diff --git a/doc/pub/week34/ipynb/week34.ipynb b/doc/pub/week34/ipynb/week34.ipynb
index 6ce29208d..950682c6f 100644
--- a/doc/pub/week34/ipynb/week34.ipynb
+++ b/doc/pub/week34/ipynb/week34.ipynb
@@ -709,11 +709,8 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "execution_count": 1,
+ "metadata": {},
"outputs": [],
"source": [
"import numpy as np"
@@ -728,12 +725,18 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
- "outputs": [],
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ 0.83685501 -0.85701074 1.64314707 1.00755389 -1.04107604 1.73472194\n",
+ " 0.02127074 1.27828984 0.52885865 0.09337075]\n"
+ ]
+ }
+ ],
"source": [
"n = 10\n",
"x = np.random.normal(size=n)\n",
@@ -751,10 +754,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
@@ -773,10 +773,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
@@ -800,10 +797,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
@@ -825,10 +819,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
@@ -846,10 +837,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
@@ -867,10 +855,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
@@ -892,10 +877,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
@@ -913,10 +895,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
@@ -935,10 +914,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
@@ -957,10 +933,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
@@ -980,10 +953,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
@@ -1003,10 +973,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
@@ -1090,10 +1057,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"# Importing various packages\n",
@@ -1116,10 +1080,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
@@ -1161,12 +1122,81 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
- "outputs": [],
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " First Name | \n",
+ " Last Name | \n",
+ " Place of birth | \n",
+ " Date of Birth T.A. | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " Frodo | \n",
+ " Baggins | \n",
+ " Shire | \n",
+ " 2968 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " Bilbo | \n",
+ " Baggins | \n",
+ " Shire | \n",
+ " 2890 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " Aragorn II | \n",
+ " Elessar | \n",
+ " Eriador | \n",
+ " 2931 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " Samwise | \n",
+ " Gamgee | \n",
+ " Shire | \n",
+ " 2980 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " First Name Last Name Place of birth Date of Birth T.A.\n",
+ "0 Frodo Baggins Shire 2968\n",
+ "1 Bilbo Baggins Shire 2890\n",
+ "2 Aragorn II Elessar Eriador 2931\n",
+ "3 Samwise Gamgee Shire 2980"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"import pandas as pd\n",
"from IPython.display import display\n",
@@ -1191,12 +1221,81 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
- "outputs": [],
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " First Name | \n",
+ " Last Name | \n",
+ " Place of birth | \n",
+ " Date of Birth T.A. | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | Frodo | \n",
+ " Frodo | \n",
+ " Baggins | \n",
+ " Shire | \n",
+ " 2968 | \n",
+ "
\n",
+ " \n",
+ " | Bilbo | \n",
+ " Bilbo | \n",
+ " Baggins | \n",
+ " Shire | \n",
+ " 2890 | \n",
+ "
\n",
+ " \n",
+ " | Aragorn | \n",
+ " Aragorn II | \n",
+ " Elessar | \n",
+ " Eriador | \n",
+ " 2931 | \n",
+ "
\n",
+ " \n",
+ " | Sam | \n",
+ " Samwise | \n",
+ " Gamgee | \n",
+ " Shire | \n",
+ " 2980 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " First Name Last Name Place of birth Date of Birth T.A.\n",
+ "Frodo Frodo Baggins Shire 2968\n",
+ "Bilbo Bilbo Baggins Shire 2890\n",
+ "Aragorn Aragorn II Elessar Eriador 2931\n",
+ "Sam Samwise Gamgee Shire 2980"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"data_pandas = pd.DataFrame(data,index=['Frodo','Bilbo','Aragorn','Sam'])\n",
"display(data_pandas)"
@@ -1211,12 +1310,23 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
- "outputs": [],
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "First Name Aragorn II\n",
+ "Last Name Elessar\n",
+ "Place of birth Eriador\n",
+ "Date of Birth T.A. 2931\n",
+ "Name: Aragorn, dtype: object"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"display(data_pandas.loc['Aragorn'])"
]
@@ -1230,12 +1340,89 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
- "outputs": [],
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " First Name | \n",
+ " Last Name | \n",
+ " Place of birth | \n",
+ " Date of Birth T.A. | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | Frodo | \n",
+ " Frodo | \n",
+ " Baggins | \n",
+ " Shire | \n",
+ " 2968 | \n",
+ "
\n",
+ " \n",
+ " | Bilbo | \n",
+ " Bilbo | \n",
+ " Baggins | \n",
+ " Shire | \n",
+ " 2890 | \n",
+ "
\n",
+ " \n",
+ " | Aragorn | \n",
+ " Aragorn II | \n",
+ " Elessar | \n",
+ " Eriador | \n",
+ " 2931 | \n",
+ "
\n",
+ " \n",
+ " | Sam | \n",
+ " Samwise | \n",
+ " Gamgee | \n",
+ " Shire | \n",
+ " 2980 | \n",
+ "
\n",
+ " \n",
+ " | Pippin | \n",
+ " Peregrin | \n",
+ " Took | \n",
+ " Shire | \n",
+ " 2990 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " First Name Last Name Place of birth Date of Birth T.A.\n",
+ "Frodo Frodo Baggins Shire 2968\n",
+ "Bilbo Bilbo Baggins Shire 2890\n",
+ "Aragorn Aragorn II Elessar Eriador 2931\n",
+ "Sam Samwise Gamgee Shire 2980\n",
+ "Pippin Peregrin Took Shire 2990"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"new_hobbit = {'First Name': [\"Peregrin\"],\n",
" 'Last Name': [\"Took\"],\n",
@@ -1256,12 +1443,288 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
- "outputs": [],
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 2 | \n",
+ " 3 | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " -1.749765 | \n",
+ " 0.342680 | \n",
+ " 1.153036 | \n",
+ " -0.252436 | \n",
+ " 0.981321 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " 0.514219 | \n",
+ " 0.221180 | \n",
+ " -1.070043 | \n",
+ " -0.189496 | \n",
+ " 0.255001 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " -0.458027 | \n",
+ " 0.435163 | \n",
+ " -0.583595 | \n",
+ " 0.816847 | \n",
+ " 0.672721 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " -0.104411 | \n",
+ " -0.531280 | \n",
+ " 1.029733 | \n",
+ " -0.438136 | \n",
+ " -1.118318 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 1.618982 | \n",
+ " 1.541605 | \n",
+ " -0.251879 | \n",
+ " -0.842436 | \n",
+ " 0.184519 | \n",
+ "
\n",
+ " \n",
+ " | 5 | \n",
+ " 0.937082 | \n",
+ " 0.731000 | \n",
+ " 1.361556 | \n",
+ " -0.326238 | \n",
+ " 0.055676 | \n",
+ "
\n",
+ " \n",
+ " | 6 | \n",
+ " 0.222400 | \n",
+ " -1.443217 | \n",
+ " -0.756352 | \n",
+ " 0.816454 | \n",
+ " 0.750445 | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " -0.455947 | \n",
+ " 1.189622 | \n",
+ " -1.690617 | \n",
+ " -1.356399 | \n",
+ " -1.232435 | \n",
+ "
\n",
+ " \n",
+ " | 8 | \n",
+ " -0.544439 | \n",
+ " -0.668172 | \n",
+ " 0.007315 | \n",
+ " -0.612939 | \n",
+ " 1.299748 | \n",
+ "
\n",
+ " \n",
+ " | 9 | \n",
+ " -1.733096 | \n",
+ " -0.983310 | \n",
+ " 0.357508 | \n",
+ " -1.613579 | \n",
+ " 1.470714 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 0 1 2 3 4\n",
+ "0 -1.749765 0.342680 1.153036 -0.252436 0.981321\n",
+ "1 0.514219 0.221180 -1.070043 -0.189496 0.255001\n",
+ "2 -0.458027 0.435163 -0.583595 0.816847 0.672721\n",
+ "3 -0.104411 -0.531280 1.029733 -0.438136 -1.118318\n",
+ "4 1.618982 1.541605 -0.251879 -0.842436 0.184519\n",
+ "5 0.937082 0.731000 1.361556 -0.326238 0.055676\n",
+ "6 0.222400 -1.443217 -0.756352 0.816454 0.750445\n",
+ "7 -0.455947 1.189622 -1.690617 -1.356399 -1.232435\n",
+ "8 -0.544439 -0.668172 0.007315 -0.612939 1.299748\n",
+ "9 -1.733096 -0.983310 0.357508 -1.613579 1.470714"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0 -0.175300\n",
+ "1 0.083527\n",
+ "2 -0.044334\n",
+ "3 -0.399836\n",
+ "4 0.331939\n",
+ "dtype: float64\n",
+ "0 1.069584\n",
+ "1 0.965548\n",
+ "2 1.018232\n",
+ "3 0.793167\n",
+ "4 0.918992\n",
+ "dtype: float64\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 2 | \n",
+ " 3 | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 3.061679 | \n",
+ " 0.117430 | \n",
+ " 1.329492 | \n",
+ " 0.063724 | \n",
+ " 0.962990 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " 0.264421 | \n",
+ " 0.048920 | \n",
+ " 1.144993 | \n",
+ " 0.035909 | \n",
+ " 0.065026 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 0.209789 | \n",
+ " 0.189367 | \n",
+ " 0.340583 | \n",
+ " 0.667239 | \n",
+ " 0.452553 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 0.010902 | \n",
+ " 0.282259 | \n",
+ " 1.060349 | \n",
+ " 0.191963 | \n",
+ " 1.250636 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 2.621102 | \n",
+ " 2.376547 | \n",
+ " 0.063443 | \n",
+ " 0.709698 | \n",
+ " 0.034047 | \n",
+ "
\n",
+ " \n",
+ " | 5 | \n",
+ " 0.878123 | \n",
+ " 0.534362 | \n",
+ " 1.853835 | \n",
+ " 0.106431 | \n",
+ " 0.003100 | \n",
+ "
\n",
+ " \n",
+ " | 6 | \n",
+ " 0.049462 | \n",
+ " 2.082875 | \n",
+ " 0.572069 | \n",
+ " 0.666597 | \n",
+ " 0.563167 | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " 0.207888 | \n",
+ " 1.415201 | \n",
+ " 2.858185 | \n",
+ " 1.839818 | \n",
+ " 1.518895 | \n",
+ "
\n",
+ " \n",
+ " | 8 | \n",
+ " 0.296414 | \n",
+ " 0.446453 | \n",
+ " 0.000054 | \n",
+ " 0.375694 | \n",
+ " 1.689345 | \n",
+ "
\n",
+ " \n",
+ " | 9 | \n",
+ " 3.003620 | \n",
+ " 0.966899 | \n",
+ " 0.127812 | \n",
+ " 2.603636 | \n",
+ " 2.162999 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 0 1 2 3 4\n",
+ "0 3.061679 0.117430 1.329492 0.063724 0.962990\n",
+ "1 0.264421 0.048920 1.144993 0.035909 0.065026\n",
+ "2 0.209789 0.189367 0.340583 0.667239 0.452553\n",
+ "3 0.010902 0.282259 1.060349 0.191963 1.250636\n",
+ "4 2.621102 2.376547 0.063443 0.709698 0.034047\n",
+ "5 0.878123 0.534362 1.853835 0.106431 0.003100\n",
+ "6 0.049462 2.082875 0.572069 0.666597 0.563167\n",
+ "7 0.207888 1.415201 2.858185 1.839818 1.518895\n",
+ "8 0.296414 0.446453 0.000054 0.375694 1.689345\n",
+ "9 3.003620 0.966899 0.127812 2.603636 2.162999"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
@@ -1288,10 +1751,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"df.columns = ['First', 'Second', 'Third', 'Fourth', 'Fifth']\n",
@@ -1324,10 +1784,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"b = np.arange(16).reshape((4,4))\n",
@@ -1451,10 +1908,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"# Importing various packages\n",
@@ -1583,10 +2037,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
@@ -1628,10 +2079,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"import numpy as np \n",
@@ -1801,10 +2249,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
@@ -2008,10 +2453,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"# Common imports\n",
@@ -2059,10 +2501,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"from pylab import plt, mpl\n",
@@ -2095,10 +2534,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"\"\"\" \n",
@@ -2126,10 +2562,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"# Read the experimental data with Pandas\n",
@@ -2171,10 +2604,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"A = Masses['A']\n",
@@ -2196,10 +2626,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"# Now we set up the design matrix X\n",
@@ -2221,10 +2648,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"clf = skl.LinearRegression().fit(X, Energies)\n",
@@ -2242,10 +2666,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"# The mean squared error \n",
@@ -2282,10 +2703,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"\n",
@@ -2333,10 +2751,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"from sklearn.neural_network import MLPRegressor\n",
@@ -2388,7 +2803,25 @@
]
}
],
- "metadata": {},
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.5"
+ }
+ },
"nbformat": 4,
"nbformat_minor": 4
}