diff --git a/.gitignore b/.gitignore
index 906396122e3c17d2e4d9bed86eac79a3efe0fc62..94cd0d2e22d1b1c2d368c9802d2cd77b486e6a04 100755
--- a/.gitignore
+++ b/.gitignore
@@ -3,5 +3,9 @@
__pycache__
*/__pycache__/*
run/
-data/
-!/BHPD/data/BostonHousing.csv
+GTSRB/data
+IMDB/data
+MNIST/data
+VAE/data
+BHPD/data/*
+!BHPD/data/BostonHousing.csv
diff --git a/BHPD/01-DNN-Regression.ipynb b/BHPD/01-DNN-Regression.ipynb
index 8d27450b6a735cc5c8a73827b288033cc11310e2..82736a589a9297c77d8126634360db2dcdd509bc 100644
--- a/BHPD/01-DNN-Regression.ipynb
+++ b/BHPD/01-DNN-Regression.ipynb
@@ -4,14 +4,17 @@
"cell_type": "markdown",
"metadata": {},
"source": [
+ "<div style=\"text-align: left\">\n",
+ " <img src=\"../fidle/img/00-Fidle-header-01.svg\" style=\"width:800px\"/>\n",
+ "</div>\n",
+ "\n",
"Deep Neural Network (DNN) - BHPD dataset\n",
"========================================\n",
- "---\n",
- "Introduction au Deep Learning (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020 \n",
"\n",
- "## A very simple example of **regression** :\n",
+ "A very simple and classic example of **regression** :\n",
"\n",
- "Objective is to predicts **housing prices** from a set of house features. \n",
+ "## Objectives :\n",
+ "Predicts **housing prices** from a set of house features. \n",
"\n",
"The **[Boston Housing Dataset](https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html)** consists of price of houses in various places in Boston. \n",
"Alongside with price, the dataset also provide information such as Crime, areas of non-retail business in the town, \n",
@@ -35,7 +38,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 2,
"metadata": {},
"outputs": [
{
@@ -78,8 +81,8 @@
"text": [
"\n",
"FIDLE 2020 - Practical Work Module\n",
- "Version : 0.2.8\n",
- "Run time : Saturday 15 February 2020, 12:38:00\n",
+ "Version : 0.2.9\n",
+ "Run time : Monday 17 February 2020, 22:08:38\n",
"TensorFlow version : 2.0.0\n",
"Keras version : 2.2.4-tf\n"
]
@@ -131,103 +134,103 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- "</style><table id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826\" ><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_aac3e7e8_51c9_11ea_b50a_1f46721d93b7\" ><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",
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+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row0_col13\" class=\"data row0 col13\" >24.00</td>\n",
" </tr>\n",
" <tr>\n",
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- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row1_col6\" class=\"data row1 col6\" >78.90</td>\n",
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- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row1_col10\" class=\"data row1 col10\" >17.80</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row1_col11\" class=\"data row1 col11\" >396.90</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row1_col12\" class=\"data row1 col12\" >9.14</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row1_col13\" class=\"data row1 col13\" >21.60</td>\n",
+ " <th id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row1_col0\" class=\"data row1 col0\" >0.03</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row1_col2\" class=\"data row1 col2\" >7.07</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row1_col4\" class=\"data row1 col4\" >0.47</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row1_col5\" class=\"data row1 col5\" >6.42</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row1_col6\" class=\"data row1 col6\" >78.90</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row1_col7\" class=\"data row1 col7\" >4.97</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row1_col8\" class=\"data row1 col8\" >2.00</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row1_col9\" class=\"data row1 col9\" >242.00</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row1_col10\" class=\"data row1 col10\" >17.80</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row1_col11\" class=\"data row1 col11\" >396.90</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row1_col12\" class=\"data row1 col12\" >9.14</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row1_col13\" class=\"data row1 col13\" >21.60</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row2_col0\" class=\"data row2 col0\" >0.03</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row2_col2\" class=\"data row2 col2\" >7.07</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row2_col3\" class=\"data row2 col3\" >0.00</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row2_col4\" class=\"data row2 col4\" >0.47</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row2_col5\" class=\"data row2 col5\" >7.18</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row2_col6\" class=\"data row2 col6\" >61.10</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row2_col7\" class=\"data row2 col7\" >4.97</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row2_col8\" class=\"data row2 col8\" >2.00</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row2_col9\" class=\"data row2 col9\" >242.00</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row2_col10\" class=\"data row2 col10\" >17.80</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row2_col11\" class=\"data row2 col11\" >392.83</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row2_col12\" class=\"data row2 col12\" >4.03</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row2_col13\" class=\"data row2 col13\" >34.70</td>\n",
+ " <th id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row2_col0\" class=\"data row2 col0\" >0.03</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row2_col2\" class=\"data row2 col2\" >7.07</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row2_col3\" class=\"data row2 col3\" >0.00</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row2_col4\" class=\"data row2 col4\" >0.47</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row2_col5\" class=\"data row2 col5\" >7.18</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row2_col6\" class=\"data row2 col6\" >61.10</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row2_col7\" class=\"data row2 col7\" >4.97</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row2_col8\" class=\"data row2 col8\" >2.00</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row2_col9\" class=\"data row2 col9\" >242.00</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row2_col10\" class=\"data row2 col10\" >17.80</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row2_col11\" class=\"data row2 col11\" >392.83</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row2_col12\" class=\"data row2 col12\" >4.03</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row2_col13\" class=\"data row2 col13\" >34.70</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row3_col0\" class=\"data row3 col0\" >0.03</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row3_col2\" class=\"data row3 col2\" >2.18</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row3_col4\" class=\"data row3 col4\" >0.46</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row3_col5\" class=\"data row3 col5\" >7.00</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row3_col6\" class=\"data row3 col6\" >45.80</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row3_col7\" class=\"data row3 col7\" >6.06</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row3_col8\" class=\"data row3 col8\" >3.00</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row3_col9\" class=\"data row3 col9\" >222.00</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row3_col10\" class=\"data row3 col10\" >18.70</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row3_col11\" class=\"data row3 col11\" >394.63</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row3_col12\" class=\"data row3 col12\" >2.94</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row3_col13\" class=\"data row3 col13\" >33.40</td>\n",
+ " <th id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row3_col0\" class=\"data row3 col0\" >0.03</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row3_col2\" class=\"data row3 col2\" >2.18</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row3_col4\" class=\"data row3 col4\" >0.46</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row3_col5\" class=\"data row3 col5\" >7.00</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row3_col6\" class=\"data row3 col6\" >45.80</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row3_col7\" class=\"data row3 col7\" >6.06</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row3_col8\" class=\"data row3 col8\" >3.00</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row3_col9\" class=\"data row3 col9\" >222.00</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row3_col10\" class=\"data row3 col10\" >18.70</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row3_col11\" class=\"data row3 col11\" >394.63</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row3_col12\" class=\"data row3 col12\" >2.94</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row3_col13\" class=\"data row3 col13\" >33.40</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row4_col0\" class=\"data row4 col0\" >0.07</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row4_col2\" class=\"data row4 col2\" >2.18</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row4_col4\" class=\"data row4 col4\" >0.46</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row4_col5\" class=\"data row4 col5\" >7.15</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row4_col6\" class=\"data row4 col6\" >54.20</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row4_col7\" class=\"data row4 col7\" >6.06</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row4_col8\" class=\"data row4 col8\" >3.00</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row4_col9\" class=\"data row4 col9\" >222.00</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row4_col10\" class=\"data row4 col10\" >18.70</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row4_col11\" class=\"data row4 col11\" >396.90</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row4_col12\" class=\"data row4 col12\" >5.33</td>\n",
- " <td id=\"T_9eb52d06_4fe7_11ea_bfba_2f4388d94826row4_col13\" class=\"data row4 col13\" >36.20</td>\n",
+ " <th id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row4_col0\" class=\"data row4 col0\" >0.07</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row4_col2\" class=\"data row4 col2\" >2.18</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row4_col4\" class=\"data row4 col4\" >0.46</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row4_col5\" class=\"data row4 col5\" >7.15</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row4_col6\" class=\"data row4 col6\" >54.20</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row4_col7\" class=\"data row4 col7\" >6.06</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row4_col8\" class=\"data row4 col8\" >3.00</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row4_col9\" class=\"data row4 col9\" >222.00</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row4_col10\" class=\"data row4 col10\" >18.70</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row4_col11\" class=\"data row4 col11\" >396.90</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row4_col12\" class=\"data row4 col12\" >5.33</td>\n",
+ " <td id=\"T_aac3e7e8_51c9_11ea_b50a_1f46721d93b7row4_col13\" class=\"data row4 col13\" >36.20</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x7f7b6c0c8050>"
+ "<pandas.io.formats.style.Styler at 0x7f953611fb50>"
]
},
"metadata": {},
@@ -260,7 +263,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
@@ -305,146 +308,146 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- "</style><table id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826\" ><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_aaccf306_51c9_11ea_b50a_1f46721d93b7\" ><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_9ebc7b06_4fe7_11ea_bfba_2f4388d94826level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
+ " <th id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row1_col0\" class=\"data row1 col0\" >3.73</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row1_col1\" class=\"data row1 col1\" >10.85</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row1_col2\" class=\"data row1 col2\" >11.11</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row1_col3\" class=\"data row1 col3\" >0.08</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row1_col4\" class=\"data row1 col4\" >0.55</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row1_col5\" class=\"data row1 col5\" >6.26</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row1_col6\" class=\"data row1 col6\" >69.55</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row1_col7\" class=\"data row1 col7\" >3.86</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row1_col8\" class=\"data row1 col8\" >9.69</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row1_col9\" class=\"data row1 col9\" >410.44</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row1_col10\" class=\"data row1 col10\" >18.56</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row1_col11\" class=\"data row1 col11\" >355.37</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row1_col12\" class=\"data row1 col12\" >12.98</td>\n",
+ " <th id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row1_col0\" class=\"data row1 col0\" >3.72</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row1_col1\" class=\"data row1 col1\" >11.78</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row1_col2\" class=\"data row1 col2\" >11.07</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row1_col3\" class=\"data row1 col3\" >0.06</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row1_col4\" class=\"data row1 col4\" >0.55</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row1_col5\" class=\"data row1 col5\" >6.30</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row1_col6\" class=\"data row1 col6\" >69.19</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row1_col7\" class=\"data row1 col7\" >3.78</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row1_col8\" class=\"data row1 col8\" >9.85</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row1_col9\" class=\"data row1 col9\" >412.77</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row1_col10\" class=\"data row1 col10\" >18.47</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row1_col11\" class=\"data row1 col11\" >357.65</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row1_col12\" class=\"data row1 col12\" >12.59</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row2_col0\" class=\"data row2 col0\" >8.61</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row2_col1\" class=\"data row2 col1\" >22.71</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row2_col2\" class=\"data row2 col2\" >6.86</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row2_col3\" class=\"data row2 col3\" >0.27</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row2_col4\" class=\"data row2 col4\" >0.11</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row2_col5\" class=\"data row2 col5\" >0.67</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row2_col6\" class=\"data row2 col6\" >27.52</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row2_col7\" class=\"data row2 col7\" >2.17</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row2_col8\" class=\"data row2 col8\" >8.79</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row2_col9\" class=\"data row2 col9\" >170.55</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row2_col10\" class=\"data row2 col10\" >2.07</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row2_col11\" class=\"data row2 col11\" >93.33</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row2_col12\" class=\"data row2 col12\" >7.13</td>\n",
+ " <th id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row2_col0\" class=\"data row2 col0\" >8.30</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row2_col1\" class=\"data row2 col1\" >23.59</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row2_col2\" class=\"data row2 col2\" >6.78</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row2_col3\" class=\"data row2 col3\" >0.24</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row2_col4\" class=\"data row2 col4\" >0.11</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row2_col5\" class=\"data row2 col5\" >0.71</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row2_col6\" class=\"data row2 col6\" >27.24</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row2_col7\" class=\"data row2 col7\" >2.08</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row2_col8\" class=\"data row2 col8\" >8.88</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row2_col9\" class=\"data row2 col9\" >171.88</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row2_col10\" class=\"data row2 col10\" >2.17</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row2_col11\" class=\"data row2 col11\" >90.85</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row2_col12\" class=\"data row2 col12\" >6.82</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row3_col0\" class=\"data row3 col0\" >0.01</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row3_col2\" class=\"data row3 col2\" >0.74</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row3_col4\" class=\"data row3 col4\" >0.39</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row3_col5\" class=\"data row3 col5\" >3.56</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row3_col6\" class=\"data row3 col6\" >6.50</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row3_col7\" class=\"data row3 col7\" >1.17</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row3_col8\" class=\"data row3 col8\" >1.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row3_col9\" class=\"data row3 col9\" >187.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row3_col10\" class=\"data row3 col10\" >12.60</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row3_col11\" class=\"data row3 col11\" >0.32</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row3_col12\" class=\"data row3 col12\" >1.92</td>\n",
+ " <th id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row3_col0\" class=\"data row3 col0\" >0.01</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row3_col2\" class=\"data row3 col2\" >0.74</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row3_col4\" class=\"data row3 col4\" >0.39</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row3_col5\" class=\"data row3 col5\" >3.86</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row3_col6\" class=\"data row3 col6\" >6.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row3_col7\" class=\"data row3 col7\" >1.14</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row3_col8\" class=\"data row3 col8\" >1.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row3_col9\" class=\"data row3 col9\" >188.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row3_col10\" class=\"data row3 col10\" >12.60</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row3_col11\" class=\"data row3 col11\" >0.32</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row3_col12\" class=\"data row3 col12\" >1.73</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row4_col0\" class=\"data row4 col0\" >0.08</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row4_col2\" class=\"data row4 col2\" >5.19</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row4_col4\" class=\"data row4 col4\" >0.45</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row4_col5\" class=\"data row4 col5\" >5.89</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row4_col6\" class=\"data row4 col6\" >47.45</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row4_col7\" class=\"data row4 col7\" >2.07</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row4_col8\" class=\"data row4 col8\" >4.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row4_col9\" class=\"data row4 col9\" >279.50</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row4_col10\" class=\"data row4 col10\" >17.40</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row4_col11\" class=\"data row4 col11\" >374.59</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row4_col12\" class=\"data row4 col12\" >7.46</td>\n",
+ " <th id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row4_col0\" class=\"data row4 col0\" >0.08</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row4_col2\" class=\"data row4 col2\" >5.15</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row4_col4\" class=\"data row4 col4\" >0.45</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row4_col5\" class=\"data row4 col5\" >5.89</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row4_col6\" class=\"data row4 col6\" >47.45</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row4_col7\" class=\"data row4 col7\" >2.11</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row4_col8\" class=\"data row4 col8\" >4.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row4_col9\" class=\"data row4 col9\" >279.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row4_col10\" class=\"data row4 col10\" >17.40</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row4_col11\" class=\"data row4 col11\" >375.91</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row4_col12\" class=\"data row4 col12\" >7.28</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row5_col0\" class=\"data row5 col0\" >0.25</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row5_col2\" class=\"data row5 col2\" >9.12</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row5_col3\" class=\"data row5 col3\" >0.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row5_col4\" class=\"data row5 col4\" >0.53</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row5_col5\" class=\"data row5 col5\" >6.17</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row5_col6\" class=\"data row5 col6\" >78.50</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row5_col7\" class=\"data row5 col7\" >3.32</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row5_col8\" class=\"data row5 col8\" >5.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row5_col9\" class=\"data row5 col9\" >330.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row5_col10\" class=\"data row5 col10\" >19.05</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row5_col11\" class=\"data row5 col11\" >391.30</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row5_col12\" class=\"data row5 col12\" >11.57</td>\n",
+ " <th id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row5_col0\" class=\"data row5 col0\" >0.25</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row5_col2\" class=\"data row5 col2\" >9.79</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row5_col3\" class=\"data row5 col3\" >0.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row5_col4\" class=\"data row5 col4\" >0.54</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row5_col5\" class=\"data row5 col5\" >6.23</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row5_col6\" class=\"data row5 col6\" >77.95</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row5_col7\" class=\"data row5 col7\" >3.13</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row5_col8\" class=\"data row5 col8\" >5.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row5_col9\" class=\"data row5 col9\" >332.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row5_col10\" class=\"data row5 col10\" >18.90</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row5_col11\" class=\"data row5 col11\" >392.22</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row5_col12\" class=\"data row5 col12\" >11.39</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row6_col0\" class=\"data row6 col0\" >3.99</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row6_col1\" class=\"data row6 col1\" >12.50</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row6_col2\" class=\"data row6 col2\" >18.10</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row6_col3\" class=\"data row6 col3\" >0.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row6_col4\" class=\"data row6 col4\" >0.62</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row6_col5\" class=\"data row6 col5\" >6.55</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row6_col6\" class=\"data row6 col6\" >94.38</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row6_col7\" class=\"data row6 col7\" >5.27</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row6_col8\" class=\"data row6 col8\" >24.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row6_col9\" class=\"data row6 col9\" >666.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row6_col10\" class=\"data row6 col10\" >20.20</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row6_col11\" class=\"data row6 col11\" >396.21</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row6_col12\" class=\"data row6 col12\" >17.27</td>\n",
+ " <th id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row6_col0\" class=\"data row6 col0\" >3.85</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row6_col1\" class=\"data row6 col1\" >19.50</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row6_col2\" class=\"data row6 col2\" >18.10</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row6_col3\" class=\"data row6 col3\" >0.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row6_col4\" class=\"data row6 col4\" >0.62</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row6_col5\" class=\"data row6 col5\" >6.64</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row6_col6\" class=\"data row6 col6\" >93.97</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row6_col7\" class=\"data row6 col7\" >5.08</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row6_col8\" class=\"data row6 col8\" >24.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row6_col9\" class=\"data row6 col9\" >666.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row6_col10\" class=\"data row6 col10\" >20.20</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row6_col11\" class=\"data row6 col11\" >396.90</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row6_col12\" class=\"data row6 col12\" >16.57</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row7_col0\" class=\"data row7 col0\" >73.53</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row7_col1\" class=\"data row7 col1\" >95.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row7_col2\" class=\"data row7 col2\" >27.74</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row7_col3\" class=\"data row7 col3\" >1.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row7_col4\" class=\"data row7 col4\" >0.87</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row7_col5\" class=\"data row7 col5\" >8.78</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row7_col6\" class=\"data row7 col6\" >100.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row7_col7\" class=\"data row7 col7\" >12.13</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row7_col8\" class=\"data row7 col8\" >24.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row7_col9\" class=\"data row7 col9\" >711.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row7_col10\" class=\"data row7 col10\" >22.00</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row7_col11\" class=\"data row7 col11\" >396.90</td>\n",
- " <td id=\"T_9ebc7b06_4fe7_11ea_bfba_2f4388d94826row7_col12\" class=\"data row7 col12\" >36.98</td>\n",
+ " <th id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row7_col0\" class=\"data row7 col0\" >73.53</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row7_col1\" class=\"data row7 col1\" >100.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row7_col2\" class=\"data row7 col2\" >27.74</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row7_col3\" class=\"data row7 col3\" >1.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row7_col4\" class=\"data row7 col4\" >0.87</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row7_col5\" class=\"data row7 col5\" >8.78</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row7_col6\" class=\"data row7 col6\" >100.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row7_col7\" class=\"data row7 col7\" >10.71</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row7_col8\" class=\"data row7 col8\" >24.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row7_col9\" class=\"data row7 col9\" >711.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row7_col10\" class=\"data row7 col10\" >22.00</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row7_col11\" class=\"data row7 col11\" >396.90</td>\n",
+ " <td id=\"T_aaccf306_51c9_11ea_b50a_1f46721d93b7row7_col12\" class=\"data row7 col12\" >37.97</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x7f7af2b97cd0>"
+ "<pandas.io.formats.style.Styler at 0x7f953611f5d0>"
]
},
"metadata": {},
@@ -454,139 +457,139 @@
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- "</style><table id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826\" ><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_aadc40b8_51c9_11ea_b50a_1f46721d93b7\" ><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_9ec335cc_4fe7_11ea_bfba_2f4388d94826level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
+ " <th id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row1_col0\" class=\"data row1 col0\" >-0.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row1_col4\" class=\"data row1 col4\" >-0.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row1_col5\" class=\"data row1 col5\" >0.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row1_col6\" class=\"data row1 col6\" >-0.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row1_col7\" class=\"data row1 col7\" >0.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row1_col8\" class=\"data row1 col8\" >-0.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row1_col9\" class=\"data row1 col9\" >0.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row1_col10\" class=\"data row1 col10\" >0.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row1_col11\" class=\"data row1 col11\" >0.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row1_col12\" class=\"data row1 col12\" >-0.00</td>\n",
+ " <th id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row1_col0\" class=\"data row1 col0\" >0.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row1_col1\" class=\"data row1 col1\" >-0.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row1_col3\" class=\"data row1 col3\" >-0.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row1_col4\" class=\"data row1 col4\" >-0.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row1_col5\" class=\"data row1 col5\" >0.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row1_col6\" class=\"data row1 col6\" >0.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row1_col7\" class=\"data row1 col7\" >0.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row1_col8\" class=\"data row1 col8\" >0.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row1_col9\" class=\"data row1 col9\" >0.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row1_col10\" class=\"data row1 col10\" >0.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row1_col11\" class=\"data row1 col11\" >0.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row1_col12\" class=\"data row1 col12\" >-0.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row2_col0\" class=\"data row2 col0\" >1.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row2_col1\" class=\"data row2 col1\" >1.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row2_col3\" class=\"data row2 col3\" >1.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row2_col4\" class=\"data row2 col4\" >1.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row2_col5\" class=\"data row2 col5\" >1.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row2_col6\" class=\"data row2 col6\" >1.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row2_col7\" class=\"data row2 col7\" >1.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row2_col8\" class=\"data row2 col8\" >1.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row2_col9\" class=\"data row2 col9\" >1.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row2_col10\" class=\"data row2 col10\" >1.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row2_col11\" class=\"data row2 col11\" >1.00</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row2_col12\" class=\"data row2 col12\" >1.00</td>\n",
+ " <th id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row2_col0\" class=\"data row2 col0\" >1.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row2_col1\" class=\"data row2 col1\" >1.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row2_col3\" class=\"data row2 col3\" >1.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row2_col4\" class=\"data row2 col4\" >1.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row2_col5\" class=\"data row2 col5\" >1.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row2_col6\" class=\"data row2 col6\" >1.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row2_col7\" class=\"data row2 col7\" >1.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row2_col8\" class=\"data row2 col8\" >1.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row2_col9\" class=\"data row2 col9\" >1.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row2_col10\" class=\"data row2 col10\" >1.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row2_col11\" class=\"data row2 col11\" >1.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row2_col12\" class=\"data row2 col12\" >1.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row3_col0\" class=\"data row3 col0\" >-0.43</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row3_col1\" class=\"data row3 col1\" >-0.48</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row3_col2\" class=\"data row3 col2\" >-1.51</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row3_col3\" class=\"data row3 col3\" >-0.29</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row3_col4\" class=\"data row3 col4\" >-1.48</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row3_col5\" class=\"data row3 col5\" >-4.03</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row3_col6\" class=\"data row3 col6\" >-2.29</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row3_col7\" class=\"data row3 col7\" >-1.24</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row3_col8\" class=\"data row3 col8\" >-0.99</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row3_col9\" class=\"data row3 col9\" >-1.31</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row3_col10\" class=\"data row3 col10\" >-2.88</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row3_col11\" class=\"data row3 col11\" >-3.80</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row3_col12\" class=\"data row3 col12\" >-1.55</td>\n",
+ " <th id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row3_col0\" class=\"data row3 col0\" >-0.45</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row3_col1\" class=\"data row3 col1\" >-0.50</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row3_col2\" class=\"data row3 col2\" >-1.52</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row3_col3\" class=\"data row3 col3\" >-0.26</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row3_col4\" class=\"data row3 col4\" >-1.49</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row3_col5\" class=\"data row3 col5\" >-3.44</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row3_col6\" class=\"data row3 col6\" >-2.32</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row3_col7\" class=\"data row3 col7\" >-1.27</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row3_col8\" class=\"data row3 col8\" >-1.00</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row3_col9\" class=\"data row3 col9\" >-1.31</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row3_col10\" class=\"data row3 col10\" >-2.70</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row3_col11\" class=\"data row3 col11\" >-3.93</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row3_col12\" class=\"data row3 col12\" >-1.59</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row4_col0\" class=\"data row4 col0\" >-0.42</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row4_col1\" class=\"data row4 col1\" >-0.48</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row4_col2\" class=\"data row4 col2\" >-0.86</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row4_col3\" class=\"data row4 col3\" >-0.29</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row4_col4\" class=\"data row4 col4\" >-0.88</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row4_col5\" class=\"data row4 col5\" >-0.56</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row4_col6\" class=\"data row4 col6\" >-0.80</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row4_col7\" class=\"data row4 col7\" >-0.83</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row4_col8\" class=\"data row4 col8\" >-0.65</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row4_col9\" class=\"data row4 col9\" >-0.77</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row4_col10\" class=\"data row4 col10\" >-0.56</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row4_col11\" class=\"data row4 col11\" >0.21</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row4_col12\" class=\"data row4 col12\" >-0.77</td>\n",
+ " <th id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row4_col0\" class=\"data row4 col0\" >-0.44</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row4_col1\" class=\"data row4 col1\" >-0.50</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row4_col2\" class=\"data row4 col2\" >-0.87</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row4_col3\" class=\"data row4 col3\" >-0.26</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row4_col4\" class=\"data row4 col4\" >-0.88</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row4_col5\" class=\"data row4 col5\" >-0.58</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row4_col6\" class=\"data row4 col6\" >-0.80</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row4_col7\" class=\"data row4 col7\" >-0.81</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row4_col8\" class=\"data row4 col8\" >-0.66</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row4_col9\" class=\"data row4 col9\" >-0.78</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row4_col10\" class=\"data row4 col10\" >-0.49</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row4_col11\" class=\"data row4 col11\" >0.20</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row4_col12\" class=\"data row4 col12\" >-0.78</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row5_col0\" class=\"data row5 col0\" >-0.40</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row5_col1\" class=\"data row5 col1\" >-0.48</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row5_col2\" class=\"data row5 col2\" >-0.29</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row5_col3\" class=\"data row5 col3\" >-0.29</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row5_col4\" class=\"data row5 col4\" >-0.21</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row5_col5\" class=\"data row5 col5\" >-0.13</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row5_col6\" class=\"data row5 col6\" >0.33</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row5_col7\" class=\"data row5 col7\" >-0.25</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row5_col8\" class=\"data row5 col8\" >-0.53</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row5_col9\" class=\"data row5 col9\" >-0.47</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row5_col10\" class=\"data row5 col10\" >0.24</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row5_col11\" class=\"data row5 col11\" >0.39</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row5_col12\" class=\"data row5 col12\" >-0.20</td>\n",
+ " <th id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row5_col0\" class=\"data row5 col0\" >-0.42</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row5_col1\" class=\"data row5 col1\" >-0.50</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row5_col2\" class=\"data row5 col2\" >-0.19</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row5_col3\" class=\"data row5 col3\" >-0.26</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row5_col4\" class=\"data row5 col4\" >-0.13</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row5_col5\" class=\"data row5 col5\" >-0.10</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row5_col6\" class=\"data row5 col6\" >0.32</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row5_col7\" class=\"data row5 col7\" >-0.31</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row5_col8\" class=\"data row5 col8\" >-0.55</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row5_col9\" class=\"data row5 col9\" >-0.47</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row5_col10\" class=\"data row5 col10\" >0.20</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row5_col11\" class=\"data row5 col11\" >0.38</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row5_col12\" class=\"data row5 col12\" >-0.18</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row6_col0\" class=\"data row6 col0\" >0.03</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row6_col1\" class=\"data row6 col1\" >0.07</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row6_col2\" class=\"data row6 col2\" >1.02</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row6_col3\" class=\"data row6 col3\" >-0.29</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row6_col4\" class=\"data row6 col4\" >0.63</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row6_col5\" class=\"data row6 col5\" >0.44</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row6_col6\" class=\"data row6 col6\" >0.90</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row6_col7\" class=\"data row6 col7\" >0.65</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row6_col8\" class=\"data row6 col8\" >1.63</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row6_col9\" class=\"data row6 col9\" >1.50</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row6_col10\" class=\"data row6 col10\" >0.80</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row6_col11\" class=\"data row6 col11\" >0.44</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row6_col12\" class=\"data row6 col12\" >0.60</td>\n",
+ " <th id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row6_col0\" class=\"data row6 col0\" >0.02</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row6_col1\" class=\"data row6 col1\" >0.33</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row6_col2\" class=\"data row6 col2\" >1.04</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row6_col3\" class=\"data row6 col3\" >-0.26</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row6_col4\" class=\"data row6 col4\" >0.63</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row6_col5\" class=\"data row6 col5\" >0.48</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row6_col6\" class=\"data row6 col6\" >0.91</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row6_col7\" class=\"data row6 col7\" >0.63</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row6_col8\" class=\"data row6 col8\" >1.59</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row6_col9\" class=\"data row6 col9\" >1.47</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row6_col10\" class=\"data row6 col10\" >0.80</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row6_col11\" class=\"data row6 col11\" >0.43</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row6_col12\" class=\"data row6 col12\" >0.58</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row7_col0\" class=\"data row7 col0\" >8.11</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row7_col1\" class=\"data row7 col1\" >3.71</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row7_col2\" class=\"data row7 col2\" >2.42</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row7_col3\" class=\"data row7 col3\" >3.48</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row7_col4\" class=\"data row7 col4\" >2.81</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row7_col5\" class=\"data row7 col5\" >3.77</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row7_col6\" class=\"data row7 col6\" >1.11</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row7_col7\" class=\"data row7 col7\" >3.81</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row7_col8\" class=\"data row7 col8\" >1.63</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row7_col9\" class=\"data row7 col9\" >1.76</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row7_col10\" class=\"data row7 col10\" >1.67</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row7_col11\" class=\"data row7 col11\" >0.44</td>\n",
- " <td id=\"T_9ec335cc_4fe7_11ea_bfba_2f4388d94826row7_col12\" class=\"data row7 col12\" >3.36</td>\n",
+ " <th id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row7_col0\" class=\"data row7 col0\" >8.41</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row7_col1\" class=\"data row7 col1\" >3.74</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row7_col2\" class=\"data row7 col2\" >2.46</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row7_col3\" class=\"data row7 col3\" >3.88</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row7_col4\" class=\"data row7 col4\" >2.82</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row7_col5\" class=\"data row7 col5\" >3.50</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row7_col6\" class=\"data row7 col6\" >1.13</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row7_col7\" class=\"data row7 col7\" >3.34</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row7_col8\" class=\"data row7 col8\" >1.59</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row7_col9\" class=\"data row7 col9\" >1.74</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row7_col10\" class=\"data row7 col10\" >1.63</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row7_col11\" class=\"data row7 col11\" >0.43</td>\n",
+ " <td id=\"T_aadc40b8_51c9_11ea_b50a_1f46721d93b7row7_col12\" class=\"data row7 col12\" >3.72</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x7f7af238e790>"
+ "<pandas.io.formats.style.Styler at 0x7f952e97ff50>"
]
},
"metadata": {},
@@ -621,7 +624,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
@@ -649,7 +652,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
@@ -679,7 +682,7 @@
"<IPython.core.display.Image object>"
]
},
- "execution_count": 6,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -700,7 +703,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
@@ -709,205 +712,205 @@
"text": [
"Train on 354 samples, validate on 152 samples\n",
"Epoch 1/100\n",
- "354/354 [==============================] - 1s 3ms/sample - loss: 418.8435 - mae: 18.6926 - mse: 418.8436 - val_loss: 363.6250 - val_mae: 17.0712 - val_mse: 363.6249\n",
+ "354/354 [==============================] - 1s 2ms/sample - loss: 414.5603 - mae: 18.2577 - mse: 414.5602 - val_loss: 266.3728 - val_mae: 13.9913 - val_mse: 266.3728\n",
"Epoch 2/100\n",
- "354/354 [==============================] - 0s 280us/sample - loss: 185.7644 - mae: 11.5769 - mse: 185.7644 - val_loss: 130.8274 - val_mae: 9.1964 - val_mse: 130.8273\n",
+ "354/354 [==============================] - 0s 190us/sample - loss: 165.4507 - mae: 10.4618 - mse: 165.4507 - val_loss: 74.4125 - val_mae: 6.0372 - val_mse: 74.4125\n",
"Epoch 3/100\n",
- "354/354 [==============================] - 0s 272us/sample - loss: 63.7021 - mae: 6.1090 - mse: 63.7021 - val_loss: 54.9533 - val_mae: 5.5778 - val_mse: 54.9533\n",
+ "354/354 [==============================] - 0s 187us/sample - loss: 54.2313 - mae: 5.3763 - mse: 54.2313 - val_loss: 47.0203 - val_mae: 4.7399 - val_mse: 47.0203\n",
"Epoch 4/100\n",
- "354/354 [==============================] - 0s 273us/sample - loss: 33.5469 - mae: 4.2459 - mse: 33.5469 - val_loss: 38.7432 - val_mae: 4.5027 - val_mse: 38.7432\n",
+ "354/354 [==============================] - 0s 166us/sample - loss: 32.3303 - mae: 4.2632 - mse: 32.3303 - val_loss: 38.0120 - val_mae: 4.2484 - val_mse: 38.0120\n",
"Epoch 5/100\n",
- "354/354 [==============================] - 0s 272us/sample - loss: 25.0842 - mae: 3.5697 - mse: 25.0842 - val_loss: 31.9627 - val_mae: 3.9794 - val_mse: 31.9627\n",
+ "354/354 [==============================] - 0s 153us/sample - loss: 25.3763 - mae: 3.7745 - mse: 25.3763 - val_loss: 32.4707 - val_mae: 3.8465 - val_mse: 32.4707\n",
"Epoch 6/100\n",
- "354/354 [==============================] - 0s 270us/sample - loss: 20.9567 - mae: 3.2382 - mse: 20.9567 - val_loss: 30.0260 - val_mae: 3.7664 - val_mse: 30.0260\n",
+ "354/354 [==============================] - 0s 153us/sample - loss: 22.2331 - mae: 3.4720 - mse: 22.2331 - val_loss: 29.6142 - val_mae: 3.4844 - val_mse: 29.6142\n",
"Epoch 7/100\n",
- "354/354 [==============================] - 0s 275us/sample - loss: 18.9505 - mae: 3.0151 - mse: 18.9505 - val_loss: 23.8960 - val_mae: 3.3547 - val_mse: 23.8960\n",
+ "354/354 [==============================] - 0s 154us/sample - loss: 19.7834 - mae: 3.2245 - mse: 19.7834 - val_loss: 27.1649 - val_mae: 3.5465 - val_mse: 27.1649\n",
"Epoch 8/100\n",
- "354/354 [==============================] - 0s 273us/sample - loss: 17.1925 - mae: 2.8789 - mse: 17.1925 - val_loss: 22.9789 - val_mae: 3.1954 - val_mse: 22.9789\n",
+ "354/354 [==============================] - 0s 155us/sample - loss: 18.0991 - mae: 3.0669 - mse: 18.0991 - val_loss: 26.0093 - val_mae: 3.5617 - val_mse: 26.0093\n",
"Epoch 9/100\n",
- "354/354 [==============================] - 0s 272us/sample - loss: 15.8211 - mae: 2.7289 - mse: 15.8211 - val_loss: 20.5825 - val_mae: 3.1199 - val_mse: 20.5825\n",
+ "354/354 [==============================] - 0s 161us/sample - loss: 16.9247 - mae: 2.9184 - mse: 16.9247 - val_loss: 23.2549 - val_mae: 3.3243 - val_mse: 23.2549\n",
"Epoch 10/100\n",
- "354/354 [==============================] - 0s 274us/sample - loss: 15.0976 - mae: 2.6753 - mse: 15.0976 - val_loss: 21.2327 - val_mae: 3.0570 - val_mse: 21.2327\n",
+ "354/354 [==============================] - 0s 150us/sample - loss: 16.0827 - mae: 2.8116 - mse: 16.0827 - val_loss: 21.1365 - val_mae: 3.0248 - val_mse: 21.1365\n",
"Epoch 11/100\n",
- "354/354 [==============================] - 0s 273us/sample - loss: 14.0510 - mae: 2.5988 - mse: 14.0510 - val_loss: 18.8729 - val_mae: 2.9371 - val_mse: 18.8729\n",
+ "354/354 [==============================] - 0s 170us/sample - loss: 15.0334 - mae: 2.7214 - mse: 15.0334 - val_loss: 20.0163 - val_mae: 2.9800 - val_mse: 20.0163\n",
"Epoch 12/100\n",
- "354/354 [==============================] - 0s 278us/sample - loss: 13.9241 - mae: 2.5217 - mse: 13.9241 - val_loss: 18.5040 - val_mae: 2.8935 - val_mse: 18.5040\n",
+ "354/354 [==============================] - 0s 180us/sample - loss: 14.4011 - mae: 2.6949 - mse: 14.4011 - val_loss: 19.8958 - val_mae: 2.9262 - val_mse: 19.8958\n",
"Epoch 13/100\n",
- "354/354 [==============================] - 0s 276us/sample - loss: 13.2203 - mae: 2.4659 - mse: 13.2203 - val_loss: 17.7626 - val_mae: 2.8627 - val_mse: 17.7626\n",
+ "354/354 [==============================] - 0s 184us/sample - loss: 13.9168 - mae: 2.5674 - mse: 13.9168 - val_loss: 18.5729 - val_mae: 2.7302 - val_mse: 18.5729\n",
"Epoch 14/100\n",
- "354/354 [==============================] - 0s 277us/sample - loss: 13.0399 - mae: 2.4507 - mse: 13.0399 - val_loss: 18.7882 - val_mae: 2.8812 - val_mse: 18.7882\n",
+ "354/354 [==============================] - 0s 161us/sample - loss: 13.5575 - mae: 2.5442 - mse: 13.5575 - val_loss: 17.8812 - val_mae: 2.6748 - val_mse: 17.8812\n",
"Epoch 15/100\n",
- "354/354 [==============================] - 0s 274us/sample - loss: 12.7152 - mae: 2.3897 - mse: 12.7152 - val_loss: 17.5327 - val_mae: 2.7973 - val_mse: 17.5327\n",
+ "354/354 [==============================] - 0s 166us/sample - loss: 12.8689 - mae: 2.4779 - mse: 12.8689 - val_loss: 18.9649 - val_mae: 2.7560 - val_mse: 18.9649\n",
"Epoch 16/100\n",
- "354/354 [==============================] - 0s 276us/sample - loss: 12.4044 - mae: 2.3936 - mse: 12.4044 - val_loss: 18.3104 - val_mae: 2.8215 - val_mse: 18.3104\n",
+ "354/354 [==============================] - 0s 159us/sample - loss: 12.6470 - mae: 2.4670 - mse: 12.6470 - val_loss: 16.5834 - val_mae: 2.6016 - val_mse: 16.5834\n",
"Epoch 17/100\n",
- "354/354 [==============================] - 0s 280us/sample - loss: 12.0216 - mae: 2.3488 - mse: 12.0216 - val_loss: 16.1507 - val_mae: 2.7517 - val_mse: 16.1507\n",
+ "354/354 [==============================] - 0s 159us/sample - loss: 12.3566 - mae: 2.4280 - mse: 12.3566 - val_loss: 16.7371 - val_mae: 2.6670 - val_mse: 16.7371\n",
"Epoch 18/100\n",
- "354/354 [==============================] - 0s 272us/sample - loss: 11.9575 - mae: 2.3477 - mse: 11.9575 - val_loss: 15.5722 - val_mae: 2.6895 - val_mse: 15.5722\n",
+ "354/354 [==============================] - 0s 158us/sample - loss: 12.3328 - mae: 2.4060 - mse: 12.3328 - val_loss: 16.3754 - val_mae: 2.6027 - val_mse: 16.3754\n",
"Epoch 19/100\n",
- "354/354 [==============================] - 0s 272us/sample - loss: 11.7747 - mae: 2.3273 - mse: 11.7747 - val_loss: 15.7311 - val_mae: 2.6832 - val_mse: 15.7311\n",
+ "354/354 [==============================] - 0s 152us/sample - loss: 11.8357 - mae: 2.3106 - mse: 11.8357 - val_loss: 16.1015 - val_mae: 2.6255 - val_mse: 16.1015\n",
"Epoch 20/100\n",
- "354/354 [==============================] - 0s 273us/sample - loss: 11.6233 - mae: 2.3188 - mse: 11.6233 - val_loss: 16.9861 - val_mae: 2.7485 - val_mse: 16.9861\n",
+ "354/354 [==============================] - 0s 163us/sample - loss: 11.6722 - mae: 2.3482 - mse: 11.6722 - val_loss: 16.1405 - val_mae: 2.6889 - val_mse: 16.1405\n",
"Epoch 21/100\n",
- "354/354 [==============================] - 0s 275us/sample - loss: 11.3040 - mae: 2.2359 - mse: 11.3040 - val_loss: 20.0885 - val_mae: 3.0260 - val_mse: 20.0885\n",
+ "354/354 [==============================] - 0s 175us/sample - loss: 11.2774 - mae: 2.3344 - mse: 11.2774 - val_loss: 15.2110 - val_mae: 2.5038 - val_mse: 15.2110\n",
"Epoch 22/100\n",
- "354/354 [==============================] - 0s 267us/sample - loss: 11.5525 - mae: 2.2839 - mse: 11.5525 - val_loss: 16.2819 - val_mae: 2.6818 - val_mse: 16.2819\n",
+ "354/354 [==============================] - 0s 180us/sample - loss: 11.2491 - mae: 2.3055 - mse: 11.2491 - val_loss: 15.4745 - val_mae: 2.4494 - val_mse: 15.4744\n",
"Epoch 23/100\n",
- "354/354 [==============================] - 0s 270us/sample - loss: 10.9328 - mae: 2.2393 - mse: 10.9328 - val_loss: 14.8984 - val_mae: 2.6211 - val_mse: 14.8984\n",
+ "354/354 [==============================] - 0s 187us/sample - loss: 10.9102 - mae: 2.2171 - mse: 10.9102 - val_loss: 15.1145 - val_mae: 2.4282 - val_mse: 15.1145\n",
"Epoch 24/100\n",
- "354/354 [==============================] - 0s 271us/sample - loss: 10.6702 - mae: 2.2566 - mse: 10.6702 - val_loss: 15.2297 - val_mae: 2.5943 - val_mse: 15.2297\n",
+ "354/354 [==============================] - 0s 168us/sample - loss: 10.7952 - mae: 2.2533 - mse: 10.7952 - val_loss: 14.3789 - val_mae: 2.3683 - val_mse: 14.3789\n",
"Epoch 25/100\n",
- "354/354 [==============================] - 0s 268us/sample - loss: 10.6099 - mae: 2.2306 - mse: 10.6099 - val_loss: 14.0894 - val_mae: 2.5234 - val_mse: 14.0894\n",
+ "354/354 [==============================] - 0s 171us/sample - loss: 10.7250 - mae: 2.2489 - mse: 10.7250 - val_loss: 15.1102 - val_mae: 2.3422 - val_mse: 15.1102\n",
"Epoch 26/100\n",
- "354/354 [==============================] - 0s 271us/sample - loss: 10.5236 - mae: 2.1899 - mse: 10.5236 - val_loss: 16.2054 - val_mae: 2.6445 - val_mse: 16.2054\n",
+ "354/354 [==============================] - 0s 158us/sample - loss: 10.4010 - mae: 2.1702 - mse: 10.4010 - val_loss: 14.3260 - val_mae: 2.3176 - val_mse: 14.3260\n",
"Epoch 27/100\n",
- "354/354 [==============================] - 0s 276us/sample - loss: 10.3134 - mae: 2.1293 - mse: 10.3134 - val_loss: 14.1194 - val_mae: 2.5172 - val_mse: 14.1194\n",
+ "354/354 [==============================] - 0s 149us/sample - loss: 10.1442 - mae: 2.1797 - mse: 10.1442 - val_loss: 13.6694 - val_mae: 2.3864 - val_mse: 13.6694\n",
"Epoch 28/100\n",
- "354/354 [==============================] - 0s 252us/sample - loss: 10.1060 - mae: 2.1281 - mse: 10.1060 - val_loss: 15.9710 - val_mae: 2.6624 - val_mse: 15.9710\n",
+ "354/354 [==============================] - 0s 168us/sample - loss: 10.1391 - mae: 2.1809 - mse: 10.1391 - val_loss: 14.0177 - val_mae: 2.3467 - val_mse: 14.0177\n",
"Epoch 29/100\n",
- "354/354 [==============================] - 0s 272us/sample - loss: 10.3668 - mae: 2.1627 - mse: 10.3668 - val_loss: 14.1934 - val_mae: 2.4820 - val_mse: 14.1934\n",
+ "354/354 [==============================] - 0s 149us/sample - loss: 9.9119 - mae: 2.1267 - mse: 9.9119 - val_loss: 14.0739 - val_mae: 2.4617 - val_mse: 14.0739\n",
"Epoch 30/100\n",
- "354/354 [==============================] - 0s 271us/sample - loss: 9.9118 - mae: 2.0982 - mse: 9.9118 - val_loss: 14.3858 - val_mae: 2.4987 - val_mse: 14.3858\n",
+ "354/354 [==============================] - 0s 164us/sample - loss: 10.0176 - mae: 2.1669 - mse: 10.0176 - val_loss: 13.5116 - val_mae: 2.3158 - val_mse: 13.5116\n",
"Epoch 31/100\n",
- "354/354 [==============================] - 0s 272us/sample - loss: 9.8766 - mae: 2.0939 - mse: 9.8766 - val_loss: 13.8227 - val_mae: 2.5071 - val_mse: 13.8227\n",
+ "354/354 [==============================] - 0s 189us/sample - loss: 9.8259 - mae: 2.1407 - mse: 9.8259 - val_loss: 13.7364 - val_mae: 2.3531 - val_mse: 13.7364\n",
"Epoch 32/100\n",
- "354/354 [==============================] - 0s 273us/sample - loss: 9.8629 - mae: 2.1158 - mse: 9.8629 - val_loss: 13.7714 - val_mae: 2.4909 - val_mse: 13.7714\n",
+ "354/354 [==============================] - 0s 178us/sample - loss: 9.4495 - mae: 2.0922 - mse: 9.4495 - val_loss: 14.1936 - val_mae: 2.3887 - val_mse: 14.1936\n",
"Epoch 33/100\n",
- "354/354 [==============================] - 0s 273us/sample - loss: 9.7673 - mae: 2.0776 - mse: 9.7673 - val_loss: 13.6226 - val_mae: 2.4226 - val_mse: 13.6226\n",
+ "354/354 [==============================] - 0s 164us/sample - loss: 9.6721 - mae: 2.0870 - mse: 9.6721 - val_loss: 13.4267 - val_mae: 2.3508 - val_mse: 13.4267\n",
"Epoch 34/100\n",
- "354/354 [==============================] - 0s 269us/sample - loss: 9.9778 - mae: 2.1046 - mse: 9.9778 - val_loss: 13.7940 - val_mae: 2.4524 - val_mse: 13.7940\n",
+ "354/354 [==============================] - 0s 167us/sample - loss: 9.1042 - mae: 2.0644 - mse: 9.1042 - val_loss: 13.3821 - val_mae: 2.4709 - val_mse: 13.3821\n",
"Epoch 35/100\n",
- "354/354 [==============================] - 0s 269us/sample - loss: 9.5368 - mae: 2.0359 - mse: 9.5368 - val_loss: 14.8796 - val_mae: 2.7218 - val_mse: 14.8796\n",
+ "354/354 [==============================] - 0s 155us/sample - loss: 9.0129 - mae: 2.0482 - mse: 9.0129 - val_loss: 14.2184 - val_mae: 2.2754 - val_mse: 14.2184\n",
"Epoch 36/100\n",
- "354/354 [==============================] - 0s 272us/sample - loss: 9.6137 - mae: 2.0622 - mse: 9.6137 - val_loss: 13.3884 - val_mae: 2.4377 - val_mse: 13.3884\n",
+ "354/354 [==============================] - 0s 160us/sample - loss: 9.2470 - mae: 2.0661 - mse: 9.2470 - val_loss: 14.3466 - val_mae: 2.5561 - val_mse: 14.3466\n",
"Epoch 37/100\n",
- "354/354 [==============================] - 0s 272us/sample - loss: 9.0956 - mae: 2.0223 - mse: 9.0956 - val_loss: 14.0526 - val_mae: 2.4567 - val_mse: 14.0526\n",
+ "354/354 [==============================] - 0s 169us/sample - loss: 9.1695 - mae: 2.0766 - mse: 9.1695 - val_loss: 13.3818 - val_mae: 2.2373 - val_mse: 13.3818\n",
"Epoch 38/100\n",
- "354/354 [==============================] - 0s 269us/sample - loss: 9.1988 - mae: 2.0116 - mse: 9.1988 - val_loss: 13.9418 - val_mae: 2.4329 - val_mse: 13.9418\n",
+ "354/354 [==============================] - 0s 165us/sample - loss: 9.1663 - mae: 2.0617 - mse: 9.1663 - val_loss: 14.7461 - val_mae: 2.5061 - val_mse: 14.7461\n",
"Epoch 39/100\n",
- "354/354 [==============================] - 0s 267us/sample - loss: 9.2909 - mae: 2.0380 - mse: 9.2909 - val_loss: 12.7459 - val_mae: 2.4097 - val_mse: 12.7459\n",
+ "354/354 [==============================] - 0s 159us/sample - loss: 8.7273 - mae: 2.0208 - mse: 8.7273 - val_loss: 12.5890 - val_mae: 2.3037 - val_mse: 12.5890\n",
"Epoch 40/100\n",
- "354/354 [==============================] - 0s 272us/sample - loss: 9.0192 - mae: 2.0256 - mse: 9.0192 - val_loss: 14.2976 - val_mae: 2.4998 - val_mse: 14.2976\n",
+ "354/354 [==============================] - 0s 166us/sample - loss: 8.9038 - mae: 2.0352 - mse: 8.9038 - val_loss: 12.9754 - val_mae: 2.2079 - val_mse: 12.9754\n",
"Epoch 41/100\n",
- "354/354 [==============================] - 0s 281us/sample - loss: 8.7552 - mae: 1.9830 - mse: 8.7552 - val_loss: 14.4870 - val_mae: 2.4739 - val_mse: 14.4870\n",
+ "354/354 [==============================] - 0s 153us/sample - loss: 8.6155 - mae: 2.0267 - mse: 8.6155 - val_loss: 13.9239 - val_mae: 2.3525 - val_mse: 13.9239\n",
"Epoch 42/100\n",
- "354/354 [==============================] - 0s 273us/sample - loss: 8.8108 - mae: 1.9751 - mse: 8.8108 - val_loss: 12.7398 - val_mae: 2.4317 - val_mse: 12.7398\n",
+ "354/354 [==============================] - 0s 163us/sample - loss: 8.5479 - mae: 2.0170 - mse: 8.5479 - val_loss: 13.6362 - val_mae: 2.2694 - val_mse: 13.6362\n",
"Epoch 43/100\n",
- "354/354 [==============================] - 0s 270us/sample - loss: 8.6617 - mae: 1.9931 - mse: 8.6617 - val_loss: 15.7144 - val_mae: 2.5868 - val_mse: 15.7144\n",
+ "354/354 [==============================] - 0s 165us/sample - loss: 8.7087 - mae: 2.0062 - mse: 8.7087 - val_loss: 13.1138 - val_mae: 2.2386 - val_mse: 13.1138\n",
"Epoch 44/100\n",
- "354/354 [==============================] - 0s 274us/sample - loss: 8.6111 - mae: 1.9638 - mse: 8.6111 - val_loss: 12.5787 - val_mae: 2.3649 - val_mse: 12.5787\n",
+ "354/354 [==============================] - 0s 160us/sample - loss: 8.3942 - mae: 1.9622 - mse: 8.3942 - val_loss: 12.3461 - val_mae: 2.2337 - val_mse: 12.3461\n",
"Epoch 45/100\n",
- "354/354 [==============================] - 0s 273us/sample - loss: 8.3551 - mae: 1.9166 - mse: 8.3551 - val_loss: 12.4471 - val_mae: 2.3693 - val_mse: 12.4471\n",
+ "354/354 [==============================] - 0s 168us/sample - loss: 8.4101 - mae: 2.0098 - mse: 8.4101 - val_loss: 13.2116 - val_mae: 2.2682 - val_mse: 13.2116\n",
"Epoch 46/100\n",
- "354/354 [==============================] - 0s 266us/sample - loss: 8.3524 - mae: 1.9671 - mse: 8.3524 - val_loss: 14.4275 - val_mae: 2.4847 - val_mse: 14.4275\n",
+ "354/354 [==============================] - 0s 156us/sample - loss: 8.3264 - mae: 1.9483 - mse: 8.3264 - val_loss: 12.5519 - val_mae: 2.4063 - val_mse: 12.5519\n",
"Epoch 47/100\n",
- "354/354 [==============================] - 0s 274us/sample - loss: 8.4545 - mae: 1.9071 - mse: 8.4545 - val_loss: 13.2229 - val_mae: 2.4060 - val_mse: 13.2229\n",
+ "354/354 [==============================] - 0s 158us/sample - loss: 8.1445 - mae: 1.9549 - mse: 8.1445 - val_loss: 12.1838 - val_mae: 2.2591 - val_mse: 12.1838\n",
"Epoch 48/100\n",
- "354/354 [==============================] - 0s 272us/sample - loss: 8.5690 - mae: 1.9217 - mse: 8.5690 - val_loss: 13.4104 - val_mae: 2.3992 - val_mse: 13.4104\n",
+ "354/354 [==============================] - 0s 156us/sample - loss: 8.0389 - mae: 1.9304 - mse: 8.0389 - val_loss: 12.6978 - val_mae: 2.1907 - val_mse: 12.6978\n",
"Epoch 49/100\n",
- "354/354 [==============================] - 0s 278us/sample - loss: 8.1166 - mae: 1.9338 - mse: 8.1166 - val_loss: 14.6198 - val_mae: 2.5067 - val_mse: 14.6198\n",
+ "354/354 [==============================] - 0s 164us/sample - loss: 8.0705 - mae: 1.9493 - mse: 8.0705 - val_loss: 12.4833 - val_mae: 2.4720 - val_mse: 12.4833\n",
"Epoch 50/100\n",
- "354/354 [==============================] - 0s 249us/sample - loss: 8.1556 - mae: 1.8854 - mse: 8.1556 - val_loss: 13.2502 - val_mae: 2.4204 - val_mse: 13.2502\n",
+ "354/354 [==============================] - 0s 158us/sample - loss: 8.1872 - mae: 1.9630 - mse: 8.1872 - val_loss: 12.0043 - val_mae: 2.2610 - val_mse: 12.0043\n",
"Epoch 51/100\n",
- "354/354 [==============================] - 0s 266us/sample - loss: 8.1675 - mae: 1.8848 - mse: 8.1675 - val_loss: 13.0517 - val_mae: 2.4092 - val_mse: 13.0517\n",
+ "354/354 [==============================] - 0s 158us/sample - loss: 8.0357 - mae: 1.8946 - mse: 8.0357 - val_loss: 11.3982 - val_mae: 2.1770 - val_mse: 11.3982\n",
"Epoch 52/100\n",
- "354/354 [==============================] - 0s 268us/sample - loss: 7.8965 - mae: 1.8619 - mse: 7.8965 - val_loss: 12.2905 - val_mae: 2.4068 - val_mse: 12.2905\n",
+ "354/354 [==============================] - 0s 162us/sample - loss: 7.6882 - mae: 1.8951 - mse: 7.6882 - val_loss: 13.0714 - val_mae: 2.4109 - val_mse: 13.0714\n",
"Epoch 53/100\n",
- "354/354 [==============================] - 0s 268us/sample - loss: 7.7485 - mae: 1.8806 - mse: 7.7485 - val_loss: 14.3344 - val_mae: 2.4619 - val_mse: 14.3344\n",
+ "354/354 [==============================] - 0s 162us/sample - loss: 7.9639 - mae: 1.9103 - mse: 7.9639 - val_loss: 12.4297 - val_mae: 2.2996 - val_mse: 12.4297\n",
"Epoch 54/100\n",
- "354/354 [==============================] - 0s 273us/sample - loss: 7.8410 - mae: 1.8223 - mse: 7.8410 - val_loss: 12.4339 - val_mae: 2.3516 - val_mse: 12.4339\n",
+ "354/354 [==============================] - 0s 183us/sample - loss: 7.7929 - mae: 1.8971 - mse: 7.7929 - val_loss: 11.9751 - val_mae: 2.2491 - val_mse: 11.9751\n",
"Epoch 55/100\n",
- "354/354 [==============================] - 0s 270us/sample - loss: 7.7763 - mae: 1.8629 - mse: 7.7763 - val_loss: 12.4900 - val_mae: 2.3219 - val_mse: 12.4900\n",
+ "354/354 [==============================] - 0s 185us/sample - loss: 7.4411 - mae: 1.8631 - mse: 7.4411 - val_loss: 11.3761 - val_mae: 2.3416 - val_mse: 11.3761\n",
"Epoch 56/100\n",
- "354/354 [==============================] - 0s 276us/sample - loss: 7.7260 - mae: 1.8532 - mse: 7.7260 - val_loss: 12.1347 - val_mae: 2.4580 - val_mse: 12.1347\n",
+ "354/354 [==============================] - 0s 186us/sample - loss: 7.6105 - mae: 1.9111 - mse: 7.6105 - val_loss: 12.4939 - val_mae: 2.4095 - val_mse: 12.4939\n",
"Epoch 57/100\n",
- "354/354 [==============================] - 0s 276us/sample - loss: 7.7718 - mae: 1.8427 - mse: 7.7718 - val_loss: 12.2219 - val_mae: 2.3501 - val_mse: 12.2219\n",
+ "354/354 [==============================] - 0s 190us/sample - loss: 7.5013 - mae: 1.9146 - mse: 7.5013 - val_loss: 11.6668 - val_mae: 2.1468 - val_mse: 11.6668\n",
"Epoch 58/100\n",
- "354/354 [==============================] - 0s 276us/sample - loss: 7.4303 - mae: 1.8239 - mse: 7.4303 - val_loss: 14.0691 - val_mae: 2.4818 - val_mse: 14.0691\n",
+ "354/354 [==============================] - 0s 195us/sample - loss: 7.4096 - mae: 1.8515 - mse: 7.4096 - val_loss: 13.8000 - val_mae: 2.5222 - val_mse: 13.8000\n",
"Epoch 59/100\n",
- "354/354 [==============================] - 0s 272us/sample - loss: 7.4273 - mae: 1.8028 - mse: 7.4273 - val_loss: 11.7311 - val_mae: 2.3401 - val_mse: 11.7311\n",
+ "354/354 [==============================] - 0s 180us/sample - loss: 7.2263 - mae: 1.8241 - mse: 7.2263 - val_loss: 10.8964 - val_mae: 2.2130 - val_mse: 10.8964\n",
"Epoch 60/100\n",
- "354/354 [==============================] - 0s 271us/sample - loss: 7.5121 - mae: 1.8147 - mse: 7.5121 - val_loss: 11.3120 - val_mae: 2.2758 - val_mse: 11.3120\n",
+ "354/354 [==============================] - 0s 161us/sample - loss: 7.1773 - mae: 1.8526 - mse: 7.1773 - val_loss: 10.7862 - val_mae: 2.1088 - val_mse: 10.7862\n",
"Epoch 61/100\n",
- "354/354 [==============================] - 0s 264us/sample - loss: 7.2880 - mae: 1.8049 - mse: 7.2880 - val_loss: 11.9505 - val_mae: 2.3743 - val_mse: 11.9505\n",
+ "354/354 [==============================] - 0s 165us/sample - loss: 7.0812 - mae: 1.8308 - mse: 7.0812 - val_loss: 10.8147 - val_mae: 2.3209 - val_mse: 10.8147\n",
"Epoch 62/100\n",
- "354/354 [==============================] - 0s 267us/sample - loss: 7.4047 - mae: 1.8147 - mse: 7.4047 - val_loss: 11.3717 - val_mae: 2.3193 - val_mse: 11.3717\n",
+ "354/354 [==============================] - 0s 155us/sample - loss: 7.2235 - mae: 1.8367 - mse: 7.2235 - val_loss: 11.0399 - val_mae: 2.2583 - val_mse: 11.0399\n",
"Epoch 63/100\n",
- "354/354 [==============================] - 0s 271us/sample - loss: 7.1160 - mae: 1.8047 - mse: 7.1160 - val_loss: 11.4990 - val_mae: 2.3168 - val_mse: 11.4990\n",
+ "354/354 [==============================] - 0s 155us/sample - loss: 7.0341 - mae: 1.8172 - mse: 7.0341 - val_loss: 10.9894 - val_mae: 2.1429 - val_mse: 10.9894\n",
"Epoch 64/100\n",
- "354/354 [==============================] - 0s 271us/sample - loss: 7.2246 - mae: 1.7902 - mse: 7.2246 - val_loss: 11.6231 - val_mae: 2.3270 - val_mse: 11.6231\n",
+ "354/354 [==============================] - 0s 157us/sample - loss: 6.8729 - mae: 1.7492 - mse: 6.8729 - val_loss: 10.5465 - val_mae: 2.1532 - val_mse: 10.5465\n",
"Epoch 65/100\n",
- "354/354 [==============================] - 0s 270us/sample - loss: 7.2051 - mae: 1.7780 - mse: 7.2051 - val_loss: 11.8574 - val_mae: 2.3124 - val_mse: 11.8574\n",
+ "354/354 [==============================] - 0s 164us/sample - loss: 6.9345 - mae: 1.7837 - mse: 6.9345 - val_loss: 11.5379 - val_mae: 2.1963 - val_mse: 11.5379\n",
"Epoch 66/100\n",
- "354/354 [==============================] - 0s 275us/sample - loss: 7.2065 - mae: 1.7675 - mse: 7.2065 - val_loss: 11.4090 - val_mae: 2.2963 - val_mse: 11.4090\n",
+ "354/354 [==============================] - 0s 166us/sample - loss: 6.8218 - mae: 1.7714 - mse: 6.8218 - val_loss: 10.1486 - val_mae: 2.1617 - val_mse: 10.1486\n",
"Epoch 67/100\n",
- "354/354 [==============================] - 0s 270us/sample - loss: 6.8474 - mae: 1.7266 - mse: 6.8474 - val_loss: 12.4625 - val_mae: 2.3632 - val_mse: 12.4625\n",
+ "354/354 [==============================] - 0s 157us/sample - loss: 6.8711 - mae: 1.8045 - mse: 6.8711 - val_loss: 10.3196 - val_mae: 2.2297 - val_mse: 10.3196\n",
"Epoch 68/100\n",
- "354/354 [==============================] - 0s 276us/sample - loss: 6.9649 - mae: 1.7497 - mse: 6.9649 - val_loss: 12.4027 - val_mae: 2.4041 - val_mse: 12.4027\n",
+ "354/354 [==============================] - 0s 162us/sample - loss: 6.7281 - mae: 1.7762 - mse: 6.7281 - val_loss: 11.2361 - val_mae: 2.2046 - val_mse: 11.2361\n",
"Epoch 69/100\n",
- "354/354 [==============================] - 0s 271us/sample - loss: 7.0694 - mae: 1.7344 - mse: 7.0694 - val_loss: 12.8411 - val_mae: 2.4197 - val_mse: 12.8411\n",
+ "354/354 [==============================] - 0s 158us/sample - loss: 6.5518 - mae: 1.7292 - mse: 6.5518 - val_loss: 10.2378 - val_mae: 2.1494 - val_mse: 10.2378\n",
"Epoch 70/100\n",
- "354/354 [==============================] - 0s 271us/sample - loss: 6.6178 - mae: 1.6949 - mse: 6.6178 - val_loss: 10.8244 - val_mae: 2.2712 - val_mse: 10.8244\n",
+ "354/354 [==============================] - 0s 161us/sample - loss: 6.6489 - mae: 1.7383 - mse: 6.6489 - val_loss: 11.1613 - val_mae: 2.2212 - val_mse: 11.1613\n",
"Epoch 71/100\n",
- "354/354 [==============================] - 0s 265us/sample - loss: 6.5987 - mae: 1.7189 - mse: 6.5987 - val_loss: 11.3479 - val_mae: 2.2654 - val_mse: 11.3479\n",
+ "354/354 [==============================] - 0s 176us/sample - loss: 6.5827 - mae: 1.7564 - mse: 6.5827 - val_loss: 10.0177 - val_mae: 2.2440 - val_mse: 10.0177\n",
"Epoch 72/100\n",
- "354/354 [==============================] - 0s 272us/sample - loss: 6.6659 - mae: 1.7367 - mse: 6.6659 - val_loss: 13.3760 - val_mae: 2.5578 - val_mse: 13.3760\n",
+ "354/354 [==============================] - 0s 168us/sample - loss: 6.3411 - mae: 1.7463 - mse: 6.3411 - val_loss: 10.7929 - val_mae: 2.1946 - val_mse: 10.7929\n",
"Epoch 73/100\n",
- "354/354 [==============================] - 0s 267us/sample - loss: 6.7449 - mae: 1.7191 - mse: 6.7449 - val_loss: 10.6301 - val_mae: 2.2385 - val_mse: 10.6301\n",
+ "354/354 [==============================] - 0s 163us/sample - loss: 6.3621 - mae: 1.7466 - mse: 6.3621 - val_loss: 9.7344 - val_mae: 2.1441 - val_mse: 9.7344\n",
"Epoch 74/100\n",
- "354/354 [==============================] - 0s 269us/sample - loss: 6.4514 - mae: 1.7079 - mse: 6.4514 - val_loss: 11.2719 - val_mae: 2.4001 - val_mse: 11.2719\n",
+ "354/354 [==============================] - 0s 158us/sample - loss: 6.2298 - mae: 1.7411 - mse: 6.2298 - val_loss: 11.2495 - val_mae: 2.1948 - val_mse: 11.2495\n",
"Epoch 75/100\n",
- "354/354 [==============================] - 0s 273us/sample - loss: 6.4869 - mae: 1.6759 - mse: 6.4869 - val_loss: 12.1220 - val_mae: 2.3984 - val_mse: 12.1220\n",
+ "354/354 [==============================] - 0s 159us/sample - loss: 6.3037 - mae: 1.7169 - mse: 6.3037 - val_loss: 10.1339 - val_mae: 2.1716 - val_mse: 10.1339\n",
"Epoch 76/100\n",
- "354/354 [==============================] - 0s 274us/sample - loss: 6.5897 - mae: 1.6894 - mse: 6.5897 - val_loss: 11.1440 - val_mae: 2.2544 - val_mse: 11.1440\n",
+ "354/354 [==============================] - 0s 158us/sample - loss: 6.0780 - mae: 1.6686 - mse: 6.0780 - val_loss: 11.9975 - val_mae: 2.3317 - val_mse: 11.9975\n",
"Epoch 77/100\n",
- "354/354 [==============================] - 0s 272us/sample - loss: 6.4586 - mae: 1.6888 - mse: 6.4586 - val_loss: 12.3773 - val_mae: 2.3903 - val_mse: 12.3773\n",
+ "354/354 [==============================] - 0s 165us/sample - loss: 6.3311 - mae: 1.7082 - mse: 6.3311 - val_loss: 11.6433 - val_mae: 2.2756 - val_mse: 11.6433\n",
"Epoch 78/100\n",
- "354/354 [==============================] - 0s 273us/sample - loss: 6.4341 - mae: 1.7145 - mse: 6.4341 - val_loss: 11.4619 - val_mae: 2.3918 - val_mse: 11.4619\n",
+ "354/354 [==============================] - 0s 155us/sample - loss: 6.0620 - mae: 1.6765 - mse: 6.0620 - val_loss: 13.0159 - val_mae: 2.5073 - val_mse: 13.0159\n",
"Epoch 79/100\n",
- "354/354 [==============================] - 0s 274us/sample - loss: 6.3822 - mae: 1.6900 - mse: 6.3822 - val_loss: 12.2106 - val_mae: 2.3721 - val_mse: 12.2106\n",
+ "354/354 [==============================] - 0s 167us/sample - loss: 6.1819 - mae: 1.7157 - mse: 6.1819 - val_loss: 10.1000 - val_mae: 2.1462 - val_mse: 10.1000\n",
"Epoch 80/100\n",
- "354/354 [==============================] - 0s 276us/sample - loss: 6.1874 - mae: 1.6462 - mse: 6.1874 - val_loss: 10.5171 - val_mae: 2.2212 - val_mse: 10.5171\n",
+ "354/354 [==============================] - 0s 158us/sample - loss: 5.9085 - mae: 1.6720 - mse: 5.9085 - val_loss: 11.7867 - val_mae: 2.5045 - val_mse: 11.7866\n",
"Epoch 81/100\n",
- "354/354 [==============================] - 0s 268us/sample - loss: 6.2406 - mae: 1.6560 - mse: 6.2406 - val_loss: 11.1654 - val_mae: 2.2688 - val_mse: 11.1654\n",
+ "354/354 [==============================] - 0s 168us/sample - loss: 6.0201 - mae: 1.6678 - mse: 6.0201 - val_loss: 10.8789 - val_mae: 2.3031 - val_mse: 10.8789\n",
"Epoch 82/100\n",
- "354/354 [==============================] - 0s 277us/sample - loss: 6.1431 - mae: 1.6326 - mse: 6.1431 - val_loss: 10.5330 - val_mae: 2.2102 - val_mse: 10.5330\n",
+ "354/354 [==============================] - 0s 159us/sample - loss: 6.1278 - mae: 1.6799 - mse: 6.1278 - val_loss: 9.8114 - val_mae: 2.1048 - val_mse: 9.8114\n",
"Epoch 83/100\n",
- "354/354 [==============================] - 0s 268us/sample - loss: 6.0614 - mae: 1.6425 - mse: 6.0614 - val_loss: 11.1468 - val_mae: 2.3205 - val_mse: 11.1468\n",
+ "354/354 [==============================] - 0s 150us/sample - loss: 5.6372 - mae: 1.6280 - mse: 5.6372 - val_loss: 10.0971 - val_mae: 2.1464 - val_mse: 10.0971\n",
"Epoch 84/100\n",
- "354/354 [==============================] - 0s 271us/sample - loss: 5.9098 - mae: 1.6575 - mse: 5.9098 - val_loss: 10.3323 - val_mae: 2.2447 - val_mse: 10.3323\n",
+ "354/354 [==============================] - 0s 153us/sample - loss: 5.9587 - mae: 1.6421 - mse: 5.9587 - val_loss: 9.4731 - val_mae: 2.1915 - val_mse: 9.4731\n",
"Epoch 85/100\n",
- "354/354 [==============================] - 0s 262us/sample - loss: 5.8809 - mae: 1.6603 - mse: 5.8809 - val_loss: 12.0661 - val_mae: 2.3902 - val_mse: 12.0661\n",
+ "354/354 [==============================] - 0s 158us/sample - loss: 5.6189 - mae: 1.6223 - mse: 5.6189 - val_loss: 9.9788 - val_mae: 2.3332 - val_mse: 9.9788\n",
"Epoch 86/100\n",
- "354/354 [==============================] - 0s 272us/sample - loss: 5.9024 - mae: 1.6258 - mse: 5.9024 - val_loss: 10.2668 - val_mae: 2.2403 - val_mse: 10.2668\n",
+ "354/354 [==============================] - 0s 158us/sample - loss: 5.8193 - mae: 1.6930 - mse: 5.8193 - val_loss: 10.4070 - val_mae: 2.1490 - val_mse: 10.4070\n",
"Epoch 87/100\n",
- "354/354 [==============================] - 0s 267us/sample - loss: 5.9340 - mae: 1.6283 - mse: 5.9340 - val_loss: 10.3232 - val_mae: 2.2364 - val_mse: 10.3232\n",
+ "354/354 [==============================] - 0s 155us/sample - loss: 5.5919 - mae: 1.6152 - mse: 5.5919 - val_loss: 9.9985 - val_mae: 2.2546 - val_mse: 9.9985\n",
"Epoch 88/100\n",
- "354/354 [==============================] - 0s 272us/sample - loss: 5.7561 - mae: 1.6597 - mse: 5.7561 - val_loss: 11.0334 - val_mae: 2.3103 - val_mse: 11.0334\n",
+ "354/354 [==============================] - 0s 160us/sample - loss: 5.6652 - mae: 1.6246 - mse: 5.6652 - val_loss: 9.1506 - val_mae: 2.0642 - val_mse: 9.1506\n",
"Epoch 89/100\n",
- "354/354 [==============================] - 0s 278us/sample - loss: 5.7652 - mae: 1.6276 - mse: 5.7652 - val_loss: 10.5884 - val_mae: 2.2664 - val_mse: 10.5884\n",
+ "354/354 [==============================] - 0s 157us/sample - loss: 5.6349 - mae: 1.6108 - mse: 5.6349 - val_loss: 9.8522 - val_mae: 2.0813 - val_mse: 9.8522\n",
"Epoch 90/100\n",
- "354/354 [==============================] - 0s 265us/sample - loss: 5.7276 - mae: 1.6284 - mse: 5.7276 - val_loss: 10.8470 - val_mae: 2.3210 - val_mse: 10.8470\n",
+ "354/354 [==============================] - 0s 159us/sample - loss: 5.6165 - mae: 1.6449 - mse: 5.6165 - val_loss: 9.1553 - val_mae: 2.0421 - val_mse: 9.1553\n",
"Epoch 91/100\n",
- "354/354 [==============================] - 0s 264us/sample - loss: 5.6704 - mae: 1.5802 - mse: 5.6704 - val_loss: 10.5757 - val_mae: 2.2699 - val_mse: 10.5757\n",
+ "354/354 [==============================] - 0s 161us/sample - loss: 5.5416 - mae: 1.6153 - mse: 5.5416 - val_loss: 10.4231 - val_mae: 2.2880 - val_mse: 10.4231\n",
"Epoch 92/100\n",
- "354/354 [==============================] - 0s 277us/sample - loss: 5.6099 - mae: 1.5911 - mse: 5.6099 - val_loss: 12.4091 - val_mae: 2.4405 - val_mse: 12.4091\n",
+ "354/354 [==============================] - 0s 158us/sample - loss: 5.3909 - mae: 1.5863 - mse: 5.3909 - val_loss: 8.8087 - val_mae: 2.1022 - val_mse: 8.8087\n",
"Epoch 93/100\n",
- "354/354 [==============================] - 0s 265us/sample - loss: 5.4673 - mae: 1.5880 - mse: 5.4673 - val_loss: 9.5978 - val_mae: 2.1851 - val_mse: 9.5978\n",
+ "354/354 [==============================] - 0s 155us/sample - loss: 5.3540 - mae: 1.5986 - mse: 5.3540 - val_loss: 9.6963 - val_mae: 2.1931 - val_mse: 9.6963\n",
"Epoch 94/100\n",
- "354/354 [==============================] - 0s 267us/sample - loss: 5.3195 - mae: 1.5474 - mse: 5.3195 - val_loss: 11.3653 - val_mae: 2.3870 - val_mse: 11.3653\n",
+ "354/354 [==============================] - 0s 161us/sample - loss: 5.3198 - mae: 1.6074 - mse: 5.3198 - val_loss: 9.1875 - val_mae: 2.1917 - val_mse: 9.1875\n",
"Epoch 95/100\n",
- "354/354 [==============================] - 0s 271us/sample - loss: 5.2298 - mae: 1.5350 - mse: 5.2298 - val_loss: 11.4847 - val_mae: 2.4010 - val_mse: 11.4847\n",
+ "354/354 [==============================] - 0s 165us/sample - loss: 5.2299 - mae: 1.5638 - mse: 5.2299 - val_loss: 8.8746 - val_mae: 2.1273 - val_mse: 8.8746\n",
"Epoch 96/100\n",
- "354/354 [==============================] - 0s 270us/sample - loss: 5.3535 - mae: 1.5478 - mse: 5.3535 - val_loss: 11.1203 - val_mae: 2.3284 - val_mse: 11.1203\n",
+ "354/354 [==============================] - 0s 163us/sample - loss: 5.2789 - mae: 1.5651 - mse: 5.2789 - val_loss: 9.7351 - val_mae: 2.2359 - val_mse: 9.7351\n",
"Epoch 97/100\n",
- "354/354 [==============================] - 0s 269us/sample - loss: 5.2177 - mae: 1.5622 - mse: 5.2177 - val_loss: 11.1940 - val_mae: 2.3487 - val_mse: 11.1940\n",
+ "354/354 [==============================] - 0s 153us/sample - loss: 5.3399 - mae: 1.6002 - mse: 5.3399 - val_loss: 9.7185 - val_mae: 2.1080 - val_mse: 9.7185\n",
"Epoch 98/100\n",
- "354/354 [==============================] - 0s 268us/sample - loss: 5.3019 - mae: 1.5416 - mse: 5.3019 - val_loss: 10.6022 - val_mae: 2.2977 - val_mse: 10.6022\n",
+ "354/354 [==============================] - 0s 159us/sample - loss: 5.0072 - mae: 1.5055 - mse: 5.0072 - val_loss: 8.3621 - val_mae: 2.0586 - val_mse: 8.3621\n",
"Epoch 99/100\n",
- "354/354 [==============================] - 0s 272us/sample - loss: 5.0745 - mae: 1.5318 - mse: 5.0745 - val_loss: 9.1810 - val_mae: 2.1663 - val_mse: 9.1810\n",
+ "354/354 [==============================] - 0s 156us/sample - loss: 5.2596 - mae: 1.5557 - mse: 5.2596 - val_loss: 8.6406 - val_mae: 2.0527 - val_mse: 8.6406\n",
"Epoch 100/100\n",
- "354/354 [==============================] - 0s 276us/sample - loss: 4.9694 - mae: 1.5170 - mse: 4.9694 - val_loss: 12.8575 - val_mae: 2.7038 - val_mse: 12.8575\n"
+ "354/354 [==============================] - 0s 159us/sample - loss: 5.0983 - mae: 1.5543 - mse: 5.0983 - val_loss: 8.4836 - val_mae: 2.0234 - val_mse: 8.4836\n"
]
}
],
@@ -932,16 +935,16 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "x_test / loss : 12.8575\n",
- "x_test / mae : 2.7038\n",
- "x_test / mse : 12.8575\n"
+ "x_test / loss : 8.4836\n",
+ "x_test / mae : 2.0234\n",
+ "x_test / mse : 8.4836\n"
]
}
],
@@ -963,7 +966,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -1007,66 +1010,66 @@
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
- " <td>15.538406</td>\n",
- " <td>2.299461</td>\n",
- " <td>15.538407</td>\n",
- " <td>19.231753</td>\n",
- " <td>2.775025</td>\n",
- " <td>19.231753</td>\n",
+ " <td>15.144930</td>\n",
+ " <td>2.312168</td>\n",
+ " <td>15.144930</td>\n",
+ " <td>17.019036</td>\n",
+ " <td>2.582618</td>\n",
+ " <td>17.019036</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
- " <td>44.914352</td>\n",
- " <td>2.003585</td>\n",
- " <td>44.914356</td>\n",
- " <td>37.177478</td>\n",
- " <td>1.657639</td>\n",
- " <td>37.177476</td>\n",
+ " <td>43.707091</td>\n",
+ " <td>1.906713</td>\n",
+ " <td>43.707090</td>\n",
+ " <td>26.587745</td>\n",
+ " <td>1.288267</td>\n",
+ " <td>26.587746</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
- " <td>4.969428</td>\n",
- " <td>1.516992</td>\n",
- " <td>4.969429</td>\n",
- " <td>9.180952</td>\n",
- " <td>2.166264</td>\n",
- " <td>9.180954</td>\n",
+ " <td>5.007155</td>\n",
+ " <td>1.505515</td>\n",
+ " <td>5.007155</td>\n",
+ " <td>8.362053</td>\n",
+ " <td>2.023406</td>\n",
+ " <td>8.362053</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
- " <td>6.479793</td>\n",
- " <td>1.693636</td>\n",
- " <td>6.479793</td>\n",
- " <td>11.448713</td>\n",
- " <td>2.328062</td>\n",
- " <td>11.448714</td>\n",
+ " <td>6.285225</td>\n",
+ " <td>1.716563</td>\n",
+ " <td>6.285225</td>\n",
+ " <td>10.419040</td>\n",
+ " <td>2.192718</td>\n",
+ " <td>10.419040</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
- " <td>8.136086</td>\n",
- " <td>1.885107</td>\n",
- " <td>8.136086</td>\n",
- " <td>12.954600</td>\n",
- " <td>2.421493</td>\n",
- " <td>12.954600</td>\n",
+ " <td>8.037316</td>\n",
+ " <td>1.922454</td>\n",
+ " <td>8.037317</td>\n",
+ " <td>12.488579</td>\n",
+ " <td>2.301342</td>\n",
+ " <td>12.488580</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
- " <td>10.545197</td>\n",
- " <td>2.200065</td>\n",
- " <td>10.545198</td>\n",
- " <td>15.315346</td>\n",
- " <td>2.648960</td>\n",
- " <td>15.315347</td>\n",
+ " <td>10.482029</td>\n",
+ " <td>2.189933</td>\n",
+ " <td>10.482029</td>\n",
+ " <td>14.470699</td>\n",
+ " <td>2.503943</td>\n",
+ " <td>14.470701</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
- " <td>418.843522</td>\n",
- " <td>18.692585</td>\n",
- " <td>418.843567</td>\n",
- " <td>363.624954</td>\n",
- " <td>17.071243</td>\n",
- " <td>363.624939</td>\n",
+ " <td>414.560260</td>\n",
+ " <td>18.257650</td>\n",
+ " <td>414.560242</td>\n",
+ " <td>266.372801</td>\n",
+ " <td>13.991282</td>\n",
+ " <td>266.372803</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -1075,16 +1078,16 @@
"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 15.538406 2.299461 15.538407 19.231753 2.775025 19.231753\n",
- "std 44.914352 2.003585 44.914356 37.177478 1.657639 37.177476\n",
- "min 4.969428 1.516992 4.969429 9.180952 2.166264 9.180954\n",
- "25% 6.479793 1.693636 6.479793 11.448713 2.328062 11.448714\n",
- "50% 8.136086 1.885107 8.136086 12.954600 2.421493 12.954600\n",
- "75% 10.545197 2.200065 10.545198 15.315346 2.648960 15.315347\n",
- "max 418.843522 18.692585 418.843567 363.624954 17.071243 363.624939"
+ "mean 15.144930 2.312168 15.144930 17.019036 2.582618 17.019036\n",
+ "std 43.707091 1.906713 43.707090 26.587745 1.288267 26.587746\n",
+ "min 5.007155 1.505515 5.007155 8.362053 2.023406 8.362053\n",
+ "25% 6.285225 1.716563 6.285225 10.419040 2.192718 10.419040\n",
+ "50% 8.037316 1.922454 8.037317 12.488579 2.301342 12.488580\n",
+ "75% 10.482029 2.189933 10.482029 14.470699 2.503943 14.470701\n",
+ "max 414.560260 18.257650 414.560242 266.372801 13.991282 266.372803"
]
},
- "execution_count": 9,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@@ -1097,14 +1100,14 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "min( val_mae ) : 2.1663\n"
+ "min( val_mae ) : 2.0234\n"
]
}
],
@@ -1114,12 +1117,12 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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QUA0uR/N//YBLgTQR7p58Sq1rJCIiMmzqnqiGWLzvaZwcGa3TICIiIaTQUA1muGhBo042g3OadikiIuGi0FAlFusPDTGXJaObVomISMgoNFRLtCQ0aAaFiIiEjEJDtcQLxjW4nKZdiohI6Cg0VEu0cDCkWhpERCR8FBqqpWRMg+50KSIiYaPQUC0F0y5jLqebVomISOgoNFRLQUtD3GU1pkFEREJHoaFaYsVjGhQaREQkbBQaqqVoymVOAyFFRCR0FBqqpbClQes0iIhICCk0VMseYxo0EFJERMJFoaFaCmdPkCWdzdawMiIiIsOn0FAtmnIpIiIhp9BQLZpyKSIiIafQUC3RktCgFSFFRCRkFBqqJV44piFHJqfQICIi4aLQUC1RTbkUEZFwU2ioltIbVmkgpIiIhIxCQ7UUDYTMaUyDiIiEjkJDtRRNuVT3hIiIhI9CQ7VEi29YpdAgIiJho9BQLbHiG1ZpnQYREQkbhYZqKblhlUKDiIiEjUJDtZSsCKnuCRERCRuFhmop7J5A954QEZHwUWiolqi6J0REJNwUGqolrtAgIiLhptBQLdHiFSE1pkFERMJGoaFaSsY0qKVBRETCRqGhWmINfU/VPSEiImGk0FAte0y51OwJEREJF4WGailZEVJjGkREJGwUGqqlcCAkWd3lUkREQkehoVo0pkFEREJOoaFaisY05MjkFBpERCRcFBqqJVa8ToO6J0REJGwUGqql5C6Xmj0hIiJho9BQLQWhQYs7iYhIGNU8NJjZ0Wb2IzNbZWa7zKzLzJ4zsxvMbNoA5e82sx1m1mlmvzGzMwc49zgzW2xmG80sZWbPmtnHzcxG/52ViOrW2CIiEm6xoYuMupnANODnwAYgA8wFLgcuNLP5zrnXAMxsDvBoUOZrwC7go8D9Znaec+6B/EnNrAH4FXAisBhYBZwHJIGpwLXVeHN9otH+pziymUxVv72IiMi+qnlocM79L/C/pfvN7BHgTuAj+IAA8BVgPHCSc25lUO4W4FngJjM7xjmXHyxwGbAQ+Fvn3OJg3xIz+ylwtZn9wDn30ii9rT2Z4WJxLJP2X2az5JwjUoNGDxERkb1R8+6JQeQ/0A8CMLNW4N3AQ/nAAOCc2w18DzgKHxLyLgK6gCUl570RiAMfHJ1qD8yKxjWoi0JERMKlbkKDmTWZ2SQzm2lm5wD/Hhy6L9jOAxqBx8q8/PFguzA4VwRYAPzBOZcqKbsMyFEcMKqjZK0GDYYUEZEwqZvQgO9O2Aq8AtyP74b4sHPuN8Hx6cF2Y5nX5vfNCLYHAc3lyjrneoBtBWWrR9MuRUQkxOopNNwN/B/gz4DrgJ3A5ILjLcG2p8xrUyVlBiubL99S7oCZXW5myyus8/CULPCk7gkREQmTugkNzrkNzrkHnHN3O+euAf4S+Bczuyoo0hVsG8u8vKmkzGBl8+W7yh1wzt3snHvj8GpfocJpl+j+EyIiEi51ExpKOeeeBv4AJIJdm4JtuW6F/L58d8QOoLtcWTNrBCZSvptjdBUOhHQ5LSUtIiKhUrehIdAMTAieP4Pvbji1TLlTgu1yAOdcDlgBnBiEhEJvwr/v0emCGMweYxoUGkREJDxqHhrM7OAB9p8BnEAwMyKYWvkL4HQze0NBuTb8IMo1+JkRebfjxy1cXnLqT+MXh7pzhN5C5UrHNOQ0EFJERMKj5os7Ad8Jlov+NX5thibgJOBCoAP4XEHZq4CzgKVm9k2gHb8i5Azg/IKFncCvz3ApcIOZzcavCPkO/EDL651zL47ieyqvaExDjt5MtupVEBER2Vv1EBpuxw96vAQ/W8Lhw8O/A193zr2cL+ice8HMFgFfBa4EGvDdEG8vXEI6KNtrZmcD1wMfwo9jWAtcAdw02m+qrLimXIqISHjVPDQ45+5kGF0FzrlVwHsqLLsT+GTwqL1o4UBIjWkQEZFwqfmYhgNK0ZgGrQgpIiLhotBQTSWzJxQaREQkTBQaqilWvLiTuidERCRMFBqqqWRxJw2EFBGRMFFoqKZo8ToN6p4QEZEwUWiopoKWhgaFBhERCRmFhmoqnD2hMQ0iIhIyCg3VtMeYBoUGEREJD4WGaiqdcqm7XIqISIgoNFRT4ZRLjWkQEZGQUWiopsLZE+R0l0sREQkVhYZqihXfe0LdEyIiEiYKDdWkZaRFRCTEFBqqqWhMg2ZPiIhIuCg0VJPWaRARkRBTaKgmdU+IiEiIKTRUU8niTmndsEpEREJEoaGaosXrNKh7QkREwkShoZoKuydQ94SIiISLQkM1FQ6E1OwJEREJGYWGaipd3EmhQUREQkShoZr2GNOggZAiIhIeCg3VFC+ccpkjncnWsDIiIiLDo9BQTdGSxZ10wyoREQkRhYZq0pgGEREJMYWGaipdEVJ3uRQRkRBRaKgm3bBKRERCTKGhmkpvWKWBkCIiEiIKDdUUieLMX/IIkFVLg4iIhIhCQ7UVTLuM5jJkNYNCRERCQqGhykw3rRIRkZBSaKi2onENGgwpIiLhodBQbaXTLhUaREQkJBQaqk2hQUREQkqhodqipbfH1kBIEREJB4WGaitc4Am1NIiISHgoNFRb6f0ntJS0iIiEhEJDtcVKplzmFBpERCQcKgoNiUTiLxKJxLySfQ2JRGLsAOXflkgkvjgSFdzvFLU0aMqliIiER6UtDf8BvLdk31XAjgHKnw5cs3dV2s8Vzp5A3RMiIhIe6p6otsLFnTTlUkREQkShodqipbfH1pRLEREJB4WGaitZ3EljGkREJCwUGqqtdMqlQoOIiISEQkO1FS3ulFNoEBGR0IgNXaTP+EQicWjh1wCJROIQwErL7mvF9lvR4oGQ6p4QEZGwGE5o+FTwKLV+ZKpygIjrhlUiIhJOlYaGlwEN8x8J0eLFnRQaREQkLCoKDclkcvYo1+PAUXLDKk25FBGRsNBAyGrTlEsREQkphYZqK1oRMqdlpEVEJDQq6p5IJBLNwDTg9WQy2V5ybBbwTeBM/CyKh4H/m0wmV49wXfcPpes06C6XIiISEpW2NHwSWAMcV7gzkUiMwYeE9wBjgTHAO4GHEonExBGs5/4jWjqmQaFBRETCodLQ8FbglWQy+XjJ/o8DhwKPAUcAU4HFwMGUn54pJWMa1D0hIiJhUemUy+OA5WX2X4CfivlXyWRyXbDvU4lE4nzgPOCL+17F/UzJmAbNnhARkbCotKVhMvBi4Y5EIhEHTgSeLzN+4df4lgcppXtPiIhISFUaGhqBaMm+44E4sKxM+deAln2o1/6rsHtCYxpERCREKg0Nm4ETSva9Gd81Ua7bYgywfR/qtf8qXNxJK0KKiEiIVBoafgecmUgkToe+KZgfDY79qkz5E4CNlZzYzI4ys+vM7HEz22pmHWa20sz+wcxay5Q/2szuNrMdZtZpZr8xszMHOPc4M1tsZhvNLGVmz5rZx82s9AZb1aMbVomISEhVGhq+GWyXJhKJFfjxDfOAh5LJ5POFBROJxFhgEVA602IgfwV8BlgLXAd8HngeuB541Mya8wXNbA7wKHAq8LWgbBtwv5mdXXhSM2vAB5q/Ae4ArgjOmwSuqbBuI6909oRCg4iIhERFoSGZTC4HPgJ0A/OBKfhuib8sU/wvgQZgaYV1+Akw0zl3sXNusXPuu865DwL/hA8mf11Q9iv4226f65z7inMuiZ8Ougm4qaQF4TJgIfBZ59xnnXNLnHMXAD8DrjazWRXWb2QVDoRE3RMiIhIeFS8jnUwmf4hff+Fk4IhkMnlyMpncUKboL/CzKn5ZyXmdc8udc7vKHLoj2J4AEHRVvBt4yDm3suD1u4HvAUfhQ0LeRUAXsKTkvDfiB3B+sJL6jbiiMQ26YZWIiIRHpes0AJBMJruBJ4cos35fKlRgZrDdEmzn4WdxPFambL4rZCGwzMwiwAJghXMuVVJ2GZCjOGBUzx7rNKilQUREwqEub1hlZlH8wlAZ4LZg9/RgW26AZX7fjGB7ENBcrqxzrgfYVlC29HtfbmblZoSMDI1pEBGRkKr0hlV/sTcnTyaTt+zN6/BdCKcAVzvn8gMt8+s+9JQpnyopM1jZfPmy60g4524Gbk4kEqPTb1A0pkGhQUREwqPS7on/wK/JUCkLyg87NJjZl/E3yLrZOfeVgkNdwbaxzMuaSsoMVjZfvmuAY6MrWrxOg7onREQkLIYzpiED/Dfwp1GqC2Z2LfCPwA/wUyULbQq25boV8vvy3RE78DM99ihrZo3ARPzdOasvXtw9oYGQIiISFpWGhoeB04D34qdbLgHuTCaTpYMM95qZXYNfP+EW4DLnXOmn6TP47oZTy7z8lGC7HMA5lzOzFcCJZtYYjGPIexN+LMfojVsYTMniTrrLpYiIhEWl6zScARwN/Cv+RlQ/AF5NJBKLE4nEvH2thJl9EbgWuBW41Dm3xydpMLXyF8DpZvaGgte24ddkWEPxfTBux49buLzkVJ/Gt5rcua/13iuF3RPkSGeyNamGiIjIcFXcPZFMJl8A/j6RSPwD8B78MtIfBxKJROL3wL8DP04mk53DqYCZfQL4EvAy8ABwUckqz1ucc/mlqq8CzgKWmtk3gfagHjOA80taJ5YAlwI3mNlsYBXwDuDPgOudc0V37ayaSAQXiWI5HxYsm8E5Ry1XthYREanEsNZpAEgmkxngp8BPE4nELPxf+R8BbgZuSCQSb08mk+XWUhhIfr2EQ4H/LHP8YYL7WzjnXjCzRcBXgSvxK0+uAN7unHug8EXOud5gaenrgQ/hxzGsxS8nfdMw6jfiLB6HHh8aoi5LzjmiCg0iIlLnhh0aCiWTyZeALyQSiUfxLQ0zgMnDOYdz7iP40FFp+VX4lo5Kyu7Ez8T45HDqNOrKjGuINtTlkhkiIiJ99jo0JBKJ6fibTf0VMAu/9sEP8X/5y2CKFnjKkc66vjmjIiIi9WpYoSGRSESAd+K7JN4evP4Z4FPArclkstw9JKRUYWggS28mi78dhoiISP2qdEXIw/B3m7wUmAZ04scfLEkmk8sGe62UESvunkilNYNCRETqX6UtDS8E2+X4tRRuH+4sCSlQctOqVG+mhpURERGpTKWhwYA0vpXhi8AXE4nEUK9xyWRy1j7Ubf8VLV4VsrtXLQ0iIlL/hjOmIU7/7aplX8SLxzR0q6VBRERCoKLQkEwmNR9wJEVLuyfU0iAiIvVPYaAWYoV3uszSnVZLg4iI1D+FhlqIaUyDiIiEj0JDLRSEhhhZzZ4QEZFQUGiohZIxDWppEBGRMFBoqIXSMQ1qaRARkRBQaKiFkjENmj0hIiJhoNBQC0VjGnJqaRARkVBQaKiFPaZcqqVBRETqn0JDLRS2NDjNnhARkXBQaKiFopYGzZ4QEZFwUGiohWjxrbE1pkFERMJAoaEWYg19T+No9oSIiISDQkMtxEoXd1JLg4iI1D+FhlrYY3GnLM65GlZIRERkaAoNtRAtDg0550hnczWskIiIyNAUGmohXry4E6AZFCIiUvcUGmohWryMNKBxDSIiUvcUGmohVjzlEtAMChERqXsKDbUQU0uDiIiEj0JDLRS2NGhMg4iIhIRCQy2UaWnQ/SdERKTeKTTUQsmUS1D3hIiI1D+FhloonHLpgu4J3R5bRETqnEJDLRTesAq1NIiISDgoNNRC2TENamkQEZH6ptBQC0WhIT97Qi0NIiJS3xQaaqHM4k6acikiIvVOoaEWonvee0JTLkVEpN4pNNRCrNyUS7U0iIhIfVNoqIVyy0in1dIgIiL1TaGhFqLR/qc4Ii6n2RMiIlL3FBpqwayotSHmcpo9ISIidU+hoVYKQwNZtTSIiEjdU2iolZLBkGppEBGReqfQUCtNLX1P23I9mj0hIiJ1T6GhVtrG9D0dm0uRzubIZHM1rJCIiMjgFBpqpW1c39Ox2RQAKd3pUkRE6phCQ620Frc0gO4/ISIi9U2hoVbaxvY9HZvrBrQqpIiI1DeFhlopDA357gm1NIiISB1TaKiVopaGfPeEWhpERKR+KTTUSmu50KCWBhERqV8KDbUypj80jOnrnlBLg4iI1C+Fhlop19KgO12KiEgdU2ioFc2eEBGRkFFoqJXCdRqyKXBOsydERKSuKTTUSmMTNDQCECdHs0urpUFEROqaQpOw0MwAACAASURBVEMtFbU2dGv2hIiI1DWFhloqvP9ELqXZEyIiUtcUGmqp5E6XamkQEZF6VvPQYGZXmdldZrbOzJyZrR+i/Mlm9oCZdZhZu5n90szmD1B2upndYmZbzazbzJab2QdG5Y3sjdbipaS7dZdLERGpYzUPDcA/A2cCa4EdgxU0s1OAh4HDgC8C1wBHAr8xs7klZScAvwUuAL4DfArYDdxpZpeO8HvYO2OK12rQ7AkREalnsVpXAJjjnFsHYGZ/BNoGKfttoBc4zTm3MXjNncAq4BvAOQVlr8SHi3c7534RlP0+8Bjwr2Z2l3Nu90i/mWFpLV6rQbMnRESkntW8pSEfGIZiZkcAC4G78oEheP1G4C7gbDM7uOAlFwFr84EhKJsFFgMTgHeMQPX3TcmdLjWmQURE6lnNQ8MwLAy2j5U59jhgwEkAZjYNmBHsL1e28Hy101baPaGWBhERqV9hCg3Tg+3GMsfy+2bsRdnaKQkNamkQEZF6FqbQ0BJse8ocS5WUGU7ZImZ2uZkt36saDlfB4k5jcilS6Sw556ryrUVERIYrTKGhK9g2ljnWVFJmOGWLOOduds69ca9qOFwFLQ3jsv6mVT2adikiInUqTKFhU7At162Q37dxL8rWTluZ22Ori0JEROpUmELDk8H21DLHTgEc8HsA59yr+FBwygBlAarTBTGYphaIRv1Tl6Ehl9G0SxERqVuhCQ3OuRfwH/QfMLP8QEeC5x8Afu2c21zwktuBOWb2roKyUeAKYCdwX1UqPhizkrUatMCTiIjUr5ov7mRmlwCzgi8nAw1m9o/B1y85524tKP4p4EH8CpCLg31X4MPP50pO/VV8mLjNzG7Atzx8CD/V8jLnXMeIv5m90TYW2v1CmFrgSURE6lnNQwPw18DbSvZ9Odg+DPSFBufco2Z2OnB98HDAo8AHnHNPFZ7AObfNzBbhw8Mn8CtN/gm40Dl3xyi8j71TMK5hjBZ4EhGROlbz0OCcO32Y5R8Dzqqw7Ebgkr2oVvUUTLscpwWeRESkjoVmTMN+a8y4vqdjcym602ppEBGR+qTQUGuFCzxlUxrTICIidUuhodYKF3jKdWv2hIiI1C2FhlorHAiZU0uDiIjUL4WGWmvV7bFFRCQcFBpqbY/uCbU0iIhIfVJoqLU9uifU0iAiIvVJoaHW2kq6J3SXSxERqVMKDbXW0orDAGhzvfSmempcIRERkfIUGmotEiXb0tb/ZdfuGlZGRERkYAoNdcAVhIZot0KDiIjUJ4WGelAw7bIh1VnDioiIiAxMoaEOWMH9JxpTamkQEZH6pNBQByJj+lsaGns7cc7VsDYiIiLlKTTUgUhBS8OYbIp0NlfD2oiIiJSn0FAPCtdqyHXr/hMiIlKXFBrqQekCT1oVUkRE6pBCQz1oHdP3dGwuRVePQoOIiNQfhYZ6UDCmYWwuxYZtmnYpIiL1R6GhHhS2NGS7Wbt5Vw0rIyIiUp5CQz0oudPl2i3tNayMiIhIeQoN9aCgpWFMrod1r6qlQURE6o9CQz2IxXFNLQBEcfS2t7Njt+52KSIi9UWhoU5YyVoN6qIQEZF6o9BQLyZM7ns6L7VRgyFFRKTuKDTUiwWL+p6e2fk8azerpUFEROqLQkO9WHgazgyAeT2b2P7yhhpXSEREpJhCQ70YNwF3zPy+L4/Z+JSWkxYRkbqi0FBHIqec0ff8jM7nWafBkCIiUkcUGurJgkVkIjEA5qRf57U/PVfjComIiPRTaKgnza1sPnRu35ctT/22hpUREREpptBQZ9Inndb3fM5LK8C5GtZGRESkn0JDnZm46DR2WwMAk3p2klnzpxrXSERExFNoqDNjx7ayfPzRfV93PvKrGtZGRESkn0JDHVp/+MK+581P/Q4ymnopIiK1p9BQh2LHzOP1aCsADd0dcM8Pa1wjERERhYa6dPj08fx3W/8sCu77MTz2QO0qJCIigkJDXZozdSx3jDuJZU2z+nf+57dgzR9rVykRETngKTTUoSnjmmlpbuQrk9/O+vgEvzOThpu+DFtfrW3lRETkgKXQUIfMjCOmjaUr0sAXp7yLrgY/voHdu+CGq+Ghe6FjZ20rKSIiBxyFhjp1/gLfNbElNpZ/OOg8ctG4P7D1VfjhYvjcRfCNq+CJB7UAlIiIVIVCQ51667EH85ZjDgbgT03TWDz9PFy8ob9ALger/gBL/gXuXKLgICIio06hoU6ZGVe84wTGtfigcF9kFt89/fNw4d/AEccVF/7Vz+De22tQSxEROZAoNNSx8a2N/O07Tuj7+u7V7Tx2yKlw5Q3wtVvhxDf3F777Fvj1PTWopYiIHCgUGurcW46dxhknTO/7+lv3PsNLWztgwmS4/Eo49sT+wrcl4bH/rUEtRUTkQGBOfeFlJRIJB5BMJmtdFdq7e/nYdx9h++4eABpjET52znG8Y8GhWE8KbrgK1j3X/4IJk2HaITDtUDjkcFiwCJpba1R7EREJGRvwgEJDefUUGgBWrHuda+54kt5Mrm/foqOn8ul3zmNsrge+/nnYuL78ixub4NSz4Yx3wozZfl8uBx27IN0DE6eCDfgzIiIiBxaFhuGqt9AAsP61Dr7ysz+wfmtH377xrQ2cPW8mZx/azOz/+T72pxWQzQ58kpmHQU8KdrzuF4wC3yLxtnfAm8+GlrZRfhf7qGMnvLgajjgeWtR6IiIyChQahqseQwNATzrLkgdW8YvlL+1x7JCJrZxx7FROm+SY2bsd2/QSPPkIbNqzbFkNjfCm032waG2DljE+REyYDAdNhEi0v2yqy7dsvLoB2sbA4cfC2PF7njPdC9Fo8Wv3Rqoblv4U7v8p9HTD2IPgkiuKB4OKiMhIUGgYrnoNDXmPr97Ct+59pm+cQ6mp45s59aipnHzEFE5IbaDhkXvhD4/6bom8ljbIZnzLw1CisSA8TPKtFOWWs550MBx+DMRi/vhrr8Ku7dDYDG98Kyw6B448fnhdIZk0/OaXcM+Pyq+C+abT4aIEtI2t/JwiIjIYhYbhqvfQAJDJ5lix7nUe/ONGHn1+C6l0+W6JWMQ4cvo4Fk6KMb+pm4OmT2XCoYfQNKbNtxg8/iA8+IuBx0SMpCnT4aS3wKSpMG4ijJ/ouxkyaf9Ip2HnNli3yg/uXL/Gt1YUikaLu2DGjIdz3gdHz4ND5/jQUqnXNsHKx8AisODNfnxHoUzG3yisazccdYL/XgeCXA42b4CJU/yYGBE5kCg0DFcYQkOhVG+Gx9e8xmPPb2HZC6/R1ZMZ8jXjWxuYNr6FWVPGcPiUMZzQ+yozNz9PPNVJpGs3dHX6+11se23Pv/KjUZg6E6Yf6j/k16/pHyNRyGzkVqs8aBK89y9g3sl+FcxytwtvbPJdJY2Nvv7dXT4YjTsIZh3pH4ccDi8+71+/5tni1x81F04503e1rPgdrHzcBwbwweLI42H+Kf1TXdNpP5g0l/OvGTcBWsfs3cDS3h4f3F5Z57fNrXDMG2DOsVC4Gij475fN+BagSAUzp7s7fVdSb8r/O/X2+H+XOcf64JbnHDy9DO7+T1+PtnHwrov8mJdYfODz53KwYZ0PWTMP811dg9nxuv836NoNhx4BM2cPvwsrm4Wtm2DsBI1vERlZCg3DFbbQUCidzfH0S9t4fPUWVr64jZdf3z3sc0QMopEI8ViECW2NzBwT44hYNzMj3UTGTaBn8nRiDY00xqM0NURpjcL47Rtp2/IiDfEY8YNnYFOmwYQp8NIa+N1SWPaw//AarkkHw9vOh7PeXfxh9NQTcOu3fWipJ7G4DxD5D3QzHzhiMX8sFoNoHHJZ34qSSfsuotc3F3cf5cUb/MDP8RNg+9b+RzYIhhbxIa6lDWYdAbOPgsOO9l+v+gP8aYVvtRlogOzso3wQmnYo3P+T4um7eZOnwfsuhZPe2h+InIOXX4BlD/mxM9u3+v3RKMw4zNdh0sGQ6fXvM90Lr2+B9at9aCjU3OpXOj38GF/veIP/t25shomTYdI0Hwyc869/4kF44iEfZqMxOH6Br9uJpw5/MG8mA88/DSt+60Niute3Op3+Tn9t6llPCna3+2teGP4Gks36n4lNL8PsI+HIEzRzKsy6O+F/7oR3XAhNzSN5ZoWG4QpzaCi1s7OHZ1/ZwR9f3s66Le1s2dXNa7u6yeZG798+YtDcEKOlMUZrY5y25jjj4zB/1xoO7nyNtlQ7rcGjIZ0iF43honFcLEa2sYWOKbPonH4EnTPmYOMn0tIY63s0xaP0ZnKk0ll629tpfvoxxm98nub1q4js2DrMikbghIX+A/zZFeDKfGhPmOx/Ib/4vO7xEW/oDz4O3xJVLa1jfEvS9kH+jaMx36qUy/l/y1wO4o0+cLS0+W005j88cznIpv1snK4BgvWsI+Et5/jw0zrWD/qNN8CObbAjCG+d7b5FZnzQ3ZYfEJzJ+GCXzfQ/z2T89+zt9a09PSm/jcd911fbWP9o3+lbbl5ZBxte9MEgEglCaMQHzc4O/9q8Q+bAyaf7cT4TJvfvz6Th5XXwxK99uGvf0X9sznFw/oUwd2FxeOjt8eOSNm+ALRv988ZmmDELps/y265OHzBffM5fw2wWDp7p14g5+BB/rbdu9mH49c3+/R89D+a+qbh++yrd60Pf6mf8v/EbTvF1GC3pXli10rdEvrbJh+NzLvCtjKV2d0Cqs/hnofDnsbQFcThWPg4/+jcfwM9+r7/FwMhRaBiu/Sk0lJPN5Xi9PcWG7Z28uKWDdVvaWbelnU3bO+nN5AjrT8Vh0S7m23Ya43GyTS24phYiTU1MTG3n4J0bmbJzI5PaX6W7aQzrZi3gxdkn0dsylng0wth0B4evX8GMdSuI5dLsOmwu7ce9iewhR2IRI7NjOy3P/57xq1fQsv1VLB7HGhqJNDYSjRiRjl1Y+3Ys1b1XdXdmMGU6dugRvol/+1Z4bqX/pV1ONNbf2lCJ6bP8B1I87n9xde2GF57ds3UjFvfdEedcAMt/6+9rMtCHaqHWMf78A9W3UGOT/0BuGwtr/wS7dgz9mnKaW/eu9Wp/d+gc33XWvsOHi6Eccrgfv7J9q/8Q6hjlMHjIHN/V19kRfM+t/gN2zLj+AddjxvtAtuN1H9Lad/hANmU6TJnhZ3Stew7++Hs/o6rQwTP9zKpDj/BBPx8gOzt8a9e2LX7bm/KBb0wQ1hqa/L5UKujKy/if1cYmH5q6dsMzT/ouz0LxBv9/5tz3+2u38jH/eHnt4Nch3uD/H+ey/UG3obF/vNf4Cb6V78jjfcBrbPL/V27/Dix/pP88ZvClf/fdxSNDoWG49vfQMJRszpHJ5uhJZ9nanuK1Xd1s2dXF1vYU3b0ZejM50hl/vDudoSuVoasnQ2dPhq7eDD0DDMo8EDTm0ozPdhHFYTgiOCLOESNH3GVpJEsDOXqc0Wsx0hal16JsjbbRG21kbEucsc0NxKMRcs4xNrWLI9tfoslypMdNJHfQZKKTphBvaSWbyZLLZsll0jR37mTStpeZvP1lpm5/mcZ0iq2TZrF5+jHsOOQ4IuPG09QQo7khRnNDlMZ4lEhXJ21rVzJ29Qoat71K9/TD2f62P8NNmEI8GiFiRiy1m3EP/ozWJ35FpLdkpk1js2/KX/g2OG6Bb4Ho7MCtX417cTW5jnZysTi5WAPZaAxrG0vjkccSnXFo/xgG5/xMmzV/9NOD0z3+L/F0jx+T8voW/5dqfkBsU4tf5fSUM/yYj22v+XDz+0f82Jq9MWGyP+eCt/i/5h++D558uPw4nXoSi/sPu93tldd13ATfDfT0suGFTqmtaMx3l736cnGIHzMOPpSAhaeNZFeTQsNwHeihYV9lczm6erJ09qTpTGXoSPWyuzvN7lSannSWrINczpFzPpykszl6Mzl6M9m+QJLf35PJ0dWTpqvHB5NUOktjLEpTMJ4iFo2ws7OHbR09pLNluhdkREScDz0xcsRcluaIo7OhlYz5D3/nwDlHJucG7foyoK05zrjmBlqafDeHw782/7L87yUzoykepSVuTHQpJuS62T1uCtbURCwaIR6N4Jz/ecvmHPHu3cSyvUSiUcwiWDRCPJumMd1NUzpFY7oLczlyFiFLhJxF6GkZR8+0WbQ2NdDWFKcxHsEwYt0dTPzj72h9dS2xrt1EU53EuncTzfSSbhtPZuxEsuMn4trGEelsJ9q+nWjHDmKd7VgkAtEYFo8TicZwsRguGiMXieIiUVy8AdfQiIs34RoaiKR7iXS2E+nswDrboamZyCGHEzl0DnbI4UFzvvN/jeYcRCPQNg7X0EjWgaW6iK58DJY9CH9aWdzNZhHfZXP8ST5oHT3PB7btW2Hpz+CR+4q7OcAHp4lT/WDng2f4v+47O/xYiI3rYcsGH1hmHwWHHQNzjvF96q++4j/UXn3Fn3PiVJh8sO/e6Un5v9Kff3rkw8rkaTDvTb5V4o/L93w/I23yNB+Wp8/2Nwp8aYCwGo351oJoLHhEffjt3A3duwdfiK8Sb/4/8OcfHY0p5woNw6XQED7OOXZ19bKtI9UXLrp7s3T3ZsgGH2T+g8mRC77O5vyHXCaboyeTpSedpTcdjJfIZOnJ+K9zztHUEKU57v9KNzM6unvZ2dVLe1cvu1Np/4HnwBF8+AX/t/JPs0FIKhSPRmiMR2iIRUn1Zunq1V9+0i8ejdDaFCMejZDNOdLZHNmsI5PLkckW/zy1NcUY29LAjGgvs7M7ScWb2d00hu54C5hvtcrmHNng5z8XhK2Wnk6O3rkWYnFSbQfRM2YCubEHYZEozrm+QOdbH/02l82AGfFYjGjEfKtUxPyYX3zYM4OIGZHgeTQSoSEWocWlmbnleca2b6WneQxdrQfR1TqeVGMbLT0dtHTtorVzB02p3WRaxpBuO4j02Ank2sbR3N1Oa/tWmnduobF9G9kJU9h99ElkJs/wQQ0Hvb00v/A0ratXEOnqwJkfB+LMcE3N5CZM9WFm0lSizc1YVwfW2UGks90Hm8bm/u6IWNQHut4Ukd4U5nJw5Fwihx4evMdgdtgzT8J/3+a7S5pbfYCZfyqccNLA9/1xzn+/XNaHuPyYlVS3H9y9c5sPQS+/4IPWppf7XztpKlzyKT8AeHQcmKHBzCLAp4CPAbOBrcCdwBedc4N2hCo0yGjI//LN5hyxaIRopPj/Zm8mS0d3ml1dvWRzzo+VMCMSMVK9GXZ09rCzs5cdu3voSWeJBueIRYxoxPq+jkYM5yCVzpLqzfSFp1Sw7U77gBQx/ws+/4s937qTyTrSGR+WcgVBqzeT6zvHUL85IubrEYsa0UiEWNTIZB27U3Xe5C9SoYjR1+IVixhtlsUa4lg0RjwW9fuj/v9BJOL/H/g/WHJ9ARAjKOcfEaPvd0Q25zCgMR5lQq6bOe0v0+LSrDv0DbiGpr7fIRPHNPGehbNH8q0NGBqGsQpOKH0T+Fvg58A3gGODr080s7OdKzdUXmT0mPkP0dgASxI0xKJMHBNl4pj6XlDJOR8gSrshIvnwEoSdcrK5XF8w6u7N+L/YoGCbL2nknPPjZvKBJ53p67rqzfpurMJfyNHgtYXdX/luDxcEH8z6wpLhpyh39mToTKXp7MnQm9mzyThq+V/8fk2MdCZLqqBlKho1YkEwipiRSmfp6sn4gNab7ftwyV8bC95k/i/54osLvZkcu1PpirrbImblzyOjLhf8W+VvJLgLoKsHGK3ukWCGxvbNRXsPmzJmpEPDgPbb0GBmxwNXAD9zzr2vYP+LwLeBC4HbalQ9kVAzMxrje3c/kWgkwvjWRsa3DrEAlNCbyfrwkMn1/SXa13JTEECyOUdnygex9m7fXZZvRHZBl1nECv7iLXgeCZrZ8+EsH3YcxV0NvkWroCUL3zKVyQbdJkH/nCsZo5LvEsxmXd+4pZ5MlkzW7dGF0f8Xdn/3S+Ff3fmWsHTGb50r/n6wZ/jMf435IJkfP5XO5MjkcljBH9UuqD9QFDhzuf7n5boZa620xXI07behAfgQ/mf+xpL9S4CvAh9GoUFE6lhDLMqEtqHDWTRijG1pYGzLPsz7l2HpG+eRDzNBkPHde/1fF4aebC7nZyQVhD/Ihy9fPuccsUikr9Uul3PBeCs/W60nk/XnCs6dyTkOaq3ev/v+HBoWAjlgWeFO51zKzFYGx0VERIatqKtxL1vdwqiCRetDazrwunOuXOfSRmCSmSmWi4iIVGh/Dg0tDDwaJVVQpoiZXW5my0etViIiIiG1P4eGLmCgkVZNBWWKOOduds69cdRqJSIiElL7c2jYhO+CKBccZuC7LnqrXCcREZHQ2p9Dw5P49/emwp1m1gTMB9QFISIiMgz7c2i4Az9999Ml+z+KH8vwo6rXSEREJMT22ymXzrlnzOwm4JNm9jPgPvpXhHwYrdEgIiIyLPttaAh8GlgPXA6cD7wOLMbfe0JLSIuIiAzDfh0anHNZ/D0nvlHruoiIiITd/jymQUREREaQQoOIiIhUZL/unhgJiUSi1lUQERGpJpdMJsveOlMtDSIiIlIRc3V2X/D9mZkt1xLV+07XcWToOo4MXceRoes4Mkb7OqqlQURERCqi0CAiIiIVUWiorptrXYH9hK7jyNB1HBm6jiND13FkjOp11JgGERERqYhaGkRERKQiCg0iIiJSEYWGUWZmETP7jJk9Z2YpM3vFzL5hZq21rlu9MbOjzOw6M3vczLaaWYeZrTSzfyh3vczsaDO728x2mFmnmf3GzM6sRd3rnZm1mNmLZubM7N/KHNe1HICZTTCzfzWzF4L/w1vN7EEze2tJuZPN7IHg57bdzH5pZvNrVe96YmZtZna1mT0TXJ/XzexRM/uImVlJ2QP+OprZVWZ2l5mtC/7Prh+ifMXXzMymm9ktwc9xt5ktN7MPVFw3jWkYXWb2LfztuH8O/A/+9txXAL8BztbdNvuZ2VeBTwD3AI8DaeAM4M+Bp4FTnHPdQdk5wDIgA9wI7AI+CpwAnOece6Dqb6COmdm/Ah8D2oCbnHOfLDimazkAM5sFPIS/bt8HVgPjgHnA/c65HwflTgnKbQTyoeyTwBTgzc65Z6pa8TpiZhHgYeDNwH/i/2+3AB8C3gR8zTn390FZXUfAzBywHVgBnAS0O+dmD1C24mtmZhOA5cGxG4ANwEXA24C/cs79YMjKOef0GKUHcDyQA35asv8KwAEX1bqO9fQA3giMK7P/+uB6fbJg351AFphfsK8NeAl4niAQ6+EAFuADwWeD6/hvJcd1LQe+dr8BXgGmDVFuGdAOzCjYNyPYt7TW76PG1/DU4OfumyX7G4B1wE5dxz2u2eEFz/8IrB+kbMXXDPha8G/xroJ90eAc24C2oeqm7onR9SHA8H+9FVoCdAEfrnqN6phzbrlzbleZQ3cE2xMAgq6KdwMPOedWFrx+N/A94Chg4ShXNxTMLIr/efsl8LMyx3UtB2BmpwFvwf8l/KqZxc2spUy5I/DX6C7n3Mb8/uD5XcDZZnZwtepdh8YG202FO51zvcDrQCfoOhZyzq2rpNxeXLOLgLXOuV8UlM0Ci4EJwDuG+p4KDaNrIb6lYVnhTudcCljJAfrLeC/MDLZbgu08oBF4rEzZx4Otrq33GeAYfHNlObqWA8v/An3ZzH4BdAOdZrbazAoDf/76DHQNDd/EfKBaBuwE/s7MPmBmhwZjaL6Cvy7XBuV0HYev4mtmZtPwLRCPD1C28HwDUmgYXdOB151zPWWObQQmmVlDlesUKsFfyl/EN6/fFuyeHmw3lnlJft+MUa5a3TOzw4AvAdc559YPUEzXcmBHB9sl+L/C/hL4a6AXuNXMLg2O6xoOwjm3A9+atR3fFfYS8Bx+/NL7nHNLgqK6jsM3nGs2ItdXt8YeXS1AucAAkCoo01ud6oTSjcApwNXOueeDffkm4nLXNlVS5kD2HeBF/ICngehaDmxMsO0Azgia0zGzn+P74v/ZzP4TXcNK7Mb3zd8DPIoPYZ8AbjOz9zjnfoWu494YzjUbkeur0DC6uvCjVMtpKigjZZjZl/HN6jc7575ScCh/zRrLvEzXFQiaz88BTnPOpQcpqms5sO5ge3s+MID/y9nM7gH+At8aoWs4CDObiw8Kn3HOfbdg/+34ILEkmMGj6zh8w7lmI3J91T0xujbhuyDK/SPNwHddqJWhDDO7FvhH4AfA35Qczg+oKteUlt9XrgnugBD8vN0A3AdsNrMjggFTs4Ii44J949G1HMyGYLu5zLFXg+1B6BoO5TP4D6W7Cnc657qAe/E/l7PRddwbw7lmI3J9FRpG15P4a/ymwp1m1gTMx8+XlRJmdg1wDXALcJkL5gUVeAbfxHZqmZefEmwP5GvbDEwGzgfWFDweCo5/OPj6MnQtB5MfwDyzzLH8vtfw/89h4GvogN+PbNVCJf+BFC1zLFaw1XUcvoqvmXPuVXwoOGWAslDJ//Vaz0fdnx/AXAZfp+HDta5jvT3wgx4dPjBEBil3F35tgTcU7MuvLbCaA3ttgTjw/jKPjwfX9n+Cr4/StRz0Oh6En+u+gYL568A0fB/96oJ9TwZlpxfsmx7se6DW76XG1/Gbwc/d35Xsz7d0bQdiuo4DXr+h1mmo+JoBX2fgdRp2AGOGqo9WhBxlZrYY3y//c3xz8bH4FSJ/B5zptCJkHzP7BH5Fs5eBL+ADV6Etzg+Yys9PXoZfNfKb+P8gH8UHtfOdc/dXq95hYWaz8QMjS1eE1LUcgJldDvw78Czw//ALEn0cHxze6ZxbGpR7M/AgPmAsDl5+BTAVWOSce6rKVa8bwaqaK/Ah7Ef4330T8D9js4FPOOeSQVldR8DMLqG/O/EK/M/dN4KvX3LO3VpQtuJrZmYT8S0PE/FdmBvx6wmdjm/V/f6Qlat1itrfH/gU9zn8yno9wT/SDVSw8taB9gD+A5+CB3o8VFL+WOC/8HPAu4Df4pfmrvl7qccH/hf0HitC6loOed0uwM9j78TPXjO1rwAABEFJREFUpFga/DIuLXcq8L/4VogO4H5gQa3rXw8PYA5+CekN+HDaDjwCXKDrWPZ6PVTp78HhXjN8d9Gt+IW1UvhA98FK66aWBhEREamIBkKKiIhIRRQaREREpCIKDSIiIlIRhQYRERGpiEKDiIiIVEShQURERCqi0CAiIiIV0V0uRWS/l0gkrsXfz+SMZDL5UG1rIxJeCg0iMqREIlHJKnD6QBbZzyk0iMhwfGmQY+urVQkRqQ2FBhGpWDKZvLbWdRCR2lFoEJERVziGAH+3vk8Dx+BvpvPfwNXJZHJzmdcdib/D6VnAZPxNdR4AvpxMJteUKR/F3y3xEuAE/N0AN+Jv+PMvA7zm/cDfBeVT+BtQfS6ZTG7cl/csciDQ7AkRGU2fAb4LPAXciL/b66XAo4lEYnJhwUQisRBYDnwYeBL4V/zdJS8GlicSiTeWlG8Afgl8BzgEuA34Nv7Wv38GLCpTnwTwQ3xXyk3AH4EPAg8kEonGfX63Ivs5tTSISMWCFoRyUslk8qtl9p8H/7+9uwex4ooCOP5fAgabrKmikdQSi6CCCrJECwk2kjRBDH4UNnJ6QURwSzvB4iCkkURjdpskKKQICBFFBNGFFH5UgmCINi4Ioqhrce+TYXizmdFsE/8/eFzemTP3zVTvvDv33sfmzLzZ6OMEZeThOHCgxiaAH4CPgD2ZebaRvwv4GTgTEWsz81U9NA1sB84D32bms8Y5H9a+2nYAGzPzr0buT8Bu4GtgtvPmJTnSIGmQYx2vwx35PzYLhmoamAe+a/y630J5fHG1WTAAZOYMcBlYA0zBm8cSATwFDjYLhnrOs8x8NOZ6TjYLhur72m7quAdJlSMNknrLzImBp/w5po/5iJgDtgKfA3PAhnr4Ykc/FykFw3rgEqXAmASuZeaDAddzfUzsfm0/HtCP9F5ypEHSUvqnIz6aBDnZav/uyB/FV7TaoZMXH4+JvajtBwP7kt47Fg2SltInHfGVtZ1vtSvH5AKsauWNvvxXv/2lSRrKokHSUtraDkTEJLCOstzxVg2P5j1s6+hnFL9R29uUwuGLiPj0v7hQSf/OokHSUtobEetbsWnK44hzjQmMVyjLMafqPgpv1PdfAncpEyLJzJdAAsuBU+3lkhGxrL2kU9K7cyKkpN4WWXIJ8GtmzrVivwNXImKWMi9hqr7u0VhxkZkLEbEf+AOYiYjfKKMJa4BvKJtC7Wsst4SypfVmYCdwNyIu1LzPgK+AQ8Dpt7pRSWNZNEga4tgix+5RVkI0nQB+oezLsAt4QvkiP5KZD5uJmXmtbvB0lLL/wk7KjpDnKDtC3mnlP4+IHcBBYB+wH5gAHtTPvDz89iQtZmJhoc+f10lSf/4VtfT/5JwGSZLUi0WDJEnqxaJBkiT14pwGSZLUiyMNkiSpF4sGSZLUi0WDJEnqxaJBkiT1YtEgSZJ6sWiQJEm9vAbzHFrpzcg1vgAAAABJRU5ErkJggg==\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
@@ -1131,7 +1134,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
@@ -1143,7 +1146,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
@@ -1169,7 +1172,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
@@ -1183,14 +1186,14 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Prédiction : 8.13 K$\n",
+ "Prédiction : 11.59 K$\n",
"Reality : 10.40 K$\n"
]
}
@@ -1207,7 +1210,9 @@
"metadata": {},
"source": [
"---\n",
- "That's all folks !"
+ "<div style=\"text-align: left\">\n",
+ " <img src=\"../fidle/img/00-Fidle-logo-01.svg\" style=\"width:80px\"/>\n",
+ "</div>"
]
}
],
@@ -1227,7 +1232,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.7.6"
}
},
"nbformat": 4,
diff --git a/BHPD/data/BostonHousing.csv b/BHPD/data/BostonHousing.csv
new file mode 100755
index 0000000000000000000000000000000000000000..63c67b4fe7497acae14424d259fbeb69d2997985
--- /dev/null
+++ b/BHPD/data/BostonHousing.csv
@@ -0,0 +1,507 @@
+"crim","zn","indus","chas","nox","rm","age","dis","rad","tax","ptratio","b","lstat","medv"
+0.00632,18,2.31,"0",0.538,6.575,65.2,4.09,1,296,15.3,396.9,4.98,24
+0.02731,0,7.07,"0",0.469,6.421,78.9,4.9671,2,242,17.8,396.9,9.14,21.6
+0.02729,0,7.07,"0",0.469,7.185,61.1,4.9671,2,242,17.8,392.83,4.03,34.7
+0.03237,0,2.18,"0",0.458,6.998,45.8,6.0622,3,222,18.7,394.63,2.94,33.4
+0.06905,0,2.18,"0",0.458,7.147,54.2,6.0622,3,222,18.7,396.9,5.33,36.2
+0.02985,0,2.18,"0",0.458,6.43,58.7,6.0622,3,222,18.7,394.12,5.21,28.7
+0.08829,12.5,7.87,"0",0.524,6.012,66.6,5.5605,5,311,15.2,395.6,12.43,22.9
+0.14455,12.5,7.87,"0",0.524,6.172,96.1,5.9505,5,311,15.2,396.9,19.15,27.1
+0.21124,12.5,7.87,"0",0.524,5.631,100,6.0821,5,311,15.2,386.63,29.93,16.5
+0.17004,12.5,7.87,"0",0.524,6.004,85.9,6.5921,5,311,15.2,386.71,17.1,18.9
+0.22489,12.5,7.87,"0",0.524,6.377,94.3,6.3467,5,311,15.2,392.52,20.45,15
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diff --git a/docs/Howto-SSH.md b/docs/Howto-SSH.md
new file mode 100644
index 0000000000000000000000000000000000000000..3a236fa5e4bd84e8d3108fa7976efad951c07bf5
--- /dev/null
+++ b/docs/Howto-SSH.md
@@ -0,0 +1,81 @@
+<div style="text-align: left">
+ <img src="../fidle/img/00-Fidle-header-01.svg" style="width:800px"/>
+</div>
+
+
+# How to - SSH for GRICAD
+How to configure your SSH environment for friendly access to GRICAD in 6 steps.
+
+## Step 1 - Generate yours ssh keys
+If you don't already have SSH keys, create one with a nice password :
+
+```
+# ssh-keygen
+```
+
+## Step 2 - ssh-agent configuration
+To avoid having to enter your ssh password all the time, we will use the ssh-agent service.
+Try :
+```
+# ssh-add
+```
+If an error occurs, add this lines in your .bash_profile and restart your session :
+```
+pgrep ssh-agent >/dev/null || ssh-agent -s > ~/.ssh_agent
+. ~/.ssh_agent
+ssh-add -l >/dev/null || ssh-add
+```
+
+## Step 3 - Allow your access to the bastions
+The password requested is your PERSEUS password.
+\<login\> is your PERSEUS login
+```
+# ssh-copy-id <login>@rotule.imag.fr
+```
+and
+```
+# ssh-copy-id <login>@trinity.ujf-grenoble.fr
+```
+To verify, these two commands must now work :
+```
+# ssh <login>@rotule.imag.fr hostname
+# ssh <login>@trinity.ujf-grenoble.fr hostname
+```
+
+## Step 4 - Configuring access through bastions
+Modify (or create if it doesn't exist) your **.ssh/config** file, with :
+```
+ForwardAgent yes
+
+Host *.ciment
+ User <login>
+ ProxyCommand ssh -q <login>@access-rr-ciment.imag.fr "nc -w 60 `basename %h .ciment` %p"
+ LocalForward 8888 f-dahu:<your uid>
+ LocalForward 6006 f-dahu:<your uid + 10000>
+```
+Where :
+ - \<login\> is your PERSEUS login
+ - \<your uid\> is your uid on rotule or trinity
+ - \<your uid + 10000\> is your uid + 10000 (if uid=6500, that makes 16500)
+
+ To get your uid, try : `# ssh <your login>@rotule.imag.fr id -u`
+
+## Step 5 - Drink a coffee
+You've earned it, but courage, it's almost over !
+
+## Step 6 - Allow your access to the frontal
+As before, the password requested is your PERSEUS password.
+```
+# ssh-copy-id f-dahu.ciment
+```
+**Fine !** Normalement, on peut maintenant accéder directement à la frontale - de manière sécurisée et simple, - sans ressaisir 18 fois son mot de passe :-)
+**To check :**
+```
+# ssh f-dahu.ciment hostname
+f-dahu
+```
+If that doesn't work, drink another coffee and check your steps...
+---
+<div style="text-align: left">
+ <img src="../fidle/img/00-Fidle-logo-01.svg" style="width:80px"/>
+</div>
\ No newline at end of file
diff --git a/fidle/custom.css b/fidle/css/custom.css
similarity index 100%
rename from fidle/custom.css
rename to fidle/css/custom.css
diff --git a/fidle/img/00-Fidle-header-01.svg b/fidle/img/00-Fidle-header-01.svg
new file mode 100755
index 0000000000000000000000000000000000000000..e01b0402c1596e00b9dc73dbbe92fad23eb30c88
--- /dev/null
+++ b/fidle/img/00-Fidle-header-01.svg
@@ -0,0 +1 @@
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diff --git a/fidle/img/00-Fidle-logo-01.svg b/fidle/img/00-Fidle-logo-01.svg
new file mode 100755
index 0000000000000000000000000000000000000000..286bff6a11bb5d88b5e9b282cac42194f0bf0f67
--- /dev/null
+++ b/fidle/img/00-Fidle-logo-01.svg
@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 140.2164 40.848"><title>00-Fidle-logo-01</title><g id="Calque_2" data-name="Calque 2"><g id="Calque_4" data-name="Calque 4"><path d="M46.1913,31.74a12.9222,12.9222,0,0,0,5.2755-1.77,6.4763,6.4763,0,0,1,2.3629-.9239,14.6364,14.6364,0,0,0-5.7616-16.4446,17.0565,17.0565,0,0,0-11.8732-2.0051c-4.1719.69-8.4957,3.8461-10.189,5.2622-1.0189.8536-13.1385,12.3424-18.1936,10.0527-3.42-1.5492,2.6862-7.1873-.1144-12.3393a.2236.2236,0,0,0-.373-.0248c-1.4257,1.9233-2.8193,4.2317-4.7179,3.1953-.8482-.4632-1.6116-1.9422-2.2-2.8775A.2216.2216,0,0,0,0,13.9917,23.35,23.35,0,0,0,5.87,28.2417a35.3776,35.3776,0,0,0,24.34,12.518c5.3439.5321,18.0193-1.1527,23.0835-10.2646a12.7681,12.7681,0,0,0-1.2217.6066,14.2177,14.2177,0,0,1-5.7629,1.9167c-.1761.0163-.3511.0236-.5261.0236a10.1733,10.1733,0,0,1-5.7446-2.303,1.0764,1.0764,0,1,1,.8227-1.0443c0,.0176-.0042.0339-.0054.0515C41.8966,30.5423,44.0669,31.9474,46.1913,31.74ZM30.0385,36.5091a19.6093,19.6093,0,0,1-4.6162.8385c-1.0425.0006-1.476-.2954-1.6824-.7392-.5431-1.1678,1.4136-2.8563,3.1493-4.0677a.6418.6418,0,1,1,.7343,1.0528,10.5781,10.5781,0,0,0-2.651,2.4368c.339.0732,1.44.12,4.733-.7616a.6422.6422,0,0,1,.333,1.24Zm14.87-15.6442a2.4512,2.4512,0,0,1,2.38,2.3617,1.6015,1.6015,0,1,0-1.4179,2.34,1.6573,1.6573,0,0,0,.2973-.03,2.28,2.28,0,0,1-1.2593.3875,2.5337,2.5337,0,0,1,0-5.06ZM36.6423,4.436A1.2835,1.2835,0,0,0,37.1466,6.18c.6211.342,1.9294-.402,2.7231.7071.4122.5763-.8627-2.6129-1.4839-2.9556A1.2827,1.2827,0,0,0,36.6423,4.436Zm6.5389.1374c-1.5995.9378-1.8961,4.8154-1.4838,4.2391a7.2989,7.2989,0,0,1,2.7231-1.9906,1.2837,1.2837,0,0,0-1.2393-2.2485ZM41.5587.2981c-.8179.9462-.2579,3.4-.1114,2.95a5.2169,5.2169,0,0,1,1.3174-1.8537A.8415.8415,0,0,0,42.7441.2054.8332.8332,0,0,0,41.5587.2981Z" style="fill:#e12229"/><path d="M65.6671,13.7493H77.3946V15.158H67.3223v9.4379h9.2271v1.4087H67.3223v11.481H65.6671Z" style="fill:#808285"/><path d="M83.5909,13.7493V37.4856H81.9356V13.7493Z" style="fill:#808285"/><path d="M89.3658,14.0662a39.0353,39.0353,0,0,1,6.0576-.4932c4.3316,0,7.607,1.1621,9.5791,3.24a11.2256,11.2256,0,0,1,2.958,8.2056,13.0738,13.0738,0,0,1-3.0991,9.0156c-2.1128,2.2891-5.67,3.6275-10.248,3.6275a50.7148,50.7148,0,0,1-5.2476-.2115Zm1.6553,22.0107a29.8576,29.8576,0,0,0,3.8388.1763c7.607,0,11.375-4.2617,11.375-11.1289.0352-6.022-3.31-10.1426-10.9174-10.1426a25.2377,25.2377,0,0,0-4.2964.352Z" style="fill:#808285"/><path d="M112.15,13.7493h1.6553V36.0769h10.6006v1.4087H112.15Z" style="fill:#808285"/><path d="M139.0894,25.6877h-9.5088V36.0769h10.6358v1.4087h-12.291V13.7493h11.7275V15.158H129.5806v9.1211h9.5088Z" style="fill:#808285"/></g></g></svg>
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diff --git a/fidle/custom.mplstyle b/fidle/mplstyles/custom.mplstyle
similarity index 100%
rename from fidle/custom.mplstyle
rename to fidle/mplstyles/custom.mplstyle
diff --git a/fidle/pwk.py b/fidle/pwk.py
index 16fccda581d360edec46b3eb12ae5fdacd70b7c3..6186a4f78ceaa6f2a30078e464928a3e6b8b660a 100644
--- a/fidle/pwk.py
+++ b/fidle/pwk.py
@@ -8,7 +8,7 @@
# module pwk
# ==================================================================
# A simple module to host some common functions for practical work
-# pjluc 2020
+# Jean-Luc Parouty 2020
import os
import glob
@@ -31,14 +31,14 @@ import matplotlib.pyplot as plt
from IPython.display import display,Markdown,HTML
-VERSION='0.2.8'
+VERSION='0.2.9'
# -------------------------------------------------------------
# init_all
# -------------------------------------------------------------
#
-def init(mplstyle='../fidle/custom.mplstyle', cssfile='../fidle/custom.css'):
+def init(mplstyle='../fidle/mplstyles/custom.mplstyle', cssfile='../fidle/css/custom.css'):
global VERSION
# ---- matplotlib and css
matplotlib.style.use(mplstyle)
@@ -376,7 +376,7 @@ def hsize(num, suffix='o'):
def load_cssfile(cssfile):
if cssfile is None: return
- styles = open("../fidle/custom.css", "r").read()
+ styles = open(cssfile, "r").read()
display(HTML(styles))