From 35bcaa7220dfa1a479a8500e1144cc13f8933648 Mon Sep 17 00:00:00 2001
From: Jean-Luc Parouty <uja62cb@jean-zay2.ib0.xa.idris.fr>
Date: Wed, 9 Sep 2020 10:50:28 +0200
Subject: [PATCH] Update pwk.py and add config.py (0.5.7 DEV)
---
.gitignore | 2 +
BHPD/01-DNN-Regression.ipynb | 1091 +++++++++++------------
BHPD/02-DNN-Regression-Premium.ipynb | 853 +++++++++---------
README.ipynb | 2 +-
README.md | 2 +-
fidle/{Example.ipynb => Template.ipynb} | 0
fidle/config.py | 38 +
fidle/pwk.py | 27 +-
8 files changed, 1027 insertions(+), 988 deletions(-)
rename fidle/{Example.ipynb => Template.ipynb} (100%)
create mode 100644 fidle/config.py
diff --git a/.gitignore b/.gitignore
index 160434e..0199a17 100755
--- a/.gitignore
+++ b/.gitignore
@@ -2,6 +2,8 @@
*/.ipynb_checkpoints/*
__pycache__
*/__pycache__/*
+stderr.txt
+stdout.txt
run/
figs/
GTSRB/data
diff --git a/BHPD/01-DNN-Regression.ipynb b/BHPD/01-DNN-Regression.ipynb
index b3e616b..3236801 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 : 0.5.2\n",
- "Run time : Tuesday 8 September 2020, 18:58:03\n",
- "TensorFlow version : 2.0.0\n",
- "Keras version : 2.2.4-tf\n",
- "Current place : Fidle at HOME\n",
- "Dataset dir : /home/pjluc/datasets\n",
- "Update keras cache : Yes\n"
+ "Version : 0.56 DEV\n",
+ "Run time : Wednesday 9 September 2020, 10:45:12\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"
]
}
],
@@ -135,7 +135,7 @@
"sys.path.append('..')\n",
"import fidle.pwk as ooo\n",
"\n",
- "place, dataset_dir = ooo.init(places={'MyLaptop':'/path/to/datasets'})"
+ "place, dataset_dir = ooo.init()"
]
},
{
@@ -174,96 +174,96 @@
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- "</style><table id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3\" ><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_c6701b60_f278_11ea_97b3_0cc47af5c7c7\" ><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_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row0_col0\" class=\"data row0 col0\" >0.01</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row0_col1\" class=\"data row0 col1\" >18.00</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row0_col2\" class=\"data row0 col2\" >2.31</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row0_col3\" class=\"data row0 col3\" >0.00</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row0_col4\" class=\"data row0 col4\" >0.54</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row0_col5\" class=\"data row0 col5\" >6.58</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row0_col6\" class=\"data row0 col6\" >65.20</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row0_col7\" class=\"data row0 col7\" >4.09</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row0_col8\" class=\"data row0 col8\" >1.00</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row0_col9\" class=\"data row0 col9\" >296.00</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row0_col10\" class=\"data row0 col10\" >15.30</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row0_col11\" class=\"data row0 col11\" >396.90</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row0_col12\" class=\"data row0 col12\" >4.98</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row0_col13\" class=\"data row0 col13\" >24.00</td>\n",
+ " <th id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row0_col0\" class=\"data row0 col0\" >0.01</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row0_col1\" class=\"data row0 col1\" >18.00</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row0_col2\" class=\"data row0 col2\" >2.31</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row0_col3\" class=\"data row0 col3\" >0.00</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row0_col4\" class=\"data row0 col4\" >0.54</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row0_col5\" class=\"data row0 col5\" >6.58</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row0_col6\" class=\"data row0 col6\" >65.20</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row0_col7\" class=\"data row0 col7\" >4.09</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row0_col8\" class=\"data row0 col8\" >1.00</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row0_col9\" class=\"data row0 col9\" >296.00</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row0_col10\" class=\"data row0 col10\" >15.30</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row0_col11\" class=\"data row0 col11\" >396.90</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row0_col12\" class=\"data row0 col12\" >4.98</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row0_col13\" class=\"data row0 col13\" >24.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row1_col0\" class=\"data row1 col0\" >0.03</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row1_col2\" class=\"data row1 col2\" >7.07</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row1_col4\" class=\"data row1 col4\" >0.47</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row1_col5\" class=\"data row1 col5\" >6.42</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row1_col6\" class=\"data row1 col6\" >78.90</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row1_col7\" class=\"data row1 col7\" >4.97</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row1_col8\" class=\"data row1 col8\" >2.00</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row1_col9\" class=\"data row1 col9\" >242.00</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row1_col10\" class=\"data row1 col10\" >17.80</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row1_col11\" class=\"data row1 col11\" >396.90</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row1_col12\" class=\"data row1 col12\" >9.14</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row1_col13\" class=\"data row1 col13\" >21.60</td>\n",
+ " <th id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row1_col0\" class=\"data row1 col0\" >0.03</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row1_col2\" class=\"data row1 col2\" >7.07</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row1_col4\" class=\"data row1 col4\" >0.47</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row1_col5\" class=\"data row1 col5\" >6.42</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row1_col6\" class=\"data row1 col6\" >78.90</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row1_col7\" class=\"data row1 col7\" >4.97</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row1_col8\" class=\"data row1 col8\" >2.00</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row1_col9\" class=\"data row1 col9\" >242.00</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row1_col10\" class=\"data row1 col10\" >17.80</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row1_col11\" class=\"data row1 col11\" >396.90</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row1_col12\" class=\"data row1 col12\" >9.14</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row1_col13\" class=\"data row1 col13\" >21.60</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row2_col0\" class=\"data row2 col0\" >0.03</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row2_col2\" class=\"data row2 col2\" >7.07</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row2_col3\" class=\"data row2 col3\" >0.00</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row2_col4\" class=\"data row2 col4\" >0.47</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row2_col5\" class=\"data row2 col5\" >7.18</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row2_col6\" class=\"data row2 col6\" >61.10</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row2_col7\" class=\"data row2 col7\" >4.97</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row2_col8\" class=\"data row2 col8\" >2.00</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row2_col9\" class=\"data row2 col9\" >242.00</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row2_col10\" class=\"data row2 col10\" >17.80</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row2_col11\" class=\"data row2 col11\" >392.83</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row2_col12\" class=\"data row2 col12\" >4.03</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row2_col13\" class=\"data row2 col13\" >34.70</td>\n",
+ " <th id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row2_col0\" class=\"data row2 col0\" >0.03</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row2_col2\" class=\"data row2 col2\" >7.07</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row2_col3\" class=\"data row2 col3\" >0.00</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row2_col4\" class=\"data row2 col4\" >0.47</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row2_col5\" class=\"data row2 col5\" >7.18</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row2_col6\" class=\"data row2 col6\" >61.10</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row2_col7\" class=\"data row2 col7\" >4.97</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row2_col8\" class=\"data row2 col8\" >2.00</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row2_col9\" class=\"data row2 col9\" >242.00</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row2_col10\" class=\"data row2 col10\" >17.80</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row2_col11\" class=\"data row2 col11\" >392.83</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row2_col12\" class=\"data row2 col12\" >4.03</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row2_col13\" class=\"data row2 col13\" >34.70</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row3_col0\" class=\"data row3 col0\" >0.03</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row3_col2\" class=\"data row3 col2\" >2.18</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row3_col4\" class=\"data row3 col4\" >0.46</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row3_col5\" class=\"data row3 col5\" >7.00</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row3_col6\" class=\"data row3 col6\" >45.80</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row3_col7\" class=\"data row3 col7\" >6.06</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row3_col8\" class=\"data row3 col8\" >3.00</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row3_col9\" class=\"data row3 col9\" >222.00</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row3_col10\" class=\"data row3 col10\" >18.70</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row3_col11\" class=\"data row3 col11\" >394.63</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row3_col12\" class=\"data row3 col12\" >2.94</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row3_col13\" class=\"data row3 col13\" >33.40</td>\n",
+ " <th id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row3_col0\" class=\"data row3 col0\" >0.03</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row3_col2\" class=\"data row3 col2\" >2.18</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row3_col4\" class=\"data row3 col4\" >0.46</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row3_col5\" class=\"data row3 col5\" >7.00</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row3_col6\" class=\"data row3 col6\" >45.80</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row3_col7\" class=\"data row3 col7\" >6.06</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row3_col8\" class=\"data row3 col8\" >3.00</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row3_col9\" class=\"data row3 col9\" >222.00</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row3_col10\" class=\"data row3 col10\" >18.70</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row3_col11\" class=\"data row3 col11\" >394.63</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row3_col12\" class=\"data row3 col12\" >2.94</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row3_col13\" class=\"data row3 col13\" >33.40</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row4_col0\" class=\"data row4 col0\" >0.07</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row4_col2\" class=\"data row4 col2\" >2.18</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row4_col4\" class=\"data row4 col4\" >0.46</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row4_col5\" class=\"data row4 col5\" >7.15</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row4_col6\" class=\"data row4 col6\" >54.20</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row4_col7\" class=\"data row4 col7\" >6.06</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row4_col8\" class=\"data row4 col8\" >3.00</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row4_col9\" class=\"data row4 col9\" >222.00</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row4_col10\" class=\"data row4 col10\" >18.70</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row4_col11\" class=\"data row4 col11\" >396.90</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row4_col12\" class=\"data row4 col12\" >5.33</td>\n",
- " <td id=\"T_75c26e2c_f1f4_11ea_a8d7_dbdf8a821ee3row4_col13\" class=\"data row4 col13\" >36.20</td>\n",
+ " <th id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row4_col0\" class=\"data row4 col0\" >0.07</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row4_col2\" class=\"data row4 col2\" >2.18</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row4_col4\" class=\"data row4 col4\" >0.46</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row4_col5\" class=\"data row4 col5\" >7.15</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row4_col6\" class=\"data row4 col6\" >54.20</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row4_col7\" class=\"data row4 col7\" >6.06</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row4_col8\" class=\"data row4 col8\" >3.00</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row4_col9\" class=\"data row4 col9\" >222.00</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row4_col10\" class=\"data row4 col10\" >18.70</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row4_col11\" class=\"data row4 col11\" >396.90</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row4_col12\" class=\"data row4 col12\" >5.33</td>\n",
+ " <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row4_col13\" class=\"data row4 col13\" >36.20</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x7f7591be21d0>"
+ "<pandas.io.formats.style.Styler at 0x154ab65c8990>"
]
},
"metadata": {},
@@ -349,139 +349,139 @@
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- "</style><table id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3\" ><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_c67974bc_f278_11ea_97b3_0cc47af5c7c7\" ><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_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
+ " <th id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row1_col0\" class=\"data row1 col0\" >3.92</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row1_col1\" class=\"data row1 col1\" >10.43</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row1_col2\" class=\"data row1 col2\" >11.36</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row1_col3\" class=\"data row1 col3\" >0.08</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row1_col4\" class=\"data row1 col4\" >0.56</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row1_col5\" class=\"data row1 col5\" >6.27</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row1_col6\" class=\"data row1 col6\" >69.73</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row1_col7\" class=\"data row1 col7\" >3.78</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row1_col8\" class=\"data row1 col8\" >9.67</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row1_col9\" class=\"data row1 col9\" >410.66</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row1_col10\" class=\"data row1 col10\" >18.49</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row1_col11\" class=\"data row1 col11\" >352.04</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row1_col12\" class=\"data row1 col12\" >12.91</td>\n",
+ " <th id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row1_col0\" class=\"data row1 col0\" >3.53</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row1_col1\" class=\"data row1 col1\" >12.31</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row1_col2\" class=\"data row1 col2\" >11.13</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row1_col3\" class=\"data row1 col3\" >0.08</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row1_col4\" class=\"data row1 col4\" >0.55</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row1_col5\" class=\"data row1 col5\" >6.30</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row1_col6\" class=\"data row1 col6\" >68.27</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row1_col7\" class=\"data row1 col7\" >3.82</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row1_col8\" class=\"data row1 col8\" >9.25</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row1_col9\" class=\"data row1 col9\" >404.34</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row1_col10\" class=\"data row1 col10\" >18.29</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row1_col11\" class=\"data row1 col11\" >356.99</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row1_col12\" class=\"data row1 col12\" >12.78</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row2_col0\" class=\"data row2 col0\" >9.18</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row2_col1\" class=\"data row2 col1\" >21.83</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row2_col2\" class=\"data row2 col2\" >6.91</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row2_col3\" class=\"data row2 col3\" >0.27</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row2_col4\" class=\"data row2 col4\" >0.12</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row2_col5\" class=\"data row2 col5\" >0.74</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row2_col6\" class=\"data row2 col6\" >28.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row2_col7\" class=\"data row2 col7\" >2.15</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row2_col8\" class=\"data row2 col8\" >8.81</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row2_col9\" class=\"data row2 col9\" >170.66</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row2_col10\" class=\"data row2 col10\" >2.18</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row2_col11\" class=\"data row2 col11\" >97.98</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row2_col12\" class=\"data row2 col12\" >7.34</td>\n",
+ " <th id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row2_col0\" class=\"data row2 col0\" >8.82</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row2_col1\" class=\"data row2 col1\" >24.61</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row2_col2\" class=\"data row2 col2\" >6.90</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row2_col3\" class=\"data row2 col3\" >0.27</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row2_col4\" class=\"data row2 col4\" >0.12</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row2_col5\" class=\"data row2 col5\" >0.71</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row2_col6\" class=\"data row2 col6\" >28.39</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row2_col7\" class=\"data row2 col7\" >2.12</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row2_col8\" class=\"data row2 col8\" >8.60</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row2_col9\" class=\"data row2 col9\" >166.70</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row2_col10\" class=\"data row2 col10\" >2.25</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row2_col11\" class=\"data row2 col11\" >91.85</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row2_col12\" class=\"data row2 col12\" >7.53</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row3_col0\" class=\"data row3 col0\" >0.01</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row3_col2\" class=\"data row3 col2\" >0.74</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row3_col4\" class=\"data row3 col4\" >0.39</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row3_col5\" class=\"data row3 col5\" >3.56</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row3_col6\" class=\"data row3 col6\" >2.90</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row3_col7\" class=\"data row3 col7\" >1.13</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row3_col8\" class=\"data row3 col8\" >1.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row3_col9\" class=\"data row3 col9\" >187.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row3_col10\" class=\"data row3 col10\" >12.60</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row3_col11\" class=\"data row3 col11\" >0.32</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row3_col12\" class=\"data row3 col12\" >1.73</td>\n",
+ " <th id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row3_col0\" class=\"data row3 col0\" >0.01</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row3_col2\" class=\"data row3 col2\" >0.46</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row3_col4\" class=\"data row3 col4\" >0.39</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row3_col5\" class=\"data row3 col5\" >4.14</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row3_col6\" class=\"data row3 col6\" >2.90</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row3_col7\" class=\"data row3 col7\" >1.13</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row3_col8\" class=\"data row3 col8\" >1.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row3_col9\" class=\"data row3 col9\" >187.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row3_col10\" class=\"data row3 col10\" >12.60</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row3_col11\" class=\"data row3 col11\" >0.32</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row3_col12\" class=\"data row3 col12\" >1.73</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row4_col0\" class=\"data row4 col0\" >0.09</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row4_col2\" class=\"data row4 col2\" >5.22</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row4_col4\" class=\"data row4 col4\" >0.45</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row4_col5\" class=\"data row4 col5\" >5.87</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row4_col6\" class=\"data row4 col6\" >47.40</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row4_col7\" class=\"data row4 col7\" >2.08</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row4_col8\" class=\"data row4 col8\" >4.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row4_col9\" class=\"data row4 col9\" >277.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row4_col10\" class=\"data row4 col10\" >17.33</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row4_col11\" class=\"data row4 col11\" >374.59</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row4_col12\" class=\"data row4 col12\" >7.23</td>\n",
+ " <th id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row4_col0\" class=\"data row4 col0\" >0.08</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row4_col2\" class=\"data row4 col2\" >5.15</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row4_col4\" class=\"data row4 col4\" >0.45</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row4_col5\" class=\"data row4 col5\" >5.89</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row4_col6\" class=\"data row4 col6\" >42.32</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row4_col7\" class=\"data row4 col7\" >2.10</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row4_col8\" class=\"data row4 col8\" >4.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row4_col9\" class=\"data row4 col9\" >277.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row4_col10\" class=\"data row4 col10\" >16.60</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row4_col11\" class=\"data row4 col11\" >376.25</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row4_col12\" class=\"data row4 col12\" >6.88</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row5_col0\" class=\"data row5 col0\" >0.29</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row5_col2\" class=\"data row5 col2\" >9.69</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row5_col3\" class=\"data row5 col3\" >0.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row5_col4\" class=\"data row5 col4\" >0.54</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row5_col5\" class=\"data row5 col5\" >6.18</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row5_col6\" class=\"data row5 col6\" >79.50</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row5_col7\" class=\"data row5 col7\" >3.10</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row5_col8\" class=\"data row5 col8\" >5.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row5_col9\" class=\"data row5 col9\" >332.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row5_col10\" class=\"data row5 col10\" >19.10</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row5_col11\" class=\"data row5 col11\" >390.84</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row5_col12\" class=\"data row5 col12\" >11.43</td>\n",
+ " <th id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row5_col0\" class=\"data row5 col0\" >0.26</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row5_col2\" class=\"data row5 col2\" >9.69</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row5_col3\" class=\"data row5 col3\" >0.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row5_col4\" class=\"data row5 col4\" >0.54</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row5_col5\" class=\"data row5 col5\" >6.19</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row5_col6\" class=\"data row5 col6\" >78.20</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row5_col7\" class=\"data row5 col7\" >3.35</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row5_col8\" class=\"data row5 col8\" >5.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row5_col9\" class=\"data row5 col9\" >329.50</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row5_col10\" class=\"data row5 col10\" >18.60</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row5_col11\" class=\"data row5 col11\" >391.88</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row5_col12\" class=\"data row5 col12\" >11.23</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row6_col0\" class=\"data row6 col0\" >3.85</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row6_col1\" class=\"data row6 col1\" >12.50</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row6_col2\" class=\"data row6 col2\" >18.10</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row6_col3\" class=\"data row6 col3\" >0.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row6_col4\" class=\"data row6 col4\" >0.62</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row6_col5\" class=\"data row6 col5\" >6.62</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row6_col6\" class=\"data row6 col6\" >94.30</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row6_col7\" class=\"data row6 col7\" >5.23</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row6_col8\" class=\"data row6 col8\" >24.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row6_col9\" class=\"data row6 col9\" >666.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row6_col10\" class=\"data row6 col10\" >20.20</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row6_col11\" class=\"data row6 col11\" >395.63</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row6_col12\" class=\"data row6 col12\" >17.26</td>\n",
+ " <th id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row6_col0\" class=\"data row6 col0\" >3.10</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row6_col1\" class=\"data row6 col1\" >16.25</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row6_col2\" class=\"data row6 col2\" >18.10</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row6_col3\" class=\"data row6 col3\" >0.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row6_col4\" class=\"data row6 col4\" >0.62</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row6_col5\" class=\"data row6 col5\" >6.61</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row6_col6\" class=\"data row6 col6\" >93.80</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row6_col7\" class=\"data row6 col7\" >5.12</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row6_col8\" class=\"data row6 col8\" >8.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row6_col9\" class=\"data row6 col9\" >666.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row6_col10\" class=\"data row6 col10\" >20.20</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row6_col11\" class=\"data row6 col11\" >396.12</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row6_col12\" class=\"data row6 col12\" >16.72</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row7_col0\" class=\"data row7 col0\" >88.98</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row7_col1\" class=\"data row7 col1\" >95.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row7_col2\" class=\"data row7 col2\" >27.74</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row7_col3\" class=\"data row7 col3\" >1.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row7_col4\" class=\"data row7 col4\" >0.87</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row7_col5\" class=\"data row7 col5\" >8.78</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row7_col6\" class=\"data row7 col6\" >100.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row7_col7\" class=\"data row7 col7\" >12.13</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row7_col8\" class=\"data row7 col8\" >24.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row7_col9\" class=\"data row7 col9\" >711.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row7_col10\" class=\"data row7 col10\" >22.00</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row7_col11\" class=\"data row7 col11\" >396.90</td>\n",
- " <td id=\"T_75cb9736_f1f4_11ea_a8d7_dbdf8a821ee3row7_col12\" class=\"data row7 col12\" >37.97</td>\n",
+ " <th id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row7_col0\" class=\"data row7 col0\" >88.98</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row7_col1\" class=\"data row7 col1\" >100.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row7_col2\" class=\"data row7 col2\" >27.74</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row7_col3\" class=\"data row7 col3\" >1.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row7_col4\" class=\"data row7 col4\" >0.87</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row7_col5\" class=\"data row7 col5\" >8.78</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row7_col6\" class=\"data row7 col6\" >100.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row7_col7\" class=\"data row7 col7\" >12.13</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row7_col8\" class=\"data row7 col8\" >24.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row7_col9\" class=\"data row7 col9\" >711.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row7_col10\" class=\"data row7 col10\" >22.00</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row7_col11\" class=\"data row7 col11\" >396.90</td>\n",
+ " <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row7_col12\" class=\"data row7 col12\" >37.97</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x7f760c015bd0>"
+ "<pandas.io.formats.style.Styler at 0x154ab3d10f50>"
]
},
"metadata": {},
@@ -491,139 +491,139 @@
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- "</style><table id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3\" ><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_c6811690_f278_11ea_97b3_0cc47af5c7c7\" ><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_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
+ " <th id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row1_col0\" class=\"data row1 col0\" >-0.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row1_col2\" class=\"data row1 col2\" >-0.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row1_col4\" class=\"data row1 col4\" >-0.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row1_col5\" class=\"data row1 col5\" >0.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row1_col6\" class=\"data row1 col6\" >0.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row1_col7\" class=\"data row1 col7\" >0.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row1_col8\" class=\"data row1 col8\" >0.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row1_col9\" class=\"data row1 col9\" >-0.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row1_col10\" class=\"data row1 col10\" >0.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row1_col11\" class=\"data row1 col11\" >-0.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row1_col12\" class=\"data row1 col12\" >-0.00</td>\n",
+ " <th id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row1_col0\" class=\"data row1 col0\" >0.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row1_col4\" class=\"data row1 col4\" >-0.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row1_col5\" class=\"data row1 col5\" >0.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row1_col6\" class=\"data row1 col6\" >0.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row1_col7\" class=\"data row1 col7\" >0.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row1_col8\" class=\"data row1 col8\" >0.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row1_col9\" class=\"data row1 col9\" >0.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row1_col10\" class=\"data row1 col10\" >0.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row1_col11\" class=\"data row1 col11\" >0.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row1_col12\" class=\"data row1 col12\" >-0.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row2_col0\" class=\"data row2 col0\" >1.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row2_col1\" class=\"data row2 col1\" >1.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row2_col3\" class=\"data row2 col3\" >1.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row2_col4\" class=\"data row2 col4\" >1.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row2_col5\" class=\"data row2 col5\" >1.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row2_col6\" class=\"data row2 col6\" >1.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row2_col7\" class=\"data row2 col7\" >1.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row2_col8\" class=\"data row2 col8\" >1.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row2_col9\" class=\"data row2 col9\" >1.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row2_col10\" class=\"data row2 col10\" >1.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row2_col11\" class=\"data row2 col11\" >1.00</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row2_col12\" class=\"data row2 col12\" >1.00</td>\n",
+ " <th id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row2_col0\" class=\"data row2 col0\" >1.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row2_col1\" class=\"data row2 col1\" >1.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row2_col3\" class=\"data row2 col3\" >1.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row2_col4\" class=\"data row2 col4\" >1.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row2_col5\" class=\"data row2 col5\" >1.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row2_col6\" class=\"data row2 col6\" >1.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row2_col7\" class=\"data row2 col7\" >1.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row2_col8\" class=\"data row2 col8\" >1.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row2_col9\" class=\"data row2 col9\" >1.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row2_col10\" class=\"data row2 col10\" >1.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row2_col11\" class=\"data row2 col11\" >1.00</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row2_col12\" class=\"data row2 col12\" >1.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row3_col0\" class=\"data row3 col0\" >-0.43</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row3_col1\" class=\"data row3 col1\" >-0.48</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row3_col2\" class=\"data row3 col2\" >-1.54</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row3_col3\" class=\"data row3 col3\" >-0.30</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row3_col4\" class=\"data row3 col4\" >-1.42</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row3_col5\" class=\"data row3 col5\" >-3.67</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row3_col6\" class=\"data row3 col6\" >-2.39</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row3_col7\" class=\"data row3 col7\" >-1.23</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row3_col8\" class=\"data row3 col8\" >-0.98</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row3_col9\" class=\"data row3 col9\" >-1.31</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row3_col10\" class=\"data row3 col10\" >-2.70</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row3_col11\" class=\"data row3 col11\" >-3.59</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row3_col12\" class=\"data row3 col12\" >-1.52</td>\n",
+ " <th id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row3_col0\" class=\"data row3 col0\" >-0.40</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row3_col1\" class=\"data row3 col1\" >-0.50</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row3_col2\" class=\"data row3 col2\" >-1.55</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row3_col3\" class=\"data row3 col3\" >-0.30</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row3_col4\" class=\"data row3 col4\" >-1.40</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row3_col5\" class=\"data row3 col5\" >-3.03</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row3_col6\" class=\"data row3 col6\" >-2.30</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row3_col7\" class=\"data row3 col7\" >-1.27</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row3_col8\" class=\"data row3 col8\" >-0.96</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row3_col9\" class=\"data row3 col9\" >-1.30</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row3_col10\" class=\"data row3 col10\" >-2.52</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row3_col11\" class=\"data row3 col11\" >-3.88</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row3_col12\" class=\"data row3 col12\" >-1.47</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row4_col0\" class=\"data row4 col0\" >-0.42</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row4_col1\" class=\"data row4 col1\" >-0.48</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row4_col2\" class=\"data row4 col2\" >-0.89</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row4_col3\" class=\"data row4 col3\" >-0.30</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row4_col4\" class=\"data row4 col4\" >-0.90</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row4_col5\" class=\"data row4 col5\" >-0.53</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row4_col6\" class=\"data row4 col6\" >-0.80</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row4_col7\" class=\"data row4 col7\" >-0.79</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row4_col8\" class=\"data row4 col8\" >-0.64</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row4_col9\" class=\"data row4 col9\" >-0.78</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row4_col10\" class=\"data row4 col10\" >-0.53</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row4_col11\" class=\"data row4 col11\" >0.23</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row4_col12\" class=\"data row4 col12\" >-0.77</td>\n",
+ " <th id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row4_col0\" class=\"data row4 col0\" >-0.39</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row4_col1\" class=\"data row4 col1\" >-0.50</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row4_col2\" class=\"data row4 col2\" >-0.87</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row4_col3\" class=\"data row4 col3\" >-0.30</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row4_col4\" class=\"data row4 col4\" >-0.90</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row4_col5\" class=\"data row4 col5\" >-0.57</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row4_col6\" class=\"data row4 col6\" >-0.91</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row4_col7\" class=\"data row4 col7\" >-0.81</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row4_col8\" class=\"data row4 col8\" >-0.61</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row4_col9\" class=\"data row4 col9\" >-0.76</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row4_col10\" class=\"data row4 col10\" >-0.75</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row4_col11\" class=\"data row4 col11\" >0.21</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row4_col12\" class=\"data row4 col12\" >-0.78</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row5_col0\" class=\"data row5 col0\" >-0.40</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row5_col1\" class=\"data row5 col1\" >-0.48</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row5_col2\" class=\"data row5 col2\" >-0.24</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row5_col3\" class=\"data row5 col3\" >-0.30</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row5_col4\" class=\"data row5 col4\" >-0.17</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row5_col5\" class=\"data row5 col5\" >-0.11</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row5_col6\" class=\"data row5 col6\" >0.35</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row5_col7\" class=\"data row5 col7\" >-0.32</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row5_col8\" class=\"data row5 col8\" >-0.53</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row5_col9\" class=\"data row5 col9\" >-0.46</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row5_col10\" class=\"data row5 col10\" >0.28</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row5_col11\" class=\"data row5 col11\" >0.40</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row5_col12\" class=\"data row5 col12\" >-0.20</td>\n",
+ " <th id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row5_col0\" class=\"data row5 col0\" >-0.37</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row5_col1\" class=\"data row5 col1\" >-0.50</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row5_col2\" class=\"data row5 col2\" >-0.21</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row5_col3\" class=\"data row5 col3\" >-0.30</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row5_col4\" class=\"data row5 col4\" >-0.13</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row5_col5\" class=\"data row5 col5\" >-0.15</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row5_col6\" class=\"data row5 col6\" >0.35</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row5_col7\" class=\"data row5 col7\" >-0.22</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row5_col8\" class=\"data row5 col8\" >-0.49</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row5_col9\" class=\"data row5 col9\" >-0.45</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row5_col10\" class=\"data row5 col10\" >0.14</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row5_col11\" class=\"data row5 col11\" >0.38</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row5_col12\" class=\"data row5 col12\" >-0.21</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row6_col0\" class=\"data row6 col0\" >-0.01</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row6_col1\" class=\"data row6 col1\" >0.09</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row6_col2\" class=\"data row6 col2\" >0.98</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row6_col3\" class=\"data row6 col3\" >-0.30</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row6_col4\" class=\"data row6 col4\" >0.57</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row6_col5\" class=\"data row6 col5\" >0.47</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row6_col6\" class=\"data row6 col6\" >0.88</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row6_col7\" class=\"data row6 col7\" >0.68</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row6_col8\" class=\"data row6 col8\" >1.63</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row6_col9\" class=\"data row6 col9\" >1.50</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row6_col10\" class=\"data row6 col10\" >0.79</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row6_col11\" class=\"data row6 col11\" >0.44</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row6_col12\" class=\"data row6 col12\" >0.59</td>\n",
+ " <th id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row6_col0\" class=\"data row6 col0\" >-0.05</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row6_col1\" class=\"data row6 col1\" >0.16</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row6_col2\" class=\"data row6 col2\" >1.01</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row6_col3\" class=\"data row6 col3\" >-0.30</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row6_col4\" class=\"data row6 col4\" >0.60</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row6_col5\" class=\"data row6 col5\" >0.44</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row6_col6\" class=\"data row6 col6\" >0.90</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row6_col7\" class=\"data row6 col7\" >0.61</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row6_col8\" class=\"data row6 col8\" >-0.15</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row6_col9\" class=\"data row6 col9\" >1.57</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row6_col10\" class=\"data row6 col10\" >0.85</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row6_col11\" class=\"data row6 col11\" >0.43</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row6_col12\" class=\"data row6 col12\" >0.52</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row7_col0\" class=\"data row7 col0\" >9.26</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row7_col1\" class=\"data row7 col1\" >3.87</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row7_col2\" class=\"data row7 col2\" >2.37</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row7_col3\" class=\"data row7 col3\" >3.34</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row7_col4\" class=\"data row7 col4\" >2.69</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row7_col5\" class=\"data row7 col5\" >3.41</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row7_col6\" class=\"data row7 col6\" >1.08</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row7_col7\" class=\"data row7 col7\" >3.89</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row7_col8\" class=\"data row7 col8\" >1.63</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row7_col9\" class=\"data row7 col9\" >1.76</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row7_col10\" class=\"data row7 col10\" >1.61</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row7_col11\" class=\"data row7 col11\" >0.46</td>\n",
- " <td id=\"T_75d25418_f1f4_11ea_a8d7_dbdf8a821ee3row7_col12\" class=\"data row7 col12\" >3.41</td>\n",
+ " <th id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row7_col0\" class=\"data row7 col0\" >9.68</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row7_col1\" class=\"data row7 col1\" >3.56</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row7_col2\" class=\"data row7 col2\" >2.41</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row7_col3\" class=\"data row7 col3\" >3.34</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row7_col4\" class=\"data row7 col4\" >2.71</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row7_col5\" class=\"data row7 col5\" >3.49</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row7_col6\" class=\"data row7 col6\" >1.12</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row7_col7\" class=\"data row7 col7\" >3.92</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row7_col8\" class=\"data row7 col8\" >1.72</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row7_col9\" class=\"data row7 col9\" >1.84</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row7_col10\" class=\"data row7 col10\" >1.65</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row7_col11\" class=\"data row7 col11\" >0.43</td>\n",
+ " <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row7_col12\" class=\"data row7 col12\" >3.35</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x7f758fd31190>"
+ "<pandas.io.formats.style.Styler at 0x154ab5f15590>"
]
},
"metadata": {},
@@ -633,91 +633,91 @@
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- "</style><table id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3\" ><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_c681f33a_f278_11ea_97b3_0cc47af5c7c7\" ><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_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3level0_row0\" class=\"row_heading level0 row0\" >186</th>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row0_col0\" class=\"data row0 col0\" >-0.42</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row0_col1\" class=\"data row0 col1\" >-0.48</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row0_col2\" class=\"data row0 col2\" >-1.29</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row0_col3\" class=\"data row0 col3\" >-0.30</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row0_col4\" class=\"data row0 col4\" >-0.60</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row0_col5\" class=\"data row0 col5\" >2.12</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row0_col6\" class=\"data row0 col6\" >-0.58</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row0_col7\" class=\"data row0 col7\" >-0.27</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row0_col8\" class=\"data row0 col8\" >-0.76</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row0_col9\" class=\"data row0 col9\" >-1.28</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row0_col10\" class=\"data row0 col10\" >-0.32</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row0_col11\" class=\"data row0 col11\" >0.41</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row0_col12\" class=\"data row0 col12\" >-1.15</td>\n",
+ " <th id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7level0_row0\" class=\"row_heading level0 row0\" >191</th>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row0_col0\" class=\"data row0 col0\" >-0.39</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row0_col1\" class=\"data row0 col1\" >1.33</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row0_col2\" class=\"data row0 col2\" >-1.11</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row0_col3\" class=\"data row0 col3\" >-0.30</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row0_col4\" class=\"data row0 col4\" >-0.99</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row0_col5\" class=\"data row0 col5\" >0.62</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row0_col6\" class=\"data row0 col6\" >-1.32</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row0_col7\" class=\"data row0 col7\" >1.26</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row0_col8\" class=\"data row0 col8\" >-0.49</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row0_col9\" class=\"data row0 col9\" >-0.04</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row0_col10\" class=\"data row0 col10\" >-1.37</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row0_col11\" class=\"data row0 col11\" >0.36</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row0_col12\" class=\"data row0 col12\" >-1.07</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3level0_row1\" class=\"row_heading level0 row1\" >452</th>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row1_col0\" class=\"data row1 col0\" >0.13</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row1_col1\" class=\"data row1 col1\" >-0.48</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row1_col2\" class=\"data row1 col2\" >0.98</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row1_col3\" class=\"data row1 col3\" >-0.30</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row1_col4\" class=\"data row1 col4\" >1.33</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row1_col5\" class=\"data row1 col5\" >0.04</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row1_col6\" class=\"data row1 col6\" >0.79</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row1_col7\" class=\"data row1 col7\" >-0.66</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row1_col8\" class=\"data row1 col8\" >1.63</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row1_col9\" class=\"data row1 col9\" >1.50</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row1_col10\" class=\"data row1 col10\" >0.79</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row1_col11\" class=\"data row1 col11\" >0.34</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row1_col12\" class=\"data row1 col12\" >0.59</td>\n",
+ " <th id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7level0_row1\" class=\"row_heading level0 row1\" >300</th>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row1_col0\" class=\"data row1 col0\" >-0.39</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row1_col1\" class=\"data row1 col1\" >2.34</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row1_col2\" class=\"data row1 col2\" >-1.29</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row1_col3\" class=\"data row1 col3\" >-0.30</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row1_col4\" class=\"data row1 col4\" >-1.31</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row1_col5\" class=\"data row1 col5\" >0.81</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row1_col6\" class=\"data row1 col6\" >-0.74</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row1_col7\" class=\"data row1 col7\" >1.89</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row1_col8\" class=\"data row1 col8\" >-0.49</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row1_col9\" class=\"data row1 col9\" >-0.28</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row1_col10\" class=\"data row1 col10\" >-1.55</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row1_col11\" class=\"data row1 col11\" >0.37</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row1_col12\" class=\"data row1 col12\" >-0.89</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3level0_row2\" class=\"row_heading level0 row2\" >465</th>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row2_col0\" class=\"data row2 col0\" >-0.08</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row2_col1\" class=\"data row2 col1\" >-0.48</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row2_col2\" class=\"data row2 col2\" >0.98</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row2_col3\" class=\"data row2 col3\" >-0.30</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row2_col4\" class=\"data row2 col4\" >0.84</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row2_col5\" class=\"data row2 col5\" >-0.69</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row2_col6\" class=\"data row2 col6\" >-0.77</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row2_col7\" class=\"data row2 col7\" >-0.33</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row2_col8\" class=\"data row2 col8\" >1.63</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row2_col9\" class=\"data row2 col9\" >1.50</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row2_col10\" class=\"data row2 col10\" >0.79</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row2_col11\" class=\"data row2 col11\" >-0.18</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row2_col12\" class=\"data row2 col12\" >0.17</td>\n",
+ " <th id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7level0_row2\" class=\"row_heading level0 row2\" >65</th>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row2_col0\" class=\"data row2 col0\" >-0.40</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row2_col1\" class=\"data row2 col1\" >2.75</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row2_col2\" class=\"data row2 col2\" >-1.12</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row2_col3\" class=\"data row2 col3\" >-0.30</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row2_col4\" class=\"data row2 col4\" >-1.33</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row2_col5\" class=\"data row2 col5\" >-0.01</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row2_col6\" class=\"data row2 col6\" >-1.78</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row2_col7\" class=\"data row2 col7\" >1.32</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row2_col8\" class=\"data row2 col8\" >-0.61</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row2_col9\" class=\"data row2 col9\" >-0.40</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row2_col10\" class=\"data row2 col10\" >-0.97</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row2_col11\" class=\"data row2 col11\" >0.43</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row2_col12\" class=\"data row2 col12\" >-1.08</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3level0_row3\" class=\"row_heading level0 row3\" >193</th>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row3_col0\" class=\"data row3 col0\" >-0.42</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row3_col1\" class=\"data row3 col1\" >2.27</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row3_col2\" class=\"data row3 col2\" >-1.22</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row3_col3\" class=\"data row3 col3\" >-0.30</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row3_col4\" class=\"data row3 col4\" >-1.34</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row3_col5\" class=\"data row3 col5\" >0.72</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row3_col6\" class=\"data row3 col6\" >-2.14</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row3_col7\" class=\"data row3 col7\" >1.14</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row3_col8\" class=\"data row3 col8\" >-0.98</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row3_col9\" class=\"data row3 col9\" >-0.85</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row3_col10\" class=\"data row3 col10\" >-1.33</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row3_col11\" class=\"data row3 col11\" >0.42</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row3_col12\" class=\"data row3 col12\" >-1.07</td>\n",
+ " <th id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7level0_row3\" class=\"row_heading level0 row3\" >119</th>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row3_col0\" class=\"data row3 col0\" >-0.38</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row3_col1\" class=\"data row3 col1\" >-0.50</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row3_col2\" class=\"data row3 col2\" >-0.16</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row3_col3\" class=\"data row3 col3\" >-0.30</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row3_col4\" class=\"data row3 col4\" >-0.05</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row3_col5\" class=\"data row3 col5\" >-0.79</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row3_col6\" class=\"data row3 col6\" >-0.11</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row3_col7\" class=\"data row3 col7\" >-0.50</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row3_col8\" class=\"data row3 col8\" >-0.38</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row3_col9\" class=\"data row3 col9\" >0.17</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row3_col10\" class=\"data row3 col10\" >-0.22</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row3_col11\" class=\"data row3 col11\" >0.38</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row3_col12\" class=\"data row3 col12\" >0.11</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3level0_row4\" class=\"row_heading level0 row4\" >32</th>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row4_col0\" class=\"data row4 col0\" >-0.28</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row4_col1\" class=\"data row4 col1\" >-0.48</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row4_col2\" class=\"data row4 col2\" >-0.47</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row4_col3\" class=\"data row4 col3\" >-0.30</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row4_col4\" class=\"data row4 col4\" >-0.17</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row4_col5\" class=\"data row4 col5\" >-0.43</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row4_col6\" class=\"data row4 col6\" >0.44</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row4_col7\" class=\"data row4 col7\" >0.10</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row4_col8\" class=\"data row4 col8\" >-0.64</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row4_col9\" class=\"data row4 col9\" >-0.61</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row4_col10\" class=\"data row4 col10\" >1.15</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row4_col11\" class=\"data row4 col11\" >-1.22</td>\n",
- " <td id=\"T_75d30dae_f1f4_11ea_a8d7_dbdf8a821ee3row4_col12\" class=\"data row4 col12\" >2.02</td>\n",
+ " <th id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7level0_row4\" class=\"row_heading level0 row4\" >128</th>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row4_col0\" class=\"data row4 col0\" >-0.36</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row4_col1\" class=\"data row4 col1\" >-0.50</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row4_col2\" class=\"data row4 col2\" >1.56</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row4_col3\" class=\"data row4 col3\" >-0.30</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row4_col4\" class=\"data row4 col4\" >0.60</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row4_col5\" class=\"data row4 col5\" >0.19</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row4_col6\" class=\"data row4 col6\" >1.08</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row4_col7\" class=\"data row4 col7\" >-0.95</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row4_col8\" class=\"data row4 col8\" >-0.61</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row4_col9\" class=\"data row4 col9\" >0.20</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row4_col10\" class=\"data row4 col10\" >1.29</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row4_col11\" class=\"data row4 col11\" >0.43</td>\n",
+ " <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row4_col12\" class=\"data row4 col12\" >0.35</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x7f760c0155d0>"
+ "<pandas.io.formats.style.Styler at 0x154b37589ad0>"
]
},
"metadata": {},
@@ -801,14 +801,14 @@
"Total params: 5,121\n",
"Trainable params: 5,121\n",
"Non-trainable params: 0\n",
- "_________________________________________________________________\n"
+ "_________________________________________________________________\n",
+ "Failed to import pydot. You must install pydot and graphviz for `pydotprint` to work.\n"
]
},
{
"data": {
- "image/png": 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\n",
"text/plain": [
- "<IPython.core.display.Image object>"
+ "None"
]
},
"metadata": {},
@@ -840,207 +840,206 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Train on 354 samples, validate on 152 samples\n",
"Epoch 1/100\n",
- "354/354 [==============================] - 1s 2ms/sample - loss: 496.9229 - mae: 20.3398 - mse: 496.9229 - val_loss: 419.5401 - val_mae: 18.4986 - val_mse: 419.5401\n",
+ "36/36 [==============================] - 0s 9ms/step - loss: 547.8102 - mae: 21.3789 - mse: 547.8102 - val_loss: 426.4636 - val_mae: 18.9591 - val_mse: 426.4636\n",
"Epoch 2/100\n",
- "354/354 [==============================] - 0s 177us/sample - loss: 295.5164 - mae: 14.8616 - mse: 295.5164 - val_loss: 186.6298 - val_mae: 11.5436 - val_mse: 186.6298\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 359.6963 - mae: 16.8183 - mse: 359.6963 - val_loss: 226.9481 - val_mae: 13.1573 - val_mse: 226.9481\n",
"Epoch 3/100\n",
- "354/354 [==============================] - 0s 171us/sample - loss: 114.9580 - mae: 8.2730 - mse: 114.9580 - val_loss: 59.9437 - val_mae: 5.8003 - val_mse: 59.9437\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 160.5077 - mae: 10.3363 - mse: 160.5077 - val_loss: 84.0127 - val_mae: 7.2714 - val_mse: 84.0127\n",
"Epoch 4/100\n",
- "354/354 [==============================] - 0s 164us/sample - loss: 51.2817 - mae: 5.2744 - mse: 51.2817 - val_loss: 35.3415 - val_mae: 4.3495 - val_mse: 35.3415\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 68.7662 - mae: 6.2226 - mse: 68.7662 - val_loss: 42.7031 - val_mae: 5.0207 - val_mse: 42.7031\n",
"Epoch 5/100\n",
- "354/354 [==============================] - 0s 169us/sample - loss: 32.2583 - mae: 4.1435 - mse: 32.2583 - val_loss: 25.3969 - val_mae: 3.6065 - val_mse: 25.3969\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 39.3656 - mae: 4.5027 - mse: 39.3656 - val_loss: 28.1898 - val_mae: 3.9144 - val_mse: 28.1898\n",
"Epoch 6/100\n",
- "354/354 [==============================] - 0s 268us/sample - loss: 25.0037 - mae: 3.6255 - mse: 25.0037 - val_loss: 21.0705 - val_mae: 3.3281 - val_mse: 21.0705\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 27.7994 - mae: 3.7284 - mse: 27.7994 - val_loss: 22.5556 - val_mae: 3.4554 - val_mse: 22.5556\n",
"Epoch 7/100\n",
- "354/354 [==============================] - 0s 183us/sample - loss: 21.5637 - mae: 3.3349 - mse: 21.5637 - val_loss: 18.8010 - val_mae: 3.0725 - val_mse: 18.8010\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 23.3525 - mae: 3.4556 - mse: 23.3525 - val_loss: 21.7096 - val_mae: 3.2439 - val_mse: 21.7096\n",
"Epoch 8/100\n",
- "354/354 [==============================] - 0s 208us/sample - loss: 19.2727 - mae: 3.1629 - mse: 19.2727 - val_loss: 17.3783 - val_mae: 2.8856 - val_mse: 17.3783\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 21.6431 - mae: 3.1815 - mse: 21.6431 - val_loss: 19.3019 - val_mae: 3.0922 - val_mse: 19.3019\n",
"Epoch 9/100\n",
- "354/354 [==============================] - 0s 248us/sample - loss: 17.6151 - mae: 2.9420 - mse: 17.6151 - val_loss: 17.7303 - val_mae: 2.9088 - val_mse: 17.7303\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 19.4970 - mae: 3.0120 - mse: 19.4970 - val_loss: 18.5831 - val_mae: 2.9942 - val_mse: 18.5831\n",
"Epoch 10/100\n",
- "354/354 [==============================] - 0s 201us/sample - loss: 16.7331 - mae: 2.8489 - mse: 16.7331 - val_loss: 16.6203 - val_mae: 2.8102 - val_mse: 16.6203\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 18.0860 - mae: 2.8919 - mse: 18.0860 - val_loss: 17.3483 - val_mae: 2.9057 - val_mse: 17.3483\n",
"Epoch 11/100\n",
- "354/354 [==============================] - 0s 199us/sample - loss: 15.4812 - mae: 2.7647 - mse: 15.4812 - val_loss: 15.9232 - val_mae: 2.8161 - val_mse: 15.9232\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 16.9134 - mae: 2.7540 - mse: 16.9134 - val_loss: 16.5643 - val_mae: 2.8748 - val_mse: 16.5643\n",
"Epoch 12/100\n",
- "354/354 [==============================] - 0s 215us/sample - loss: 14.9469 - mae: 2.7133 - mse: 14.9469 - val_loss: 14.7353 - val_mae: 2.7331 - val_mse: 14.7353\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 15.9337 - mae: 2.6675 - mse: 15.9337 - val_loss: 16.0137 - val_mae: 2.8492 - val_mse: 16.0137\n",
"Epoch 13/100\n",
- "354/354 [==============================] - 0s 222us/sample - loss: 14.2610 - mae: 2.6598 - mse: 14.2610 - val_loss: 15.5891 - val_mae: 2.7066 - val_mse: 15.5891\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 15.3994 - mae: 2.6116 - mse: 15.3994 - val_loss: 15.4497 - val_mae: 2.7655 - val_mse: 15.4497\n",
"Epoch 14/100\n",
- "354/354 [==============================] - 0s 207us/sample - loss: 13.9148 - mae: 2.5804 - mse: 13.9148 - val_loss: 13.8518 - val_mae: 2.6489 - val_mse: 13.8518\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 14.7345 - mae: 2.5935 - mse: 14.7345 - val_loss: 15.5044 - val_mae: 2.7343 - val_mse: 15.5044\n",
"Epoch 15/100\n",
- "354/354 [==============================] - 0s 206us/sample - loss: 13.2557 - mae: 2.5414 - mse: 13.2557 - val_loss: 14.0806 - val_mae: 2.6050 - val_mse: 14.0806\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 13.9952 - mae: 2.4952 - mse: 13.9952 - val_loss: 15.4225 - val_mae: 2.7673 - val_mse: 15.4225\n",
"Epoch 16/100\n",
- "354/354 [==============================] - 0s 176us/sample - loss: 13.0313 - mae: 2.5098 - mse: 13.0313 - val_loss: 13.0845 - val_mae: 2.5645 - val_mse: 13.0845\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 13.6492 - mae: 2.4715 - mse: 13.6492 - val_loss: 15.9129 - val_mae: 2.7843 - val_mse: 15.9129\n",
"Epoch 17/100\n",
- "354/354 [==============================] - 0s 162us/sample - loss: 12.6932 - mae: 2.4334 - mse: 12.6932 - val_loss: 12.7918 - val_mae: 2.5210 - val_mse: 12.7918\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 13.1960 - mae: 2.4395 - mse: 13.1960 - val_loss: 13.8508 - val_mae: 2.6311 - val_mse: 13.8508\n",
"Epoch 18/100\n",
- "354/354 [==============================] - 0s 161us/sample - loss: 12.1471 - mae: 2.3744 - mse: 12.1471 - val_loss: 12.7903 - val_mae: 2.5516 - val_mse: 12.7903\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 12.8516 - mae: 2.4187 - mse: 12.8516 - val_loss: 14.0965 - val_mae: 2.6200 - val_mse: 14.0965\n",
"Epoch 19/100\n",
- "354/354 [==============================] - 0s 156us/sample - loss: 11.7664 - mae: 2.3935 - mse: 11.7664 - val_loss: 12.7772 - val_mae: 2.5006 - val_mse: 12.7772\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 12.5172 - mae: 2.3631 - mse: 12.5172 - val_loss: 14.6048 - val_mae: 2.6703 - val_mse: 14.6048\n",
"Epoch 20/100\n",
- "354/354 [==============================] - 0s 166us/sample - loss: 11.7673 - mae: 2.3442 - mse: 11.7673 - val_loss: 12.2528 - val_mae: 2.4773 - val_mse: 12.2528\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 12.0961 - mae: 2.3411 - mse: 12.0961 - val_loss: 13.6234 - val_mae: 2.6658 - val_mse: 13.6234\n",
"Epoch 21/100\n",
- "354/354 [==============================] - 0s 166us/sample - loss: 11.5515 - mae: 2.3222 - mse: 11.5515 - val_loss: 11.9364 - val_mae: 2.4422 - val_mse: 11.9364\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 11.9956 - mae: 2.3587 - mse: 11.9956 - val_loss: 14.0870 - val_mae: 2.6412 - val_mse: 14.0870\n",
"Epoch 22/100\n",
- "354/354 [==============================] - 0s 163us/sample - loss: 11.1944 - mae: 2.2782 - mse: 11.1944 - val_loss: 12.1765 - val_mae: 2.4741 - val_mse: 12.1765\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 11.7839 - mae: 2.3047 - mse: 11.7839 - val_loss: 13.5144 - val_mae: 2.5665 - val_mse: 13.5144\n",
"Epoch 23/100\n",
- "354/354 [==============================] - 0s 156us/sample - loss: 10.8719 - mae: 2.2615 - mse: 10.8719 - val_loss: 11.6195 - val_mae: 2.4146 - val_mse: 11.6195\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 11.5465 - mae: 2.2461 - mse: 11.5465 - val_loss: 13.4045 - val_mae: 2.6249 - val_mse: 13.4045\n",
"Epoch 24/100\n",
- "354/354 [==============================] - 0s 163us/sample - loss: 10.7249 - mae: 2.2482 - mse: 10.7249 - val_loss: 11.6477 - val_mae: 2.4269 - val_mse: 11.6477\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 11.3203 - mae: 2.2785 - mse: 11.3203 - val_loss: 13.4534 - val_mae: 2.5680 - val_mse: 13.4534\n",
"Epoch 25/100\n",
- "354/354 [==============================] - 0s 167us/sample - loss: 10.4705 - mae: 2.2339 - mse: 10.4705 - val_loss: 11.7473 - val_mae: 2.4594 - val_mse: 11.7473\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 10.9813 - mae: 2.2193 - mse: 10.9813 - val_loss: 13.4274 - val_mae: 2.6316 - val_mse: 13.4274\n",
"Epoch 26/100\n",
- "354/354 [==============================] - 0s 162us/sample - loss: 10.4140 - mae: 2.2127 - mse: 10.4140 - val_loss: 11.5549 - val_mae: 2.4450 - val_mse: 11.5549\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 10.8622 - mae: 2.2494 - mse: 10.8622 - val_loss: 13.2414 - val_mae: 2.5662 - val_mse: 13.2414\n",
"Epoch 27/100\n",
- "354/354 [==============================] - 0s 163us/sample - loss: 10.0311 - mae: 2.2345 - mse: 10.0311 - val_loss: 11.6549 - val_mae: 2.3777 - val_mse: 11.6549\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 10.5865 - mae: 2.2073 - mse: 10.5865 - val_loss: 13.4347 - val_mae: 2.6453 - val_mse: 13.4347\n",
"Epoch 28/100\n",
- "354/354 [==============================] - 0s 163us/sample - loss: 10.0233 - mae: 2.1886 - mse: 10.0233 - val_loss: 11.2612 - val_mae: 2.3638 - val_mse: 11.2612\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 10.4764 - mae: 2.1905 - mse: 10.4764 - val_loss: 13.6223 - val_mae: 2.6343 - val_mse: 13.6223\n",
"Epoch 29/100\n",
- "354/354 [==============================] - 0s 165us/sample - loss: 9.7396 - mae: 2.1549 - mse: 9.7396 - val_loss: 11.7192 - val_mae: 2.3995 - val_mse: 11.7192\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 10.1884 - mae: 2.1639 - mse: 10.1884 - val_loss: 13.2782 - val_mae: 2.5879 - val_mse: 13.2782\n",
"Epoch 30/100\n",
- "354/354 [==============================] - 0s 175us/sample - loss: 9.4578 - mae: 2.1502 - mse: 9.4578 - val_loss: 11.0006 - val_mae: 2.3374 - val_mse: 11.0006\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 10.2006 - mae: 2.1927 - mse: 10.2006 - val_loss: 13.2758 - val_mae: 2.5862 - val_mse: 13.2758\n",
"Epoch 31/100\n",
- "354/354 [==============================] - 0s 167us/sample - loss: 9.2345 - mae: 2.1387 - mse: 9.2345 - val_loss: 10.8882 - val_mae: 2.3251 - val_mse: 10.8882\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 9.9884 - mae: 2.1413 - mse: 9.9884 - val_loss: 14.8800 - val_mae: 2.7099 - val_mse: 14.8800\n",
"Epoch 32/100\n",
- "354/354 [==============================] - 0s 161us/sample - loss: 9.4173 - mae: 2.1318 - mse: 9.4173 - val_loss: 11.6230 - val_mae: 2.3616 - val_mse: 11.6230\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 9.8956 - mae: 2.1278 - mse: 9.8956 - val_loss: 13.9123 - val_mae: 2.6520 - val_mse: 13.9123\n",
"Epoch 33/100\n",
- "354/354 [==============================] - 0s 187us/sample - loss: 9.1474 - mae: 2.0741 - mse: 9.1474 - val_loss: 10.8005 - val_mae: 2.3326 - val_mse: 10.8005\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 9.5863 - mae: 2.1298 - mse: 9.5863 - val_loss: 13.7808 - val_mae: 2.6443 - val_mse: 13.7808\n",
"Epoch 34/100\n",
- "354/354 [==============================] - 0s 182us/sample - loss: 8.7565 - mae: 2.0961 - mse: 8.7565 - val_loss: 10.5611 - val_mae: 2.2676 - val_mse: 10.5611\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 9.7643 - mae: 2.1279 - mse: 9.7643 - val_loss: 13.5272 - val_mae: 2.6014 - val_mse: 13.5272\n",
"Epoch 35/100\n",
- "354/354 [==============================] - 0s 165us/sample - loss: 8.8445 - mae: 2.0441 - mse: 8.8445 - val_loss: 10.7529 - val_mae: 2.2829 - val_mse: 10.7529\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 9.2945 - mae: 2.0820 - mse: 9.2945 - val_loss: 14.6006 - val_mae: 2.7305 - val_mse: 14.6006\n",
"Epoch 36/100\n",
- "354/354 [==============================] - 0s 161us/sample - loss: 8.7197 - mae: 2.0306 - mse: 8.7197 - val_loss: 10.5885 - val_mae: 2.3312 - val_mse: 10.5885\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 9.1900 - mae: 2.1095 - mse: 9.1900 - val_loss: 13.5662 - val_mae: 2.6306 - val_mse: 13.5662\n",
"Epoch 37/100\n",
- "354/354 [==============================] - 0s 160us/sample - loss: 8.3803 - mae: 1.9988 - mse: 8.3803 - val_loss: 10.4625 - val_mae: 2.2977 - val_mse: 10.4625\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 9.3998 - mae: 2.0957 - mse: 9.3998 - val_loss: 14.1139 - val_mae: 2.6710 - val_mse: 14.1139\n",
"Epoch 38/100\n",
- "354/354 [==============================] - 0s 161us/sample - loss: 8.1797 - mae: 1.9841 - mse: 8.1797 - val_loss: 10.8993 - val_mae: 2.4313 - val_mse: 10.8993\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 9.0737 - mae: 2.0781 - mse: 9.0737 - val_loss: 13.3742 - val_mae: 2.6184 - val_mse: 13.3742\n",
"Epoch 39/100\n",
- "354/354 [==============================] - 0s 162us/sample - loss: 8.4897 - mae: 2.0293 - mse: 8.4897 - val_loss: 10.3395 - val_mae: 2.3076 - val_mse: 10.3395\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 8.7388 - mae: 2.0449 - mse: 8.7388 - val_loss: 13.0439 - val_mae: 2.5409 - val_mse: 13.0439\n",
"Epoch 40/100\n",
- "354/354 [==============================] - 0s 169us/sample - loss: 8.2173 - mae: 2.0265 - mse: 8.2173 - val_loss: 10.4234 - val_mae: 2.3516 - val_mse: 10.4234\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 8.7623 - mae: 2.0635 - mse: 8.7623 - val_loss: 13.0694 - val_mae: 2.5882 - val_mse: 13.0694\n",
"Epoch 41/100\n",
- "354/354 [==============================] - 0s 162us/sample - loss: 8.0208 - mae: 1.9914 - mse: 8.0208 - val_loss: 10.1297 - val_mae: 2.2798 - val_mse: 10.1297\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 8.6891 - mae: 2.0567 - mse: 8.6891 - val_loss: 16.1179 - val_mae: 2.9252 - val_mse: 16.1179\n",
"Epoch 42/100\n",
- "354/354 [==============================] - 0s 163us/sample - loss: 7.9852 - mae: 1.9667 - mse: 7.9852 - val_loss: 10.9296 - val_mae: 2.4653 - val_mse: 10.9296\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 8.6387 - mae: 2.0479 - mse: 8.6387 - val_loss: 13.0465 - val_mae: 2.5522 - val_mse: 13.0465\n",
"Epoch 43/100\n",
- "354/354 [==============================] - 0s 167us/sample - loss: 7.8901 - mae: 1.9684 - mse: 7.8901 - val_loss: 10.3993 - val_mae: 2.3660 - val_mse: 10.3993\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 8.7518 - mae: 2.0440 - mse: 8.7518 - val_loss: 13.5344 - val_mae: 2.5933 - val_mse: 13.5344\n",
"Epoch 44/100\n",
- "354/354 [==============================] - 0s 166us/sample - loss: 7.7134 - mae: 1.9914 - mse: 7.7134 - val_loss: 10.1382 - val_mae: 2.1999 - val_mse: 10.1382\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 8.2242 - mae: 1.9886 - mse: 8.2242 - val_loss: 14.1849 - val_mae: 2.6695 - val_mse: 14.1849\n",
"Epoch 45/100\n",
- "354/354 [==============================] - 0s 160us/sample - loss: 7.4651 - mae: 1.8810 - mse: 7.4651 - val_loss: 9.9405 - val_mae: 2.2293 - val_mse: 9.9405\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 8.3604 - mae: 2.0113 - mse: 8.3604 - val_loss: 13.0178 - val_mae: 2.5636 - val_mse: 13.0178\n",
"Epoch 46/100\n",
- "354/354 [==============================] - 0s 158us/sample - loss: 7.6525 - mae: 1.9254 - mse: 7.6525 - val_loss: 10.6396 - val_mae: 2.3426 - val_mse: 10.6396\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 8.0855 - mae: 1.9936 - mse: 8.0855 - val_loss: 13.6547 - val_mae: 2.5996 - val_mse: 13.6547\n",
"Epoch 47/100\n",
- "354/354 [==============================] - 0s 177us/sample - loss: 7.4940 - mae: 1.9148 - mse: 7.4940 - val_loss: 10.3031 - val_mae: 2.3603 - val_mse: 10.3031\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 8.0649 - mae: 1.9796 - mse: 8.0649 - val_loss: 13.9731 - val_mae: 2.6992 - val_mse: 13.9731\n",
"Epoch 48/100\n",
- "354/354 [==============================] - 0s 166us/sample - loss: 7.3784 - mae: 1.8920 - mse: 7.3784 - val_loss: 10.3574 - val_mae: 2.2241 - val_mse: 10.3574\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 7.9916 - mae: 1.9805 - mse: 7.9916 - val_loss: 14.1935 - val_mae: 2.6618 - val_mse: 14.1935\n",
"Epoch 49/100\n",
- "354/354 [==============================] - 0s 182us/sample - loss: 7.3094 - mae: 1.8977 - mse: 7.3094 - val_loss: 9.6455 - val_mae: 2.1697 - val_mse: 9.6455\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 7.9540 - mae: 1.9707 - mse: 7.9540 - val_loss: 13.7080 - val_mae: 2.6203 - val_mse: 13.7080\n",
"Epoch 50/100\n",
- "354/354 [==============================] - 0s 213us/sample - loss: 7.0373 - mae: 1.8537 - mse: 7.0373 - val_loss: 9.9072 - val_mae: 2.2264 - val_mse: 9.9072\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 7.9801 - mae: 1.9259 - mse: 7.9801 - val_loss: 14.0406 - val_mae: 2.6032 - val_mse: 14.0406\n",
"Epoch 51/100\n",
- "354/354 [==============================] - 0s 189us/sample - loss: 6.9772 - mae: 1.8815 - mse: 6.9772 - val_loss: 10.8260 - val_mae: 2.3366 - val_mse: 10.8260\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 7.6427 - mae: 1.9189 - mse: 7.6427 - val_loss: 14.1556 - val_mae: 2.6252 - val_mse: 14.1556\n",
"Epoch 52/100\n",
- "354/354 [==============================] - 0s 196us/sample - loss: 6.8625 - mae: 1.8743 - mse: 6.8625 - val_loss: 9.8249 - val_mae: 2.2377 - val_mse: 9.8249\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 7.5178 - mae: 1.9350 - mse: 7.5178 - val_loss: 13.5717 - val_mae: 2.5349 - val_mse: 13.5717\n",
"Epoch 53/100\n",
- "354/354 [==============================] - 0s 194us/sample - loss: 6.8944 - mae: 1.8752 - mse: 6.8944 - val_loss: 9.6898 - val_mae: 2.1971 - val_mse: 9.6898\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 7.3679 - mae: 1.9285 - mse: 7.3679 - val_loss: 13.3949 - val_mae: 2.5432 - val_mse: 13.3949\n",
"Epoch 54/100\n",
- "354/354 [==============================] - 0s 165us/sample - loss: 6.7095 - mae: 1.8559 - mse: 6.7095 - val_loss: 10.6151 - val_mae: 2.2746 - val_mse: 10.6151\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 7.3311 - mae: 1.8902 - mse: 7.3311 - val_loss: 14.0604 - val_mae: 2.6227 - val_mse: 14.0604\n",
"Epoch 55/100\n",
- "354/354 [==============================] - 0s 166us/sample - loss: 6.7723 - mae: 1.8727 - mse: 6.7723 - val_loss: 10.3318 - val_mae: 2.2836 - val_mse: 10.3318\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 7.1482 - mae: 1.8984 - mse: 7.1482 - val_loss: 13.4580 - val_mae: 2.5299 - val_mse: 13.4580\n",
"Epoch 56/100\n",
- "354/354 [==============================] - 0s 166us/sample - loss: 6.5750 - mae: 1.8187 - mse: 6.5750 - val_loss: 10.2085 - val_mae: 2.2743 - val_mse: 10.2085\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 7.1295 - mae: 1.9292 - mse: 7.1295 - val_loss: 13.4544 - val_mae: 2.5697 - val_mse: 13.4544\n",
"Epoch 57/100\n",
- "354/354 [==============================] - 0s 155us/sample - loss: 6.6257 - mae: 1.7959 - mse: 6.6257 - val_loss: 10.2118 - val_mae: 2.2346 - val_mse: 10.2118\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 7.0592 - mae: 1.9154 - mse: 7.0592 - val_loss: 13.8496 - val_mae: 2.5861 - val_mse: 13.8496\n",
"Epoch 58/100\n",
- "354/354 [==============================] - 0s 153us/sample - loss: 6.2614 - mae: 1.8075 - mse: 6.2614 - val_loss: 9.5504 - val_mae: 2.2475 - val_mse: 9.5504\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 7.0913 - mae: 1.8850 - mse: 7.0913 - val_loss: 13.8990 - val_mae: 2.5791 - val_mse: 13.8990\n",
"Epoch 59/100\n",
- "354/354 [==============================] - 0s 152us/sample - loss: 6.4591 - mae: 1.8022 - mse: 6.4591 - val_loss: 9.5048 - val_mae: 2.1846 - val_mse: 9.5048\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.7415 - mae: 1.8537 - mse: 6.7415 - val_loss: 15.4353 - val_mae: 2.7890 - val_mse: 15.4353\n",
"Epoch 60/100\n",
- "354/354 [==============================] - 0s 151us/sample - loss: 6.2679 - mae: 1.7856 - mse: 6.2679 - val_loss: 9.8863 - val_mae: 2.1472 - val_mse: 9.8863\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.6908 - mae: 1.8085 - mse: 6.6908 - val_loss: 15.1765 - val_mae: 2.6976 - val_mse: 15.1765\n",
"Epoch 61/100\n",
- "354/354 [==============================] - 0s 172us/sample - loss: 6.1997 - mae: 1.7521 - mse: 6.1997 - val_loss: 9.5469 - val_mae: 2.2168 - val_mse: 9.5469\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.7490 - mae: 1.8432 - mse: 6.7490 - val_loss: 14.6076 - val_mae: 2.6679 - val_mse: 14.6076\n",
"Epoch 62/100\n",
- "354/354 [==============================] - 0s 164us/sample - loss: 5.9001 - mae: 1.7677 - mse: 5.9001 - val_loss: 10.3969 - val_mae: 2.2894 - val_mse: 10.3969\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.6512 - mae: 1.8354 - mse: 6.6512 - val_loss: 14.6138 - val_mae: 2.6351 - val_mse: 14.6138\n",
"Epoch 63/100\n",
- "354/354 [==============================] - 0s 161us/sample - loss: 5.9727 - mae: 1.7715 - mse: 5.9727 - val_loss: 9.2806 - val_mae: 2.1635 - val_mse: 9.2806\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.5442 - mae: 1.8366 - mse: 6.5442 - val_loss: 13.5825 - val_mae: 2.5514 - val_mse: 13.5825\n",
"Epoch 64/100\n",
- "354/354 [==============================] - 0s 159us/sample - loss: 5.9357 - mae: 1.7691 - mse: 5.9357 - val_loss: 9.7865 - val_mae: 2.1970 - val_mse: 9.7865\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.2777 - mae: 1.8151 - mse: 6.2777 - val_loss: 14.7046 - val_mae: 2.6135 - val_mse: 14.7046\n",
"Epoch 65/100\n",
- "354/354 [==============================] - 0s 173us/sample - loss: 5.7502 - mae: 1.7583 - mse: 5.7502 - val_loss: 9.4449 - val_mae: 2.1681 - val_mse: 9.4449\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.2568 - mae: 1.8048 - mse: 6.2568 - val_loss: 13.9574 - val_mae: 2.5623 - val_mse: 13.9574\n",
"Epoch 66/100\n",
- "354/354 [==============================] - 0s 161us/sample - loss: 5.8152 - mae: 1.7191 - mse: 5.8152 - val_loss: 9.6538 - val_mae: 2.2230 - val_mse: 9.6538\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.3788 - mae: 1.7895 - mse: 6.3788 - val_loss: 13.3885 - val_mae: 2.5273 - val_mse: 13.3885\n",
"Epoch 67/100\n",
- "354/354 [==============================] - 0s 161us/sample - loss: 5.7099 - mae: 1.7531 - mse: 5.7099 - val_loss: 10.2712 - val_mae: 2.2829 - val_mse: 10.2712\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.1612 - mae: 1.7715 - mse: 6.1612 - val_loss: 14.8357 - val_mae: 2.6192 - val_mse: 14.8357\n",
"Epoch 68/100\n",
- "354/354 [==============================] - 0s 164us/sample - loss: 5.6872 - mae: 1.7396 - mse: 5.6872 - val_loss: 9.8613 - val_mae: 2.2475 - val_mse: 9.8614\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.1632 - mae: 1.7848 - mse: 6.1632 - val_loss: 13.6717 - val_mae: 2.5406 - val_mse: 13.6717\n",
"Epoch 69/100\n",
- "354/354 [==============================] - 0s 164us/sample - loss: 5.5726 - mae: 1.6874 - mse: 5.5726 - val_loss: 9.6871 - val_mae: 2.2940 - val_mse: 9.6871\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.0074 - mae: 1.7391 - mse: 6.0074 - val_loss: 13.8504 - val_mae: 2.6783 - val_mse: 13.8504\n",
"Epoch 70/100\n",
- "354/354 [==============================] - 0s 160us/sample - loss: 5.6172 - mae: 1.6898 - mse: 5.6172 - val_loss: 9.6192 - val_mae: 2.2869 - val_mse: 9.6192\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.1040 - mae: 1.7755 - mse: 6.1040 - val_loss: 13.5444 - val_mae: 2.6011 - val_mse: 13.5444\n",
"Epoch 71/100\n",
- "354/354 [==============================] - 0s 163us/sample - loss: 5.2962 - mae: 1.6887 - mse: 5.2962 - val_loss: 11.9677 - val_mae: 2.6445 - val_mse: 11.9677\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.7638 - mae: 1.7447 - mse: 5.7638 - val_loss: 13.4598 - val_mae: 2.4891 - val_mse: 13.4598\n",
"Epoch 72/100\n",
- "354/354 [==============================] - 0s 157us/sample - loss: 5.4289 - mae: 1.6882 - mse: 5.4289 - val_loss: 9.0920 - val_mae: 2.1635 - val_mse: 9.0920\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.6415 - mae: 1.7166 - mse: 5.6415 - val_loss: 13.9506 - val_mae: 2.5667 - val_mse: 13.9506\n",
"Epoch 73/100\n",
- "354/354 [==============================] - 0s 163us/sample - loss: 5.3055 - mae: 1.6638 - mse: 5.3055 - val_loss: 9.0646 - val_mae: 2.1701 - val_mse: 9.0646\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.6369 - mae: 1.7073 - mse: 5.6369 - val_loss: 13.0920 - val_mae: 2.4877 - val_mse: 13.0920\n",
"Epoch 74/100\n",
- "354/354 [==============================] - 0s 161us/sample - loss: 5.3235 - mae: 1.6849 - mse: 5.3235 - val_loss: 9.1451 - val_mae: 2.1315 - val_mse: 9.1451\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.6331 - mae: 1.7188 - mse: 5.6331 - val_loss: 13.4528 - val_mae: 2.5475 - val_mse: 13.4528\n",
"Epoch 75/100\n",
- "354/354 [==============================] - 0s 163us/sample - loss: 5.2496 - mae: 1.6685 - mse: 5.2496 - val_loss: 9.3699 - val_mae: 2.2101 - val_mse: 9.3699\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.6567 - mae: 1.7286 - mse: 5.6567 - val_loss: 14.3292 - val_mae: 2.5823 - val_mse: 14.3292\n",
"Epoch 76/100\n",
- "354/354 [==============================] - 0s 156us/sample - loss: 5.0007 - mae: 1.6417 - mse: 5.0007 - val_loss: 9.0921 - val_mae: 2.1279 - val_mse: 9.0921\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.4564 - mae: 1.7111 - mse: 5.4564 - val_loss: 13.3819 - val_mae: 2.5767 - val_mse: 13.3819\n",
"Epoch 77/100\n",
- "354/354 [==============================] - 0s 172us/sample - loss: 5.2315 - mae: 1.6528 - mse: 5.2315 - val_loss: 9.0284 - val_mae: 2.1283 - val_mse: 9.0284\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.5796 - mae: 1.6924 - mse: 5.5796 - val_loss: 14.2068 - val_mae: 2.5854 - val_mse: 14.2068\n",
"Epoch 78/100\n",
- "354/354 [==============================] - 0s 157us/sample - loss: 5.0492 - mae: 1.6244 - mse: 5.0492 - val_loss: 9.7080 - val_mae: 2.2000 - val_mse: 9.7080\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.2629 - mae: 1.6593 - mse: 5.2629 - val_loss: 16.1614 - val_mae: 2.8351 - val_mse: 16.1614\n",
"Epoch 79/100\n",
- "354/354 [==============================] - 0s 165us/sample - loss: 4.9512 - mae: 1.6168 - mse: 4.9512 - val_loss: 9.3491 - val_mae: 2.2524 - val_mse: 9.3491\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.4147 - mae: 1.7023 - mse: 5.4147 - val_loss: 13.4259 - val_mae: 2.4891 - val_mse: 13.4259\n",
"Epoch 80/100\n",
- "354/354 [==============================] - 0s 161us/sample - loss: 4.8233 - mae: 1.5951 - mse: 4.8233 - val_loss: 10.4702 - val_mae: 2.3727 - val_mse: 10.4702\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.2169 - mae: 1.6532 - mse: 5.2169 - val_loss: 15.0308 - val_mae: 2.6102 - val_mse: 15.0308\n",
"Epoch 81/100\n",
- "354/354 [==============================] - 0s 163us/sample - loss: 4.8356 - mae: 1.6313 - mse: 4.8356 - val_loss: 10.8360 - val_mae: 2.5334 - val_mse: 10.8360\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 4.9672 - mae: 1.6381 - mse: 4.9672 - val_loss: 13.1356 - val_mae: 2.4735 - val_mse: 13.1356\n",
"Epoch 82/100\n",
- "354/354 [==============================] - 0s 165us/sample - loss: 4.7664 - mae: 1.5728 - mse: 4.7664 - val_loss: 9.0297 - val_mae: 2.1707 - val_mse: 9.0297\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.0431 - mae: 1.6363 - mse: 5.0431 - val_loss: 13.3402 - val_mae: 2.4705 - val_mse: 13.3402\n",
"Epoch 83/100\n",
- "354/354 [==============================] - 0s 160us/sample - loss: 4.8996 - mae: 1.5917 - mse: 4.8996 - val_loss: 9.0345 - val_mae: 2.1374 - val_mse: 9.0345\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 4.9814 - mae: 1.6268 - mse: 4.9814 - val_loss: 13.6050 - val_mae: 2.5131 - val_mse: 13.6050\n",
"Epoch 84/100\n",
- "354/354 [==============================] - 0s 160us/sample - loss: 4.7986 - mae: 1.6048 - mse: 4.7986 - val_loss: 9.4659 - val_mae: 2.2548 - val_mse: 9.4659\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 4.9594 - mae: 1.6109 - mse: 4.9594 - val_loss: 14.5256 - val_mae: 2.5736 - val_mse: 14.5256\n",
"Epoch 85/100\n",
- "354/354 [==============================] - 0s 171us/sample - loss: 4.5527 - mae: 1.5404 - mse: 4.5527 - val_loss: 9.5788 - val_mae: 2.1823 - val_mse: 9.5788\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 4.8949 - mae: 1.6083 - mse: 4.8949 - val_loss: 13.7125 - val_mae: 2.5208 - val_mse: 13.7125\n",
"Epoch 86/100\n",
- "354/354 [==============================] - 0s 162us/sample - loss: 4.6033 - mae: 1.5829 - mse: 4.6033 - val_loss: 9.7029 - val_mae: 2.2306 - val_mse: 9.7029\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 4.9217 - mae: 1.6250 - mse: 4.9217 - val_loss: 13.9564 - val_mae: 2.5172 - val_mse: 13.9564\n",
"Epoch 87/100\n",
- "354/354 [==============================] - 0s 158us/sample - loss: 4.5717 - mae: 1.5727 - mse: 4.5717 - val_loss: 9.1632 - val_mae: 2.1194 - val_mse: 9.1632\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 4.6410 - mae: 1.5842 - mse: 4.6410 - val_loss: 15.8558 - val_mae: 2.7166 - val_mse: 15.8558\n",
"Epoch 88/100\n",
- "354/354 [==============================] - 0s 158us/sample - loss: 4.4614 - mae: 1.5356 - mse: 4.4614 - val_loss: 8.9921 - val_mae: 2.1007 - val_mse: 8.9921\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 4.7807 - mae: 1.5999 - mse: 4.7807 - val_loss: 16.3003 - val_mae: 2.7580 - val_mse: 16.3003\n",
"Epoch 89/100\n",
- "354/354 [==============================] - 0s 164us/sample - loss: 4.6582 - mae: 1.5248 - mse: 4.6582 - val_loss: 9.0494 - val_mae: 2.1288 - val_mse: 9.0494\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 4.6485 - mae: 1.5754 - mse: 4.6485 - val_loss: 14.7663 - val_mae: 2.6457 - val_mse: 14.7663\n",
"Epoch 90/100\n",
- "354/354 [==============================] - 0s 158us/sample - loss: 4.3272 - mae: 1.5289 - mse: 4.3272 - val_loss: 9.8204 - val_mae: 2.3003 - val_mse: 9.8204\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 4.6064 - mae: 1.5620 - mse: 4.6064 - val_loss: 13.7809 - val_mae: 2.5864 - val_mse: 13.7809\n",
"Epoch 91/100\n",
- "354/354 [==============================] - 0s 157us/sample - loss: 4.4199 - mae: 1.5274 - mse: 4.4199 - val_loss: 10.0219 - val_mae: 2.2723 - val_mse: 10.0219\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 4.6342 - mae: 1.6050 - mse: 4.6342 - val_loss: 14.7906 - val_mae: 2.5932 - val_mse: 14.7906\n",
"Epoch 92/100\n",
- "354/354 [==============================] - 0s 163us/sample - loss: 4.3447 - mae: 1.5423 - mse: 4.3447 - val_loss: 11.5811 - val_mae: 2.5490 - val_mse: 11.5811\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 4.4944 - mae: 1.5783 - mse: 4.4944 - val_loss: 14.5350 - val_mae: 2.5457 - val_mse: 14.5350\n",
"Epoch 93/100\n",
- "354/354 [==============================] - 0s 163us/sample - loss: 4.3355 - mae: 1.5369 - mse: 4.3355 - val_loss: 8.8431 - val_mae: 2.1337 - val_mse: 8.8431\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 4.4326 - mae: 1.5426 - mse: 4.4326 - val_loss: 14.9994 - val_mae: 2.6641 - val_mse: 14.9994\n",
"Epoch 94/100\n",
- "354/354 [==============================] - 0s 156us/sample - loss: 4.3791 - mae: 1.5442 - mse: 4.3791 - val_loss: 9.4194 - val_mae: 2.1541 - val_mse: 9.4194\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 4.4441 - mae: 1.5272 - mse: 4.4441 - val_loss: 14.1781 - val_mae: 2.5117 - val_mse: 14.1781\n",
"Epoch 95/100\n",
- "354/354 [==============================] - 0s 157us/sample - loss: 4.3247 - mae: 1.5456 - mse: 4.3247 - val_loss: 8.9440 - val_mae: 2.0844 - val_mse: 8.9440\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 4.4125 - mae: 1.5533 - mse: 4.4125 - val_loss: 14.1736 - val_mae: 2.5013 - val_mse: 14.1736\n",
"Epoch 96/100\n",
- "354/354 [==============================] - 0s 164us/sample - loss: 4.2525 - mae: 1.4911 - mse: 4.2525 - val_loss: 9.1399 - val_mae: 2.1911 - val_mse: 9.1399\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 4.2680 - mae: 1.5250 - mse: 4.2680 - val_loss: 14.4141 - val_mae: 2.5394 - val_mse: 14.4141\n",
"Epoch 97/100\n",
- "354/354 [==============================] - 0s 161us/sample - loss: 4.2203 - mae: 1.4947 - mse: 4.2203 - val_loss: 9.4216 - val_mae: 2.2468 - val_mse: 9.4216\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 4.3037 - mae: 1.5324 - mse: 4.3037 - val_loss: 14.5133 - val_mae: 2.5606 - val_mse: 14.5133\n",
"Epoch 98/100\n",
- "354/354 [==============================] - 0s 158us/sample - loss: 4.0427 - mae: 1.5021 - mse: 4.0427 - val_loss: 9.8772 - val_mae: 2.2684 - val_mse: 9.8772\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 4.2143 - mae: 1.5106 - mse: 4.2143 - val_loss: 14.8538 - val_mae: 2.5982 - val_mse: 14.8538\n",
"Epoch 99/100\n",
- "354/354 [==============================] - 0s 156us/sample - loss: 4.1551 - mae: 1.4745 - mse: 4.1551 - val_loss: 9.2742 - val_mae: 2.1680 - val_mse: 9.2742\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 4.1531 - mae: 1.5010 - mse: 4.1531 - val_loss: 14.7132 - val_mae: 2.6151 - val_mse: 14.7132\n",
"Epoch 100/100\n",
- "354/354 [==============================] - 0s 166us/sample - loss: 4.1385 - mae: 1.4979 - mse: 4.1385 - val_loss: 9.1496 - val_mae: 2.1970 - val_mse: 9.1496\n"
+ "36/36 [==============================] - 0s 3ms/step - loss: 4.1666 - mae: 1.4817 - mse: 4.1666 - val_loss: 15.1563 - val_mae: 2.6203 - val_mse: 15.1563\n"
]
}
],
@@ -1072,9 +1071,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "x_test / loss : 9.1496\n",
- "x_test / mae : 2.1970\n",
- "x_test / mse : 9.1496\n"
+ "x_test / loss : 15.1563\n",
+ "x_test / mae : 2.6203\n",
+ "x_test / mse : 15.1563\n"
]
}
],
@@ -1140,66 +1139,66 @@
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
- " <td>17.575156</td>\n",
- " <td>2.393877</td>\n",
- " <td>17.575157</td>\n",
- " <td>17.672065</td>\n",
- " <td>2.668328</td>\n",
- " <td>17.672065</td>\n",
+ " <td>19.964298</td>\n",
+ " <td>2.495103</td>\n",
+ " <td>19.964298</td>\n",
+ " <td>21.819469</td>\n",
+ " <td>2.996733</td>\n",
+ " <td>21.819469</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
- " <td>57.558606</td>\n",
- " <td>2.374958</td>\n",
- " <td>57.558606</td>\n",
- " <td>44.606822</td>\n",
- " <td>1.899542</td>\n",
- " <td>44.606823</td>\n",
+ " <td>65.950543</td>\n",
+ " <td>2.623174</td>\n",
+ " <td>65.950543</td>\n",
+ " <td>46.660749</td>\n",
+ " <td>1.997806</td>\n",
+ " <td>46.660749</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
- " <td>4.042664</td>\n",
- " <td>1.474455</td>\n",
- " <td>4.042665</td>\n",
- " <td>8.843062</td>\n",
- " <td>2.084356</td>\n",
- " <td>8.843062</td>\n",
+ " <td>4.153061</td>\n",
+ " <td>1.481748</td>\n",
+ " <td>4.153061</td>\n",
+ " <td>13.017827</td>\n",
+ " <td>2.470451</td>\n",
+ " <td>13.017827</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
- " <td>5.245075</td>\n",
- " <td>1.661062</td>\n",
- " <td>5.245075</td>\n",
- " <td>9.571723</td>\n",
- " <td>2.199997</td>\n",
- " <td>9.571724</td>\n",
+ " <td>5.619731</td>\n",
+ " <td>1.710129</td>\n",
+ " <td>5.619731</td>\n",
+ " <td>13.532590</td>\n",
+ " <td>2.566433</td>\n",
+ " <td>13.532590</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
- " <td>7.007228</td>\n",
- " <td>1.878118</td>\n",
- " <td>7.007228</td>\n",
- " <td>10.348436</td>\n",
- " <td>2.288131</td>\n",
- " <td>10.348436</td>\n",
+ " <td>7.798356</td>\n",
+ " <td>1.932091</td>\n",
+ " <td>7.798356</td>\n",
+ " <td>14.073690</td>\n",
+ " <td>2.618842</td>\n",
+ " <td>14.073690</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
- " <td>10.428114</td>\n",
- " <td>2.234086</td>\n",
- " <td>10.428113</td>\n",
- " <td>11.670978</td>\n",
- " <td>2.460876</td>\n",
- " <td>11.670978</td>\n",
+ " <td>10.892002</td>\n",
+ " <td>2.226007</td>\n",
+ " <td>10.892002</td>\n",
+ " <td>14.909881</td>\n",
+ " <td>2.683100</td>\n",
+ " <td>14.909881</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
- " <td>496.922868</td>\n",
- " <td>20.339752</td>\n",
- " <td>496.922882</td>\n",
- " <td>419.540121</td>\n",
- " <td>18.498610</td>\n",
- " <td>419.540131</td>\n",
+ " <td>547.810181</td>\n",
+ " <td>21.378906</td>\n",
+ " <td>547.810181</td>\n",
+ " <td>426.463562</td>\n",
+ " <td>18.959126</td>\n",
+ " <td>426.463562</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -1208,13 +1207,13 @@
"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 17.575156 2.393877 17.575157 17.672065 2.668328 17.672065\n",
- "std 57.558606 2.374958 57.558606 44.606822 1.899542 44.606823\n",
- "min 4.042664 1.474455 4.042665 8.843062 2.084356 8.843062\n",
- "25% 5.245075 1.661062 5.245075 9.571723 2.199997 9.571724\n",
- "50% 7.007228 1.878118 7.007228 10.348436 2.288131 10.348436\n",
- "75% 10.428114 2.234086 10.428113 11.670978 2.460876 11.670978\n",
- "max 496.922868 20.339752 496.922882 419.540121 18.498610 419.540131"
+ "mean 19.964298 2.495103 19.964298 21.819469 2.996733 21.819469\n",
+ "std 65.950543 2.623174 65.950543 46.660749 1.997806 46.660749\n",
+ "min 4.153061 1.481748 4.153061 13.017827 2.470451 13.017827\n",
+ "25% 5.619731 1.710129 5.619731 13.532590 2.566433 13.532590\n",
+ "50% 7.798356 1.932091 7.798356 14.073690 2.618842 14.073690\n",
+ "75% 10.892002 2.226007 10.892002 14.909881 2.683100 14.909881\n",
+ "max 547.810181 21.378906 547.810181 426.463562 18.959126 426.463562"
]
},
"execution_count": 10,
@@ -1237,7 +1236,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "min( val_mae ) : 2.0844\n"
+ "min( val_mae ) : 2.4705\n"
]
}
],
@@ -1252,7 +1251,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
@@ -1264,7 +1263,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
@@ -1276,7 +1275,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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LiIhMuEgEhxH8EMgDJwQ/9wXLdI2ybcPKVNZrlR2p/CDn3LXOuUPrr2p92lIKDiIi0toiGxyccwVgNTA72LQ6WM6vUbyyLdyNsRqYbWa1wsN8fDfGmLspdkQ6dFGrnIKDiIi0oMgGBzNrAxYAlWtDL8N3PRxRo/jhwTLcvfAE/vW9t8bjLh5WtiHakgoOIiLS2poeHMxs1gh3fRc/ePMuGDzt8i7gKDM7OLR/F3A68DJDz6C4BXDA+cMe9wz82IabxqP+Y6ExDiIi0uqicFbFt8zscOC3wOtAF/6siqOB/wKuDJW9EPgg8BszuwLYjA8C84ETXGhiBefcMjO7CjjbzO4A7qE6c+SD7MDkT9tLYxxERKTVRSE4PAC8C/g8MAso4VsPvglc7pyrnAGBc+4VM3s/8APg60AKeBr4SI3ppsG3NqwAzsQPsuzGB5GLGj3dNGiMg4iItL6mBwfn3K+AX42h/PPAiXWWLeGvUXHZ9tVufKmrQkREWl3TxzjsTBQcRESk1Sk4NJDOqhARkVan4NBAGhwpIiKtTsGhgZLxGJXrYxZKZUqT/RKZIiIy6Sg4NJCZaZyDiIi0NAWHBlNwEBGRVqbg0GAa5yAiIq1MwaHBNAmUiIi0MgWHBtMpmSIi0soUHBpMYxxERKSVKTg0mIKDiIi0MgWHBlNXhYiItDIFhwZTi4OIiLQyBYcGS6vFQUREWpiCQ4O1qcVBRERamIJDgyk4iIhIK1NwaDB1VYiISCtTcGgwDY4UEZFWpuDQYAoOIiLSyhQcGkzzOIiISCtTcGgwBQcREWllCg4Npq4KERFpZQoODabgICIirUzBocEUHEREpJUpODSYxjiIiEgrU3BoMLU4iIhIK1NwaDC1OIiISCtTcGgwtTiIiEgrU3BosFQihgXrhVKZUtk1tT4iIiJjoeDQYGamVgcREWlZiWZXYKfxm1/Aw/dBPsefJg/i9vT+gA8OHWn9N4iISGvQJ1aj9GyBN18HYMauucHNanEQEZFWoq6KRkmlBlc7rBoWdGaFiIi0EgWHRkmlB1fbUXAQEZHWpODQKKm2wdU2ioPr6qoQEZFWouDQKMlqV0Wbq4YFBQcREWklCg6Nkq52VaTV4iAiIi1KwaFRQmMc0uVqcNAYBxERaSUKDo2SDAUHVxhcV3AQEZFWouDQKKEWh2RZXRUiItKaFBwaJRQcUqVqi4OCg4iItJLIBQcz6zCzV83Mmdk/1Lh/PzO708w2mFmvmT1sZseM8FgxM7vAzF4ws5yZvWFml5lZ58S/kmFCwSFRVnAQEZHWFLngAHwHmF3rDjPbG3gEOAK4FPgK0AXcZ2bH1tjlCuBy4A/AOcBtwLnAXWbW2NceDg4ljXEQEZHWFKlrVZjZe4Dzga8Cl9UocgkwHTjEObc02OdG4DngKjNb6JxzwfYD8GHhDufcSaHneBX4MXAycPMEvpyhwsGhmB9cV4uDiIi0ksi0OJhZHLgOuBe4o8b9ncDHgQcqoQHAOdcDXA/sCywJ7XIKYMCPhj3UdUAfcOp41n+bQsEhHgoOanEQEZFWEpngAFwALATOHuH+g4A08GiN+x4LluHgsAQoA4+HCzrncsDSYWUnXiIJZgDEyiVirgyoxUFERFpLJIKDme0JfBv4jnNuxQjF5gXLVTXuq2ybP6x8t3NuYITys80sVeM+zOxMM3tymxUfC7Mh006nnT8lUy0OIiLSSiIRHICrgVfxAxlH0hEsawWB3LAylfVaZUcqP8g5d61z7tBR6rJ9Qhe6qgQHtTiIiEgrafrgSDM7FfgwcKRzoSkVt9YXLNM17msbVqayvssIj1Wr/MQLTzut4CAiIi2oqcHBzNL4VoZ7gLfMbJ/grkqXw7RgWzeweth9YZVt4W6M1cC7zCxdo7tiPr4bI08jpUJdFWUFBxERaT3N7qpoB+YAJwAvh24PBPefGvx8OrAM3/VwRI3HOTxYhsclPIF/fe8NFzSzNmDxsLKNkdr6ehUa4yAiIq2k2V0VvcCf19g+B8jiT838R+D3zrkeM7sL+DMzO9g59wyAmXXhg8XLDD2D4hbgG/h5IR4ObT8DP7bhpnF+LdtWo6tCwUFERFpJU4NDMKbh9uHbzWyPYPV/nHPh+y8EPgj8xsyuADbjg8B84ITK5E/BYy8zs6uAs83sDnx3yP74mSMfpJGTP1UkNcZBRERaW7NbHMbEOfeKmb0f+AHwdSAFPA18xDl3f41dzgdWAGfiu0O6gSuBi5wLJlJopHToQldBcCiUypTKjnjMGl4dERGRsYpkcAjmcqj5Seqcex44sc7HKeGnrq41fXXjhboqumLV3JIvlmhPRfK/QkREZIhmD47cuYS6Krqs2kWRy6u7QkREWoOCQyOFWhw6Qy0OGucgIiKtQsGhkULBoSPc4qDgICIiLULBoZFCwaGdalgYKCo4iIhIa1BwaKRwcNAYBxERaUEKDo0UujpmW3A6JmiMg4iItA4Fh0YKtTi0oeAgIiKtR8GhkdLVy2q3OQ2OFBGR1qPg0EihropUuXoFcQUHERFpFQoOjVTjIlegrgoREWkdCg6NlKp2VSRDLQ4KDiIi0ioUHBopVe2qSJYUHEREpPUoODRSqKtiSHDQBFAiItIiFBwaKRQcEqHgoAmgRESkVSg4NFLo6pjxYn5wXS0OIiLSKhQcGikdCg4a4yAiIi1IwaGRQi0OsUIenANgoFgeaQ8REZFIUXBopHgc4gkADEcyuEKmWhxERKRVKDg0WngSqLKfBErBQUREWoWCQ6OF5nKozB6p4CAiIq1CwaHRQrNHDgYHnVUhIiItQsGh0Wpcr0ItDiIi0ioUHBotuXVXRb6gsypERKQ1KDg02pDBkX4uh1yhhAtOzRQREYkyBYdGC41xaAtOxyw7R7Gs4CAiItGn4NBoobMqpsSqXRR5jXMQEZEWoODQaKGuis5YNSzkFBxERKQFKDg0Wig4tFs1LOjMChERaQUKDo0WbnGwUFeFrlchIiItQMGh0UZocVBXhYiItAIFh0YLBweKg+vqqhARkVag4NBooUtrV07HBMhr2mkREWkBCg6NFmpxaHPVFgd1VYiISCtQcGi0EYKDuipERKQVKDg0Wo2LXIHOqhARkdag4NBooeCQKqurQkREWktiPB4kk8kkgAMBA57NZrOF8XjcSSk05XSyXD1M6qoQEZFWUFeLQyaT2TOTyfxFJpPZt8Z9xwMrgaeAJ4HVmUzmpPGt5iQypMWhGhx0rQoREWkF9XZVfBG4DsiHN2YymT2B24BdgDeAF4CZwM2ZTOaAcazn5BG6OmaiVA0OOZ2OKSIiLaDe4PDHwLJsNrti2PZzgXZ8qNgzm80eAHwaSALnjFclJ5VkqKuipK4KERFpLfUGhz2BZ2ts/whQBL6WzWYdQDabvR14FPhAPQ9sZvuZ2U1m9ryZbTKzPjN7wcwuN7PdRih/p5ltMLNeM3vYzI4Z4bFjZnZB8Hg5M3vDzC4zs846X/f4C3VVxIvVBpx8QWdViIhI9NU7OHIO8Hp4QyaTmQLsC/xXNpvdOKz808BpdT72AmA34Jf4sRJFYBFwJnCymS12zr0NYGZ7A48EZS4FNgFnAPeZ2Uedc/cPe+wr8K0ivwQuA/YPfn63mR3rnGv8p3WoqyIe7qpQi4OIiLSAeoODA6YN27YYfxbF0zXKb6r3sZ1z/w78+/DtZvYQcCs+gFwabL4EmA4c4pxbGpS7EXgOuMrMFjrnXLD9AHx3yR3OuZNCj/sq8GPgZODmeuo4rkJnVYRbHAY0xkFERFpAvV0VK4D3D9t2ND5Q/FeN8nOANdtfLQBeC5YzAILuhY8DD1RCA4Bzrge4Ht/6sSS0/yn4YPOjYY97HdAHnLqD9ds+oTEOsVKRWNDoobMqRESkFdTb4vAb4NxMJvMt/Lf1fYGz8F0G99YofyjDuja2xczagC6gDXgX8LfBXfcEy4OANH78xHCPBcslwOOh9XLoZwCcczkzW8rQkNE4Zn6cQ34AgJQrkrOUBkeKiEhLqLfF4VJgI/BtYAO+lWFX4KfZbHZtuGAmk9kdeDfw4BjrcjqwFn9a5334LolTnXMPB/fPC5arauxb2TY/tG0e0O2cGxih/GwzS9W4b+LVmHZaYxxERKQV1BUcstnsm8CRwG+BHP6D9zL8QMPhPo8f4/CvY6zLncCHgE8A38EHlTmh+zuCZa0gkBtWprJeq+xI5QeZ2Zlm9mQddd4+NYKDrlUhIiKtoO4pp7PZ7HPAsXWU+w7+g39MnHMr8WdVANxpZr8AnjCzdufcJfhxCeC7K4arnKrQF9rWh5+YqpZa5cN1uRa4NpPJuHrrPyY1goO6KkREpBVE9iJXzrnfA/8NZIJNq4Pl/BrFK9vC3Rir8d0RtYLGfHw3Rr7GfRMvGQoOZXVViIhI6xivi1wdjD/LwoCHs9nseDXzt+OnsAZYhu96OKJGucODZfh5nwA+DLwXqIyTqAzCXAw8NE51HLuaXRUKDiIiEn31XuTqyEwmc2Mmkzm8xn0X4+dyuAz4O+C/MpnM5fVWwMzmjrD9aPwVNx+DwdMu7wKOMrODQ+W68AMrX2boGRS34E8XPX/YQ5+BH9twU711HHehuRzCYxzKbmJ6RkRERMZLvV0Vfw58Cng+vDGTyfwJcBH+tMebgGuAdcB5wVUz63G1mT1mZt83sy+Z2XnBpE73AVuAvwqVvRA/8PI3ZvZ1M8vgWxPmA+dUJn8CcM4tA64C/szM7jCz083sMuBy/BkfjZ/8qSLU4tAVq7Y0aICkiIhEXb1dFUfgp5beNGz7l/Df6s/NZrNXA2QymSuBZ4AvUJ2DYTQ/x5+J8Tn8WRQOP/nTT4AfOucG54Nwzr1iZu8HfgB8HUjhWzs+UmO6afCtDSvw01efAHQDVwIXNWW66YrQtNMdsWo1Bgol2pLxZtRIRESkLvUGh3nUDgHHAL342RgByGazL2Qymfuoc4Il59yt+Kml6+Kcex44sc6yJXwXymX1Pn5DhGaP7KTa4qAzK0REJOrq7aqYxbAppDOZzFxgLvBINpstDiv/Mn6CKKklXe2q6IwpOIiISOuoNzj0s3UQeE+w/O8a5Qfw01FLLaExDu02tKtCREQkyuoNDi8AH81kMuGujRPw4xEeqVH+HcCbO1i3ySs0j0OHq+YrXSFTRESirt4xDrfjr1fx60wmcw3+IldfxJ/h8G81yr+fYWdgSEioxaEt1DAzUNBZFSIiEm31Bocr8Zep/ghwXLDNgP+TzWZz4YKZTOYwYI9gH6llSHDQGAcREWkd9V7kagB/kauL8JfRvgn4WDab/YcaxRcDv8JP1iS1hINDuKtCwUFERCJuLBe56gW+V0e5n+DnYJCR1JhyGjTGQUREoi+yF7ma1EYKDmpxEBGRiBvzRa4ymUwn8En8AMh5+DMr3gT+E7g9aJmQ0YSCQ7JcGFxXcBARkagbU4tDJpM5FT+F8/+Hv7DU8fjTMk8Ptq3IZDKfHec6Tj6h4JAqh7sqdFaFiIhEW93BIZPJnAP8DD+L5JPA94GzgEyw/kRw342ZTObs8a/qJBKaclotDiIi0krq6qrIZDL74q8quQH4bDabva9GsW9lMpkP4a86eXkmk/lNNpt9afyqOomEWhwSJQUHERFpHfW2OJyHn7fhxBFCAwDZbPbf8BegigPn7nj1JqnQ1TGHBAedVSEiIhFXb3D4IPBANpv9z20VzGazjwC/BY7dkYpNaqlqV0WimB9cV4uDiIhEXb3BYQHw1Bge96lgH6kl1FURV3AQEZEWUm9wcPiuinqNpezOJ3SRq1gxD84BOqtCRESir97gsBI4dAyPewjwxtirs5NIJCAeB8CcI4kPDGpxEBGRqKs3OPwHcGQmk3n/tgpmMpn3AUcF+8hIwrNHBqdkKjiIiEjU1RscfoTvrvhlJpMZcdBjJpP5IHAnUAL+fserN4mFJ4EKpp1WcBARkairax6HbDb7ciaT+St8gLgvk8k8DtyP745wwDvxZ1G8Fz++4S81h8M2JLe+QqZOxxQRkagby9Uxf5zJZDbiw8Nh+JAQZsBG4K+y2exPx6+Kk1SNC12pxUFERKJuTNeqyGazN+JbF/4C+ClwL3AfcAPwRWB3hYY61eiqyBd0VoWIiMmnXMUAACAASURBVETbmK+Omc1me/BB4YaRymQymTYglc1mN293zSa71NZdFblCCeccZjqbVUREomlMLQ5jcDWwfoIee3KoERzKzlEsu2bVSEREZJsmKjiAJoEaXSg4dMWqXRR5jXMQEZEIm8jgIKMJXVq7MxQccgoOIiISYQoOzZKuXiGzI1YNCzqzQkREokzBoVlCLQ4dVMNCXterEBGRCFNwaJbQGIcOqwYHdVWIiEiUKTg0S/isCtRVISIiraGueRwymYw+zcZbKDi0Uxxcz2vaaRERibB6J4DanlMrNSHBaMLBwRUG19VVISIiUVbvRa7UpTHe2tqrq+UCxP26uipERCTKFAiapa1zcLW9lB9c11kVIiISZQoOzRJucSgNDK6rq0JERKJMwaFZ2jsGV1OhFgd1VYiISJQpODRLqMUhVay2OOhaFSIiEmUKDs3SFmpxKOQG13M6HVNERCJMwaFZQsEhUawGB3VViIhIlCk4NEu6DcxPj5EoFog5fzZFvqCzKkREJLoUHJrFbMg4h46yHyCpsypERCTKmh4czGxfM/uOmT1mZmvNbIuZLTWzb5pZZ43y+5nZnWa2wcx6zexhMztmhMeOmdkFZvaCmeXM7A0zu6zW4zZFqLuiw/ngoCmnRUQkypoeHIC/AC4A/gf4DvAV4EXge8AjZjb4tdzM9gYeAY4ALg3KdgH3mdmxNR77CuBy4A/AOcBtwLnAXWbW/NceCg7tZT/ttMY4iIhIlNV7rYqJdDtwiXNuU2jbNWb2MvBN4IvAPwTbLwGmA4c455YCmNmNwHPAVWa20Dnngu0H4MPCHc65kyoPbGavAj8GTgZuntBXti2hrorOoKtCwUFERKKs6d+6nXNPDgsNFbcEywMBgu6FjwMPVEJDsH8PcD2wL7AktP8p+Itz/WjY414H9AGnjssL2BHhFoegq2JAU06LiEiENT04jGJBsFwTLA8C0sCjNco+FizDwWEJUAYeDxd0zuWApcPKNkeNwZFqcRARkSiLZHAwszhwEVCk2p0wL1iuqrFLZdv80LZ5QLdzbmCE8rPNLDXC859pZk+OueJj1R5ucdAYBxERib5IBgd898LhwEXOuReDbZVP2VpBIDesTGW9VtmRyg9yzl3rnDu0/upup1BXxeAYB51VISIiERa54GBm3wXOBq51zl0SuqsvWKZr7NY2rExlvVbZkco33pCzKtRVISIi0Rep4GBmFwPfAn4KfHnY3auD5Xy2VtkW7sZYje+OqBUe5uO7MfI17muc0BiHSldFvlim7E8MERERiZzIBAcz+xvgb4AbgdMrp1WGLMN3PRxRY/fDg2V4XMIT+Nf33mHP0wYsHla2OUJjHKZQzTB5nVkhIiIRFYngYGYXARcD/wR8wTm31SdncNrlXcBRZnZwaN8u4HTgZYaeQXEL4IDzhz3UGfixDTeN40vYPqEWhy6Kg+vqrhARkahq+gRQZva/gW8DrwP3A5+x4OJPgTXOuX8L1i8EPgj8xsyuADbjg8B84IRwK4VzbpmZXQWcbWZ3APcA++NnjnyQZk/+BNBWnfm6M+iqAAUHERGJrqYHB6rzKbwT+FmN+x8E/g3AOfeKmb0f+AHwdSAFPA18xDl3f419zwdWAGcCJwDdwJX4szWa3x8QnschNNxCwUFERKKq6cHBOXcacNoYyj8PnFhn2RJwWXCLntAYh8oEUKDgICIi0RWJMQ47rfBZFeVQV4XmchARkYhScGim0DwObaXqXFUDheb3ooiIiNSi4NBMoeCQLqmrQkREok/BoZnSbRCcQZIqF4gF4zUVHEREJKoUHJrJrPYVMjXGQUREIkrBodlC3RWVUzLV4iAiIlGl4NBsQy50pUtri4hItCk4NFuNSaAGdK0KERGJKAWHZmvbehIotTiIiEhUKTg0W41JoBQcREQkqhQcmq29xuBInVUhIiIRpeDQbOqqEBGRFqLg0Gw1gkMur+AgIiLRpODQbOExDs6Pcdjcnx+ptIiISFMpODRbaIxDZ9DisKlPwUFERKJJwaHZhpxVoeAgIiLRpuDQbDWmnN7SX6BU1iRQIiISPQoOzRYKDlMoDq5v7is0ozYiIiKjUnBotlBXRRfVsKDuChERiSIFh2YbMgGUgoOIiESbgkOzDbk6ZjUsKDiIiEgUKTg0Wyg4pEsDg+ub+gZqlRYREWkqBYdmS7eBGQDJUoGY82dTbOpVi4OIiESPgkOzmQ0ZIFmZdnqjuipERCSCFByiIDzOIRggqTEOIiISRQoOUVDjQlcKDiIiEkUKDlEQ7qoIZo/UGAcREYkiBYcoUIuDiIi0CAWHKBhyoavqGIeyc82qkYiISE0KDlEQmj1yRsxfr6LsHD05Xa9CRESiRcEhCkJdFTOS1atiapyDiIhEjYJDFIS6KqbHqlfI1DgHERGJGgWHKAi1OEyLlQbXFRxERCRqFByiYMiltdXiICIi0aXgEAWhwZFdVMPCxl5d6EpERKJFwSEKwvM4uOqZFGpxEBGRqFFwiIJQcGgbcmltBQcREYkWBYcoCI1xSBerYUHBQUREokbBIQpCYxyShdzguuZxEBGRqFFwiIJQV0U8HBzU4iAiIhHT9OBgZhea2W1mttzMnJmt2Eb5/czsTjPbYGa9ZvawmR0zQtmYmV1gZi+YWc7M3jCzy8ysc0JezPYKBYdYrn9wfVNfHqfrVYiISIQ0PTgA3weOAf4H2DBaQTPbG3gEOAK4FPgK0AXcZ2bH1tjlCuBy4A/AOcBtwLnAXWYWhdfupdvADAArDNAe95sLpTJ9+eIoO4qIiDRWotkVAPZ2zi0HMLNn8UFgJJcA04FDnHNLg31uBJ4DrjKzhS74im5mB+DDwh3OuZMqD2BmrwI/Bk4Gbp6A1zN2Zn6AZH8fALu2GSt6fUvDpt48nelkM2snIiIyqOnfuiuhYVuC7oWPAw9UQkOwfw9wPbAvsCS0yymAAT8a9lDXAX3AqTtQ7fEX6q7YJV3tntA4BxERiZKmB4cxOAhIA4/WuO+xYBkODkuAMvB4uKBzLgcsHVa2+ULBYXb4CpkKDiIiEiGtFBzmBctVNe6rbJs/rHy3c67WvM2rgNlmlhrH+u2Y0FwOsxQcREQkolopOFS+ktcKArlhZSrrI13soVb5QWZ2ppk9OeYa7ohQcJiRqAaHjZrLQUREIqSVgkNfsEzXuK9tWJnKeq2yI5Uf5Jy71jl36JhruCNCXRXTY+ErZOpCVyIiEh2tFBxWB8v5Ne6rbAt3Y6zGd0fUCg/z8d0Y0fk6HwoOU02X1hYRkWhqpeCwDN/1cESN+w4PluHuhSfwr++94YJm1gYsHla2+YZcWlvBQUREoqllgkNw2uVdwFFmdnBlu5l1AacDLzP0DIpbAAecP+yhzsCPbbhpQis8VqExDp2hhhBdr0JERKKk6RNAmdnngN2DH+cAKTP7VvDza865fwoVvxD4IPAbM7sC2IwPAvOBE1xofmbn3DIzuwo428zuAO4B9sfPHPkgUZn8qSLUVdFe1hUyRUQkmpoeHIAvAh8Ytu27wfJBYDA4OOdeMbP3Az8Avg6kgKeBjzjn7q/x2OcDK4AzgROAbuBK4CLnXLlG+eYJtTi0laphYaOCg4iIREjTg4Nz7qgxln8eOLHOsiXgsuAWbaExDol8P4mYUSw7BgolcoUSbcl4EysnIiLitcwYh0mvvXqJDtuykWmd1bmpNvXqlEwREYkGBYeo2O2d1fWVK5jWUT2LVOMcREQkKhQcomLOXH95bYAtG5kfr7YyKDiIiEhUKDhERSwG8/cY/HGf4rrBdQUHERGJCgWHKHnHXoOruw+sHVxXcBARkahQcIiSBXsOrs7tWTO4rkmgREQkKhQcomRBtcVh1sbVg+tqcRARkahQcIiSBXsMrnZtfIuEKwGaBEpERKJDwSFK2jth9lwAYuUS7yysB2DdllwzayUiIjJIwSFqQgMk98p3A/A/b22mb6A40h4iIiINo+AQNaEBkouTWwAoO8ezr69vVo1EREQGKThETSg47O82DK4/89q6WqVFREQaSsEhakJnVuyy5a3B9aWvdjejNiIiIkMoOERNaOrpVN9mZpV6AT/OYUt/oZk1ExERUXCInGFTT79/Sj8ADlim7goREWkyBYcoCp1ZsSTdO7iucQ4iItJsCg5RFBoguXehGhaeWaHgICIizaXgEEWhAZIzNqwiHjMAXn17Cxt7B0baS0REZMIpOERRaOrp2Jo3OGC3KYM///41zecgIiLNo+AQRaGppymV+JPp1bMplq7QaZkiItI8Cg5RFRogeXBi0+C6xjmIiEgzKThEVWiA5IK+t0nG/X/VynW9uuiViIg0jYJDVIWCQ3z58+y/YPrgz2p1EBGRZlFwiKq99/eTQQEsf4Hj0tWwoHEOIiLSLAoOUTV9FvzxcYM/vu8P94JzADzy4hqdlikiIk2h4BBlf/oZSCQB6Fj1CsfFVgOwpb/A1ff9oZk1ExGRnZSCQ5TNnAMfOH7wxy/3PYkFrQ4PPLeaR19c06yaiYjITkrBIeqO/zSk0gB0vP06Z82pTgB15b8uoyenK2aKiEjjKDhE3bSZcMyJgz/+6cqHmNGRAGDdlgGuv//5ZtVMRER2QgoOreAjfw7tHQDE16zkO++oTgj1r//9Bktf1VkWIiLSGAoOraBrCnzozwZ//KN/v5Hz25cPnmVx0S1P8vPfvUK+WGpWDUVEZCeh4NAqPvQJmDEbACsV+egLd/Pt9ffSWR5goFDiht++yJd+8hD/9bIGTIqIyMQxF3xrla1lMhkHkM1mm10V7+3VcM334fVXBjetTU/nV+3vYm1iCmvjXbydmEL7rnM54J0zedc7ZvCuBTOYP7MTM2tixUVEpIWM+oGRaFQtZBzsMg8uvBxuux7+49cAzBnYyOkDjwwptuatKTzy8l7c17E3V6R3Y+bUDg7dew6H7jOH9+w5m862ZDNqLyIik4CCQ6tJpuAzGdh3EfzsCujv26rIrqUtfGLLM3xiyzNsjLXzSmoO61/tYNVvO3ku0YnNmUvqnXsye8892HvedPaYM4WOtH4VRERk2/Rp0aoO/RN/PYsnH4Z1a2D9Wli/FvfWSixXDRPTy/0cmnt96L7rgRdhwOK8kZjBU8lp9LZPozxtJolZu9AxZzZTZs9m5tzZzNltF9q6OkFdHSIyFuUSxOLNrkVthTz09UI+56f3T6aaXaOWouDQymbM9oMmQ6xYhJd+D08/Av/9n7Bpw4i7p12JfQrd7FPohj5gHbB863J5i9ObaKcv2cFAqoNSqp1U3EgO3mIkE3GSyQTJZALD+T/Kvh5/K+ThnfvAAYfAAe+BebtvHURKpWr5vh5o74RZu7TmH/TGdbDiJf+aZu8Ks+dC55T69y+XYUM3bNnkZw+dMm18g9sby+Ghe3zojMXhsKPhAyfArvPG7zlaXV+vH0u04iV/vEolSKUgmfbL9k7omlq9JdND929rhzm7+WVYrh/eXuUff+p0P09LR9fI/7/FIrz5uh/fNH0m7PoOf5ZVo5VLkB+AdPvIde3dAi/+Hl54Bp5f6uu963w4/Bh/m7ObL1fI+2P6xnL/2vddBNNmjL0+G9fDQM4fl/bOkcu9sRxe+D28+Ay89jL09kAxNHFePAHz94A994Xd/8i/P82ZC1NnVF9rIe+/oHWv8evJlJ+YL5nyz9HfF7x39fry02fBzNn+Pbpr2N+vc/75C/nqrVQGV/J/+6WSf10DORjo98fdDOJxX9dY3G/r64H+3uDWBx880R/vBtDgyFFEbnDkWJXLsGqFb43YtB42rWegey35la+TfPsN2vq3NKVa/e1TKaXaiZcLxEtF4sU88Xxuq3LODJs+y7/hxOPVMNLf5/+QpkyFzuCNu73DX000FvdlLQauDGUHuMFTV/0fsEHMIJ6ERMLf4gkoFaFQCC0L1Z+LRf8mkW6r3uKh3O3K8OYbsPwF/6E/XHun/6CIB88Vj/nnT6X84yZT/nneXg1vvzn0jS3dDrvsBjPm+G9IPZv9m3Suz7/xTpvpb5U30FSb/8BKpYNvfMHrH8jBEw/B/4xwnZN3vQcOeq8vVwlwPZthy0YfYjZv8Mdhl3kwd4G/zZzj/182b4SeTb784Jtezr/BFfJQzPtjWSz4Y9fRFdw6/e9pLniDHMj5MuWyf0MulfyxGfyQngYdHUP/74pFX7dKHQcGquWnTPPPEwudQFYu++fp74OBPsjl/HO54DgV8tD91lh/rWubMt3/3yWS/v+21u9GMuU/OLtCv8/xhP/bXbVi6O8C+Ne063xIpPxxLRb970484Y9tKu1viaR/3Rbzy0Lef2j2bfH/Z7GY//+bOcd/wLV3+t+vXPCB1bM5aMl829e7XPZ/Z7N29YF46gz//76h2982j/wlBfAtpOWy/yAf/prmvRP2O8j/rRcL1d+VQgEKA5APPmB7NldbWEvF6v7pdv8apkwL/n7z/vdp80b/wbo9UmmYuYs/Jhu6q+8h22N4cJgI530XFi0Zr0cb9ZuKgsMoWj44bEvPZnjzdcrda9jy5hr61rxFcd1a6NlMor+HdL6XzmI/aaf5IURkEoknfFBKJmuHuVZ05tfhvUeN16PprAoZQddU+KMDif3RgUwDptUoUio7Nm7aQt+69eQ2rGdg40ZyPb30DJToGSjQmyuxJVegp3+Avv48ubz/FtBrKXribfRYijiOg3MrOST3OotzK5lS3vqS4GWgN5ZmSyxNbyzNtFI/s0s9LTnRSD6WZNWUeeQTaWbkNjCjfyPJ8tiuKTLQPoVixxTaNq8nXti6NWZHuHgC9+73wQeOJ1bIwwN3w7LHJ+6bUCuKxWD+nrDHH/nm6/aO6rfYQr7aGlNp/SmE/3+d3969Zui3YvCtYXN289+MN2/03VoD2/j/nbULzH2HbzVcs8o/fzMkklu3FITFYv5YLTwYFi6G3feBP/w3PPbv8OyTvrWhYpd5vuzGdb6VbvhxqkfXNP//snHd6Mdk2gzY72DforHfIt9yl0pXWwF6t/gujBUv++6pt9/0LU59PdXHsJjveqh0P+Xz1ZaQWMy3arV3VlvQNq6rtsSEH6cikay2NFZaPCstprHY0JbNVBocQQtcMeg2S/vX3h602rV3wjv3Hvsx3E5qcRjFpG9xmAD5YolNfXn68yVy+SL9+RL9+SK9uQK9A0X6+geId7/JQLFMb8noKcXYUoLNpQT9xTK5QpFcvkTfQJFiPs8uxS3MLW4GoCeWpjeWojeWJuYcU8v9TC3nmFrK0e4KxFyZOI44ZWLOUTbDd1QYDgv+BXDEnCNBmYQrkXQlEq5M0eIUBm8xCsQpWpyixShajJQr0VYu0OYKtJWLxKi+ERqwLt7Ji+ldWZGcRdlCkcc5ZpT76CoPEHdlEq5MHL9MuyJJVyLtijjgzcQ03kxOozeWHtx3WrmfecVNzCz10mcptsTa2Bxvo99STCnnmFnqZWapj5mlXtor9RusowtesT8ObyRncH/XQjbGO4bUfdfSFj7Y8wKzSz30WIotsTQ9sTZ6Ymn601Modk6lNGUaqVSS2b3dzO5by5zebqYUehhIddCf7iLXNoV8WyeFRJpCIkUhnqIQS1FOJinHE5SD7oVUMU9bsZ/2Qo62Qj+lWJxCPEU+nqQQT0EqRTqdIpVKkk4naKNMe76X9oFe2gZ6SBXzxMslEpRIuBIWi1HsnEahcyqlzul+/3wfqVwvqf4tJPN9oVfqe6lcuh3aOvyHQFs7Lhb3R8oMZzHic3alvauTdDJObHvHl5RL/oOj0vW0yzzfzJ8Y9n0t1+fHIvVsht7NsGWzbx6fuwDesc/QMQ3lsu86WPum/7nSZROP+y6L/EC1y6dU9OVd2S+TSeiY4j9oOrp8ndZ3w4a1sO5t/5xpfzxItwfjjOb45voZs/2HXM8mH4i61/gurKnT/X0z5vjusvgIgyE3b/RjH9o7YM/9/JeWioEcLH8eXnrWdx8lKt1QwYfr8LEls3b1YSrd5vd3zn/4b+j2y2SwXyLlX8uM2ds3Rqh3iz8uqbTvmkls52ns4cAEvi7RH2y+83ZVmFkMOA/4ErAHsBa4FbjIObfNji8Fh+Yqlcv0DZToHShQLJUplx2l4FYsOwqlMvliiUKx7G/Bz/limYHQ9nypzEChRK7gw4xflig7R7HkKJXLlMqOgWKJfKEcLEuDzyM7t7ZknETcj42JGYOTqZWdwzlH2fl32VjMiMeMmPllPGZ+mxmJeIxUIk466ZfJeMx/foD/EHGOkmPwd7HsnB90XLklYltN4haPGYmYf+xEPIZzbnDfUtnv35aM055K0J6Kk0zEB+sVjxlmNvqnAz5wUvkTMEjGYyTiRjLuj0n4b7LsHMmEf862ZIK2ZHywLoWSf13xmA15TamEX6aDulVeo3POh13HkGMuDbNTd1VcAZwL/BK4DNg/+PndZnasc6482s7SXPFYjCntMaa0N3fCKh8wfAjJFUoMFHw4KQeh2/DvrZU3yELRB5hS2fk3VueXhSDAVEJMseTfaBPBmSnOQe+Ab53pyRXozxeHvCmXy45E8KabSsSIx2PkK4EoCEO5QjUY5fJFyqExoZV6lsv+w648ypcGw38wlcqOnT065Qol0NXrJ1zMfDArB2EszPDhofJ7XAkSFoSKmPn9gwxWDTwGqUSMdDJOOhEnlYhRdlAMgkypXCYWMxKx2NBAZWBBUKwEmMpfQiUYJuJD94mF6lhLJSTGzL+3JeL+eWOxUFgK/b2Gg2jltfll9QnCX/w/dPACdp/TmDNuJm1wMLMDgHOAO5xzJ4W2vwr8GDgZuLlJ1ZMWEjMjlYiTSsQn3ayb1W921TfrypsV+DfxvoEiW/oLbOnPkyuUgjdL/6ZpQL5UJl8oDbbYuFqPHQor4W+TzrkhHwLAYEDrz/tuq0LoTb5U9q1ExVKZYmjb4Bu7Y7AlqVjyrVDFUrVOOCjjy5eDN+py0GQQC30DHyiW6M/7kCeNUSswVFR+j4b8R9apb+shVZPSu94xQ8FhHJyCD3k/Grb9OuAHwKkoOMhObrC52oxavdMxM7raknS1JdltRkeNEpNbqewYCLqtKoGnXHZDvv1VvuX6ZvmyPxW/XKYcdD0MtjYVqwGrWKo+XkXl22siFgNjsJWrEASg4Q1EPkT5AFUslf2p/lbtIqkGMB/CKmXD3Quj81/ZK9/yK9/UC6FAVgmRibg/FvlQ69dAsTT47TwZfDsvO0c+9JoqrXMDhfJWLWAWqoVEy2QODkvwg/UfD290zuXMbGlwv4jIiOIx03TsDVJpGYoNa5If3ioWbp4vB01XJVdtRbJQ2HEwGEwGCr71qtJVUAlqlXEY4W7BSmtYOWgR890jAEH5UGCrlh+6T9hgSBwMmI5iuRyMsaoG0XD5Svdkpbsw3EI29OH9D7vPbtzEYJP5L2Ie0O2cq9VQtQp4n5mlnHNbncdjZmcCZ5511lkTXUcREQES8donX2+rVWzbJlf3YhS04mny9eoARurdyoXKbMU5d61z7tAJqZWIiEgLm8zBoQ9Ij3BfW6iMiIiI1GkyB4fVwGwzqxUe5uO7MZo0BZuIiEhrmszB4Qn863tveKOZtQGLgSebUSkREZFWNpmDwy34QbXnD9t+Bn5sw00Nr5GIiEiLm7RnVTjnlpnZVcDZZnYHcA/VmSMfRHM4iIiIjNmkDQ6B84EVwJnACUA3cCX+WhWablpERGSMJnVwcM6V8NeouKzZdREREZkMJvMYBxERERlnCg4iIiJSNwUHERERqdukHuMwXjKZTLOrICIi0igum83aSHeqxUFERETqZm74Rd5lQpnZk7qA1o7TcRwfOo7jQ8dxfOg4jo+JPo5qcRAREZG6KTiIiIhI3RQcGu/aZldgktBxHB86juNDx3F86DiOjwk9jhrjICIiInVTi4OIiIjUTcFBRERE6qbgMMHMLGZmF5jZC2aWM7M3zOwyM+tsdt2iyMz2NbPvmNljZrbWzLaY2VIz+2atY2Zm+5nZnWa2wcx6zexhMzumGXWPMjPrMLNXzcyZ2T/UuF/HcRRmNtPM/s7MXgn+jtea2W/N7E+GldNxHIGZdZnZN8xsWfB33W1mj5jZaWZmw8ru9MfRzC40s9vMbHnwd7tiG+XrPmY7+rmkmSMn3hXAucAv8Vfp3D/4+d1mdqwu772VvwD+N/Br4CagABwNfA/4lJkd7pzrBzCzvYFHgCJwKbAJOAO4z8w+6py7vwn1j6rvALNr3aHjODoz2x14AOgC/hF4CZgGHATMD5XTcRyBmcWAfwXeB/wMuBLoAE4Bfop/X/xaUFbH0fs+sB54Gpg+WsHtOGY79rnknNNtgm7AAUAZ+MWw7ecADvhMs+sYtRtwKDCtxvbvBcfs7NC2W4ESsDi0rQt4DXiRYPDvzn4D3hO8ofxlcAz/Ydj9Oo6jH7+HgTeA3bZRTsdx5GNzRPC7d8Ww7SlgObBRx3GrY7ZXaP1ZYMUoZes+ZuPxuaSuiol1CmDAj4Ztvw7oA05teI0izjn3pHNuU427bgmWBwIETWofBx5wzi0N7d8DXA/sCyyZ4OpGnpnF8b9v9wJ31Lhfx3EUZnYk8MfApc65N80saWYdNcrpOI5uarBcHd7onMsD3UAv6DiGOeeW11NuO47ZDn8uKThMrCX4ZPd4eKNzLgcsZSf5AxgnC4LlmmB5EJAGHq1R9rFgqeMLFwALgbNHuF/HcXTHB8vXzewuoB/oNbOXzCz8BqvjOLrHgY3AV83sz83snUGf/CXAIcDFQTkdx7Eb6zHb4c8lBYeJNQ/ods4N1LhvFTDbzFINrlPLCb41X4Rvbr852DwvWK6qsUtl2/wa9+00zGxP4NvAd5xzK0YopuM4uv2C5XXATODzwBeBPPBPZvaF4H4dx1E45zbgvxWvxzervwa8gB/PdJJz7rqgqI7j2I31mO3w55IGR06sDqDWfw5ALlQm35jqtKwfr5SP0wAABwJJREFUAYcD33DOvRhsqzQX1zq+uWFldlZXA68Cl49SRsdxdFOC5Rbg6KBpHTP7Jb5v/vtm9jN0HOvRg++r/zV+IN9MfHC42cxOdM79GzqO22Osx2yHP5cUHCZWH7DLCPe1hcrICMzsu/hm9mudc5eE7qoct3SN3Xb6Yxs0o38YONI5VxilqI7j6PqD5c8roQH8N2gz+zXw/+BbJXQcR2Fmi/Bh4QLn3DWh7T/Hh4nrgjMDdBzHbqzHbIc/l9RVMbFW45t9av2Hzsc3F6m1YQRmdjHwLfzpWl8edndlkFWtZsvKtlpNd5Ne8Pt2OXAP8JaZ7WNm+wC7B0WmBdumo+O4LSuD5Vs17nszWM5Ax3FbLsB/KN0W3uic6wPuxv9u7oGO4/YY6zHb4c8lBYeJ9QT+GL83vNHM2oDFwJPNqFQrMLO/Af4GuBE43QXnC4Uswze3HVFj98OD5c56fNuBOcAJwMuh2wPB/acGP5+OjuO2VAaQLahxX2Xb2+g4bkvlAyxe475EaKnjOHZjPWY7/rnU7HNVJ/MNWMTo58ue2uw6RvGGHwjp8KEhNkq52/DnLh8c2lY5d/kldpLzvWsclyTwyRq3s4Lj+q/Bz/vqOG7zWM4ANuNbHrpC23fD99m/FNqm4zjycbwi+N376rDtlVav9UBCx3HE47eteRzqPmbj8bn0f9u7t1ArqjCA4/9BUHrJCiQrhCDCjIiMTEjJboRR0oVCKjUiClsWFFGEFJ7opaAyLL+MICQ10xeThMpETLSwzIyKyl7sYmX1kNFFo5oe1hwbd3vrbPV40vP/wTDstdfMWbPYZ8+316yLq2P2saIoniI/o19GbjrunaFrPXBR6cyReyiKYgbwNPAl8CD5A163vcydqKia398hzy45m/wFfyv5H+PysixfP1TlPhwURXEyubPk3LIs76ilW497URTFbcCzwMfA8+RJi24nBw9XlGW5sspnPXZQzb65iRyILSJ//x1Hrp+TgRllWUaV13oEiqKYyr+PF+8kf+4er15/UZblglrerursgO9L/R1JHekbuWnuHvLsXbvIz5qeoPbrxW2P+ppPjno7bWta8o8ClpPHiP8GrAMu6e/r+D9u5C/o/8wcaT02qrtryGPifyWPsFgJjLMeu6rDU8jTTX9d3eB+BtYC11iPbetrTdPvwW7r7EDvS7Y4SJKkxuwcKUmSGjNwkCRJjRk4SJKkxgwcJElSYwYOkiSpMQMHSZLUmIGDJElqzNUxJR3xUko95LVPLoyINf1bGunwZuAgaZ9SSk1mivOmLA0ABg6SuvHQXt7beqgKIan/GDhIaiwievq7DJL6l4GDpIOu3qeAvMLfXcBp5AWiVgAzI+K7NsedSl4V9WJgGPAjsAp4OCI+b5N/EHkVwKnAGeQVBLeRFwh6tMMx1wL3Vfl3khesuicith3INUsDhaMqJPWlu4F5wAfAk+TV+G4G3kopDatnTCmNATYCU4B3gcfIK1LeCGxMKZ3Tkn8w8BrwDDACeBGYA7wHXA2Ma1OeBCwkP1aZC3wETAZWpZSGHPDVSgOALQ6SGqtaEtrZGRGPtEm/DBgbEe/XzjGb3ALxCHBLlVYALwBHA1MiYlEt/2TgJWBhSun0iPi7eqsHuAR4BbguInbVjhlSnavVRGBMRHxYy/sicD1wJbC048VLAmxxkNSdWR22+zvkX1APGio9wA7ghtqv/PPIjzLergcNABGxBFgHjATGw+5HFAn4HZheDxqqY3ZFxA9tyjOnHjRUnqv253a4Bkk1tjhIaiwiii4PebPNOXaklDYDE4BRwGbg7Ort1R3Os5ocNIwG1pKDjKHAhoj4povybGyT9lW1P7aL80gDli0OkvrS9g7pvR0jh7bsv+2Qvzf9mJZ9tx0af2qT9me1H9TluaQBycBBUl86vkP68Gq/o2U/vE1egBNa8vUGACftf9Ek7Q8DB0l9aUJrQkppKHAWeSjkJ1Vybz+ICzqcpzd9U7X/lBw8nJlSOvFgFFRSMwYOkvrS1JTS6Ja0HvKjicW1To3ryUM1x1fzLOxWvT4f2ELuJElE/AUEcBQwr3UoZUppcOtwT0kHh50jJTW2l+GYAC9HxOaWtFeB9SmlpeR+CuOrbSu1kRgRUaaUbgLeAJaklJaTWxVGAleRJ46aVhuKCXn667HAJGBLSmlFlW8EcClwLzB/vy5UUkcGDpK6MWsv720lj5Comw0sI8/bMBn4hXwznxkR39czRsSGahKoB8jzM0wizxy5mDxz5Gct+f9IKU0EpgPTgJuAAvim+pvrur88SftSlGWTRe8kqTmXsZaOXPZxkCRJjRk4SJKkxgwcJElSY/ZxkCRJjdniIEmSGjNwkCRJjRk4SJKkxgwcJElSYwYOkiSpMQMHSZLU2D+ObwhBSjtdwAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
@@ -1324,7 +1323,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Prediction : 10.66 K$\n",
+ "Prediction : 9.41 K$\n",
"Reality : 10.40 K$\n"
]
}
diff --git a/BHPD/02-DNN-Regression-Premium.ipynb b/BHPD/02-DNN-Regression-Premium.ipynb
index 91d757d..e1e3819 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 : 0.5.4\n",
- "Run time : Tuesday 8 September 2020, 19:03:13\n",
- "TensorFlow version : 2.0.0\n",
- "Keras version : 2.2.4-tf\n",
- "Current place : Fidle at HOME\n",
- "Dataset dir : /home/pjluc/datasets\n",
- "Update keras cache : Yes\n"
+ "Version : 0.56 DEV\n",
+ "Run time : Wednesday 9 September 2020, 10:45:46\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"
]
}
],
@@ -178,96 +178,96 @@
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- "</style><table id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08\" ><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_da570864_f278_11ea_a3cd_0cc47af5c7c7\" ><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_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row0_col0\" class=\"data row0 col0\" >0.01</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row0_col1\" class=\"data row0 col1\" >18.00</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row0_col2\" class=\"data row0 col2\" >2.31</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row0_col3\" class=\"data row0 col3\" >0.00</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row0_col4\" class=\"data row0 col4\" >0.54</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row0_col5\" class=\"data row0 col5\" >6.58</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row0_col6\" class=\"data row0 col6\" >65.20</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row0_col7\" class=\"data row0 col7\" >4.09</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row0_col8\" class=\"data row0 col8\" >1.00</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row0_col9\" class=\"data row0 col9\" >296.00</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row0_col10\" class=\"data row0 col10\" >15.30</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row0_col11\" class=\"data row0 col11\" >396.90</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row0_col12\" class=\"data row0 col12\" >4.98</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row0_col13\" class=\"data row0 col13\" >24.00</td>\n",
+ " <th id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row0_col0\" class=\"data row0 col0\" >0.01</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row0_col1\" class=\"data row0 col1\" >18.00</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row0_col2\" class=\"data row0 col2\" >2.31</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row0_col3\" class=\"data row0 col3\" >0.00</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row0_col4\" class=\"data row0 col4\" >0.54</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row0_col5\" class=\"data row0 col5\" >6.58</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row0_col6\" class=\"data row0 col6\" >65.20</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row0_col7\" class=\"data row0 col7\" >4.09</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row0_col8\" class=\"data row0 col8\" >1.00</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row0_col9\" class=\"data row0 col9\" >296.00</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row0_col10\" class=\"data row0 col10\" >15.30</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row0_col11\" class=\"data row0 col11\" >396.90</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row0_col12\" class=\"data row0 col12\" >4.98</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row0_col13\" class=\"data row0 col13\" >24.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row1_col0\" class=\"data row1 col0\" >0.03</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row1_col2\" class=\"data row1 col2\" >7.07</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row1_col4\" class=\"data row1 col4\" >0.47</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row1_col5\" class=\"data row1 col5\" >6.42</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row1_col6\" class=\"data row1 col6\" >78.90</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row1_col7\" class=\"data row1 col7\" >4.97</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row1_col8\" class=\"data row1 col8\" >2.00</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row1_col9\" class=\"data row1 col9\" >242.00</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row1_col10\" class=\"data row1 col10\" >17.80</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row1_col11\" class=\"data row1 col11\" >396.90</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row1_col12\" class=\"data row1 col12\" >9.14</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row1_col13\" class=\"data row1 col13\" >21.60</td>\n",
+ " <th id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row1_col0\" class=\"data row1 col0\" >0.03</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row1_col2\" class=\"data row1 col2\" >7.07</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row1_col4\" class=\"data row1 col4\" >0.47</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row1_col5\" class=\"data row1 col5\" >6.42</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row1_col6\" class=\"data row1 col6\" >78.90</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row1_col7\" class=\"data row1 col7\" >4.97</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row1_col8\" class=\"data row1 col8\" >2.00</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row1_col9\" class=\"data row1 col9\" >242.00</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row1_col10\" class=\"data row1 col10\" >17.80</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row1_col11\" class=\"data row1 col11\" >396.90</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row1_col12\" class=\"data row1 col12\" >9.14</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row1_col13\" class=\"data row1 col13\" >21.60</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row2_col0\" class=\"data row2 col0\" >0.03</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row2_col2\" class=\"data row2 col2\" >7.07</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row2_col3\" class=\"data row2 col3\" >0.00</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row2_col4\" class=\"data row2 col4\" >0.47</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row2_col5\" class=\"data row2 col5\" >7.18</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row2_col6\" class=\"data row2 col6\" >61.10</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row2_col7\" class=\"data row2 col7\" >4.97</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row2_col8\" class=\"data row2 col8\" >2.00</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row2_col9\" class=\"data row2 col9\" >242.00</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row2_col10\" class=\"data row2 col10\" >17.80</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row2_col11\" class=\"data row2 col11\" >392.83</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row2_col12\" class=\"data row2 col12\" >4.03</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row2_col13\" class=\"data row2 col13\" >34.70</td>\n",
+ " <th id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row2_col0\" class=\"data row2 col0\" >0.03</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row2_col2\" class=\"data row2 col2\" >7.07</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row2_col3\" class=\"data row2 col3\" >0.00</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row2_col4\" class=\"data row2 col4\" >0.47</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row2_col5\" class=\"data row2 col5\" >7.18</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row2_col6\" class=\"data row2 col6\" >61.10</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row2_col7\" class=\"data row2 col7\" >4.97</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row2_col8\" class=\"data row2 col8\" >2.00</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row2_col9\" class=\"data row2 col9\" >242.00</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row2_col10\" class=\"data row2 col10\" >17.80</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row2_col11\" class=\"data row2 col11\" >392.83</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row2_col12\" class=\"data row2 col12\" >4.03</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row2_col13\" class=\"data row2 col13\" >34.70</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row3_col0\" class=\"data row3 col0\" >0.03</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row3_col2\" class=\"data row3 col2\" >2.18</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row3_col4\" class=\"data row3 col4\" >0.46</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row3_col5\" class=\"data row3 col5\" >7.00</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row3_col6\" class=\"data row3 col6\" >45.80</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row3_col7\" class=\"data row3 col7\" >6.06</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row3_col8\" class=\"data row3 col8\" >3.00</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row3_col9\" class=\"data row3 col9\" >222.00</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row3_col10\" class=\"data row3 col10\" >18.70</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row3_col11\" class=\"data row3 col11\" >394.63</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row3_col12\" class=\"data row3 col12\" >2.94</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row3_col13\" class=\"data row3 col13\" >33.40</td>\n",
+ " <th id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row3_col0\" class=\"data row3 col0\" >0.03</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row3_col2\" class=\"data row3 col2\" >2.18</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row3_col4\" class=\"data row3 col4\" >0.46</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row3_col5\" class=\"data row3 col5\" >7.00</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row3_col6\" class=\"data row3 col6\" >45.80</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row3_col7\" class=\"data row3 col7\" >6.06</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row3_col8\" class=\"data row3 col8\" >3.00</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row3_col9\" class=\"data row3 col9\" >222.00</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row3_col10\" class=\"data row3 col10\" >18.70</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row3_col11\" class=\"data row3 col11\" >394.63</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row3_col12\" class=\"data row3 col12\" >2.94</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row3_col13\" class=\"data row3 col13\" >33.40</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row4_col0\" class=\"data row4 col0\" >0.07</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row4_col2\" class=\"data row4 col2\" >2.18</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row4_col4\" class=\"data row4 col4\" >0.46</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row4_col5\" class=\"data row4 col5\" >7.15</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row4_col6\" class=\"data row4 col6\" >54.20</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row4_col7\" class=\"data row4 col7\" >6.06</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row4_col8\" class=\"data row4 col8\" >3.00</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row4_col9\" class=\"data row4 col9\" >222.00</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row4_col10\" class=\"data row4 col10\" >18.70</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row4_col11\" class=\"data row4 col11\" >396.90</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row4_col12\" class=\"data row4 col12\" >5.33</td>\n",
- " <td id=\"T_2e2c9e06_f1f5_11ea_9e87_677b8e0bcd08row4_col13\" class=\"data row4 col13\" >36.20</td>\n",
+ " <th id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row4_col0\" class=\"data row4 col0\" >0.07</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row4_col2\" class=\"data row4 col2\" >2.18</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row4_col4\" class=\"data row4 col4\" >0.46</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row4_col5\" class=\"data row4 col5\" >7.15</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row4_col6\" class=\"data row4 col6\" >54.20</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row4_col7\" class=\"data row4 col7\" >6.06</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row4_col8\" class=\"data row4 col8\" >3.00</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row4_col9\" class=\"data row4 col9\" >222.00</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row4_col10\" class=\"data row4 col10\" >18.70</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row4_col11\" class=\"data row4 col11\" >396.90</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row4_col12\" class=\"data row4 col12\" >5.33</td>\n",
+ " <td id=\"T_da570864_f278_11ea_a3cd_0cc47af5c7c7row4_col13\" class=\"data row4 col13\" >36.20</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x7ffe29c64d10>"
+ "<pandas.io.formats.style.Styler at 0x153c0f2ab5d0>"
]
},
"metadata": {},
@@ -352,139 +352,139 @@
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- "</style><table id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08\" ><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_da5fe308_f278_11ea_a3cd_0cc47af5c7c7\" ><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_2e34d652_f1f5_11ea_9e87_677b8e0bcd08level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
+ " <th id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row1_col0\" class=\"data row1 col0\" >3.32</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row1_col1\" class=\"data row1 col1\" >12.33</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row1_col2\" class=\"data row1 col2\" >11.39</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row1_col3\" class=\"data row1 col3\" >0.07</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row1_col4\" class=\"data row1 col4\" >0.55</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row1_col5\" class=\"data row1 col5\" >6.27</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row1_col6\" class=\"data row1 col6\" >67.04</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row1_col7\" class=\"data row1 col7\" >3.83</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row1_col8\" class=\"data row1 col8\" >9.43</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row1_col9\" class=\"data row1 col9\" >408.21</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row1_col10\" class=\"data row1 col10\" >18.45</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row1_col11\" class=\"data row1 col11\" >356.12</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row1_col12\" class=\"data row1 col12\" >12.41</td>\n",
+ " <th id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row1_col0\" class=\"data row1 col0\" >3.24</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row1_col1\" class=\"data row1 col1\" >10.63</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row1_col2\" class=\"data row1 col2\" >11.34</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row1_col3\" class=\"data row1 col3\" >0.06</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row1_col4\" class=\"data row1 col4\" >0.56</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row1_col5\" class=\"data row1 col5\" >6.28</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row1_col6\" class=\"data row1 col6\" >69.69</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row1_col7\" class=\"data row1 col7\" >3.74</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row1_col8\" class=\"data row1 col8\" >9.27</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row1_col9\" class=\"data row1 col9\" >404.71</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row1_col10\" class=\"data row1 col10\" >18.54</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row1_col11\" class=\"data row1 col11\" >359.32</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row1_col12\" class=\"data row1 col12\" >12.54</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row2_col0\" class=\"data row2 col0\" >7.78</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row2_col1\" class=\"data row2 col1\" >24.49</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row2_col2\" class=\"data row2 col2\" >7.06</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row2_col3\" class=\"data row2 col3\" >0.26</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row2_col4\" class=\"data row2 col4\" >0.12</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row2_col5\" class=\"data row2 col5\" >0.67</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row2_col6\" class=\"data row2 col6\" >29.01</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row2_col7\" class=\"data row2 col7\" >2.10</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row2_col8\" class=\"data row2 col8\" >8.75</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row2_col9\" class=\"data row2 col9\" >170.18</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row2_col10\" class=\"data row2 col10\" >2.17</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row2_col11\" class=\"data row2 col11\" >94.01</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row2_col12\" class=\"data row2 col12\" >6.77</td>\n",
+ " <th id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row2_col0\" class=\"data row2 col0\" >7.34</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row2_col1\" class=\"data row2 col1\" >21.95</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row2_col2\" class=\"data row2 col2\" >7.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row2_col3\" class=\"data row2 col3\" >0.25</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row2_col4\" class=\"data row2 col4\" >0.11</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row2_col5\" class=\"data row2 col5\" >0.72</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row2_col6\" class=\"data row2 col6\" >27.79</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row2_col7\" class=\"data row2 col7\" >2.07</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row2_col8\" class=\"data row2 col8\" >8.60</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row2_col9\" class=\"data row2 col9\" >168.73</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row2_col10\" class=\"data row2 col10\" >2.16</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row2_col11\" class=\"data row2 col11\" >85.20</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row2_col12\" class=\"data row2 col12\" >7.02</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row3_col0\" class=\"data row3 col0\" >0.01</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row3_col2\" class=\"data row3 col2\" >0.46</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row3_col4\" class=\"data row3 col4\" >0.39</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row3_col5\" class=\"data row3 col5\" >3.56</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row3_col6\" class=\"data row3 col6\" >6.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row3_col7\" class=\"data row3 col7\" >1.17</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row3_col8\" class=\"data row3 col8\" >1.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row3_col9\" class=\"data row3 col9\" >188.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row3_col10\" class=\"data row3 col10\" >12.60</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row3_col11\" class=\"data row3 col11\" >2.52</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row3_col12\" class=\"data row3 col12\" >1.92</td>\n",
+ " <th id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row3_col0\" class=\"data row3 col0\" >0.01</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row3_col2\" class=\"data row3 col2\" >0.46</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row3_col4\" class=\"data row3 col4\" >0.39</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row3_col5\" class=\"data row3 col5\" >3.56</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row3_col6\" class=\"data row3 col6\" >2.90</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row3_col7\" class=\"data row3 col7\" >1.13</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row3_col8\" class=\"data row3 col8\" >1.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row3_col9\" class=\"data row3 col9\" >187.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row3_col10\" class=\"data row3 col10\" >12.60</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row3_col11\" class=\"data row3 col11\" >0.32</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row3_col12\" class=\"data row3 col12\" >1.73</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row4_col0\" class=\"data row4 col0\" >0.08</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row4_col2\" class=\"data row4 col2\" >5.19</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row4_col4\" class=\"data row4 col4\" >0.45</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row4_col5\" class=\"data row4 col5\" >5.88</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row4_col6\" class=\"data row4 col6\" >41.20</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row4_col7\" class=\"data row4 col7\" >2.09</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row4_col8\" class=\"data row4 col8\" >4.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row4_col9\" class=\"data row4 col9\" >277.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row4_col10\" class=\"data row4 col10\" >17.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row4_col11\" class=\"data row4 col11\" >376.60</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row4_col12\" class=\"data row4 col12\" >7.12</td>\n",
+ " <th id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row4_col0\" class=\"data row4 col0\" >0.08</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row4_col2\" class=\"data row4 col2\" >5.19</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row4_col4\" class=\"data row4 col4\" >0.46</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row4_col5\" class=\"data row4 col5\" >5.91</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row4_col6\" class=\"data row4 col6\" >46.83</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row4_col7\" class=\"data row4 col7\" >2.11</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row4_col8\" class=\"data row4 col8\" >4.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row4_col9\" class=\"data row4 col9\" >277.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row4_col10\" class=\"data row4 col10\" >17.40</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row4_col11\" class=\"data row4 col11\" >374.59</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row4_col12\" class=\"data row4 col12\" >6.92</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row5_col0\" class=\"data row5 col0\" >0.25</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row5_col2\" class=\"data row5 col2\" >9.90</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row5_col3\" class=\"data row5 col3\" >0.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row5_col4\" class=\"data row5 col4\" >0.54</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row5_col5\" class=\"data row5 col5\" >6.21</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row5_col6\" class=\"data row5 col6\" >76.50</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row5_col7\" class=\"data row5 col7\" >3.27</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row5_col8\" class=\"data row5 col8\" >5.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row5_col9\" class=\"data row5 col9\" >330.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row5_col10\" class=\"data row5 col10\" >19.10</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row5_col11\" class=\"data row5 col11\" >392.28</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row5_col12\" class=\"data row5 col12\" >11.38</td>\n",
+ " <th id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row5_col0\" class=\"data row5 col0\" >0.24</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row5_col2\" class=\"data row5 col2\" >9.69</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row5_col3\" class=\"data row5 col3\" >0.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row5_col4\" class=\"data row5 col4\" >0.54</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row5_col5\" class=\"data row5 col5\" >6.19</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row5_col6\" class=\"data row5 col6\" >79.45</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row5_col7\" class=\"data row5 col7\" >3.10</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row5_col8\" class=\"data row5 col8\" >5.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row5_col9\" class=\"data row5 col9\" >330.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row5_col10\" class=\"data row5 col10\" >19.10</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row5_col11\" class=\"data row5 col11\" >391.34</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row5_col12\" class=\"data row5 col12\" >11.17</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row6_col0\" class=\"data row6 col0\" >3.52</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row6_col1\" class=\"data row6 col1\" >20.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row6_col2\" class=\"data row6 col2\" >18.10</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row6_col3\" class=\"data row6 col3\" >0.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row6_col4\" class=\"data row6 col4\" >0.62</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row6_col5\" class=\"data row6 col5\" >6.60</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row6_col6\" class=\"data row6 col6\" >94.07</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row6_col7\" class=\"data row6 col7\" >5.23</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row6_col8\" class=\"data row6 col8\" >24.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row6_col9\" class=\"data row6 col9\" >666.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row6_col10\" class=\"data row6 col10\" >20.20</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row6_col11\" class=\"data row6 col11\" >396.78</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row6_col12\" class=\"data row6 col12\" >16.82</td>\n",
+ " <th id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row6_col0\" class=\"data row6 col0\" >2.90</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row6_col1\" class=\"data row6 col1\" >12.50</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row6_col2\" class=\"data row6 col2\" >18.10</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row6_col3\" class=\"data row6 col3\" >0.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row6_col4\" class=\"data row6 col4\" >0.62</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row6_col5\" class=\"data row6 col5\" >6.63</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row6_col6\" class=\"data row6 col6\" >94.25</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row6_col7\" class=\"data row6 col7\" >5.08</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row6_col8\" class=\"data row6 col8\" >8.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row6_col9\" class=\"data row6 col9\" >666.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row6_col10\" class=\"data row6 col10\" >20.20</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row6_col11\" class=\"data row6 col11\" >395.76</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row6_col12\" class=\"data row6 col12\" >17.06</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row7_col0\" class=\"data row7 col0\" >73.53</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row7_col1\" class=\"data row7 col1\" >100.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row7_col2\" class=\"data row7 col2\" >27.74</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row7_col3\" class=\"data row7 col3\" >1.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row7_col4\" class=\"data row7 col4\" >0.87</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row7_col5\" class=\"data row7 col5\" >8.78</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row7_col6\" class=\"data row7 col6\" >100.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row7_col7\" class=\"data row7 col7\" >10.71</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row7_col8\" class=\"data row7 col8\" >24.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row7_col9\" class=\"data row7 col9\" >711.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row7_col10\" class=\"data row7 col10\" >22.00</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row7_col11\" class=\"data row7 col11\" >396.90</td>\n",
- " <td id=\"T_2e34d652_f1f5_11ea_9e87_677b8e0bcd08row7_col12\" class=\"data row7 col12\" >34.77</td>\n",
+ " <th id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row7_col0\" class=\"data row7 col0\" >73.53</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row7_col1\" class=\"data row7 col1\" >100.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row7_col2\" class=\"data row7 col2\" >27.74</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row7_col3\" class=\"data row7 col3\" >1.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row7_col4\" class=\"data row7 col4\" >0.87</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row7_col5\" class=\"data row7 col5\" >8.72</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row7_col6\" class=\"data row7 col6\" >100.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row7_col7\" class=\"data row7 col7\" >12.13</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row7_col8\" class=\"data row7 col8\" >24.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row7_col9\" class=\"data row7 col9\" >711.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row7_col10\" class=\"data row7 col10\" >22.00</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row7_col11\" class=\"data row7 col11\" >396.90</td>\n",
+ " <td id=\"T_da5fe308_f278_11ea_a3cd_0cc47af5c7c7row7_col12\" class=\"data row7 col12\" >37.97</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x7ffea457afd0>"
+ "<pandas.io.formats.style.Styler at 0x153b8aded2d0>"
]
},
"metadata": {},
@@ -494,139 +494,139 @@
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- "</style><table id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08\" ><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_da6798c8_f278_11ea_a3cd_0cc47af5c7c7\" ><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_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
+ " <th id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row1_col0\" class=\"data row1 col0\" >0.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row1_col1\" class=\"data row1 col1\" >-0.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row1_col2\" class=\"data row1 col2\" >-0.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row1_col4\" class=\"data row1 col4\" >-0.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row1_col5\" class=\"data row1 col5\" >0.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row1_col6\" class=\"data row1 col6\" >0.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row1_col7\" class=\"data row1 col7\" >0.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row1_col8\" class=\"data row1 col8\" >-0.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row1_col9\" class=\"data row1 col9\" >0.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row1_col10\" class=\"data row1 col10\" >0.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row1_col11\" class=\"data row1 col11\" >-0.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row1_col12\" class=\"data row1 col12\" >0.00</td>\n",
+ " <th id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row1_col0\" class=\"data row1 col0\" >0.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row1_col4\" class=\"data row1 col4\" >-0.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row1_col5\" class=\"data row1 col5\" >-0.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row1_col6\" class=\"data row1 col6\" >0.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row1_col7\" class=\"data row1 col7\" >-0.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row1_col8\" class=\"data row1 col8\" >0.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row1_col9\" class=\"data row1 col9\" >-0.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row1_col10\" class=\"data row1 col10\" >0.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row1_col11\" class=\"data row1 col11\" >0.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row1_col12\" class=\"data row1 col12\" >0.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row2_col0\" class=\"data row2 col0\" >1.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row2_col1\" class=\"data row2 col1\" >1.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row2_col3\" class=\"data row2 col3\" >1.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row2_col4\" class=\"data row2 col4\" >1.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row2_col5\" class=\"data row2 col5\" >1.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row2_col6\" class=\"data row2 col6\" >1.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row2_col7\" class=\"data row2 col7\" >1.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row2_col8\" class=\"data row2 col8\" >1.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row2_col9\" class=\"data row2 col9\" >1.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row2_col10\" class=\"data row2 col10\" >1.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row2_col11\" class=\"data row2 col11\" >1.00</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row2_col12\" class=\"data row2 col12\" >1.00</td>\n",
+ " <th id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row2_col0\" class=\"data row2 col0\" >1.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row2_col1\" class=\"data row2 col1\" >1.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row2_col3\" class=\"data row2 col3\" >1.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row2_col4\" class=\"data row2 col4\" >1.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row2_col5\" class=\"data row2 col5\" >1.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row2_col6\" class=\"data row2 col6\" >1.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row2_col7\" class=\"data row2 col7\" >1.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row2_col8\" class=\"data row2 col8\" >1.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row2_col9\" class=\"data row2 col9\" >1.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row2_col10\" class=\"data row2 col10\" >1.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row2_col11\" class=\"data row2 col11\" >1.00</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row2_col12\" class=\"data row2 col12\" >1.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row3_col0\" class=\"data row3 col0\" >-0.43</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row3_col1\" class=\"data row3 col1\" >-0.50</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row3_col2\" class=\"data row3 col2\" >-1.55</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row3_col3\" class=\"data row3 col3\" >-0.28</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row3_col4\" class=\"data row3 col4\" >-1.44</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row3_col5\" class=\"data row3 col5\" >-4.04</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row3_col6\" class=\"data row3 col6\" >-2.10</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row3_col7\" class=\"data row3 col7\" >-1.27</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row3_col8\" class=\"data row3 col8\" >-0.96</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row3_col9\" class=\"data row3 col9\" >-1.29</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row3_col10\" class=\"data row3 col10\" >-2.69</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row3_col11\" class=\"data row3 col11\" >-3.76</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row3_col12\" class=\"data row3 col12\" >-1.55</td>\n",
+ " <th id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row3_col0\" class=\"data row3 col0\" >-0.44</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row3_col1\" class=\"data row3 col1\" >-0.48</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row3_col2\" class=\"data row3 col2\" >-1.56</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row3_col3\" class=\"data row3 col3\" >-0.26</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row3_col4\" class=\"data row3 col4\" >-1.46</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row3_col5\" class=\"data row3 col5\" >-3.81</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row3_col6\" class=\"data row3 col6\" >-2.40</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row3_col7\" class=\"data row3 col7\" >-1.26</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row3_col8\" class=\"data row3 col8\" >-0.96</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row3_col9\" class=\"data row3 col9\" >-1.29</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row3_col10\" class=\"data row3 col10\" >-2.75</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row3_col11\" class=\"data row3 col11\" >-4.21</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row3_col12\" class=\"data row3 col12\" >-1.54</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row4_col0\" class=\"data row4 col0\" >-0.42</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row4_col1\" class=\"data row4 col1\" >-0.50</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row4_col2\" class=\"data row4 col2\" >-0.88</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row4_col3\" class=\"data row4 col3\" >-0.28</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row4_col4\" class=\"data row4 col4\" >-0.90</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row4_col5\" class=\"data row4 col5\" >-0.59</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row4_col6\" class=\"data row4 col6\" >-0.89</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row4_col7\" class=\"data row4 col7\" >-0.83</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row4_col8\" class=\"data row4 col8\" >-0.62</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row4_col9\" class=\"data row4 col9\" >-0.77</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row4_col10\" class=\"data row4 col10\" >-0.67</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row4_col11\" class=\"data row4 col11\" >0.22</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row4_col12\" class=\"data row4 col12\" >-0.78</td>\n",
+ " <th id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row4_col0\" class=\"data row4 col0\" >-0.43</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row4_col1\" class=\"data row4 col1\" >-0.48</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row4_col2\" class=\"data row4 col2\" >-0.88</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row4_col3\" class=\"data row4 col3\" >-0.26</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row4_col4\" class=\"data row4 col4\" >-0.87</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row4_col5\" class=\"data row4 col5\" >-0.53</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row4_col6\" class=\"data row4 col6\" >-0.82</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row4_col7\" class=\"data row4 col7\" >-0.79</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row4_col8\" class=\"data row4 col8\" >-0.61</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row4_col9\" class=\"data row4 col9\" >-0.76</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row4_col10\" class=\"data row4 col10\" >-0.53</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row4_col11\" class=\"data row4 col11\" >0.18</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row4_col12\" class=\"data row4 col12\" >-0.80</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row5_col0\" class=\"data row5 col0\" >-0.39</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row5_col1\" class=\"data row5 col1\" >-0.50</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row5_col2\" class=\"data row5 col2\" >-0.21</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row5_col3\" class=\"data row5 col3\" >-0.28</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row5_col4\" class=\"data row5 col4\" >-0.13</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row5_col5\" class=\"data row5 col5\" >-0.09</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row5_col6\" class=\"data row5 col6\" >0.33</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row5_col7\" class=\"data row5 col7\" >-0.26</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row5_col8\" class=\"data row5 col8\" >-0.51</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row5_col9\" class=\"data row5 col9\" >-0.46</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row5_col10\" class=\"data row5 col10\" >0.30</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row5_col11\" class=\"data row5 col11\" >0.38</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row5_col12\" class=\"data row5 col12\" >-0.15</td>\n",
+ " <th id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row5_col0\" class=\"data row5 col0\" >-0.41</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row5_col1\" class=\"data row5 col1\" >-0.48</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row5_col2\" class=\"data row5 col2\" >-0.24</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row5_col3\" class=\"data row5 col3\" >-0.26</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row5_col4\" class=\"data row5 col4\" >-0.16</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row5_col5\" class=\"data row5 col5\" >-0.12</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row5_col6\" class=\"data row5 col6\" >0.35</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row5_col7\" class=\"data row5 col7\" >-0.31</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row5_col8\" class=\"data row5 col8\" >-0.50</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row5_col9\" class=\"data row5 col9\" >-0.44</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row5_col10\" class=\"data row5 col10\" >0.26</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row5_col11\" class=\"data row5 col11\" >0.38</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row5_col12\" class=\"data row5 col12\" >-0.19</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row6_col0\" class=\"data row6 col0\" >0.03</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row6_col1\" class=\"data row6 col1\" >0.31</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row6_col2\" class=\"data row6 col2\" >0.95</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row6_col3\" class=\"data row6 col3\" >-0.28</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row6_col4\" class=\"data row6 col4\" >0.60</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row6_col5\" class=\"data row6 col5\" >0.49</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row6_col6\" class=\"data row6 col6\" >0.93</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row6_col7\" class=\"data row6 col7\" >0.67</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row6_col8\" class=\"data row6 col8\" >1.66</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row6_col9\" class=\"data row6 col9\" >1.51</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row6_col10\" class=\"data row6 col10\" >0.81</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row6_col11\" class=\"data row6 col11\" >0.43</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row6_col12\" class=\"data row6 col12\" >0.65</td>\n",
+ " <th id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row6_col0\" class=\"data row6 col0\" >-0.05</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row6_col1\" class=\"data row6 col1\" >0.09</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row6_col2\" class=\"data row6 col2\" >0.97</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row6_col3\" class=\"data row6 col3\" >-0.26</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row6_col4\" class=\"data row6 col4\" >0.61</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row6_col5\" class=\"data row6 col5\" >0.49</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row6_col6\" class=\"data row6 col6\" >0.88</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row6_col7\" class=\"data row6 col7\" >0.65</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row6_col8\" class=\"data row6 col8\" >-0.15</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row6_col9\" class=\"data row6 col9\" >1.55</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row6_col10\" class=\"data row6 col10\" >0.77</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row6_col11\" class=\"data row6 col11\" >0.43</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row6_col12\" class=\"data row6 col12\" >0.64</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row7_col0\" class=\"data row7 col0\" >9.02</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row7_col1\" class=\"data row7 col1\" >3.58</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row7_col2\" class=\"data row7 col2\" >2.32</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row7_col3\" class=\"data row7 col3\" >3.55</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row7_col4\" class=\"data row7 col4\" >2.71</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row7_col5\" class=\"data row7 col5\" >3.74</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row7_col6\" class=\"data row7 col6\" >1.14</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row7_col7\" class=\"data row7 col7\" >3.29</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row7_col8\" class=\"data row7 col8\" >1.66</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row7_col9\" class=\"data row7 col9\" >1.78</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row7_col10\" class=\"data row7 col10\" >1.64</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row7_col11\" class=\"data row7 col11\" >0.43</td>\n",
- " <td id=\"T_2e3c00da_f1f5_11ea_9e87_677b8e0bcd08row7_col12\" class=\"data row7 col12\" >3.30</td>\n",
+ " <th id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row7_col0\" class=\"data row7 col0\" >9.58</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row7_col1\" class=\"data row7 col1\" >4.07</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row7_col2\" class=\"data row7 col2\" >2.34</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row7_col3\" class=\"data row7 col3\" >3.79</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row7_col4\" class=\"data row7 col4\" >2.82</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row7_col5\" class=\"data row7 col5\" >3.42</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row7_col6\" class=\"data row7 col6\" >1.09</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row7_col7\" class=\"data row7 col7\" >4.05</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row7_col8\" class=\"data row7 col8\" >1.71</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row7_col9\" class=\"data row7 col9\" >1.82</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row7_col10\" class=\"data row7 col10\" >1.60</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row7_col11\" class=\"data row7 col11\" >0.44</td>\n",
+ " <td id=\"T_da6798c8_f278_11ea_a3cd_0cc47af5c7c7row7_col12\" class=\"data row7 col12\" >3.62</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x7ffea452d1d0>"
+ "<pandas.io.formats.style.Styler at 0x153b8adbe050>"
]
},
"metadata": {},
@@ -709,14 +709,14 @@
"Total params: 5,121\n",
"Trainable params: 5,121\n",
"Non-trainable params: 0\n",
- "_________________________________________________________________\n"
+ "_________________________________________________________________\n",
+ "Failed to import pydot. You must install pydot and graphviz for `pydotprint` to work.\n"
]
},
{
"data": {
- "image/png": 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\n",
"text/plain": [
- "<IPython.core.display.Image object>"
+ "None"
]
},
"metadata": {},
@@ -766,207 +766,206 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Train on 354 samples, validate on 152 samples\n",
"Epoch 1/100\n",
- "354/354 [==============================] - 1s 2ms/sample - loss: 429.1682 - mae: 18.8468 - mse: 429.1683 - val_loss: 352.0686 - val_mae: 16.2867 - val_mse: 352.0686\n",
+ "36/36 [==============================] - 0s 10ms/step - loss: 493.8600 - mae: 20.1628 - mse: 493.8600 - val_loss: 344.0921 - val_mae: 16.4232 - val_mse: 344.0921\n",
"Epoch 2/100\n",
- "354/354 [==============================] - 0s 220us/sample - loss: 187.3996 - mae: 11.6919 - mse: 187.3996 - val_loss: 124.8861 - val_mae: 8.8280 - val_mse: 124.8861\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 271.4029 - mae: 13.9558 - mse: 271.4029 - val_loss: 135.1553 - val_mae: 9.4587 - val_mse: 135.1553\n",
"Epoch 3/100\n",
- "354/354 [==============================] - 0s 226us/sample - loss: 60.9587 - mae: 6.1852 - mse: 60.9587 - val_loss: 64.7766 - val_mae: 5.7120 - val_mse: 64.7766\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 98.2466 - mae: 7.5100 - mse: 98.2466 - val_loss: 66.5462 - val_mae: 6.2643 - val_mse: 66.5462\n",
"Epoch 4/100\n",
- "354/354 [==============================] - 0s 215us/sample - loss: 33.4901 - mae: 4.3295 - mse: 33.4901 - val_loss: 42.7490 - val_mae: 4.3789 - val_mse: 42.7490\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 49.8627 - mae: 5.1989 - mse: 49.8627 - val_loss: 44.7207 - val_mae: 5.0417 - val_mse: 44.7207\n",
"Epoch 5/100\n",
- "354/354 [==============================] - 0s 218us/sample - loss: 23.7739 - mae: 3.5450 - mse: 23.7739 - val_loss: 35.5486 - val_mae: 3.8631 - val_mse: 35.5486\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 32.4176 - mae: 4.1260 - mse: 32.4176 - val_loss: 32.3549 - val_mae: 4.2057 - val_mse: 32.3549\n",
"Epoch 6/100\n",
- "354/354 [==============================] - 0s 224us/sample - loss: 18.6308 - mae: 3.0728 - mse: 18.6308 - val_loss: 31.7546 - val_mae: 3.6386 - val_mse: 31.7546\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 24.6088 - mae: 3.4733 - mse: 24.6088 - val_loss: 27.5344 - val_mae: 3.8070 - val_mse: 27.5344\n",
"Epoch 7/100\n",
- "354/354 [==============================] - 0s 219us/sample - loss: 16.3552 - mae: 2.8485 - mse: 16.3552 - val_loss: 29.6702 - val_mae: 3.5472 - val_mse: 29.6702\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 20.7117 - mae: 3.2537 - mse: 20.7117 - val_loss: 24.9330 - val_mae: 3.5383 - val_mse: 24.9330\n",
"Epoch 8/100\n",
- "354/354 [==============================] - 0s 225us/sample - loss: 15.0688 - mae: 2.7065 - mse: 15.0688 - val_loss: 27.7940 - val_mae: 3.4281 - val_mse: 27.7940\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 18.2772 - mae: 3.0100 - mse: 18.2772 - val_loss: 23.2481 - val_mae: 3.3901 - val_mse: 23.2481\n",
"Epoch 9/100\n",
- "354/354 [==============================] - 0s 219us/sample - loss: 13.7102 - mae: 2.5252 - mse: 13.7102 - val_loss: 25.8524 - val_mae: 3.3030 - val_mse: 25.8524\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 16.4003 - mae: 2.8432 - mse: 16.4003 - val_loss: 21.9234 - val_mae: 3.2054 - val_mse: 21.9234\n",
"Epoch 10/100\n",
- "354/354 [==============================] - 0s 214us/sample - loss: 13.0620 - mae: 2.4623 - mse: 13.0620 - val_loss: 24.1970 - val_mae: 3.2497 - val_mse: 24.1970\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 15.1889 - mae: 2.7275 - mse: 15.1889 - val_loss: 22.4510 - val_mae: 3.2592 - val_mse: 22.4510\n",
"Epoch 11/100\n",
- "354/354 [==============================] - 0s 181us/sample - loss: 12.3652 - mae: 2.4164 - mse: 12.3652 - val_loss: 25.3418 - val_mae: 3.2958 - val_mse: 25.3418\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 13.8576 - mae: 2.6184 - mse: 13.8576 - val_loss: 20.2455 - val_mae: 3.0453 - val_mse: 20.2455\n",
"Epoch 12/100\n",
- "354/354 [==============================] - 0s 209us/sample - loss: 11.8866 - mae: 2.3633 - mse: 11.8866 - val_loss: 23.2097 - val_mae: 3.1934 - val_mse: 23.2097\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 12.8444 - mae: 2.5178 - mse: 12.8444 - val_loss: 24.1662 - val_mae: 3.3867 - val_mse: 24.1662\n",
"Epoch 13/100\n",
- "354/354 [==============================] - 0s 175us/sample - loss: 11.5238 - mae: 2.2892 - mse: 11.5238 - val_loss: 23.2434 - val_mae: 3.1315 - val_mse: 23.2434\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 12.5199 - mae: 2.4892 - mse: 12.5199 - val_loss: 20.2512 - val_mae: 3.0096 - val_mse: 20.2512\n",
"Epoch 14/100\n",
- "354/354 [==============================] - 0s 208us/sample - loss: 11.1692 - mae: 2.2504 - mse: 11.1692 - val_loss: 22.5373 - val_mae: 3.0818 - val_mse: 22.5373\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 11.7703 - mae: 2.3801 - mse: 11.7703 - val_loss: 19.5756 - val_mae: 2.9569 - val_mse: 19.5756\n",
"Epoch 15/100\n",
- "354/354 [==============================] - 0s 215us/sample - loss: 10.8398 - mae: 2.2416 - mse: 10.8398 - val_loss: 21.5891 - val_mae: 3.0361 - val_mse: 21.5891\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 11.4068 - mae: 2.3343 - mse: 11.4068 - val_loss: 19.8973 - val_mae: 2.9276 - val_mse: 19.8973\n",
"Epoch 16/100\n",
- "354/354 [==============================] - 0s 171us/sample - loss: 10.7131 - mae: 2.2010 - mse: 10.7131 - val_loss: 22.0238 - val_mae: 3.0444 - val_mse: 22.0238\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 10.8153 - mae: 2.3001 - mse: 10.8153 - val_loss: 19.1401 - val_mae: 2.8576 - val_mse: 19.1401\n",
"Epoch 17/100\n",
- "354/354 [==============================] - 0s 207us/sample - loss: 10.4554 - mae: 2.2160 - mse: 10.4554 - val_loss: 20.3310 - val_mae: 3.0210 - val_mse: 20.3310\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 10.5820 - mae: 2.2517 - mse: 10.5820 - val_loss: 19.8560 - val_mae: 2.9379 - val_mse: 19.8560\n",
"Epoch 18/100\n",
- "354/354 [==============================] - 0s 215us/sample - loss: 10.1969 - mae: 2.1522 - mse: 10.1969 - val_loss: 19.8989 - val_mae: 3.0406 - val_mse: 19.8989\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 10.1091 - mae: 2.2229 - mse: 10.1091 - val_loss: 19.5092 - val_mae: 2.8537 - val_mse: 19.5092\n",
"Epoch 19/100\n",
- "354/354 [==============================] - 0s 231us/sample - loss: 9.8782 - mae: 2.1273 - mse: 9.8782 - val_loss: 19.5894 - val_mae: 3.0204 - val_mse: 19.5894\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 10.1551 - mae: 2.1879 - mse: 10.1551 - val_loss: 19.4200 - val_mae: 2.8337 - val_mse: 19.4200\n",
"Epoch 20/100\n",
- "354/354 [==============================] - 0s 179us/sample - loss: 9.6757 - mae: 2.1045 - mse: 9.6757 - val_loss: 20.2566 - val_mae: 2.9530 - val_mse: 20.2566\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 9.7575 - mae: 2.1486 - mse: 9.7575 - val_loss: 19.8508 - val_mae: 2.8520 - val_mse: 19.8508\n",
"Epoch 21/100\n",
- "354/354 [==============================] - 0s 209us/sample - loss: 9.7305 - mae: 2.1052 - mse: 9.7305 - val_loss: 19.3397 - val_mae: 2.9417 - val_mse: 19.3397\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 9.5391 - mae: 2.1334 - mse: 9.5391 - val_loss: 19.6073 - val_mae: 2.8094 - val_mse: 19.6073\n",
"Epoch 22/100\n",
- "354/354 [==============================] - 0s 173us/sample - loss: 9.3255 - mae: 2.0713 - mse: 9.3255 - val_loss: 20.6716 - val_mae: 2.9769 - val_mse: 20.6716\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 9.5457 - mae: 2.1677 - mse: 9.5457 - val_loss: 19.4711 - val_mae: 2.8027 - val_mse: 19.4711\n",
"Epoch 23/100\n",
- "354/354 [==============================] - 0s 174us/sample - loss: 9.4971 - mae: 2.0784 - mse: 9.4971 - val_loss: 19.5308 - val_mae: 2.8951 - val_mse: 19.5308\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 9.4080 - mae: 2.1558 - mse: 9.4080 - val_loss: 19.0961 - val_mae: 2.7937 - val_mse: 19.0961\n",
"Epoch 24/100\n",
- "354/354 [==============================] - 0s 171us/sample - loss: 9.3786 - mae: 2.0279 - mse: 9.3786 - val_loss: 20.0007 - val_mae: 2.9042 - val_mse: 20.0007\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 9.0595 - mae: 2.0982 - mse: 9.0595 - val_loss: 18.5873 - val_mae: 2.7763 - val_mse: 18.5873\n",
"Epoch 25/100\n",
- "354/354 [==============================] - 0s 164us/sample - loss: 9.2494 - mae: 2.0473 - mse: 9.2494 - val_loss: 19.8543 - val_mae: 2.9153 - val_mse: 19.8543\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 8.8593 - mae: 2.1166 - mse: 8.8593 - val_loss: 20.5131 - val_mae: 2.8059 - val_mse: 20.5131\n",
"Epoch 26/100\n",
- "354/354 [==============================] - 0s 178us/sample - loss: 8.8924 - mae: 1.9611 - mse: 8.8924 - val_loss: 20.1059 - val_mae: 3.0175 - val_mse: 20.1059\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 8.9069 - mae: 2.0790 - mse: 8.9069 - val_loss: 18.8169 - val_mae: 2.7318 - val_mse: 18.8169\n",
"Epoch 27/100\n",
- "354/354 [==============================] - 0s 211us/sample - loss: 8.8493 - mae: 1.9850 - mse: 8.8493 - val_loss: 19.0924 - val_mae: 2.9133 - val_mse: 19.0924\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 8.6986 - mae: 2.0321 - mse: 8.6986 - val_loss: 18.8858 - val_mae: 2.7567 - val_mse: 18.8858\n",
"Epoch 28/100\n",
- "354/354 [==============================] - 0s 222us/sample - loss: 8.7235 - mae: 1.9539 - mse: 8.7235 - val_loss: 18.8848 - val_mae: 2.8254 - val_mse: 18.8848\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 8.6039 - mae: 2.0581 - mse: 8.6039 - val_loss: 18.5083 - val_mae: 2.7327 - val_mse: 18.5083\n",
"Epoch 29/100\n",
- "354/354 [==============================] - 0s 212us/sample - loss: 8.4714 - mae: 1.9410 - mse: 8.4714 - val_loss: 18.6757 - val_mae: 2.8632 - val_mse: 18.6757\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 8.4062 - mae: 2.0187 - mse: 8.4062 - val_loss: 18.5227 - val_mae: 2.7294 - val_mse: 18.5227\n",
"Epoch 30/100\n",
- "354/354 [==============================] - 0s 213us/sample - loss: 8.5340 - mae: 1.9305 - mse: 8.5340 - val_loss: 17.5472 - val_mae: 2.9041 - val_mse: 17.5472\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 8.5707 - mae: 2.0294 - mse: 8.5707 - val_loss: 19.1018 - val_mae: 2.7214 - val_mse: 19.1018\n",
"Epoch 31/100\n",
- "354/354 [==============================] - 0s 173us/sample - loss: 8.2329 - mae: 1.9490 - mse: 8.2329 - val_loss: 18.3023 - val_mae: 2.8256 - val_mse: 18.3023\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 8.2422 - mae: 1.9886 - mse: 8.2422 - val_loss: 18.4019 - val_mae: 2.7168 - val_mse: 18.4019\n",
"Epoch 32/100\n",
- "354/354 [==============================] - 0s 179us/sample - loss: 8.2141 - mae: 1.8936 - mse: 8.2141 - val_loss: 19.1499 - val_mae: 2.9304 - val_mse: 19.1499\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 7.9650 - mae: 1.9721 - mse: 7.9650 - val_loss: 17.9165 - val_mae: 2.6871 - val_mse: 17.9165\n",
"Epoch 33/100\n",
- "354/354 [==============================] - 0s 168us/sample - loss: 8.1556 - mae: 1.8934 - mse: 8.1556 - val_loss: 17.6018 - val_mae: 2.7829 - val_mse: 17.6018\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 8.0356 - mae: 1.9331 - mse: 8.0356 - val_loss: 20.1362 - val_mae: 2.8835 - val_mse: 20.1362\n",
"Epoch 34/100\n",
- "354/354 [==============================] - 0s 213us/sample - loss: 8.0088 - mae: 1.8932 - mse: 8.0088 - val_loss: 17.0437 - val_mae: 2.7469 - val_mse: 17.0437\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 8.1133 - mae: 1.9798 - mse: 8.1133 - val_loss: 18.7192 - val_mae: 2.7234 - val_mse: 18.7192\n",
"Epoch 35/100\n",
- "354/354 [==============================] - 0s 233us/sample - loss: 7.7964 - mae: 1.8813 - mse: 7.7964 - val_loss: 16.5385 - val_mae: 2.7644 - val_mse: 16.5385\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 7.8014 - mae: 1.9488 - mse: 7.8014 - val_loss: 19.4508 - val_mae: 2.7816 - val_mse: 19.4508\n",
"Epoch 36/100\n",
- "354/354 [==============================] - 0s 172us/sample - loss: 7.9428 - mae: 1.9060 - mse: 7.9428 - val_loss: 17.1548 - val_mae: 2.8017 - val_mse: 17.1548\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 7.8598 - mae: 1.9583 - mse: 7.8598 - val_loss: 19.5103 - val_mae: 2.9375 - val_mse: 19.5103\n",
"Epoch 37/100\n",
- "354/354 [==============================] - 0s 166us/sample - loss: 7.8799 - mae: 1.8151 - mse: 7.8798 - val_loss: 18.0839 - val_mae: 2.7838 - val_mse: 18.0839\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 7.7984 - mae: 1.9296 - mse: 7.7984 - val_loss: 19.4718 - val_mae: 2.7538 - val_mse: 19.4718\n",
"Epoch 38/100\n",
- "354/354 [==============================] - 0s 166us/sample - loss: 7.9034 - mae: 1.8507 - mse: 7.9034 - val_loss: 16.7612 - val_mae: 2.7060 - val_mse: 16.7612\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 7.5976 - mae: 1.9239 - mse: 7.5976 - val_loss: 19.2051 - val_mae: 2.7307 - val_mse: 19.2051\n",
"Epoch 39/100\n",
- "354/354 [==============================] - 0s 171us/sample - loss: 7.6441 - mae: 1.8198 - mse: 7.6441 - val_loss: 16.7067 - val_mae: 2.7185 - val_mse: 16.7067\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 7.6630 - mae: 1.9172 - mse: 7.6630 - val_loss: 18.6308 - val_mae: 2.7413 - val_mse: 18.6308\n",
"Epoch 40/100\n",
- "354/354 [==============================] - 0s 173us/sample - loss: 7.7784 - mae: 1.8554 - mse: 7.7784 - val_loss: 16.5735 - val_mae: 2.6828 - val_mse: 16.5735\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 7.4104 - mae: 1.8826 - mse: 7.4104 - val_loss: 17.8465 - val_mae: 2.7210 - val_mse: 17.8465\n",
"Epoch 41/100\n",
- "354/354 [==============================] - 0s 222us/sample - loss: 7.4901 - mae: 1.8260 - mse: 7.4901 - val_loss: 16.4810 - val_mae: 2.6926 - val_mse: 16.4810\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 7.4981 - mae: 1.8841 - mse: 7.4981 - val_loss: 19.4714 - val_mae: 2.7838 - val_mse: 19.4714\n",
"Epoch 42/100\n",
- "354/354 [==============================] - 0s 171us/sample - loss: 7.4583 - mae: 1.7847 - mse: 7.4583 - val_loss: 16.6520 - val_mae: 2.6816 - val_mse: 16.6520\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 7.3673 - mae: 1.8906 - mse: 7.3673 - val_loss: 17.8742 - val_mae: 2.6332 - val_mse: 17.8742\n",
"Epoch 43/100\n",
- "354/354 [==============================] - 0s 213us/sample - loss: 7.1234 - mae: 1.8169 - mse: 7.1234 - val_loss: 16.1394 - val_mae: 2.8231 - val_mse: 16.1394\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 7.3283 - mae: 1.8515 - mse: 7.3283 - val_loss: 18.3729 - val_mae: 2.6627 - val_mse: 18.3729\n",
"Epoch 44/100\n",
- "354/354 [==============================] - 0s 176us/sample - loss: 7.3209 - mae: 1.8194 - mse: 7.3209 - val_loss: 17.1395 - val_mae: 2.7080 - val_mse: 17.1395\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 7.1782 - mae: 1.8650 - mse: 7.1782 - val_loss: 20.5136 - val_mae: 3.0215 - val_mse: 20.5136\n",
"Epoch 45/100\n",
- "354/354 [==============================] - 0s 175us/sample - loss: 7.2618 - mae: 1.7858 - mse: 7.2618 - val_loss: 16.5276 - val_mae: 2.6765 - val_mse: 16.5276\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 7.2115 - mae: 1.8789 - mse: 7.2115 - val_loss: 17.6324 - val_mae: 2.6560 - val_mse: 17.6324\n",
"Epoch 46/100\n",
- "354/354 [==============================] - 0s 174us/sample - loss: 7.0747 - mae: 1.7662 - mse: 7.0747 - val_loss: 16.8039 - val_mae: 2.7428 - val_mse: 16.8039\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.9389 - mae: 1.8178 - mse: 6.9389 - val_loss: 17.7074 - val_mae: 2.7116 - val_mse: 17.7074\n",
"Epoch 47/100\n",
- "354/354 [==============================] - 0s 205us/sample - loss: 6.9520 - mae: 1.7333 - mse: 6.9520 - val_loss: 15.9968 - val_mae: 2.6659 - val_mse: 15.9968\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 7.1571 - mae: 1.8432 - mse: 7.1571 - val_loss: 17.9549 - val_mae: 2.6420 - val_mse: 17.9549\n",
"Epoch 48/100\n",
- "354/354 [==============================] - 0s 178us/sample - loss: 6.9232 - mae: 1.7301 - mse: 6.9232 - val_loss: 16.2660 - val_mae: 2.6494 - val_mse: 16.2660\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 7.0388 - mae: 1.8194 - mse: 7.0388 - val_loss: 18.3145 - val_mae: 2.8232 - val_mse: 18.3145\n",
"Epoch 49/100\n",
- "354/354 [==============================] - 0s 207us/sample - loss: 7.0229 - mae: 1.7404 - mse: 7.0229 - val_loss: 15.6720 - val_mae: 2.6691 - val_mse: 15.6720\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.9670 - mae: 1.8063 - mse: 6.9670 - val_loss: 17.6338 - val_mae: 2.6524 - val_mse: 17.6338\n",
"Epoch 50/100\n",
- "354/354 [==============================] - 0s 184us/sample - loss: 6.7251 - mae: 1.7580 - mse: 6.7251 - val_loss: 17.0077 - val_mae: 2.7250 - val_mse: 17.0077\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.8445 - mae: 1.7940 - mse: 6.8445 - val_loss: 16.7234 - val_mae: 2.6413 - val_mse: 16.7234\n",
"Epoch 51/100\n",
- "354/354 [==============================] - 0s 185us/sample - loss: 6.8393 - mae: 1.7131 - mse: 6.8393 - val_loss: 16.5671 - val_mae: 2.6707 - val_mse: 16.5671\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.9393 - mae: 1.8154 - mse: 6.9393 - val_loss: 18.0841 - val_mae: 2.8057 - val_mse: 18.0841\n",
"Epoch 52/100\n",
- "354/354 [==============================] - 0s 175us/sample - loss: 6.6647 - mae: 1.6831 - mse: 6.6647 - val_loss: 16.2175 - val_mae: 2.6816 - val_mse: 16.2175\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.7668 - mae: 1.7698 - mse: 6.7668 - val_loss: 18.0858 - val_mae: 2.7448 - val_mse: 18.0858\n",
"Epoch 53/100\n",
- "354/354 [==============================] - 0s 174us/sample - loss: 6.5925 - mae: 1.7133 - mse: 6.5925 - val_loss: 16.8129 - val_mae: 2.6882 - val_mse: 16.8129\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.6034 - mae: 1.7724 - mse: 6.6034 - val_loss: 16.6955 - val_mae: 2.6054 - val_mse: 16.6955\n",
"Epoch 54/100\n",
- "354/354 [==============================] - 0s 211us/sample - loss: 6.5071 - mae: 1.6980 - mse: 6.5071 - val_loss: 15.6172 - val_mae: 2.7470 - val_mse: 15.6172\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.7387 - mae: 1.7751 - mse: 6.7387 - val_loss: 20.1177 - val_mae: 2.8366 - val_mse: 20.1177\n",
"Epoch 55/100\n",
- "354/354 [==============================] - 0s 237us/sample - loss: 6.4762 - mae: 1.7064 - mse: 6.4762 - val_loss: 15.1044 - val_mae: 2.6354 - val_mse: 15.1044\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.4552 - mae: 1.7589 - mse: 6.4552 - val_loss: 17.7537 - val_mae: 2.6217 - val_mse: 17.7537\n",
"Epoch 56/100\n",
- "354/354 [==============================] - 0s 173us/sample - loss: 6.2826 - mae: 1.6902 - mse: 6.2826 - val_loss: 17.5892 - val_mae: 2.8422 - val_mse: 17.5892\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.4993 - mae: 1.7678 - mse: 6.4993 - val_loss: 17.1823 - val_mae: 2.6101 - val_mse: 17.1823\n",
"Epoch 57/100\n",
- "354/354 [==============================] - 0s 162us/sample - loss: 6.5736 - mae: 1.7001 - mse: 6.5736 - val_loss: 15.5415 - val_mae: 2.6102 - val_mse: 15.5415\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.4921 - mae: 1.7340 - mse: 6.4921 - val_loss: 19.8667 - val_mae: 3.0042 - val_mse: 19.8667\n",
"Epoch 58/100\n",
- "354/354 [==============================] - 0s 158us/sample - loss: 6.3667 - mae: 1.6807 - mse: 6.3667 - val_loss: 15.3699 - val_mae: 2.6143 - val_mse: 15.3699\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.5311 - mae: 1.7701 - mse: 6.5311 - val_loss: 16.7387 - val_mae: 2.6019 - val_mse: 16.7387\n",
"Epoch 59/100\n",
- "354/354 [==============================] - 0s 175us/sample - loss: 6.3116 - mae: 1.6672 - mse: 6.3116 - val_loss: 15.1690 - val_mae: 2.6191 - val_mse: 15.1690\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.2846 - mae: 1.6964 - mse: 6.2846 - val_loss: 16.7143 - val_mae: 2.6060 - val_mse: 16.7143\n",
"Epoch 60/100\n",
- "354/354 [==============================] - 0s 166us/sample - loss: 6.0480 - mae: 1.6076 - mse: 6.0480 - val_loss: 15.4159 - val_mae: 2.5666 - val_mse: 15.4159\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.3824 - mae: 1.7143 - mse: 6.3824 - val_loss: 16.2439 - val_mae: 2.6744 - val_mse: 16.2439\n",
"Epoch 61/100\n",
- "354/354 [==============================] - 0s 168us/sample - loss: 5.9372 - mae: 1.6170 - mse: 5.9372 - val_loss: 16.3723 - val_mae: 2.7008 - val_mse: 16.3723\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.2568 - mae: 1.7032 - mse: 6.2568 - val_loss: 16.7933 - val_mae: 2.6208 - val_mse: 16.7933\n",
"Epoch 62/100\n",
- "354/354 [==============================] - 0s 209us/sample - loss: 6.2295 - mae: 1.6809 - mse: 6.2295 - val_loss: 14.4432 - val_mae: 2.5659 - val_mse: 14.4432\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.3085 - mae: 1.7262 - mse: 6.3085 - val_loss: 16.5479 - val_mae: 2.5937 - val_mse: 16.5479\n",
"Epoch 63/100\n",
- "354/354 [==============================] - 0s 173us/sample - loss: 6.0430 - mae: 1.6175 - mse: 6.0430 - val_loss: 15.7230 - val_mae: 2.6120 - val_mse: 15.7230\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.2031 - mae: 1.6910 - mse: 6.2031 - val_loss: 16.2833 - val_mae: 2.6115 - val_mse: 16.2833\n",
"Epoch 64/100\n",
- "354/354 [==============================] - 0s 165us/sample - loss: 5.8128 - mae: 1.5988 - mse: 5.8128 - val_loss: 15.3515 - val_mae: 2.6344 - val_mse: 15.3515\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.0996 - mae: 1.7162 - mse: 6.0996 - val_loss: 18.4672 - val_mae: 2.9381 - val_mse: 18.4672\n",
"Epoch 65/100\n",
- "354/354 [==============================] - 0s 163us/sample - loss: 6.0937 - mae: 1.6535 - mse: 6.0937 - val_loss: 15.0359 - val_mae: 2.6071 - val_mse: 15.0359\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.1800 - mae: 1.7108 - mse: 6.1800 - val_loss: 17.0333 - val_mae: 2.7235 - val_mse: 17.0333\n",
"Epoch 66/100\n",
- "354/354 [==============================] - 0s 175us/sample - loss: 5.9493 - mae: 1.6212 - mse: 5.9493 - val_loss: 14.5079 - val_mae: 2.5570 - val_mse: 14.5079\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.0228 - mae: 1.6654 - mse: 6.0228 - val_loss: 18.1264 - val_mae: 2.8055 - val_mse: 18.1264\n",
"Epoch 67/100\n",
- "354/354 [==============================] - 0s 167us/sample - loss: 5.7467 - mae: 1.5815 - mse: 5.7467 - val_loss: 15.6834 - val_mae: 2.6418 - val_mse: 15.6834\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.1494 - mae: 1.6927 - mse: 6.1494 - val_loss: 17.0323 - val_mae: 2.6064 - val_mse: 17.0323\n",
"Epoch 68/100\n",
- "354/354 [==============================] - 0s 175us/sample - loss: 5.7754 - mae: 1.5822 - mse: 5.7754 - val_loss: 15.3485 - val_mae: 2.6661 - val_mse: 15.3485\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.9137 - mae: 1.6858 - mse: 5.9137 - val_loss: 17.1055 - val_mae: 2.6391 - val_mse: 17.1055\n",
"Epoch 69/100\n",
- "354/354 [==============================] - 0s 166us/sample - loss: 5.8671 - mae: 1.6219 - mse: 5.8671 - val_loss: 14.7226 - val_mae: 2.5691 - val_mse: 14.7226\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.0769 - mae: 1.6926 - mse: 6.0769 - val_loss: 16.8573 - val_mae: 2.6361 - val_mse: 16.8573\n",
"Epoch 70/100\n",
- "354/354 [==============================] - 0s 161us/sample - loss: 5.5371 - mae: 1.5618 - mse: 5.5371 - val_loss: 15.4694 - val_mae: 2.7397 - val_mse: 15.4694\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.8309 - mae: 1.6378 - mse: 5.8309 - val_loss: 17.2946 - val_mae: 2.6609 - val_mse: 17.2946\n",
"Epoch 71/100\n",
- "354/354 [==============================] - 0s 184us/sample - loss: 5.7798 - mae: 1.6298 - mse: 5.7798 - val_loss: 14.9847 - val_mae: 2.5864 - val_mse: 14.9847\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.9778 - mae: 1.7016 - mse: 5.9778 - val_loss: 15.9038 - val_mae: 2.5838 - val_mse: 15.9038\n",
"Epoch 72/100\n",
- "354/354 [==============================] - 0s 215us/sample - loss: 5.6852 - mae: 1.5909 - mse: 5.6852 - val_loss: 14.4344 - val_mae: 2.5542 - val_mse: 14.4344\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.8563 - mae: 1.6335 - mse: 5.8563 - val_loss: 17.4159 - val_mae: 2.6298 - val_mse: 17.4159\n",
"Epoch 73/100\n",
- "354/354 [==============================] - 0s 226us/sample - loss: 5.4654 - mae: 1.5450 - mse: 5.4654 - val_loss: 13.7925 - val_mae: 2.5750 - val_mse: 13.7925\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.7478 - mae: 1.6517 - mse: 5.7478 - val_loss: 16.3856 - val_mae: 2.5893 - val_mse: 16.3856\n",
"Epoch 74/100\n",
- "354/354 [==============================] - 0s 174us/sample - loss: 5.4204 - mae: 1.5595 - mse: 5.4204 - val_loss: 14.3083 - val_mae: 2.5919 - val_mse: 14.3083\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.6123 - mae: 1.6471 - mse: 5.6123 - val_loss: 16.3227 - val_mae: 2.6746 - val_mse: 16.3227\n",
"Epoch 75/100\n",
- "354/354 [==============================] - 0s 170us/sample - loss: 5.3183 - mae: 1.5827 - mse: 5.3183 - val_loss: 14.8913 - val_mae: 2.6033 - val_mse: 14.8913\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.6092 - mae: 1.6309 - mse: 5.6092 - val_loss: 16.3552 - val_mae: 2.5841 - val_mse: 16.3552\n",
"Epoch 76/100\n",
- "354/354 [==============================] - 0s 162us/sample - loss: 5.4595 - mae: 1.5874 - mse: 5.4595 - val_loss: 14.0410 - val_mae: 2.6021 - val_mse: 14.0410\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.7278 - mae: 1.6690 - mse: 5.7278 - val_loss: 16.2516 - val_mae: 2.5807 - val_mse: 16.2516\n",
"Epoch 77/100\n",
- "354/354 [==============================] - 0s 165us/sample - loss: 5.3295 - mae: 1.5586 - mse: 5.3295 - val_loss: 14.4499 - val_mae: 2.6315 - val_mse: 14.4499\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.5015 - mae: 1.6626 - mse: 5.5015 - val_loss: 16.2511 - val_mae: 2.6601 - val_mse: 16.2511\n",
"Epoch 78/100\n",
- "354/354 [==============================] - 0s 170us/sample - loss: 5.3187 - mae: 1.5144 - mse: 5.3187 - val_loss: 14.8048 - val_mae: 2.6123 - val_mse: 14.8048\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.6931 - mae: 1.6550 - mse: 5.6931 - val_loss: 16.6842 - val_mae: 2.6523 - val_mse: 16.6842\n",
"Epoch 79/100\n",
- "354/354 [==============================] - 0s 163us/sample - loss: 5.0078 - mae: 1.5458 - mse: 5.0078 - val_loss: 16.8855 - val_mae: 2.7085 - val_mse: 16.8855\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.4391 - mae: 1.6007 - mse: 5.4391 - val_loss: 18.1906 - val_mae: 2.7731 - val_mse: 18.1906\n",
"Epoch 80/100\n",
- "354/354 [==============================] - 0s 165us/sample - loss: 5.2642 - mae: 1.5158 - mse: 5.2642 - val_loss: 14.1548 - val_mae: 2.5029 - val_mse: 14.1548\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.4225 - mae: 1.6033 - mse: 5.4225 - val_loss: 15.6912 - val_mae: 2.5630 - val_mse: 15.6912\n",
"Epoch 81/100\n",
- "354/354 [==============================] - 0s 157us/sample - loss: 5.1865 - mae: 1.5163 - mse: 5.1865 - val_loss: 14.0614 - val_mae: 2.5335 - val_mse: 14.0614\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.5329 - mae: 1.6150 - mse: 5.5329 - val_loss: 16.4829 - val_mae: 2.6005 - val_mse: 16.4829\n",
"Epoch 82/100\n",
- "354/354 [==============================] - 0s 185us/sample - loss: 5.0460 - mae: 1.5366 - mse: 5.0460 - val_loss: 14.5370 - val_mae: 2.5429 - val_mse: 14.5370\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.3777 - mae: 1.5899 - mse: 5.3777 - val_loss: 16.6022 - val_mae: 2.6415 - val_mse: 16.6022\n",
"Epoch 83/100\n",
- "354/354 [==============================] - 0s 174us/sample - loss: 4.9748 - mae: 1.5005 - mse: 4.9748 - val_loss: 14.7812 - val_mae: 2.6546 - val_mse: 14.7812\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.3488 - mae: 1.6064 - mse: 5.3488 - val_loss: 15.7812 - val_mae: 2.6189 - val_mse: 15.7812\n",
"Epoch 84/100\n",
- "354/354 [==============================] - 0s 173us/sample - loss: 5.1114 - mae: 1.5233 - mse: 5.1114 - val_loss: 14.6264 - val_mae: 2.5917 - val_mse: 14.6264\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.4109 - mae: 1.6151 - mse: 5.4109 - val_loss: 16.1101 - val_mae: 2.6002 - val_mse: 16.1101\n",
"Epoch 85/100\n",
- "354/354 [==============================] - 0s 173us/sample - loss: 4.9709 - mae: 1.4471 - mse: 4.9709 - val_loss: 14.4156 - val_mae: 2.5713 - val_mse: 14.4156\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.2320 - mae: 1.5687 - mse: 5.2320 - val_loss: 17.5305 - val_mae: 2.7975 - val_mse: 17.5305\n",
"Epoch 86/100\n",
- "354/354 [==============================] - 0s 218us/sample - loss: 4.7742 - mae: 1.4547 - mse: 4.7742 - val_loss: 13.4703 - val_mae: 2.6523 - val_mse: 13.4703\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.2325 - mae: 1.5457 - mse: 5.2325 - val_loss: 18.4276 - val_mae: 2.7352 - val_mse: 18.4276\n",
"Epoch 87/100\n",
- "354/354 [==============================] - 0s 176us/sample - loss: 5.0550 - mae: 1.4842 - mse: 5.0550 - val_loss: 13.4970 - val_mae: 2.5958 - val_mse: 13.4970\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.2545 - mae: 1.5747 - mse: 5.2545 - val_loss: 17.2209 - val_mae: 2.7965 - val_mse: 17.2209\n",
"Epoch 88/100\n",
- "354/354 [==============================] - 0s 167us/sample - loss: 4.6993 - mae: 1.5044 - mse: 4.6993 - val_loss: 13.9216 - val_mae: 2.5554 - val_mse: 13.9216\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.3344 - mae: 1.6059 - mse: 5.3344 - val_loss: 15.7674 - val_mae: 2.5888 - val_mse: 15.7674\n",
"Epoch 89/100\n",
- "354/354 [==============================] - 0s 164us/sample - loss: 4.7660 - mae: 1.4815 - mse: 4.7660 - val_loss: 13.6762 - val_mae: 2.5521 - val_mse: 13.6762\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.2197 - mae: 1.5489 - mse: 5.2197 - val_loss: 16.0532 - val_mae: 2.5752 - val_mse: 16.0532\n",
"Epoch 90/100\n",
- "354/354 [==============================] - 0s 166us/sample - loss: 4.6544 - mae: 1.4618 - mse: 4.6544 - val_loss: 14.6759 - val_mae: 2.5828 - val_mse: 14.6759\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.0072 - mae: 1.5649 - mse: 5.0072 - val_loss: 18.6302 - val_mae: 3.0371 - val_mse: 18.6302\n",
"Epoch 91/100\n",
- "354/354 [==============================] - 0s 240us/sample - loss: 4.7288 - mae: 1.4415 - mse: 4.7288 - val_loss: 13.2609 - val_mae: 2.6077 - val_mse: 13.2609\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 4.9996 - mae: 1.5426 - mse: 4.9996 - val_loss: 17.1384 - val_mae: 2.8235 - val_mse: 17.1384\n",
"Epoch 92/100\n",
- "354/354 [==============================] - 0s 177us/sample - loss: 4.6730 - mae: 1.4331 - mse: 4.6730 - val_loss: 13.4463 - val_mae: 2.7238 - val_mse: 13.4463\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.0666 - mae: 1.5521 - mse: 5.0666 - val_loss: 16.0273 - val_mae: 2.6984 - val_mse: 16.0273\n",
"Epoch 93/100\n",
- "354/354 [==============================] - 0s 163us/sample - loss: 4.4141 - mae: 1.4512 - mse: 4.4141 - val_loss: 13.6472 - val_mae: 2.5763 - val_mse: 13.6472\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.0523 - mae: 1.5273 - mse: 5.0523 - val_loss: 15.9783 - val_mae: 2.5942 - val_mse: 15.9783\n",
"Epoch 94/100\n",
- "354/354 [==============================] - 0s 165us/sample - loss: 4.5136 - mae: 1.4138 - mse: 4.5136 - val_loss: 15.7465 - val_mae: 2.7020 - val_mse: 15.7465\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 4.9561 - mae: 1.5317 - mse: 4.9561 - val_loss: 17.4211 - val_mae: 2.7392 - val_mse: 17.4211\n",
"Epoch 95/100\n",
- "354/354 [==============================] - 0s 168us/sample - loss: 4.6733 - mae: 1.4679 - mse: 4.6733 - val_loss: 13.3843 - val_mae: 2.5652 - val_mse: 13.3843\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 4.7807 - mae: 1.5195 - mse: 4.7807 - val_loss: 17.4444 - val_mae: 2.6350 - val_mse: 17.4444\n",
"Epoch 96/100\n",
- "354/354 [==============================] - 0s 174us/sample - loss: 4.6895 - mae: 1.4674 - mse: 4.6895 - val_loss: 13.7346 - val_mae: 2.5467 - val_mse: 13.7346\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 4.7558 - mae: 1.5327 - mse: 4.7558 - val_loss: 16.5095 - val_mae: 2.7889 - val_mse: 16.5095\n",
"Epoch 97/100\n",
- "354/354 [==============================] - 0s 166us/sample - loss: 4.3689 - mae: 1.4277 - mse: 4.3689 - val_loss: 13.6955 - val_mae: 2.5702 - val_mse: 13.6955\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 4.9151 - mae: 1.5311 - mse: 4.9151 - val_loss: 17.1855 - val_mae: 2.7893 - val_mse: 17.1855\n",
"Epoch 98/100\n",
- "354/354 [==============================] - 0s 167us/sample - loss: 4.3953 - mae: 1.3996 - mse: 4.3953 - val_loss: 13.6977 - val_mae: 2.5888 - val_mse: 13.6977\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 4.7915 - mae: 1.5052 - mse: 4.7915 - val_loss: 16.9043 - val_mae: 2.8458 - val_mse: 16.9043\n",
"Epoch 99/100\n",
- "354/354 [==============================] - 0s 160us/sample - loss: 4.3799 - mae: 1.4405 - mse: 4.3799 - val_loss: 15.0689 - val_mae: 2.6300 - val_mse: 15.0689\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 4.6241 - mae: 1.4739 - mse: 4.6241 - val_loss: 15.9920 - val_mae: 2.5725 - val_mse: 15.9920\n",
"Epoch 100/100\n",
- "354/354 [==============================] - 0s 205us/sample - loss: 4.3640 - mae: 1.4099 - mse: 4.3640 - val_loss: 12.8820 - val_mae: 2.5248 - val_mse: 12.8820\n"
+ "36/36 [==============================] - 0s 3ms/step - loss: 4.8180 - mae: 1.4844 - mse: 4.8180 - val_loss: 16.6288 - val_mae: 2.6493 - val_mse: 16.6288\n"
]
}
],
@@ -999,9 +998,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "x_test / loss : 12.8820\n",
- "x_test / mae : 2.5248\n",
- "x_test / mse : 12.8820\n"
+ "x_test / loss : 16.6288\n",
+ "x_test / mae : 2.6493\n",
+ "x_test / mse : 16.6288\n"
]
}
],
@@ -1030,7 +1029,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "min( val_mae ) : 2.5029\n"
+ "min( val_mae ) : 2.5630\n"
]
}
],
@@ -1045,7 +1044,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
@@ -1057,7 +1056,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
@@ -1069,7 +1068,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
@@ -1149,9 +1148,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "x_test / loss : 12.8820\n",
- "x_test / mae : 2.5248\n",
- "x_test / mse : 12.8820\n"
+ "x_test / loss : 15.6912\n",
+ "x_test / mae : 2.5630\n",
+ "x_test / mse : 15.6912\n"
]
}
],
@@ -1193,7 +1192,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Prediction : 10.39 K$ Reality : 10.40 K$\n"
+ "Prediction : 11.54 K$ Reality : 10.40 K$\n"
]
}
],
diff --git a/README.ipynb b/README.ipynb
index 0a325b0..e0c9db9 100644
--- a/README.ipynb
+++ b/README.ipynb
@@ -35,7 +35,7 @@
"\n",
"For more information, you can contact us at : \n",
"[<img width=\"200px\" style=\"vertical-align:middle\" src=\"fidle/img/00-Mail_contact.svg\"></img>](#top) \n",
- "Current Version : 0.5.5 DEV \n",
+ "Current Version : 0.5.7 DEV \n",
"\n",
"\n",
"## Course materials\n",
diff --git a/README.md b/README.md
index a7af8bc..e8030ab 100644
--- a/README.md
+++ b/README.md
@@ -21,7 +21,7 @@ The objectives of this training are :
For more information, you can contact us at :
[<img width="200px" style="vertical-align:middle" src="fidle/img/00-Mail_contact.svg"></img>](#top)
-Current Version : 0.5.5 DEV
+Current Version : 0.5.7 DEV
## Course materials
diff --git a/fidle/Example.ipynb b/fidle/Template.ipynb
similarity index 100%
rename from fidle/Example.ipynb
rename to fidle/Template.ipynb
diff --git a/fidle/config.py b/fidle/config.py
new file mode 100644
index 0000000..0177d11
--- /dev/null
+++ b/fidle/config.py
@@ -0,0 +1,38 @@
+
+
+# ==================================================================
+# ____ _ _ _ __ __ _
+# | _ \ _ __ __ _ ___| |_(_) ___ __ _| | \ \ / /__ _ __| | __
+# | |_) | '__/ _` |/ __| __| |/ __/ _` | | \ \ /\ / / _ \| '__| |/ /
+# | __/| | | (_| | (__| |_| | (_| (_| | | \ V V / (_) | | | <
+# |_| |_| \__,_|\___|\__|_|\___\__,_|_| \_/\_/ \___/|_| |_|\_\
+# Configuration
+# ==================================================================
+# Few configuration stuffs for the Fidle practical work notebooks
+# Jean-Luc Parouty 2020
+
+import os
+
+# ---- Current version ---------------------------------------------
+#
+VERSION = '0.57 DEV'
+
+# ---- Locations ---------------------------------------------------
+#
+# A list of locations where this notebooks can be executed, with the
+# location of the datasets folder.
+# You can complete this list by adding specifics locations.
+#
+# Syntax is :
+# locations = { <location name>:<datasets path> , ...}
+#
+# Example :
+# locations = { 'My laptop':'/usr/local/datasets' }
+#
+# This locations are defaults locations :
+#
+locations = { 'Fidle at GRICAD' : f"{os.getenv('SCRATCH_DIR', 'nowhere' )}/PROJECTS/pr-fidle/datasets",
+ 'Fidle at IDRIS' : f"{os.getenv('ALL_CCFRWORK', 'nowhere' )}/datasets",
+ 'Fidle at HOME' : f"{os.getenv('HOME', 'nowhere' )}/datasets"}
+
+# ------------------------------------------------------------------
\ No newline at end of file
diff --git a/fidle/pwk.py b/fidle/pwk.py
index 0455739..2aa0edc 100644
--- a/fidle/pwk.py
+++ b/fidle/pwk.py
@@ -31,7 +31,8 @@ import matplotlib.pyplot as plt
import seaborn as sn #IDRIS : module en cours d'installation
from IPython.display import display,Image,Markdown,HTML
-VERSION='0.5.5'
+
+import fidle.config as config
_save_figs = False
_figs_dir = './figs'
@@ -46,14 +47,11 @@ def init( mplstyle='../fidle/mplstyles/custom.mplstyle',
cssfile='../fidle/css/custom.css',
places={ 'SOMEWHERE' : '/path/to/datasets'}):
- global VERSION
update_keras_cache=False
# ---- Predifined places
#
- predefined_places = { 'Fidle at GRICAD' : f"{os.getenv('SCRATCH_DIR','nowhere')}/PROJECTS/pr-fidle/datasets",
- 'Fidle at IDRIS' : f"{os.getenv('WORK', 'nowhere')}/datasets",
- 'Fidle at HOME' : f"{os.getenv('HOME', 'nowhere')}/datasets"}
+ predefined_places = config.locations
# ---- Load matplotlib style and css
#
@@ -84,14 +82,14 @@ def init( mplstyle='../fidle/mplstyles/custom.mplstyle',
# ---- Hello world
print('\nFIDLE 2020 - Practical Work Module')
- print('Version :', VERSION)
+ print('Version :', config.VERSION)
print('Run time : {}'.format(time.strftime("%A %-d %B %Y, %H:%M:%S")))
print('TensorFlow version :',tf.__version__)
print('Keras version :',tf.keras.__version__)
print('Current place :',place_name )
print('Dataset dir :',dataset_dir)
if update_keras_cache:
- print('Update keras cache : Yes')
+ print('Update keras cache : Done')
return place_name, dataset_dir
@@ -129,14 +127,17 @@ def where_we_are(places):
if os.path.isdir(place_dir):
return place_name,place_dir
- print('** ERROR ** : Le dossier datasets est introuvable')
- print(' Vous devez récupérer celui-ci et en préciser la localisation.')
- print(' Une archive (datasets.tar) est disponible via le repo Fidle.\n')
- print(' Une liste de localisations peut être donnée en paramètre de la fonction init()\n')
+ print('** ERROR ** : Le dossier datasets est introuvable\n')
+ print(' Vous devez :\n')
+ print(' 1/ Récupérer le dossier datasets')
+ print(' Une archive (datasets.tar) est disponible via le repo Fidle.\n')
+ print(" 2/ Préciser la localisation de ce dossier datasets")
+ print(" Soit dans le fichier fidle/config.py (préférable)")
+ print(" Soit via un paramètre à la fonction ooo.init()\n")
print(' Par exemple :')
print(" ooo.init( places={ 'Chez-moi':'/tmp/datasets', 'Sur-mon-cluster':'/tests/datasets'}')\n")
- print(' Vous pouvez également recopier le dossier datasets directement dans votre home : ~/datasets\n\n')
- assert False, 'datasets folder not found : Abort all...'
+ print(' Note : Vous pouvez également déposer le dossier datasets directement dans votre home : ~/datasets\n\n')
+ assert False, 'datasets folder not found : Abort all.'
# -------------------------------------------------------------
--
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