Make First Code Example Work "out of the box"

.gitignore

@@ -0,0 +1,3 @@
+.vscode/
+.venv/
+Illustrations/

README.md

@@ -4,3 +4,21 @@ The Python code to reproduce the illustrations from [The Hundred-Page Machine Le
 ![](http://themlbook.com/images/og-image3.png)
 
 **WARNING!** To avoid buying counterfeit on Amazon, click on **[See All Buying Options](https://www.amazon.com/gp/offer-listing/199957950X/)** and choose "Amazon.com" and not a third-party seller.
+
+# Getting Started
+1. Create a virtual environment
+   ```bash
+    py -m venv .venv
+    ```
+2. Activate the virtual environment
+    ```bash
+    .venv/Scripts/activate
+    ```
+3. Install dependencies
+   ```bash
+   pip install -r requirements.txt
+   ```
+4. Run one of the examples
+   ```
+   py gradient_descent.py
+   ```

data.txt

@@ -1 +1,201 @@
-The dataset for gradient descent example can be downloaded from: http://themlbook.com/wiki/doku.php?id=gradient_descent
+,TV,radio,newspaper,sales
+1,230.1,37.8,69.2,22.1
+2,44.5,39.3,45.1,10.4
+3,17.2,45.9,69.3,9.3
+4,151.5,41.3,58.5,18.5
+5,180.8,10.8,58.4,12.9
+6,8.7,48.9,75,7.2
+7,57.5,32.8,23.5,11.8
+8,120.2,19.6,11.6,13.2
+9,8.6,2.1,1,4.8
+10,199.8,2.6,21.2,10.6
+11,66.1,5.8,24.2,8.6
+12,214.7,24,4,17.4
+13,23.8,35.1,65.9,9.2
+14,97.5,7.6,7.2,9.7
+15,204.1,32.9,46,19
+16,195.4,47.7,52.9,22.4
+17,67.8,36.6,114,12.5
+18,281.4,39.6,55.8,24.4
+19,69.2,20.5,18.3,11.3
+20,147.3,23.9,19.1,14.6
+21,218.4,27.7,53.4,18
+22,237.4,5.1,23.5,12.5
+23,13.2,15.9,49.6,5.6
+24,228.3,16.9,26.2,15.5
+25,62.3,12.6,18.3,9.7
+26,262.9,3.5,19.5,12
+27,142.9,29.3,12.6,15
+28,240.1,16.7,22.9,15.9
+29,248.8,27.1,22.9,18.9
+30,70.6,16,40.8,10.5
+31,292.9,28.3,43.2,21.4
+32,112.9,17.4,38.6,11.9
+33,97.2,1.5,30,9.6
+34,265.6,20,0.3,17.4
+35,95.7,1.4,7.4,9.5
+36,290.7,4.1,8.5,12.8
+37,266.9,43.8,5,25.4
+38,74.7,49.4,45.7,14.7
+39,43.1,26.7,35.1,10.1
+40,228,37.7,32,21.5
+41,202.5,22.3,31.6,16.6
+42,177,33.4,38.7,17.1
+43,293.6,27.7,1.8,20.7
+44,206.9,8.4,26.4,12.9
+45,25.1,25.7,43.3,8.5
+46,175.1,22.5,31.5,14.9
+47,89.7,9.9,35.7,10.6
+48,239.9,41.5,18.5,23.2
+49,227.2,15.8,49.9,14.8
+50,66.9,11.7,36.8,9.7
+51,199.8,3.1,34.6,11.4
+52,100.4,9.6,3.6,10.7
+53,216.4,41.7,39.6,22.6
+54,182.6,46.2,58.7,21.2
+55,262.7,28.8,15.9,20.2
+56,198.9,49.4,60,23.7
+57,7.3,28.1,41.4,5.5
+58,136.2,19.2,16.6,13.2
+59,210.8,49.6,37.7,23.8
+60,210.7,29.5,9.3,18.4
+61,53.5,2,21.4,8.1
+62,261.3,42.7,54.7,24.2
+63,239.3,15.5,27.3,15.7
+64,102.7,29.6,8.4,14
+65,131.1,42.8,28.9,18
+66,69,9.3,0.9,9.3
+67,31.5,24.6,2.2,9.5
+68,139.3,14.5,10.2,13.4
+69,237.4,27.5,11,18.9
+70,216.8,43.9,27.2,22.3
+71,199.1,30.6,38.7,18.3
+72,109.8,14.3,31.7,12.4
+73,26.8,33,19.3,8.8
+74,129.4,5.7,31.3,11
+75,213.4,24.6,13.1,17
+76,16.9,43.7,89.4,8.7
+77,27.5,1.6,20.7,6.9
+78,120.5,28.5,14.2,14.2
+79,5.4,29.9,9.4,5.3
+80,116,7.7,23.1,11
+81,76.4,26.7,22.3,11.8
+82,239.8,4.1,36.9,12.3
+83,75.3,20.3,32.5,11.3
+84,68.4,44.5,35.6,13.6
+85,213.5,43,33.8,21.7
+86,193.2,18.4,65.7,15.2
+87,76.3,27.5,16,12
+88,110.7,40.6,63.2,16
+89,88.3,25.5,73.4,12.9
+90,109.8,47.8,51.4,16.7
+91,134.3,4.9,9.3,11.2
+92,28.6,1.5,33,7.3
+93,217.7,33.5,59,19.4
+94,250.9,36.5,72.3,22.2
+95,107.4,14,10.9,11.5
+96,163.3,31.6,52.9,16.9
+97,197.6,3.5,5.9,11.7
+98,184.9,21,22,15.5
+99,289.7,42.3,51.2,25.4
+100,135.2,41.7,45.9,17.2
+101,222.4,4.3,49.8,11.7
+102,296.4,36.3,100.9,23.8
+103,280.2,10.1,21.4,14.8
+104,187.9,17.2,17.9,14.7
+105,238.2,34.3,5.3,20.7
+106,137.9,46.4,59,19.2
+107,25,11,29.7,7.2
+108,90.4,0.3,23.2,8.7
+109,13.1,0.4,25.6,5.3
+110,255.4,26.9,5.5,19.8
+111,225.8,8.2,56.5,13.4
+112,241.7,38,23.2,21.8
+113,175.7,15.4,2.4,14.1
+114,209.6,20.6,10.7,15.9
+115,78.2,46.8,34.5,14.6
+116,75.1,35,52.7,12.6
+117,139.2,14.3,25.6,12.2
+118,76.4,0.8,14.8,9.4
+119,125.7,36.9,79.2,15.9
+120,19.4,16,22.3,6.6
+121,141.3,26.8,46.2,15.5
+122,18.8,21.7,50.4,7
+123,224,2.4,15.6,11.6
+124,123.1,34.6,12.4,15.2
+125,229.5,32.3,74.2,19.7
+126,87.2,11.8,25.9,10.6
+127,7.8,38.9,50.6,6.6
+128,80.2,0,9.2,8.8
+129,220.3,49,3.2,24.7
+130,59.6,12,43.1,9.7
+131,0.7,39.6,8.7,1.6
+132,265.2,2.9,43,12.7
+133,8.4,27.2,2.1,5.7
+134,219.8,33.5,45.1,19.6
+135,36.9,38.6,65.6,10.8
+136,48.3,47,8.5,11.6
+137,25.6,39,9.3,9.5
+138,273.7,28.9,59.7,20.8
+139,43,25.9,20.5,9.6
+140,184.9,43.9,1.7,20.7
+141,73.4,17,12.9,10.9
+142,193.7,35.4,75.6,19.2
+143,220.5,33.2,37.9,20.1
+144,104.6,5.7,34.4,10.4
+145,96.2,14.8,38.9,11.4
+146,140.3,1.9,9,10.3
+147,240.1,7.3,8.7,13.2
+148,243.2,49,44.3,25.4
+149,38,40.3,11.9,10.9
+150,44.7,25.8,20.6,10.1
+151,280.7,13.9,37,16.1
+152,121,8.4,48.7,11.6
+153,197.6,23.3,14.2,16.6
+154,171.3,39.7,37.7,19
+155,187.8,21.1,9.5,15.6
+156,4.1,11.6,5.7,3.2
+157,93.9,43.5,50.5,15.3
+158,149.8,1.3,24.3,10.1
+159,11.7,36.9,45.2,7.3
+160,131.7,18.4,34.6,12.9
+161,172.5,18.1,30.7,14.4
+162,85.7,35.8,49.3,13.3
+163,188.4,18.1,25.6,14.9
+164,163.5,36.8,7.4,18
+165,117.2,14.7,5.4,11.9
+166,234.5,3.4,84.8,11.9
+167,17.9,37.6,21.6,8
+168,206.8,5.2,19.4,12.2
+169,215.4,23.6,57.6,17.1
+170,284.3,10.6,6.4,15
+171,50,11.6,18.4,8.4
+172,164.5,20.9,47.4,14.5
+173,19.6,20.1,17,7.6
+174,168.4,7.1,12.8,11.7
+175,222.4,3.4,13.1,11.5
+176,276.9,48.9,41.8,27
+177,248.4,30.2,20.3,20.2
+178,170.2,7.8,35.2,11.7
+179,276.7,2.3,23.7,11.8
+180,165.6,10,17.6,12.6
+181,156.6,2.6,8.3,10.5
+182,218.5,5.4,27.4,12.2
+183,56.2,5.7,29.7,8.7
+184,287.6,43,71.8,26.2
+185,253.8,21.3,30,17.6
+186,205,45.1,19.6,22.6
+187,139.5,2.1,26.6,10.3
+188,191.1,28.7,18.2,17.3
+189,286,13.9,3.7,15.9
+190,18.7,12.1,23.4,6.7
+191,39.5,41.1,5.8,10.8
+192,75.5,10.8,6,9.9
+193,17.2,4.1,31.6,5.9
+194,166.8,42,3.6,19.6
+195,149.7,35.6,6,17.3
+196,38.2,3.7,13.8,7.6
+197,94.2,4.9,8.1,9.7
+198,177,9.3,6.4,12.8
+199,283.6,42,66.2,25.5
+200,232.1,8.6,8.7,13.4

gradient_descent.py

@@ -1,15 +1,31 @@
 from __future__ import print_function
 import numpy as np
 import matplotlib.pyplot as plt
+import pathlib
 
 import matplotlib
 matplotlib.rcParams['mathtext.fontset'] = 'stix'
 matplotlib.rcParams['font.family'] = 'STIXGeneral'
 matplotlib.rcParams.update({'font.size': 18})
 
+# constants for columns in data.txt
+TV_COL=1;
+RADIO_COL=2;
+PAPER_COL=3;
+SALES_COL=4
+
+# platform agnostic path to the output directory
+output_dir = pathlib.Path.cwd() / 'Illustrations'
+output_dir.mkdir(parents=True, exist_ok=True)
 
 def plot_original_data():
-    x, y = np.loadtxt("data.txt", delimiter= "\t", unpack = True)
+    x, y = np.loadtxt(
+        "data.txt",
+        skiprows=1,
+        usecols=(RADIO_COL, SALES_COL),
+        delimiter= ",",
+        unpack = True,
+    )
 
     plt.scatter(x, y, color='#1f77b4', marker='o')
 
@@ -19,9 +35,9 @@ def plot_original_data():
     #plt.show()
     fig1 = plt.gcf()
     fig1.subplots_adjust(top = 0.98, bottom = 0.1, right = 0.98, left = 0.08, hspace = 0, wspace = 0)
-    fig1.savefig('../../Illustrations/gradient_descent-1.eps', format='eps', dpi=1000, bbox_inches = 'tight', pad_inches = 0)
-    fig1.savefig('../../Illustrations/gradient_descent-1.pdf', format='pdf', dpi=1000, bbox_inches = 'tight', pad_inches = 0)
-    fig1.savefig('../../Illustrations/gradient_descent-1.png', dpi=1000, bbox_inches = 'tight', pad_inches = 0)
+    fig1.savefig(output_dir / 'gradient_descent-1.eps', format='eps', dpi=1000, bbox_inches = 'tight', pad_inches = 0)
+    fig1.savefig(output_dir / 'gradient_descent-1.pdf', format='pdf', dpi=1000, bbox_inches = 'tight', pad_inches = 0)
+    fig1.savefig(output_dir / 'gradient_descent-1.png', dpi=1000, bbox_inches = 'tight', pad_inches = 0)
 
 def update_w_and_b(spendings, sales, w, b, alpha):
     dr_dw = 0.0
@@ -57,9 +73,9 @@ def train(spendings, sales, w, b, alpha, epochs):
             #plt.show()
             fig1 = plt.gcf()
             fig1.subplots_adjust(top = 0.98, bottom = 0.1, right = 0.98, left = 0.08, hspace = 0, wspace = 0)
-            fig1.savefig('../../Illustrations/gradient_descent-' + str(image_counter) + '.eps', format='eps', dpi=1000, bbox_inches = 'tight', pad_inches = 0)
-            fig1.savefig('../../Illustrations/gradient_descent-' + str(image_counter) + '.pdf', format='pdf', dpi=1000, bbox_inches = 'tight', pad_inches = 0)
-            fig1.savefig('../../Illustrations/gradient_descent-' + str(image_counter) + '.png', dpi=1000, bbox_inches = 'tight', pad_inches = 0)
+            fig1.savefig(output_dir / ('gradient_descent-' + str(image_counter) + '.eps'), format='eps', dpi=1000, bbox_inches = 'tight', pad_inches = 0)
+            fig1.savefig(output_dir / ('gradient_descent-' + str(image_counter) + '.pdf'), format='pdf', dpi=1000, bbox_inches = 'tight', pad_inches = 0)
+            fig1.savefig(output_dir / ('gradient_descent-' + str(image_counter) + '.png'), dpi=1000, bbox_inches = 'tight', pad_inches = 0)
             image_counter += 1
     return w, b
 
@@ -70,8 +86,15 @@ def loss(spendings, sales, w, b):
         total_error += (sales[i] - (w*spendings[i] + b))**2
     return total_error / N
 
-x, y = np.loadtxt("data.txt", delimiter= "\t", unpack = True)
-#w, b = train(x, y, 0.0, 0.0, 0.001, 15000)
+x, y = np.loadtxt(
+    "data.txt",
+    skiprows=1,
+    usecols=(RADIO_COL, SALES_COL),
+    delimiter= ",",
+    unpack = True,
+)
+
+w, b = train(x, y, 0.0, 0.0, 0.001, 15000)
 
 plot_original_data()
 

requirements.txt

@@ -0,0 +1,56 @@
+absl-py==2.1.0
+astunparse==1.6.3
+certifi==2024.12.14
+charset-normalizer==3.4.0
+colorama==0.4.6
+contourpy==1.3.1
+cycler==0.12.1
+flatbuffers==24.3.25
+fonttools==4.55.3
+gast==0.6.0
+google-pasta==0.2.0
+grpcio==1.68.1
+h5py==3.12.1
+idna==3.10
+joblib==1.4.2
+keras==3.7.0
+kiwisolver==1.4.7
+libclang==18.1.1
+llvmlite==0.43.0
+Markdown==3.7
+markdown-it-py==3.0.0
+MarkupSafe==3.0.2
+matplotlib==3.10.0
+mdurl==0.1.2
+ml-dtypes==0.4.1
+namex==0.0.8
+numba==0.60.0
+numpy==2.0.2
+opt_einsum==3.4.0
+optree==0.13.1
+packaging==24.2
+pillow==11.0.0
+protobuf==5.29.1
+Pygments==2.18.0
+pynndescent==0.5.13
+pyparsing==3.2.0
+python-dateutil==2.9.0.post0
+requests==2.32.3
+rich==13.9.4
+scikit-learn==1.6.0
+scipy==1.14.1
+setuptools==75.6.0
+six==1.17.0
+tensorboard==2.18.0
+tensorboard-data-server==0.7.2
+tensorflow==2.18.0
+tensorflow_intel==2.18.0
+termcolor==2.5.0
+threadpoolctl==3.5.0
+tqdm==4.67.1
+typing_extensions==4.12.2
+umap-learn==0.5.7
+urllib3==2.2.3
+Werkzeug==3.1.3
+wheel==0.45.1
+wrapt==1.17.0