From 98fccb2d9be4cf71a00dd7af558571507e1a6cbe Mon Sep 17 00:00:00 2001
From: Jean-Luc Parouty <Jean-Luc.Parouty@grenoble-inp.fr>
Date: Tue, 15 Dec 2020 22:09:41 +0100
Subject: [PATCH] Update notebooks for continous integration
---
BHPD/01-DNN-Regression.ipynb | 1116 +++++++++++++-------------
BHPD/02-DNN-Regression-Premium.ipynb | 878 ++++++++++----------
IRIS/01-Simple-Perceptron.ipynb | 39 +-
README.ipynb | 5 +
fidle/02 - Finished report.ipynb | 78 +-
fidle/cooker.py | 12 +-
fidle/log/ci_report.html | 76 +-
fidle/log/finished.json | 18 +
8 files changed, 1180 insertions(+), 1042 deletions(-)
diff --git a/BHPD/01-DNN-Regression.ipynb b/BHPD/01-DNN-Regression.ipynb
index 1166c53..ea620c3 100644
--- a/BHPD/01-DNN-Regression.ipynb
+++ b/BHPD/01-DNN-Regression.ipynb
@@ -113,13 +113,13 @@
"text": [
"\n",
"FIDLE 2020 - Practical Work Module\n",
- "Version : 0.57 DEV\n",
- "Run time : Monday 14 September 2020, 08:57:10\n",
- "TensorFlow version : 2.2.0\n",
- "Keras version : 2.3.0-tf\n",
- "Current place : Fidle at IDRIS\n",
- "Datasets dir : /gpfswork/rech/mlh/uja62cb/datasets\n",
- "Update keras cache : Done\n"
+ "Version : 0.6.1 DEV\n",
+ "Notebook id : BHP1\n",
+ "Run time : Tuesday 15 December 2020, 21:51:22\n",
+ "TensorFlow version : 2.0.0\n",
+ "Keras version : 2.2.4-tf\n",
+ "Datasets dir : /home/pjluc/datasets/fidle\n",
+ "Update keras cache : False\n"
]
}
],
@@ -133,9 +133,9 @@
"import os,sys\n",
"\n",
"sys.path.append('..')\n",
- "import fidle.pwk as ooo\n",
+ "import fidle.pwk as pwk\n",
"\n",
- "place, datasets_dir = ooo.init()"
+ "datasets_dir = pwk.init('BHP1')"
]
},
{
@@ -174,96 +174,96 @@
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- "</style><table id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763\" ><caption>Few lines of the dataset :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> <th class=\"col_heading level0 col13\" >medv</th> </tr></thead><tbody>\n",
+ "</style><table id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0\" ><caption>Few lines of the dataset :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> <th class=\"col_heading level0 col13\" >medv</th> </tr></thead><tbody>\n",
" <tr>\n",
- " <th id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row0_col0\" class=\"data row0 col0\" >0.01</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row0_col1\" class=\"data row0 col1\" >18.00</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row0_col2\" class=\"data row0 col2\" >2.31</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row0_col3\" class=\"data row0 col3\" >0.00</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row0_col4\" class=\"data row0 col4\" >0.54</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row0_col5\" class=\"data row0 col5\" >6.58</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row0_col6\" class=\"data row0 col6\" >65.20</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row0_col7\" class=\"data row0 col7\" >4.09</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row0_col8\" class=\"data row0 col8\" >1.00</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row0_col9\" class=\"data row0 col9\" >296.00</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row0_col10\" class=\"data row0 col10\" >15.30</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row0_col11\" class=\"data row0 col11\" >396.90</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row0_col12\" class=\"data row0 col12\" >4.98</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row0_col13\" class=\"data row0 col13\" >24.00</td>\n",
+ " <th id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row0_col0\" class=\"data row0 col0\" >0.01</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row0_col1\" class=\"data row0 col1\" >18.00</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row0_col2\" class=\"data row0 col2\" >2.31</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row0_col3\" class=\"data row0 col3\" >0.00</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row0_col4\" class=\"data row0 col4\" >0.54</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row0_col5\" class=\"data row0 col5\" >6.58</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row0_col6\" class=\"data row0 col6\" >65.20</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row0_col7\" class=\"data row0 col7\" >4.09</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row0_col8\" class=\"data row0 col8\" >1.00</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row0_col9\" class=\"data row0 col9\" >296.00</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row0_col10\" class=\"data row0 col10\" >15.30</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row0_col11\" class=\"data row0 col11\" >396.90</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row0_col12\" class=\"data row0 col12\" >4.98</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row0_col13\" class=\"data row0 col13\" >24.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row1_col0\" class=\"data row1 col0\" >0.03</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row1_col2\" class=\"data row1 col2\" >7.07</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row1_col4\" class=\"data row1 col4\" >0.47</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row1_col5\" class=\"data row1 col5\" >6.42</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row1_col6\" class=\"data row1 col6\" >78.90</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row1_col7\" class=\"data row1 col7\" >4.97</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row1_col8\" class=\"data row1 col8\" >2.00</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row1_col9\" class=\"data row1 col9\" >242.00</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row1_col10\" class=\"data row1 col10\" >17.80</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row1_col11\" class=\"data row1 col11\" >396.90</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row1_col12\" class=\"data row1 col12\" >9.14</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row1_col13\" class=\"data row1 col13\" >21.60</td>\n",
+ " <th id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row1_col0\" class=\"data row1 col0\" >0.03</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row1_col2\" class=\"data row1 col2\" >7.07</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row1_col4\" class=\"data row1 col4\" >0.47</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row1_col5\" class=\"data row1 col5\" >6.42</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row1_col6\" class=\"data row1 col6\" >78.90</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row1_col7\" class=\"data row1 col7\" >4.97</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row1_col8\" class=\"data row1 col8\" >2.00</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row1_col9\" class=\"data row1 col9\" >242.00</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row1_col10\" class=\"data row1 col10\" >17.80</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row1_col11\" class=\"data row1 col11\" >396.90</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row1_col12\" class=\"data row1 col12\" >9.14</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row1_col13\" class=\"data row1 col13\" >21.60</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row2_col0\" class=\"data row2 col0\" >0.03</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row2_col2\" class=\"data row2 col2\" >7.07</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row2_col3\" class=\"data row2 col3\" >0.00</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row2_col4\" class=\"data row2 col4\" >0.47</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row2_col5\" class=\"data row2 col5\" >7.18</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row2_col6\" class=\"data row2 col6\" >61.10</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row2_col7\" class=\"data row2 col7\" >4.97</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row2_col8\" class=\"data row2 col8\" >2.00</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row2_col9\" class=\"data row2 col9\" >242.00</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row2_col10\" class=\"data row2 col10\" >17.80</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row2_col11\" class=\"data row2 col11\" >392.83</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row2_col12\" class=\"data row2 col12\" >4.03</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row2_col13\" class=\"data row2 col13\" >34.70</td>\n",
+ " <th id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row2_col0\" class=\"data row2 col0\" >0.03</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row2_col2\" class=\"data row2 col2\" >7.07</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row2_col3\" class=\"data row2 col3\" >0.00</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row2_col4\" class=\"data row2 col4\" >0.47</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row2_col5\" class=\"data row2 col5\" >7.18</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row2_col6\" class=\"data row2 col6\" >61.10</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row2_col7\" class=\"data row2 col7\" >4.97</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row2_col8\" class=\"data row2 col8\" >2.00</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row2_col9\" class=\"data row2 col9\" >242.00</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row2_col10\" class=\"data row2 col10\" >17.80</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row2_col11\" class=\"data row2 col11\" >392.83</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row2_col12\" class=\"data row2 col12\" >4.03</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row2_col13\" class=\"data row2 col13\" >34.70</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row3_col0\" class=\"data row3 col0\" >0.03</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row3_col2\" class=\"data row3 col2\" >2.18</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row3_col4\" class=\"data row3 col4\" >0.46</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row3_col5\" class=\"data row3 col5\" >7.00</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row3_col6\" class=\"data row3 col6\" >45.80</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row3_col7\" class=\"data row3 col7\" >6.06</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row3_col8\" class=\"data row3 col8\" >3.00</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row3_col9\" class=\"data row3 col9\" >222.00</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row3_col10\" class=\"data row3 col10\" >18.70</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row3_col11\" class=\"data row3 col11\" >394.63</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row3_col12\" class=\"data row3 col12\" >2.94</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row3_col13\" class=\"data row3 col13\" >33.40</td>\n",
+ " <th id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row3_col0\" class=\"data row3 col0\" >0.03</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row3_col2\" class=\"data row3 col2\" >2.18</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row3_col4\" class=\"data row3 col4\" >0.46</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row3_col5\" class=\"data row3 col5\" >7.00</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row3_col6\" class=\"data row3 col6\" >45.80</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row3_col7\" class=\"data row3 col7\" >6.06</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row3_col8\" class=\"data row3 col8\" >3.00</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row3_col9\" class=\"data row3 col9\" >222.00</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row3_col10\" class=\"data row3 col10\" >18.70</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row3_col11\" class=\"data row3 col11\" >394.63</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row3_col12\" class=\"data row3 col12\" >2.94</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row3_col13\" class=\"data row3 col13\" >33.40</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row4_col0\" class=\"data row4 col0\" >0.07</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row4_col2\" class=\"data row4 col2\" >2.18</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row4_col4\" class=\"data row4 col4\" >0.46</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row4_col5\" class=\"data row4 col5\" >7.15</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row4_col6\" class=\"data row4 col6\" >54.20</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row4_col7\" class=\"data row4 col7\" >6.06</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row4_col8\" class=\"data row4 col8\" >3.00</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row4_col9\" class=\"data row4 col9\" >222.00</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row4_col10\" class=\"data row4 col10\" >18.70</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row4_col11\" class=\"data row4 col11\" >396.90</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row4_col12\" class=\"data row4 col12\" >5.33</td>\n",
- " <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row4_col13\" class=\"data row4 col13\" >36.20</td>\n",
+ " <th id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row4_col0\" class=\"data row4 col0\" >0.07</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row4_col2\" class=\"data row4 col2\" >2.18</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row4_col4\" class=\"data row4 col4\" >0.46</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row4_col5\" class=\"data row4 col5\" >7.15</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row4_col6\" class=\"data row4 col6\" >54.20</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row4_col7\" class=\"data row4 col7\" >6.06</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row4_col8\" class=\"data row4 col8\" >3.00</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row4_col9\" class=\"data row4 col9\" >222.00</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row4_col10\" class=\"data row4 col10\" >18.70</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row4_col11\" class=\"data row4 col11\" >396.90</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row4_col12\" class=\"data row4 col12\" >5.33</td>\n",
+ " <td id=\"T_4a11dda6_3f17_11eb_bc41_a3018241c6f0row4_col13\" class=\"data row4 col13\" >36.20</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x146f52e8b310>"
+ "<pandas.io.formats.style.Styler at 0x7f5dcbd67410>"
]
},
"metadata": {},
@@ -349,139 +349,139 @@
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- "</style><table id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763\" ><caption>Before normalization :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> </tr></thead><tbody>\n",
+ "</style><table id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0\" ><caption>Before normalization :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> </tr></thead><tbody>\n",
" <tr>\n",
- " <th id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
+ " <th id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row1_col0\" class=\"data row1 col0\" >3.74</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row1_col1\" class=\"data row1 col1\" >10.51</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row1_col2\" class=\"data row1 col2\" >11.22</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row1_col3\" class=\"data row1 col3\" >0.06</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row1_col4\" class=\"data row1 col4\" >0.56</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row1_col5\" class=\"data row1 col5\" >6.29</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row1_col6\" class=\"data row1 col6\" >69.82</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row1_col7\" class=\"data row1 col7\" >3.72</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row1_col8\" class=\"data row1 col8\" >9.62</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row1_col9\" class=\"data row1 col9\" >407.45</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row1_col10\" class=\"data row1 col10\" >18.46</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row1_col11\" class=\"data row1 col11\" >353.89</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row1_col12\" class=\"data row1 col12\" >12.75</td>\n",
+ " <th id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row1_col0\" class=\"data row1 col0\" >3.92</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row1_col1\" class=\"data row1 col1\" >12.18</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row1_col2\" class=\"data row1 col2\" >11.21</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row1_col3\" class=\"data row1 col3\" >0.07</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row1_col4\" class=\"data row1 col4\" >0.56</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row1_col5\" class=\"data row1 col5\" >6.29</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row1_col6\" class=\"data row1 col6\" >68.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row1_col7\" class=\"data row1 col7\" >3.80</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row1_col8\" class=\"data row1 col8\" >9.62</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row1_col9\" class=\"data row1 col9\" >412.68</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row1_col10\" class=\"data row1 col10\" >18.38</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row1_col11\" class=\"data row1 col11\" >358.11</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row1_col12\" class=\"data row1 col12\" >12.49</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row2_col0\" class=\"data row2 col0\" >8.87</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row2_col1\" class=\"data row2 col1\" >22.27</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row2_col2\" class=\"data row2 col2\" >6.75</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row2_col3\" class=\"data row2 col3\" >0.25</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row2_col4\" class=\"data row2 col4\" >0.11</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row2_col5\" class=\"data row2 col5\" >0.72</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row2_col6\" class=\"data row2 col6\" >27.50</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row2_col7\" class=\"data row2 col7\" >2.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row2_col8\" class=\"data row2 col8\" >8.71</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row2_col9\" class=\"data row2 col9\" >167.90</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row2_col10\" class=\"data row2 col10\" >2.19</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row2_col11\" class=\"data row2 col11\" >95.77</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row2_col12\" class=\"data row2 col12\" >7.23</td>\n",
+ " <th id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row2_col0\" class=\"data row2 col0\" >9.58</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row2_col1\" class=\"data row2 col1\" >23.79</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row2_col2\" class=\"data row2 col2\" >6.95</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row2_col3\" class=\"data row2 col3\" >0.26</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row2_col4\" class=\"data row2 col4\" >0.12</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row2_col5\" class=\"data row2 col5\" >0.73</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row2_col6\" class=\"data row2 col6\" >28.35</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row2_col7\" class=\"data row2 col7\" >2.13</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row2_col8\" class=\"data row2 col8\" >8.78</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row2_col9\" class=\"data row2 col9\" >169.62</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row2_col10\" class=\"data row2 col10\" >2.18</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row2_col11\" class=\"data row2 col11\" >87.83</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row2_col12\" class=\"data row2 col12\" >7.22</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row3_col0\" class=\"data row3 col0\" >0.01</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row3_col2\" class=\"data row3 col2\" >1.21</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row3_col4\" class=\"data row3 col4\" >0.39</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row3_col5\" class=\"data row3 col5\" >3.56</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row3_col6\" class=\"data row3 col6\" >2.90</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row3_col7\" class=\"data row3 col7\" >1.17</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row3_col8\" class=\"data row3 col8\" >1.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row3_col9\" class=\"data row3 col9\" >188.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row3_col10\" class=\"data row3 col10\" >12.60</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row3_col11\" class=\"data row3 col11\" >2.52</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row3_col12\" class=\"data row3 col12\" >1.73</td>\n",
+ " <th id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row3_col0\" class=\"data row3 col0\" >0.01</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row3_col2\" class=\"data row3 col2\" >1.21</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row3_col4\" class=\"data row3 col4\" >0.39</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row3_col5\" class=\"data row3 col5\" >3.56</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row3_col6\" class=\"data row3 col6\" >6.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row3_col7\" class=\"data row3 col7\" >1.13</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row3_col8\" class=\"data row3 col8\" >1.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row3_col9\" class=\"data row3 col9\" >187.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row3_col10\" class=\"data row3 col10\" >12.60</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row3_col11\" class=\"data row3 col11\" >2.60</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row3_col12\" class=\"data row3 col12\" >1.98</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row4_col0\" class=\"data row4 col0\" >0.08</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row4_col2\" class=\"data row4 col2\" >5.19</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row4_col4\" class=\"data row4 col4\" >0.45</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row4_col5\" class=\"data row4 col5\" >5.90</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row4_col6\" class=\"data row4 col6\" >47.25</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row4_col7\" class=\"data row4 col7\" >2.09</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row4_col8\" class=\"data row4 col8\" >4.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row4_col9\" class=\"data row4 col9\" >279.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row4_col10\" class=\"data row4 col10\" >17.40</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row4_col11\" class=\"data row4 col11\" >374.83</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row4_col12\" class=\"data row4 col12\" >6.86</td>\n",
+ " <th id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row4_col0\" class=\"data row4 col0\" >0.07</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row4_col2\" class=\"data row4 col2\" >5.13</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row4_col4\" class=\"data row4 col4\" >0.45</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row4_col5\" class=\"data row4 col5\" >5.89</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row4_col6\" class=\"data row4 col6\" >42.65</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row4_col7\" class=\"data row4 col7\" >2.11</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row4_col8\" class=\"data row4 col8\" >4.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row4_col9\" class=\"data row4 col9\" >281.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row4_col10\" class=\"data row4 col10\" >16.80</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row4_col11\" class=\"data row4 col11\" >374.71</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row4_col12\" class=\"data row4 col12\" >6.74</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row5_col0\" class=\"data row5 col0\" >0.29</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row5_col2\" class=\"data row5 col2\" >9.79</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row5_col3\" class=\"data row5 col3\" >0.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row5_col4\" class=\"data row5 col4\" >0.54</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row5_col5\" class=\"data row5 col5\" >6.20</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row5_col6\" class=\"data row5 col6\" >79.50</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row5_col7\" class=\"data row5 col7\" >3.16</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row5_col8\" class=\"data row5 col8\" >5.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row5_col9\" class=\"data row5 col9\" >330.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row5_col10\" class=\"data row5 col10\" >19.05</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row5_col11\" class=\"data row5 col11\" >391.96</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row5_col12\" class=\"data row5 col12\" >11.49</td>\n",
+ " <th id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row5_col0\" class=\"data row5 col0\" >0.23</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row5_col2\" class=\"data row5 col2\" >9.69</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row5_col3\" class=\"data row5 col3\" >0.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row5_col4\" class=\"data row5 col4\" >0.54</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row5_col5\" class=\"data row5 col5\" >6.21</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row5_col6\" class=\"data row5 col6\" >77.50</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row5_col7\" class=\"data row5 col7\" >3.10</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row5_col8\" class=\"data row5 col8\" >5.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row5_col9\" class=\"data row5 col9\" >336.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row5_col10\" class=\"data row5 col10\" >18.80</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row5_col11\" class=\"data row5 col11\" >392.22</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row5_col12\" class=\"data row5 col12\" >11.29</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row6_col0\" class=\"data row6 col0\" >3.69</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row6_col1\" class=\"data row6 col1\" >12.50</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row6_col2\" class=\"data row6 col2\" >18.10</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row6_col3\" class=\"data row6 col3\" >0.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row6_col4\" class=\"data row6 col4\" >0.62</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row6_col5\" class=\"data row6 col5\" >6.63</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row6_col6\" class=\"data row6 col6\" >94.05</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row6_col7\" class=\"data row6 col7\" >4.98</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row6_col8\" class=\"data row6 col8\" >24.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row6_col9\" class=\"data row6 col9\" >666.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row6_col10\" class=\"data row6 col10\" >20.20</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row6_col11\" class=\"data row6 col11\" >396.32</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row6_col12\" class=\"data row6 col12\" >17.10</td>\n",
+ " <th id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row6_col0\" class=\"data row6 col0\" >3.69</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row6_col1\" class=\"data row6 col1\" >20.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row6_col2\" class=\"data row6 col2\" >18.10</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row6_col3\" class=\"data row6 col3\" >0.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row6_col4\" class=\"data row6 col4\" >0.62</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row6_col5\" class=\"data row6 col5\" >6.63</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row6_col6\" class=\"data row6 col6\" >93.60</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row6_col7\" class=\"data row6 col7\" >5.24</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row6_col8\" class=\"data row6 col8\" >24.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row6_col9\" class=\"data row6 col9\" >666.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row6_col10\" class=\"data row6 col10\" >20.20</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row6_col11\" class=\"data row6 col11\" >396.90</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row6_col12\" class=\"data row6 col12\" >16.27</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row7_col0\" class=\"data row7 col0\" >88.98</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row7_col1\" class=\"data row7 col1\" >100.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row7_col2\" class=\"data row7 col2\" >27.74</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row7_col3\" class=\"data row7 col3\" >1.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row7_col4\" class=\"data row7 col4\" >0.87</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row7_col5\" class=\"data row7 col5\" >8.78</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row7_col6\" class=\"data row7 col6\" >100.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row7_col7\" class=\"data row7 col7\" >10.71</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row7_col8\" class=\"data row7 col8\" >24.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row7_col9\" class=\"data row7 col9\" >711.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row7_col10\" class=\"data row7 col10\" >22.00</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row7_col11\" class=\"data row7 col11\" >396.90</td>\n",
- " <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row7_col12\" class=\"data row7 col12\" >36.98</td>\n",
+ " <th id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row7_col0\" class=\"data row7 col0\" >88.98</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row7_col1\" class=\"data row7 col1\" >95.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row7_col2\" class=\"data row7 col2\" >27.74</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row7_col3\" class=\"data row7 col3\" >1.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row7_col4\" class=\"data row7 col4\" >0.87</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row7_col5\" class=\"data row7 col5\" >8.78</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row7_col6\" class=\"data row7 col6\" >100.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row7_col7\" class=\"data row7 col7\" >12.13</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row7_col8\" class=\"data row7 col8\" >24.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row7_col9\" class=\"data row7 col9\" >711.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row7_col10\" class=\"data row7 col10\" >22.00</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row7_col11\" class=\"data row7 col11\" >396.90</td>\n",
+ " <td id=\"T_4a1b17e0_3f17_11eb_bc41_a3018241c6f0row7_col12\" class=\"data row7 col12\" >37.97</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x146f52aed590>"
+ "<pandas.io.formats.style.Styler at 0x7f5dcbd672d0>"
]
},
"metadata": {},
@@ -491,139 +491,139 @@
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- "</style><table id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763\" ><caption>After normalization :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> </tr></thead><tbody>\n",
+ "</style><table id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0\" ><caption>After normalization :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> </tr></thead><tbody>\n",
" <tr>\n",
- " <th id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
+ " <th id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row1_col0\" class=\"data row1 col0\" >-0.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row1_col1\" class=\"data row1 col1\" >-0.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row1_col4\" class=\"data row1 col4\" >-0.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row1_col5\" class=\"data row1 col5\" >0.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row1_col6\" class=\"data row1 col6\" >0.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row1_col7\" class=\"data row1 col7\" >0.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row1_col8\" class=\"data row1 col8\" >-0.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row1_col9\" class=\"data row1 col9\" >0.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row1_col10\" class=\"data row1 col10\" >0.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row1_col11\" class=\"data row1 col11\" >0.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row1_col12\" class=\"data row1 col12\" >0.00</td>\n",
+ " <th id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row1_col0\" class=\"data row1 col0\" >-0.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row1_col1\" class=\"data row1 col1\" >-0.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row1_col3\" class=\"data row1 col3\" >-0.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row1_col4\" class=\"data row1 col4\" >-0.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row1_col5\" class=\"data row1 col5\" >0.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row1_col6\" class=\"data row1 col6\" >0.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row1_col7\" class=\"data row1 col7\" >-0.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row1_col8\" class=\"data row1 col8\" >-0.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row1_col9\" class=\"data row1 col9\" >-0.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row1_col10\" class=\"data row1 col10\" >0.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row1_col11\" class=\"data row1 col11\" >-0.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row1_col12\" class=\"data row1 col12\" >0.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row2_col0\" class=\"data row2 col0\" >1.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row2_col1\" class=\"data row2 col1\" >1.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row2_col3\" class=\"data row2 col3\" >1.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row2_col4\" class=\"data row2 col4\" >1.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row2_col5\" class=\"data row2 col5\" >1.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row2_col6\" class=\"data row2 col6\" >1.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row2_col7\" class=\"data row2 col7\" >1.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row2_col8\" class=\"data row2 col8\" >1.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row2_col9\" class=\"data row2 col9\" >1.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row2_col10\" class=\"data row2 col10\" >1.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row2_col11\" class=\"data row2 col11\" >1.00</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row2_col12\" class=\"data row2 col12\" >1.00</td>\n",
+ " <th id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row2_col0\" class=\"data row2 col0\" >1.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row2_col1\" class=\"data row2 col1\" >1.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row2_col3\" class=\"data row2 col3\" >1.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row2_col4\" class=\"data row2 col4\" >1.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row2_col5\" class=\"data row2 col5\" >1.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row2_col6\" class=\"data row2 col6\" >1.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row2_col7\" class=\"data row2 col7\" >1.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row2_col8\" class=\"data row2 col8\" >1.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row2_col9\" class=\"data row2 col9\" >1.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row2_col10\" class=\"data row2 col10\" >1.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row2_col11\" class=\"data row2 col11\" >1.00</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row2_col12\" class=\"data row2 col12\" >1.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row3_col0\" class=\"data row3 col0\" >-0.42</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row3_col1\" class=\"data row3 col1\" >-0.47</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row3_col2\" class=\"data row3 col2\" >-1.48</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row3_col3\" class=\"data row3 col3\" >-0.26</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row3_col4\" class=\"data row3 col4\" >-1.48</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row3_col5\" class=\"data row3 col5\" >-3.80</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row3_col6\" class=\"data row3 col6\" >-2.43</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row3_col7\" class=\"data row3 col7\" >-1.28</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row3_col8\" class=\"data row3 col8\" >-0.99</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row3_col9\" class=\"data row3 col9\" >-1.31</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row3_col10\" class=\"data row3 col10\" >-2.68</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row3_col11\" class=\"data row3 col11\" >-3.67</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row3_col12\" class=\"data row3 col12\" >-1.52</td>\n",
+ " <th id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row3_col0\" class=\"data row3 col0\" >-0.41</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row3_col1\" class=\"data row3 col1\" >-0.51</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row3_col2\" class=\"data row3 col2\" >-1.44</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row3_col3\" class=\"data row3 col3\" >-0.28</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row3_col4\" class=\"data row3 col4\" >-1.43</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row3_col5\" class=\"data row3 col5\" >-3.73</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row3_col6\" class=\"data row3 col6\" >-2.19</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row3_col7\" class=\"data row3 col7\" >-1.26</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row3_col8\" class=\"data row3 col8\" >-0.98</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row3_col9\" class=\"data row3 col9\" >-1.33</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row3_col10\" class=\"data row3 col10\" >-2.65</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row3_col11\" class=\"data row3 col11\" >-4.05</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row3_col12\" class=\"data row3 col12\" >-1.46</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row4_col0\" class=\"data row4 col0\" >-0.41</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row4_col1\" class=\"data row4 col1\" >-0.47</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row4_col2\" class=\"data row4 col2\" >-0.89</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row4_col3\" class=\"data row4 col3\" >-0.26</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row4_col4\" class=\"data row4 col4\" >-0.91</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row4_col5\" class=\"data row4 col5\" >-0.54</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row4_col6\" class=\"data row4 col6\" >-0.82</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row4_col7\" class=\"data row4 col7\" >-0.82</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row4_col8\" class=\"data row4 col8\" >-0.65</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row4_col9\" class=\"data row4 col9\" >-0.77</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row4_col10\" class=\"data row4 col10\" >-0.48</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row4_col11\" class=\"data row4 col11\" >0.22</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row4_col12\" class=\"data row4 col12\" >-0.81</td>\n",
+ " <th id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row4_col0\" class=\"data row4 col0\" >-0.40</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row4_col1\" class=\"data row4 col1\" >-0.51</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row4_col2\" class=\"data row4 col2\" >-0.87</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row4_col3\" class=\"data row4 col3\" >-0.28</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row4_col4\" class=\"data row4 col4\" >-0.91</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row4_col5\" class=\"data row4 col5\" >-0.55</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row4_col6\" class=\"data row4 col6\" >-0.89</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row4_col7\" class=\"data row4 col7\" >-0.79</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row4_col8\" class=\"data row4 col8\" >-0.64</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row4_col9\" class=\"data row4 col9\" >-0.78</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row4_col10\" class=\"data row4 col10\" >-0.72</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row4_col11\" class=\"data row4 col11\" >0.19</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row4_col12\" class=\"data row4 col12\" >-0.80</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row5_col0\" class=\"data row5 col0\" >-0.39</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row5_col1\" class=\"data row5 col1\" >-0.47</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row5_col2\" class=\"data row5 col2\" >-0.21</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row5_col3\" class=\"data row5 col3\" >-0.26</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row5_col4\" class=\"data row5 col4\" >-0.16</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row5_col5\" class=\"data row5 col5\" >-0.12</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row5_col6\" class=\"data row5 col6\" >0.35</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row5_col7\" class=\"data row5 col7\" >-0.28</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row5_col8\" class=\"data row5 col8\" >-0.53</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row5_col9\" class=\"data row5 col9\" >-0.46</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row5_col10\" class=\"data row5 col10\" >0.27</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row5_col11\" class=\"data row5 col11\" >0.40</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row5_col12\" class=\"data row5 col12\" >-0.17</td>\n",
+ " <th id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row5_col0\" class=\"data row5 col0\" >-0.39</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row5_col1\" class=\"data row5 col1\" >-0.51</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row5_col2\" class=\"data row5 col2\" >-0.22</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row5_col3\" class=\"data row5 col3\" >-0.28</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row5_col4\" class=\"data row5 col4\" >-0.15</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row5_col5\" class=\"data row5 col5\" >-0.11</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row5_col6\" class=\"data row5 col6\" >0.34</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row5_col7\" class=\"data row5 col7\" >-0.33</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row5_col8\" class=\"data row5 col8\" >-0.53</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row5_col9\" class=\"data row5 col9\" >-0.45</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row5_col10\" class=\"data row5 col10\" >0.19</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row5_col11\" class=\"data row5 col11\" >0.39</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row5_col12\" class=\"data row5 col12\" >-0.17</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row6_col0\" class=\"data row6 col0\" >-0.01</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row6_col1\" class=\"data row6 col1\" >0.09</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row6_col2\" class=\"data row6 col2\" >1.02</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row6_col3\" class=\"data row6 col3\" >-0.26</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row6_col4\" class=\"data row6 col4\" >0.60</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row6_col5\" class=\"data row6 col5\" >0.48</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row6_col6\" class=\"data row6 col6\" >0.88</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row6_col7\" class=\"data row6 col7\" >0.63</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row6_col8\" class=\"data row6 col8\" >1.65</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row6_col9\" class=\"data row6 col9\" >1.54</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row6_col10\" class=\"data row6 col10\" >0.80</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row6_col11\" class=\"data row6 col11\" >0.44</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row6_col12\" class=\"data row6 col12\" >0.60</td>\n",
+ " <th id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row6_col0\" class=\"data row6 col0\" >-0.02</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row6_col1\" class=\"data row6 col1\" >0.33</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row6_col2\" class=\"data row6 col2\" >0.99</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row6_col3\" class=\"data row6 col3\" >-0.28</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row6_col4\" class=\"data row6 col4\" >0.57</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row6_col5\" class=\"data row6 col5\" >0.46</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row6_col6\" class=\"data row6 col6\" >0.90</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row6_col7\" class=\"data row6 col7\" >0.67</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row6_col8\" class=\"data row6 col8\" >1.64</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row6_col9\" class=\"data row6 col9\" >1.49</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row6_col10\" class=\"data row6 col10\" >0.84</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row6_col11\" class=\"data row6 col11\" >0.44</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row6_col12\" class=\"data row6 col12\" >0.52</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row7_col0\" class=\"data row7 col0\" >9.60</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row7_col1\" class=\"data row7 col1\" >4.02</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row7_col2\" class=\"data row7 col2\" >2.45</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row7_col3\" class=\"data row7 col3\" >3.79</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row7_col4\" class=\"data row7 col4\" >2.79</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row7_col5\" class=\"data row7 col5\" >3.48</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row7_col6\" class=\"data row7 col6\" >1.10</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row7_col7\" class=\"data row7 col7\" >3.49</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row7_col8\" class=\"data row7 col8\" >1.65</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row7_col9\" class=\"data row7 col9\" >1.81</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row7_col10\" class=\"data row7 col10\" >1.62</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row7_col11\" class=\"data row7 col11\" >0.45</td>\n",
- " <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row7_col12\" class=\"data row7 col12\" >3.35</td>\n",
+ " <th id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row7_col0\" class=\"data row7 col0\" >8.88</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row7_col1\" class=\"data row7 col1\" >3.48</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row7_col2\" class=\"data row7 col2\" >2.38</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row7_col3\" class=\"data row7 col3\" >3.62</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row7_col4\" class=\"data row7 col4\" >2.65</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row7_col5\" class=\"data row7 col5\" >3.39</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row7_col6\" class=\"data row7 col6\" >1.13</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row7_col7\" class=\"data row7 col7\" >3.91</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row7_col8\" class=\"data row7 col8\" >1.64</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row7_col9\" class=\"data row7 col9\" >1.76</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row7_col10\" class=\"data row7 col10\" >1.66</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row7_col11\" class=\"data row7 col11\" >0.44</td>\n",
+ " <td id=\"T_4a21ceaa_3f17_11eb_bc41_a3018241c6f0row7_col12\" class=\"data row7 col12\" >3.53</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x146f52aeb0d0>"
+ "<pandas.io.formats.style.Styler at 0x7f5e482d29d0>"
]
},
"metadata": {},
@@ -633,91 +633,91 @@
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- "</style><table id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763\" ><caption>Few lines of the dataset :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> </tr></thead><tbody>\n",
+ "</style><table id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0\" ><caption>Few lines of the dataset :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> </tr></thead><tbody>\n",
" <tr>\n",
- " <th id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763level0_row0\" class=\"row_heading level0 row0\" >256</th>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row0_col0\" class=\"data row0 col0\" >-0.42</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row0_col1\" class=\"data row0 col1\" >3.57</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row0_col2\" class=\"data row0 col2\" >-1.11</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row0_col3\" class=\"data row0 col3\" >-0.26</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row0_col4\" class=\"data row0 col4\" >-1.44</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row0_col5\" class=\"data row0 col5\" >1.63</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row0_col6\" class=\"data row0 col6\" >-1.30</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row0_col7\" class=\"data row0 col7\" >1.31</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row0_col8\" class=\"data row0 col8\" >-0.76</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row0_col9\" class=\"data row0 col9\" >-0.97</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row0_col10\" class=\"data row0 col10\" >-1.17</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row0_col11\" class=\"data row0 col11\" >0.34</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row0_col12\" class=\"data row0 col12\" >-1.33</td>\n",
+ " <th id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0level0_row0\" class=\"row_heading level0 row0\" >38</th>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row0_col0\" class=\"data row0 col0\" >-0.39</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row0_col1\" class=\"data row0 col1\" >-0.51</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row0_col2\" class=\"data row0 col2\" >-0.76</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row0_col3\" class=\"data row0 col3\" >-0.28</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row0_col4\" class=\"data row0 col4\" >-0.48</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row0_col5\" class=\"data row0 col5\" >-0.45</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row0_col6\" class=\"data row0 col6\" >-1.33</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row0_col7\" class=\"data row0 col7\" >0.02</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row0_col8\" class=\"data row0 col8\" >-0.53</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row0_col9\" class=\"data row0 col9\" >-0.79</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row0_col10\" class=\"data row0 col10\" >0.38</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row0_col11\" class=\"data row0 col11\" >0.40</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row0_col12\" class=\"data row0 col12\" >-0.33</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763level0_row1\" class=\"row_heading level0 row1\" >124</th>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row1_col0\" class=\"data row1 col0\" >-0.41</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row1_col1\" class=\"data row1 col1\" >-0.47</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row1_col2\" class=\"data row1 col2\" >2.14</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row1_col3\" class=\"data row1 col3\" >-0.26</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row1_col4\" class=\"data row1 col4\" >0.22</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row1_col5\" class=\"data row1 col5\" >-0.57</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row1_col6\" class=\"data row1 col6\" >0.94</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row1_col7\" class=\"data row1 col7\" >-0.86</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row1_col8\" class=\"data row1 col8\" >-0.88</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row1_col9\" class=\"data row1 col9\" >-1.31</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row1_col10\" class=\"data row1 col10\" >0.29</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row1_col11\" class=\"data row1 col11\" >0.27</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row1_col12\" class=\"data row1 col12\" >0.67</td>\n",
+ " <th id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0level0_row1\" class=\"row_heading level0 row1\" >391</th>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row1_col0\" class=\"data row1 col0\" >0.14</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row1_col1\" class=\"data row1 col1\" >-0.51</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row1_col2\" class=\"data row1 col2\" >0.99</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row1_col3\" class=\"data row1 col3\" >-0.28</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row1_col4\" class=\"data row1 col4\" >1.21</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row1_col5\" class=\"data row1 col5\" >-0.33</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row1_col6\" class=\"data row1 col6\" >0.51</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row1_col7\" class=\"data row1 col7\" >-0.77</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row1_col8\" class=\"data row1 col8\" >1.64</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row1_col9\" class=\"data row1 col9\" >1.49</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row1_col10\" class=\"data row1 col10\" >0.84</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row1_col11\" class=\"data row1 col11\" >0.23</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row1_col12\" class=\"data row1 col12\" >0.87</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763level0_row2\" class=\"row_heading level0 row2\" >268</th>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row2_col0\" class=\"data row2 col0\" >-0.36</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row2_col1\" class=\"data row2 col1\" >0.43</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row2_col2\" class=\"data row2 col2\" >-1.07</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row2_col3\" class=\"data row2 col3\" >-0.26</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row2_col4\" class=\"data row2 col4\" >0.17</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row2_col5\" class=\"data row2 col5\" >1.65</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row2_col6\" class=\"data row2 col6\" >-0.63</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row2_col7\" class=\"data row2 col7\" >-0.43</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row2_col8\" class=\"data row2 col8\" >-0.53</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row2_col9\" class=\"data row2 col9\" >-0.85</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row2_col10\" class=\"data row2 col10\" >-2.50</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row2_col11\" class=\"data row2 col11\" >0.38</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row2_col12\" class=\"data row2 col12\" >-1.33</td>\n",
+ " <th id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0level0_row2\" class=\"row_heading level0 row2\" >307</th>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row2_col0\" class=\"data row2 col0\" >-0.40</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row2_col1\" class=\"data row2 col1\" >0.88</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row2_col2\" class=\"data row2 col2\" >-1.30</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row2_col3\" class=\"data row2 col3\" >-0.28</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row2_col4\" class=\"data row2 col4\" >-0.70</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row2_col5\" class=\"data row2 col5\" >0.76</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row2_col6\" class=\"data row2 col6\" >0.08</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row2_col7\" class=\"data row2 col7\" >-0.29</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row2_col8\" class=\"data row2 col8\" >-0.30</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row2_col9\" class=\"data row2 col9\" >-1.12</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row2_col10\" class=\"data row2 col10\" >0.01</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row2_col11\" class=\"data row2 col11\" >0.44</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row2_col12\" class=\"data row2 col12\" >-0.69</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763level0_row3\" class=\"row_heading level0 row3\" >489</th>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row3_col0\" class=\"data row3 col0\" >-0.40</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row3_col1\" class=\"data row3 col1\" >-0.47</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row3_col2\" class=\"data row3 col2\" >2.45</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row3_col3\" class=\"data row3 col3\" >-0.26</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row3_col4\" class=\"data row3 col4\" >0.47</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row3_col5\" class=\"data row3 col5\" >-1.21</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row3_col6\" class=\"data row3 col6\" >1.04</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row3_col7\" class=\"data row3 col7\" >-0.98</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row3_col8\" class=\"data row3 col8\" >-0.65</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row3_col9\" class=\"data row3 col9\" >1.81</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row3_col10\" class=\"data row3 col10\" >0.75</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row3_col11\" class=\"data row3 col11\" >-0.10</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row3_col12\" class=\"data row3 col12\" >1.55</td>\n",
+ " <th id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0level0_row3\" class=\"row_heading level0 row3\" >384</th>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row3_col0\" class=\"data row3 col0\" >1.69</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row3_col1\" class=\"data row3 col1\" >-0.51</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row3_col2\" class=\"data row3 col2\" >0.99</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row3_col3\" class=\"data row3 col3\" >-0.28</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row3_col4\" class=\"data row3 col4\" >1.21</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row3_col5\" class=\"data row3 col5\" >-2.63</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row3_col6\" class=\"data row3 col6\" >0.82</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row3_col7\" class=\"data row3 col7\" >-1.11</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row3_col8\" class=\"data row3 col8\" >1.64</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row3_col9\" class=\"data row3 col9\" >1.49</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row3_col10\" class=\"data row3 col10\" >0.84</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row3_col11\" class=\"data row3 col11\" >-0.82</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row3_col12\" class=\"data row3 col12\" >2.51</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763level0_row4\" class=\"row_heading level0 row4\" >332</th>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row4_col0\" class=\"data row4 col0\" >-0.42</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row4_col1\" class=\"data row4 col1\" >1.10</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row4_col2\" class=\"data row4 col2\" >-0.76</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row4_col3\" class=\"data row4 col3\" >-0.26</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row4_col4\" class=\"data row4 col4\" >-1.05</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row4_col5\" class=\"data row4 col5\" >-0.35</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row4_col6\" class=\"data row4 col6\" >-1.69</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row4_col7\" class=\"data row4 col7\" >1.46</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row4_col8\" class=\"data row4 col8\" >-0.99</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row4_col9\" class=\"data row4 col9\" >-0.62</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row4_col10\" class=\"data row4 col10\" >-0.71</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row4_col11\" class=\"data row4 col11\" >0.09</td>\n",
- " <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row4_col12\" class=\"data row4 col12\" >-0.68</td>\n",
+ " <th id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0level0_row4\" class=\"row_heading level0 row4\" >17</th>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row4_col0\" class=\"data row4 col0\" >-0.33</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row4_col1\" class=\"data row4 col1\" >-0.51</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row4_col2\" class=\"data row4 col2\" >-0.44</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row4_col3\" class=\"data row4 col3\" >-0.28</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row4_col4\" class=\"data row4 col4\" >-0.15</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row4_col5\" class=\"data row4 col5\" >-0.41</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row4_col6\" class=\"data row4 col6\" >0.48</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row4_col7\" class=\"data row4 col7\" >0.21</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row4_col8\" class=\"data row4 col8\" >-0.64</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row4_col9\" class=\"data row4 col9\" >-0.62</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row4_col10\" class=\"data row4 col10\" >1.20</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row4_col11\" class=\"data row4 col11\" >0.33</td>\n",
+ " <td id=\"T_4a228cfa_3f17_11eb_bc41_a3018241c6f0row4_col12\" class=\"data row4 col12\" >0.30</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x146f52cc8810>"
+ "<pandas.io.formats.style.Styler at 0x7f5e482c1e50>"
]
},
"metadata": {},
@@ -801,14 +801,14 @@
"Total params: 5,121\n",
"Trainable params: 5,121\n",
"Non-trainable params: 0\n",
- "_________________________________________________________________\n",
- "Failed to import pydot. You must install pydot and graphviz for `pydotprint` to work.\n"
+ "_________________________________________________________________\n"
]
},
{
"data": {
+ "image/png": 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\n",
"text/plain": [
- "None"
+ "<IPython.core.display.Image object>"
]
},
"metadata": {},
@@ -840,206 +840,207 @@
"name": "stdout",
"output_type": "stream",
"text": [
+ "Train on 354 samples, validate on 152 samples\n",
"Epoch 1/100\n",
- "36/36 [==============================] - 0s 9ms/step - loss: 503.0777 - mae: 20.2737 - mse: 503.0777 - val_loss: 393.9827 - val_mae: 17.6556 - val_mse: 393.9827\n",
+ "354/354 [==============================] - 1s 2ms/sample - loss: 513.4509 - mae: 20.7256 - mse: 513.4510 - val_loss: 430.5565 - val_mae: 18.2928 - val_mse: 430.5565\n",
"Epoch 2/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 279.2001 - mae: 14.1952 - mse: 279.2001 - val_loss: 149.7944 - val_mae: 9.8029 - val_mse: 149.7944\n",
+ "354/354 [==============================] - 0s 231us/sample - loss: 299.3683 - mae: 15.1641 - mse: 299.3683 - val_loss: 204.4792 - val_mae: 11.5829 - val_mse: 204.4792\n",
"Epoch 3/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 92.0400 - mae: 7.3086 - mse: 92.0400 - val_loss: 56.1463 - val_mae: 5.1862 - val_mse: 56.1463\n",
+ "354/354 [==============================] - 0s 239us/sample - loss: 111.2687 - mae: 8.3739 - mse: 111.2687 - val_loss: 76.9330 - val_mae: 6.3440 - val_mse: 76.9330\n",
"Epoch 4/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 40.7789 - mae: 4.5229 - mse: 40.7789 - val_loss: 39.5256 - val_mae: 4.1634 - val_mse: 39.5256\n",
+ "354/354 [==============================] - 0s 222us/sample - loss: 44.0149 - mae: 5.1225 - mse: 44.0149 - val_loss: 45.7564 - val_mae: 4.7400 - val_mse: 45.7564\n",
"Epoch 5/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 29.7879 - mae: 3.8496 - mse: 29.7879 - val_loss: 31.2629 - val_mae: 3.6489 - val_mse: 31.2629\n",
+ "354/354 [==============================] - 0s 230us/sample - loss: 26.6644 - mae: 3.9499 - mse: 26.6644 - val_loss: 30.6785 - val_mae: 3.7247 - val_mse: 30.6785\n",
"Epoch 6/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 24.4654 - mae: 3.5160 - mse: 24.4654 - val_loss: 26.0581 - val_mae: 3.4063 - val_mse: 26.0581\n",
+ "354/354 [==============================] - 0s 217us/sample - loss: 20.6117 - mae: 3.3932 - mse: 20.6117 - val_loss: 26.4334 - val_mae: 3.3416 - val_mse: 26.4334\n",
"Epoch 7/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 21.0785 - mae: 3.2459 - mse: 21.0785 - val_loss: 23.6567 - val_mae: 3.2537 - val_mse: 23.6567\n",
+ "354/354 [==============================] - 0s 217us/sample - loss: 17.6924 - mae: 3.0659 - mse: 17.6924 - val_loss: 23.7837 - val_mae: 3.2338 - val_mse: 23.7837\n",
"Epoch 8/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 19.4353 - mae: 3.1121 - mse: 19.4353 - val_loss: 21.8422 - val_mae: 3.0457 - val_mse: 21.8422\n",
+ "354/354 [==============================] - 0s 227us/sample - loss: 15.9457 - mae: 2.8998 - mse: 15.9457 - val_loss: 21.4864 - val_mae: 3.0186 - val_mse: 21.4864\n",
"Epoch 9/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 17.8308 - mae: 2.9690 - mse: 17.8308 - val_loss: 20.7882 - val_mae: 3.0116 - val_mse: 20.7882\n",
+ "354/354 [==============================] - 0s 217us/sample - loss: 14.6125 - mae: 2.7675 - mse: 14.6125 - val_loss: 22.9072 - val_mae: 2.9882 - val_mse: 22.9072\n",
"Epoch 10/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 16.8658 - mae: 2.8324 - mse: 16.8658 - val_loss: 19.7575 - val_mae: 2.8680 - val_mse: 19.7575\n",
+ "354/354 [==============================] - 0s 245us/sample - loss: 14.1301 - mae: 2.6367 - mse: 14.1301 - val_loss: 19.5298 - val_mae: 2.8492 - val_mse: 19.5298\n",
"Epoch 11/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 15.8717 - mae: 2.7445 - mse: 15.8717 - val_loss: 18.9251 - val_mae: 2.7688 - val_mse: 18.9251\n",
+ "354/354 [==============================] - 0s 237us/sample - loss: 13.1397 - mae: 2.5622 - mse: 13.1397 - val_loss: 18.6511 - val_mae: 2.8039 - val_mse: 18.6511\n",
"Epoch 12/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 15.0276 - mae: 2.6749 - mse: 15.0276 - val_loss: 18.8640 - val_mae: 2.9710 - val_mse: 18.8640\n",
+ "354/354 [==============================] - 0s 250us/sample - loss: 12.5476 - mae: 2.5026 - mse: 12.5476 - val_loss: 17.8982 - val_mae: 2.7973 - val_mse: 17.8982\n",
"Epoch 13/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 14.3917 - mae: 2.5725 - mse: 14.3917 - val_loss: 17.7454 - val_mae: 2.8248 - val_mse: 17.7454\n",
+ "354/354 [==============================] - 0s 224us/sample - loss: 12.1809 - mae: 2.4823 - mse: 12.1809 - val_loss: 18.7780 - val_mae: 2.8541 - val_mse: 18.7780\n",
"Epoch 14/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 13.5518 - mae: 2.5363 - mse: 13.5518 - val_loss: 17.7311 - val_mae: 2.7501 - val_mse: 17.7311\n",
+ "354/354 [==============================] - 0s 234us/sample - loss: 11.5024 - mae: 2.4120 - mse: 11.5024 - val_loss: 17.7346 - val_mae: 2.7236 - val_mse: 17.7346\n",
"Epoch 15/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 13.4551 - mae: 2.4748 - mse: 13.4551 - val_loss: 17.3188 - val_mae: 2.8649 - val_mse: 17.3188\n",
+ "354/354 [==============================] - 0s 217us/sample - loss: 11.1445 - mae: 2.3840 - mse: 11.1445 - val_loss: 16.1831 - val_mae: 2.7346 - val_mse: 16.1831\n",
"Epoch 16/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 12.9481 - mae: 2.4157 - mse: 12.9481 - val_loss: 16.7981 - val_mae: 2.7170 - val_mse: 16.7981\n",
+ "354/354 [==============================] - 0s 218us/sample - loss: 10.9601 - mae: 2.3061 - mse: 10.9601 - val_loss: 15.8746 - val_mae: 2.5763 - val_mse: 15.8746\n",
"Epoch 17/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 12.3689 - mae: 2.3805 - mse: 12.3689 - val_loss: 16.6066 - val_mae: 2.7056 - val_mse: 16.6066\n",
+ "354/354 [==============================] - 0s 217us/sample - loss: 10.6611 - mae: 2.3021 - mse: 10.6611 - val_loss: 15.6866 - val_mae: 2.5988 - val_mse: 15.6866\n",
"Epoch 18/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 12.2772 - mae: 2.3851 - mse: 12.2772 - val_loss: 16.6416 - val_mae: 2.7872 - val_mse: 16.6416\n",
+ "354/354 [==============================] - 0s 214us/sample - loss: 10.4118 - mae: 2.2787 - mse: 10.4118 - val_loss: 16.9243 - val_mae: 2.5444 - val_mse: 16.9243\n",
"Epoch 19/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 11.7965 - mae: 2.3402 - mse: 11.7965 - val_loss: 16.8247 - val_mae: 2.8627 - val_mse: 16.8247\n",
+ "354/354 [==============================] - 0s 214us/sample - loss: 10.3306 - mae: 2.2449 - mse: 10.3306 - val_loss: 16.9934 - val_mae: 2.6862 - val_mse: 16.9934\n",
"Epoch 20/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 11.6902 - mae: 2.3298 - mse: 11.6902 - val_loss: 16.3016 - val_mae: 2.7847 - val_mse: 16.3016\n",
+ "354/354 [==============================] - 0s 237us/sample - loss: 9.8463 - mae: 2.2128 - mse: 9.8463 - val_loss: 15.5787 - val_mae: 2.6066 - val_mse: 15.5787\n",
"Epoch 21/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 11.6052 - mae: 2.2741 - mse: 11.6052 - val_loss: 14.8726 - val_mae: 2.5843 - val_mse: 14.8726\n",
+ "354/354 [==============================] - 0s 243us/sample - loss: 9.7320 - mae: 2.2203 - mse: 9.7320 - val_loss: 15.4724 - val_mae: 2.5029 - val_mse: 15.4724\n",
"Epoch 22/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 11.0375 - mae: 2.2424 - mse: 11.0375 - val_loss: 14.7919 - val_mae: 2.5355 - val_mse: 14.7919\n",
+ "354/354 [==============================] - 0s 246us/sample - loss: 9.3334 - mae: 2.1828 - mse: 9.3334 - val_loss: 15.5829 - val_mae: 2.5425 - val_mse: 15.5829\n",
"Epoch 23/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 10.6963 - mae: 2.2055 - mse: 10.6963 - val_loss: 15.4898 - val_mae: 2.7335 - val_mse: 15.4898\n",
+ "354/354 [==============================] - 0s 250us/sample - loss: 9.2649 - mae: 2.1177 - mse: 9.2649 - val_loss: 15.3732 - val_mae: 2.5681 - val_mse: 15.3732\n",
"Epoch 24/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 10.6611 - mae: 2.2158 - mse: 10.6611 - val_loss: 16.7863 - val_mae: 2.7952 - val_mse: 16.7863\n",
+ "354/354 [==============================] - 0s 254us/sample - loss: 9.1117 - mae: 2.1026 - mse: 9.1117 - val_loss: 15.3212 - val_mae: 2.5937 - val_mse: 15.3212\n",
"Epoch 25/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 10.5480 - mae: 2.1719 - mse: 10.5480 - val_loss: 16.3515 - val_mae: 2.8322 - val_mse: 16.3515\n",
+ "354/354 [==============================] - 0s 236us/sample - loss: 9.0936 - mae: 2.1371 - mse: 9.0936 - val_loss: 15.4627 - val_mae: 2.4987 - val_mse: 15.4627\n",
"Epoch 26/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 10.3655 - mae: 2.1437 - mse: 10.3655 - val_loss: 14.6908 - val_mae: 2.5558 - val_mse: 14.6908\n",
+ "354/354 [==============================] - 0s 259us/sample - loss: 8.9899 - mae: 2.1038 - mse: 8.9899 - val_loss: 15.6777 - val_mae: 2.5340 - val_mse: 15.6777\n",
"Epoch 27/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 9.9280 - mae: 2.1000 - mse: 9.9280 - val_loss: 14.5626 - val_mae: 2.6410 - val_mse: 14.5626\n",
+ "354/354 [==============================] - 0s 247us/sample - loss: 8.7228 - mae: 2.0762 - mse: 8.7228 - val_loss: 15.2530 - val_mae: 2.4652 - val_mse: 15.2530\n",
"Epoch 28/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 9.9190 - mae: 2.1106 - mse: 9.9190 - val_loss: 14.9380 - val_mae: 2.6679 - val_mse: 14.9380\n",
+ "354/354 [==============================] - 0s 241us/sample - loss: 8.8868 - mae: 2.1030 - mse: 8.8868 - val_loss: 15.2783 - val_mae: 2.4830 - val_mse: 15.2783\n",
"Epoch 29/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 9.8377 - mae: 2.1032 - mse: 9.8377 - val_loss: 14.6915 - val_mae: 2.5614 - val_mse: 14.6915\n",
+ "354/354 [==============================] - 0s 244us/sample - loss: 8.4892 - mae: 2.0355 - mse: 8.4892 - val_loss: 15.5838 - val_mae: 2.4354 - val_mse: 15.5838\n",
"Epoch 30/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 9.6516 - mae: 2.0634 - mse: 9.6516 - val_loss: 14.0798 - val_mae: 2.4975 - val_mse: 14.0798\n",
+ "354/354 [==============================] - 0s 234us/sample - loss: 8.5458 - mae: 2.0438 - mse: 8.5458 - val_loss: 15.7169 - val_mae: 2.4373 - val_mse: 15.7169\n",
"Epoch 31/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 9.5721 - mae: 2.0818 - mse: 9.5721 - val_loss: 14.6150 - val_mae: 2.4934 - val_mse: 14.6150\n",
+ "354/354 [==============================] - 0s 238us/sample - loss: 8.1906 - mae: 2.0414 - mse: 8.1906 - val_loss: 15.2374 - val_mae: 2.3660 - val_mse: 15.2374\n",
"Epoch 32/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 9.2323 - mae: 2.0112 - mse: 9.2323 - val_loss: 13.9540 - val_mae: 2.4628 - val_mse: 13.9540\n",
+ "354/354 [==============================] - 0s 239us/sample - loss: 8.2735 - mae: 2.0165 - mse: 8.2735 - val_loss: 14.5404 - val_mae: 2.3494 - val_mse: 14.5404\n",
"Epoch 33/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 8.9889 - mae: 2.0254 - mse: 8.9889 - val_loss: 13.2860 - val_mae: 2.5377 - val_mse: 13.2860\n",
+ "354/354 [==============================] - 0s 249us/sample - loss: 8.2003 - mae: 1.9736 - mse: 8.2003 - val_loss: 14.3508 - val_mae: 2.4532 - val_mse: 14.3508\n",
"Epoch 34/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 9.1012 - mae: 2.0766 - mse: 9.1012 - val_loss: 12.8317 - val_mae: 2.4198 - val_mse: 12.8317\n",
+ "354/354 [==============================] - 0s 245us/sample - loss: 7.7961 - mae: 1.9638 - mse: 7.7961 - val_loss: 14.5859 - val_mae: 2.4202 - val_mse: 14.5859\n",
"Epoch 35/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 8.8631 - mae: 2.0241 - mse: 8.8631 - val_loss: 13.3025 - val_mae: 2.4192 - val_mse: 13.3025\n",
+ "354/354 [==============================] - 0s 227us/sample - loss: 7.6356 - mae: 1.9727 - mse: 7.6356 - val_loss: 15.9552 - val_mae: 2.4375 - val_mse: 15.9552\n",
"Epoch 36/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 8.8026 - mae: 1.9883 - mse: 8.8026 - val_loss: 12.9319 - val_mae: 2.4633 - val_mse: 12.9319\n",
+ "354/354 [==============================] - 0s 241us/sample - loss: 7.9449 - mae: 1.9577 - mse: 7.9449 - val_loss: 14.6588 - val_mae: 2.3436 - val_mse: 14.6588\n",
"Epoch 37/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 8.8867 - mae: 1.9741 - mse: 8.8867 - val_loss: 12.6364 - val_mae: 2.3791 - val_mse: 12.6364\n",
+ "354/354 [==============================] - 0s 224us/sample - loss: 7.7020 - mae: 1.9771 - mse: 7.7020 - val_loss: 14.8468 - val_mae: 2.3969 - val_mse: 14.8468\n",
"Epoch 38/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 8.4074 - mae: 1.9460 - mse: 8.4074 - val_loss: 14.1145 - val_mae: 2.6373 - val_mse: 14.1145\n",
+ "354/354 [==============================] - 0s 240us/sample - loss: 7.5701 - mae: 1.9369 - mse: 7.5701 - val_loss: 15.1807 - val_mae: 2.3786 - val_mse: 15.1807\n",
"Epoch 39/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 8.3968 - mae: 1.9564 - mse: 8.3968 - val_loss: 12.9021 - val_mae: 2.3632 - val_mse: 12.9021\n",
+ "354/354 [==============================] - 0s 228us/sample - loss: 7.2280 - mae: 1.9195 - mse: 7.2280 - val_loss: 14.7364 - val_mae: 2.3831 - val_mse: 14.7364\n",
"Epoch 40/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 8.2169 - mae: 1.9806 - mse: 8.2169 - val_loss: 13.8061 - val_mae: 2.4158 - val_mse: 13.8061\n",
+ "354/354 [==============================] - 0s 242us/sample - loss: 7.5389 - mae: 1.9141 - mse: 7.5389 - val_loss: 15.3474 - val_mae: 2.3752 - val_mse: 15.3474\n",
"Epoch 41/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 8.3619 - mae: 1.9439 - mse: 8.3619 - val_loss: 12.8874 - val_mae: 2.5040 - val_mse: 12.8874\n",
+ "354/354 [==============================] - 0s 227us/sample - loss: 7.3597 - mae: 1.8916 - mse: 7.3597 - val_loss: 14.1774 - val_mae: 2.3974 - val_mse: 14.1774\n",
"Epoch 42/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 8.2899 - mae: 1.9434 - mse: 8.2899 - val_loss: 12.2988 - val_mae: 2.3410 - val_mse: 12.2988\n",
+ "354/354 [==============================] - 0s 239us/sample - loss: 7.0825 - mae: 1.8568 - mse: 7.0825 - val_loss: 14.2668 - val_mae: 2.3858 - val_mse: 14.2668\n",
"Epoch 43/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 8.2357 - mae: 1.9633 - mse: 8.2357 - val_loss: 12.6261 - val_mae: 2.3339 - val_mse: 12.6261\n",
+ "354/354 [==============================] - 0s 254us/sample - loss: 7.0469 - mae: 1.8867 - mse: 7.0469 - val_loss: 14.9425 - val_mae: 2.3592 - val_mse: 14.9425\n",
"Epoch 44/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 7.8609 - mae: 1.8751 - mse: 7.8609 - val_loss: 13.0507 - val_mae: 2.3719 - val_mse: 13.0507\n",
+ "354/354 [==============================] - 0s 223us/sample - loss: 7.0406 - mae: 1.8601 - mse: 7.0406 - val_loss: 14.0913 - val_mae: 2.3614 - val_mse: 14.0913\n",
"Epoch 45/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 8.0495 - mae: 1.9139 - mse: 8.0495 - val_loss: 13.0642 - val_mae: 2.4887 - val_mse: 13.0642\n",
+ "354/354 [==============================] - 0s 218us/sample - loss: 7.0613 - mae: 1.8465 - mse: 7.0613 - val_loss: 14.8358 - val_mae: 2.3776 - val_mse: 14.8358\n",
"Epoch 46/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 8.0163 - mae: 1.9104 - mse: 8.0163 - val_loss: 12.3198 - val_mae: 2.3351 - val_mse: 12.3198\n",
+ "354/354 [==============================] - 0s 240us/sample - loss: 6.7649 - mae: 1.8648 - mse: 6.7649 - val_loss: 13.9273 - val_mae: 2.3785 - val_mse: 13.9273\n",
"Epoch 47/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 7.9405 - mae: 1.9327 - mse: 7.9405 - val_loss: 12.6989 - val_mae: 2.3331 - val_mse: 12.6989\n",
+ "354/354 [==============================] - 0s 233us/sample - loss: 6.8243 - mae: 1.8643 - mse: 6.8243 - val_loss: 14.8333 - val_mae: 2.3090 - val_mse: 14.8333\n",
"Epoch 48/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 7.8142 - mae: 1.8923 - mse: 7.8142 - val_loss: 12.0313 - val_mae: 2.3155 - val_mse: 12.0313\n",
+ "354/354 [==============================] - 0s 241us/sample - loss: 6.6334 - mae: 1.8277 - mse: 6.6334 - val_loss: 15.1938 - val_mae: 2.3739 - val_mse: 15.1938\n",
"Epoch 49/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 7.6443 - mae: 1.8980 - mse: 7.6443 - val_loss: 12.3676 - val_mae: 2.3588 - val_mse: 12.3676\n",
+ "354/354 [==============================] - 0s 240us/sample - loss: 6.8629 - mae: 1.8398 - mse: 6.8629 - val_loss: 14.6093 - val_mae: 2.5040 - val_mse: 14.6093\n",
"Epoch 50/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 7.5691 - mae: 1.8896 - mse: 7.5691 - val_loss: 12.0860 - val_mae: 2.3235 - val_mse: 12.0860\n",
+ "354/354 [==============================] - 0s 239us/sample - loss: 6.5647 - mae: 1.7984 - mse: 6.5647 - val_loss: 16.0258 - val_mae: 2.4205 - val_mse: 16.0258\n",
"Epoch 51/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 7.5890 - mae: 1.8923 - mse: 7.5890 - val_loss: 12.0311 - val_mae: 2.3124 - val_mse: 12.0311\n",
+ "354/354 [==============================] - 0s 234us/sample - loss: 6.6074 - mae: 1.8102 - mse: 6.6074 - val_loss: 15.2152 - val_mae: 2.4258 - val_mse: 15.2153\n",
"Epoch 52/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 7.4704 - mae: 1.8626 - mse: 7.4704 - val_loss: 12.6293 - val_mae: 2.4518 - val_mse: 12.6293\n",
+ "354/354 [==============================] - 0s 237us/sample - loss: 6.4244 - mae: 1.8012 - mse: 6.4244 - val_loss: 14.8104 - val_mae: 2.3019 - val_mse: 14.8104\n",
"Epoch 53/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 7.3612 - mae: 1.8371 - mse: 7.3612 - val_loss: 11.9190 - val_mae: 2.3333 - val_mse: 11.9190\n",
+ "354/354 [==============================] - 0s 243us/sample - loss: 6.4423 - mae: 1.8020 - mse: 6.4423 - val_loss: 14.0713 - val_mae: 2.3033 - val_mse: 14.0713\n",
"Epoch 54/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 7.4012 - mae: 1.8808 - mse: 7.4012 - val_loss: 11.9248 - val_mae: 2.3689 - val_mse: 11.9248\n",
+ "354/354 [==============================] - 0s 223us/sample - loss: 6.4478 - mae: 1.7550 - mse: 6.4478 - val_loss: 14.0326 - val_mae: 2.3117 - val_mse: 14.0326\n",
"Epoch 55/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 7.2038 - mae: 1.8266 - mse: 7.2038 - val_loss: 12.0944 - val_mae: 2.3533 - val_mse: 12.0944\n",
+ "354/354 [==============================] - 0s 244us/sample - loss: 6.3945 - mae: 1.7955 - mse: 6.3945 - val_loss: 14.4762 - val_mae: 2.3475 - val_mse: 14.4762\n",
"Epoch 56/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 7.1204 - mae: 1.8215 - mse: 7.1204 - val_loss: 11.7066 - val_mae: 2.3448 - val_mse: 11.7066\n",
+ "354/354 [==============================] - 0s 231us/sample - loss: 6.2068 - mae: 1.7539 - mse: 6.2068 - val_loss: 14.4932 - val_mae: 2.3048 - val_mse: 14.4932\n",
"Epoch 57/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 7.1178 - mae: 1.8433 - mse: 7.1178 - val_loss: 11.7769 - val_mae: 2.2965 - val_mse: 11.7769\n",
+ "354/354 [==============================] - 0s 242us/sample - loss: 6.1200 - mae: 1.7404 - mse: 6.1200 - val_loss: 13.9329 - val_mae: 2.3452 - val_mse: 13.9329\n",
"Epoch 58/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 7.1196 - mae: 1.8110 - mse: 7.1196 - val_loss: 12.2767 - val_mae: 2.3149 - val_mse: 12.2767\n",
+ "354/354 [==============================] - 0s 237us/sample - loss: 6.2618 - mae: 1.7416 - mse: 6.2618 - val_loss: 14.6890 - val_mae: 2.3846 - val_mse: 14.6890\n",
"Epoch 59/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 7.0974 - mae: 1.8381 - mse: 7.0974 - val_loss: 11.5640 - val_mae: 2.2697 - val_mse: 11.5640\n",
+ "354/354 [==============================] - 0s 236us/sample - loss: 5.8514 - mae: 1.7118 - mse: 5.8514 - val_loss: 15.6700 - val_mae: 2.3766 - val_mse: 15.6700\n",
"Epoch 60/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.7409 - mae: 1.7891 - mse: 6.7409 - val_loss: 12.3791 - val_mae: 2.3625 - val_mse: 12.3791\n",
+ "354/354 [==============================] - 0s 245us/sample - loss: 6.0458 - mae: 1.7110 - mse: 6.0458 - val_loss: 14.5858 - val_mae: 2.3417 - val_mse: 14.5858\n",
"Epoch 61/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.8470 - mae: 1.7871 - mse: 6.8470 - val_loss: 11.7725 - val_mae: 2.3698 - val_mse: 11.7725\n",
+ "354/354 [==============================] - 0s 246us/sample - loss: 6.0823 - mae: 1.7243 - mse: 6.0823 - val_loss: 15.1163 - val_mae: 2.3449 - val_mse: 15.1163\n",
"Epoch 62/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.7225 - mae: 1.8152 - mse: 6.7225 - val_loss: 11.9121 - val_mae: 2.2868 - val_mse: 11.9121\n",
+ "354/354 [==============================] - 0s 242us/sample - loss: 5.6626 - mae: 1.6985 - mse: 5.6626 - val_loss: 15.0898 - val_mae: 2.3515 - val_mse: 15.0898\n",
"Epoch 63/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.7131 - mae: 1.7605 - mse: 6.7131 - val_loss: 11.4516 - val_mae: 2.2583 - val_mse: 11.4516\n",
+ "354/354 [==============================] - 0s 240us/sample - loss: 5.9696 - mae: 1.7284 - mse: 5.9696 - val_loss: 14.2939 - val_mae: 2.3462 - val_mse: 14.2939\n",
"Epoch 64/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.3812 - mae: 1.7668 - mse: 6.3812 - val_loss: 11.4067 - val_mae: 2.2894 - val_mse: 11.4067\n",
+ "354/354 [==============================] - 0s 245us/sample - loss: 5.8503 - mae: 1.7085 - mse: 5.8503 - val_loss: 13.7826 - val_mae: 2.3353 - val_mse: 13.7826\n",
"Epoch 65/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.6222 - mae: 1.7565 - mse: 6.6222 - val_loss: 11.5903 - val_mae: 2.2998 - val_mse: 11.5903\n",
+ "354/354 [==============================] - 0s 227us/sample - loss: 5.7394 - mae: 1.6938 - mse: 5.7394 - val_loss: 14.0608 - val_mae: 2.3246 - val_mse: 14.0608\n",
"Epoch 66/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.4743 - mae: 1.7497 - mse: 6.4743 - val_loss: 13.4242 - val_mae: 2.4360 - val_mse: 13.4242\n",
+ "354/354 [==============================] - 0s 240us/sample - loss: 5.5783 - mae: 1.6769 - mse: 5.5783 - val_loss: 15.5371 - val_mae: 2.4635 - val_mse: 15.5371\n",
"Epoch 67/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.5273 - mae: 1.7640 - mse: 6.5273 - val_loss: 11.3342 - val_mae: 2.2677 - val_mse: 11.3342\n",
+ "354/354 [==============================] - 0s 232us/sample - loss: 5.6714 - mae: 1.6874 - mse: 5.6714 - val_loss: 13.9092 - val_mae: 2.2392 - val_mse: 13.9092\n",
"Epoch 68/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.4266 - mae: 1.7519 - mse: 6.4266 - val_loss: 11.0310 - val_mae: 2.2586 - val_mse: 11.0310\n",
+ "354/354 [==============================] - 0s 241us/sample - loss: 5.6935 - mae: 1.6664 - mse: 5.6935 - val_loss: 14.0716 - val_mae: 2.3146 - val_mse: 14.0716\n",
"Epoch 69/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.2695 - mae: 1.7240 - mse: 6.2695 - val_loss: 11.0090 - val_mae: 2.3480 - val_mse: 11.0090\n",
+ "354/354 [==============================] - 0s 225us/sample - loss: 5.5212 - mae: 1.6871 - mse: 5.5212 - val_loss: 14.3383 - val_mae: 2.3069 - val_mse: 14.3383\n",
"Epoch 70/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.0646 - mae: 1.6954 - mse: 6.0646 - val_loss: 11.5025 - val_mae: 2.4072 - val_mse: 11.5025\n",
+ "354/354 [==============================] - 0s 240us/sample - loss: 5.5734 - mae: 1.6781 - mse: 5.5734 - val_loss: 13.6603 - val_mae: 2.3011 - val_mse: 13.6603\n",
"Epoch 71/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.2148 - mae: 1.7431 - mse: 6.2148 - val_loss: 13.4257 - val_mae: 2.4724 - val_mse: 13.4257\n",
+ "354/354 [==============================] - 0s 239us/sample - loss: 5.2691 - mae: 1.6260 - mse: 5.2691 - val_loss: 16.1045 - val_mae: 2.3393 - val_mse: 16.1045\n",
"Epoch 72/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.1323 - mae: 1.7067 - mse: 6.1323 - val_loss: 11.6827 - val_mae: 2.3037 - val_mse: 11.6827\n",
+ "354/354 [==============================] - 0s 241us/sample - loss: 5.3612 - mae: 1.6277 - mse: 5.3612 - val_loss: 13.5673 - val_mae: 2.2625 - val_mse: 13.5673\n",
"Epoch 73/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.1373 - mae: 1.6878 - mse: 6.1373 - val_loss: 11.3448 - val_mae: 2.2659 - val_mse: 11.3448\n",
+ "354/354 [==============================] - 0s 245us/sample - loss: 5.4357 - mae: 1.6398 - mse: 5.4357 - val_loss: 13.5269 - val_mae: 2.2802 - val_mse: 13.5269\n",
"Epoch 74/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.8058 - mae: 1.6580 - mse: 5.8058 - val_loss: 12.2608 - val_mae: 2.4840 - val_mse: 12.2608\n",
+ "354/354 [==============================] - 0s 238us/sample - loss: 5.4292 - mae: 1.6488 - mse: 5.4292 - val_loss: 14.3962 - val_mae: 2.2998 - val_mse: 14.3962\n",
"Epoch 75/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.0826 - mae: 1.7135 - mse: 6.0826 - val_loss: 10.9988 - val_mae: 2.3573 - val_mse: 10.9988\n",
+ "354/354 [==============================] - 0s 237us/sample - loss: 5.2124 - mae: 1.6044 - mse: 5.2124 - val_loss: 13.7880 - val_mae: 2.3240 - val_mse: 13.7880\n",
"Epoch 76/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.0445 - mae: 1.7116 - mse: 6.0445 - val_loss: 10.5661 - val_mae: 2.2273 - val_mse: 10.5661\n",
+ "354/354 [==============================] - 0s 242us/sample - loss: 5.0692 - mae: 1.6496 - mse: 5.0692 - val_loss: 15.2243 - val_mae: 2.3084 - val_mse: 15.2243\n",
"Epoch 77/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.8601 - mae: 1.6876 - mse: 5.8601 - val_loss: 11.8910 - val_mae: 2.3362 - val_mse: 11.8910\n",
+ "354/354 [==============================] - 0s 240us/sample - loss: 5.2713 - mae: 1.6358 - mse: 5.2713 - val_loss: 14.4513 - val_mae: 2.3700 - val_mse: 14.4513\n",
"Epoch 78/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.9526 - mae: 1.6862 - mse: 5.9526 - val_loss: 10.9216 - val_mae: 2.2957 - val_mse: 10.9216\n",
+ "354/354 [==============================] - 0s 231us/sample - loss: 5.1036 - mae: 1.6274 - mse: 5.1036 - val_loss: 14.5156 - val_mae: 2.3600 - val_mse: 14.5156\n",
"Epoch 79/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.6953 - mae: 1.6600 - mse: 5.6953 - val_loss: 11.3075 - val_mae: 2.3553 - val_mse: 11.3075\n",
+ "354/354 [==============================] - 0s 222us/sample - loss: 5.2459 - mae: 1.6031 - mse: 5.2459 - val_loss: 13.8835 - val_mae: 2.3314 - val_mse: 13.8836\n",
"Epoch 80/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.7693 - mae: 1.6746 - mse: 5.7693 - val_loss: 11.1568 - val_mae: 2.3284 - val_mse: 11.1568\n",
+ "354/354 [==============================] - 0s 231us/sample - loss: 4.9007 - mae: 1.5727 - mse: 4.9007 - val_loss: 13.8099 - val_mae: 2.3869 - val_mse: 13.8099\n",
"Epoch 81/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.6595 - mae: 1.6718 - mse: 5.6595 - val_loss: 12.0236 - val_mae: 2.3323 - val_mse: 12.0235\n",
+ "354/354 [==============================] - 0s 234us/sample - loss: 5.0184 - mae: 1.5977 - mse: 5.0184 - val_loss: 13.9236 - val_mae: 2.2918 - val_mse: 13.9236\n",
"Epoch 82/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.5099 - mae: 1.6473 - mse: 5.5099 - val_loss: 10.7671 - val_mae: 2.2284 - val_mse: 10.7671\n",
+ "354/354 [==============================] - 0s 226us/sample - loss: 4.9304 - mae: 1.5690 - mse: 4.9304 - val_loss: 13.2486 - val_mae: 2.2700 - val_mse: 13.2486\n",
"Epoch 83/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.3901 - mae: 1.6293 - mse: 5.3901 - val_loss: 10.4340 - val_mae: 2.2395 - val_mse: 10.4340\n",
+ "354/354 [==============================] - 0s 235us/sample - loss: 4.8900 - mae: 1.5585 - mse: 4.8900 - val_loss: 14.7053 - val_mae: 2.2975 - val_mse: 14.7053\n",
"Epoch 84/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.2272 - mae: 1.5880 - mse: 5.2272 - val_loss: 10.5282 - val_mae: 2.2358 - val_mse: 10.5282\n",
+ "354/354 [==============================] - 0s 237us/sample - loss: 4.8613 - mae: 1.5754 - mse: 4.8613 - val_loss: 13.6969 - val_mae: 2.2389 - val_mse: 13.6969\n",
"Epoch 85/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.2817 - mae: 1.5993 - mse: 5.2817 - val_loss: 12.7236 - val_mae: 2.5487 - val_mse: 12.7236\n",
+ "354/354 [==============================] - 0s 238us/sample - loss: 4.8000 - mae: 1.5498 - mse: 4.8000 - val_loss: 14.4436 - val_mae: 2.3210 - val_mse: 14.4436\n",
"Epoch 86/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.4569 - mae: 1.6151 - mse: 5.4569 - val_loss: 9.8936 - val_mae: 2.1521 - val_mse: 9.8936\n",
+ "354/354 [==============================] - 0s 235us/sample - loss: 4.7117 - mae: 1.5672 - mse: 4.7117 - val_loss: 14.9045 - val_mae: 2.2897 - val_mse: 14.9045\n",
"Epoch 87/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.2211 - mae: 1.6136 - mse: 5.2211 - val_loss: 10.2496 - val_mae: 2.2236 - val_mse: 10.2496\n",
+ "354/354 [==============================] - 0s 235us/sample - loss: 4.7052 - mae: 1.5525 - mse: 4.7052 - val_loss: 13.7441 - val_mae: 2.3163 - val_mse: 13.7441\n",
"Epoch 88/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.4119 - mae: 1.6099 - mse: 5.4119 - val_loss: 10.1610 - val_mae: 2.2395 - val_mse: 10.1610\n",
+ "354/354 [==============================] - 0s 239us/sample - loss: 4.6802 - mae: 1.5365 - mse: 4.6802 - val_loss: 13.4314 - val_mae: 2.2807 - val_mse: 13.4314\n",
"Epoch 89/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.1924 - mae: 1.5901 - mse: 5.1924 - val_loss: 10.8264 - val_mae: 2.3490 - val_mse: 10.8264\n",
+ "354/354 [==============================] - 0s 241us/sample - loss: 4.6374 - mae: 1.5332 - mse: 4.6374 - val_loss: 15.0482 - val_mae: 2.3000 - val_mse: 15.0482\n",
"Epoch 90/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.1459 - mae: 1.5858 - mse: 5.1459 - val_loss: 12.5025 - val_mae: 2.6935 - val_mse: 12.5025\n",
+ "354/354 [==============================] - 0s 241us/sample - loss: 4.5433 - mae: 1.5285 - mse: 4.5433 - val_loss: 13.7702 - val_mae: 2.3456 - val_mse: 13.7702\n",
"Epoch 91/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.3007 - mae: 1.6294 - mse: 5.3007 - val_loss: 11.1890 - val_mae: 2.3350 - val_mse: 11.1890\n",
+ "354/354 [==============================] - 0s 232us/sample - loss: 4.5477 - mae: 1.5450 - mse: 4.5477 - val_loss: 13.9801 - val_mae: 2.2605 - val_mse: 13.9801\n",
"Epoch 92/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.1556 - mae: 1.5787 - mse: 5.1556 - val_loss: 10.2351 - val_mae: 2.1923 - val_mse: 10.2351\n",
+ "354/354 [==============================] - 0s 248us/sample - loss: 4.4808 - mae: 1.5036 - mse: 4.4808 - val_loss: 14.1505 - val_mae: 2.3413 - val_mse: 14.1505\n",
"Epoch 93/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 4.8326 - mae: 1.5545 - mse: 4.8326 - val_loss: 9.4646 - val_mae: 2.1306 - val_mse: 9.4646\n",
+ "354/354 [==============================] - 0s 236us/sample - loss: 4.5110 - mae: 1.4664 - mse: 4.5110 - val_loss: 14.2742 - val_mae: 2.4985 - val_mse: 14.2742\n",
"Epoch 94/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 4.9053 - mae: 1.5734 - mse: 4.9053 - val_loss: 10.5321 - val_mae: 2.2610 - val_mse: 10.5321\n",
+ "354/354 [==============================] - 0s 244us/sample - loss: 4.2063 - mae: 1.4888 - mse: 4.2063 - val_loss: 15.0578 - val_mae: 2.4181 - val_mse: 15.0578\n",
"Epoch 95/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 4.9277 - mae: 1.5788 - mse: 4.9277 - val_loss: 10.3036 - val_mae: 2.2465 - val_mse: 10.3036\n",
+ "354/354 [==============================] - 0s 248us/sample - loss: 4.5301 - mae: 1.5132 - mse: 4.5301 - val_loss: 15.0761 - val_mae: 2.4323 - val_mse: 15.0761\n",
"Epoch 96/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 4.9132 - mae: 1.5412 - mse: 4.9132 - val_loss: 10.5573 - val_mae: 2.2525 - val_mse: 10.5573\n",
+ "354/354 [==============================] - 0s 233us/sample - loss: 4.4600 - mae: 1.4974 - mse: 4.4600 - val_loss: 13.3793 - val_mae: 2.2611 - val_mse: 13.3793\n",
"Epoch 97/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 4.7811 - mae: 1.5319 - mse: 4.7811 - val_loss: 10.7507 - val_mae: 2.2576 - val_mse: 10.7507\n",
+ "354/354 [==============================] - 0s 248us/sample - loss: 4.2927 - mae: 1.4881 - mse: 4.2927 - val_loss: 13.3131 - val_mae: 2.2616 - val_mse: 13.3131\n",
"Epoch 98/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.1049 - mae: 1.5598 - mse: 5.1049 - val_loss: 9.8901 - val_mae: 2.1534 - val_mse: 9.8901\n",
+ "354/354 [==============================] - 0s 223us/sample - loss: 4.1763 - mae: 1.4515 - mse: 4.1763 - val_loss: 13.8829 - val_mae: 2.4999 - val_mse: 13.8829\n",
"Epoch 99/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 4.6737 - mae: 1.5074 - mse: 4.6737 - val_loss: 10.0904 - val_mae: 2.2182 - val_mse: 10.0904\n",
+ "354/354 [==============================] - 0s 236us/sample - loss: 4.3079 - mae: 1.4784 - mse: 4.3079 - val_loss: 12.9524 - val_mae: 2.2145 - val_mse: 12.9524\n",
"Epoch 100/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 4.7541 - mae: 1.5518 - mse: 4.7541 - val_loss: 9.6605 - val_mae: 2.1656 - val_mse: 9.6605\n"
+ "354/354 [==============================] - 0s 221us/sample - loss: 4.0813 - mae: 1.4686 - mse: 4.0813 - val_loss: 13.4295 - val_mae: 2.1932 - val_mse: 13.4295\n"
]
}
],
@@ -1071,9 +1072,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "x_test / loss : 9.6605\n",
- "x_test / mae : 2.1656\n",
- "x_test / mse : 9.6605\n"
+ "x_test / loss : 13.4295\n",
+ "x_test / mae : 2.1932\n",
+ "x_test / mse : 13.4295\n"
]
}
],
@@ -1139,66 +1140,66 @@
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
- " <td>17.502493</td>\n",
- " <td>2.361973</td>\n",
- " <td>17.502493</td>\n",
- " <td>19.327123</td>\n",
- " <td>2.742917</td>\n",
- " <td>19.327123</td>\n",
+ " <td>16.917902</td>\n",
+ " <td>2.323967</td>\n",
+ " <td>16.917902</td>\n",
+ " <td>22.374289</td>\n",
+ " <td>2.762395</td>\n",
+ " <td>22.374289</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
- " <td>56.817240</td>\n",
- " <td>2.297174</td>\n",
- " <td>56.817240</td>\n",
- " <td>40.660030</td>\n",
- " <td>1.724474</td>\n",
- " <td>40.660030</td>\n",
+ " <td>59.089760</td>\n",
+ " <td>2.424422</td>\n",
+ " <td>59.089763</td>\n",
+ " <td>45.905594</td>\n",
+ " <td>1.882063</td>\n",
+ " <td>45.905594</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
- " <td>4.673726</td>\n",
- " <td>1.507414</td>\n",
- " <td>4.673726</td>\n",
- " <td>9.464625</td>\n",
- " <td>2.130583</td>\n",
- " <td>9.464625</td>\n",
+ " <td>4.081289</td>\n",
+ " <td>1.451479</td>\n",
+ " <td>4.081289</td>\n",
+ " <td>12.952355</td>\n",
+ " <td>2.193249</td>\n",
+ " <td>12.952354</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
- " <td>6.021543</td>\n",
- " <td>1.687790</td>\n",
- " <td>6.021543</td>\n",
- " <td>11.342185</td>\n",
- " <td>2.299006</td>\n",
- " <td>11.342185</td>\n",
+ " <td>5.263275</td>\n",
+ " <td>1.627658</td>\n",
+ " <td>5.263275</td>\n",
+ " <td>14.071505</td>\n",
+ " <td>2.315892</td>\n",
+ " <td>14.071506</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
- " <td>7.579046</td>\n",
- " <td>1.885187</td>\n",
- " <td>7.579046</td>\n",
- " <td>12.440843</td>\n",
- " <td>2.369379</td>\n",
- " <td>12.440843</td>\n",
+ " <td>6.586041</td>\n",
+ " <td>1.806143</td>\n",
+ " <td>6.586040</td>\n",
+ " <td>14.834556</td>\n",
+ " <td>2.377117</td>\n",
+ " <td>14.834556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
- " <td>10.411116</td>\n",
- " <td>2.150775</td>\n",
- " <td>10.411116</td>\n",
- " <td>14.812038</td>\n",
- " <td>2.647739</td>\n",
- " <td>14.812038</td>\n",
+ " <td>9.015857</td>\n",
+ " <td>2.103222</td>\n",
+ " <td>9.015858</td>\n",
+ " <td>15.605363</td>\n",
+ " <td>2.503167</td>\n",
+ " <td>15.605363</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
- " <td>503.077698</td>\n",
- " <td>20.273706</td>\n",
- " <td>503.077698</td>\n",
- " <td>393.982666</td>\n",
- " <td>17.655603</td>\n",
- " <td>393.982666</td>\n",
+ " <td>513.450921</td>\n",
+ " <td>20.725618</td>\n",
+ " <td>513.450989</td>\n",
+ " <td>430.556491</td>\n",
+ " <td>18.292789</td>\n",
+ " <td>430.556488</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -1207,13 +1208,13 @@
"text/plain": [
" loss mae mse val_loss val_mae val_mse\n",
"count 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000\n",
- "mean 17.502493 2.361973 17.502493 19.327123 2.742917 19.327123\n",
- "std 56.817240 2.297174 56.817240 40.660030 1.724474 40.660030\n",
- "min 4.673726 1.507414 4.673726 9.464625 2.130583 9.464625\n",
- "25% 6.021543 1.687790 6.021543 11.342185 2.299006 11.342185\n",
- "50% 7.579046 1.885187 7.579046 12.440843 2.369379 12.440843\n",
- "75% 10.411116 2.150775 10.411116 14.812038 2.647739 14.812038\n",
- "max 503.077698 20.273706 503.077698 393.982666 17.655603 393.982666"
+ "mean 16.917902 2.323967 16.917902 22.374289 2.762395 22.374289\n",
+ "std 59.089760 2.424422 59.089763 45.905594 1.882063 45.905594\n",
+ "min 4.081289 1.451479 4.081289 12.952355 2.193249 12.952354\n",
+ "25% 5.263275 1.627658 5.263275 14.071505 2.315892 14.071506\n",
+ "50% 6.586041 1.806143 6.586040 14.834556 2.377117 14.834556\n",
+ "75% 9.015857 2.103222 9.015858 15.605363 2.503167 15.605363\n",
+ "max 513.450921 20.725618 513.450989 430.556491 18.292789 430.556488"
]
},
"execution_count": 10,
@@ -1236,7 +1237,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "min( val_mae ) : 2.1306\n"
+ "min( val_mae ) : 2.1932\n"
]
}
],
@@ -1251,7 +1252,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
@@ -1263,7 +1264,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
@@ -1275,7 +1276,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
@@ -1287,7 +1288,7 @@
}
],
"source": [
- "ooo.plot_history(history, plot={'MSE' :['mse', 'val_mse'],\n",
+ "pwk.plot_history(history, plot={'MSE' :['mse', 'val_mse'],\n",
" 'MAE' :['mae', 'val_mae'],\n",
" 'LOSS':['loss','val_loss']})"
]
@@ -1316,14 +1317,14 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Prediction : 9.46 K$\n",
+ "Prediction : 8.92 K$\n",
"Reality : 10.40 K$\n"
]
}
@@ -1335,6 +1336,25 @@
"print(\"Reality : {:.2f} K$\".format(real_price))"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "End time is : Tuesday 15 December 2020, 21:51:32\n",
+ "Duration is : 00:00:10 080ms\n",
+ "This notebook ends here\n"
+ ]
+ }
+ ],
+ "source": [
+ "pwk.end()"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
diff --git a/BHPD/02-DNN-Regression-Premium.ipynb b/BHPD/02-DNN-Regression-Premium.ipynb
index 14cd403..8ffee0f 100644
--- a/BHPD/02-DNN-Regression-Premium.ipynb
+++ b/BHPD/02-DNN-Regression-Premium.ipynb
@@ -114,13 +114,13 @@
"text": [
"\n",
"FIDLE 2020 - Practical Work Module\n",
- "Version : 0.57 DEV\n",
- "Run time : Friday 11 September 2020, 08:51:26\n",
- "TensorFlow version : 2.2.0\n",
- "Keras version : 2.3.0-tf\n",
- "Current place : Fidle at IDRIS\n",
- "Datasets dir : /gpfswork/rech/mlh/commun/datasets\n",
- "Update keras cache : Done\n"
+ "Version : 0.6.1 DEV\n",
+ "Notebook id : BHP2\n",
+ "Run time : Tuesday 15 December 2020, 22:05:15\n",
+ "TensorFlow version : 2.0.0\n",
+ "Keras version : 2.2.4-tf\n",
+ "Datasets dir : /home/pjluc/datasets/fidle\n",
+ "Update keras cache : False\n"
]
}
],
@@ -137,9 +137,9 @@
"from importlib import reload\n",
"\n",
"sys.path.append('..')\n",
- "import fidle.pwk as ooo\n",
+ "import fidle.pwk as pwk\n",
"\n",
- "place, datasets_dir = ooo.init()"
+ "datasets_dir = pwk.init('BHP2')"
]
},
{
@@ -178,96 +178,96 @@
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- "</style><table id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63b\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> <th class=\"col_heading level0 col13\" >medv</th> </tr></thead><tbody>\n",
+ "</style><table id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> <th class=\"col_heading level0 col13\" >medv</th> </tr></thead><tbody>\n",
" <tr>\n",
- " <th id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63blevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow0_col0\" class=\"data row0 col0\" >0.01</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow0_col1\" class=\"data row0 col1\" >18.00</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow0_col2\" class=\"data row0 col2\" >2.31</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow0_col3\" class=\"data row0 col3\" >0.00</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow0_col4\" class=\"data row0 col4\" >0.54</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow0_col5\" class=\"data row0 col5\" >6.58</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow0_col6\" class=\"data row0 col6\" >65.20</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow0_col7\" class=\"data row0 col7\" >4.09</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow0_col8\" class=\"data row0 col8\" >1.00</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow0_col9\" class=\"data row0 col9\" >296.00</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow0_col10\" class=\"data row0 col10\" >15.30</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow0_col11\" class=\"data row0 col11\" >396.90</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow0_col12\" class=\"data row0 col12\" >4.98</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow0_col13\" class=\"data row0 col13\" >24.00</td>\n",
+ " <th id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row0_col0\" class=\"data row0 col0\" >0.01</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row0_col1\" class=\"data row0 col1\" >18.00</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row0_col2\" class=\"data row0 col2\" >2.31</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row0_col3\" class=\"data row0 col3\" >0.00</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row0_col4\" class=\"data row0 col4\" >0.54</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row0_col5\" class=\"data row0 col5\" >6.58</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row0_col6\" class=\"data row0 col6\" >65.20</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row0_col7\" class=\"data row0 col7\" >4.09</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row0_col8\" class=\"data row0 col8\" >1.00</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row0_col9\" class=\"data row0 col9\" >296.00</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row0_col10\" class=\"data row0 col10\" >15.30</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row0_col11\" class=\"data row0 col11\" >396.90</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row0_col12\" class=\"data row0 col12\" >4.98</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row0_col13\" class=\"data row0 col13\" >24.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63blevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow1_col0\" class=\"data row1 col0\" >0.03</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow1_col1\" class=\"data row1 col1\" >0.00</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow1_col2\" class=\"data row1 col2\" >7.07</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow1_col3\" class=\"data row1 col3\" >0.00</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow1_col4\" class=\"data row1 col4\" >0.47</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow1_col5\" class=\"data row1 col5\" >6.42</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow1_col6\" class=\"data row1 col6\" >78.90</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow1_col7\" class=\"data row1 col7\" >4.97</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow1_col8\" class=\"data row1 col8\" >2.00</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow1_col9\" class=\"data row1 col9\" >242.00</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow1_col10\" class=\"data row1 col10\" >17.80</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow1_col11\" class=\"data row1 col11\" >396.90</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow1_col12\" class=\"data row1 col12\" >9.14</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow1_col13\" class=\"data row1 col13\" >21.60</td>\n",
+ " <th id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row1_col0\" class=\"data row1 col0\" >0.03</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row1_col2\" class=\"data row1 col2\" >7.07</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row1_col4\" class=\"data row1 col4\" >0.47</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row1_col5\" class=\"data row1 col5\" >6.42</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row1_col6\" class=\"data row1 col6\" >78.90</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row1_col7\" class=\"data row1 col7\" >4.97</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row1_col8\" class=\"data row1 col8\" >2.00</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row1_col9\" class=\"data row1 col9\" >242.00</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row1_col10\" class=\"data row1 col10\" >17.80</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row1_col11\" class=\"data row1 col11\" >396.90</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row1_col12\" class=\"data row1 col12\" >9.14</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row1_col13\" class=\"data row1 col13\" >21.60</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63blevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow2_col0\" class=\"data row2 col0\" >0.03</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow2_col1\" class=\"data row2 col1\" >0.00</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow2_col2\" class=\"data row2 col2\" >7.07</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow2_col3\" class=\"data row2 col3\" >0.00</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow2_col4\" class=\"data row2 col4\" >0.47</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow2_col5\" class=\"data row2 col5\" >7.18</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow2_col6\" class=\"data row2 col6\" >61.10</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow2_col7\" class=\"data row2 col7\" >4.97</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow2_col8\" class=\"data row2 col8\" >2.00</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow2_col9\" class=\"data row2 col9\" >242.00</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow2_col10\" class=\"data row2 col10\" >17.80</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow2_col11\" class=\"data row2 col11\" >392.83</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow2_col12\" class=\"data row2 col12\" >4.03</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow2_col13\" class=\"data row2 col13\" >34.70</td>\n",
+ " <th id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row2_col0\" class=\"data row2 col0\" >0.03</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row2_col2\" class=\"data row2 col2\" >7.07</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row2_col3\" class=\"data row2 col3\" >0.00</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row2_col4\" class=\"data row2 col4\" >0.47</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row2_col5\" class=\"data row2 col5\" >7.18</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row2_col6\" class=\"data row2 col6\" >61.10</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row2_col7\" class=\"data row2 col7\" >4.97</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row2_col8\" class=\"data row2 col8\" >2.00</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row2_col9\" class=\"data row2 col9\" >242.00</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row2_col10\" class=\"data row2 col10\" >17.80</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row2_col11\" class=\"data row2 col11\" >392.83</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row2_col12\" class=\"data row2 col12\" >4.03</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row2_col13\" class=\"data row2 col13\" >34.70</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63blevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow3_col0\" class=\"data row3 col0\" >0.03</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow3_col1\" class=\"data row3 col1\" >0.00</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow3_col2\" class=\"data row3 col2\" >2.18</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow3_col3\" class=\"data row3 col3\" >0.00</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow3_col4\" class=\"data row3 col4\" >0.46</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow3_col5\" class=\"data row3 col5\" >7.00</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow3_col6\" class=\"data row3 col6\" >45.80</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow3_col7\" class=\"data row3 col7\" >6.06</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow3_col8\" class=\"data row3 col8\" >3.00</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow3_col9\" class=\"data row3 col9\" >222.00</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow3_col10\" class=\"data row3 col10\" >18.70</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow3_col11\" class=\"data row3 col11\" >394.63</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow3_col12\" class=\"data row3 col12\" >2.94</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow3_col13\" class=\"data row3 col13\" >33.40</td>\n",
+ " <th id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row3_col0\" class=\"data row3 col0\" >0.03</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row3_col2\" class=\"data row3 col2\" >2.18</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row3_col4\" class=\"data row3 col4\" >0.46</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row3_col5\" class=\"data row3 col5\" >7.00</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row3_col6\" class=\"data row3 col6\" >45.80</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row3_col7\" class=\"data row3 col7\" >6.06</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row3_col8\" class=\"data row3 col8\" >3.00</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row3_col9\" class=\"data row3 col9\" >222.00</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row3_col10\" class=\"data row3 col10\" >18.70</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row3_col11\" class=\"data row3 col11\" >394.63</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row3_col12\" class=\"data row3 col12\" >2.94</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row3_col13\" class=\"data row3 col13\" >33.40</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63blevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow4_col0\" class=\"data row4 col0\" >0.07</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow4_col1\" class=\"data row4 col1\" >0.00</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow4_col2\" class=\"data row4 col2\" >2.18</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow4_col3\" class=\"data row4 col3\" >0.00</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow4_col4\" class=\"data row4 col4\" >0.46</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow4_col5\" class=\"data row4 col5\" >7.15</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow4_col6\" class=\"data row4 col6\" >54.20</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow4_col7\" class=\"data row4 col7\" >6.06</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow4_col8\" class=\"data row4 col8\" >3.00</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow4_col9\" class=\"data row4 col9\" >222.00</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow4_col10\" class=\"data row4 col10\" >18.70</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow4_col11\" class=\"data row4 col11\" >396.90</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow4_col12\" class=\"data row4 col12\" >5.33</td>\n",
- " <td id=\"T_369eb1e8_f3fb_11ea_bf65_0cc47af5c63brow4_col13\" class=\"data row4 col13\" >36.20</td>\n",
+ " <th id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row4_col0\" class=\"data row4 col0\" >0.07</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row4_col2\" class=\"data row4 col2\" >2.18</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row4_col4\" class=\"data row4 col4\" >0.46</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row4_col5\" class=\"data row4 col5\" >7.15</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row4_col6\" class=\"data row4 col6\" >54.20</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row4_col7\" class=\"data row4 col7\" >6.06</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row4_col8\" class=\"data row4 col8\" >3.00</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row4_col9\" class=\"data row4 col9\" >222.00</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row4_col10\" class=\"data row4 col10\" >18.70</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row4_col11\" class=\"data row4 col11\" >396.90</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row4_col12\" class=\"data row4 col12\" >5.33</td>\n",
+ " <td id=\"T_3ad09fba_3f19_11eb_8e70_3dad9513ffb3row4_col13\" class=\"data row4 col13\" >36.20</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x145717c33190>"
+ "<pandas.io.formats.style.Styler at 0x7f27698151d0>"
]
},
"metadata": {},
@@ -352,139 +352,139 @@
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- "</style><table id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63b\" ><caption>Before normalization :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> </tr></thead><tbody>\n",
+ "</style><table id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3\" ><caption>Before normalization :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> </tr></thead><tbody>\n",
" <tr>\n",
- " <th id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63blevel0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow0_col0\" class=\"data row0 col0\" >354.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow0_col1\" class=\"data row0 col1\" >354.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow0_col2\" class=\"data row0 col2\" >354.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow0_col3\" class=\"data row0 col3\" >354.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow0_col4\" class=\"data row0 col4\" >354.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow0_col5\" class=\"data row0 col5\" >354.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow0_col6\" class=\"data row0 col6\" >354.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow0_col7\" class=\"data row0 col7\" >354.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow0_col8\" class=\"data row0 col8\" >354.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow0_col9\" class=\"data row0 col9\" >354.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow0_col10\" class=\"data row0 col10\" >354.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow0_col11\" class=\"data row0 col11\" >354.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow0_col12\" class=\"data row0 col12\" >354.00</td>\n",
+ " <th id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63blevel0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow1_col0\" class=\"data row1 col0\" >3.74</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow1_col1\" class=\"data row1 col1\" >10.92</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow1_col2\" class=\"data row1 col2\" >11.27</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow1_col3\" class=\"data row1 col3\" >0.06</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow1_col4\" class=\"data row1 col4\" >0.56</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow1_col5\" class=\"data row1 col5\" >6.28</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow1_col6\" class=\"data row1 col6\" >70.19</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow1_col7\" class=\"data row1 col7\" >3.73</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow1_col8\" class=\"data row1 col8\" >9.73</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow1_col9\" class=\"data row1 col9\" >412.53</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow1_col10\" class=\"data row1 col10\" >18.44</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow1_col11\" class=\"data row1 col11\" >355.09</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow1_col12\" class=\"data row1 col12\" >12.85</td>\n",
+ " <th id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row1_col0\" class=\"data row1 col0\" >3.58</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row1_col1\" class=\"data row1 col1\" >10.87</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row1_col2\" class=\"data row1 col2\" >11.19</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row1_col3\" class=\"data row1 col3\" >0.08</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row1_col4\" class=\"data row1 col4\" >0.56</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row1_col5\" class=\"data row1 col5\" >6.29</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row1_col6\" class=\"data row1 col6\" >69.35</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row1_col7\" class=\"data row1 col7\" >3.75</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row1_col8\" class=\"data row1 col8\" >9.66</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row1_col9\" class=\"data row1 col9\" >408.53</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row1_col10\" class=\"data row1 col10\" >18.45</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row1_col11\" class=\"data row1 col11\" >357.38</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row1_col12\" class=\"data row1 col12\" >12.64</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63blevel0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow2_col0\" class=\"data row2 col0\" >8.36</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow2_col1\" class=\"data row2 col1\" >23.31</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow2_col2\" class=\"data row2 col2\" >6.88</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow2_col3\" class=\"data row2 col3\" >0.25</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow2_col4\" class=\"data row2 col4\" >0.12</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow2_col5\" class=\"data row2 col5\" >0.69</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow2_col6\" class=\"data row2 col6\" >27.43</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow2_col7\" class=\"data row2 col7\" >2.13</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow2_col8\" class=\"data row2 col8\" >8.82</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow2_col9\" class=\"data row2 col9\" >169.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow2_col10\" class=\"data row2 col10\" >2.19</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow2_col11\" class=\"data row2 col11\" >93.20</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow2_col12\" class=\"data row2 col12\" >7.21</td>\n",
+ " <th id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row2_col0\" class=\"data row2 col0\" >8.38</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row2_col1\" class=\"data row2 col1\" >23.05</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row2_col2\" class=\"data row2 col2\" >6.85</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row2_col3\" class=\"data row2 col3\" >0.27</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row2_col4\" class=\"data row2 col4\" >0.12</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row2_col5\" class=\"data row2 col5\" >0.70</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row2_col6\" class=\"data row2 col6\" >27.89</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row2_col7\" class=\"data row2 col7\" >2.11</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row2_col8\" class=\"data row2 col8\" >8.74</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row2_col9\" class=\"data row2 col9\" >168.51</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row2_col10\" class=\"data row2 col10\" >2.20</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row2_col11\" class=\"data row2 col11\" >90.23</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row2_col12\" class=\"data row2 col12\" >7.18</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63blevel0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow3_col0\" class=\"data row3 col0\" >0.01</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow3_col1\" class=\"data row3 col1\" >0.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow3_col2\" class=\"data row3 col2\" >0.74</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow3_col3\" class=\"data row3 col3\" >0.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow3_col4\" class=\"data row3 col4\" >0.39</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow3_col5\" class=\"data row3 col5\" >3.56</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow3_col6\" class=\"data row3 col6\" >6.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow3_col7\" class=\"data row3 col7\" >1.13</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow3_col8\" class=\"data row3 col8\" >1.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow3_col9\" class=\"data row3 col9\" >187.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow3_col10\" class=\"data row3 col10\" >12.60</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow3_col11\" class=\"data row3 col11\" >0.32</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow3_col12\" class=\"data row3 col12\" >1.73</td>\n",
+ " <th id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row3_col0\" class=\"data row3 col0\" >0.01</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row3_col2\" class=\"data row3 col2\" >0.46</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row3_col4\" class=\"data row3 col4\" >0.39</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row3_col5\" class=\"data row3 col5\" >3.86</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row3_col6\" class=\"data row3 col6\" >2.90</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row3_col7\" class=\"data row3 col7\" >1.14</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row3_col8\" class=\"data row3 col8\" >1.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row3_col9\" class=\"data row3 col9\" >187.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row3_col10\" class=\"data row3 col10\" >12.60</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row3_col11\" class=\"data row3 col11\" >0.32</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row3_col12\" class=\"data row3 col12\" >1.73</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63blevel0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow4_col0\" class=\"data row4 col0\" >0.08</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow4_col1\" class=\"data row4 col1\" >0.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow4_col2\" class=\"data row4 col2\" >5.13</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow4_col3\" class=\"data row4 col3\" >0.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow4_col4\" class=\"data row4 col4\" >0.45</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow4_col5\" class=\"data row4 col5\" >5.88</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow4_col6\" class=\"data row4 col6\" >47.45</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow4_col7\" class=\"data row4 col7\" >2.06</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow4_col8\" class=\"data row4 col8\" >4.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow4_col9\" class=\"data row4 col9\" >281.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow4_col10\" class=\"data row4 col10\" >17.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow4_col11\" class=\"data row4 col11\" >374.46</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow4_col12\" class=\"data row4 col12\" >7.14</td>\n",
+ " <th id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row4_col0\" class=\"data row4 col0\" >0.09</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row4_col2\" class=\"data row4 col2\" >5.15</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row4_col4\" class=\"data row4 col4\" >0.45</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row4_col5\" class=\"data row4 col5\" >5.88</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row4_col6\" class=\"data row4 col6\" >45.80</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row4_col7\" class=\"data row4 col7\" >2.09</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row4_col8\" class=\"data row4 col8\" >4.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row4_col9\" class=\"data row4 col9\" >279.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row4_col10\" class=\"data row4 col10\" >17.40</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row4_col11\" class=\"data row4 col11\" >375.38</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row4_col12\" class=\"data row4 col12\" >7.18</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63blevel0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow5_col0\" class=\"data row5 col0\" >0.27</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow5_col1\" class=\"data row5 col1\" >0.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow5_col2\" class=\"data row5 col2\" >9.79</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow5_col3\" class=\"data row5 col3\" >0.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow5_col4\" class=\"data row5 col4\" >0.54</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow5_col5\" class=\"data row5 col5\" >6.21</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow5_col6\" class=\"data row5 col6\" >79.45</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow5_col7\" class=\"data row5 col7\" >3.10</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow5_col8\" class=\"data row5 col8\" >5.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow5_col9\" class=\"data row5 col9\" >345.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow5_col10\" class=\"data row5 col10\" >19.05</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow5_col11\" class=\"data row5 col11\" >391.18</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow5_col12\" class=\"data row5 col12\" >11.65</td>\n",
+ " <th id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row5_col0\" class=\"data row5 col0\" >0.30</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row5_col2\" class=\"data row5 col2\" >9.69</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row5_col3\" class=\"data row5 col3\" >0.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row5_col4\" class=\"data row5 col4\" >0.54</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row5_col5\" class=\"data row5 col5\" >6.20</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row5_col6\" class=\"data row5 col6\" >78.80</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row5_col7\" class=\"data row5 col7\" >3.10</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row5_col8\" class=\"data row5 col8\" >5.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row5_col9\" class=\"data row5 col9\" >330.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row5_col10\" class=\"data row5 col10\" >19.10</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row5_col11\" class=\"data row5 col11\" >391.48</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row5_col12\" class=\"data row5 col12\" >11.27</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63blevel0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow6_col0\" class=\"data row6 col0\" >3.76</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow6_col1\" class=\"data row6 col1\" >0.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow6_col2\" class=\"data row6 col2\" >18.10</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow6_col3\" class=\"data row6 col3\" >0.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow6_col4\" class=\"data row6 col4\" >0.64</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow6_col5\" class=\"data row6 col5\" >6.63</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow6_col6\" class=\"data row6 col6\" >94.60</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow6_col7\" class=\"data row6 col7\" >4.80</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow6_col8\" class=\"data row6 col8\" >24.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow6_col9\" class=\"data row6 col9\" >666.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow6_col10\" class=\"data row6 col10\" >20.20</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow6_col11\" class=\"data row6 col11\" >395.69</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow6_col12\" class=\"data row6 col12\" >17.12</td>\n",
+ " <th id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row6_col0\" class=\"data row6 col0\" >3.66</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row6_col1\" class=\"data row6 col1\" >0.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row6_col2\" class=\"data row6 col2\" >18.10</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row6_col3\" class=\"data row6 col3\" >0.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row6_col4\" class=\"data row6 col4\" >0.62</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row6_col5\" class=\"data row6 col5\" >6.62</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row6_col6\" class=\"data row6 col6\" >94.30</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row6_col7\" class=\"data row6 col7\" >5.11</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row6_col8\" class=\"data row6 col8\" >24.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row6_col9\" class=\"data row6 col9\" >666.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row6_col10\" class=\"data row6 col10\" >20.20</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row6_col11\" class=\"data row6 col11\" >396.24</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row6_col12\" class=\"data row6 col12\" >16.63</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63blevel0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow7_col0\" class=\"data row7 col0\" >73.53</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow7_col1\" class=\"data row7 col1\" >100.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow7_col2\" class=\"data row7 col2\" >27.74</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow7_col3\" class=\"data row7 col3\" >1.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow7_col4\" class=\"data row7 col4\" >0.87</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow7_col5\" class=\"data row7 col5\" >8.78</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow7_col6\" class=\"data row7 col6\" >100.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow7_col7\" class=\"data row7 col7\" >12.13</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow7_col8\" class=\"data row7 col8\" >24.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow7_col9\" class=\"data row7 col9\" >711.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow7_col10\" class=\"data row7 col10\" >22.00</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow7_col11\" class=\"data row7 col11\" >396.90</td>\n",
- " <td id=\"T_36a7f8f2_f3fb_11ea_bf65_0cc47af5c63brow7_col12\" class=\"data row7 col12\" >34.77</td>\n",
+ " <th id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row7_col0\" class=\"data row7 col0\" >88.98</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row7_col1\" class=\"data row7 col1\" >100.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row7_col2\" class=\"data row7 col2\" >27.74</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row7_col3\" class=\"data row7 col3\" >1.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row7_col4\" class=\"data row7 col4\" >0.87</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row7_col5\" class=\"data row7 col5\" >8.78</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row7_col6\" class=\"data row7 col6\" >100.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row7_col7\" class=\"data row7 col7\" >12.13</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row7_col8\" class=\"data row7 col8\" >24.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row7_col9\" class=\"data row7 col9\" >711.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row7_col10\" class=\"data row7 col10\" >22.00</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row7_col11\" class=\"data row7 col11\" >396.90</td>\n",
+ " <td id=\"T_3ada7058_3f19_11eb_8e70_3dad9513ffb3row7_col12\" class=\"data row7 col12\" >37.97</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x14578e541410>"
+ "<pandas.io.formats.style.Styler at 0x7f27e44e7490>"
]
},
"metadata": {},
@@ -494,139 +494,139 @@
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- "</style><table id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63b\" ><caption>After normalization :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> </tr></thead><tbody>\n",
+ "</style><table id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3\" ><caption>After normalization :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> </tr></thead><tbody>\n",
" <tr>\n",
- " <th id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63blevel0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow0_col0\" class=\"data row0 col0\" >354.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow0_col1\" class=\"data row0 col1\" >354.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow0_col2\" class=\"data row0 col2\" >354.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow0_col3\" class=\"data row0 col3\" >354.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow0_col4\" class=\"data row0 col4\" >354.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow0_col5\" class=\"data row0 col5\" >354.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow0_col6\" class=\"data row0 col6\" >354.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow0_col7\" class=\"data row0 col7\" >354.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow0_col8\" class=\"data row0 col8\" >354.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow0_col9\" class=\"data row0 col9\" >354.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow0_col10\" class=\"data row0 col10\" >354.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow0_col11\" class=\"data row0 col11\" >354.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow0_col12\" class=\"data row0 col12\" >354.00</td>\n",
+ " <th id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63blevel0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow1_col0\" class=\"data row1 col0\" >0.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow1_col1\" class=\"data row1 col1\" >0.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow1_col2\" class=\"data row1 col2\" >0.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow1_col3\" class=\"data row1 col3\" >0.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow1_col4\" class=\"data row1 col4\" >-0.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow1_col5\" class=\"data row1 col5\" >0.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow1_col6\" class=\"data row1 col6\" >0.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow1_col7\" class=\"data row1 col7\" >0.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow1_col8\" class=\"data row1 col8\" >0.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow1_col9\" class=\"data row1 col9\" >-0.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow1_col10\" class=\"data row1 col10\" >0.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow1_col11\" class=\"data row1 col11\" >0.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow1_col12\" class=\"data row1 col12\" >-0.00</td>\n",
+ " <th id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row1_col0\" class=\"data row1 col0\" >0.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row1_col1\" class=\"data row1 col1\" >-0.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row1_col4\" class=\"data row1 col4\" >-0.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row1_col5\" class=\"data row1 col5\" >-0.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row1_col6\" class=\"data row1 col6\" >0.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row1_col7\" class=\"data row1 col7\" >-0.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row1_col8\" class=\"data row1 col8\" >0.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row1_col9\" class=\"data row1 col9\" >0.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row1_col10\" class=\"data row1 col10\" >0.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row1_col11\" class=\"data row1 col11\" >0.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row1_col12\" class=\"data row1 col12\" >-0.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63blevel0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow2_col0\" class=\"data row2 col0\" >1.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow2_col1\" class=\"data row2 col1\" >1.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow2_col2\" class=\"data row2 col2\" >1.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow2_col3\" class=\"data row2 col3\" >1.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow2_col4\" class=\"data row2 col4\" >1.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow2_col5\" class=\"data row2 col5\" >1.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow2_col6\" class=\"data row2 col6\" >1.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow2_col7\" class=\"data row2 col7\" >1.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow2_col8\" class=\"data row2 col8\" >1.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow2_col9\" class=\"data row2 col9\" >1.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow2_col10\" class=\"data row2 col10\" >1.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow2_col11\" class=\"data row2 col11\" >1.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow2_col12\" class=\"data row2 col12\" >1.00</td>\n",
+ " <th id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row2_col0\" class=\"data row2 col0\" >1.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row2_col1\" class=\"data row2 col1\" >1.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row2_col3\" class=\"data row2 col3\" >1.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row2_col4\" class=\"data row2 col4\" >1.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row2_col5\" class=\"data row2 col5\" >1.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row2_col6\" class=\"data row2 col6\" >1.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row2_col7\" class=\"data row2 col7\" >1.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row2_col8\" class=\"data row2 col8\" >1.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row2_col9\" class=\"data row2 col9\" >1.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row2_col10\" class=\"data row2 col10\" >1.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row2_col11\" class=\"data row2 col11\" >1.00</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row2_col12\" class=\"data row2 col12\" >1.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63blevel0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow3_col0\" class=\"data row3 col0\" >-0.45</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow3_col1\" class=\"data row3 col1\" >-0.47</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow3_col2\" class=\"data row3 col2\" >-1.53</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow3_col3\" class=\"data row3 col3\" >-0.26</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow3_col4\" class=\"data row3 col4\" >-1.47</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow3_col5\" class=\"data row3 col5\" >-3.92</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow3_col6\" class=\"data row3 col6\" >-2.34</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow3_col7\" class=\"data row3 col7\" >-1.22</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow3_col8\" class=\"data row3 col8\" >-0.99</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow3_col9\" class=\"data row3 col9\" >-1.33</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow3_col10\" class=\"data row3 col10\" >-2.66</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow3_col11\" class=\"data row3 col11\" >-3.81</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow3_col12\" class=\"data row3 col12\" >-1.54</td>\n",
+ " <th id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row3_col0\" class=\"data row3 col0\" >-0.43</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row3_col1\" class=\"data row3 col1\" >-0.47</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row3_col2\" class=\"data row3 col2\" >-1.57</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row3_col3\" class=\"data row3 col3\" >-0.29</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row3_col4\" class=\"data row3 col4\" >-1.45</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row3_col5\" class=\"data row3 col5\" >-3.48</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row3_col6\" class=\"data row3 col6\" >-2.38</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row3_col7\" class=\"data row3 col7\" >-1.24</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row3_col8\" class=\"data row3 col8\" >-0.99</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row3_col9\" class=\"data row3 col9\" >-1.31</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row3_col10\" class=\"data row3 col10\" >-2.66</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row3_col11\" class=\"data row3 col11\" >-3.96</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row3_col12\" class=\"data row3 col12\" >-1.52</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63blevel0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow4_col0\" class=\"data row4 col0\" >-0.44</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow4_col1\" class=\"data row4 col1\" >-0.47</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow4_col2\" class=\"data row4 col2\" >-0.89</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow4_col3\" class=\"data row4 col3\" >-0.26</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow4_col4\" class=\"data row4 col4\" >-0.90</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow4_col5\" class=\"data row4 col5\" >-0.59</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow4_col6\" class=\"data row4 col6\" >-0.83</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow4_col7\" class=\"data row4 col7\" >-0.78</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow4_col8\" class=\"data row4 col8\" >-0.65</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow4_col9\" class=\"data row4 col9\" >-0.78</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow4_col10\" class=\"data row4 col10\" >-0.66</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow4_col11\" class=\"data row4 col11\" >0.21</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow4_col12\" class=\"data row4 col12\" >-0.79</td>\n",
+ " <th id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row4_col0\" class=\"data row4 col0\" >-0.42</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row4_col1\" class=\"data row4 col1\" >-0.47</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row4_col2\" class=\"data row4 col2\" >-0.88</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row4_col3\" class=\"data row4 col3\" >-0.29</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row4_col4\" class=\"data row4 col4\" >-0.89</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row4_col5\" class=\"data row4 col5\" >-0.58</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row4_col6\" class=\"data row4 col6\" >-0.84</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row4_col7\" class=\"data row4 col7\" >-0.79</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row4_col8\" class=\"data row4 col8\" >-0.65</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row4_col9\" class=\"data row4 col9\" >-0.77</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row4_col10\" class=\"data row4 col10\" >-0.48</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row4_col11\" class=\"data row4 col11\" >0.20</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row4_col12\" class=\"data row4 col12\" >-0.76</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63blevel0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow5_col0\" class=\"data row5 col0\" >-0.41</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow5_col1\" class=\"data row5 col1\" >-0.47</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow5_col2\" class=\"data row5 col2\" >-0.21</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow5_col3\" class=\"data row5 col3\" >-0.26</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow5_col4\" class=\"data row5 col4\" >-0.19</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow5_col5\" class=\"data row5 col5\" >-0.10</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow5_col6\" class=\"data row5 col6\" >0.34</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow5_col7\" class=\"data row5 col7\" >-0.30</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow5_col8\" class=\"data row5 col8\" >-0.54</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow5_col9\" class=\"data row5 col9\" >-0.40</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow5_col10\" class=\"data row5 col10\" >0.28</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow5_col11\" class=\"data row5 col11\" >0.39</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow5_col12\" class=\"data row5 col12\" >-0.17</td>\n",
+ " <th id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row5_col0\" class=\"data row5 col0\" >-0.39</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row5_col1\" class=\"data row5 col1\" >-0.47</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row5_col2\" class=\"data row5 col2\" >-0.22</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row5_col3\" class=\"data row5 col3\" >-0.29</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row5_col4\" class=\"data row5 col4\" >-0.16</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row5_col5\" class=\"data row5 col5\" >-0.13</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row5_col6\" class=\"data row5 col6\" >0.34</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row5_col7\" class=\"data row5 col7\" >-0.31</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row5_col8\" class=\"data row5 col8\" >-0.53</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row5_col9\" class=\"data row5 col9\" >-0.47</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row5_col10\" class=\"data row5 col10\" >0.29</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row5_col11\" class=\"data row5 col11\" >0.38</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row5_col12\" class=\"data row5 col12\" >-0.19</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63blevel0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow6_col0\" class=\"data row6 col0\" >0.00</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow6_col1\" class=\"data row6 col1\" >-0.47</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow6_col2\" class=\"data row6 col2\" >0.99</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow6_col3\" class=\"data row6 col3\" >-0.26</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow6_col4\" class=\"data row6 col4\" >0.69</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow6_col5\" class=\"data row6 col5\" >0.50</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow6_col6\" class=\"data row6 col6\" >0.89</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow6_col7\" class=\"data row6 col7\" >0.50</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow6_col8\" class=\"data row6 col8\" >1.62</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow6_col9\" class=\"data row6 col9\" >1.50</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow6_col10\" class=\"data row6 col10\" >0.80</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow6_col11\" class=\"data row6 col11\" >0.44</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow6_col12\" class=\"data row6 col12\" >0.59</td>\n",
+ " <th id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row6_col0\" class=\"data row6 col0\" >0.01</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row6_col1\" class=\"data row6 col1\" >-0.47</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row6_col2\" class=\"data row6 col2\" >1.01</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row6_col3\" class=\"data row6 col3\" >-0.29</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row6_col4\" class=\"data row6 col4\" >0.58</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row6_col5\" class=\"data row6 col5\" >0.48</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row6_col6\" class=\"data row6 col6\" >0.89</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row6_col7\" class=\"data row6 col7\" >0.65</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row6_col8\" class=\"data row6 col8\" >1.64</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row6_col9\" class=\"data row6 col9\" >1.53</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row6_col10\" class=\"data row6 col10\" >0.79</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row6_col11\" class=\"data row6 col11\" >0.43</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row6_col12\" class=\"data row6 col12\" >0.56</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63blevel0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow7_col0\" class=\"data row7 col0\" >8.35</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow7_col1\" class=\"data row7 col1\" >3.82</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow7_col2\" class=\"data row7 col2\" >2.39</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow7_col3\" class=\"data row7 col3\" >3.79</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow7_col4\" class=\"data row7 col4\" >2.59</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow7_col5\" class=\"data row7 col5\" >3.60</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow7_col6\" class=\"data row7 col6\" >1.09</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow7_col7\" class=\"data row7 col7\" >3.94</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow7_col8\" class=\"data row7 col8\" >1.62</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow7_col9\" class=\"data row7 col9\" >1.77</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow7_col10\" class=\"data row7 col10\" >1.62</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow7_col11\" class=\"data row7 col11\" >0.45</td>\n",
- " <td id=\"T_36afadf4_f3fb_11ea_bf65_0cc47af5c63brow7_col12\" class=\"data row7 col12\" >3.04</td>\n",
+ " <th id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row7_col0\" class=\"data row7 col0\" >10.19</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row7_col1\" class=\"data row7 col1\" >3.87</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row7_col2\" class=\"data row7 col2\" >2.42</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row7_col3\" class=\"data row7 col3\" >3.48</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row7_col4\" class=\"data row7 col4\" >2.72</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row7_col5\" class=\"data row7 col5\" >3.57</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row7_col6\" class=\"data row7 col6\" >1.10</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row7_col7\" class=\"data row7 col7\" >3.98</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row7_col8\" class=\"data row7 col8\" >1.64</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row7_col9\" class=\"data row7 col9\" >1.80</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row7_col10\" class=\"data row7 col10\" >1.61</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row7_col11\" class=\"data row7 col11\" >0.44</td>\n",
+ " <td id=\"T_3ae1805a_3f19_11eb_8e70_3dad9513ffb3row7_col12\" class=\"data row7 col12\" >3.53</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x145717e78d50>"
+ "<pandas.io.formats.style.Styler at 0x7f2767f08490>"
]
},
"metadata": {},
@@ -709,14 +709,14 @@
"Total params: 5,121\n",
"Trainable params: 5,121\n",
"Non-trainable params: 0\n",
- "_________________________________________________________________\n",
- "Failed to import pydot. You must install pydot and graphviz for `pydotprint` to work.\n"
+ "_________________________________________________________________\n"
]
},
{
"data": {
+ "image/png": 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zV7oz/gjfk9He3u7g4EB/T0ZgYGBGRgb1saenRyQSGRoaNjc3qx/VokWLduzY0dfXh9dV7vDbb78ZGRm1tbXRN2RtbW1iYtLd3U014vdkHD9+XHmEYb8nA94vBJ46kDcVjDBvfvTRR0ZGRvX19SrWfe211xBC//73v9WP6tGjR/gfg+XNAeFn5GQyGb0xLCzMwcGhr69PoTO8XwgAwAKSJNPT0z09Pe3t7VV0q62tRQiNGzdO/ZEV3kmpDplMduPGDTc3N4VnzEJCQurq6k6dOsV0wMFA3gRgUHgW1JQpUzgcjpWV1RtvvEE9cT+SWny6U+tv5CorK5ubm3EdmcFkZmbW1NQ4OztPnTp1lMLo6OgoLi4ODg4WCoVHjhxRWIoPQnEdGY2AvAnAwJqammbOnPnVV1+lpqbev3//p59+MjMz8/Pzw6/w3rBhA/nnqayvv/46SZLUHFj0x0sy6OfR+DEn3C4Wi2UyWWxs7Pbt25uamv7973+3tbX5+vr++OOPIxwf08KzFQihK1euIITotQLorl27Fhsbu2zZMisrK3w7fjRi2L59u0Ag8PLyMjQ0zM3NnT59ukIHkUhEhaoRkDcBGFhiYuLNmzdTUlLmzZvH5/OdnZ2/+uorOzu7mJiYwe7wMtXV1bV//36JRMLj8Tw8PLKysnp7exUeNh22/v5+nEw1MtpgcHmtAWuvIIRcXV2zs7NXrlx55coVegUvzdqwYUNPT89vv/3m4uLi5uam/IZhPp9PEIQGK4FB3gRgYHjKYWBgINViYmLi5+f3+PFjTZ3xjWqtv5HXKlRHd3c3Qogq/afs3Llzqampqq9+jhyHw3Fxcfn888+Dg4M3bdr0r3/9S6GDkZGRcnmEYYO8CcAAcBU7U1NTCwsLerutrS1CqKmpSSNbGbDWH/qjcpVeMDU1RQj19fWxHch/BQUFIYTy8/MV2uVy+TBuNA0G8iYAAzAxMREIBN3d3Q8fPqS34zN0oVCIP46wFh+u9Udv0XKtv5HD5bVwpVRdYGJighBqa2ujN3Z0dJAkqcGys5A3ARhYSEgIQog+eaWnp6egoIDL5QYEBOCWEdbiY6vWnwbhmzAK5fcpcrncxcVllDYdHx8fERGh0Hj69Gn057chIITwPlS+XzRskDcBGNiOHTsmTZoUFxeXn5//8OHD6urqd955p7GxMTU1FZ+toxHX4hvVWn/auZ8uFottbGwqKyuVF6WlpfF4vLVr1yovioiIIAji5s2bI9z6V199tXXr1lu3bvX09Ny6dWvdunVZWVnu7u6RkZH0bvgdqP7+/iPc3P+hT4KH54XA00D9v/P79+/HxcVNmjTJ2NhYIBAEBAQUFBTQOwy7Fh85yrX+SDVqFVJG+LzQ+vXrB3xe6MCBA1wul/4GU4qvr6+5ublcLlexOfw+VAVpaWlUh/b29vT09ICAAPyaaHNzc3d39x07dlAPGlHCwsLo7/ujwHOWAKhLR/7OdafW3wjzpkwmE4lE9OfTVXvw4AGXy42MjGQc6LDg59OPHTumvAieswQAsEMgEOTl5eXk5Ozbt2/IziRJxsTE8Pl85VmWo6G2tlYqlSYmJi5cuFCDw0LeBAAw8MEHHyjX33RzcysvLz99+jSuv6lCc3NzbW1tQUEBNSdhVB08eDApKSkpKYneSNXffPLkyfCGhbwJgLbh58orKyvr6+sJgtiwYQPbEaklIiKCOlFVqL+JEHJycsrPz8f1N1UQCoVFRUXTpk0btTD/ZOfOncpHmlT9TZIkh3ffDF5uBYC2xcfHx8fHsx0FGD443gQAAGYgbwIAADOQNwEAgBnImwAAwMwA94VOnDih/TgA0JqSkhIEf+c0+Oly2CGDqaurUyzMTJ8Er/yiYQAAAArPCxHkKJeDBmB48Ltb4b3qQAfB9U0AAGAG8iYAADADeRMAAJiBvAkAAMxA3gQAAGYgbwIAADOQNwEAgBnImwAAwAzkTQAAYAbyJgAAMAN5EwAAmIG8CQAAzEDeBAAAZiBvAgAAM5A3AQCAGcibAADADORNAABgBvImAAAwA3kTAACYgbwJAADMQN4EAABmIG8CAAAzkDcBAIAZyJsAAMAM5E0AAGAG8iYAADADeRMAAJiBvAkAAMxA3gQAAGYgbwIAADOQNwEAgBnImwAAwAzkTQAAYIYgSZLtGABACKHo6Ojr169TH3/++edJkyZZWVnhj4aGhpmZmQ4ODixFB8D/MWI7AAD+y9bW9tChQ/SWy5cvU/+ePHkyJE2gI+A8HeiKd955Z7BFHA7nvffe02IsAKgC5+lAh0yfPv3atWsD/k1ev37d2dlZ+yEBoAyON4EOWbp0qaGhoUIjQRBisRiSJtAdkDeBDlm0aNGTJ08UGg0NDd99911W4gFgQHCeDnTLrFmzfvrpp/7+fqqFIIi7d++KRCIWowKADo43gW5ZsmQJQRDURwMDAy8vL0iaQKdA3gS6JSwsjP6RIIilS5eyFQwAA4K8CXTLM8884+fnR90dIggiJCSE3ZAAUAB5E+iciIgIfNnd0NAwICDA2tqa7YgA+BPIm0DnSKVSDoeDECJJMiIigu1wAFAEeRPoHB6PN2/ePIQQh8MJCgpiOxwAFEHeBLpo8eLFCKGQkBAej8d2LAAoIVnF9rcHAOil48ePs5i42K+HFBcXJ5FI2I4C6JysrKyFCxcaGf3fn2hJSUlKSsrx48dZjEqn7NmzByH04Ycfsh2ItoWHh7MbAPt5UyKRLFiwgO0ogM4JDg42NTVVaExJSYG/Fkp2djZC6CncIaznTbi+CXSUctIEQEdA3gQAAGYgbwIAADOQNwEAgBnImwA8LW7fvh0cHNzR0YEQamxsTEpK8vDw4PP5QqHQx8cnNzd3eMN+9913zs7O9JkPFJIki4uLV6xY4ezsbGJiYmNj4+XllZWVRZ+DmJCQoHdzJCBvgrGvs7Pzueeew88gPbUqKio8PDz8/f35fD5CKCoqKiUlZcuWLY2NjaWlpQ4ODlKpNCEhgdGYNTU1wcHBiYmJzc3NA3a4fv26l5dXdXV1Tk5Oe3t7aWnphAkTlixZsmbNGqpPVFRUYmLixo0bR/LttAzyJhj7SJLs7++n10LWMnNzcy8vL7a2jhDq6OgICgp6++23V65cSTUmJycHBgbyeDwnJ6cvvvhCJBIlJye3tLSoP+zGjRtnzZp16dIlCwuLwfoYGRmdOHHC1dXV1NR08uTJGRkZ1tbWe/fu7enpwR2mTJmSm5ublJR04sSJYX9BLWN//iYAo83CwqKmpobtKNi0a9eupqamTZs2US35+fn0DhwOZ+rUqfX19devX7exsVFz2H/+859cLldFBxcXl76+PoUNOTo6VlRUdHd3m5iY4EaxWBwaGrp69WqpVDrg+b6ugeNNAMY4kiTT09M9PT3t7e1VdKutrUUIjRs3Tv2RVSfNAclkshs3bri5uQkEAnp7SEhIXV3dqVOnmA7ICsibYIw7efIk8Yfu7m6Fllu3boWHh1taWlpbW8+bN486LE1OTsYdHBwcysrK/Pz8LCwszMzM5syZU1xcjPts374d96HOwc+cOYNbnnnmGfo4XV1dxcXFeJH2j6cqKyubm5vFYrGKPpmZmTU1Nc7OzlOnTh2lMDo6OoqLi4ODg4VC4ZEjRxSWvvjiiwih77//fpS2rmEsPhuP76mx+3w+0CP4ruvw1p0/fz5C6PHjxwot8+fPv3DhQmdn59mzZ7lc7syZM+lricViHo8nkUhwn7KyMldXVw6HU1hYSPXh8XivvPIKfS13d3dra2t6i3IfbM6cOePGjSspKRnelwoNDQ0NDR2y29GjRxFCH3/88YBLr169GhMTY2BgYGVldfHixeFFIhKJDA0NVXTYtm0bTjizZ8++fPmycof29naEkLe3tzqbYz1vwPEmeKpFRkZKJBIejzd37tzAwMCysrL79+/TO3R1de3fvx/38fDwyMrK6u3tjY2N1cjW+/v7qUQwehobGxFCCufFFFdX1+zs7JUrV165cmXmzJmjFMOGDRt6enp+++03FxcXNzc3Ko1S+Hw+QRA4VN2nB5dgARg99Ezh6OiIEGpoaKDOshFCPB4Pn0JiM2bMsLe3r6ysbGxstLOzG+HWCwsLRziCOvDVCWNj48E6nDt3zsXFZbTD4HA4Li4un3/+eXNz86ZNmyQSydy5c+kdjIyMHj9+PNphaAQcb4KnGv0oDL+cQ2G6kqWlpcIq+HYzo/k67MIVUhTua7MI1/BXuKGPEJLL5cO40cQKyJsAqNLa2qpwHo0zJjVZx8DAoLe3l95BJpMpDEJ/I7z24eNifAFRF+DpR21tbfTGjo4OkiRHfgivHZA3AVClu7u7rKyM+vjrr782NDSIxWLqF25nZ1dfX091aGpqunPnjsIgZmZmVG59/vnnDx06NMpR/8n06dMRQnV1dQMulcvlo3eSHh8fr/xmvdOnT6M/XyFBCOF9iEPVfZA3AVBFIBCsX7++pKSkq6urvLw8IiKCw+GkpqZSHfz9/RsaGvbu3dvZ2VlTUxMbG6s8b/yll16qrq6+e/duSUlJbW2tt7c3bvf19bW2ti4tLR3VryAWi21sbCorK5UXpaWl8Xi8tWvXKi+KiIggCOLmzZsj3PpXX321devWW7du9fT03Lp1a926dVlZWe7u7pGRkfRuFRUVCCF/f/8Rbk5LWLyXT+rAfAKgR4Y3D0mhXMXixYtLSkroLR999BH55zPxwMBAvK5YLBaJRNeuXQsICLCwsOByuT4+PkVFRfTxZTJZZGSknZ0dl8v18vIqKytzd3fH46xbtw73qaqq8vb25vF4jo6O+/bto9b19va2srK6cOHC8HaImvOQSJJcv369kZFRfX29QvuBAwe4XG58fLzyKr6+vubm5nK5XMWweXl5yiklLS2N6tDe3p6enh4QEODk5MThcMzNzd3d3Xfs2PHo0SOFocLCwkQiUW9vrzpfh/W8AXkT6I2RzN8cHpw3tblFRtTPmzKZTCQSRUdHqznygwcPuFxuZGTkCKJjoKKigiCIY8eOqdmf9byhB+fp5ubmBA2eoCsWi5cvX37p0iW2oxtdKip0qeNp3nWATiAQ5OXl5eTk7Nu3b8jOJEnGxMTw+XzlWZajoba2ViqVJiYmLly4UAub0wg9yJudnZ2//PILQmj+/PkkSfb19VVVVW3durWqqsrDw+Mvf/nLo0eP2I5R84as0KWOp3PXgQG5ubmVl5efPn0a199Uobm5uba2tqCgQCgUaiGwgwcPJiUlJSUlaWFbmqIHeVOBoaGhra3t/Pnzz507t3bt2oyMjEWLFpFj7lXs6lToYuop2XUagZ8rr6ysrK+vJwhiw4YNbEekAU5OTvn5+bj+pgpCobCoqGjatGnaiWrnzp16dKSJ6V/epPvkk088PT2//fbbr7/+mu1YNOyf//xnQkLC6NWAGMO7TiMUbpVs376d7YiADtHvvEkQBK7Dun//frZj0bDRfnBiDO86AEabfudNhBAu4VVaWko9Rnbv3r2YmBg872H8+PFSqRRPDUPqFRBDCPX09GzatMnFxcXMzGzcuHFBQUHffvvtkydPqA4qNqGamgFoh37tOgB0CAv38GmQevMJ6Dc3FFCFABoaGkiSbGhomDhxoq2t7alTpx4+fHjlyhUfHx9TU1P6FLkhC4hFRkYKBIIffvjh0aNHTU1N8fHxCKHz58/jpepsQjV1KphRVFToUqcK2Vjaddqfh6Tj1J+HNMaomTdGMQAWt01qIm9Sd4Txj//dd99FCH355ZdUh8bGRhMTE3d3d6oF//jz8vKoltDQUITQvXv38MdJkybNmjWLvhVnZ2fqx6/OJlQbMgA6FXnTx8dnyFnTY2nXQd5UAHmTLXpfRw4X7DM2Nsa1v06ePGlgYEB/c6FQKJw2bdqlS5fq6uocHByodhUFxF5//fXPP//8/fffX7Zs2cyZMw0NDa9fv051Vn8Tqg1ZwWxII6xCpqe7To/e3jXa8CPnsEO0T+/zZlFREUJIIjcOHrUAACAASURBVJEYGxv39PTgoi8Dlmi9ceMG/ZepooDYvn37JBJJZmamn58fQsjb2zs6OjokJAQhxGgTqg1ZwWy06emuCw8PV/cbPh1gh2ifft8X6u/vx88/rFixAiFkYmJiaWlpZGTU19enfGg9Z84cNYclCGLJkiX/+te/ZDLZyZMnSZKUSqWfffaZBjfBOv3ddSM9xRpDnubzdHbpd95MTEy8ePFiSEhIWFgYbpFKpXK5nHpzFrZz584JEybI5XI1h7W0tKyqqkIIGRsbv/baa/hWMvWmPY1sgnWw6wAYNv3Lm/39/S0tLd98842fn9+uXbuWLVv25ZdfUnVhd+zYMWXKlGXLlp0+fbq9vb2tre3gwYNbt25NTk5mNIf8f/7nfy5fvtzT09PS0rJr1y6SJH19fTW7iZFjWoUMdh0AmsH68faQ98V4PB49YIIgBALBjBkzPvjgg0uXLin3b21tXbVq1eTJk42NjcePH+/v73/27Fm8SM0CYhUVFdHR0S+88AKehPjyyy+npaVRr9BSvQnV1K9gNmSFLlKNKmRjadeRcD9dydN8ns7u/XSCZPViAUEQx48fX7BgAYsxAH1x4sSJ8PBwdv9idQq+xpKdnc12INrGet7Qv/N0AABgF+RNAJ4Wt2/fDg4OxnXkGhsbk5KSPDw8+Hy+UCj08fFRKIyvpr6+vj179ri7u1tYWNjY2Lzxxhv4sYgBOwcHBxMEoVAkJSEhAV+B0SOQNzWMGNzmzZvZjg48vSoqKjw8PPz9/XEduaioqJSUlC1btjQ2NpaWljo4OEil0oSEBEZjdnV1+fr6ZmRk7Nmzp6Wlpby83NzcPDg4+OrVq8qdjxw5MuBV+6ioqMTExI0bNw7ve7GDxWurpA5c3wV6RMv3hXg83iuvvKLL46t/X6i9vd3BwYH+nozAwMCMjAzqY09PD36it7m5Wf0APvjgAz6f39TURLV0dnaamJj8+uuvCj3r6+utrKyWLFmCENq2bZvCUvyeDPVTAet5A443ARj7du3a1dTUtGnTJqolPz8flwvAOBzO1KlTnzx5Qn8uVrXm5uZDhw4tXrzY1taWauTxeN3d3cqv842KigoLCxvsdZVisTg0NHT16tX6Mo0X8iYAYxxJkunp6Z6envb29iq61dbWIoTGjRun5rC4QiCuRqja4cOHr169mpycrKJPSEhIXV0d9YiEjoO8CcYgPEt0ypQpHA7HysrqjTfeOH/+PF60fft2fLmZ+sGfOXMGt1BFVfBLMrq6uoqLi/EiPC0ftxME4eDgUFZW5ufnZ2FhYWZmNmfOHOohqJGMP0oqKyubm5vFYrGKPpmZmTU1Nc7OzlOnTlVz2J9//hkhZGVltXr1akdHRw6HM3HixJiYmLa2Nnq3urq61atXHz58WPUbX1588UWE0Pfff6/m1lnG4jUCUgeuUwA9oub1zcbGxkmTJtna2ubl5bW3t1+/fl0qlRIEQX9qQPnaoru7u7W1Nb1lsOuPYrGYx+NJJBJchLSsrMzV1ZXD4RQWFmpkfHXKqmJqXt88evQoQujjjz8ecOnVq1djYmLwu04vXrw45GgUXFRQKBQuXry4pqbmwYMHmZmZPB7P2dlZJpNR3QICApYvX06PRPn6JkmSuOaLt7e3OptmPW/A8SYYaxITE2/evJmSkjJv3jw+n+/s7PzVV1/Z2dnFxMSM5OWgdF1dXfv375dIJDwez8PDIysrq7e3NzY2ViODU49XaWQ09EfBwAHLUCGEXF1ds7OzV65ceeXKFXqFwCF1d3cjhLhcbkZGxuTJky0tLZcuXZqYmFhdXb17927cJy0t7caNG7t27RpyND6fTxAEDlX3Qd4EYw2ehxgYGEi1mJiY+Pn5PX78WFOngTweD59XYjNmzLC3t6+srNTIz76wsLCtrU0ikYx8KAwnOGNj48E6nDt3LjU1VfXVT2X4Kd65c+fSLzIEBQWhP06379y5s2bNmsOHDys87zsYIyMj6h0EOg7yJhhTcJVPU1NThatp+J5vU1OTRrZiaWmp0GJjY4MQamlp0cj4mmVqaooQot4ipSlOTk4IIWtra3oj3g/37t1DCOHrJLNnz6amMON5SBs3bsQff//9d/q6crl8tF9HqCmQN8GYYmJiIhAIuru7Hz58SG/HZ+hCoRB/NDAw6O3tpXeQyWQKQ1GVopS1trYqnEfjjImzxsjH1yw7OzuEEL6AqEH4xpfCITbeD/j/UitWrFC4LKhwffPZZ5+lVuzo6CBJEoeq+yBvgrEGl5enz2jp6ekpKCjgcrkBAQG4xc7Orr6+nurQ1NR0584dhXHMzMyo3Pf8888fOnSIWtTd3V1WVkZ9/PXXXxsaGsRiMfWzH+H4moVnU+KXaiiTy+UuLi7DGPbNN98UiURnzpzB1wEw/ETQW2+9xXQ0vLuUJ37qJsibYKzZsWPHpEmT4uLi8vPzHz58WF1d/c477zQ2NqamplIztP39/RsaGvbu3dvZ2VlTUxMbG0sdKlJeeuml6urqu3fvlpSU1NbWent7U4sEAsH69etLSkq6urrKy8sjIiI4HE5qairVYSTjMy2rOiSxWGxjY1NZWam8KC0tjcfjrV27VnlRREQEQRA3b94cbFgTE5P09PTW1taFCxfeuHFDJpMdPXp0x44dnp6eMTExTIPEr4MebGK8ztHivfsBILbnEwA9ov5zlvfv34+Li5s0aZKxsbFAIAgICCgoKKB3kMlkkZGRdnZ2XC7Xy8urrKzM3d0d/yLWrVuH+1RVVXl7e/N4PEdHx3379lHrisVikUh07dq1gIAACwsLLpfr4+NTVFSkqfGHLKtKUf85y/Xr1xsZGdXX1yu0HzhwgMvlxsfHK6/i6+trbm4ul8tVj3zhwoWAgACBQMDhcFxcXDZv3vzo0SPlbtHR0QqZJyAggN4hLCxMJBL19vaq83VYzxuQN4He0JG6xThvsh0FSTLJmzKZTCQS0Z9PV+3BgwdcLjcyMnIE0TGAn08/duyYmv1Zzxtwng7A2CcQCPLy8nJycvDL+FQjSTImJobP52/btk0LsdXW1kql0sTExIULF2phcxoBeROAp4Kbm1t5efnp06dx/U0Vmpuba2trCwoKqOkHo+rgwYNJSUlJSUla2JamQN4EQF34ufLKysr6+nqCIDZs2MB2RMw4OTnl5+fj+psqCIXCoqKiadOmaSeqnTt36tGRJgYvEQRAXfHx8fHx8WxHAdgHx5sAAMAM5E0AAGAG8iYAADADeRMAAJhh/77Qnj17srOz2Y4C6AH8hHVYWBjbgegK/Cwm7BDtI0jNlUcdBvhPDgZz+vRpNzc37UwhBHpn1apVGixRyhTLeROAweAXwy5YsIDtQABQBNc3AQCAGcibAADADORNAABgBvImAAAwA3kTAACYgbwJAADMQN4EAABmIG8CAAAzkDcBAIAZyJsAAMAM5E0AAGAG8iYAADADeRMAAJiBvAkAAMxA3gQAAGYgbwIAADOQNwEAgBnImwAAwAzkTQAAYAbyJgAAMAN5EwAAmIG8CQAAzEDeBAAAZiBvAgAAM5A3AQCAGcibAADADORNAABgBvImAAAwA3kTAACYgbwJAADMQN4EAABmIG8CAAAzRmwHAMB/yWQykiTpLV1dXQ8ePKA+mpubGxsbaz0uABQRCn+pALDF19f3/Pnzgy01NDSsr6+3tbXVZkgADAjO04GuWLRoEUEQAy4yMDB49dVXIWkCHQF5E+iK0NBQI6OBLxwRBLF06VItxwPAYCBvAl1hZWXl7+9vaGiovMjAwCAkJET7IQEwIMibQIdERET09/crNBoZGQUGBgoEAlZCAkAZ5E2gQ4KDg01MTBQanzx5EhERwUo8AAwI8ibQIWZmZiEhIQqTjbhc7ptvvslWSAAog7wJdMs777zT19dHfTQ2Ng4NDeVyuSyGBIACyJtAtwQEBNAvZfb19b3zzjssxgOAMsibQLcYGxsvXLiQw+Hgj5aWln5+fuyGBIACyJtA5yxatKi3txchZGxsHBERMdikTgDYAs9ZAp3T399vb2/f3NyMECoqKnrllVfYjgiAP4HjTaBzDAwMlixZghCys7ObNWsW2+EAoIjlM6ATJ06wGwDQTc888wxCyNPTMzs7m+1YgC6aNWuWg4MDa5snWcXa1wYA6LPjx4+zmLjYv+J+/PjxBQsWsB0F0Dk5OTmhoaH0lhMnToSHh8P/bilhYWEIoafwkHywullaA9c3gY5SSJoA6A7ImwAAwAzkTQAAYAbyJgAAMAN5EwAAmIG8CcDT4vbt28HBwR0dHQihxsbGpKQkDw8PPp8vFAp9fHxyc3OHMWZfX9+ePXvc3d0tLCxsbGzeeOONvLy8weY8BAcHEwSxfft2emNCQsLx48eHsWkWQd4EY19nZ+dzzz03b948tgNhU0VFhYeHh7+/P5/PRwhFRUWlpKRs2bKlsbGxtLTUwcFBKpUmJCQwGrOrq8vX1zcjI2PPnj0tLS3l5eXm5ubBwcFXr15V7nzkyJG8vDzl9qioqMTExI0bNw7ve7EC8iYY+0iS7O/vV34Dh9aYm5t7eXmxtXWEUEdHR1BQ0Ntvv71y5UqqMTk5OTAwkMfjOTk5ffHFFyKRKDk5uaWlRf1h16xZc/ny5R9++OHVV1/lcrkTJkzIyMhQrtiPEGpoaIiLi8OPzyqYMmVKbm5uUlKSHj09CHkTjH0WFhY1NTXfffcd24GwZteuXU1NTZs2baJa8vPz3333Xeojh8OZOnXqkydPrl+/ruaYzc3Nhw4dWrx4Mf39zDwer7u7e/r06Qqdo6KiwsLC/P39BxxKLBaHhoauXr1aLper+5VYBXkTgDGOJMn09HRPT097e3sV3WpraxFC48aNU3PYb7/99smTJ+ocRx8+fPjq1avJyckq+oSEhNTV1Z06dUrNrbML8iYY406ePEn8obu7W6Hl1q1b4eHhlpaW1tbW8+bNq6mpwWslJyfjDg4ODmVlZX5+fhYWFmZmZnPmzCkuLsZ9tm/fjvtQuePMmTO4Bdclocbp6uoqLi7Gi7RfTrSysrK5uVksFqvok5mZWVNT4+zsPHXqVDWH/fnnnxFCVlZWq1evdnR05HA4EydOjImJaWtro3erq6tbvXr14cOHLSwsVIz24osvIoS+//57NbfOMhafjcc33dh9Ph/oEXzXdXjrzp8/HyH0+PFjhZb58+dfuHChs7Pz7NmzXC535syZ9LXEYjGPx5NIJLhPWVmZq6srh8MpLCyk+vB4vFdeeYW+lru7u7W1Nb1FuQ82Z86ccePGlZSUDO9LhYaGhoaGDtnt6NGjCKGPP/54wKVXr16NiYkxMDCwsrK6ePGi+lvHO1AoFC5evLimpubBgweZmZk8Hs/Z2Vkmk1HdAgICli9fTo9k27ZtyqO1t7cjhLy9vdXZNOt5A443wVMtMjJSIpHweLy5c+cGBgaWlZXdv3+f3qGrq2v//v24j4eHR1ZWVm9vb2xsrEa23t/fTyWC0dPY2IgQGuwF9K6urtnZ2StXrrxy5crMmTPVHxYfvHO53IyMjMmTJ1taWi5dujQxMbG6unr37t24T1pa2o0bN3bt2jXkaHw+nyAIHKrug7wJnmr0TOHo6IgQamhooHfg8Xj4FBKbMWOGvb19ZWWlRn7hhYWFbW1tEolk5EOpgBOcwtuV6c6dO5eamqr66qcyHo+HEJo7dy79ykNQUBD643T7zp07a9asOXz4MO45JCMjo8ePHzOKgS2QN8FTjX4Uhl8GpzBdydLSUmEVGxsbhBCj+TrsMjU1RQjR366sEU5OTggha2treiPeOffu3UMI5eXltbe3z549m7qajOchbdy4EX/8/fff6evK5XJ9eeEz5E0AVGltbVU4j8YZEycIhJCBgQF+ixxFJpMpDMJuvUg7OzuEEL6AqEH4bpjCcTfeOXhm0ooVKxQuCypc33z22WepFTs6OkiSxKHqPsibAKjS3d1dVlZGffz1118bGhrEYjH1C7ezs6uvr6c6NDU13blzR2EQMzMzKrc+//zzhw4dGuWo/wTPpqyrqxtwqVwud3FxGcawb775pkgkOnPmDL4OgOEngt566y2mo+F9qDzxUzdB3gRAFYFAsH79+pKSkq6urvLy8oiICA6Hk5qaSnXw9/dvaGjYu3dvZ2dnTU1NbGwsdShKeemll6qrq+/evVtSUlJbW+vt7Y3bfX19ra2tS0tLR/UriMViGxubyspK5UVpaWk8Hm/t2rXKiyIiIgiCuHnz5mDDmpiYpKent7a2Lly48MaNGzKZ7OjRozt27PD09IyJiWEaZEVFBUJosInxOkeL9+4HgNieTwD0yPDmISmUq1i8eHFJSQm95aOPPiL/fCYeGBiI1xWLxSKR6Nq1awEBARYWFlwu18fHp6ioiD6+TCaLjIy0s7PjcrleXl5lZWXu7u54nHXr1uE+VVVV3t7ePB7P0dFx37591Lre3t5WVlYXLlwY3g5Rcx4SSZLr1683MjKqr69XaD9w4ACXy42Pj1dexdfX19zcXC6Xqx75woULAQEBAoGAw+G4uLhs3rz50aNHyt2io6MVMk9AQAC9Q1hYmEgk6u3tVefrsJ43IG8CvTGS+ZvDg/OmNrfIiPp5UyaTiUSi6OhoNUd+8OABl8uNjIwcQXQMVFRUEARx7NgxNfuznjf04Dzd3NycoMETdMVi8fLlyy9dusR2dKPiwYMHBw4c8PX1HTduHJfLfe655xYvXjzgeZZqT+GuAwMSCAR5eXk5OTn79u0bsjNJkjExMXw+f9u2bVqIrba2ViqVJiYmLly4UAub0wg9yJudnZ2//PILQmj+/PkkSfb19VVVVW3durWqqsrDw+Mvf/nLo0eP2I5Rw9asWfO3v/1t/vz5165da21tPXz4cEVFhbu7+8mTJxmN8xTuOjAYNze38vLy06dP4/qbKjQ3N9fW1hYUFAiFQi0EdvDgwaSkpKSkJC1sS2NYPNYl1T7epv/46fD17ODgYOq5i7Hhr3/96/vvv09vwVfNn3vuOaZDjaVdp83z9E8//ZT+M8HXQHWN+ufpY4yaeWP06MHxpgqffPKJp6fnt99++/XXX7Mdiyalp6cfPHiQ3iIWi7lcbk1NDamhZ/LG6q7TFIVbJQolysFTTr/zJkEQuA7r/v372Y5ldHV1dT1+/Hj69OmamkH99Ow6ADROv/Mm+uOhhdLSUuoxsnv37sXExDg5OXE4nPHjx0ulUnySi9QrIIYQ6unp2bRpk4uLi5mZ2bhx44KCgnCpQaqDik2opmYAyrKzsxFCH330EcPdo4p+7ToAdAhL1wf+C43s+iZJklQhgIaGBpIkGxoaJk6caGtre+rUqYcPH165csXHx8fU1JQ+RW7IAmKRkZECgeCHH3549OhRU1NTfHw8Quj8+fN4qTqbUE2dCmZ0TU1Ntra2ypNC1KlCNpZ2nfbnIek4uL7JWgAsbpvURN6k7gjjHz8u/f/ll19SHRobG01MTNzd3akW/OPHb93DQkNDEUL37t3DHydNmjRr1iz6VpydnakfvzqbUG3IAOju37//4osvhoeHK89A9vHxGXLW9FjadZA3FUDeZIu2S09rHC4rYGxsjCtsnzx50sDAgP7mQqFQOG3atEuXLtXV1Tk4OFDtAxYQw4O8/vrrn3/++fvvv79s2bKZM2caGhrSX7qi/iZUUxEApaurKyAgYOrUqUeOHDE0NFQYobCwUM1tDUhPd11YWNiwv/IYgx/QhB2ifXp/fbOoqAghJJFIjI2Ne3p62tvb+/v7BQIBfb43Luh/48YN+ooqCojt27fvyJEjtbW1fn5+fD7/9ddfp57VY7QJ1YasYCaXy/HDZ5mZmcpJc+T0d9cBwC79Pt7s7+/Hzz+sWLECIWRiYmJpadnZ2fn48eORvMUFFwpcsmRJX19fYWFhcnKyVCrdvXv3qlWrNLUJdURHR/f09OTm5lIbevbZZ7Oysl5++eWRD66/uw7fIgPojyPNp3CHsFuXD+n78WZiYuLFixdDQkKoUxWpVCqXy6k3Z2E7d+6cMGGC+q8YtbS0rKqqQggZGxu/9tpr+FYy9aY9jWxiSJs3b7569eo333wz4NuoR24M7zoARpv+5c3+/v6WlpZvvvnGz89v165dy5Yt+/LLL6n//+zYsWPKlCnLli07ffp0e3t7W1vbwYMHt27dmpyczOgY53/+538uX77c09PT0tKya9cukiR9fX01uwkVMjIytmzZ8tNPP1lYWNBPaRXmKjGtQvY07DoAtIHFe1KkevfFFF5OQhCEQCCYMWPGBx98cOnSJeX+ra2tq1atmjx5srGx8fjx4/39/c+ePYsXqVlArKKiIjo6+oUXXsCTEF9++eW0tDT684gqNqGamgEEBgYO9t+LPutoyCpkY2nXkXA/XQncT2cLQY7yu/RUIwji+PHjCxYsYDEGoC9OnDgRHh7O7l+sTnmar2+ymzf07zwdADA8t2/fDg4OxvWQGhsbk5KSPDw8+Hy+UCj08fFRKPCspr6+vj179ri7u1tYWNjY2Lzxxht4eu+AnYODgwmCUHjYPyEhAZ9J6BHImwA8FSoqKjw8PPz9/fl8PkIoKioqJSVly5YtjY2NpaWlDg4OUqk0ISGB0ZhdXV2+vr4ZGRl79uxpaWkpLy83NzcPDg6+evWqcucjR47gtw8piIqKSkxM3Lhx4/C+Fysgb2oYMbjNmzezHR1gwNzcHD/Cr6fj03V0dAQFBb399tu4mAuWnJwcGBjI4/GcnJy++OILkUiUnJzM6P3Ga9asuXz58g8//PDqq69yudwJEyZkZGQMOAOkoaEhLi4OvwdYwZQpU3Jzc5OSkk6cODGMr8YKuImpYXD1DeigXbt2NTU1bdq0iWrJz8+nd+BwOFOnTq2vr79+/brye+UG1NzcfOjQoffffx+/9Rfj8Xj011tSoqKiwsLCvL298auAFYjF4tDQ0NWrV0ulUr2YWQHHmwCMcSRJpqene3p62tvbq+hWW1uLEBo3bpyaw+JKV+ocMh8+fPjq1avJyckq+oSEhNTV1VFTfXUc5E0wBuHZTlOmTOFwOFZWVm+88cb58+fxou3bt+PLJtQP/syZM7iFKg6QnJxMEERXV1dxcTFehA+CcDtBEA4ODmVlZX5+fhYWFmZmZnPmzKEm849k/FFSWVnZ3NwsFotV9MnMzKypqXF2dp46daqaw+IHZK2srFavXu3o6MjhcCZOnBgTE9PW1kbvVldXt3r16sOHD1tYWKgY7cUXX0QIff/992punWUszoEidWAeFtAjas7fbGxsnDRpkq2tbV5eXnt7+/Xr16VSKUEQaWlpVB8ej/fKK6/Q13J3d7e2tqa3KPfBxGIxj8eTSCS4mF5ZWZmrqyuHwyksLNTI+OqUB8TUnL+JT40//vjjAZdevXo1JiYGv7Pv4sWLQ45GwcWxhELh4sWLa2pqHjx4kJmZyePxnJ2dZTIZ1S0gIGD58uX0SLZt26Y8Wnt7O0LI29tbnU2znjfgeBOMNYmJiTdv3kxJSZk3bx6fz3d2dv7qq6/s7OxiYmKam5s1somurq79+/dLJBIej+fh4ZGVldXb2xsbG6uRwanHBDQyGvqj8BW9Ggudq6trdnb2ypUrr1y5Qq90NSR8HZPL5WZkZEyePNnS0nLp0qWJiYnV1dW7d+/GfdLS0m7cuLFr164hR+Pz+QRB4FB1H+RNMNbgeYj0Z65MTEz8/PweP36sqdNAHo+HzyuxGTNm2NvbV1ZWauRnX1hY2NbWJpFIRj4UhhOcsbHxYB3OnTuXmpqq+uqnMvw02ty5c+kXGYKCgtAfp9t37txZs2bN4cOHFZ5bG4yRkRFVS1vHQd4EYwquVmdqaqpwNQ3f821qatLIViwtLRVa8D1oRpN4tMbU1BQhRL0NRVOcnJwQQtbW1vRGvB/u3buHEMLXSWbPnk1NxcPzkDZu3Ig//v777/R15XI5l8vVbJCjBPImGFNMTEwEAkF3d/fDhw/p7fgMnXohuIGBQW9vL72DTCZTGEpFsbLW1laF82icMakZPCMcX7Ps7OwQQvgCogbhG18Kh9h4P+D/S61YsULhsqDC9c1nn32WWrGjo4MkSRyq7oO8CcaakJAQhBB9RktPT09BQQGXyw0ICMAtdnZ29fX1VIempqY7d+4ojGNmZkblvueff/7QoUPUou7u7rKyMurjr7/+2tDQIBaLqZ/9CMfXrOnTpyOE6urqBlwql8tdXFyGMeybb74pEonOnDlDn7CJnwh66623mI6GdxcOVfdB3gRjzY4dOyZNmhQXF5efn//w4cPq6up33nmnsbExNTWVmqHt7+/f0NCwd+/ezs7Ompqa2NhY5cneL730UnV19d27d0tKSmpra729valFAoFg/fr1JSUlXV1d5eXlERERHA4nNTWV6jCS8ZmWBxySWCy2sbGprKxUXpSWlsbj8dauXau8KCIigiCImzdvDjasiYlJenp6a2vrwoULb9y4IZPJjh49umPHDk9Pz5iYGKZB4tea+vv7M12RHVq8dz8AxPZ8AqBH1K8jd//+/bi4uEmTJhkbGwsEgoCAgIKCAnoHmUwWGRlpZ2fH5XK9vLzKysrc3d3xL2LdunW4T1VVlbe3N4/Hc3R03LdvH7WuWCwWiUTXrl0LCAiwsLDgcrk+Pj5FRUWaGn/I8oAU9evIrV+/3sjIqL6+XqH9wIEDXC43Pj5eeRVfX19zc3PltwEquHDhQkBAgEAg4HA4Li4umzdvfvTokXK36OhohcwTEBBA74BfCdPb26vO12E9b0DeBHpDR+pv4rzJdhQkySRvymQykUgUHR2t5sgPHjzgcrnKr54eJRUVFQRBHDt2TM3+rOcNOE8HYOwTCAR5eXk5OTn4pVKqkSQZExPD5/O3bdumhdhqa2ulUmliYuLChQu1sDmNgLwJwFPBzc2tvLz89OnTuP6mCs3NzbW1tQUFBdT0g4kfVAAAFiBJREFUg1F18ODBpKSkpKQkLWxLUyBvAqAu/Fx5ZWVlfX09QRAbNmxgOyJmnJyc8vPzcf1NFYRCYVFR0bRp07QT1c6dO/XoSBPTg5JNAOiI+Pj4+Ph4tqMA7IPjTQAAYAbyJgAAMAN5EwAAmIG8CQAAzEDeBAAAhlicc0/CK8wAAMPC7vNCLM9D0rv3zQOtCQ8Pj4uL02D5XjCWzJo1i8WtE3DQB3QTQRDHjx9fsGAB24EAoAiubwIAADOQNwEAgBnImwAAwAzkTQAAYAbyJgAAMAN5EwAAmIG8CQAAzEDeBAAAZiBvAgAAM5A3AQCAGcibAADADORNAABgBvImAAAwA3kTAACYgbwJAADMQN4EAABmIG8CAAAzkDcBAIAZyJsAAMAM5E0AAGAG8iYAADADeRMAAJiBvAkAAMxA3gQAAGYgbwIAADOQNwEAgBnImwAAwAzkTQAAYAbyJgAAMAN5EwAAmIG8CQAAzEDeBAAAZozYDgCA/zp27NjDhw/pLf/6179kMhn1MSQkZPz48VqPCwBFBEmSbMcAAEIIvffee5mZmcbGxvgj/sskCAIh9OTJE3Nz85aWFhMTEzZDBAAhBOfpQHcsWrQIIdT3B7lcLpfL8b8NDQ3DwsIgaQIdAcebQFfI5XJbW9u2trYBlxYUFPj6+mo5JAAGBMebQFcYGRktWrSIOk+ne+aZZ3x8fLQfEgADgrwJdMiiRYv6+voUGo2NjZcsWWJoaMhKSAAog/N0oENIkpwwYUJdXZ1C+8WLF2fOnMlKSAAog+NNoEMIgoiIiFA4VXd0dPTw8GArJACUQd4EukXhVN3Y2Pi9997Ds5EA0BFwng50jouLy/Xr16mPV65cmTZtGovxAKAAjjeBzlmyZAl1qj516lRImkDXQN4EOiciIkIulyOEjI2N3333XbbDAUARnKcDXeTh4XHp0iWCIG7dujVhwgS2wwHgT+B4E+iipUuXIoQ8PT0haQIdpAf1kEpKSj777DO2owBa1d3dTRBET09PWFgY27EArZJIJKtWrWI7iiHowfHm3bt3c3Jy2I4CjK6cnBz6dHdTU1NbW1sHBwcWQ2JRXV3d0/k3X1paWlJSwnYUQ9OD400sOzub7RDAKCII4sMPP1ywYAHV8vvvvz/77LMshsSiEydOhIeHP4V/8/pyeqEHx5vg6fTUJk2g+yBvAgAAM5A3AQCAGcibAADADORNAPTe7du3g4ODOzo6EEKNjY1JSUkeHh58Pl8oFPr4+OTm5g5jzL6+vj179ri7u1tYWNjY2Lzxxht5eXmDPSYTHBxMEMT27dvpjQkJCcePHx/GpnUf5E2gxzo7O5977rl58+axHQibKioqPDw8/P39+Xw+QigqKiolJWXLli2NjY2lpaUODg5SqTQhIYHRmF1dXb6+vhkZGXv27GlpaSkvLzc3Nw8ODr569apy5yNHjuTl5Sm3R0VFJSYmbty4cXjfS5dB3gR6jCTJ/v7+/v5+tgIwNzf38vJia+sIoY6OjqCgoLfffnvlypVUY3JycmBgII/Hc3Jy+uKLL0QiUXJycktLi/rDrlmz5vLlyz/88MOrr77K5XInTJiQkZEx4HvxGhoa4uLilixZorxoypQpubm5SUlJJ06cGMZX02WQN4Ees7CwqKmp+e6779gOhDW7du1qamratGkT1ZKfn08vhsLhcKZOnfrkyRN6aT7VmpubDx06tHjxYltbW6qRx+N1d3dPnz5doXNUVFRYWJi/v/+AQ4nF4tDQ0NWrV+NCLWMG5E0A9BVJkunp6Z6envb29iq61dbWIoTGjRun5rDffvvtkydP1DmOPnz48NWrV5OTk1X0CQkJqaurO3XqlJpb1wuQN4G+OnnyJPGH7u5uhZZbt26Fh4dbWlpaW1vPmzevpqYGr5WcnIw7ODg4lJWV+fn5WVhYmJmZzZkzp7i4GPfZvn077kPljjNnzuCWZ555hj5OV1dXcXExXmRkpO2n7yorK5ubm8VisYo+mZmZNTU1zs7OU6dOVXPYn3/+GSFkZWW1evVqR0dHDoczceLEmJgYhVc019XVrV69+vDhwxYWFipGe/HFFxFC33//vZpb1w+kzsO35NiOAowuhNDx48eHseL8+fMRQo8fP1ZomT9//oULFzo7O8+ePcvlcmfOnElfSywW83g8iUSC+5SVlbm6unI4nMLCQqoPj8d75ZVX6Gu5u7tbW1vTW5T7YHPmzBk3blxJSckwvhGp9t/80aNHEUIff/zxgEuvXr0aExNjYGBgZWV18eJF9beOd6BQKFy8eHFNTc2DBw8yMzN5PJ6zs7NMJqO6BQQELF++nB7Jtm3blEdrb29HCHl7e6uz6dDQ0NDQUPVDZQscb4KxKTIyUiKR8Hi8uXPnBgYGlpWV3b9/n96hq6tr//79uI+Hh0dWVlZvb29sbKxGtt7f349/YBoZbTCNjY0IIYFAMOBSV1fX7OzslStXXrlyhdHbQPHBO5fLzcjImDx5sqWl5dKlSxMTE6urq3fv3o37pKWl3bhxY9euXUOOxufzCYLAoY4ZkDfB2ETPFI6OjgihhoYGegcej4dPIbEZM2bY29tXVlZq5BdeWFjY1tYmkUhGPpQKOMEpvP6T7ty5c6mpqaqvfirj8XgIoblz59KvPAQFBaE/Trfv3LmzZs2aw4cP455DMjIyevz4MaMYdBzkTTA20Y/COBwOQkhhupKlpaXCKjY2NgghRvN12GVqaooQor/+UyOcnJwQQtbW1vRGvHPu3buHEMrLy2tvb589ezZ1NRnPQ9q4cSP++Pvvv9PXlcvlXC5Xs0GyC/ImeEq1trYqnEfjjIkTBELIwMCgt7eX3kEmkykMwu4Liu3s7BBC+AKiBuG7YQrH3Xjn4JlJK1asULjep3B9k17LqqOjgyRJHOqYAXkTPKW6u7vLysqoj7/++mtDQ4NYLKZ+4XZ2dvX19VSHpqamO3fuKAxiZmZG5dbnn3/+0KFDoxz1n+DZlPR6z3RyudzFxWUYw7755psikejMmTP4OgCGnwh66623mI6G96HyxE+9BnkTPKUEAsH69etLSkq6urrKy8sjIiI4HE5qairVwd/fv6GhYe/evZ2dnTU1NbGxsdShKOWll16qrq6+e/duSUlJbW2tt7c3bvf19bW2ti4tLR3VryAWi21sbCorK5UXpaWl8Xi8tWvXKi+KiIggCOLmzZuDDWtiYpKent7a2rpw4cIbN27IZLKjR4/u2LHD09MzJiaGaZAVFRUIocEmxusrLd67HyaYh/Q0QMznISmUq1i8eLHCKxY++ugj8s9n4oGBgXhdsVgsEomuXbsWEBBgYWHB5XJ9fHyKioro48tkssjISDs7Oy6X6+XlVVZW5u7ujsdZt24d7lNVVeXt7c3j8RwdHfft20et6+3tbWVldeHCheHtDfX/5tevX29kZFRfX6/QfuDAAS6XGx8fr7yKr6+vubm5XC5XPfKFCxcCAgIEAgGHw3Fxcdm8efOjR4+Uu0VHRyuklICAAHqHsLAwkUjU29urztfRl3lIepCPIG8+DYaRN0cC502tbY4p9f/mZTKZSCSKjo5Wc+QHDx5wudzIyMgRRMdARUUFQRDHjh1Ts7++5E04TwdAjwkEgry8vJycnH379g3ZmSTJmJgYPp+/bds2LcRWW1srlUoTExMXLlyohc1p0xjMm+Xl5e+9956Tk5OpqamlpeXMmTO3bt2qfCdU35mbmxM0+LEQsVi8fPnyS5cusR0d0B43N7fy8vLTp0/j+psqNDc319bWFhQUCIVCLQR28ODBpKSkpKQkLWxLy8Za3kxMTHz55ZetrKzy8/NlMtnNmzf//ve/5+bmOjs7U08fjw2dnZ2//PILQmj+/PkkSfb19VVVVW3durWqqsrDw+Mvf/nLo0eP2I5RF+HnyisrK+vr6wmC2LBhA9sRaYCTk1N+fj6uv6mCUCgsKiqaNm2adqLauXPn2DvS/C+2LxQMTf1rPfjs48CBAwrtXV1duGz1b7/9xnTrgz2ArCkjGZ+eN+nwXdTg4GDqaT/dh7R7fVPHPbXX9OH6prb9/vvvW7Zseemll5Rv8JmZme3Zs+fhw4fDmEWhjz755BNPT89vv/3266+/ZjsWAMagsZM3Dxw4IJfLB3tvvbe3t729/dmzZ3EtwrGNIAhc/Xv//v1sxwLAGDR28uaPP/6IEFJRixAv+s9//oNGVmBRLwo44u2WlpZSDy/fu3cvJibGycmJw+GMHz9eKpXiCclIvbKVCKGenp5Nmza5uLiYmZmNGzcuKCgIF7ilOqjYBABjCtsXCoam5rUe/HjcTz/9NFiHiIgI9OdihSMpsKgLBRwHu75JkiRVfqahoYEkyYaGhokTJ9ra2p46derhw4dXrlzx8fExNTWlT8wesmxlZGSkQCD44YcfHj161NTUFB8fjxA6f/48XqrOJlRAcH2TBq5v6rixc7yJDVlnQYOFGHS5gKPCiomJibdv3/7ss8/efPNNc3PzadOmff311yRJ/u1vf1NYUUXZyoKCgmnTpr322mtcLtfW1vbTTz91dnYexiYA0Hfaruw/euzt7RsbG1tbWwfrgBcxrUWogooCjiOv/lJYWDiS1XExG2NjY3xZ4OTJkwYGBvT35QqFwmnTpl26dKmurs7BwYFqH7BsJR7k9ddf//zzz99///1ly5bNnDnT0NCQ/qov9TcxmPDw8PDw8JF86zGG3WJLbAkNDWU7hKGNnbzp4+Nz6dKlioqK119/fcAOuPzB7NmzNbXFAQs4NjQ0tLS0sF41q6ioCCEkkUiMjY17enpwqbEBC4PfuHGDntRUlK3ct2+fRCLJzMz08/NDCHl7e0dHR4eEhCCEGG1iMHFxcaNd6FdflJSUpKSk4LP1p8qePXvYDkEtYydvRkdH/+Mf/8jOzk5ISFBeWlRU1NDQEBQUNGHCBKpxhAUWcQFHegcdKeDY39+Pn7pbsWIFQsjExMTS0rKzs/Px48cjufWEy9MuWbKkr6+vsLAwOTlZKpXu3r171apVGtmERCJZsGDBsMMbY1JSUp7CvZGdnc12CGoZO9c3nZ2d//73v//8888HDx5UWPTo0aO4uDhra+uUlBR6+wgLLOpsAcfExMSLFy+GhIRQs7KkUqlcLld4Ymrnzp0TJkxQ/8XWlpaWVVVVCCFjY+PXXnsN34Wn3u+qkU0AoB/YuyWlLkb3FhMTEw0NDT/88MMrV650d3c/ePAgLy/Pzc1NJBKVl5crdMaTHP/3f/9/e/cf0sQbxwH8OdqmS91RUS5OyDkYZT9WMigCESbMwhJavyQEIRIJYklJNcEockThH45vfxgrIoIiCwo2jIhVf0STVJpggdImlTYPtMwVLVmsPx467uvmdbeb7e72ef3n3eNzj/P4sN3z7P38F41G3717d+DAAYqi5s1379ixgyTJDx8+vHz5UqVSvX37Fh83m80kSVZXV3PMp4vpX+h8+q9fv2iafvjwodVqRQgdPnyYnfpF07TRaCwrK+vt7Z2ZmZmenu7u7l66dCl7Cjt5Y8jTp08jhF6/fo1/JEmyqqpqaGgoFovRNH3u3DmEUEdHB/9LcEAwn84C8+kSJ4P/jdB7qL+/v7Gxcc2aNRqNpqioyGKxdHR0sPcvZYgJWMx6gOO8LbEIgiBJcuPGjUePHh0cHExuPz09feLEibKyMrVavXLlSpvN9uTJE3yKZ2xlMBhsbm5et24dXr+5bds2j8fD/ionxyX+CuomG9RNiSMSi7xVqXg9PT0HDx6U2jg3b948NTW10BYFQCiCIO7evZuDT/RSkuY9/w/gJ0vSf8qpnOebAADG+/fv6+rqcLJcJBJxuVwWi0Wn0+n1+qqqqnlR+fz19vaaTKaUU39nzpzJnQUAUDcBUJpgMGixWGw2G06Wa2pq6urqOn/+fCQS6evrKykpsdvtKZedcAiFQnV1dU6nk6bplA2ampqcTmd7e3sG/gDJg7opmCIDHHNKYWEhkxsgx/65zc7O7t69e+/evXhaEuvs7KytrS0oKCgtLb1x4wZFUZ2dnYJ2im9vb9++ffvg4GBRUVHKBkaj8cGDBy6Xq6enR+zfIHnKWb/5z7S2tuKvZgMgQZcvX56cnDx79ixzxOfzsRtoNJry8vKJiYmRkZHkHToXcv36da1Wy93GbDbv27fv5MmTdrs9gyE1EgTvNwFQjkQice3ata1bt3J/nxinKS5fvpx/z38tmtiePXvGx8eZVb1KBXUTyAle6mQ0GjUazbJly3bu3Pns2TN8SvHZgHwMDQ3RNM2RpogQunnzZigUMplM5eXlGR8ATmx4/PhxxnuWluwug+IjZ9ey5RTEY/1mJBIxGAzFxcVer/fr168jIyN2u50gCI/Hw7SRezYglvY9f+vWLfT/sES2N2/eOBwOvIXfq1ev0ug/kUhQFLVkyZKFzuKYgsrKyvQ6l8v6TXi/CWTD6XSOjY11dXXt2rVLp9OZTKbbt2+vXr3a4XAsNMkrlJSzAfnAOVgp01UQQps2bbp3796xY8eGh4fZwVcZpNPpCILAw1AwqJtANvCqw9raWuZIXl5edXX1jx8/MvXBkCMbUHznz58///z586JmPsViMYSQWq1eqMHTp0/dbncG0xSTqVQqJjZbqaBuAnnAUXX5+fnz1sEUFxcjhCYnJzNylZTZgOhP0pX05efnI4SYzVGyIh6P85xEki+om0Ae8vLySJKMxWLRaJR9HH9C1+v1+MeMZAOyj0gkG5AnnMWFHzJmxezsbOLPpjUKBnUTyAbOSGavcfn586ff79dqtTU1NfiIUrMBedqwYQNCaKHYhHg8vnbt2kUdAH5x8DAUDOomkI2LFy8aDIaWlhafzxeNRkdHRw8dOhSJRNxuN/60jhCy2WyfPn26cuXKt2/fQqHQ8ePHk5d2V1RUjI6Ofvz4MRAIhMPhyspK5hRJkm1tbYFA4Pv37wMDAw0NDRqNxu12Mw3E9G+1WlesWNHX15f5l+YPs9m8atUqvLXBPB6Pp6Cg4NSpU8mnGhoaCIIYGxsTPwC8g6nNZhPflaRlczKfH1iHlAsQvxy5qamplpYWg8GgVqtJkqypqfH7/ewGss4GZIi559va2lQq1cTExLzj3d3dWq22tbU1+VesVmthYWE8Hufo1uv1JlcP9gowbP/+/RRFzc3NpTd4uaxDkkE9grqZC3jWzUWF62Z2x4CJuednZmYoimpububZ/suXL1qt9siRI+ldji0YDBIEcefOnbR7kEvdhM/pACgKSZJer/f+/ft4jyluiUTC4XDodLoLFy6IvG44HLbb7U6ns76+XmRX0gd1EwCl2bJly8DAwKNHj3D+JgeapsPhsN/vZxYkpO3q1asul8vlconsRxagbgKgwGzA0tJSn8+H8zc56PX6Fy9erF+/XvwVL126lAvvNDElZz0BwBNkAwJB4P0mAAAIA3UTAACEgboJAADCQN0EAABhZDMvlAubPeW4QCCQ7SFIBX4pcvCeHx8fLykpyfYoeMj2wvu/y51NmQEAsvi+EJFYzPRpAABQHni+CQAAwkDdBAAAYaBuAgCAMFA3AQBAmN/I1Q74MRFjcwAAAABJRU5ErkJggg==\n",
"text/plain": [
- "None"
+ "<IPython.core.display.Image object>"
]
},
"metadata": {},
@@ -766,206 +766,207 @@
"name": "stdout",
"output_type": "stream",
"text": [
+ "Train on 354 samples, validate on 152 samples\n",
"Epoch 1/100\n",
- "36/36 [==============================] - 0s 10ms/step - loss: 499.2093 - mae: 20.4430 - mse: 499.2093 - val_loss: 420.4774 - val_mae: 18.8304 - val_mse: 420.4775\n",
+ "354/354 [==============================] - 1s 2ms/sample - loss: 481.3753 - mae: 19.8670 - mse: 481.3753 - val_loss: 314.3385 - val_mae: 15.8008 - val_mse: 314.3385\n",
"Epoch 2/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 292.3440 - mae: 15.0259 - mse: 292.3440 - val_loss: 169.6111 - val_mae: 11.3853 - val_mse: 169.6111\n",
+ "354/354 [==============================] - 0s 260us/sample - loss: 229.3621 - mae: 12.6174 - mse: 229.3621 - val_loss: 88.5334 - val_mae: 7.5604 - val_mse: 88.5334\n",
"Epoch 3/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 102.2642 - mae: 7.9369 - mse: 102.2642 - val_loss: 47.8630 - val_mae: 5.2687 - val_mse: 47.8630\n",
+ "354/354 [==============================] - 0s 257us/sample - loss: 68.8611 - mae: 6.3557 - mse: 68.8611 - val_loss: 31.3818 - val_mae: 4.0464 - val_mse: 31.3818\n",
"Epoch 4/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 39.8329 - mae: 4.7167 - mse: 39.8329 - val_loss: 27.7145 - val_mae: 3.9110 - val_mse: 27.7145\n",
+ "354/354 [==============================] - 0s 276us/sample - loss: 35.8664 - mae: 4.2830 - mse: 35.8664 - val_loss: 22.6644 - val_mae: 3.3926 - val_mse: 22.6644\n",
"Epoch 5/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 26.4996 - mae: 3.7357 - mse: 26.4996 - val_loss: 21.7246 - val_mae: 3.3270 - val_mse: 21.7246\n",
+ "354/354 [==============================] - 0s 308us/sample - loss: 27.5686 - mae: 3.7422 - mse: 27.5686 - val_loss: 19.3083 - val_mae: 3.1289 - val_mse: 19.3083\n",
"Epoch 6/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 21.9933 - mae: 3.3116 - mse: 21.9933 - val_loss: 19.8916 - val_mae: 3.1714 - val_mse: 19.8916\n",
+ "354/354 [==============================] - 0s 219us/sample - loss: 23.4305 - mae: 3.4043 - mse: 23.4305 - val_loss: 20.6539 - val_mae: 3.4142 - val_mse: 20.6539\n",
"Epoch 7/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 19.7898 - mae: 3.0751 - mse: 19.7898 - val_loss: 17.2688 - val_mae: 2.9405 - val_mse: 17.2688\n",
+ "354/354 [==============================] - 0s 255us/sample - loss: 21.1150 - mae: 3.2773 - mse: 21.1150 - val_loss: 18.6601 - val_mae: 3.1530 - val_mse: 18.6601\n",
"Epoch 8/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 17.9261 - mae: 2.9466 - mse: 17.9261 - val_loss: 16.5394 - val_mae: 2.8745 - val_mse: 16.5394\n",
+ "354/354 [==============================] - 0s 260us/sample - loss: 19.8029 - mae: 3.0902 - mse: 19.8029 - val_loss: 15.9759 - val_mae: 2.7631 - val_mse: 15.9759\n",
"Epoch 9/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 17.0427 - mae: 2.8894 - mse: 17.0427 - val_loss: 15.0774 - val_mae: 2.7670 - val_mse: 15.0774\n",
+ "354/354 [==============================] - 0s 245us/sample - loss: 18.2403 - mae: 2.9607 - mse: 18.2403 - val_loss: 15.4413 - val_mae: 2.7050 - val_mse: 15.4413\n",
"Epoch 10/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 16.1931 - mae: 2.8067 - mse: 16.1931 - val_loss: 14.6675 - val_mae: 2.7240 - val_mse: 14.6675\n",
+ "354/354 [==============================] - 0s 270us/sample - loss: 17.1524 - mae: 2.8301 - mse: 17.1524 - val_loss: 15.1125 - val_mae: 2.7898 - val_mse: 15.1125\n",
"Epoch 11/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 15.5665 - mae: 2.6927 - mse: 15.5665 - val_loss: 14.1492 - val_mae: 2.6652 - val_mse: 14.1492\n",
+ "354/354 [==============================] - 0s 270us/sample - loss: 16.2984 - mae: 2.7811 - mse: 16.2984 - val_loss: 14.8470 - val_mae: 2.7025 - val_mse: 14.8470\n",
"Epoch 12/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 15.1335 - mae: 2.6444 - mse: 15.1335 - val_loss: 13.8187 - val_mae: 2.7048 - val_mse: 13.8187\n",
+ "354/354 [==============================] - 0s 252us/sample - loss: 15.2709 - mae: 2.6902 - mse: 15.2709 - val_loss: 14.4838 - val_mae: 2.7250 - val_mse: 14.4838\n",
"Epoch 13/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 14.7162 - mae: 2.6326 - mse: 14.7162 - val_loss: 13.3821 - val_mae: 2.6124 - val_mse: 13.3821\n",
+ "354/354 [==============================] - 0s 260us/sample - loss: 15.0772 - mae: 2.6599 - mse: 15.0772 - val_loss: 13.1951 - val_mae: 2.5494 - val_mse: 13.1951\n",
"Epoch 14/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 14.5406 - mae: 2.5951 - mse: 14.5406 - val_loss: 13.1009 - val_mae: 2.6227 - val_mse: 13.1009\n",
+ "354/354 [==============================] - 0s 239us/sample - loss: 14.4671 - mae: 2.6048 - mse: 14.4671 - val_loss: 12.9212 - val_mae: 2.5698 - val_mse: 12.9212\n",
"Epoch 15/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 13.7831 - mae: 2.5532 - mse: 13.7831 - val_loss: 12.9629 - val_mae: 2.5355 - val_mse: 12.9629\n",
+ "354/354 [==============================] - 0s 246us/sample - loss: 13.8594 - mae: 2.5400 - mse: 13.8594 - val_loss: 12.7734 - val_mae: 2.5859 - val_mse: 12.7734\n",
"Epoch 16/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 13.7281 - mae: 2.4935 - mse: 13.7281 - val_loss: 12.6765 - val_mae: 2.6053 - val_mse: 12.6765\n",
+ "354/354 [==============================] - 0s 236us/sample - loss: 13.6460 - mae: 2.5167 - mse: 13.6460 - val_loss: 13.0759 - val_mae: 2.6484 - val_mse: 13.0759\n",
"Epoch 17/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 13.3781 - mae: 2.4750 - mse: 13.3781 - val_loss: 11.9486 - val_mae: 2.4801 - val_mse: 11.9486\n",
+ "354/354 [==============================] - 0s 264us/sample - loss: 13.2246 - mae: 2.4772 - mse: 13.2246 - val_loss: 12.1817 - val_mae: 2.5219 - val_mse: 12.1817\n",
"Epoch 18/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 12.9392 - mae: 2.3964 - mse: 12.9392 - val_loss: 13.0057 - val_mae: 2.6542 - val_mse: 13.0057\n",
+ "354/354 [==============================] - 0s 235us/sample - loss: 12.5195 - mae: 2.4540 - mse: 12.5195 - val_loss: 12.6745 - val_mae: 2.5802 - val_mse: 12.6745\n",
"Epoch 19/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 12.8964 - mae: 2.4341 - mse: 12.8964 - val_loss: 11.8803 - val_mae: 2.4624 - val_mse: 11.8803\n",
+ "354/354 [==============================] - 0s 261us/sample - loss: 12.4546 - mae: 2.4254 - mse: 12.4546 - val_loss: 11.9251 - val_mae: 2.4647 - val_mse: 11.9251\n",
"Epoch 20/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 12.6664 - mae: 2.3981 - mse: 12.6664 - val_loss: 11.9270 - val_mae: 2.4685 - val_mse: 11.9270\n",
+ "354/354 [==============================] - 0s 225us/sample - loss: 12.0475 - mae: 2.3894 - mse: 12.0475 - val_loss: 12.0472 - val_mae: 2.4925 - val_mse: 12.0472\n",
"Epoch 21/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 12.4017 - mae: 2.3463 - mse: 12.4017 - val_loss: 11.2440 - val_mae: 2.4397 - val_mse: 11.2440\n",
+ "354/354 [==============================] - 0s 225us/sample - loss: 11.4262 - mae: 2.3048 - mse: 11.4262 - val_loss: 13.1727 - val_mae: 2.6941 - val_mse: 13.1727\n",
"Epoch 22/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 12.1234 - mae: 2.3006 - mse: 12.1234 - val_loss: 11.1839 - val_mae: 2.4669 - val_mse: 11.1839\n",
+ "354/354 [==============================] - 0s 214us/sample - loss: 11.4323 - mae: 2.3778 - mse: 11.4323 - val_loss: 12.2495 - val_mae: 2.5638 - val_mse: 12.2495\n",
"Epoch 23/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 12.0749 - mae: 2.3447 - mse: 12.0749 - val_loss: 11.2207 - val_mae: 2.4656 - val_mse: 11.2207\n",
+ "354/354 [==============================] - 0s 255us/sample - loss: 11.2458 - mae: 2.2963 - mse: 11.2458 - val_loss: 11.7263 - val_mae: 2.4637 - val_mse: 11.7263\n",
"Epoch 24/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 11.8094 - mae: 2.3250 - mse: 11.8094 - val_loss: 11.0406 - val_mae: 2.4306 - val_mse: 11.0406\n",
+ "354/354 [==============================] - 0s 261us/sample - loss: 10.9569 - mae: 2.3280 - mse: 10.9569 - val_loss: 11.6823 - val_mae: 2.4107 - val_mse: 11.6823\n",
"Epoch 25/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 11.7316 - mae: 2.3286 - mse: 11.7316 - val_loss: 10.9188 - val_mae: 2.4019 - val_mse: 10.9188\n",
+ "354/354 [==============================] - 0s 231us/sample - loss: 10.9952 - mae: 2.2930 - mse: 10.9952 - val_loss: 11.6954 - val_mae: 2.3762 - val_mse: 11.6954\n",
"Epoch 26/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 11.5838 - mae: 2.2687 - mse: 11.5838 - val_loss: 10.5081 - val_mae: 2.3415 - val_mse: 10.5081\n",
+ "354/354 [==============================] - 0s 242us/sample - loss: 10.3608 - mae: 2.2362 - mse: 10.3608 - val_loss: 11.7024 - val_mae: 2.4272 - val_mse: 11.7024\n",
"Epoch 27/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 11.2892 - mae: 2.2301 - mse: 11.2892 - val_loss: 10.5226 - val_mae: 2.3555 - val_mse: 10.5226\n",
+ "354/354 [==============================] - 0s 260us/sample - loss: 10.6636 - mae: 2.2256 - mse: 10.6636 - val_loss: 11.4609 - val_mae: 2.3786 - val_mse: 11.4609\n",
"Epoch 28/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 11.1164 - mae: 2.2479 - mse: 11.1164 - val_loss: 10.4150 - val_mae: 2.3400 - val_mse: 10.4150\n",
+ "354/354 [==============================] - 0s 267us/sample - loss: 10.3853 - mae: 2.2194 - mse: 10.3853 - val_loss: 11.3762 - val_mae: 2.3642 - val_mse: 11.3762\n",
"Epoch 29/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 10.9612 - mae: 2.2179 - mse: 10.9612 - val_loss: 10.2046 - val_mae: 2.3435 - val_mse: 10.2046\n",
+ "354/354 [==============================] - 0s 223us/sample - loss: 10.1887 - mae: 2.1885 - mse: 10.1887 - val_loss: 11.9517 - val_mae: 2.5128 - val_mse: 11.9517\n",
"Epoch 30/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 10.9214 - mae: 2.2458 - mse: 10.9214 - val_loss: 10.8314 - val_mae: 2.4200 - val_mse: 10.8314\n",
+ "354/354 [==============================] - 0s 230us/sample - loss: 9.9776 - mae: 2.1494 - mse: 9.9776 - val_loss: 13.0448 - val_mae: 2.6374 - val_mse: 13.0448\n",
"Epoch 31/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 10.8019 - mae: 2.1876 - mse: 10.8019 - val_loss: 10.3296 - val_mae: 2.3254 - val_mse: 10.3296\n",
+ "354/354 [==============================] - 0s 239us/sample - loss: 9.6249 - mae: 2.1446 - mse: 9.6249 - val_loss: 12.0983 - val_mae: 2.5684 - val_mse: 12.0983\n",
"Epoch 32/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 10.6908 - mae: 2.1993 - mse: 10.6908 - val_loss: 9.9282 - val_mae: 2.2934 - val_mse: 9.9282\n",
+ "354/354 [==============================] - 0s 230us/sample - loss: 9.8706 - mae: 2.1513 - mse: 9.8706 - val_loss: 11.4932 - val_mae: 2.4160 - val_mse: 11.4932\n",
"Epoch 33/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 10.2687 - mae: 2.1525 - mse: 10.2687 - val_loss: 10.3924 - val_mae: 2.3241 - val_mse: 10.3924\n",
+ "354/354 [==============================] - 0s 228us/sample - loss: 9.4976 - mae: 2.0707 - mse: 9.4976 - val_loss: 12.2362 - val_mae: 2.4694 - val_mse: 12.2362\n",
"Epoch 34/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 10.3882 - mae: 2.1708 - mse: 10.3882 - val_loss: 9.8941 - val_mae: 2.2892 - val_mse: 9.8941\n",
+ "354/354 [==============================] - 0s 222us/sample - loss: 9.3964 - mae: 2.1224 - mse: 9.3964 - val_loss: 12.7674 - val_mae: 2.6478 - val_mse: 12.7674\n",
"Epoch 35/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 10.1249 - mae: 2.1561 - mse: 10.1249 - val_loss: 10.2400 - val_mae: 2.3040 - val_mse: 10.2400\n",
+ "354/354 [==============================] - 0s 239us/sample - loss: 9.4643 - mae: 2.1037 - mse: 9.4643 - val_loss: 11.3943 - val_mae: 2.3635 - val_mse: 11.3943\n",
"Epoch 36/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 10.1762 - mae: 2.1386 - mse: 10.1762 - val_loss: 10.3141 - val_mae: 2.3525 - val_mse: 10.3141\n",
+ "354/354 [==============================] - 0s 230us/sample - loss: 8.8058 - mae: 2.0187 - mse: 8.8058 - val_loss: 11.5623 - val_mae: 2.3851 - val_mse: 11.5623\n",
"Epoch 37/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 9.6215 - mae: 2.1006 - mse: 9.6215 - val_loss: 9.8773 - val_mae: 2.2859 - val_mse: 9.8773\n",
+ "354/354 [==============================] - 0s 270us/sample - loss: 8.9810 - mae: 2.0965 - mse: 8.9810 - val_loss: 11.1572 - val_mae: 2.3400 - val_mse: 11.1572\n",
"Epoch 38/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 10.0097 - mae: 2.1486 - mse: 10.0097 - val_loss: 9.7549 - val_mae: 2.2745 - val_mse: 9.7549\n",
+ "354/354 [==============================] - 0s 251us/sample - loss: 8.6998 - mae: 2.0867 - mse: 8.6998 - val_loss: 12.4127 - val_mae: 2.5576 - val_mse: 12.4127\n",
"Epoch 39/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 9.6103 - mae: 2.0716 - mse: 9.6103 - val_loss: 9.7774 - val_mae: 2.2508 - val_mse: 9.7774\n",
+ "354/354 [==============================] - 0s 255us/sample - loss: 8.9094 - mae: 2.0972 - mse: 8.9094 - val_loss: 11.8589 - val_mae: 2.4271 - val_mse: 11.8589\n",
"Epoch 40/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 9.7722 - mae: 2.1225 - mse: 9.7722 - val_loss: 9.6608 - val_mae: 2.2962 - val_mse: 9.6608\n",
+ "354/354 [==============================] - 0s 234us/sample - loss: 8.6066 - mae: 2.0621 - mse: 8.6066 - val_loss: 11.5191 - val_mae: 2.3642 - val_mse: 11.5191\n",
"Epoch 41/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 9.5963 - mae: 2.0797 - mse: 9.5963 - val_loss: 9.5160 - val_mae: 2.2512 - val_mse: 9.5160\n",
+ "354/354 [==============================] - 0s 244us/sample - loss: 8.7560 - mae: 2.0669 - mse: 8.7560 - val_loss: 11.2479 - val_mae: 2.3179 - val_mse: 11.2479\n",
"Epoch 42/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 9.5719 - mae: 2.0775 - mse: 9.5719 - val_loss: 9.3480 - val_mae: 2.2093 - val_mse: 9.3480\n",
+ "354/354 [==============================] - 0s 230us/sample - loss: 8.3698 - mae: 2.0177 - mse: 8.3698 - val_loss: 11.5014 - val_mae: 2.4096 - val_mse: 11.5014\n",
"Epoch 43/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 9.4643 - mae: 2.0375 - mse: 9.4643 - val_loss: 9.2934 - val_mae: 2.2373 - val_mse: 9.2934\n",
+ "354/354 [==============================] - 0s 243us/sample - loss: 8.3662 - mae: 2.0202 - mse: 8.3662 - val_loss: 13.6899 - val_mae: 2.6890 - val_mse: 13.6899\n",
"Epoch 44/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 9.2036 - mae: 2.0558 - mse: 9.2036 - val_loss: 9.4987 - val_mae: 2.2673 - val_mse: 9.4987\n",
+ "354/354 [==============================] - 0s 228us/sample - loss: 8.3175 - mae: 1.9934 - mse: 8.3175 - val_loss: 12.2908 - val_mae: 2.4973 - val_mse: 12.2908\n",
"Epoch 45/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 9.2458 - mae: 2.0325 - mse: 9.2458 - val_loss: 9.6669 - val_mae: 2.3039 - val_mse: 9.6669\n",
+ "354/354 [==============================] - 0s 239us/sample - loss: 8.0425 - mae: 1.9670 - mse: 8.0425 - val_loss: 11.5553 - val_mae: 2.3337 - val_mse: 11.5553\n",
"Epoch 46/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 9.1519 - mae: 2.0477 - mse: 9.1519 - val_loss: 9.6261 - val_mae: 2.2777 - val_mse: 9.6261\n",
+ "354/354 [==============================] - 0s 222us/sample - loss: 7.9485 - mae: 1.9926 - mse: 7.9485 - val_loss: 11.2622 - val_mae: 2.3386 - val_mse: 11.2622\n",
"Epoch 47/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 8.8660 - mae: 2.0486 - mse: 8.8660 - val_loss: 9.2118 - val_mae: 2.2124 - val_mse: 9.2118\n",
+ "354/354 [==============================] - 0s 242us/sample - loss: 7.8891 - mae: 2.0168 - mse: 7.8891 - val_loss: 11.4137 - val_mae: 2.3604 - val_mse: 11.4137\n",
"Epoch 48/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 9.0046 - mae: 2.0213 - mse: 9.0046 - val_loss: 8.9364 - val_mae: 2.1414 - val_mse: 8.9364\n",
+ "354/354 [==============================] - 0s 230us/sample - loss: 7.9028 - mae: 1.9734 - mse: 7.9028 - val_loss: 12.7944 - val_mae: 2.6490 - val_mse: 12.7944\n",
"Epoch 49/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 8.6198 - mae: 1.9905 - mse: 8.6198 - val_loss: 8.9992 - val_mae: 2.1603 - val_mse: 8.9992\n",
+ "354/354 [==============================] - 0s 258us/sample - loss: 7.7419 - mae: 1.9250 - mse: 7.7419 - val_loss: 11.0452 - val_mae: 2.3072 - val_mse: 11.0452\n",
"Epoch 50/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 8.6736 - mae: 1.9742 - mse: 8.6736 - val_loss: 9.3506 - val_mae: 2.2071 - val_mse: 9.3506\n",
+ "354/354 [==============================] - 0s 255us/sample - loss: 7.7409 - mae: 1.9717 - mse: 7.7409 - val_loss: 10.9745 - val_mae: 2.2683 - val_mse: 10.9745\n",
"Epoch 51/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 8.5662 - mae: 1.9625 - mse: 8.5662 - val_loss: 8.8453 - val_mae: 2.1646 - val_mse: 8.8453\n",
+ "354/354 [==============================] - 0s 225us/sample - loss: 7.6033 - mae: 1.9730 - mse: 7.6034 - val_loss: 11.0436 - val_mae: 2.3185 - val_mse: 11.0436\n",
"Epoch 52/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 8.6659 - mae: 1.9890 - mse: 8.6659 - val_loss: 8.9524 - val_mae: 2.1324 - val_mse: 8.9524\n",
+ "354/354 [==============================] - 0s 244us/sample - loss: 7.3048 - mae: 1.9423 - mse: 7.3048 - val_loss: 11.2731 - val_mae: 2.3358 - val_mse: 11.2731\n",
"Epoch 53/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 8.4001 - mae: 1.9698 - mse: 8.4001 - val_loss: 9.1100 - val_mae: 2.1893 - val_mse: 9.1100\n",
+ "354/354 [==============================] - 0s 230us/sample - loss: 7.4668 - mae: 1.8864 - mse: 7.4668 - val_loss: 11.5785 - val_mae: 2.4433 - val_mse: 11.5785\n",
"Epoch 54/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 8.4276 - mae: 1.9754 - mse: 8.4276 - val_loss: 9.0500 - val_mae: 2.1473 - val_mse: 9.0500\n",
+ "354/354 [==============================] - 0s 241us/sample - loss: 7.3823 - mae: 1.8967 - mse: 7.3823 - val_loss: 11.1408 - val_mae: 2.3028 - val_mse: 11.1408\n",
"Epoch 55/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 8.3976 - mae: 1.9540 - mse: 8.3976 - val_loss: 9.2225 - val_mae: 2.2563 - val_mse: 9.2225\n",
+ "354/354 [==============================] - 0s 261us/sample - loss: 7.2735 - mae: 1.8909 - mse: 7.2735 - val_loss: 11.3305 - val_mae: 2.3024 - val_mse: 11.3305\n",
"Epoch 56/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 8.1493 - mae: 1.9598 - mse: 8.1493 - val_loss: 8.8117 - val_mae: 2.1391 - val_mse: 8.8117\n",
+ "354/354 [==============================] - 0s 248us/sample - loss: 7.2339 - mae: 1.8862 - mse: 7.2339 - val_loss: 12.2850 - val_mae: 2.5301 - val_mse: 12.2850\n",
"Epoch 57/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 8.0737 - mae: 1.9515 - mse: 8.0737 - val_loss: 8.7231 - val_mae: 2.1213 - val_mse: 8.7231\n",
+ "354/354 [==============================] - 0s 248us/sample - loss: 7.0889 - mae: 1.8855 - mse: 7.0889 - val_loss: 11.2573 - val_mae: 2.3870 - val_mse: 11.2573\n",
"Epoch 58/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 8.0246 - mae: 1.9310 - mse: 8.0246 - val_loss: 8.6850 - val_mae: 2.1204 - val_mse: 8.6850\n",
+ "354/354 [==============================] - 0s 255us/sample - loss: 7.2343 - mae: 1.9144 - mse: 7.2343 - val_loss: 11.6382 - val_mae: 2.4304 - val_mse: 11.6382\n",
"Epoch 59/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 7.9543 - mae: 1.9113 - mse: 7.9543 - val_loss: 8.4364 - val_mae: 2.1018 - val_mse: 8.4364\n",
+ "354/354 [==============================] - 0s 242us/sample - loss: 7.1102 - mae: 1.9082 - mse: 7.1102 - val_loss: 11.4438 - val_mae: 2.4327 - val_mse: 11.4438\n",
"Epoch 60/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 7.9279 - mae: 1.9548 - mse: 7.9279 - val_loss: 8.5545 - val_mae: 2.1434 - val_mse: 8.5545\n",
+ "354/354 [==============================] - 0s 243us/sample - loss: 7.0405 - mae: 1.8855 - mse: 7.0405 - val_loss: 11.0125 - val_mae: 2.3220 - val_mse: 11.0125\n",
"Epoch 61/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 7.7351 - mae: 1.9272 - mse: 7.7351 - val_loss: 8.8891 - val_mae: 2.2113 - val_mse: 8.8891\n",
+ "354/354 [==============================] - 0s 250us/sample - loss: 6.7857 - mae: 1.8399 - mse: 6.7857 - val_loss: 11.0973 - val_mae: 2.3080 - val_mse: 11.0973\n",
"Epoch 62/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 7.8114 - mae: 1.9405 - mse: 7.8114 - val_loss: 8.3726 - val_mae: 2.0777 - val_mse: 8.3726\n",
+ "354/354 [==============================] - 0s 228us/sample - loss: 6.8898 - mae: 1.8708 - mse: 6.8898 - val_loss: 11.1531 - val_mae: 2.3710 - val_mse: 11.1531\n",
"Epoch 63/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 7.5490 - mae: 1.9089 - mse: 7.5490 - val_loss: 9.2360 - val_mae: 2.2077 - val_mse: 9.2360\n",
+ "354/354 [==============================] - 0s 271us/sample - loss: 6.6358 - mae: 1.8618 - mse: 6.6358 - val_loss: 10.9051 - val_mae: 2.3133 - val_mse: 10.9051\n",
"Epoch 64/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 7.6260 - mae: 1.9067 - mse: 7.6260 - val_loss: 8.3816 - val_mae: 2.0638 - val_mse: 8.3816\n",
+ "354/354 [==============================] - 0s 253us/sample - loss: 6.5401 - mae: 1.7681 - mse: 6.5401 - val_loss: 12.8580 - val_mae: 2.7144 - val_mse: 12.8580\n",
"Epoch 65/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 7.4021 - mae: 1.8836 - mse: 7.4021 - val_loss: 9.2083 - val_mae: 2.2493 - val_mse: 9.2083\n",
+ "354/354 [==============================] - 0s 240us/sample - loss: 6.6050 - mae: 1.8580 - mse: 6.6050 - val_loss: 11.3343 - val_mae: 2.3782 - val_mse: 11.3343\n",
"Epoch 66/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 7.5104 - mae: 1.8849 - mse: 7.5104 - val_loss: 8.0362 - val_mae: 2.0381 - val_mse: 8.0362\n",
+ "354/354 [==============================] - 0s 250us/sample - loss: 6.3123 - mae: 1.8428 - mse: 6.3123 - val_loss: 11.8693 - val_mae: 2.4310 - val_mse: 11.8693\n",
"Epoch 67/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 7.3472 - mae: 1.8806 - mse: 7.3472 - val_loss: 8.5162 - val_mae: 2.1142 - val_mse: 8.5162\n",
+ "354/354 [==============================] - 0s 260us/sample - loss: 6.6103 - mae: 1.8086 - mse: 6.6103 - val_loss: 11.7004 - val_mae: 2.4491 - val_mse: 11.7004\n",
"Epoch 68/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 7.2918 - mae: 1.8475 - mse: 7.2918 - val_loss: 8.2594 - val_mae: 2.0755 - val_mse: 8.2594\n",
+ "354/354 [==============================] - 0s 224us/sample - loss: 6.2110 - mae: 1.7989 - mse: 6.2110 - val_loss: 12.3957 - val_mae: 2.5979 - val_mse: 12.3957\n",
"Epoch 69/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.8707 - mae: 1.8768 - mse: 6.8707 - val_loss: 8.6912 - val_mae: 2.1383 - val_mse: 8.6912\n",
+ "354/354 [==============================] - 0s 245us/sample - loss: 6.4645 - mae: 1.7947 - mse: 6.4645 - val_loss: 11.1198 - val_mae: 2.3530 - val_mse: 11.1198\n",
"Epoch 70/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 7.3130 - mae: 1.8596 - mse: 7.3130 - val_loss: 8.3363 - val_mae: 2.0389 - val_mse: 8.3363\n",
+ "354/354 [==============================] - 0s 228us/sample - loss: 6.1215 - mae: 1.7740 - mse: 6.1215 - val_loss: 11.1679 - val_mae: 2.3572 - val_mse: 11.1679\n",
"Epoch 71/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.7877 - mae: 1.8276 - mse: 6.7877 - val_loss: 8.5638 - val_mae: 2.0866 - val_mse: 8.5638\n",
+ "354/354 [==============================] - 0s 247us/sample - loss: 6.2289 - mae: 1.7995 - mse: 6.2289 - val_loss: 11.2329 - val_mae: 2.3668 - val_mse: 11.2329\n",
"Epoch 72/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.9769 - mae: 1.8271 - mse: 6.9769 - val_loss: 8.8546 - val_mae: 2.1616 - val_mse: 8.8546\n",
+ "354/354 [==============================] - 0s 224us/sample - loss: 6.2470 - mae: 1.7568 - mse: 6.2470 - val_loss: 11.1376 - val_mae: 2.3489 - val_mse: 11.1376\n",
"Epoch 73/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.8571 - mae: 1.8383 - mse: 6.8571 - val_loss: 8.1234 - val_mae: 2.0600 - val_mse: 8.1234\n",
+ "354/354 [==============================] - 0s 250us/sample - loss: 5.9799 - mae: 1.7246 - mse: 5.9799 - val_loss: 13.7858 - val_mae: 2.7892 - val_mse: 13.7858\n",
"Epoch 74/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.8301 - mae: 1.8219 - mse: 6.8301 - val_loss: 8.2102 - val_mae: 2.0354 - val_mse: 8.2102\n",
+ "354/354 [==============================] - 0s 245us/sample - loss: 6.1388 - mae: 1.7908 - mse: 6.1388 - val_loss: 13.4726 - val_mae: 2.7951 - val_mse: 13.4726\n",
"Epoch 75/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.7562 - mae: 1.8214 - mse: 6.7562 - val_loss: 8.4574 - val_mae: 2.1038 - val_mse: 8.4574\n",
+ "354/354 [==============================] - 0s 257us/sample - loss: 5.8487 - mae: 1.7590 - mse: 5.8487 - val_loss: 10.8045 - val_mae: 2.3233 - val_mse: 10.8045\n",
"Epoch 76/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.5747 - mae: 1.7726 - mse: 6.5747 - val_loss: 9.3396 - val_mae: 2.2729 - val_mse: 9.3396\n",
+ "354/354 [==============================] - 0s 235us/sample - loss: 6.0557 - mae: 1.7344 - mse: 6.0557 - val_loss: 11.0407 - val_mae: 2.3530 - val_mse: 11.0407\n",
"Epoch 77/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.6467 - mae: 1.7824 - mse: 6.6467 - val_loss: 8.9306 - val_mae: 2.1530 - val_mse: 8.9306\n",
+ "354/354 [==============================] - 0s 255us/sample - loss: 5.9265 - mae: 1.7060 - mse: 5.9265 - val_loss: 12.1121 - val_mae: 2.5807 - val_mse: 12.1121\n",
"Epoch 78/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.5600 - mae: 1.7864 - mse: 6.5600 - val_loss: 8.2530 - val_mae: 2.0533 - val_mse: 8.2530\n",
+ "354/354 [==============================] - 0s 245us/sample - loss: 5.8707 - mae: 1.7385 - mse: 5.8707 - val_loss: 11.2911 - val_mae: 2.3766 - val_mse: 11.2911\n",
"Epoch 79/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.3958 - mae: 1.7473 - mse: 6.3958 - val_loss: 7.8694 - val_mae: 2.0041 - val_mse: 7.8694\n",
+ "354/354 [==============================] - 0s 250us/sample - loss: 5.9036 - mae: 1.7217 - mse: 5.9036 - val_loss: 10.8621 - val_mae: 2.3029 - val_mse: 10.8621\n",
"Epoch 80/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.2345 - mae: 1.7558 - mse: 6.2345 - val_loss: 7.9594 - val_mae: 2.0160 - val_mse: 7.9594\n",
+ "354/354 [==============================] - 0s 227us/sample - loss: 5.7450 - mae: 1.7397 - mse: 5.7450 - val_loss: 11.2010 - val_mae: 2.3977 - val_mse: 11.2010\n",
"Epoch 81/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.1966 - mae: 1.7368 - mse: 6.1966 - val_loss: 7.9694 - val_mae: 2.0042 - val_mse: 7.9694\n",
+ "354/354 [==============================] - 0s 235us/sample - loss: 5.7671 - mae: 1.7236 - mse: 5.7671 - val_loss: 10.9266 - val_mae: 2.3568 - val_mse: 10.9266\n",
"Epoch 82/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.0636 - mae: 1.7692 - mse: 6.0636 - val_loss: 7.6473 - val_mae: 1.9737 - val_mse: 7.6473\n",
+ "354/354 [==============================] - 0s 232us/sample - loss: 5.7146 - mae: 1.7455 - mse: 5.7146 - val_loss: 11.0089 - val_mae: 2.3166 - val_mse: 11.0089\n",
"Epoch 83/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.0333 - mae: 1.7385 - mse: 6.0333 - val_loss: 9.3755 - val_mae: 2.2669 - val_mse: 9.3755\n",
+ "354/354 [==============================] - 0s 233us/sample - loss: 5.8056 - mae: 1.7146 - mse: 5.8056 - val_loss: 11.7801 - val_mae: 2.4932 - val_mse: 11.7801\n",
"Epoch 84/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.2546 - mae: 1.7511 - mse: 6.2546 - val_loss: 7.9122 - val_mae: 2.0064 - val_mse: 7.9122\n",
+ "354/354 [==============================] - 0s 216us/sample - loss: 5.4241 - mae: 1.6707 - mse: 5.4241 - val_loss: 11.2013 - val_mae: 2.3401 - val_mse: 11.2013\n",
"Epoch 85/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.7537 - mae: 1.7018 - mse: 5.7537 - val_loss: 7.6653 - val_mae: 1.9648 - val_mse: 7.6653\n",
+ "354/354 [==============================] - 0s 266us/sample - loss: 5.6327 - mae: 1.6767 - mse: 5.6327 - val_loss: 10.7487 - val_mae: 2.3300 - val_mse: 10.7487\n",
"Epoch 86/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.9497 - mae: 1.7138 - mse: 5.9497 - val_loss: 8.1256 - val_mae: 2.0543 - val_mse: 8.1256\n",
+ "354/354 [==============================] - 0s 222us/sample - loss: 5.5686 - mae: 1.7089 - mse: 5.5686 - val_loss: 11.0816 - val_mae: 2.3402 - val_mse: 11.0816\n",
"Epoch 87/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.8180 - mae: 1.6937 - mse: 5.8180 - val_loss: 8.2221 - val_mae: 2.0478 - val_mse: 8.2221\n",
+ "354/354 [==============================] - 0s 219us/sample - loss: 5.4004 - mae: 1.7063 - mse: 5.4004 - val_loss: 11.4167 - val_mae: 2.4616 - val_mse: 11.4167\n",
"Epoch 88/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.8592 - mae: 1.6974 - mse: 5.8592 - val_loss: 7.7128 - val_mae: 1.9818 - val_mse: 7.7128\n",
+ "354/354 [==============================] - 0s 247us/sample - loss: 5.4674 - mae: 1.6716 - mse: 5.4674 - val_loss: 10.7439 - val_mae: 2.3166 - val_mse: 10.7439\n",
"Epoch 89/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.7088 - mae: 1.6802 - mse: 5.7088 - val_loss: 7.9647 - val_mae: 1.9949 - val_mse: 7.9647\n",
+ "354/354 [==============================] - 0s 234us/sample - loss: 5.4125 - mae: 1.6612 - mse: 5.4125 - val_loss: 10.8343 - val_mae: 2.3358 - val_mse: 10.8343\n",
"Epoch 90/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.6122 - mae: 1.6606 - mse: 5.6122 - val_loss: 9.0089 - val_mae: 2.1664 - val_mse: 9.0089\n",
+ "354/354 [==============================] - 0s 235us/sample - loss: 5.2508 - mae: 1.6515 - mse: 5.2508 - val_loss: 10.8623 - val_mae: 2.3208 - val_mse: 10.8623\n",
"Epoch 91/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.7143 - mae: 1.6638 - mse: 5.7143 - val_loss: 7.9427 - val_mae: 1.9977 - val_mse: 7.9427\n",
+ "354/354 [==============================] - 0s 230us/sample - loss: 5.1373 - mae: 1.6527 - mse: 5.1373 - val_loss: 11.4705 - val_mae: 2.3904 - val_mse: 11.4705\n",
"Epoch 92/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.4997 - mae: 1.6691 - mse: 5.4997 - val_loss: 7.5745 - val_mae: 1.9386 - val_mse: 7.5745\n",
+ "354/354 [==============================] - 0s 275us/sample - loss: 5.4516 - mae: 1.6815 - mse: 5.4516 - val_loss: 10.5732 - val_mae: 2.2805 - val_mse: 10.5732\n",
"Epoch 93/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.2810 - mae: 1.6264 - mse: 5.2810 - val_loss: 8.3712 - val_mae: 2.0859 - val_mse: 8.3712\n",
+ "354/354 [==============================] - 0s 239us/sample - loss: 5.1915 - mae: 1.6121 - mse: 5.1915 - val_loss: 11.8951 - val_mae: 2.5364 - val_mse: 11.8951\n",
"Epoch 94/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.2594 - mae: 1.5943 - mse: 5.2594 - val_loss: 9.5673 - val_mae: 2.2581 - val_mse: 9.5673\n",
+ "354/354 [==============================] - 0s 229us/sample - loss: 5.0416 - mae: 1.6107 - mse: 5.0416 - val_loss: 10.9742 - val_mae: 2.3353 - val_mse: 10.9742\n",
"Epoch 95/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.4449 - mae: 1.6097 - mse: 5.4449 - val_loss: 7.9021 - val_mae: 1.9875 - val_mse: 7.9021\n",
+ "354/354 [==============================] - 0s 235us/sample - loss: 4.9773 - mae: 1.6085 - mse: 4.9773 - val_loss: 12.6817 - val_mae: 2.6664 - val_mse: 12.6817\n",
"Epoch 96/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.2279 - mae: 1.6336 - mse: 5.2279 - val_loss: 8.5401 - val_mae: 2.0221 - val_mse: 8.5401\n",
+ "354/354 [==============================] - 0s 233us/sample - loss: 5.1372 - mae: 1.6370 - mse: 5.1372 - val_loss: 12.1917 - val_mae: 2.5880 - val_mse: 12.1917\n",
"Epoch 97/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.4997 - mae: 1.6162 - mse: 5.4997 - val_loss: 7.8757 - val_mae: 2.0099 - val_mse: 7.8757\n",
+ "354/354 [==============================] - 0s 235us/sample - loss: 4.8466 - mae: 1.6235 - mse: 4.8466 - val_loss: 11.0726 - val_mae: 2.4107 - val_mse: 11.0726\n",
"Epoch 98/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.1612 - mae: 1.6110 - mse: 5.1612 - val_loss: 7.9834 - val_mae: 2.0433 - val_mse: 7.9834\n",
+ "354/354 [==============================] - 0s 230us/sample - loss: 5.1305 - mae: 1.6269 - mse: 5.1305 - val_loss: 11.4330 - val_mae: 2.5100 - val_mse: 11.4330\n",
"Epoch 99/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.2353 - mae: 1.6433 - mse: 5.2353 - val_loss: 8.1701 - val_mae: 2.0359 - val_mse: 8.1701\n",
+ "354/354 [==============================] - 0s 234us/sample - loss: 4.9402 - mae: 1.5922 - mse: 4.9402 - val_loss: 12.0358 - val_mae: 2.6035 - val_mse: 12.0358\n",
"Epoch 100/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.1282 - mae: 1.6134 - mse: 5.1282 - val_loss: 7.8599 - val_mae: 1.9740 - val_mse: 7.8599\n"
+ "354/354 [==============================] - 0s 232us/sample - loss: 5.0016 - mae: 1.5761 - mse: 5.0016 - val_loss: 12.4178 - val_mae: 2.5545 - val_mse: 12.4178\n"
]
}
],
@@ -998,9 +999,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "x_test / loss : 7.8599\n",
- "x_test / mae : 1.9740\n",
- "x_test / mse : 7.8599\n"
+ "x_test / loss : 12.4178\n",
+ "x_test / mae : 2.5545\n",
+ "x_test / mse : 12.4178\n"
]
}
],
@@ -1029,7 +1030,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "min( val_mae ) : 1.9386\n"
+ "min( val_mae ) : 2.2683\n"
]
}
],
@@ -1044,7 +1045,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
@@ -1056,7 +1057,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
@@ -1068,7 +1069,7 @@
},
{
"data": {
- "image/png": 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00UU89thjfOITnyCVSpHNZvnWt761fW9IRETagmkVw+EVCgUHUCwWN9v/zDPPcOCBB4769V5YsZ5qzd8iY+9dJpPNpMeglsm2rddKRERaZsTmcA2ObKKUVo8UEZE2p66KJrIWrh65aNGigRkTdR0dHfzqV79qaj1ERKS9KTg0UStXj5w7dy4LFy5s7jcVEZEJR10V22hbxobsaHfI3BHeo4jIjkbBYRvk83lWrVo16g/GHekOmc45Vq1aRT6fb3VVRERkDKmrYhvMmTOHpUuXsnLlylGdt7a3n/6Kn1XR15OjIzuxZ1Xk83nmzJnT6mqIiMgYUnDYBtlslr333nvU511y26+57+lXAPjUn8/juANnj3XVRERExpW6Kpoomxm83JVwPQcREZF2ouDQRLlIcOiv1FpYExERkW2j4NBEHZGVIstVtTiIiEj7UXBoomhXRbmqFgcREWk/Cg5NpBYHERFpdwoOTbTZGAe1OIiISBtScGiiXGTdhopaHEREpA0pODRRTmMcRESkzSk4NFF0jEN9BUkREZF2ouDQRJstAKUWBxERaUMKDk20WYuDxjiIiEgbUnBoIo1xEBGRdqfg0ESbLwClFgcREWk/Cg5NpAWgRESk3Sk4NNFmXRW6yZWIiLQhBYcmii4AVdZttUVEpA0pODSRbqstIiLtTsGhiaJjHLTktIiItCMFhybSbbVFRKTdZVpdgR3Gz/+L7MP3ctUrq/jJ5EO4p/uN1AJHOmWtrpmIiEjDFByaZU0P9sKzvB6YUe0F/LLT6Zz+CUREpH2oq6JZstnBp/huCi07LSIi7UbBoVkykeDgfHDQOAcREWk3Cg7NEhccdGttERFpMwoOzRIJDjm1OIiISJtScGiW7JYtDhrjICIi7UbBoVliuioqanEQEZE2o+DQLJnBaZeaVSEiIu0qccHBzLrM7EUzc2b2zZjj+5vZj81sjZn1mtmDZnbcMK+VMrMLzOxZMyuZ2ctmdrmZTRr/dzKEZlWIiMgEkLjgAHwRmBl3wMz2BR4CjgYuBT4BdAN3m9nxMadcCVwB/A44F/gBcB5wu5k1973HBge1OIiISHtJ1LKFZvYm4Hzgk8DlMUUuAaYBhzvnFobn3AA8DVxlZgc451y4/yB8WLjNOffeyPd4Efg6cDJw0zi+nc2pxUFERCaAxLQ4mFkauBa4C7gt5vgk4ETgvnpoAHDObQSuA/YD5kdOOQUw4GtDXupaoA84dSzrv1UxsyrU4iAiIu0mMcEBuAA4ADhnmOOHAB3AwzHHHgm30eAwHwiAR6MFnXMlYOGQsuMvdgEotTiIiEh7SURwMLO9gS8AX3TOLR6m2KxwuyzmWH3f7CHle5xz/cOUn2lmuWHqc7aZPb7Vio+GxjiIiMgEkIjgAHwLeBE/kHE4XeE2LgiUhpSpP48rO1z5Ac65a5xzR4xQl9HLDGaUwemYanEQEZH20vLBkWZ2KvB2YIFzrjJC0b5w2xFzLD+kTP35LsO8Vlz58RUzxqGiFgcREWkzLQ0OZtaBb2W4E3jVzF4fHqp3OUwN9/UAy4cci6rvi3ZjLAfeaGYdMd0Vs/HdGOXtfQ8Ni+mqUIuDiIi0m1Z3VXQCOwMnAM9HHveFx08Nvz4TWITvejg65nWOCrfRcQmP4d/fm6MFzSwPzBtSdvxpjIOIiEwAre6q6AX+Kmb/zkARPzXz34DfOOc2mtntwF+a2aHOuacAzKwbHyyeZ/MZFDcDn8GvC/FgZP9Z+LENN47xexlZ3HRMzaoQEZE209LgEI5puHXofjPbK3z6f8656PELgbcB95jZlcB6fBCYDZxQX/wpfO1FZnYVcI6Z3YbvDjkQv3Lk/TRz8SeAdPReFQHmnFocRESk7bS6xWFUnHN/MLO3AF8FPg3kgCeBdzrn7o055XxgMXA2vjukB/gGcJFzrrmf2qmUDw+1KgAZapRrCg4iItJeEhkcwrUcbJhjzwAnNfg6NfzS1XHLVzdfJjsQHLKupq4KERFpO60eHLljGTLOQV0VIiLSbhQcmikysyLnarrJlYiItB0Fh2bKqMVBRETam4JDMw0JDloASkRE2o2CQzMNGeOgJadFRKTdKDg0U6TFIUNAv2ZViIhIm1FwaCaNcRARkTan4NBMQ4JDpRYQDC52KSIikngKDs2kW2uLiEibU3BoJt0hU0RE2pyCQzPFBgcNkBQRkfah4NBM0eCAWhxERKT9KDg0U3bzJacBTckUEZG2ouDQTDFdFRXdWltERNqIgkMzxQQHtTiIiEg7UXBoppjpmBrjICIi7UTBoZk0q0JERNqcgkMzaVaFiIi0OQWHZlKLg4iItDkFh2bSypEiItLmFByaSS0OIiLS5hQcmilmVkV/RS0OIiLSPhQcmiluASi1OIiISBtRcGimuAWgNMZBRETaiIJDM8VOx1SLg4iItA8Fh2bSypEiItLmFByaSbMqRESkzSk4NFPsTa7U4iAiIu1DwaGZNKtCRETanIJDM8WNcaipxUFERNqHgkMzxcyq6K+oxUFERNqHgkMzRYJDbqCrQi0OIiLSPhQcmiluyWmNcRARkTai4NBMujumiIi0OQWHZkpnBp/iSLlA6ziIiEhbUXBoJrMtWh3U4iAiIu1EwaHZhoxzKGtWhYiItBEFh2YbMiWzXA1wzrWwQiIiIo1TcGi2IV0VDqhoESgREWkTCg7NFrvstIKDiIi0BwWHZtOttUVEpI0pODRbpMUh43xg0CJQIiLSLhQcmi1m2Wm1OIiISLtQcGi2mBtdaUqmiIi0CwWHZtOttUVEpI0pODRb3P0q1OIgIiJtouXBwcz2N7MbzewZM1tnZn1m9qyZXWFmuw9T/sdmtsbMes3sQTM7bpjXTpnZBeHrlczsZTO73Mwmjf87G4ZudCUiIm0ss/Ui424OsDvwI2ApUAXmAmcDJ5vZPOfcawBmti/wUFjmUmAdcBZwt5m9yzl375DXvhI4L3zty4EDw68PM7PjnXPN/8SOCQ6aVSEiIu2i5cHBOfc/wP8M3W9mDwC3AGfgQwLAJcA04HDn3MKw3A3A08BVZnaAC9dvNrODgHOB25xz74287ovA14GTgZvG6W0NL2aMgxaAEhGRdtHyrooRLAm3OwGE3QsnAvfVQwOAc24jcB2wHzA/cv4pgAFfG/K61wJ9wKnjU+2tyOQGntZnVajFQURE2kVigoOZ5c1sppnNMbO3A98OD90Zbg8BOoCHY05/JNxGg8N8IAAejRZ0zpWAhUPKNo/GOIiISBtLTHAAzgRWAi8Dd+O7JE51zj0YHp8VbpfFnFvfNzuybxbQ45zrH6b8TDPLxRzDzM42s8dHWf/GxAYHtTiIiEh7SFJw+DHwp8BfAF8E1gI7R453hdu4IFAaUqb+PK7scOUHOOeucc4d0UCdRy9uHYeKWhxERKQ9tHxwZJ1zbil+VgXAj83sh8BjZtbpnLsEPy4BfHfFUPlw2xfZ1wfsMsy3iyvfHOnBS64WBxERaTdJanHYjHPuN8CvgUK4a3m4nR1TvL4v2o2xHN8dERc0ZuO7McpjUddR0d0xRUSkjSU2OIQ6genh80X4roejY8odFW6j4xIew7+/N0cLmlkemDekbPNoHQcREWljLQ8OZrbbMPuPBQ4mnDERTru8HTjGzA6NlOvGD6x8ns1nUNwMOOD8IS99Fn5sw41j9BZGJ/YmV2pxEBGR9jAmYxwKhUIG/yFvwG+LxWJlFKd/K1xa+n/xazfkgcPxCzRtAP4hUvZC4G3APWZ2JbAeHwRmAyfUF38CcM4tMrOrgHPM7Db8tM76ypH304rFn0AtDiIi0tYaCg6FQmFv4FjgF8Vi8bkhx94N/DuDMyBWFwqFjxSLxR82WIfvA6cDp4Wv4fAB4tvAZc65l+oFnXN/MLO3AF8FPg3kgCeBd8YsNw2+tWExfvnqE4Ae4BvARS1Zbho2G+OQ002uRESkzTTa4vBh/F/7+0Z3hoHiB/ixCC8BvcABwE2FQuFNxWLx6a29sHPuFvzS0g1xzj0DnNRg2Rr+HhWXN/r64y62xUFdFSIi0h4aHePwx8CiYrG4eMj+8/Ch4Vpg72KxeBDw10AWf58IGUoLQImISBtrNDjsDfw2Zv878Xeq/FSxWHQAxWLxVvyy0G8dkxpONDHTMfvVVSEiIm2i0eCwM74rYkChUJiMv7HU48Vice2Q8k/ib5ctQ8XMqlBwEBGRdtFocHDA1CH75uFnUTwZU34dCVqVMlF0kysREWljjQaHxcBbhuw7Fh8ofhVTfmdgxbZXawLTdEwREWljjbYK3AOcVygUPgd8Hd9F8VH8+Ia7YsofwZCuDQnpJlciItLGGm1xuBR/t8ovAGvwrQy7At8pFosrowULhcKewGH4RZZkqJgWh5LGOIiISJtoKDgUi8VXgAXAz/G3pF6GXxvhvJjip+PHOPz3GNVxYokJDoFzVGtqdRARkeRreABjuJjT8Q2U+yLwxe2p1ISWyQ08rc+qAD/OIZNu+a1DRERERqRPqmbbbMnpwVYGjXMQEZF2MFY3uToUP8vCgAeLxWJrblndDiJdFRlXBefATDMrRESkLTTU4lAoFBYUCoUbCoXCUTHHLsav5XA58C/ArwqFwhVjWsuJJJ0G85c9BaTxLQ1aBEpERNpBo10VfwW8H3gmurNQKPwJcBEQADcCVwOrgI+Hd82UOHFTMrUIlIiItIFGg8PRwK+KxeK6Ifv/Dr8I1HnFYvFvisXix/CzL6rAh8aumhNM3CJQanEQEZE20GhwmAU8H7P/OPyttK+t7ygWi88CdwPzt7t2E5VWjxQRkTbVaHCYwZAlpAuFwm7AbsBDxWKxOqT88/gFoiRO3P0qNKtCRETaQKPBYRNbBoE3hdtfx5Tvx3dXSJzolEzdIVNERNpIo8HhWeBdhUIhOn3zBPz4hodiyr8OeGU76zZxbTYlM5xVoa4KERFpA42u43Ar/n4VPy0UClfjb3L1YfzS0j+LKf8WhszAkIjYW2srOIiISPI1Ghy+AZwCvBN4R7jPgP9XLBZL0YKFQuFIYK/wHIkTO6tCYxxERCT5Gr3JVT9+muVF+Nto3wi8p1gsfjOm+DzgJ8DtY1XJCSd2HQe1OIiISPKN5iZXvcCXGyj3beDb21OpCS/a4oBurS0iIu1DN7lqhdgxDuqqEBGR5Bv1Ta4KhcIk4H34AZCz8DMrXgF+CdwatkzISLRypIiItKlRtTgUCoVTgcXAvwNnAu/GT8s8M9y3uFAofHCM6zjxaIyDiIi0qYaDQ6FQOBf4Ln4VyceBrwAfBQrh88fCYzcUCoVzxr6qE4hmVYiISJtqqKuiUCjsB1wBrAE+WCwW744p9rlCofCnwE3AFYVC4Z5isfjc2FV1AskMXnbdq0JERNpJoy0OH8ev23DSMKEBgGKx+DPgJCANnLf91ZugIi0OuYF7VSg4iIhI8jUaHN4G3FcsFn+5tYLFYvEh4OfA8dtTsQktZjqmWhxERKQdNBoc5gBPjOJ1nwjPkTi6O6aIiLSpRoODw3dVNGo0ZXc8MbMq1OIgIiLtoNHgsBQ4YhSvezjw8uirs4PQOg4iItKmGg0O/wssKBQKb9lawUKh8EfAMeE5EkcrR4qISJtqNDh8Dd9d8aNCoTDsoMdCofA24MdADfjX7a/eBKUWBxERaVMNreNQLBafLxQK/4APEHcXCoVHgXvx3REO2AM/i+LN+PENf681HEYwzMqRzjnMNDxERESSazR3x/x6oVBYiw8PR+JDQpQBa4F/KBaL3xm7Kk5AmdzA01w4HTNwUA0c2bSCg4iIJNeo7lVRLBZvwLcu/C3wHeAu4G7geuDDwJ4KDQ2IdFV02ODYBnVXiIhI0o367pjFYnEjPihcP1yZQqGQB3LFYnH9NtdsIot0VeTYPDh057NxZ4iIiCTCqFocRuFbwOpxeu32F21xYLCVQTMrREQk6cYrOIAWgRreZrMq1FUhIiLtYzyDgwwnepOrzVocFBxERCTZFBxaIWY6JqjFQUREkk/BoRViFoAC6NcYBxERSTgFh1aIBIdMJDiU1eIgIiIJp+DQCtHgEFQHnusOmSIiknQNreNQKBT0iTaWssO0OKirQkREEq7RFgfbhkdjL2y2n5l90cweMbOVZrbBzLmZP8gAACAASURBVBaa2WfNbFJM+f3N7MdmtsbMes3sQTM7bpjXTpnZBWb2rJmVzOxlM7s87nWbapgWh5K6KkREJOEavcnVeHZp/C3wMeCnwI1ABTgW+DLwfjM7yjm3CcDM9gUeAqrApcA64CzgbjN7l3Pu3iGvfSVwHvAj4HLgwPDrw8zseOdca/7EjwSHdFAD58BMYxxERCTxRr3k9Di4FbjEObcusu9qM3se+Cz+HhjfDPdfAkwDDnfOLQQwsxuAp4GrzOwA55wL9x8EnAvc5px7b/2FzexF4OvAycBN4/rOhpNKQToNNR8UsgRUSGtWhYiIJF7LB0c65x4fEhrqbg63BwOE3QsnAvfVQ0N4/kbgOmA/YH7k/FPwXSZfG/K61wJ9wKlj8ga2VcyUTLU4iIhI0rU8OIxgTrhdEW4PATqAh2PKPhJuo8FhPhAAj0YLOudKwMIhZZsvJjhoVoWIiCRdIoODmaWBi/BjGerdCbPC7bKYU+r7Zkf2zQJ6nHP9w5SfaWa5Yb7/2Wb2+KgrPhpxwUEtDiIiknCJDA747oWjgIucc78P93WF27ggUBpSpv48ruxw5Qc4565xzh3ReHW3Qcyy05qOKSIiSZe44GBmXwLOAa5xzl0SOdQXbjtiTssPKVN/Hld2uPLNFZ2SiVocRESkPSQqOJjZxcDngO8AHxlyeHm4nc2W6vui3RjL8d0RceFhNr4bo7zttd1OcYMjNcZBREQSLjHBwcz+EfhH4AbgzPq0yohF+K6Ho2NOPyrcRsclPIZ/f28e8n3ywLwhZZsvdnCkuipERCTZEhEczOwi4GLge8CH4hZmCqdd3g4cY2aHRs7tBs4EnmfzGRQ3Aw44f8hLnYUf23DjGL6F0YsZ46CuChERSbqWLwBlZh8DvgC8BNwLfMBssxWrVzjnfhY+vxB4G3CPmV0JrMcHgdnACdFWCufcIjO7CjjHzG4D7mRw5cj7adXiT3WRFoecgoOIiLSJlgcHBtdT2AP4bszx+4GfATjn/mBmbwG+CnwayAFPAu+MWW4afGvDYuBs4ASgB/gGfrZGa/sFYsc4qKtCRESSreXBwTl3BnDGKMo/A5zUYNka/h4Vl29L3caVFoASEZE2lIgxDjukaHBAS06LiEh7UHBoFa0cKSIibUjBoVXiZlVUA7achSoiIpIcCg6tEmlx6GBwUGSlpgGSIiKSXAoOrRIJDvnUYFjoryg4iIhIcik4tEokOHTaYPeElp0WEZEkU3BolcgYh7wNtjKUNEBSREQSTMGhVaJjHCLBQVMyRUQkyRQcWmWY4KAbXYmISJIpOLRKLjfwNO+qA881xkFERJJMwaFV8l0DT7tceeC5FoESEZEkU3Bolc5JA0+7agoOIiLSHhQcWqVzsMUhX+sfeK47ZIqISJIpOLRKPj446A6ZIiKSZAoOrdI12FXRUS0NPNd0TBERSTIFh1aJtDh0VKMtDuqqEBGR5FJwaJVIcMhW+rHwrpgaHCkiIkmm4NAq6TR05AEwHJ2uAig4iIhIsik4tFJ0LYfAT8nUrAoREUkyBYdWikzJnBT4cQ6aVSEiIkmm4NBK0UWgwtUjNatCRESSTMGhlWK6KjTGQUREkkzBoZUiazlMqgcHjXEQEZEEU3BopdjBkWpxEBGR5FJwaKXOLe+Q2V9Ri4OIiCSXgkMrdUa7KvysCrU4iIhIkik4tNJmXRV+AaiSBkeKiEiCKTi0UqfGOIiISHtRcGilaFeFCxeA0hgHERFJMAWHVlKLg4iItBkFh1bKR5ecHrxXRRDeKVNERCRpFBxaKXqvinA6JuhGVyIiklwKDq0Uc68K0P0qREQkuRQcWqlzy64K0B0yRUQkuRQcWqmjc+BpPqiQcr6LoqyZFSIiklAKDq2USm02QLLT+UWg1OIgIiJJpeDQapt1V9TXclBwEBGRZFJwaLWYtRzU4iAiIkml4NBqcbfW1hgHERFJKAWHVtts2Wm1OIiISLIpOLRa3LLTGuMgIiIJpeDQatFFoAbGOKirQkREkknBodVi7lehWRUiIpJUCg6tFu2qcLpDpoiIJJuCQ6tFB0cOrOOgrgoREUkmBYdWixscqRYHERFJqJYHBzO70Mx+YGYvmJkzs8VbKb+/mf3YzNaYWa+ZPWhmxw1TNmVmF5jZs2ZWMrOXzexyM5sUV74lYtZxKGmMg4iIJFTLgwPwFeA44P+ANSMVNLN9gYeAo4FLgU8A3cDdZnZ8zClXAlcAvwPOBX4AnAfcbmZJeO+x6zioxUFERJIq0+oKAPs6514AMLPf4oPAcC4BpgGHO+cWhufcADwNXGVmBzjnXLj/IHxYuM059976C5jZi8DXgZOBm8bh/YxO3JLTGuMgIiIJ1fK/uuuhYWvC7oUTgfvqoSE8fyNwHbAfMD9yyimAAV8b8lLXAn3AqdtR7bGjrgoREWkjLQ8Oo3AI0AE8HHPskXAbDQ7zgQB4NFrQOVcCFg4p2zpd0VkVPjis2djfqtqIiIiMqJ2Cw6xwuyzmWH3f7CHle5xzcZ/Cy4CZZpaL+0ZmdraZPb7NNR2N/JbrOPRs2NSUby0iIjJa7RQc6p+wcUGgNKRM/flwf7rHlR/gnLvGOXfEqGu4LTryEI7TzLsqaVdj9YZ+qjWNcxARkeRpp+DQF247Yo7lh5SpP48rO1z51jDbYoCkA1aru0JERBKonYLD8nA7O+ZYfV+0G2M5vjsiLjzMxndjlMewftsuZoBkz4bScKVFRERapp2CwyJ818PRMceOCrfRcQmP4d/fm6MFzSwPzBtStrUiLQ71tRx61is4iIhI8rRNcAinXd4OHGNmh9b3m1k3cCbwPJvPoLgZcMD5Q17qLPzYhhvHtcKjEbOWQ896DZAUEZHkafkCUGZ2GrBn+OXOQM7MPhd+vcQ5971I8QuBtwH3mNmVwHp8EJgNnFBf/AnAObfIzK4CzjGz24A7gQPxK0feTxIWf6rLbzklc6W6KkREJIFaHhyADwNvHbLvS+H2fmAgODjn/mBmbwG+CnwayAFPAu90zt0b89rnA4uBs4ETgB7gG8BFzrnkTFvoimtxUHAQEZHkaXlwcM4dM8ryzwAnNVi2BlwePpIrrzEOIiLSHtpmjMOEFjfGQV0VIiKSQAoOSRC9Q2bg129YtaFELXDDnSEiItISCg5JEOmqmJb2N7iqBY61vVoESkREkkXBIQkiLQ47pQbvjKnuChERSRoFhySIjHGYYpWB5xogKSIiSaPgkASR4DCZaHDQIlAiIpIsCg5JEOmq6AoGxzWsVIuDiIgkjIJDEkQGR+ZrCg4iIpJcCg5JEOmqyFYGg4MGR4qISNIoOCRBpKsi09838FxjHEREJGkUHJIgm4N0GgCrVck6PyVz1YZ+AqdFoEREJDkUHJLAbLNWh52zPjhUagHr+8qtqpWIiMgWFBySIjJAcnanDTzXAEkREUkSBYekiAyQ3L1zcLcWgRIRkSRRcEiKSFfFLrnBcQ09GzRAUkREkkPBISkiXRX1MQ6grgoREUkWBYekiHRVTM9EbnSl4CAiIgmi4JAUka6KqbpDpoiIJJSCQ1JEuiqmoDtkiohIMik4JEWkq2KSG1y7oWf9JpwWgRIRkYRQcEiK6P0qypvozPmVJPurARtKleHOEhERaSoFh6SIBAcrbWLm5PzA1+quEBGRpFBwSIrJ0wafv/x/zJwyuAqUgoOIiCSFgkNSvOFgyHX458tf4gDWDhzSzAoREUkKBYek6MjDIUcOfHlYz+8Gnq/U7bVFRCQhFBySZP6Cgaf7vrwQwtkU6qoQEZGkUHBIkrnzocOPbehet4J9Kj0AvLq2r5W1EhERGaDgkCS5Dph31MCXb+19HoBFS1azZOWGVtVKRERkgIJD0kS6K95eeQGcwwHfu/+51tVJREQkpOCQNAcdPrCmw/RNa3hD+TUAHnzmVf7v1XWtrJmIiIiCQ+JkczDvjwa+/EDH8oHnN9z/fCtqJCIiMkDBIYki3RXz1z07MLvikedW8Pvla4c7S0REZNwpOCTRGw+Drm4Asmt7OHlWdeDQDfdprIOIiLSOgkMSZbLwpsHuivf3/44UvtXh8f9bydMvr25VzUREZAen4JBU848ZeDrp1w9wWfWXZFwNgK/+aCG/frGnRRUTEZEdmYJDUr3xMDhssNXh4GW/5p9e+yldQT+vrdvEp//jV/zrHYvo7dctt0VEpHkUHJLKDD7yWVjwroFd80pL+dqKHzK3tIyMq3Hnky/xd1c/wB1PLGHNxv4WVlZERHYUmVZXQEaQTsNp58GMXeFH1wOwZ3kV/7LiNjZZlt/kZ/Hr9Xvw/Z+u5JvZyRz0uun88YG7ceQbdmX3nbpaW3cREZmQzIVT/WRLhULBARSLxVZXBR7+H7j+SqhVtzgUAE/m9+Du7jfycNc+VCzN7jt1cfg+Mzl8n515/e5TmTE5Tzplza+3iIi0mxE/LNTi0C6OfhvM2gPuvxN+92voeXXgUAo4ovQSR5ReYn0qz3O5XVjVM4meF7t57L5ubs1OZ3F+F6bsNIXdpnWx27ROdp3Wxa5TO9l1Wic7T+lk2qQcuUy6de9PRETagoJDO9nzDfA3H/fPV74Cv3sSnvglPPPrgUWipgQljii9tMWpNYwXl8/g9x278UpmCitSHbyYyrEx1cHGVAfrU53UurrJTZ7M1EkddOezdOezTMpn6O7IMimfpTufYVI+S1cuQyadIptJkU2n6MimmTk5T1eH/juJiEx0+k3frnbeHd56gn+sWgG//Bn88h5Y9Vps8TSO11d6eH1l5GmcVVJsTOUoWY6+VJZSKkufDQaMZakO+lI5SpalZBn6U1n6LUPVUmSyWbon5emc1InLdlBJZ6lmclQyedKdnXSGoaOrwweQyR0ZpgZ97NS3Gtc9jdr0XbB0GgMyYSDJZVLkMmk6sj6kmKm7RUSklRQcJoIZu8KJp8KffQCWL4HVr8GaHlizyoeKxc/jXn0Za2A8S4aAaUEJKEFt7KpYsgw96W560t2sT+fZubqBOdW1TA4GZ4OUSfNydideyk5nY6oDw2E4Ujj6LMeazCTW5SazsWMy1VwnmWyGbDZDLpsmk05hQMoghcMsRTqXI9ORI53rIJ3LQiYDqTSkM6TSaTKZFJm0f2TTKToyKfK5DPlsmnwuTTbcH21dSadsYJ8ZpMwwAzPDQMFGRCY8BYeJJJWCOXv7xxDW1wtLnoPFf4ANa6FvI2zqhb5e6N2A610PG9Zj5dK4VC3vqsyprmVOdfh7beSosW+lh3230ioyFgKgz3L0pjroTeXoS+VwGBau0OmATaToNaNGipqlqFiaMmnKYQvLpKDMtKCP6bU+ptb6cBgb0nk2pDvpTefpzXSyKd1BKd1BKeMHp06zMlNcmclBP1kL2JTpojfbSW+mk0o6S5er0uXK5IMKHbUy5gLfDeUCDHD5LlKTJpPp7ibTPZmKpSkFUKpBKYBMynzwyaboyGbAUlSx8JGGVIpUNksmlyWVzWHZDC6dgXQGsjlS6XTkkSJjkLeADmrkXI1UrQrVSvio+sG6ZmApv02l/N1dOyf5ZdNzHX5/I5yDcr+vS0a/mhKt/m+Vzfl/82YIahAEfmXdduAcrF8L5ZL/465Z16kJ9NO5o+iaBAce5h8xBn61V8qwqQ9KfdBf8ttNfT5k9G7wQaPU539p9Jf8tlzC1WrUyhUq5TK1/n5S1QqpSolUuUy6vIl0tRz7fUvpDlblp9Fd7mVqZeP4vPcYKaDblemulce0ZWWnYBNU1ozdC7a5GkY5naOc8t1aZctiZqTNkcaRNke2ViFX7SdXLQ8Et/5MB6VcF6XcJGqpNBYEmPOPdFAj4wLSrkY6qJEi8G1TlsKZAY5UrUY6qJIKaphzVHN5qrlOqh1dBB2dkE7jUin/y9zSvqkKw1I+BLlMFpfN+Ucm57NRrYa5GlYLSFf7SVf6SZdLpCv9kE4TdE2m1tVN0DUZq1XJrl1JZvVr2OrXsGoFpu+Cm7ELbsau0NlFak2PbxFc9Zr/GdtlFszeC2bv6Z9Xyv5nrv5zl8n4IJbLQzbrf/76Nvpjm3p94Mp3ho9wOnal379OuRz5mS35bSYD02bCTjNhpxkwabL/YA4CqNV8KKyfW+n336vnVVj5qq9zqc+HwnyX//3S1Q1TpsHU6TBtht92dfsQ2dnlH5byAaAWgKv5hB5tCc1koCN8D7k8rFwOz/4Gfv8UPP+0fw+z9oB9DoC994fX7eO/f64Dcjn/+n0b/aN3o69jrTr4npzzde2e4t9v/T2X+8Prs8m/v1eXwoql8Npy6MjDbq+D3V/ntzvN9O+lfp0rZX89Vq/0rb2vLoVli2HpYti4zr+v7qnwhoNgv7mwx77+32z9ali3xv/bTZoC06b7azZlmr+utVp4rWqDYb1S8e+n/m/d0enr15H353bkm/JzremYI0jUdMx25pz/4VjTA2tXwYZ1/hfLbnP8f/b6X6QbN8ArL8GrL/sfEmzgFzp9GwnWriJYvQq3dhX0l3BBQBAEuCAAF/hf+AAYzgVYtYrVBh/+QyQgFdRIuaBll0NEZMyd9Sk48tixerUddzqmmaWAjwN/B+wFrARuAS5yzvW2sGo7FjP/l0dXt/+Lajjdk30qf8NBsYdTjOFSp7XaYEvKpo1Q2hStMODCv77C5tF6s3ylPPjId8HUnWDKTjBlGkGtRm3jetz6ddQ2rMeFfw26Tf4vwlo1oJTrZFPGd2VUnJEr99FR7iNX6iVdLVNO5+ivP1I5XCr8aziVxrmAWm8vrncD1tdLur+PrDly5shZQBZH4KAaOKqBoxb4MSIZAtIuIOMCUvXgFNRIBVW/P6j6v97Dv+YNhzk/tsQBZctQsTT9pKmYf1TDbS38/VKPbBkX0BWUmeT6mRSUybst1x0ZSb+lfWsC+oMm6SqkyKIAPpJyOkctlaazsmnrhbfTihLsOu7fxZvQwQG4EjgP+BFwOXBg+PVhZna8c/qzc4eVTvug0j15zF4yBaSm7zximSlj9t2aJxdunfOBpD4ItN5QFASOwPmgUq05ytUapXKNtZUq/aUymWqZbK1MNqj4cFQN2FSpsanq2FSuUcvkqOXyuI5OH5RcgG3qI923gdSmjb6lKJ0mlc5AOkXN0vQ7oxQYmwKjUnPhQNpwYCyOWipDNZWhlkoTBM4Hw029pEp9WKlvs1BoQa3+BnHOYYHvAsnUKmRqZTK1CgF+rEtgfrxLyTL04h8bXYa0C5galJhcKzGl1kcV49X0FJZaNy8zibJl2KW2gd2q69mtuoEuV+a1dDevpSfzWmYyfakcr6usYa/KKvYqr2KX6gb6Ujk2pPJsSHXQm+ogE44zyQcVcq5GKZWhN5zt1Gs5MgR0BhW6XJmuoEyAUbYMZUvTbxn/SPnZUCXL0uGqzKj1MrO2kZnVjXQ5f05Ailo4tqc/5c8rW4ZNluW1zGReyUzl1cwU1qfypAmDYlCmO+hneq2XGbVeptf6mF7rZVJQpsuVmRT00xX47sqapcLvY2HXEgPjizIuoDMok3cVuoIKG1MdLMrP4qn8HJ7qmM36dCf7lV9j//5XOaB/BbtX15FzNTpclZzzQbg+A2xjKk9vKkfVUgPjlAC6g36mBCWm1DbRHfRTtRT9lh24Tiszk1mamcbS7E4sy06jKygzp7KG11XX8LrKGqbWNtEVuc5VSw38O67MdLMiM4XF2Rm8mJ3Bioz/id+jspq5/cuZW1rGrtX1rE93sjrdxZpUF72pDqYGmwau3dTaJv//Lax3YOYDO2nKYWjPuhqdrkI+qJB3FTpdhZ5KWsFhe5nZQcC5wG3OufdG9r8IfB04GbipRdUTaTtmRja9ZQtmaot9bTJ4rUlcGKpSKSMVGShaCwL6KwHlao1qzZFO2cADIwxiAdWao1ILcM63KNVfb1O5ysZShd5Sld7+CrlMerO1Vyq1gPWbyqzvq7Cur0zg3MCMoFzGzwqqh71SENAbjjeoDzuof7/AOQgcucCxWy1gRi1g/1pAtRb48SopG5hhFDhHEDjWOOgJButerQXhtv6e/Nf19+PC7zMYQANqYatZ/Ro6IGfGsswUejL781gmReAcG0sV1vdVqNS2/DswZRCMQePVk517DPeP29Dg3yW5GSzJzeC/Js/d/soM4wt77D9urz3UhA0OwCn4NuevDdl/LfBV4FQUHERknJkZmZjAlU6l6OpIaeG0MeCco79SY1O5NjB1OptJkTIbaCmrhWGE+jRqADNqQUAtEnBqkRa0WuBImZFKGelwGzhHpeoDX381GHheDre1wA0EHfAfQr4+6XAtGqjWAspVH54qtYCOTJqObH29mnT4XqrhozYQzHz49GHI1y/w4z6DgNfN6G7a9Z7I/2Pn42fdPRrd6ZwrmdnC8LiIiLQ5M/NrsOS2/Eirt5Rl08ONkNJS+6M1cSaWbmkW0OOci7vf9DJgppnlYo5hZmeb2ePjWjsREZE2NJGDQxcQFxoASpEyW3DOXeOcO2JcaiUiItLGJnJw6AM6hjmWj5QRERGRBk3k4LAc3x0RFx5m47sx4pczFBERkVgTOTg8hn9/b47uNLM8MA/QGAYREZFRmsjB4Wb8SujnD9l/Fn5sw41Nr5GIiEibm7DTMZ1zi8zsKuAcM7sNuJPBlSPvR2s4iIiIjNqEDQ6h84HFwNnACUAP8A38vSq03LSIiMgoTejg4Jyr4e9RcXmr6yIiIjIRTOQxDiIiIjLGFBxERESkYRO6q2KsFAqFVldBRESkWVyxWBz2tp9qcRAREZGGmXNjcLNyaZiZPa77YGw/Xcexoes4NnQdx4au49gY7+uoFgcRERFpmIKDiIiINEzBofmuaXUFJghdx7Gh6zg2dB3Hhq7j2BjX66gxDiIiItIwtTiIiIhIwxQcREREpGEKDuPMzFJmdoGZPWtmJTN72cwuN7NJra5bEpnZfmb2RTN7xMxWmtkGM1toZp+Nu2Zmtr+Z/djM1phZr5k9aGbHtaLuSWZmXWb2opk5M/tmzHFdxxGY2XQz+xcz+0P4c7zSzH5uZn8ypJyu4zDMrNvMPmNmi8Kf6x4ze8jMzjAzG1J2h7+OZnahmf3AzF4If24Xb6V8w9dsez+XtHLk+LsSfyvvH+FvtlW/tfdhZna87tK5hb8FPgb8FLgRqADHAl8G3m9mRznnNgGY2b7AQ0AVuBRYB5wF3G1m73LO3duC+ifVF4GZcQd0HUdmZnsC9wHdwL8BzwFTgUOA2ZFyuo7DMLMU8N/AHwHfxd+luAs4BfgO/vfip8Kyuo7eV4DVwJPAtJEKbsM1277PJeecHuP0AA4CAuCHQ/afCzjgA62uY9IewBHA1Jj9Xw6v2TmRfbcANWBeZF83sAT4PeHg3x39Abwp/IXy9+E1/OaQ47qOI1+/B4GXgd23Uk7Xcfhrc3T4f+/KIftzwAvAWl3HLa7ZPpHnvwUWj1C24Ws2Fp9L6qoYX6cABnxtyP5rgT7g1KbXKOGcc48759bFHLo53B4MEDapnQjc55xbGDl/I3AdsB8wf5yrm3hmlsb/f7sLuC3muK7jCMxsAfDHwKXOuVfMLGtmXTHldB1HNiXcLo/udM6VgR6gF3Qdo5xzLzRSbhuu2XZ/Lik4jK/5+GT3aHSnc64ELGQH+QEYI3PC7YpwewjQATwcU/aRcKvrCxcABwDnDHNc13Fk7w63L5nZ7cAmoNfMnjOz6C9YXceRPQqsBT5pZn9lZnuEffKXAIcDF4fldB1Hb7TXbLs/lxQcxtcsoMc51x9zbBkw08xyTa5T2wn/ar4I39x+U7h7VrhdFnNKfd/smGM7DDPbG/gC8EXn3OJhiuk6jmz/cHstMB04HfgwUAa+Z2YfCo/rOo7AObcG/1fxanyz+hLgWfx4pvc6564Ni+o6jt5or9l2fy5pcOT46gLi/nEASpEy5eZUp219DTgK+Ixz7vfhvnpzcdz1LQ0ps6P6FvAicMUIZXQdRzY53G4Ajg2b1jGzH+H75r9iZt9F17ERG/F99T/FD+Sbjg8ON5nZSc65n6HruC1Ge822+3NJwWF89QG7DHMsHykjwzCzL+Gb2a9xzl0SOVS/bh0xp+3w1zZsRn87sMA5VxmhqK7jyDaF2+/XQwP4v6DN7KfA3+BbJXQdR2Bmc/Fh4QLn3NWR/d/Hh4lrw5kBuo6jN9prtt2fS+qqGF/L8c0+cf+gs/HNRWptGIaZXQx8Dj9d6yNDDtcHWcU1W9b3xTXdTXjh/7crgDuBV83s9Wb2emDPsMjUcN80dB23Zmm4fTXm2Cvhdid0HbfmAvyH0g+iO51zfcAd+P+be6HruC1Ge822+3NJwWF8PYa/xm+O7jSzPDAPeLwVlWoHZvaPwD8CNwBnunC+UMQifHPb0TGnHxVud9Tr2wnsDJwAPB953BcePzX8+kx0HbemPoBsTsyx+r7X0HXcmvoHWDrmWCay1XUcvdFes+3/XGr1XNWJ/ADmMvJ82VNbXcckPvADIR0+NKRGKPcD/NzlQyP76nOXn2MHme8dc12ywPtiHh8Nr+t/h1/vp+u41Wu5E7Ae3/LQHdm/O77P/rnIPl3H4a/jleH/vU8O2V9v9VoNZHQdh71+W1vHoeFrNhafS7o75jgzs2/g++h/hG86rq/Q9UvgOKeVIzdjZh8Dvgm8BHwe/x88aoXzg6gIm98fxa8ueSX+F/xZ+B+ME5xzdzer3u3AzPbCD5a8yjl3TmS/ruMIzOxs4NvA08C/4xct+ig+PPyZc+6esJyu4zDC1TefxAexG/G//6bjr89ewMecc8WwrK4jYGanMdi9eC7+/93l4ddLnHPfi5Qd1TXb7s+lViepif7AN839A371rn58X9MVRP560WOz63U9PvUO97hvSPkDgZ/g54j3Ab8Ajm/1+0jiA/8LeouVI3UdG7p2f4mfE9+Ln2FxD/AWXcdRXcN98ctNLw0/4NYDDwB/qesYe73ua/T3fXtucQAAA/BJREFU4Giv2fZ+LqnFQURE/n97dxOiVRXHcfw7CEqbphaSFS7DioiMTKghiyJsIRUUQ+UL0Ub+q0KKkMCJNgaBIfRPaBNlmm4qEgoSqdBCmsooqGwzENrrooEgjWpanDtyuTzPeB51GnK+H3g4zHnOvXPuan5z7nmRqjk5UpIkVTM4SJKkagYHSZJUzeAgSZKqGRwkSVI1g4MkSapmcJAkSdU8HVPSeS8ixihnn9yWme/PbW+k/zeDg6TTioianeL8oyzNAwYHSYN4eobvJv6rTkiaOwYHSdUyc2yu+yBpbhkcJJ1z7TkFlBP+HgWupBwQtQ/YnJk/9rjuCsqpqLcDi4Ffgf3AM5n5XY/2CyinAK4DrqGcIHiMckDQs32uuQ94oml/gnJg1abMPHY2zyzNF66qkDSbHgN2AF8Az1NO43sY+CgiFrcbRsQKYBxYC3wCPEc5kfIhYDwibui0Xwi8C7wILAV2AduBT4F7gZt79CeAnZTXKi8AXwGjwP6IWHTWTyvNA444SKrWjCT0ciIzt/aovwtYmZmft+6xjTICsRV4pKkbAl4BLgTWZuZrrfajwOvAzoi4OjP/ab4aA+4A3gbuz8yTrWsWNffqWg2syMwvW213AQ8AdwN7+z68JMARB0mD2dLn82Sf9q+2Q0NjDJgEHmz9l38T5VXGx+3QAJCZe4CDwDJgBE69ogjgD2BjOzQ015zMzF969Gd7OzQ0XmrKG/s8g6QWRxwkVcvMoQEv+aDHPSYj4giwCrgKOAJc33x9oM99DlBCw3LgQ0rIGAYOZ+bxAfoz3qPu+6a8eID7SPOWIw6SZtNPfeqnJ0YOd8of+rSfrr+oUw46ofG3HnV/NeWCAe8lzUsGB0mz6ZI+9UuacrJTLunRFuDSTrvpAHD5mXdN0pkwOEiaTau6FRExDFxHWQr5dVM9PQ/i1j73ma7/rCm/oYSHayPisnPRUUl1DA6SZtO6iFjeqRujvJrY3ZrUeIiyVHOk2WfhlObnW4CjlEmSZObfQAIXADu6SykjYmF3uaekc8PJkZKqzbAcE+DNzDzSqXsHOBQReynzFEaazwStlRiZORURG4D3gD0R8RZlVGEZcA9l46j1raWYULa/XgmsAY5GxL6m3VLgTuBx4OUzelBJfRkcJA1iywzfTVBWSLRtA96g7NswCvxO+WO+OTN/bjfMzMPNJlBPUfZnWEPZOXI3ZefIbzvt/4yI1cBGYD2wARgCjje/8+DgjyfpdIampmoOvZOkeh5jLZ2/nOMgSZKqGRwkSVI1g4MkSarmHAdJklTNEQdJklTN4CBJkqoZHCRJUjWDgyRJqmZwkCRJ1QwOkiSp2r8xSKqzjhY/yAAAAABJRU5ErkJggg==\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
@@ -1080,7 +1081,7 @@
}
],
"source": [
- "ooo.plot_history(history, plot={'MSE' :['mse', 'val_mse'],\n",
+ "pwk.plot_history(history, plot={'MSE' :['mse', 'val_mse'],\n",
" 'MAE' :['mae', 'val_mae'],\n",
" 'LOSS':['loss','val_loss']})"
]
@@ -1148,9 +1149,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "x_test / loss : 7.5745\n",
- "x_test / mae : 1.9386\n",
- "x_test / mse : 7.5745\n"
+ "x_test / loss : 10.5732\n",
+ "x_test / mae : 2.2805\n",
+ "x_test / mse : 10.5732\n"
]
}
],
@@ -1192,7 +1193,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Prediction : 10.28 K$ Reality : 10.40 K$\n"
+ "Prediction : 9.75 K$ Reality : 10.40 K$\n"
]
}
],
@@ -1201,6 +1202,25 @@
"print(\"Prediction : {:.2f} K$ Reality : {:.2f} K$\".format(predictions[0][0], real_price))"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "End time is : Tuesday 15 December 2020, 22:05:26\n",
+ "Duration is : 00:00:11 601ms\n",
+ "This notebook ends here\n"
+ ]
+ }
+ ],
+ "source": [
+ "pwk.end()"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
diff --git a/IRIS/01-Simple-Perceptron.ipynb b/IRIS/01-Simple-Perceptron.ipynb
index f0a7faf..99391cd 100644
--- a/IRIS/01-Simple-Perceptron.ipynb
+++ b/IRIS/01-Simple-Perceptron.ipynb
@@ -88,13 +88,13 @@
"text": [
"\n",
"FIDLE 2020 - Practical Work Module\n",
- "Version : 0.57 DEV\n",
- "Run time : Wednesday 9 September 2020, 14:42:06\n",
- "TensorFlow version : 2.2.0\n",
- "Keras version : 2.3.0-tf\n",
- "Current place : Fidle at IDRIS\n",
- "Dataset dir : /gpfswork/rech/mlh/commun/datasets\n",
- "Update keras cache : Done\n"
+ "Version : 0.6.1 DEV\n",
+ "Notebook id : PER57\n",
+ "Run time : Tuesday 15 December 2020, 21:49:41\n",
+ "TensorFlow version : 2.0.0\n",
+ "Keras version : 2.2.4-tf\n",
+ "Datasets dir : /home/pjluc/datasets/fidle\n",
+ "Update keras cache : False\n"
]
}
],
@@ -109,9 +109,9 @@
"import os,sys\n",
"\n",
"sys.path.append('..')\n",
- "import fidle.pwk as ooo\n",
+ "import fidle.pwk as pwk\n",
"\n",
- "place, datasets_dir = ooo.init()"
+ "datasets_dir = pwk.init('PER57')"
]
},
{
@@ -251,7 +251,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
@@ -284,6 +284,25 @@
"plt.show()"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "End time is : Tuesday 15 December 2020, 21:49:41\n",
+ "Duration is : 00:00:00 203ms\n",
+ "This notebook ends here\n"
+ ]
+ }
+ ],
+ "source": [
+ "pwk.end()"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
diff --git a/README.ipynb b/README.ipynb
index a1952ae..1cd87bc 100644
--- a/README.ipynb
+++ b/README.ipynb
@@ -123,6 +123,11 @@
}
],
"metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
"language_info": {
"codemirror_mode": {
"name": "ipython",
diff --git a/fidle/02 - Finished report.ipynb b/fidle/02 - Finished report.ipynb
index 96a0812..cf7d545 100644
--- a/fidle/02 - Finished report.ipynb
+++ b/fidle/02 - Finished report.ipynb
@@ -36,52 +36,80 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- " #T_51149260_3ee0_11eb_ab9c_bf552d03deff td {\n",
+ " #T_5accec2e_3f19_11eb_8e56_19607a97f796 td {\n",
" font-size: 110%;\n",
" text-align: left;\n",
- " } #T_51149260_3ee0_11eb_ab9c_bf552d03deff th {\n",
+ " } #T_5accec2e_3f19_11eb_8e56_19607a97f796 th {\n",
" font-size: 110%;\n",
" text-align: left;\n",
- " }</style><table id=\"T_51149260_3ee0_11eb_ab9c_bf552d03deff\" ><thead> <tr> <th class=\"col_heading level0 col0\" >id</th> <th class=\"col_heading level0 col1\" >name</th> <th class=\"col_heading level0 col2\" >start</th> <th class=\"col_heading level0 col3\" >end</th> <th class=\"col_heading level0 col4\" >duration</th> </tr></thead><tbody>\n",
+ " }</style><table id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796\" ><thead> <tr> <th class=\"col_heading level0 col0\" >id</th> <th class=\"col_heading level0 col1\" >repo</th> <th class=\"col_heading level0 col2\" >name</th> <th class=\"col_heading level0 col3\" >start</th> <th class=\"col_heading level0 col4\" >end</th> <th class=\"col_heading level0 col5\" >duration</th> </tr></thead><tbody>\n",
" <tr>\n",
- " <td id=\"T_51149260_3ee0_11eb_ab9c_bf552d03deffrow0_col0\" class=\"data row0 col0\" ><a href=\"../LinearReg/01-Linear-Regression.ipynb\">LINR1</a></td>\n",
- " <td id=\"T_51149260_3ee0_11eb_ab9c_bf552d03deffrow0_col1\" class=\"data row0 col1\" ><a href=\"../LinearReg/01-Linear-Regression.ipynb\"><b>01-Linear-Regression.ipynb</b></a></td>\n",
- " <td id=\"T_51149260_3ee0_11eb_ab9c_bf552d03deffrow0_col2\" class=\"data row0 col2\" >Tuesday 15 December 2020, 14:04:04</td>\n",
- " <td id=\"T_51149260_3ee0_11eb_ab9c_bf552d03deffrow0_col3\" class=\"data row0 col3\" >Tuesday 15 December 2020, 14:04:04</td>\n",
- " <td id=\"T_51149260_3ee0_11eb_ab9c_bf552d03deffrow0_col4\" class=\"data row0 col4\" >00:00:00 295ms</td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row0_col0\" class=\"data row0 col0\" ><a href=\"../LinearReg/01-Linear-Regression.ipynb\">LINR1</a></td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row0_col1\" class=\"data row0 col1\" >LinearReg</td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row0_col2\" class=\"data row0 col2\" ><a href=\"../LinearReg/01-Linear-Regression.ipynb\"><b>01-Linear-Regression.ipynb</b></a></td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row0_col3\" class=\"data row0 col3\" >Tuesday 15 December 2020, 14:04:04</td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row0_col4\" class=\"data row0 col4\" >Tuesday 15 December 2020, 14:04:04</td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row0_col5\" class=\"data row0 col5\" >00:00:00 295ms</td>\n",
" </tr>\n",
" <tr>\n",
- " <td id=\"T_51149260_3ee0_11eb_ab9c_bf552d03deffrow1_col0\" class=\"data row1 col0\" ><a href=\"../LinearReg/02-Gradient-descent.ipynb\">GRAD1</a></td>\n",
- " <td id=\"T_51149260_3ee0_11eb_ab9c_bf552d03deffrow1_col1\" class=\"data row1 col1\" ><a href=\"../LinearReg/02-Gradient-descent.ipynb\"><b>02-Gradient-descent.ipynb</b></a></td>\n",
- " <td id=\"T_51149260_3ee0_11eb_ab9c_bf552d03deffrow1_col2\" class=\"data row1 col2\" >Tuesday 15 December 2020, 15:05:11</td>\n",
- " <td id=\"T_51149260_3ee0_11eb_ab9c_bf552d03deffrow1_col3\" class=\"data row1 col3\" >Tuesday 15 December 2020, 15:05:14</td>\n",
- " <td id=\"T_51149260_3ee0_11eb_ab9c_bf552d03deffrow1_col4\" class=\"data row1 col4\" >00:00:03 120ms</td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row1_col0\" class=\"data row1 col0\" ><a href=\"../LinearReg/02-Gradient-descent.ipynb\">GRAD1</a></td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row1_col1\" class=\"data row1 col1\" >LinearReg</td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row1_col2\" class=\"data row1 col2\" ><a href=\"../LinearReg/02-Gradient-descent.ipynb\"><b>02-Gradient-descent.ipynb</b></a></td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row1_col3\" class=\"data row1 col3\" >Tuesday 15 December 2020, 15:05:11</td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row1_col4\" class=\"data row1 col4\" >Tuesday 15 December 2020, 15:05:14</td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row1_col5\" class=\"data row1 col5\" >00:00:03 120ms</td>\n",
" </tr>\n",
" <tr>\n",
- " <td id=\"T_51149260_3ee0_11eb_ab9c_bf552d03deffrow2_col0\" class=\"data row2 col0\" ><a href=\"../LinearReg/03-Polynomial-Regression.ipynb\">POLR1</a></td>\n",
- " <td id=\"T_51149260_3ee0_11eb_ab9c_bf552d03deffrow2_col1\" class=\"data row2 col1\" ><a href=\"../LinearReg/03-Polynomial-Regression.ipynb\"><b>03-Polynomial-Regression.ipynb</b></a></td>\n",
- " <td id=\"T_51149260_3ee0_11eb_ab9c_bf552d03deffrow2_col2\" class=\"data row2 col2\" >Tuesday 15 December 2020, 15:05:27</td>\n",
- " <td id=\"T_51149260_3ee0_11eb_ab9c_bf552d03deffrow2_col3\" class=\"data row2 col3\" >Tuesday 15 December 2020, 15:05:28</td>\n",
- " <td id=\"T_51149260_3ee0_11eb_ab9c_bf552d03deffrow2_col4\" class=\"data row2 col4\" >00:00:01 686ms</td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row2_col0\" class=\"data row2 col0\" ><a href=\"../LinearReg/03-Polynomial-Regression.ipynb\">POLR1</a></td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row2_col1\" class=\"data row2 col1\" >LinearReg</td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row2_col2\" class=\"data row2 col2\" ><a href=\"../LinearReg/03-Polynomial-Regression.ipynb\"><b>03-Polynomial-Regression.ipynb</b></a></td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row2_col3\" class=\"data row2 col3\" >Tuesday 15 December 2020, 15:05:27</td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row2_col4\" class=\"data row2 col4\" >Tuesday 15 December 2020, 15:05:28</td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row2_col5\" class=\"data row2 col5\" >00:00:01 686ms</td>\n",
" </tr>\n",
" <tr>\n",
- " <td id=\"T_51149260_3ee0_11eb_ab9c_bf552d03deffrow3_col0\" class=\"data row3 col0\" ><a href=\"../LinearReg/04-Logistic-Regression.ipynb\">LOGR1</a></td>\n",
- " <td id=\"T_51149260_3ee0_11eb_ab9c_bf552d03deffrow3_col1\" class=\"data row3 col1\" ><a href=\"../LinearReg/04-Logistic-Regression.ipynb\"><b>04-Logistic-Regression.ipynb</b></a></td>\n",
- " <td id=\"T_51149260_3ee0_11eb_ab9c_bf552d03deffrow3_col2\" class=\"data row3 col2\" >Tuesday 15 December 2020, 15:05:42</td>\n",
- " <td id=\"T_51149260_3ee0_11eb_ab9c_bf552d03deffrow3_col3\" class=\"data row3 col3\" >Tuesday 15 December 2020, 15:06:44</td>\n",
- " <td id=\"T_51149260_3ee0_11eb_ab9c_bf552d03deffrow3_col4\" class=\"data row3 col4\" >00:01:02 112ms</td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row3_col0\" class=\"data row3 col0\" ><a href=\"../LinearReg/04-Logistic-Regression.ipynb\">LOGR1</a></td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row3_col1\" class=\"data row3 col1\" >LinearReg</td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row3_col2\" class=\"data row3 col2\" ><a href=\"../LinearReg/04-Logistic-Regression.ipynb\"><b>04-Logistic-Regression.ipynb</b></a></td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row3_col3\" class=\"data row3 col3\" >Tuesday 15 December 2020, 15:05:42</td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row3_col4\" class=\"data row3 col4\" >Tuesday 15 December 2020, 15:06:44</td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row3_col5\" class=\"data row3 col5\" >00:01:02 112ms</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row4_col0\" class=\"data row4 col0\" ><a href=\"../IRIS/01-Simple-Perceptron.ipynb\">PER57</a></td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row4_col1\" class=\"data row4 col1\" >IRIS</td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row4_col2\" class=\"data row4 col2\" ><a href=\"../IRIS/01-Simple-Perceptron.ipynb\"><b>01-Simple-Perceptron.ipynb</b></a></td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row4_col3\" class=\"data row4 col3\" >Tuesday 15 December 2020, 21:49:41</td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row4_col4\" class=\"data row4 col4\" >Tuesday 15 December 2020, 21:49:41</td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row4_col5\" class=\"data row4 col5\" >00:00:00 203ms</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row5_col0\" class=\"data row5 col0\" ><a href=\"../BHPD/01-DNN-Regression.ipynb\">BHP1</a></td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row5_col1\" class=\"data row5 col1\" >BHPD</td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row5_col2\" class=\"data row5 col2\" ><a href=\"../BHPD/01-DNN-Regression.ipynb\"><b>01-DNN-Regression.ipynb</b></a></td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row5_col3\" class=\"data row5 col3\" >Tuesday 15 December 2020, 21:51:22</td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row5_col4\" class=\"data row5 col4\" >Tuesday 15 December 2020, 21:51:32</td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row5_col5\" class=\"data row5 col5\" >00:00:10 080ms</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row6_col0\" class=\"data row6 col0\" ><a href=\"../BHPD/02-DNN-Regression-Premium.ipynb\">BHP2</a></td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row6_col1\" class=\"data row6 col1\" >BHPD</td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row6_col2\" class=\"data row6 col2\" ><a href=\"../BHPD/02-DNN-Regression-Premium.ipynb\"><b>02-DNN-Regression-Premium.ipynb</b></a></td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row6_col3\" class=\"data row6 col3\" >Tuesday 15 December 2020, 22:05:15</td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row6_col4\" class=\"data row6 col4\" >Tuesday 15 December 2020, 22:05:26</td>\n",
+ " <td id=\"T_5accec2e_3f19_11eb_8e56_19607a97f796row6_col5\" class=\"data row6 col5\" >00:00:11 601ms</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x7f6d1d0dd050>"
+ "<pandas.io.formats.style.Styler at 0x7f4b4996e6d0>"
]
},
"metadata": {},
diff --git a/fidle/cooker.py b/fidle/cooker.py
index 67a5b60..9aba6a8 100644
--- a/fidle/cooker.py
+++ b/fidle/cooker.py
@@ -129,6 +129,8 @@ def tag(tag, text, document):
def get_ci_report():
+ columns=['id','repo','name','start','end','duration']
+
# ---- Load catalog (notebooks descriptions)
#
with open(config.CATALOG_FILE) as fp:
@@ -140,7 +142,7 @@ def get_ci_report():
dict_finished = json.load( infile )
if dict_finished == {}:
- df=pd.DataFrame({}, columns=['id','name','start','end','duration'])
+ df=pd.DataFrame({}, columns=columns)
else:
df=pd.DataFrame(dict_finished).transpose()
df.reset_index(inplace=True)
@@ -148,7 +150,7 @@ def get_ci_report():
# ---- Add usefull html columns
#
- df['name']=''
+ df[ ['name','repo'] ]=''
for index, row in df.iterrows():
id = row['id']
@@ -158,8 +160,8 @@ def get_ci_report():
description = catalog[id]['description']
row['id'] = f'<a href="../{dirname}/{basename}">{id}</a>'
row['name'] = f'<a href="../{dirname}/{basename}"><b>{basename}</b></a>'
-
- columns=['id','name','start','end','duration']
+ row['repo'] = dirname
+
df=df[columns]
# ---- Add styles to be nice
@@ -171,8 +173,6 @@ def get_ci_report():
def still_pending(v):
return 'background-color: OrangeRed; color:white' if v == 'Unfinished...' else ''
- columns=['id','name','start','end','duration']
-
output = df[columns].style.set_table_styles(styles).hide_index().applymap(still_pending)
# ---- Get mail report
diff --git a/fidle/log/ci_report.html b/fidle/log/ci_report.html
index 13a6cc9..629a444 100644
--- a/fidle/log/ci_report.html
+++ b/fidle/log/ci_report.html
@@ -24,43 +24,71 @@
<body>
<br>Hi,
<p>Below is the result of the continuous integration tests of the Fidle project:</p>
- <div class="header"><b>Report date :</b> Tuesday 15 December 2020, 15:17:51</div>
+ <div class="header"><b>Report date :</b> Tuesday 15 December 2020, 22:06:09</div>
<div class="result">
<style type="text/css" >
- #T_51134338_3ee0_11eb_ab9c_bf552d03deff td {
+ #T_5acb85fa_3f19_11eb_8e56_19607a97f796 td {
font-size: 110%;
text-align: left;
- } #T_51134338_3ee0_11eb_ab9c_bf552d03deff th {
+ } #T_5acb85fa_3f19_11eb_8e56_19607a97f796 th {
font-size: 110%;
text-align: left;
- }</style><table id="T_51134338_3ee0_11eb_ab9c_bf552d03deff" ><thead> <tr> <th class="col_heading level0 col0" >id</th> <th class="col_heading level0 col1" >name</th> <th class="col_heading level0 col2" >start</th> <th class="col_heading level0 col3" >end</th> <th class="col_heading level0 col4" >duration</th> </tr></thead><tbody>
+ }</style><table id="T_5acb85fa_3f19_11eb_8e56_19607a97f796" ><thead> <tr> <th class="col_heading level0 col0" >id</th> <th class="col_heading level0 col1" >repo</th> <th class="col_heading level0 col2" >name</th> <th class="col_heading level0 col3" >start</th> <th class="col_heading level0 col4" >end</th> <th class="col_heading level0 col5" >duration</th> </tr></thead><tbody>
<tr>
- <td id="T_51134338_3ee0_11eb_ab9c_bf552d03deffrow0_col0" class="data row0 col0" ><a href="../LinearReg/01-Linear-Regression.ipynb">LINR1</a></td>
- <td id="T_51134338_3ee0_11eb_ab9c_bf552d03deffrow0_col1" class="data row0 col1" ><a href="../LinearReg/01-Linear-Regression.ipynb"><b>01-Linear-Regression.ipynb</b></a></td>
- <td id="T_51134338_3ee0_11eb_ab9c_bf552d03deffrow0_col2" class="data row0 col2" >Tuesday 15 December 2020, 14:04:04</td>
- <td id="T_51134338_3ee0_11eb_ab9c_bf552d03deffrow0_col3" class="data row0 col3" >Tuesday 15 December 2020, 14:04:04</td>
- <td id="T_51134338_3ee0_11eb_ab9c_bf552d03deffrow0_col4" class="data row0 col4" >00:00:00 295ms</td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row0_col0" class="data row0 col0" ><a href="../LinearReg/01-Linear-Regression.ipynb">LINR1</a></td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row0_col1" class="data row0 col1" >LinearReg</td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row0_col2" class="data row0 col2" ><a href="../LinearReg/01-Linear-Regression.ipynb"><b>01-Linear-Regression.ipynb</b></a></td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row0_col3" class="data row0 col3" >Tuesday 15 December 2020, 14:04:04</td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row0_col4" class="data row0 col4" >Tuesday 15 December 2020, 14:04:04</td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row0_col5" class="data row0 col5" >00:00:00 295ms</td>
</tr>
<tr>
- <td id="T_51134338_3ee0_11eb_ab9c_bf552d03deffrow1_col0" class="data row1 col0" ><a href="../LinearReg/02-Gradient-descent.ipynb">GRAD1</a></td>
- <td id="T_51134338_3ee0_11eb_ab9c_bf552d03deffrow1_col1" class="data row1 col1" ><a href="../LinearReg/02-Gradient-descent.ipynb"><b>02-Gradient-descent.ipynb</b></a></td>
- <td id="T_51134338_3ee0_11eb_ab9c_bf552d03deffrow1_col2" class="data row1 col2" >Tuesday 15 December 2020, 15:05:11</td>
- <td id="T_51134338_3ee0_11eb_ab9c_bf552d03deffrow1_col3" class="data row1 col3" >Tuesday 15 December 2020, 15:05:14</td>
- <td id="T_51134338_3ee0_11eb_ab9c_bf552d03deffrow1_col4" class="data row1 col4" >00:00:03 120ms</td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row1_col0" class="data row1 col0" ><a href="../LinearReg/02-Gradient-descent.ipynb">GRAD1</a></td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row1_col1" class="data row1 col1" >LinearReg</td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row1_col2" class="data row1 col2" ><a href="../LinearReg/02-Gradient-descent.ipynb"><b>02-Gradient-descent.ipynb</b></a></td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row1_col3" class="data row1 col3" >Tuesday 15 December 2020, 15:05:11</td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row1_col4" class="data row1 col4" >Tuesday 15 December 2020, 15:05:14</td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row1_col5" class="data row1 col5" >00:00:03 120ms</td>
</tr>
<tr>
- <td id="T_51134338_3ee0_11eb_ab9c_bf552d03deffrow2_col0" class="data row2 col0" ><a href="../LinearReg/03-Polynomial-Regression.ipynb">POLR1</a></td>
- <td id="T_51134338_3ee0_11eb_ab9c_bf552d03deffrow2_col1" class="data row2 col1" ><a href="../LinearReg/03-Polynomial-Regression.ipynb"><b>03-Polynomial-Regression.ipynb</b></a></td>
- <td id="T_51134338_3ee0_11eb_ab9c_bf552d03deffrow2_col2" class="data row2 col2" >Tuesday 15 December 2020, 15:05:27</td>
- <td id="T_51134338_3ee0_11eb_ab9c_bf552d03deffrow2_col3" class="data row2 col3" >Tuesday 15 December 2020, 15:05:28</td>
- <td id="T_51134338_3ee0_11eb_ab9c_bf552d03deffrow2_col4" class="data row2 col4" >00:00:01 686ms</td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row2_col0" class="data row2 col0" ><a href="../LinearReg/03-Polynomial-Regression.ipynb">POLR1</a></td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row2_col1" class="data row2 col1" >LinearReg</td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row2_col2" class="data row2 col2" ><a href="../LinearReg/03-Polynomial-Regression.ipynb"><b>03-Polynomial-Regression.ipynb</b></a></td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row2_col3" class="data row2 col3" >Tuesday 15 December 2020, 15:05:27</td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row2_col4" class="data row2 col4" >Tuesday 15 December 2020, 15:05:28</td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row2_col5" class="data row2 col5" >00:00:01 686ms</td>
</tr>
<tr>
- <td id="T_51134338_3ee0_11eb_ab9c_bf552d03deffrow3_col0" class="data row3 col0" ><a href="../LinearReg/04-Logistic-Regression.ipynb">LOGR1</a></td>
- <td id="T_51134338_3ee0_11eb_ab9c_bf552d03deffrow3_col1" class="data row3 col1" ><a href="../LinearReg/04-Logistic-Regression.ipynb"><b>04-Logistic-Regression.ipynb</b></a></td>
- <td id="T_51134338_3ee0_11eb_ab9c_bf552d03deffrow3_col2" class="data row3 col2" >Tuesday 15 December 2020, 15:05:42</td>
- <td id="T_51134338_3ee0_11eb_ab9c_bf552d03deffrow3_col3" class="data row3 col3" >Tuesday 15 December 2020, 15:06:44</td>
- <td id="T_51134338_3ee0_11eb_ab9c_bf552d03deffrow3_col4" class="data row3 col4" >00:01:02 112ms</td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row3_col0" class="data row3 col0" ><a href="../LinearReg/04-Logistic-Regression.ipynb">LOGR1</a></td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row3_col1" class="data row3 col1" >LinearReg</td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row3_col2" class="data row3 col2" ><a href="../LinearReg/04-Logistic-Regression.ipynb"><b>04-Logistic-Regression.ipynb</b></a></td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row3_col3" class="data row3 col3" >Tuesday 15 December 2020, 15:05:42</td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row3_col4" class="data row3 col4" >Tuesday 15 December 2020, 15:06:44</td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row3_col5" class="data row3 col5" >00:01:02 112ms</td>
+ </tr>
+ <tr>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row4_col0" class="data row4 col0" ><a href="../IRIS/01-Simple-Perceptron.ipynb">PER57</a></td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row4_col1" class="data row4 col1" >IRIS</td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row4_col2" class="data row4 col2" ><a href="../IRIS/01-Simple-Perceptron.ipynb"><b>01-Simple-Perceptron.ipynb</b></a></td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row4_col3" class="data row4 col3" >Tuesday 15 December 2020, 21:49:41</td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row4_col4" class="data row4 col4" >Tuesday 15 December 2020, 21:49:41</td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row4_col5" class="data row4 col5" >00:00:00 203ms</td>
+ </tr>
+ <tr>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row5_col0" class="data row5 col0" ><a href="../BHPD/01-DNN-Regression.ipynb">BHP1</a></td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row5_col1" class="data row5 col1" >BHPD</td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row5_col2" class="data row5 col2" ><a href="../BHPD/01-DNN-Regression.ipynb"><b>01-DNN-Regression.ipynb</b></a></td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row5_col3" class="data row5 col3" >Tuesday 15 December 2020, 21:51:22</td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row5_col4" class="data row5 col4" >Tuesday 15 December 2020, 21:51:32</td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row5_col5" class="data row5 col5" >00:00:10 080ms</td>
+ </tr>
+ <tr>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row6_col0" class="data row6 col0" ><a href="../BHPD/02-DNN-Regression-Premium.ipynb">BHP2</a></td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row6_col1" class="data row6 col1" >BHPD</td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row6_col2" class="data row6 col2" ><a href="../BHPD/02-DNN-Regression-Premium.ipynb"><b>02-DNN-Regression-Premium.ipynb</b></a></td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row6_col3" class="data row6 col3" >Tuesday 15 December 2020, 22:05:15</td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row6_col4" class="data row6 col4" >Tuesday 15 December 2020, 22:05:26</td>
+ <td id="T_5acb85fa_3f19_11eb_8e56_19607a97f796row6_col5" class="data row6 col5" >00:00:11 601ms</td>
</tr>
</tbody></table>
</div>
diff --git a/fidle/log/finished.json b/fidle/log/finished.json
index 02dfb3b..c351282 100644
--- a/fidle/log/finished.json
+++ b/fidle/log/finished.json
@@ -22,5 +22,23 @@
"start": "Tuesday 15 December 2020, 15:05:42",
"end": "Tuesday 15 December 2020, 15:06:44",
"duration": "00:01:02 112ms"
+ },
+ "PER57": {
+ "path": "/home/pjluc/dev/fidle/IRIS",
+ "start": "Tuesday 15 December 2020, 21:49:41",
+ "end": "Tuesday 15 December 2020, 21:49:41",
+ "duration": "00:00:00 203ms"
+ },
+ "BHP1": {
+ "path": "/home/pjluc/dev/fidle/BHPD",
+ "start": "Tuesday 15 December 2020, 21:51:22",
+ "end": "Tuesday 15 December 2020, 21:51:32",
+ "duration": "00:00:10 080ms"
+ },
+ "BHP2": {
+ "path": "/home/pjluc/dev/fidle/BHPD",
+ "start": "Tuesday 15 December 2020, 22:05:15",
+ "end": "Tuesday 15 December 2020, 22:05:26",
+ "duration": "00:00:11 601ms"
}
}
\ No newline at end of file
--
GitLab