From b4688bda6884a934a528c8726fdb1ed26695d0f1 Mon Sep 17 00:00:00 2001 From: udlbook <110402648+udlbook@users.noreply.github.com> Date: Mon, 27 Jan 2025 17:38:54 -0500 Subject: [PATCH 01/29] Created using Colab --- Trees/LinearRegression_FitModel.ipynb | 51 +++++++++++++++++++++++++++ 1 file changed, 51 insertions(+) create mode 100644 Trees/LinearRegression_FitModel.ipynb diff --git a/Trees/LinearRegression_FitModel.ipynb b/Trees/LinearRegression_FitModel.ipynb new file mode 100644 index 0000000..e1dbcc7 --- /dev/null +++ b/Trees/LinearRegression_FitModel.ipynb @@ -0,0 +1,51 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyNK+Qh1d1D+PE6hFu1NbrbI", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Fitting the model" + ], + "metadata": { + "id": "uORlKyPv02ge" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bbF6SE_F0tU8" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + } + ] +} \ No newline at end of file From 60c5a484773ec11f27c0d7a4c4bdb514805d5ec2 Mon Sep 17 00:00:00 2001 From: udlbook <110402648+udlbook@users.noreply.github.com> Date: Mon, 27 Jan 2025 17:40:21 -0500 Subject: [PATCH 02/29] Delete Trees/LinearRegression_LeastSquares.ipynb --- Trees/LinearRegression_LeastSquares.ipynb | 51 ----------------------- 1 file changed, 51 deletions(-) delete mode 100644 Trees/LinearRegression_LeastSquares.ipynb diff --git a/Trees/LinearRegression_LeastSquares.ipynb b/Trees/LinearRegression_LeastSquares.ipynb deleted file mode 100644 index e8b69d8..0000000 --- a/Trees/LinearRegression_LeastSquares.ipynb +++ /dev/null @@ -1,51 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [], - "authorship_tag": "ABX9TyM1pe3HkxLrjbeKezq1MlM5", - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Least Squares Loss" - ], - "metadata": { - "id": "uORlKyPv02ge" - } - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "bbF6SE_F0tU8" - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - } - ] -} \ No newline at end of file From 623b9782e77d79244ff02e540c990ddd1db46889 Mon Sep 17 00:00:00 2001 From: udlbook <110402648+udlbook@users.noreply.github.com> Date: Tue, 28 Jan 2025 10:36:43 -0500 Subject: [PATCH 03/29] Created using Colab --- ...inearRegression_LossFunction_Answers.ipynb | 329 ++++++++++++++++++ 1 file changed, 329 insertions(+) create mode 100644 Trees/LinearRegression_LossFunction_Answers.ipynb diff --git a/Trees/LinearRegression_LossFunction_Answers.ipynb b/Trees/LinearRegression_LossFunction_Answers.ipynb new file mode 100644 index 0000000..32ab85d --- /dev/null +++ b/Trees/LinearRegression_LossFunction_Answers.ipynb @@ -0,0 +1,329 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyMmE30JSBy91pxG0DSaosF6", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "" + ], + "metadata": { + "id": "lTJK0o1TUMnq" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Loss functions\n", + "\n", + "The purpose of this Python notebook is to compute the loss and subsequently plot the loss function for the 1D linear regression model with a least squares loss.\n", + "\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and write code to complete the functions.\n", + "\n", + "You can save a local copy of this notebook in your Google account and work through it in Colab (recommended) or you can download the notebook and run it locally using Jupyter notebook or similar. If you are using CoLab, we recommend that *turn off* AI autocomplete (under cog icon in top-right corner), which will give you the answers and defeat the purpose of the exercise.\n", + "\n", + "A fully working version of this notebook with the complete answers can be found here.\n", + "\n", + "Contact me at iclimbtreesmail@gmail.com if you find any mistakes or have any suggestions." + ], + "metadata": { + "id": "uORlKyPv02ge" + } + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "bbF6SE_F0tU8" + }, + "outputs": [], + "source": [ + "# Math library\n", + "import numpy as np\n", + "# Plotting library\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib import cm\n", + "from matplotlib.colors import ListedColormap" + ] + }, + { + "cell_type": "code", + "source": [ + "# Create the same input / output data as used in the unit\n", + "x = np.array([0.03, 0.19, 0.34, 0.46, 0.78, 0.81, 1.08, 1.18, 1.39, 1.60, 1.65, 1.90])\n", + "y = np.array([0.67, 0.85, 1.05, 1.0, 1.40, 1.5, 1.3, 1.54, 1.55, 1.68, 1.73, 1.6 ])" + ], + "metadata": { + "id": "UQ1qYLOeYf61" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Function to help plot the data\n", + "def plot(x, y, phi0=None, phi1=None):\n", + " fig,ax = plt.subplots()\n", + " ax.scatter(x,y)\n", + " plt.xlim([0,2.0])\n", + " plt.ylim([0,2.0])\n", + " ax.set_xlabel('Input, $x$')\n", + " ax.set_ylabel('Output, $y$')\n", + " ax.set_aspect('equal')\n", + " # Draw line if parameters passed\n", + " x_line = np.arange(0,2,0.01)\n", + " if(phi0 is not None and phi1 is not None):\n", + " y_line = phi0 + phi1*x_line\n", + " plt.plot(x_line, y_line,'r-',lw=2)\n", + " plt.show()" + ], + "metadata": { + "id": "785wp16FYjt5" + }, + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Define the model\n", + "\n", + "The linear regression model is defined as:\n", + "\n", + "$$\\textrm{f}[x,\\boldsymbol\\phi] = \\phi_0+\\phi_1 x$$\n", + "\n", + "where $\\phi_0$ is the y-intercept and $\\phi_1$ is the slope." + ], + "metadata": { + "id": "cO0m5OaCd17h" + } + }, + { + "cell_type": "code", + "source": [ + "# Define 1D linear regression model\n", + "def f(x, phi0, phi1):\n", + " # TODO -- define the linear regression model\n", + " # REPLACE THIS CODE\n", + " y = x\n", + "\n", + " # ANSWER\n", + " y = phi0 + phi1 * x\n", + " # END ANSWER\n", + "\n", + " return y" + ], + "metadata": { + "id": "aD2g9L9hd5Bd" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "plot(x,y, phi0 = 1.5, phi1=-0.2)" + ], + "metadata": { + "id": "sONMDaj_eJ-6", + "outputId": "a7c1dda3-465c-4d0c-e7da-b2c17b1e86c2", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 458 + } + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Compute the least squares loss\n", + "\n", + "The least squares loss is defined as the sum of the squared deviations of the model output $\\textrm{f}[x_i,\\boldsymbol\\phi]$ and the true output target $y_i$:\n", + "\n", + " \\begin{align} L[\\boldsymbol\\phi] & = \\sum_{i=1}^{I} \\bigl(\\textrm{f}[x_{i}, \\boldsymbol\\phi]-y_{i}\\bigr)^{2} \\\\ &= \\sum_{i=1}^{I} \\bigl(\\phi_{0}+\\phi_{1}x_i-y_{i}\\bigr)^{2} \\tag{1.2}\\end{align}" + ], + "metadata": { + "id": "xoe2kvdreYLh" + } + }, + { + "cell_type": "code", + "source": [ + "# Function to calculate the loss\n", + "def compute_loss(x,y,phi0,phi1):\n", + "\n", + " # TODO Replace this line with the loss calculation (equation 2.5)\n", + " loss = 0\n", + "\n", + " # ANSWER\n", + " signed_distance = f(x,phi0,phi1)-y\n", + " loss = np.sum(signed_distance * signed_distance)\n", + " # END_ANSWER\n", + "\n", + " return loss" + ], + "metadata": { + "id": "PEenxRo3ePCL" + }, + "execution_count": 11, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Let's check that this is correct\n", + "loss = compute_loss(x,y, phi0 = 0.4 , phi1 = 0.2)\n", + "print(f'Your Loss = {loss:3.2f}, Ground truth =7.07')" + ], + "metadata": { + "id": "EbTeNI3fhgGw", + "outputId": "6f55f6f2-c1bf-4475-a371-810f9e208a54", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Your Loss = 7.07, Ground truth =7.07\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Draw the loss function" + ], + "metadata": { + "id": "9A32qcIAiU5x" + } + }, + { + "cell_type": "code", + "source": [ + "# Helper code to do the drawing\n", + "def draw_loss_function(compute_loss, x_in, y_in):\n", + " # Define pretty colormap\n", + " my_colormap_vals_hex =('2a0902', '2b0a03', '2c0b04', '2d0c05', '2e0c06', '2f0d07', '300d08', '310e09', '320f0a', '330f0b', '34100b', '35110c', '36110d', '37120e', '38120f', '39130f', '3a1410', '3b1411', '3c1511', '3d1612', '3e1613', '3f1713', '401714', '411814', '421915', '431915', '451a16', '461b16', '471b17', '481c17', '491d18', '4a1d18', '4b1e19', '4c1f19', '4d1f1a', '4e201b', '50211b', '51211c', '52221c', '53231d', '54231d', '55241e', '56251e', '57261f', '58261f', '592720', '5b2821', '5c2821', '5d2922', '5e2a22', '5f2b23', '602b23', '612c24', '622d25', '632e25', '652e26', '662f26', '673027', '683027', '693128', '6a3229', '6b3329', '6c342a', '6d342a', '6f352b', '70362c', '71372c', '72372d', '73382e', '74392e', '753a2f', '763a2f', '773b30', '783c31', '7a3d31', '7b3e32', '7c3e33', '7d3f33', '7e4034', '7f4134', '804235', '814236', '824336', '834437', '854538', '864638', '874739', '88473a', '89483a', '8a493b', '8b4a3c', '8c4b3c', '8d4c3d', '8e4c3e', '8f4d3f', '904e3f', '924f40', '935041', '945141', '955242', '965343', '975343', '985444', '995545', '9a5646', '9b5746', '9c5847', '9d5948', '9e5a49', '9f5a49', 'a05b4a', 'a15c4b', 'a35d4b', 'a45e4c', 'a55f4d', 'a6604e', 'a7614e', 'a8624f', 'a96350', 'aa6451', 'ab6552', 'ac6552', 'ad6653', 'ae6754', 'af6855', 'b06955', 'b16a56', 'b26b57', 'b36c58', 'b46d59', 'b56e59', 'b66f5a', 'b7705b', 'b8715c', 'b9725d', 'ba735d', 'bb745e', 'bc755f', 'bd7660', 'be7761', 'bf7862', 'c07962', 'c17a63', 'c27b64', 'c27c65', 'c37d66', 'c47e67', 'c57f68', 'c68068', 'c78169', 'c8826a', 'c9836b', 'ca846c', 'cb856d', 'cc866e', 'cd876f', 'ce886f', 'ce8970', 'cf8a71', 'd08b72', 'd18c73', 'd28d74', 'd38e75', 'd48f76', 'd59077', 'd59178', 'd69279', 'd7937a', 'd8957b', 'd9967b', 'da977c', 'da987d', 'db997e', 'dc9a7f', 'dd9b80', 'de9c81', 'de9d82', 'df9e83', 'e09f84', 'e1a185', 'e2a286', 'e2a387', 'e3a488', 'e4a589', 'e5a68a', 'e5a78b', 'e6a88c', 'e7aa8d', 'e7ab8e', 'e8ac8f', 'e9ad90', 'eaae91', 'eaaf92', 'ebb093', 'ecb295', 'ecb396', 'edb497', 'eeb598', 'eeb699', 'efb79a', 'efb99b', 'f0ba9c', 'f1bb9d', 'f1bc9e', 'f2bd9f', 'f2bfa1', 'f3c0a2', 'f3c1a3', 'f4c2a4', 'f5c3a5', 'f5c5a6', 'f6c6a7', 'f6c7a8', 'f7c8aa', 'f7c9ab', 'f8cbac', 'f8ccad', 'f8cdae', 'f9ceb0', 'f9d0b1', 'fad1b2', 'fad2b3', 'fbd3b4', 'fbd5b6', 'fbd6b7', 'fcd7b8', 'fcd8b9', 'fcdaba', 'fddbbc', 'fddcbd', 'fddebe', 'fddfbf', 'fee0c1', 'fee1c2', 'fee3c3', 'fee4c5', 'ffe5c6', 'ffe7c7', 'ffe8c9', 'ffe9ca', 'ffebcb', 'ffeccd', 'ffedce', 'ffefcf', 'fff0d1', 'fff2d2', 'fff3d3', 'fff4d5', 'fff6d6', 'fff7d8', 'fff8d9', 'fffada', 'fffbdc', 'fffcdd', 'fffedf', 'ffffe0')\n", + " my_colormap_vals_dec = np.array([int(element,base=16) for element in my_colormap_vals_hex])\n", + " r = np.floor(my_colormap_vals_dec/(256*256))\n", + " g = np.floor((my_colormap_vals_dec - r *256 *256)/256)\n", + " b = np.floor(my_colormap_vals_dec - r * 256 *256 - g * 256)\n", + " my_colormap = ListedColormap(np.vstack((r,g,b)).transpose()/255.0)\n", + "\n", + " # Make grid of intercept/slope values to plot\n", + " intercepts_mesh, slopes_mesh = np.meshgrid(np.arange(0.0,2.0,0.02), np.arange(-1.0,1.0,0.002))\n", + " loss_mesh = np.zeros_like(slopes_mesh)\n", + "\n", + " # Compute loss for every set of parameters\n", + " for idslope, slope in np.ndenumerate(slopes_mesh):\n", + " loss_mesh[idslope] = compute_loss(x_in, y_in, intercepts_mesh[idslope], slope)\n", + "\n", + " fig,ax = plt.subplots()\n", + " fig.set_size_inches(6,6)\n", + " ax.contourf(intercepts_mesh,slopes_mesh,loss_mesh,256,cmap=my_colormap)\n", + " ax.contour(intercepts_mesh,slopes_mesh,loss_mesh,40,colors=['#80808080'])\n", + "\n", + " ax.set_ylim([1,-1])\n", + " ax.set_xlabel('Intercept, $\\phi_0$')\n", + " ax.set_ylabel('Slope, $\\phi_1$')\n", + " ax.set_aspect('equal')\n", + " plt.show()" + ], + "metadata": { + "id": "zlUXJ-5zhlb9" + }, + "execution_count": 16, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "draw_loss_function(compute_loss, x, y )" + ], + "metadata": { + "id": "sUqE8Ll6iULa", + "outputId": "284aa527-7879-49cb-c5ee-ce7686193f3c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 551 + } + }, + "execution_count": 17, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "TODO -- experiment by changing the input x and y values (keeping them in the range $x\\in[0,2], y\\in[0,2]$)\n", + "Does the loss function always have a single minimum?\n", + "Think about why this might be." + ], + "metadata": { + "id": "rpJ3ZnCDjPp0" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "niGXQTgJjclv" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From f1c07f53bf3c9e7470ef95d96bd2e4f9734aee5c Mon Sep 17 00:00:00 2001 From: udlbook <110402648+udlbook@users.noreply.github.com> Date: Tue, 28 Jan 2025 10:48:39 -0500 Subject: [PATCH 04/29] Created using Colab --- Trees/LinearRegression_LossFunction.ipynb | 232 +++++++++++++++++++++- 1 file changed, 229 insertions(+), 3 deletions(-) diff --git a/Trees/LinearRegression_LossFunction.ipynb b/Trees/LinearRegression_LossFunction.ipynb index 03fbfaa..a4596d1 100644 --- a/Trees/LinearRegression_LossFunction.ipynb +++ b/Trees/LinearRegression_LossFunction.ipynb @@ -4,7 +4,7 @@ "metadata": { "colab": { "provenance": [], - "authorship_tag": "ABX9TyMIJ9DpOBppPZXAJ5wms6s8", + "authorship_tag": "ABX9TyMDnVKMFeK2nRxX862l3xmp", "include_colab_link": true }, "kernelspec": { @@ -29,7 +29,26 @@ { "cell_type": "markdown", "source": [ - "# Loss function" + "" + ], + "metadata": { + "id": "lTJK0o1TUMnq" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Loss functions\n", + "\n", + "The purpose of this Python notebook is to compute the loss and subsequently plot the loss function for the 1D linear regression model with a least squares loss.\n", + "\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and write code to complete the functions.\n", + "\n", + "You can save a local copy of this notebook in your Google account and work through it in Colab (recommended) or you can download the notebook and run it locally using Jupyter notebook or similar. If you are using CoLab, we recommend that *turn off* AI autocomplete (under cog icon in top-right corner), which will give you the answers and defeat the purpose of the exercise.\n", + "\n", + "A fully working version of this notebook with the complete answers can be found here.\n", + "\n", + "Contact me at iclimbtreesmail@gmail.com if you find any mistakes or have any suggestions." ], "metadata": { "id": "uORlKyPv02ge" @@ -43,9 +62,216 @@ }, "outputs": [], "source": [ + "# Math library\n", "import numpy as np\n", - "import matplotlib.pyplot as plt" + "# Plotting library\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib import cm\n", + "from matplotlib.colors import ListedColormap" ] + }, + { + "cell_type": "code", + "source": [ + "# Create the same input / output data as used in the unit\n", + "x = np.array([0.03, 0.19, 0.34, 0.46, 0.78, 0.81, 1.08, 1.18, 1.39, 1.60, 1.65, 1.90])\n", + "y = np.array([0.67, 0.85, 1.05, 1.0, 1.40, 1.5, 1.3, 1.54, 1.55, 1.68, 1.73, 1.6 ])" + ], + "metadata": { + "id": "UQ1qYLOeYf61" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Function to help plot the data\n", + "def plot(x, y, f, phi0=None, phi1=None):\n", + " fig,ax = plt.subplots()\n", + " ax.scatter(x,y)\n", + " plt.xlim([0,2.0])\n", + " plt.ylim([0,2.0])\n", + " ax.set_xlabel('Input, $x$')\n", + " ax.set_ylabel('Output, $y$')\n", + " ax.set_aspect('equal')\n", + " # Draw line if parameters passed\n", + " x_line = np.arange(0,2,0.01)\n", + " if(phi0 is not None and phi1 is not None):\n", + " y_line = f(x_line, phi0, phi1)\n", + " plt.plot(x_line, y_line,'r-',lw=2)\n", + " plt.show()" + ], + "metadata": { + "id": "785wp16FYjt5" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Define the model\n", + "\n", + "The linear regression model is defined as:\n", + "\n", + "$$\\textrm{f}[x,\\boldsymbol\\phi] = \\phi_0+\\phi_1 x$$\n", + "\n", + "where $\\phi_0$ is the y-intercept and $\\phi_1$ is the slope." + ], + "metadata": { + "id": "cO0m5OaCd17h" + } + }, + { + "cell_type": "code", + "source": [ + "# Define 1D linear regression model\n", + "def f(x, phi0, phi1):\n", + " # TODO -- define the linear regression model\n", + " # REPLACE THIS CODE\n", + " y = x\n", + "\n", + " return y" + ], + "metadata": { + "id": "aD2g9L9hd5Bd" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "plot(x,y, f, phi0 = 1.5, phi1=-0.2)" + ], + "metadata": { + "id": "sONMDaj_eJ-6" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Compute the least squares loss\n", + "\n", + "The least squares loss is defined as the sum of the squared deviations of the model output $\\textrm{f}[x_i,\\boldsymbol\\phi]$ and the true output target $y_i$:\n", + "\n", + " \\begin{align} L[\\boldsymbol\\phi] & = \\sum_{i=1}^{I} \\bigl(\\textrm{f}[x_{i}, \\boldsymbol\\phi]-y_{i}\\bigr)^{2} \\\\ &= \\sum_{i=1}^{I} \\bigl(\\phi_{0}+\\phi_{1}x_i-y_{i}\\bigr)^{2} \\tag{1.2}\\end{align}" + ], + "metadata": { + "id": "xoe2kvdreYLh" + } + }, + { + "cell_type": "code", + "source": [ + "# Function to calculate the loss\n", + "def compute_loss(x,y,phi0,phi1):\n", + "\n", + " # TODO Replace this line with the loss calculation (equation 2.5)\n", + " loss = 0\n", + "\n", + "\n", + " return loss" + ], + "metadata": { + "id": "PEenxRo3ePCL" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Let's check that this is correct\n", + "loss = compute_loss(x,y, phi0 = 0.4 , phi1 = 0.2)\n", + "print(f'Your Loss = {loss:3.2f}, Ground truth =7.07')" + ], + "metadata": { + "id": "EbTeNI3fhgGw" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Draw the loss function" + ], + "metadata": { + "id": "9A32qcIAiU5x" + } + }, + { + "cell_type": "code", + "source": [ + "# Helper code to do the drawing\n", + "def draw_loss_function(compute_loss, x_in, y_in):\n", + " # Define pretty colormap\n", + " my_colormap_vals_hex =('2a0902', '2b0a03', '2c0b04', '2d0c05', '2e0c06', '2f0d07', '300d08', '310e09', '320f0a', '330f0b', '34100b', '35110c', '36110d', '37120e', '38120f', '39130f', '3a1410', '3b1411', '3c1511', '3d1612', '3e1613', '3f1713', '401714', '411814', '421915', '431915', '451a16', '461b16', '471b17', '481c17', '491d18', '4a1d18', '4b1e19', '4c1f19', '4d1f1a', '4e201b', '50211b', '51211c', '52221c', '53231d', '54231d', '55241e', '56251e', '57261f', '58261f', '592720', '5b2821', '5c2821', '5d2922', '5e2a22', '5f2b23', '602b23', '612c24', '622d25', '632e25', '652e26', '662f26', '673027', '683027', '693128', '6a3229', '6b3329', '6c342a', '6d342a', '6f352b', '70362c', '71372c', '72372d', '73382e', '74392e', '753a2f', '763a2f', '773b30', '783c31', '7a3d31', '7b3e32', '7c3e33', '7d3f33', '7e4034', '7f4134', '804235', '814236', '824336', '834437', '854538', '864638', '874739', '88473a', '89483a', '8a493b', '8b4a3c', '8c4b3c', '8d4c3d', '8e4c3e', '8f4d3f', '904e3f', '924f40', '935041', '945141', '955242', '965343', '975343', '985444', '995545', '9a5646', '9b5746', '9c5847', '9d5948', '9e5a49', '9f5a49', 'a05b4a', 'a15c4b', 'a35d4b', 'a45e4c', 'a55f4d', 'a6604e', 'a7614e', 'a8624f', 'a96350', 'aa6451', 'ab6552', 'ac6552', 'ad6653', 'ae6754', 'af6855', 'b06955', 'b16a56', 'b26b57', 'b36c58', 'b46d59', 'b56e59', 'b66f5a', 'b7705b', 'b8715c', 'b9725d', 'ba735d', 'bb745e', 'bc755f', 'bd7660', 'be7761', 'bf7862', 'c07962', 'c17a63', 'c27b64', 'c27c65', 'c37d66', 'c47e67', 'c57f68', 'c68068', 'c78169', 'c8826a', 'c9836b', 'ca846c', 'cb856d', 'cc866e', 'cd876f', 'ce886f', 'ce8970', 'cf8a71', 'd08b72', 'd18c73', 'd28d74', 'd38e75', 'd48f76', 'd59077', 'd59178', 'd69279', 'd7937a', 'd8957b', 'd9967b', 'da977c', 'da987d', 'db997e', 'dc9a7f', 'dd9b80', 'de9c81', 'de9d82', 'df9e83', 'e09f84', 'e1a185', 'e2a286', 'e2a387', 'e3a488', 'e4a589', 'e5a68a', 'e5a78b', 'e6a88c', 'e7aa8d', 'e7ab8e', 'e8ac8f', 'e9ad90', 'eaae91', 'eaaf92', 'ebb093', 'ecb295', 'ecb396', 'edb497', 'eeb598', 'eeb699', 'efb79a', 'efb99b', 'f0ba9c', 'f1bb9d', 'f1bc9e', 'f2bd9f', 'f2bfa1', 'f3c0a2', 'f3c1a3', 'f4c2a4', 'f5c3a5', 'f5c5a6', 'f6c6a7', 'f6c7a8', 'f7c8aa', 'f7c9ab', 'f8cbac', 'f8ccad', 'f8cdae', 'f9ceb0', 'f9d0b1', 'fad1b2', 'fad2b3', 'fbd3b4', 'fbd5b6', 'fbd6b7', 'fcd7b8', 'fcd8b9', 'fcdaba', 'fddbbc', 'fddcbd', 'fddebe', 'fddfbf', 'fee0c1', 'fee1c2', 'fee3c3', 'fee4c5', 'ffe5c6', 'ffe7c7', 'ffe8c9', 'ffe9ca', 'ffebcb', 'ffeccd', 'ffedce', 'ffefcf', 'fff0d1', 'fff2d2', 'fff3d3', 'fff4d5', 'fff6d6', 'fff7d8', 'fff8d9', 'fffada', 'fffbdc', 'fffcdd', 'fffedf', 'ffffe0')\n", + " my_colormap_vals_dec = np.array([int(element,base=16) for element in my_colormap_vals_hex])\n", + " r = np.floor(my_colormap_vals_dec/(256*256))\n", + " g = np.floor((my_colormap_vals_dec - r *256 *256)/256)\n", + " b = np.floor(my_colormap_vals_dec - r * 256 *256 - g * 256)\n", + " my_colormap = ListedColormap(np.vstack((r,g,b)).transpose()/255.0)\n", + "\n", + " # Make grid of intercept/slope values to plot\n", + " intercepts_mesh, slopes_mesh = np.meshgrid(np.arange(0.0,2.0,0.02), np.arange(-1.0,1.0,0.002))\n", + " loss_mesh = np.zeros_like(slopes_mesh)\n", + "\n", + " # Compute loss for every set of parameters\n", + " for idslope, slope in np.ndenumerate(slopes_mesh):\n", + " loss_mesh[idslope] = compute_loss(x_in, y_in, intercepts_mesh[idslope], slope)\n", + "\n", + " fig,ax = plt.subplots()\n", + " fig.set_size_inches(6,6)\n", + " ax.contourf(intercepts_mesh,slopes_mesh,loss_mesh,256,cmap=my_colormap)\n", + " ax.contour(intercepts_mesh,slopes_mesh,loss_mesh,40,colors=['#80808080'])\n", + "\n", + " ax.set_ylim([1,-1])\n", + " ax.set_xlabel('Intercept, $\\phi_0$')\n", + " ax.set_ylabel('Slope, $\\phi_1$')\n", + " ax.set_aspect('equal')\n", + " plt.show()" + ], + "metadata": { + "id": "zlUXJ-5zhlb9" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "draw_loss_function(compute_loss, x, y )" + ], + "metadata": { + "id": "sUqE8Ll6iULa" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "TODO -- experiment by changing the input x and y values (keeping them in the range $x\\in[0,2], y\\in[0,2]$)\n", + "Does the loss function always have a single minimum?\n", + "Think about why this might be." + ], + "metadata": { + "id": "rpJ3ZnCDjPp0" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "niGXQTgJjclv" + }, + "execution_count": null, + "outputs": [] } ] } \ No newline at end of file From 4e564088a15d26a4773355dd15a7217cf60af2c8 Mon Sep 17 00:00:00 2001 From: udlbook <110402648+udlbook@users.noreply.github.com> Date: Tue, 28 Jan 2025 10:50:31 -0500 Subject: [PATCH 05/29] Created using Colab --- Trees/LinearRegression_LossFunction.ipynb | 52 +++++++++++------------ 1 file changed, 26 insertions(+), 26 deletions(-) diff --git a/Trees/LinearRegression_LossFunction.ipynb b/Trees/LinearRegression_LossFunction.ipynb index a4596d1..b269171 100644 --- a/Trees/LinearRegression_LossFunction.ipynb +++ b/Trees/LinearRegression_LossFunction.ipynb @@ -4,7 +4,7 @@ "metadata": { "colab": { "provenance": [], - "authorship_tag": "ABX9TyMDnVKMFeK2nRxX862l3xmp", + "authorship_tag": "ABX9TyPss+SDCn3GEVlRYmKVEPWE", "include_colab_link": true }, "kernelspec": { @@ -83,31 +83,6 @@ "execution_count": null, "outputs": [] }, - { - "cell_type": "code", - "source": [ - "# Function to help plot the data\n", - "def plot(x, y, f, phi0=None, phi1=None):\n", - " fig,ax = plt.subplots()\n", - " ax.scatter(x,y)\n", - " plt.xlim([0,2.0])\n", - " plt.ylim([0,2.0])\n", - " ax.set_xlabel('Input, $x$')\n", - " ax.set_ylabel('Output, $y$')\n", - " ax.set_aspect('equal')\n", - " # Draw line if parameters passed\n", - " x_line = np.arange(0,2,0.01)\n", - " if(phi0 is not None and phi1 is not None):\n", - " y_line = f(x_line, phi0, phi1)\n", - " plt.plot(x_line, y_line,'r-',lw=2)\n", - " plt.show()" - ], - "metadata": { - "id": "785wp16FYjt5" - }, - "execution_count": null, - "outputs": [] - }, { "cell_type": "markdown", "source": [ @@ -140,6 +115,31 @@ "execution_count": null, "outputs": [] }, + { + "cell_type": "code", + "source": [ + "# Function to help plot the data\n", + "def plot(x, y, f, phi0=None, phi1=None):\n", + " fig,ax = plt.subplots()\n", + " ax.scatter(x,y)\n", + " plt.xlim([0,2.0])\n", + " plt.ylim([0,2.0])\n", + " ax.set_xlabel('Input, $x$')\n", + " ax.set_ylabel('Output, $y$')\n", + " ax.set_aspect('equal')\n", + " # Draw line if parameters passed\n", + " x_line = np.arange(0,2,0.01)\n", + " if(phi0 is not None and phi1 is not None):\n", + " y_line = f(x_line, phi0, phi1)\n", + " plt.plot(x_line, y_line,'r-',lw=2)\n", + " plt.show()" + ], + "metadata": { + "id": "785wp16FYjt5" + }, + "execution_count": null, + "outputs": [] + }, { "cell_type": "code", "source": [ From 667346fbdd6e13de8ab4e7f084d9f49e8bfdc0c1 Mon Sep 17 00:00:00 2001 From: udlbook <110402648+udlbook@users.noreply.github.com> Date: Tue, 28 Jan 2025 10:57:32 -0500 Subject: [PATCH 06/29] Created using Colab --- Trees/LinearRegression_LossFunction.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Trees/LinearRegression_LossFunction.ipynb b/Trees/LinearRegression_LossFunction.ipynb index b269171..316a0ae 100644 --- a/Trees/LinearRegression_LossFunction.ipynb +++ b/Trees/LinearRegression_LossFunction.ipynb @@ -4,7 +4,7 @@ "metadata": { "colab": { "provenance": [], - "authorship_tag": "ABX9TyPss+SDCn3GEVlRYmKVEPWE", + "authorship_tag": "ABX9TyNW9je/Pk2yQ+ikZkj4p7gC", "include_colab_link": true }, "kernelspec": { @@ -170,7 +170,7 @@ "# Function to calculate the loss\n", "def compute_loss(x,y,phi0,phi1):\n", "\n", - " # TODO Replace this line with the loss calculation (equation 2.5)\n", + " # TODO Replace this line with the loss calculation\n", " loss = 0\n", "\n", "\n", From 88e8526fa714e82c7417b5b1b628622b316cfd2f Mon Sep 17 00:00:00 2001 From: udlbook <110402648+udlbook@users.noreply.github.com> Date: Tue, 28 Jan 2025 10:59:00 -0500 Subject: [PATCH 07/29] Created using Colab --- Trees/LinearRegression_LossFunction.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Trees/LinearRegression_LossFunction.ipynb b/Trees/LinearRegression_LossFunction.ipynb index 316a0ae..2aae285 100644 --- a/Trees/LinearRegression_LossFunction.ipynb +++ b/Trees/LinearRegression_LossFunction.ipynb @@ -4,7 +4,7 @@ "metadata": { "colab": { "provenance": [], - "authorship_tag": "ABX9TyNW9je/Pk2yQ+ikZkj4p7gC", + "authorship_tag": "ABX9TyMZw5ysoZN18RhdkayKdi07", "include_colab_link": true }, "kernelspec": { @@ -46,7 +46,7 @@ "\n", "You can save a local copy of this notebook in your Google account and work through it in Colab (recommended) or you can download the notebook and run it locally using Jupyter notebook or similar. If you are using CoLab, we recommend that *turn off* AI autocomplete (under cog icon in top-right corner), which will give you the answers and defeat the purpose of the exercise.\n", "\n", - "A fully working version of this notebook with the complete answers can be found here.\n", + "A fully working version of this notebook with the complete answers can be found [here](https://colab.research.google.com/github/udlbook/udlbook/blob/main/Trees/LinearRegression_LossFunction_Answers.ipynb#scrollTo=niGXQTgJjclv).\n", "\n", "Contact me at iclimbtreesmail@gmail.com if you find any mistakes or have any suggestions." ], From fb66cd682ddfe6db7013de60f9efde97e3305610 Mon Sep 17 00:00:00 2001 From: udlbook <110402648+udlbook@users.noreply.github.com> Date: Tue, 28 Jan 2025 11:43:39 -0500 Subject: [PATCH 08/29] Created using Colab --- ...inearRegression_LossFunction_Answers.ipynb | 22 +++++++++---------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/Trees/LinearRegression_LossFunction_Answers.ipynb b/Trees/LinearRegression_LossFunction_Answers.ipynb index 32ab85d..a32ce3a 100644 --- a/Trees/LinearRegression_LossFunction_Answers.ipynb +++ b/Trees/LinearRegression_LossFunction_Answers.ipynb @@ -4,7 +4,7 @@ "metadata": { "colab": { "provenance": [], - "authorship_tag": "ABX9TyMmE30JSBy91pxG0DSaosF6", + "authorship_tag": "ABX9TyM2yB29boJt3bLHqTG5AUqR", "include_colab_link": true }, "kernelspec": { @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "id": "bbF6SE_F0tU8" }, @@ -80,7 +80,7 @@ "metadata": { "id": "UQ1qYLOeYf61" }, - "execution_count": 2, + "execution_count": null, "outputs": [] }, { @@ -105,7 +105,7 @@ "metadata": { "id": "785wp16FYjt5" }, - "execution_count": 5, + "execution_count": null, "outputs": [] }, { @@ -141,7 +141,7 @@ "metadata": { "id": "aD2g9L9hd5Bd" }, - "execution_count": 6, + "execution_count": null, "outputs": [] }, { @@ -157,7 +157,7 @@ "height": 458 } }, - "execution_count": 8, + "execution_count": null, "outputs": [ { "output_type": "display_data", @@ -188,7 +188,7 @@ "cell_type": "code", "source": [ "# Function to calculate the loss\n", - "def compute_loss(x,y,phi0,phi1):\n", + "def compute_loss(x,y,f,phi0,phi1):\n", "\n", " # TODO Replace this line with the loss calculation (equation 2.5)\n", " loss = 0\n", @@ -203,7 +203,7 @@ "metadata": { "id": "PEenxRo3ePCL" }, - "execution_count": 11, + "execution_count": null, "outputs": [] }, { @@ -220,7 +220,7 @@ "base_uri": "https://localhost:8080/" } }, - "execution_count": 13, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -275,7 +275,7 @@ "metadata": { "id": "zlUXJ-5zhlb9" }, - "execution_count": 16, + "execution_count": null, "outputs": [] }, { @@ -291,7 +291,7 @@ "height": 551 } }, - "execution_count": 17, + "execution_count": null, "outputs": [ { "output_type": "display_data", From 40a2c3ca8b4d210ecf50b061c7d261ec36fa48a6 Mon Sep 17 00:00:00 2001 From: udlbook <110402648+udlbook@users.noreply.github.com> Date: Wed, 29 Jan 2025 10:17:58 -0500 Subject: [PATCH 09/29] Created using Colab --- .../LinearRegression_FitModel_Quadratic.ipynb | 343 ++++++++++++++++++ 1 file changed, 343 insertions(+) create mode 100644 Trees/LinearRegression_FitModel_Quadratic.ipynb diff --git a/Trees/LinearRegression_FitModel_Quadratic.ipynb b/Trees/LinearRegression_FitModel_Quadratic.ipynb new file mode 100644 index 0000000..b1b2195 --- /dev/null +++ b/Trees/LinearRegression_FitModel_Quadratic.ipynb @@ -0,0 +1,343 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyOTr64QzGKRRfJqS3P6I/jU", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "" + ], + "metadata": { + "id": "9Qgup23vwdll" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Fitting 1D regression model\n", + "\n", + "This notebook fits the quadratic model using coordinate descent.\n", + "\n", + "The code is complete (i.e., there is no work for you to to), but take a look at it and run the code to see the model training.\n", + "\n", + "You can save a local copy of this notebook in your Google account and work through it in Colab (recommended) or you can download the notebook and run it locally using Jupyter notebook or similar. \n", + "\n", + "Contact me at iclimbtreesmail@gmail.com if you find any mistakes or have any suggestions." + ], + "metadata": { + "id": "uORlKyPv02ge" + } + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "bbF6SE_F0tU8" + }, + "outputs": [], + "source": [ + "# Math library\n", + "import numpy as np\n", + "# Plotting library\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib import cm\n", + "from matplotlib.colors import ListedColormap\n", + "# Time library\n", + "import time\n", + "# Used to update figures\n", + "from IPython import display" + ] + }, + { + "cell_type": "code", + "source": [ + "# Create the same input / output data as used in the unit\n", + "x = np.array([0.03, 0.19, 0.34, 0.46, 0.78, 0.81, 1.08, 1.18, 1.39, 1.60, 1.65, 1.90])\n", + "y = np.array([0.67, 0.85, 1.05, 1.0, 1.40, 1.5, 1.3, 1.54, 1.55, 1.68, 1.73, 1.6 ])" + ], + "metadata": { + "id": "9fGAobBnyI7Z" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Quadratic model\n", + "\n", + "Now let's consider fitting a more complex model. The quadratic model is defined as:\n", + "\n", + "$$ \\textrm{f}[x] = \\phi_0+\\phi_1 x+ \\phi_2 x^2$$\n", + "\n", + "and has three parameters $\\phi_0, \\phi_1, \\phi_2$" + ], + "metadata": { + "id": "vkmA_5scA5mi" + } + }, + { + "cell_type": "code", + "source": [ + "# Model definition\n", + "def f_quad(x, phi0, phi1, phi2):\n", + " return phi0 + phi1 * x + phi2 * x * x\n", + "\n", + "# Function to calculate the loss\n", + "def compute_loss_quad(x,y,f,phi0,phi1,phi2):\n", + "\n", + " signed_distance = f(x,phi0,phi1,phi2)-y\n", + " loss = np.sum(signed_distance * signed_distance)\n", + "\n", + " return loss" + ], + "metadata": { + "id": "O_3gRgR1AhfT" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Draw this model for some values\n", + "phi0 = 1.35\n", + "phi1 = 0.5\n", + "phi2 = -0.2\n", + "\n", + "fig,ax = plt.subplots()\n", + "x_plot = np.linspace(0,2,100)\n", + "ax.plot(x,y,'bo')\n", + "ax.plot(x_plot,f_quad(x_plot, phi0, phi1, phi2), 'r-')\n", + "ax.set_xlim(0,2)\n", + "ax.set_ylim(0,2)\n", + "ax.set_title('Loss: {:.2f}'.format(compute_loss_quad(x,y,f_quad,phi0,phi1,phi2)))\n", + "ax.set_aspect('equal', adjustable='box')\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452 + }, + "id": "PPA7RplHCfGC", + "outputId": "2bb7dcc4-21b3-4f37-d75a-d8cfeca72a3a" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Fit the model\n", + "\n", + "We'll fit the model using a version of coordinate descent. We first choose a step size $\\alpha$ and then we alternate between updating the intercept parameter $\\phi_0$ and the slope parameters $\\phi_1$ and $\\phi_2$. \n", + "\n", + "1. Compare the loss for models with $[\\phi_0, \\phi_1, \\phi_2]$, $[\\phi_0+\\alpha, \\phi_1,\\phi_2]$, and $[\\phi_0-\\alpha, \\phi_1,\\phi_2]$. Update the parameters according to the set that have the minimum loss.\n", + "\n", + "2. Compare the loss for models with $[\\phi_0, \\phi_1, \\phi_2]$, $[\\phi_0,\\phi_1+\\alpha, \\phi_2]$, and $[\\phi_0, \\phi_1-\\alpha, \\phi_2]$.\n", + "\n", + "2. Compare the loss for models with $[\\phi_0, \\phi_1, \\phi_2]$, $[\\phi_0,\\phi_1, \\phi_2+\\alpha]$, and $[\\phi_0, \\phi_1, \\phi_2-\\alpha]$.\n", + "\n", + "We'll alternate these two steps until we cannot improve any further." + ], + "metadata": { + "id": "4BrOiVY0zTY4" + } + }, + { + "cell_type": "code", + "source": [ + "# Utility function for plotting the three models at each stage\n", + "def plot_quad(fig, ax, x,y, f_quad, phi0_1, phi1_1, phi2_1, phi0_2, phi1_2, phi2_2, phi0_3, phi1_3, phi2_3, loss1, loss2, loss3):\n", + " x_plot = np.linspace(0,2,100)\n", + " ax.clear()\n", + " ax.plot(x,y,'bo')\n", + " ax.plot(x_plot,f_quad(x_plot, phi0_1, phi1_1, phi2_1), 'r-')\n", + " ax.plot(x_plot,f_quad(x_plot, phi0_2, phi1_2, phi2_2), 'g-')\n", + " ax.plot(x_plot,f_quad(x_plot, phi0_3, phi1_3, phi2_3), 'b-')\n", + " ax.set_title('Losses: {:.2f} (red), {:.2f} (green), {:.2f} (blue)'.format(loss1, loss2, loss3))\n", + " ax.set_xlim(0,2)\n", + " ax.set_ylim(0,2)\n", + " ax.set_aspect('equal', adjustable='box')\n", + "\n", + " # Show the figure and wait 0.1 sec\n", + " display.display(fig)\n", + " time.sleep(0.05)\n", + " display.clear_output(wait=True)" + ], + "metadata": { + "id": "_3d7J8CkCLQQ" + }, + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Fit the quad model\n", + "def fit_model_quad(x,y,f_quad,compute_loss_quad,phi0_init, phi1_init, phi2_init, alpha, n_iter):\n", + "\n", + " # Create figure to display results\n", + " fig,ax = plt.subplots()\n", + "\n", + " # These two variables to store the evolution of the parameters\n", + " phi0_progress = np.zeros(n_iter)\n", + " phi1_progress = np.zeros(n_iter)\n", + " phi2_progress = np.zeros(n_iter)\n", + "\n", + " # Initialize the history with the provided values\n", + " phi0_progress[0] = phi0_init\n", + " phi1_progress[0] = phi1_init\n", + " phi2_progress[0] = phi2_init\n", + "\n", + " # Main iteration loop\n", + " for c_iter in range(1, n_iter):\n", + " # Choose parameters for first model [phi0, phi1, phi2]\n", + " phi0_1 = phi0_progress[c_iter-1]\n", + " phi1_1 = phi1_progress[c_iter-1]\n", + " phi2_1 = phi2_progress[c_iter-1]\n", + "\n", + " # Change the intercept phi0\n", + " match c_iter%3:\n", + " case 0:\n", + " # Choose parameters for second model [phi_0+alpha, phi1, phi2]\n", + " phi0_2 = phi0_progress[c_iter-1]+alpha\n", + " phi1_2 = phi1_progress[c_iter-1]\n", + " phi2_2 = phi2_progress[c_iter-1]\n", + "\n", + " # Choose parameters for third model [phi_0+alpha, phi1, phi2]\n", + " phi0_3 = phi0_progress[c_iter-1]-alpha\n", + " phi1_3 = phi1_progress[c_iter-1]\n", + " phi2_3 = phi2_progress[c_iter-1]\n", + "\n", + " # Change the slope phi1\n", + " case 1:\n", + " # Choose parameters for second model [phi_0, phi1+alpha]\n", + " phi0_2 = phi0_progress[c_iter-1]\n", + " phi1_2 = phi1_progress[c_iter-1]+alpha\n", + " phi2_2 = phi2_progress[c_iter-1]\n", + "\n", + " # Choose parameters for third model [phi_0, phi1-alpha]\n", + " phi0_3 = phi0_progress[c_iter-1]\n", + " phi1_3 = phi1_progress[c_iter-1]-alpha\n", + " phi2_3 = phi2_progress[c_iter-1]\n", + "\n", + " # Change the quadratic term phi2\n", + " case 2:\n", + " # Choose parameters for second model [phi_0, phi1+alpha]\n", + " phi0_2 = phi0_progress[c_iter-1]\n", + " phi1_2 = phi1_progress[c_iter-1]\n", + " phi2_2 = phi2_progress[c_iter-1]+alpha\n", + "\n", + " # Choose parameters for third model [phi_0, phi1-alpha]\n", + " phi0_3 = phi0_progress[c_iter-1]\n", + " phi1_3 = phi1_progress[c_iter-1]\n", + " phi2_3 = phi2_progress[c_iter-1]-alpha\n", + "\n", + " # Compute the loss for all three models\n", + " loss1 = compute_loss_quad(x,y,f_quad, phi0_1, phi1_1, phi2_1)\n", + " loss2 = compute_loss_quad(x,y,f_quad, phi0_2, phi1_2, phi2_2)\n", + " loss3 = compute_loss_quad(x,y,f_quad, phi0_3, phi1_3, phi2_3)\n", + "\n", + " # Set the parameters to the whichever model has the lowest loss\n", + " match np.argmin(np.array([loss1, loss2, loss3]))+1:\n", + " case 1:\n", + " phi0_progress[c_iter] = phi0_1\n", + " phi1_progress[c_iter] = phi1_1\n", + " phi2_progress[c_iter] = phi2_1\n", + " case 2:\n", + " phi0_progress[c_iter] = phi0_2\n", + " phi1_progress[c_iter] = phi1_2\n", + " phi2_progress[c_iter] = phi2_2\n", + " case 3:\n", + " phi0_progress[c_iter] = phi0_3\n", + " phi1_progress[c_iter] = phi1_3\n", + " phi2_progress[c_iter] = phi2_3\n", + "\n", + "\n", + " # Plot the progress\n", + " plot_quad(fig, ax, x,y, f_quad, phi0_1, phi1_1, phi2_1, phi0_2, phi1_2, phi2_2, phi0_3, phi1_3, phi2_3, loss1, loss2, loss3)\n", + "\n", + " return phi0_progress, phi1_progress, phi2_progress" + ], + "metadata": { + "id": "uOvVKcj3ElEr" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Run the fitting algorithm\n", + "phi0_progress, phi1_progress, phi2_progress = fit_model_quad(x,y,f_quad,compute_loss_quad,phi0_init=1.35, phi1_init=-0.55, phi2_init = 0, alpha=0.015, n_iter=275)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452 + }, + "id": "7v7RpAM64XuD", + "outputId": "132f0a01-a29c-4dbb-ad28-3b3b4a0fe738" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "You can see that this takes a lot longer than for the simple linear regression model, but the final loss of 0.15 is less than the best we could achieve with that model (which was around 0.20). Adding the quadratic term which allows the function to \"bend\" improves the fit." + ], + "metadata": { + "id": "0FPMbBMlobYH" + } + } + ] +} \ No newline at end of file From a7ed3e2c348dbe67035e2eee09def70593c08d29 Mon Sep 17 00:00:00 2001 From: udlbook <110402648+udlbook@users.noreply.github.com> Date: Wed, 29 Jan 2025 10:24:36 -0500 Subject: [PATCH 10/29] Created using Colab --- Trees/LinearRegression_FitModel_Answers.ipynb | 361 ++++++++++++++++++ 1 file changed, 361 insertions(+) create mode 100644 Trees/LinearRegression_FitModel_Answers.ipynb diff --git a/Trees/LinearRegression_FitModel_Answers.ipynb b/Trees/LinearRegression_FitModel_Answers.ipynb new file mode 100644 index 0000000..7401ba5 --- /dev/null +++ b/Trees/LinearRegression_FitModel_Answers.ipynb @@ -0,0 +1,361 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyOdviPInQvLKOR2WrPDqZxp", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "" + ], + "metadata": { + "id": "9Qgup23vwdll" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Fitting 1D regression model\n", + "\n", + "The purpose of this Python notebook experiment with fitting the 1D regression model with a least squares loss using coordinate descent.\n", + "\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and write code to complete the functions.\n", + "\n", + "You can save a local copy of this notebook in your Google account and work through it in Colab (recommended) or you can download the notebook and run it locally using Jupyter notebook or similar. If you are using CoLab, we recommend that *turn off* AI autocomplete (under cog icon in top-right corner), which will give you the answers and defeat the purpose of the exercise.\n", + "\n", + "A fully working version of this notebook with the complete answers can be found here.\n", + "\n", + "Contact me at iclimbtreesmail@gmail.com if you find any mistakes or have any suggestions." + ], + "metadata": { + "id": "uORlKyPv02ge" + } + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "bbF6SE_F0tU8" + }, + "outputs": [], + "source": [ + "# Math library\n", + "import numpy as np\n", + "# Plotting library\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib import cm\n", + "from matplotlib.colors import ListedColormap\n", + "# Time library\n", + "import time\n", + "# Used to update figures\n", + "from IPython import display" + ] + }, + { + "cell_type": "code", + "source": [ + "# Create the same input / output data as used in the unit\n", + "x = np.array([0.03, 0.19, 0.34, 0.46, 0.78, 0.81, 1.08, 1.18, 1.39, 1.60, 1.65, 1.90])\n", + "y = np.array([0.67, 0.85, 1.05, 1.0, 1.40, 1.5, 1.3, 1.54, 1.55, 1.68, 1.73, 1.6 ])" + ], + "metadata": { + "id": "9fGAobBnyI7Z" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Define the model and the least squares loss\n", + "\n", + "The linear regression model is defined as:\n", + "\n", + "$$\\textrm{f}[x,\\boldsymbol\\phi] = \\phi_0+\\phi_1 x$$\n", + "\n", + "where $\\phi_0$ is the y-intercept and $\\phi_1$ is the slope." + ], + "metadata": { + "id": "FylovB6YyhWA" + } + }, + { + "cell_type": "code", + "source": [ + "def f(x, phi0, phi1):\n", + " return phi0 + phi1 * x" + ], + "metadata": { + "id": "fpgM_LstyLwt" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "The least squares loss is defined as the sum of the squared deviations of the model output $\\textrm{f}[x_i,\\boldsymbol\\phi]$ and the true output target $y_i$:\n", + "\n", + " \\begin{align} L[\\boldsymbol\\phi] & = \\sum_{i=1}^{I} \\bigl(\\textrm{f}[x_{i}, \\boldsymbol\\phi]-y_{i}\\bigr)^{2} \\\\ &= \\sum_{i=1}^{I} \\bigl(\\phi_{0}+\\phi_{1}x_i-y_{i}\\bigr)^{2} \\tag{1.2}\\end{align}" + ], + "metadata": { + "id": "cG5kwmmPybZK" + } + }, + { + "cell_type": "code", + "source": [ + "# Function to calculate the loss\n", + "def compute_loss(x,y,f,phi0,phi1):\n", + "\n", + " signed_distance = f(x,phi0,phi1)-y\n", + " loss = np.sum(signed_distance * signed_distance)\n", + "\n", + " return loss" + ], + "metadata": { + "id": "I1vBlFMAyfzp" + }, + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Fit the model\n", + "\n", + "We'll fit the model using a version of coordinate descent. We first choose a step size $\\alpha$ and then we alternate between updating the intercept parameter $\\phi_0$ and the slope parameter $\\phi_1$. \n", + "\n", + "1. Compare the loss for models with $[\\phi_0, \\phi_1]$, $[\\phi_0+\\alpha, \\phi_1]$, and $[\\phi_0-\\alpha, \\phi_1]$. Update the parameters according to the set that have the minimum loss.\n", + "\n", + "2. Compare the loss for models with $[\\phi_0, \\phi_1]$, $[\\phi_0,\\phi_1+\\alpha]$, and $[\\phi_0, \\phi_1-\\alpha]$.\n", + "\n", + "We'll alternate these two steps until we cannot improve any further." + ], + "metadata": { + "id": "4BrOiVY0zTY4" + } + }, + { + "cell_type": "code", + "source": [ + "# Utility function for plotting the three models at each stage\n", + "def plot(fig,ax, x,y, f, phi0_1, phi1_1, phi0_2, phi1_2, phi0_3, phi1_3, loss1, loss2, loss3):\n", + " x_plot = np.linspace(0,2,100)\n", + "\n", + " # Clear previous drawing on these axes\n", + " ax.clear()\n", + " # Plotting code\n", + " ax.plot(x,y,'bo')\n", + " ax.plot(x_plot,f(x_plot, phi0_1, phi1_1), 'r-')\n", + " ax.plot(x_plot,f(x_plot, phi0_2, phi1_2), 'g-')\n", + " ax.plot(x_plot,f(x_plot, phi0_3, phi1_3), 'b-')\n", + " ax.set_xlim(0,2)\n", + " ax.set_ylim(0,2)\n", + " ax.set_title('Losses: {:.2f}(red), {:.2f} (green), {:.2f} (blue)'.format(loss1, loss2, loss3))\n", + " ax.set_aspect('equal', adjustable='box')\n", + "\n", + " # Show the figure and wait 0.1 sec\n", + " display.display(fig)\n", + " time.sleep(0.1)\n", + " display.clear_output(wait=True)\n" + ], + "metadata": { + "id": "UbhOL6ob6m6Y" + }, + "execution_count": 29, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Main fitting algorithm\n", + "def fit_model(x,y,f,compute_loss,phi0_init, phi1_init, alpha, n_iter):\n", + "\n", + " # Create figure to display results\n", + " fig,ax = plt.subplots()\n", + "\n", + " # These two variables to store the evolution of the parameters\n", + " phi0_progress = np.zeros(n_iter)\n", + " phi1_progress = np.zeros(n_iter)\n", + "\n", + " # Initialize the history with the provided values\n", + " phi0_progress[0] = phi0_init\n", + " phi1_progress[0] = phi1_init\n", + "\n", + " # Main iteration loop\n", + " for c_iter in range(1, n_iter):\n", + " # Choose parameters for first model [phi0, phi1]\n", + " phi0_1 = phi0_progress[c_iter-1]\n", + " phi1_1 = phi1_progress[c_iter-1]\n", + "\n", + " # Change the intercept phi0 every other iteration\n", + " if (c_iter%2==0):\n", + " # Choose parameters for second model [phi_0+alpha, phi1]\n", + " phi0_2 = phi0_progress[c_iter-1]+alpha\n", + " phi1_2 = phi1_progress[c_iter-1]\n", + "\n", + " # Choose parameters for third model [phi_0+alpha, phi1]\n", + " phi0_3 = phi0_progress[c_iter-1]-alpha\n", + " phi1_3 = phi1_progress[c_iter-1]\n", + "\n", + " # Change the slope phi1 every other iteration\n", + " else:\n", + " # Choose parameters for second model [phi_0, phi1+alpha]\n", + " phi0_2 = phi0_progress[c_iter-1]\n", + " phi1_2 = phi1_progress[c_iter-1]+alpha\n", + "\n", + " # Choose parameters for third model [phi_0, phi1-alpha]\n", + " phi0_3 = phi0_progress[c_iter-1]\n", + " phi1_3 = phi1_progress[c_iter-1]-alpha\n", + "\n", + " # Compute the loss for the three models\n", + " loss1 = compute_loss(x,y,f, phi0_1, phi1_1)\n", + " loss2 = compute_loss(x,y,f, phi0_2, phi1_2)\n", + " loss3 = compute_loss(x,y,f, phi0_3, phi1_3)\n", + "\n", + "\n", + "\n", + " # Set the parameters to the whichever model has the lowest loss\n", + " match np.argmin(np.array([loss1, loss2, loss3]))+1:\n", + " case 1:\n", + " phi0_progress[c_iter] = phi0_1\n", + " phi1_progress[c_iter] = phi1_1\n", + " case 2:\n", + " phi0_progress[c_iter] = phi0_2\n", + " phi1_progress[c_iter] = phi1_2\n", + " case 3:\n", + " phi0_progress[c_iter] = phi0_3\n", + " phi1_progress[c_iter] = phi1_3\n", + "\n", + " # Plot the progress\n", + " plot(fig, ax, x,y, f, phi0_1, phi1_1, phi0_2, phi1_2, phi0_3, phi1_3, loss1, loss2, loss3)\n", + "\n", + " return phi0_progress, phi1_progress" + ], + "metadata": { + "id": "VaonEi8gzf3z" + }, + "execution_count": 30, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Run the fitting algorithm\n", + "phi0_progress, phi1_progress = fit_model(x,y,f,compute_loss,phi0_init=1.35, phi1_init=-0.55, alpha=0.125, n_iter=20)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452 + }, + "id": "STyOoYYv9Ddz", + "outputId": "614671e1-de36-4be9-b9e2-4bacb73cc073" + }, + "execution_count": 31, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Helper code to do the drawing\n", + "def draw_loss_function(compute_loss, f, x_in, y_in, phi0_progress, phi1_progress):\n", + " # Define pretty colormap\n", + " my_colormap_vals_hex =('2a0902', '2b0a03', '2c0b04', '2d0c05', '2e0c06', '2f0d07', '300d08', '310e09', '320f0a', '330f0b', '34100b', '35110c', '36110d', '37120e', '38120f', '39130f', '3a1410', '3b1411', '3c1511', '3d1612', '3e1613', '3f1713', '401714', '411814', '421915', '431915', '451a16', '461b16', '471b17', '481c17', '491d18', '4a1d18', '4b1e19', '4c1f19', '4d1f1a', '4e201b', '50211b', '51211c', '52221c', '53231d', '54231d', '55241e', '56251e', '57261f', '58261f', '592720', '5b2821', '5c2821', '5d2922', '5e2a22', '5f2b23', '602b23', '612c24', '622d25', '632e25', '652e26', '662f26', '673027', '683027', '693128', '6a3229', '6b3329', '6c342a', '6d342a', '6f352b', '70362c', '71372c', '72372d', '73382e', '74392e', '753a2f', '763a2f', '773b30', '783c31', '7a3d31', '7b3e32', '7c3e33', '7d3f33', '7e4034', '7f4134', '804235', '814236', '824336', '834437', '854538', '864638', '874739', '88473a', '89483a', '8a493b', '8b4a3c', '8c4b3c', '8d4c3d', '8e4c3e', '8f4d3f', '904e3f', '924f40', '935041', '945141', '955242', '965343', '975343', '985444', '995545', '9a5646', '9b5746', '9c5847', '9d5948', '9e5a49', '9f5a49', 'a05b4a', 'a15c4b', 'a35d4b', 'a45e4c', 'a55f4d', 'a6604e', 'a7614e', 'a8624f', 'a96350', 'aa6451', 'ab6552', 'ac6552', 'ad6653', 'ae6754', 'af6855', 'b06955', 'b16a56', 'b26b57', 'b36c58', 'b46d59', 'b56e59', 'b66f5a', 'b7705b', 'b8715c', 'b9725d', 'ba735d', 'bb745e', 'bc755f', 'bd7660', 'be7761', 'bf7862', 'c07962', 'c17a63', 'c27b64', 'c27c65', 'c37d66', 'c47e67', 'c57f68', 'c68068', 'c78169', 'c8826a', 'c9836b', 'ca846c', 'cb856d', 'cc866e', 'cd876f', 'ce886f', 'ce8970', 'cf8a71', 'd08b72', 'd18c73', 'd28d74', 'd38e75', 'd48f76', 'd59077', 'd59178', 'd69279', 'd7937a', 'd8957b', 'd9967b', 'da977c', 'da987d', 'db997e', 'dc9a7f', 'dd9b80', 'de9c81', 'de9d82', 'df9e83', 'e09f84', 'e1a185', 'e2a286', 'e2a387', 'e3a488', 'e4a589', 'e5a68a', 'e5a78b', 'e6a88c', 'e7aa8d', 'e7ab8e', 'e8ac8f', 'e9ad90', 'eaae91', 'eaaf92', 'ebb093', 'ecb295', 'ecb396', 'edb497', 'eeb598', 'eeb699', 'efb79a', 'efb99b', 'f0ba9c', 'f1bb9d', 'f1bc9e', 'f2bd9f', 'f2bfa1', 'f3c0a2', 'f3c1a3', 'f4c2a4', 'f5c3a5', 'f5c5a6', 'f6c6a7', 'f6c7a8', 'f7c8aa', 'f7c9ab', 'f8cbac', 'f8ccad', 'f8cdae', 'f9ceb0', 'f9d0b1', 'fad1b2', 'fad2b3', 'fbd3b4', 'fbd5b6', 'fbd6b7', 'fcd7b8', 'fcd8b9', 'fcdaba', 'fddbbc', 'fddcbd', 'fddebe', 'fddfbf', 'fee0c1', 'fee1c2', 'fee3c3', 'fee4c5', 'ffe5c6', 'ffe7c7', 'ffe8c9', 'ffe9ca', 'ffebcb', 'ffeccd', 'ffedce', 'ffefcf', 'fff0d1', 'fff2d2', 'fff3d3', 'fff4d5', 'fff6d6', 'fff7d8', 'fff8d9', 'fffada', 'fffbdc', 'fffcdd', 'fffedf', 'ffffe0')\n", + " my_colormap_vals_dec = np.array([int(element,base=16) for element in my_colormap_vals_hex])\n", + " r = np.floor(my_colormap_vals_dec/(256*256))\n", + " g = np.floor((my_colormap_vals_dec - r *256 *256)/256)\n", + " b = np.floor(my_colormap_vals_dec - r * 256 *256 - g * 256)\n", + " my_colormap = ListedColormap(np.vstack((r,g,b)).transpose()/255.0)\n", + "\n", + " # Make grid of intercept/slope values to plot\n", + " intercepts_mesh, slopes_mesh = np.meshgrid(np.arange(0.0,2.0,0.02), np.arange(-1.0,1.0,0.002))\n", + " loss_mesh = np.zeros_like(slopes_mesh)\n", + "\n", + " # Compute loss for every set of parameters\n", + " for idslope, slope in np.ndenumerate(slopes_mesh):\n", + " loss_mesh[idslope] = compute_loss(x_in, y_in, f, intercepts_mesh[idslope], slope)\n", + "\n", + " fig,ax = plt.subplots()\n", + " fig.set_size_inches(6,6)\n", + " ax.contourf(intercepts_mesh,slopes_mesh,loss_mesh,256,cmap=my_colormap)\n", + " ax.contour(intercepts_mesh,slopes_mesh,loss_mesh,40,colors=['#80808080'])\n", + " ax.plot(phi0_progress, phi1_progress, 'o-', color='#7fe7dc')\n", + "\n", + " ax.set_ylim([1,-1])\n", + " ax.set_xlabel('Intercept, $\\phi_0$')\n", + " ax.set_ylabel('Slope, $\\phi_1$')\n", + " ax.set_aspect('equal')\n", + " plt.show()" + ], + "metadata": { + "id": "Q8DEVnj992AW" + }, + "execution_count": 34, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "draw_loss_function(compute_loss, f, x, y, phi0_progress, phi1_progress)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 551 + }, + "id": "RMi7WItB-I05", + "outputId": "290edb7d-2a86-4684-f441-ad55d2500cb0" + }, + "execution_count": 35, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + } + ] +} \ No newline at end of file From 653d2f7b8405124e0b8450b02a44cf211652fdc5 Mon Sep 17 00:00:00 2001 From: udlbook <110402648+udlbook@users.noreply.github.com> Date: Wed, 29 Jan 2025 10:28:29 -0500 Subject: [PATCH 11/29] Created using Colab --- Trees/LinearRegression_FitModel.ipynb | 316 +++++++++++++++++++++++++- 1 file changed, 313 insertions(+), 3 deletions(-) diff --git a/Trees/LinearRegression_FitModel.ipynb b/Trees/LinearRegression_FitModel.ipynb index e1dbcc7..9e291a2 100644 --- a/Trees/LinearRegression_FitModel.ipynb +++ b/Trees/LinearRegression_FitModel.ipynb @@ -4,7 +4,7 @@ "metadata": { "colab": { "provenance": [], - "authorship_tag": "ABX9TyNK+Qh1d1D+PE6hFu1NbrbI", + "authorship_tag": "ABX9TyOzAfTQbTNzAGSoU2bbydat", "include_colab_link": true }, "kernelspec": { @@ -29,7 +29,26 @@ { "cell_type": "markdown", "source": [ - "# Fitting the model" + "" + ], + "metadata": { + "id": "9Qgup23vwdll" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Fitting 1D regression model\n", + "\n", + "The purpose of this Python notebook experiment with fitting the 1D regression model with a least squares loss using coordinate descent.\n", + "\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and write code to complete the functions.\n", + "\n", + "You can save a local copy of this notebook in your Google account and work through it in Colab (recommended) or you can download the notebook and run it locally using Jupyter notebook or similar. If you are using CoLab, we recommend that *turn off* AI autocomplete (under cog icon in top-right corner), which will give you the answers and defeat the purpose of the exercise.\n", + "\n", + "A fully working version of this notebook with the complete answers can be found [here](https://https://colab.research.google.com/github/udlbook/udlbook/blob/main/Trees/LinearRegression_LossFunction_Answers.ipynb).\n", + "\n", + "Contact me at iclimbtreesmail@gmail.com if you find any mistakes or have any suggestions." ], "metadata": { "id": "uORlKyPv02ge" @@ -43,8 +62,299 @@ }, "outputs": [], "source": [ + "# Math library\n", "import numpy as np\n", - "import matplotlib.pyplot as plt" + "# Plotting library\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib import cm\n", + "from matplotlib.colors import ListedColormap\n", + "# Time library\n", + "import time\n", + "# Used to update figures\n", + "from IPython import display" + ] + }, + { + "cell_type": "code", + "source": [ + "# Create the same input / output data as used in the unit\n", + "x = np.array([0.03, 0.19, 0.34, 0.46, 0.78, 0.81, 1.08, 1.18, 1.39, 1.60, 1.65, 1.90])\n", + "y = np.array([0.67, 0.85, 1.05, 1.0, 1.40, 1.5, 1.3, 1.54, 1.55, 1.68, 1.73, 1.6 ])" + ], + "metadata": { + "id": "9fGAobBnyI7Z" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Define the model and the least squares loss\n", + "\n", + "The linear regression model is defined as:\n", + "\n", + "$$\\textrm{f}[x,\\boldsymbol\\phi] = \\phi_0+\\phi_1 x$$\n", + "\n", + "where $\\phi_0$ is the y-intercept and $\\phi_1$ is the slope." + ], + "metadata": { + "id": "FylovB6YyhWA" + } + }, + { + "cell_type": "code", + "source": [ + "def f(x, phi0, phi1):\n", + " return phi0 + phi1 * x" + ], + "metadata": { + "id": "fpgM_LstyLwt" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "The least squares loss is defined as the sum of the squared deviations of the model output $\\textrm{f}[x_i,\\boldsymbol\\phi]$ and the true output target $y_i$:\n", + "\n", + " \\begin{align} L[\\boldsymbol\\phi] & = \\sum_{i=1}^{I} \\bigl(\\textrm{f}[x_{i}, \\boldsymbol\\phi]-y_{i}\\bigr)^{2} \\\\ &= \\sum_{i=1}^{I} \\bigl(\\phi_{0}+\\phi_{1}x_i-y_{i}\\bigr)^{2} \\tag{1.2}\\end{align}" + ], + "metadata": { + "id": "cG5kwmmPybZK" + } + }, + { + "cell_type": "code", + "source": [ + "# Function to calculate the loss\n", + "def compute_loss(x,y,f,phi0,phi1):\n", + "\n", + " signed_distance = f(x,phi0,phi1)-y\n", + " loss = np.sum(signed_distance * signed_distance)\n", + "\n", + " return loss" + ], + "metadata": { + "id": "I1vBlFMAyfzp" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Fit the model\n", + "\n", + "We'll fit the model using a version of coordinate descent. We first choose a step size $\\alpha$ and then we alternate between updating the intercept parameter $\\phi_0$ and the slope parameter $\\phi_1$. \n", + "\n", + "1. Compare the loss for models with $[\\phi_0, \\phi_1]$, $[\\phi_0+\\alpha, \\phi_1]$, and $[\\phi_0-\\alpha, \\phi_1]$. Update the parameters according to the set that have the minimum loss.\n", + "\n", + "2. Compare the loss for models with $[\\phi_0, \\phi_1]$, $[\\phi_0,\\phi_1+\\alpha]$, and $[\\phi_0, \\phi_1-\\alpha]$.\n", + "\n", + "We'll alternate these two steps until we cannot improve any further." + ], + "metadata": { + "id": "4BrOiVY0zTY4" + } + }, + { + "cell_type": "code", + "source": [ + "# Utility function for plotting the three models at each stage\n", + "def plot(fig,ax, x,y, f, phi0_1, phi1_1, phi0_2, phi1_2, phi0_3, phi1_3, loss1, loss2, loss3):\n", + " x_plot = np.linspace(0,2,100)\n", + "\n", + " # Clear previous drawing on these axes\n", + " ax.clear()\n", + " # Plotting code\n", + " ax.plot(x,y,'bo')\n", + " ax.plot(x_plot,f(x_plot, phi0_1, phi1_1), 'r-')\n", + " ax.plot(x_plot,f(x_plot, phi0_2, phi1_2), 'g-')\n", + " ax.plot(x_plot,f(x_plot, phi0_3, phi1_3), 'b-')\n", + " ax.set_xlim(0,2)\n", + " ax.set_ylim(0,2)\n", + " ax.set_title('Losses: {:.2f}(red), {:.2f} (green), {:.2f} (blue)'.format(loss1, loss2, loss3))\n", + " ax.set_aspect('equal', adjustable='box')\n", + "\n", + " # Show the figure and wait 0.1 sec\n", + " display.display(fig)\n", + " time.sleep(0.1)\n", + " display.clear_output(wait=True)\n" + ], + "metadata": { + "id": "UbhOL6ob6m6Y" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Main fitting algorithm\n", + "def fit_model(x,y,f,compute_loss,phi0_init, phi1_init, alpha, n_iter):\n", + "\n", + " # Create figure to display results\n", + " fig,ax = plt.subplots()\n", + "\n", + " # These two variables to store the evolution of the parameters\n", + " phi0_progress = np.zeros(n_iter)\n", + " phi1_progress = np.zeros(n_iter)\n", + "\n", + " # Initialize the history with the provided values\n", + " phi0_progress[0] = phi0_init\n", + " phi1_progress[0] = phi1_init\n", + "\n", + " # Main iteration loop\n", + " for c_iter in range(1, n_iter):\n", + " # Choose parameters for first model [phi0, phi1]\n", + " phi0_1 = phi0_progress[c_iter-1]\n", + " phi1_1 = phi1_progress[c_iter-1]\n", + "\n", + " # Change the intercept phi0 every other iteration\n", + " if (c_iter%2==0):\n", + " # Choose parameters for second model [phi_0+alpha, phi1]\n", + " phi0_2 = phi0_progress[c_iter-1]+alpha\n", + " phi1_2 = phi1_progress[c_iter-1]\n", + "\n", + " # Choose parameters for third model [phi_0+alpha, phi1]\n", + " phi0_3 = phi0_progress[c_iter-1]-alpha\n", + " phi1_3 = phi1_progress[c_iter-1]\n", + "\n", + " # Change the slope phi1 every other iteration\n", + " else:\n", + " # Choose parameters for second model [phi_0, phi1+alpha]\n", + " phi0_2 = phi0_progress[c_iter-1]\n", + " phi1_2 = phi1_progress[c_iter-1]+alpha\n", + "\n", + " # Choose parameters for third model [phi_0, phi1-alpha]\n", + " phi0_3 = phi0_progress[c_iter-1]\n", + " phi1_3 = phi1_progress[c_iter-1]-alpha\n", + "\n", + " # Compute the loss for the three models\n", + " loss1 = compute_loss(x,y,f, phi0_1, phi1_1)\n", + " loss2 = compute_loss(x,y,f, phi0_2, phi1_2)\n", + " loss3 = compute_loss(x,y,f, phi0_3, phi1_3)\n", + "\n", + "\n", + "\n", + " # Set the parameters to the whichever model has the lowest loss\n", + " match np.argmin(np.array([loss1, loss2, loss3]))+1:\n", + " case 1:\n", + " phi0_progress[c_iter] = phi0_1\n", + " phi1_progress[c_iter] = phi1_1\n", + " case 2:\n", + " phi0_progress[c_iter] = phi0_2\n", + " phi1_progress[c_iter] = phi1_2\n", + " case 3:\n", + " phi0_progress[c_iter] = phi0_3\n", + " phi1_progress[c_iter] = phi1_3\n", + "\n", + " # Plot the progress\n", + " plot(fig, ax, x,y, f, phi0_1, phi1_1, phi0_2, phi1_2, phi0_3, phi1_3, loss1, loss2, loss3)\n", + "\n", + " return phi0_progress, phi1_progress" + ], + "metadata": { + "id": "VaonEi8gzf3z" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Run the fitting algorithm\n", + "phi0_progress, phi1_progress = fit_model(x,y,f,compute_loss,phi0_init=1.35, phi1_init=-0.55, alpha=0.125, n_iter=20)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452 + }, + "id": "STyOoYYv9Ddz", + "outputId": "614671e1-de36-4be9-b9e2-4bacb73cc073" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Helper code to do the drawing\n", + "def draw_loss_function(compute_loss, f, x_in, y_in, phi0_progress, phi1_progress):\n", + " # Define pretty colormap\n", + " my_colormap_vals_hex =('2a0902', '2b0a03', '2c0b04', '2d0c05', '2e0c06', '2f0d07', '300d08', '310e09', '320f0a', '330f0b', '34100b', '35110c', '36110d', '37120e', '38120f', '39130f', '3a1410', '3b1411', '3c1511', '3d1612', '3e1613', '3f1713', '401714', '411814', '421915', '431915', '451a16', '461b16', '471b17', '481c17', '491d18', '4a1d18', '4b1e19', '4c1f19', '4d1f1a', '4e201b', '50211b', '51211c', '52221c', '53231d', '54231d', '55241e', '56251e', '57261f', '58261f', '592720', '5b2821', '5c2821', '5d2922', '5e2a22', '5f2b23', '602b23', '612c24', '622d25', '632e25', '652e26', '662f26', '673027', '683027', '693128', '6a3229', '6b3329', '6c342a', '6d342a', '6f352b', '70362c', '71372c', '72372d', '73382e', '74392e', '753a2f', '763a2f', '773b30', '783c31', '7a3d31', '7b3e32', '7c3e33', '7d3f33', '7e4034', '7f4134', '804235', '814236', '824336', '834437', '854538', '864638', '874739', '88473a', '89483a', '8a493b', '8b4a3c', '8c4b3c', '8d4c3d', '8e4c3e', '8f4d3f', '904e3f', '924f40', '935041', '945141', '955242', '965343', '975343', '985444', '995545', '9a5646', '9b5746', '9c5847', '9d5948', '9e5a49', '9f5a49', 'a05b4a', 'a15c4b', 'a35d4b', 'a45e4c', 'a55f4d', 'a6604e', 'a7614e', 'a8624f', 'a96350', 'aa6451', 'ab6552', 'ac6552', 'ad6653', 'ae6754', 'af6855', 'b06955', 'b16a56', 'b26b57', 'b36c58', 'b46d59', 'b56e59', 'b66f5a', 'b7705b', 'b8715c', 'b9725d', 'ba735d', 'bb745e', 'bc755f', 'bd7660', 'be7761', 'bf7862', 'c07962', 'c17a63', 'c27b64', 'c27c65', 'c37d66', 'c47e67', 'c57f68', 'c68068', 'c78169', 'c8826a', 'c9836b', 'ca846c', 'cb856d', 'cc866e', 'cd876f', 'ce886f', 'ce8970', 'cf8a71', 'd08b72', 'd18c73', 'd28d74', 'd38e75', 'd48f76', 'd59077', 'd59178', 'd69279', 'd7937a', 'd8957b', 'd9967b', 'da977c', 'da987d', 'db997e', 'dc9a7f', 'dd9b80', 'de9c81', 'de9d82', 'df9e83', 'e09f84', 'e1a185', 'e2a286', 'e2a387', 'e3a488', 'e4a589', 'e5a68a', 'e5a78b', 'e6a88c', 'e7aa8d', 'e7ab8e', 'e8ac8f', 'e9ad90', 'eaae91', 'eaaf92', 'ebb093', 'ecb295', 'ecb396', 'edb497', 'eeb598', 'eeb699', 'efb79a', 'efb99b', 'f0ba9c', 'f1bb9d', 'f1bc9e', 'f2bd9f', 'f2bfa1', 'f3c0a2', 'f3c1a3', 'f4c2a4', 'f5c3a5', 'f5c5a6', 'f6c6a7', 'f6c7a8', 'f7c8aa', 'f7c9ab', 'f8cbac', 'f8ccad', 'f8cdae', 'f9ceb0', 'f9d0b1', 'fad1b2', 'fad2b3', 'fbd3b4', 'fbd5b6', 'fbd6b7', 'fcd7b8', 'fcd8b9', 'fcdaba', 'fddbbc', 'fddcbd', 'fddebe', 'fddfbf', 'fee0c1', 'fee1c2', 'fee3c3', 'fee4c5', 'ffe5c6', 'ffe7c7', 'ffe8c9', 'ffe9ca', 'ffebcb', 'ffeccd', 'ffedce', 'ffefcf', 'fff0d1', 'fff2d2', 'fff3d3', 'fff4d5', 'fff6d6', 'fff7d8', 'fff8d9', 'fffada', 'fffbdc', 'fffcdd', 'fffedf', 'ffffe0')\n", + " my_colormap_vals_dec = np.array([int(element,base=16) for element in my_colormap_vals_hex])\n", + " r = np.floor(my_colormap_vals_dec/(256*256))\n", + " g = np.floor((my_colormap_vals_dec - r *256 *256)/256)\n", + " b = np.floor(my_colormap_vals_dec - r * 256 *256 - g * 256)\n", + " my_colormap = ListedColormap(np.vstack((r,g,b)).transpose()/255.0)\n", + "\n", + " # Make grid of intercept/slope values to plot\n", + " intercepts_mesh, slopes_mesh = np.meshgrid(np.arange(0.0,2.0,0.02), np.arange(-1.0,1.0,0.002))\n", + " loss_mesh = np.zeros_like(slopes_mesh)\n", + "\n", + " # Compute loss for every set of parameters\n", + " for idslope, slope in np.ndenumerate(slopes_mesh):\n", + " loss_mesh[idslope] = compute_loss(x_in, y_in, f, intercepts_mesh[idslope], slope)\n", + "\n", + " fig,ax = plt.subplots()\n", + " fig.set_size_inches(6,6)\n", + " ax.contourf(intercepts_mesh,slopes_mesh,loss_mesh,256,cmap=my_colormap)\n", + " ax.contour(intercepts_mesh,slopes_mesh,loss_mesh,40,colors=['#80808080'])\n", + " ax.plot(phi0_progress, phi1_progress, 'o-', color='#7fe7dc')\n", + "\n", + " ax.set_ylim([1,-1])\n", + " ax.set_xlabel('Intercept, $\\phi_0$')\n", + " ax.set_ylabel('Slope, $\\phi_1$')\n", + " ax.set_aspect('equal')\n", + " plt.show()" + ], + "metadata": { + "id": "Q8DEVnj992AW" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "draw_loss_function(compute_loss, f, x, y, phi0_progress, phi1_progress)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 551 + }, + "id": "RMi7WItB-I05", + "outputId": "290edb7d-2a86-4684-f441-ad55d2500cb0" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } ] } ] From 84a11d68ed5ee8879b8f15fba6f0f8dd935a175b Mon Sep 17 00:00:00 2001 From: udlbook <110402648+udlbook@users.noreply.github.com> Date: Wed, 29 Jan 2025 10:29:54 -0500 Subject: [PATCH 12/29] Created using Colab --- Trees/LinearRegression_LossFunction_Answers.ipynb | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/Trees/LinearRegression_LossFunction_Answers.ipynb b/Trees/LinearRegression_LossFunction_Answers.ipynb index a32ce3a..48dd087 100644 --- a/Trees/LinearRegression_LossFunction_Answers.ipynb +++ b/Trees/LinearRegression_LossFunction_Answers.ipynb @@ -4,7 +4,7 @@ "metadata": { "colab": { "provenance": [], - "authorship_tag": "ABX9TyM2yB29boJt3bLHqTG5AUqR", + "authorship_tag": "ABX9TyOKkeAghKXCP9dP9iv7SwLO", "include_colab_link": true }, "kernelspec": { @@ -42,11 +42,7 @@ "\n", "The purpose of this Python notebook is to compute the loss and subsequently plot the loss function for the 1D linear regression model with a least squares loss.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and write code to complete the functions.\n", - "\n", - "You can save a local copy of this notebook in your Google account and work through it in Colab (recommended) or you can download the notebook and run it locally using Jupyter notebook or similar. If you are using CoLab, we recommend that *turn off* AI autocomplete (under cog icon in top-right corner), which will give you the answers and defeat the purpose of the exercise.\n", - "\n", - "A fully working version of this notebook with the complete answers can be found here.\n", + "You can save a local copy of this notebook in your Google account and work through it in Colab (recommended) or you can download the notebook and run it locally using Jupyter notebook or similar. \n", "\n", "Contact me at iclimbtreesmail@gmail.com if you find any mistakes or have any suggestions." ], From 13b39c2f723b52d3c51030721cbac170d97047e2 Mon Sep 17 00:00:00 2001 From: udlbook <110402648+udlbook@users.noreply.github.com> Date: Wed, 29 Jan 2025 10:32:57 -0500 Subject: [PATCH 13/29] Created using Colab --- Trees/LinearRegression_FitModel_Answers.ipynb | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/Trees/LinearRegression_FitModel_Answers.ipynb b/Trees/LinearRegression_FitModel_Answers.ipynb index 7401ba5..2170342 100644 --- a/Trees/LinearRegression_FitModel_Answers.ipynb +++ b/Trees/LinearRegression_FitModel_Answers.ipynb @@ -4,7 +4,7 @@ "metadata": { "colab": { "provenance": [], - "authorship_tag": "ABX9TyOdviPInQvLKOR2WrPDqZxp", + "authorship_tag": "ABX9TyM1295QftRarPE/1QkV+9xi", "include_colab_link": true }, "kernelspec": { @@ -42,11 +42,7 @@ "\n", "The purpose of this Python notebook experiment with fitting the 1D regression model with a least squares loss using coordinate descent.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and write code to complete the functions.\n", - "\n", - "You can save a local copy of this notebook in your Google account and work through it in Colab (recommended) or you can download the notebook and run it locally using Jupyter notebook or similar. If you are using CoLab, we recommend that *turn off* AI autocomplete (under cog icon in top-right corner), which will give you the answers and defeat the purpose of the exercise.\n", - "\n", - "A fully working version of this notebook with the complete answers can be found here.\n", + "You can save a local copy of this notebook in your Google account and work through it in Colab (recommended) or you can download the notebook and run it locally using Jupyter notebook or similar. \n", "\n", "Contact me at iclimbtreesmail@gmail.com if you find any mistakes or have any suggestions." ], From 472571aef0ee3d1cc7e815c3d825ef0bff1c6fe4 Mon Sep 17 00:00:00 2001 From: udlbook <110402648+udlbook@users.noreply.github.com> Date: Wed, 29 Jan 2025 10:39:29 -0500 Subject: [PATCH 14/29] Created using Colab --- Trees/LinearRegression_FitModel.ipynb | 103 +++++++++----------------- 1 file changed, 34 insertions(+), 69 deletions(-) diff --git a/Trees/LinearRegression_FitModel.ipynb b/Trees/LinearRegression_FitModel.ipynb index 9e291a2..6363dda 100644 --- a/Trees/LinearRegression_FitModel.ipynb +++ b/Trees/LinearRegression_FitModel.ipynb @@ -4,7 +4,7 @@ "metadata": { "colab": { "provenance": [], - "authorship_tag": "ABX9TyOzAfTQbTNzAGSoU2bbydat", + "authorship_tag": "ABX9TyMD3zdteWU9gy7nYCZvDeGT", "include_colab_link": true }, "kernelspec": { @@ -209,48 +209,45 @@ "\n", " # Main iteration loop\n", " for c_iter in range(1, n_iter):\n", - " # Choose parameters for first model [phi0, phi1]\n", - " phi0_1 = phi0_progress[c_iter-1]\n", - " phi1_1 = phi1_progress[c_iter-1]\n", + " # TODO Choose parameters for first model [phi0, phi1]\n", + " # REPLACE THIS CODE\n", + " phi0_1 = 0\n", + " phi1_1 = 0\n", "\n", " # Change the intercept phi0 every other iteration\n", " if (c_iter%2==0):\n", - " # Choose parameters for second model [phi_0+alpha, phi1]\n", - " phi0_2 = phi0_progress[c_iter-1]+alpha\n", - " phi1_2 = phi1_progress[c_iter-1]\n", + " # TODO Choose parameters for second model [phi_0+alpha, phi1]\n", + " # REPLACE THIS CODE\n", + " phi0_2 = 0\n", + " phi1_2 = 0\n", "\n", - " # Choose parameters for third model [phi_0+alpha, phi1]\n", - " phi0_3 = phi0_progress[c_iter-1]-alpha\n", - " phi1_3 = phi1_progress[c_iter-1]\n", + " # TODO Choose parameters for third model [phi_0+alpha, phi1]\n", + " # REPLACE THIS CODE\n", + " phi0_3 = 0\n", + " phi1_3 = 0\n", "\n", " # Change the slope phi1 every other iteration\n", " else:\n", - " # Choose parameters for second model [phi_0, phi1+alpha]\n", - " phi0_2 = phi0_progress[c_iter-1]\n", - " phi1_2 = phi1_progress[c_iter-1]+alpha\n", + " # TODO Choose parameters for second model [phi_0, phi1+alpha]\n", + " # REPLACE THIS CODE\n", + " phi0_2 = 0\n", + " phi1_2 = 0\n", "\n", - " # Choose parameters for third model [phi_0, phi1-alpha]\n", - " phi0_3 = phi0_progress[c_iter-1]\n", - " phi1_3 = phi1_progress[c_iter-1]-alpha\n", + " # TODO Choose parameters for third model [phi_0, phi1-alpha]\n", + " # REPLACE THIS CODE\n", + " phi0_3 = 0\n", + " phi1_3 = 0\n", "\n", - " # Compute the loss for the three models\n", - " loss1 = compute_loss(x,y,f, phi0_1, phi1_1)\n", - " loss2 = compute_loss(x,y,f, phi0_2, phi1_2)\n", - " loss3 = compute_loss(x,y,f, phi0_3, phi1_3)\n", + " # TODO Compute the loss for the three models\n", + " # REPLACE THIS CODE\n", + " loss1 = 0\n", + " loss2 = 0\n", + " loss3 = 0\n", "\n", - "\n", - "\n", - " # Set the parameters to the whichever model has the lowest loss\n", - " match np.argmin(np.array([loss1, loss2, loss3]))+1:\n", - " case 1:\n", - " phi0_progress[c_iter] = phi0_1\n", - " phi1_progress[c_iter] = phi1_1\n", - " case 2:\n", - " phi0_progress[c_iter] = phi0_2\n", - " phi1_progress[c_iter] = phi1_2\n", - " case 3:\n", - " phi0_progress[c_iter] = phi0_3\n", - " phi1_progress[c_iter] = phi1_3\n", + " # TODO Set the parameters to the whichever model has the lowest loss\n", + " # REPLACE THIS CODE\n", + " phi0_progress[c_iter] = 0\n", + " phi1_progress[c_iter] = 0\n", "\n", " # Plot the progress\n", " plot(fig, ax, x,y, f, phi0_1, phi1_1, phi0_2, phi1_2, phi0_3, phi1_3, loss1, loss2, loss3)\n", @@ -270,26 +267,10 @@ "phi0_progress, phi1_progress = fit_model(x,y,f,compute_loss,phi0_init=1.35, phi1_init=-0.55, alpha=0.125, n_iter=20)" ], "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 452 - }, - "id": "STyOoYYv9Ddz", - "outputId": "614671e1-de36-4be9-b9e2-4bacb73cc073" + "id": "STyOoYYv9Ddz" }, "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ] + "outputs": [] }, { "cell_type": "code", @@ -336,26 +317,10 @@ "draw_loss_function(compute_loss, f, x, y, phi0_progress, phi1_progress)" ], "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 551 - }, - "id": "RMi7WItB-I05", - "outputId": "290edb7d-2a86-4684-f441-ad55d2500cb0" + "id": "RMi7WItB-I05" }, "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ] + "outputs": [] } ] } \ No newline at end of file From f0337130cb3fd0827b084a9748f4b06ee25acf3b Mon Sep 17 00:00:00 2001 From: udlbook <110402648+udlbook@users.noreply.github.com> Date: Thu, 30 Jan 2025 11:35:39 -0500 Subject: [PATCH 15/29] Created using Colab --- .../LinearRegression_FitModel_Quadratic.ipynb | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/Trees/LinearRegression_FitModel_Quadratic.ipynb b/Trees/LinearRegression_FitModel_Quadratic.ipynb index b1b2195..00815de 100644 --- a/Trees/LinearRegression_FitModel_Quadratic.ipynb +++ b/Trees/LinearRegression_FitModel_Quadratic.ipynb @@ -4,7 +4,7 @@ "metadata": { "colab": { "provenance": [], - "authorship_tag": "ABX9TyOTr64QzGKRRfJqS3P6I/jU", + "authorship_tag": "ABX9TyOBSkQDzgifR3FuyHZMuMGd", "include_colab_link": true }, "kernelspec": { @@ -38,7 +38,7 @@ { "cell_type": "markdown", "source": [ - "# Fitting 1D regression model\n", + "# Fitting 1D quadratic model\n", "\n", "This notebook fits the quadratic model using coordinate descent.\n", "\n", @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "id": "bbF6SE_F0tU8" }, @@ -82,7 +82,7 @@ "metadata": { "id": "9fGAobBnyI7Z" }, - "execution_count": 2, + "execution_count": null, "outputs": [] }, { @@ -118,7 +118,7 @@ "metadata": { "id": "O_3gRgR1AhfT" }, - "execution_count": 3, + "execution_count": null, "outputs": [] }, { @@ -147,7 +147,7 @@ "id": "PPA7RplHCfGC", "outputId": "2bb7dcc4-21b3-4f37-d75a-d8cfeca72a3a" }, - "execution_count": 4, + "execution_count": null, "outputs": [ { "output_type": "display_data", @@ -204,7 +204,7 @@ "metadata": { "id": "_3d7J8CkCLQQ" }, - "execution_count": 8, + "execution_count": null, "outputs": [] }, { @@ -299,7 +299,7 @@ "metadata": { "id": "uOvVKcj3ElEr" }, - "execution_count": 9, + "execution_count": null, "outputs": [] }, { @@ -316,7 +316,7 @@ "id": "7v7RpAM64XuD", "outputId": "132f0a01-a29c-4dbb-ad28-3b3b4a0fe738" }, - "execution_count": 10, + "execution_count": null, "outputs": [ { "output_type": "display_data", From 666cbb02d5bda91faf108cc6359ddec72e378c6e Mon Sep 17 00:00:00 2001 From: udlbook <110402648+udlbook@users.noreply.github.com> Date: Sat, 1 Feb 2025 14:56:25 -0500 Subject: [PATCH 16/29] Created using Colab --- .../Chap05/5_3_Multiclass_Cross_entropy_Loss.ipynb | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/Notebooks/Chap05/5_3_Multiclass_Cross_entropy_Loss.ipynb b/Notebooks/Chap05/5_3_Multiclass_Cross_entropy_Loss.ipynb index 1021d74..edb5989 100644 --- a/Notebooks/Chap05/5_3_Multiclass_Cross_entropy_Loss.ipynb +++ b/Notebooks/Chap05/5_3_Multiclass_Cross_entropy_Loss.ipynb @@ -236,11 +236,10 @@ }, "outputs": [], "source": [ - "# Let's double check we get the right answer before proceeding\n", - "print(\"Correct answer = %3.3f, Your answer = %3.3f\"%(0.2,categorical_distribution(np.array([[0]]),np.array([[0.2],[0.5],[0.3]]))))\n", - "print(\"Correct answer = %3.3f, Your answer = %3.3f\"%(0.5,categorical_distribution(np.array([[1]]),np.array([[0.2],[0.5],[0.3]]))))\n", - "print(\"Correct answer = %3.3f, Your answer = %3.3f\"%(0.3,categorical_distribution(np.array([[2]]),np.array([[0.2],[0.5],[0.3]]))))\n", - "\n" + "# Here are three examples\n", + "print(categorical_distribution(np.array([[0]]),np.array([[0.2],[0.5],[0.3]])))\n", + "print(categorical_distribution(np.array([[1]]),np.array([[0.2],[0.5],[0.3]])))\n", + "print(categorical_distribution(np.array([[2]]),np.array([[0.2],[0.5],[0.3]])))" ] }, { @@ -460,4 +459,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} +} \ No newline at end of file From 9649ce382b44930c383a5a90e5eb44d699b11e00 Mon Sep 17 00:00:00 2001 From: Mark Gotham <19486660+MarkGotham@users.noreply.github.com> Date: Tue, 11 Feb 2025 15:11:06 +0000 Subject: [PATCH 17/29] "TO DO" > "TODO In [commit 6072ad4](6072ad4), @KajvanRijn kindly changed all "TO DO" to "TODO" in the code blocks. That's useful. In addition, it should be changed (as here) in the instructions. Then there's no doubt or issue for anyone searching all instances. --- Notebooks/Chap01/1_1_BackgroundMathematics.ipynb | 2 +- Notebooks/Chap02/2_1_Supervised_Learning.ipynb | 2 +- Notebooks/Chap03/3_1_Shallow_Networks_I.ipynb | 2 +- Notebooks/Chap03/3_2_Shallow_Networks_II.ipynb | 2 +- Notebooks/Chap03/3_3_Shallow_Network_Regions.ipynb | 2 +- Notebooks/Chap03/3_4_Activation_Functions.ipynb | 2 +- Notebooks/Chap04/4_1_Composing_Networks.ipynb | 4 ++-- Notebooks/Chap04/4_2_Clipping_functions.ipynb | 2 +- Notebooks/Chap04/4_3_Deep_Networks.ipynb | 2 +- Notebooks/Chap05/5_1_Least_Squares_Loss.ipynb | 2 +- Notebooks/Chap05/5_2_Binary_Cross_Entropy_Loss.ipynb | 2 +- .../Chap05/5_3_Multiclass_Cross_entropy_Loss.ipynb | 2 +- Notebooks/Chap06/6_1_Line_Search.ipynb | 2 +- Notebooks/Chap06/6_2_Gradient_Descent.ipynb | 2 +- Notebooks/Chap06/6_3_Stochastic_Gradient_Descent.ipynb | 2 +- Notebooks/Chap06/6_4_Momentum.ipynb | 2 +- Notebooks/Chap06/6_5_Adam.ipynb | 2 +- .../Chap07/7_1_Backpropagation_in_Toy_Model.ipynb | 2 +- Notebooks/Chap07/7_2_Backpropagation.ipynb | 2 +- Notebooks/Chap07/7_3_Initialization.ipynb | 10 +++++----- Notebooks/Chap08/8_1_MNIST_1D_Performance.ipynb | 8 ++++---- Notebooks/Chap08/8_2_Bias_Variance_Trade_Off.ipynb | 2 +- Notebooks/Chap08/8_3_Double_Descent.ipynb | 4 ++-- Notebooks/Chap08/8_4_High_Dimensional_Spaces.ipynb | 2 +- Notebooks/Chap09/9_1_L2_Regularization.ipynb | 2 +- Notebooks/Chap09/9_2_Implicit_Regularization.ipynb | 2 +- Notebooks/Chap09/9_3_Ensembling.ipynb | 2 +- Notebooks/Chap09/9_4_Bayesian_Approach.ipynb | 2 +- Notebooks/Chap09/9_5_Augmentation.ipynb | 2 +- Notebooks/Chap10/10_1_1D_Convolution.ipynb | 2 +- Notebooks/Chap10/10_2_Convolution_for_MNIST_1D.ipynb | 2 +- Notebooks/Chap10/10_3_2D_Convolution.ipynb | 2 +- .../Chap10/10_4_Downsampling_and_Upsampling.ipynb | 2 +- Notebooks/Chap10/10_5_Convolution_For_MNIST.ipynb | 2 +- Notebooks/Chap11/11_1_Shattered_Gradients.ipynb | 2 +- Notebooks/Chap11/11_2_Residual_Networks.ipynb | 2 +- Notebooks/Chap11/11_3_Batch_Normalization.ipynb | 2 +- Notebooks/Chap12/12_1_Self_Attention.ipynb | 2 +- Notebooks/Chap12/12_2_Multihead_Self_Attention.ipynb | 2 +- Notebooks/Chap12/12_3_Tokenization.ipynb | 2 +- Notebooks/Chap12/12_4_Decoding_Strategies.ipynb | 2 +- Notebooks/Chap13/13_1_Graph_Representation.ipynb | 2 +- Notebooks/Chap13/13_2_Graph_Classification.ipynb | 2 +- Notebooks/Chap13/13_3_Neighborhood_Sampling.ipynb | 2 +- Notebooks/Chap13/13_4_Graph_Attention_Networks.ipynb | 2 +- Notebooks/Chap15/15_1_GAN_Toy_Example.ipynb | 2 +- Notebooks/Chap15/15_2_Wasserstein_Distance.ipynb | 2 +- Notebooks/Chap16/16_1_1D_Normalizing_Flows.ipynb | 2 +- Notebooks/Chap16/16_2_Autoregressive_Flows.ipynb | 2 +- Notebooks/Chap16/16_3_Contraction_Mappings.ipynb | 2 +- Notebooks/Chap17/17_1_Latent_Variable_Models.ipynb | 2 +- Notebooks/Chap17/17_2_Reparameterization_Trick.ipynb | 2 +- Notebooks/Chap17/17_3_Importance_Sampling.ipynb | 2 +- Notebooks/Chap18/18_1_Diffusion_Encoder.ipynb | 2 +- Notebooks/Chap18/18_2_1D_Diffusion_Model.ipynb | 2 +- Notebooks/Chap18/18_3_Reparameterized_Model.ipynb | 2 +- .../Chap18/18_4_Families_of_Diffusion_Models.ipynb | 2 +- Notebooks/Chap19/19_1_Markov_Decision_Processes.ipynb | 2 +- Notebooks/Chap19/19_2_Dynamic_Programming.ipynb | 2 +- Notebooks/Chap19/19_3_Monte_Carlo_Methods.ipynb | 2 +- .../Chap19/19_4_Temporal_Difference_Methods.ipynb | 2 +- Notebooks/Chap19/19_5_Control_Variates.ipynb | 2 +- Notebooks/Chap20/20_1_Random_Data.ipynb | 2 +- .../Chap20/20_2_Full_Batch_Gradient_Descent.ipynb | 2 +- .../Chap20/20_2_Full_Batch_Gradient_Descent_GPU.ipynb | 2 +- Notebooks/Chap20/20_3_Lottery_Tickets.ipynb | 2 +- Notebooks/Chap20/20_4_Adversarial_Attacks.ipynb | 2 +- Notebooks/Chap21/21_1_Bias_Mitigation.ipynb | 2 +- Notebooks/Chap21/21_2_Explainability.ipynb | 2 +- 69 files changed, 78 insertions(+), 78 deletions(-) diff --git a/Notebooks/Chap01/1_1_BackgroundMathematics.ipynb b/Notebooks/Chap01/1_1_BackgroundMathematics.ipynb index 9378a08..ccca8c7 100644 --- a/Notebooks/Chap01/1_1_BackgroundMathematics.ipynb +++ b/Notebooks/Chap01/1_1_BackgroundMathematics.ipynb @@ -19,7 +19,7 @@ "\n", "# **Notebook 1.1 -- Background Mathematics**\n", "\n", - "The purpose of this Python notebook is to make sure you can use CoLab and to familiarize yourself with some of the background mathematical concepts that you are going to need to understand deep learning.

It's not meant to be difficult and it may be that you know some or all of this information already.

Math is *NOT* a spectator sport. You won't learn it by just listening to lectures or reading books. It really helps to interact with it and explore yourself.

Work through the cells below, running each cell in turn. In various places you will see the words **\"TO DO\"**. Follow the instructions at these places and write code to complete the functions. There are also questions interspersed in the text.\n", + "The purpose of this Python notebook is to make sure you can use CoLab and to familiarize yourself with some of the background mathematical concepts that you are going to need to understand deep learning.

It's not meant to be difficult and it may be that you know some or all of this information already.

Math is *NOT* a spectator sport. You won't learn it by just listening to lectures or reading books. It really helps to interact with it and explore yourself.

Work through the cells below, running each cell in turn. In various places you will see the words **\"TODO\"**. Follow the instructions at these places and write code to complete the functions. There are also questions interspersed in the text.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ] diff --git a/Notebooks/Chap02/2_1_Supervised_Learning.ipynb b/Notebooks/Chap02/2_1_Supervised_Learning.ipynb index 42f3344..121dac1 100644 --- a/Notebooks/Chap02/2_1_Supervised_Learning.ipynb +++ b/Notebooks/Chap02/2_1_Supervised_Learning.ipynb @@ -32,7 +32,7 @@ "\n", "The purpose of this notebook is to explore the linear regression model discussed in Chapter 2 of the book.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and write code to complete the functions. There are also questions interspersed in the text.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and write code to complete the functions. There are also questions interspersed in the text.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ], diff --git a/Notebooks/Chap03/3_1_Shallow_Networks_I.ipynb b/Notebooks/Chap03/3_1_Shallow_Networks_I.ipynb index 1c4d9e6..06ca153 100644 --- a/Notebooks/Chap03/3_1_Shallow_Networks_I.ipynb +++ b/Notebooks/Chap03/3_1_Shallow_Networks_I.ipynb @@ -20,7 +20,7 @@ "\n", "The purpose of this notebook is to gain some familiarity with shallow neural networks with 1D inputs. It works through an example similar to figure 3.3 and experiments with different activation functions.
\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and write code to complete the functions. There are also questions interspersed in the text.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and write code to complete the functions. There are also questions interspersed in the text.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ] diff --git a/Notebooks/Chap03/3_2_Shallow_Networks_II.ipynb b/Notebooks/Chap03/3_2_Shallow_Networks_II.ipynb index 28fb916..a1131d6 100644 --- a/Notebooks/Chap03/3_2_Shallow_Networks_II.ipynb +++ b/Notebooks/Chap03/3_2_Shallow_Networks_II.ipynb @@ -32,7 +32,7 @@ "\n", "The purpose of this notebook is to gain some familiarity with shallow neural networks with 2D inputs. It works through an example similar to figure 3.8 and experiments with different activation functions.

\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and write code to complete the functions. There are also questions interspersed in the text.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and write code to complete the functions. There are also questions interspersed in the text.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n" ], diff --git a/Notebooks/Chap03/3_3_Shallow_Network_Regions.ipynb b/Notebooks/Chap03/3_3_Shallow_Network_Regions.ipynb index 607710c..c2239e4 100644 --- a/Notebooks/Chap03/3_3_Shallow_Network_Regions.ipynb +++ b/Notebooks/Chap03/3_3_Shallow_Network_Regions.ipynb @@ -32,7 +32,7 @@ "\n", "The purpose of this notebook is to compute the maximum possible number of linear regions as seen in figure 3.9 of the book.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and write code to complete the functions. There are also questions interspersed in the text.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and write code to complete the functions. There are also questions interspersed in the text.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ], diff --git a/Notebooks/Chap03/3_4_Activation_Functions.ipynb b/Notebooks/Chap03/3_4_Activation_Functions.ipynb index 582ed1e..c122987 100644 --- a/Notebooks/Chap03/3_4_Activation_Functions.ipynb +++ b/Notebooks/Chap03/3_4_Activation_Functions.ipynb @@ -22,7 +22,7 @@ "\n", "The purpose of this practical is to experiment with different activation functions.
\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and write code to complete the functions. There are also questions interspersed in the text.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and write code to complete the functions. There are also questions interspersed in the text.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ] diff --git a/Notebooks/Chap04/4_1_Composing_Networks.ipynb b/Notebooks/Chap04/4_1_Composing_Networks.ipynb index a8261f0..732d2d0 100644 --- a/Notebooks/Chap04/4_1_Composing_Networks.ipynb +++ b/Notebooks/Chap04/4_1_Composing_Networks.ipynb @@ -32,7 +32,7 @@ "\n", "The purpose of this notebook is to understand what happens when we feed one neural network into another. It works through an example similar to 4.1 and varies both networks\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions" ], @@ -343,7 +343,7 @@ { "cell_type": "code", "source": [ - "# TO DO\n", + "# TODO\n", "# How many linear regions would there be if we ran N copies of the first network, feeding the result of the first\n", "# into the second, the second into the third and so on, and then passed the result into the original second\n", "# network (blue curve above)\n", diff --git a/Notebooks/Chap04/4_2_Clipping_functions.ipynb b/Notebooks/Chap04/4_2_Clipping_functions.ipynb index 95ca408..87ecab2 100644 --- a/Notebooks/Chap04/4_2_Clipping_functions.ipynb +++ b/Notebooks/Chap04/4_2_Clipping_functions.ipynb @@ -33,7 +33,7 @@ "\n", "The purpose of this notebook is to understand how a neural network with two hidden layers build more complicated functions by clipping and recombining the representations at the intermediate hidden variables.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions" ], diff --git a/Notebooks/Chap04/4_3_Deep_Networks.ipynb b/Notebooks/Chap04/4_3_Deep_Networks.ipynb index 55eb037..fe56d2f 100644 --- a/Notebooks/Chap04/4_3_Deep_Networks.ipynb +++ b/Notebooks/Chap04/4_3_Deep_Networks.ipynb @@ -32,7 +32,7 @@ "\n", "This notebook investigates converting neural networks to matrix form.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ], diff --git a/Notebooks/Chap05/5_1_Least_Squares_Loss.ipynb b/Notebooks/Chap05/5_1_Least_Squares_Loss.ipynb index 0b27864..37cf704 100644 --- a/Notebooks/Chap05/5_1_Least_Squares_Loss.ipynb +++ b/Notebooks/Chap05/5_1_Least_Squares_Loss.ipynb @@ -32,7 +32,7 @@ "\n", "This notebook investigates the least squares loss and the equivalence of maximum likelihood and minimum negative log likelihood.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ], diff --git a/Notebooks/Chap05/5_2_Binary_Cross_Entropy_Loss.ipynb b/Notebooks/Chap05/5_2_Binary_Cross_Entropy_Loss.ipynb index 72fd8b1..8548602 100644 --- a/Notebooks/Chap05/5_2_Binary_Cross_Entropy_Loss.ipynb +++ b/Notebooks/Chap05/5_2_Binary_Cross_Entropy_Loss.ipynb @@ -32,7 +32,7 @@ "\n", "This notebook investigates the binary cross-entropy loss. It follows from applying the formula in section 5.2 to a loss function based on the Bernoulli distribution.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ], diff --git a/Notebooks/Chap05/5_3_Multiclass_Cross_entropy_Loss.ipynb b/Notebooks/Chap05/5_3_Multiclass_Cross_entropy_Loss.ipynb index edb5989..7785f6c 100644 --- a/Notebooks/Chap05/5_3_Multiclass_Cross_entropy_Loss.ipynb +++ b/Notebooks/Chap05/5_3_Multiclass_Cross_entropy_Loss.ipynb @@ -20,7 +20,7 @@ "\n", "This notebook investigates the multi-class cross-entropy loss. It follows from applying the formula in section 5.2 to a loss function based on the Categorical distribution.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ] diff --git a/Notebooks/Chap06/6_1_Line_Search.ipynb b/Notebooks/Chap06/6_1_Line_Search.ipynb index 67e0095..ae8aab1 100644 --- a/Notebooks/Chap06/6_1_Line_Search.ipynb +++ b/Notebooks/Chap06/6_1_Line_Search.ipynb @@ -32,7 +32,7 @@ "\n", "This notebook investigates how to find the minimum of a 1D function using line search as described in Figure 6.10.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n" ], diff --git a/Notebooks/Chap06/6_2_Gradient_Descent.ipynb b/Notebooks/Chap06/6_2_Gradient_Descent.ipynb index c28d51a..f530a77 100644 --- a/Notebooks/Chap06/6_2_Gradient_Descent.ipynb +++ b/Notebooks/Chap06/6_2_Gradient_Descent.ipynb @@ -20,7 +20,7 @@ "\n", "This notebook recreates the gradient descent algorithm as shown in figure 6.1.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n", "\n" diff --git a/Notebooks/Chap06/6_3_Stochastic_Gradient_Descent.ipynb b/Notebooks/Chap06/6_3_Stochastic_Gradient_Descent.ipynb index ce84689..34d6ed5 100644 --- a/Notebooks/Chap06/6_3_Stochastic_Gradient_Descent.ipynb +++ b/Notebooks/Chap06/6_3_Stochastic_Gradient_Descent.ipynb @@ -20,7 +20,7 @@ "\n", "This notebook investigates gradient descent and stochastic gradient descent and recreates figure 6.5 from the book\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n", "\n", diff --git a/Notebooks/Chap06/6_4_Momentum.ipynb b/Notebooks/Chap06/6_4_Momentum.ipynb index e7f7c9d..014ec14 100644 --- a/Notebooks/Chap06/6_4_Momentum.ipynb +++ b/Notebooks/Chap06/6_4_Momentum.ipynb @@ -32,7 +32,7 @@ "\n", "This notebook investigates the use of momentum as illustrated in figure 6.7 from the book.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n", "\n", diff --git a/Notebooks/Chap06/6_5_Adam.ipynb b/Notebooks/Chap06/6_5_Adam.ipynb index bb975d4..476bbaf 100644 --- a/Notebooks/Chap06/6_5_Adam.ipynb +++ b/Notebooks/Chap06/6_5_Adam.ipynb @@ -32,7 +32,7 @@ "\n", "This notebook investigates the Adam algorithm as illustrated in figure 6.9 from the book.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ], diff --git a/Notebooks/Chap07/7_1_Backpropagation_in_Toy_Model.ipynb b/Notebooks/Chap07/7_1_Backpropagation_in_Toy_Model.ipynb index 8b757f2..e5811bc 100644 --- a/Notebooks/Chap07/7_1_Backpropagation_in_Toy_Model.ipynb +++ b/Notebooks/Chap07/7_1_Backpropagation_in_Toy_Model.ipynb @@ -22,7 +22,7 @@ "\n", "This notebook computes the derivatives of the toy function discussed in section 7.3 of the book.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ] diff --git a/Notebooks/Chap07/7_2_Backpropagation.ipynb b/Notebooks/Chap07/7_2_Backpropagation.ipynb index be113ec..796d8a7 100644 --- a/Notebooks/Chap07/7_2_Backpropagation.ipynb +++ b/Notebooks/Chap07/7_2_Backpropagation.ipynb @@ -32,7 +32,7 @@ "\n", "This notebook runs the backpropagation algorithm on a deep neural network as described in section 7.4 of the book.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ], diff --git a/Notebooks/Chap07/7_3_Initialization.ipynb b/Notebooks/Chap07/7_3_Initialization.ipynb index 34ad1e1..3ce171d 100644 --- a/Notebooks/Chap07/7_3_Initialization.ipynb +++ b/Notebooks/Chap07/7_3_Initialization.ipynb @@ -32,7 +32,7 @@ "\n", "This notebook explores weight initialization in deep neural networks as described in section 7.5 of the book.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ], @@ -191,10 +191,10 @@ "# You can see that the values of the hidden units are increasing on average (the variance is across all hidden units at the layer\n", "# and the 1000 training examples\n", "\n", - "# TO DO\n", + "# TODO\n", "# Change this to 50 layers with 80 hidden units per layer\n", "\n", - "# TO DO\n", + "# TODO\n", "# Now experiment with sigma_sq_omega to try to stop the variance of the forward computation exploding" ], "metadata": { @@ -340,10 +340,10 @@ "# You can see that the gradients of the hidden units are increasing on average (the standard deviation is across all hidden units at the layer\n", "# and the 100 training examples\n", "\n", - "# TO DO\n", + "# TODO\n", "# Change this to 50 layers with 80 hidden units per layer\n", "\n", - "# TO DO\n", + "# TODO\n", "# Now experiment with sigma_sq_omega to try to stop the variance of the gradients exploding\n" ], "metadata": { diff --git a/Notebooks/Chap08/8_1_MNIST_1D_Performance.ipynb b/Notebooks/Chap08/8_1_MNIST_1D_Performance.ipynb index a6b8208..9f06f68 100644 --- a/Notebooks/Chap08/8_1_MNIST_1D_Performance.ipynb +++ b/Notebooks/Chap08/8_1_MNIST_1D_Performance.ipynb @@ -20,7 +20,7 @@ "\n", "This notebook runs a simple neural network on the MNIST1D dataset as in figure 8.2a. It uses code from https://github.com/greydanus/mnist1d to generate the data.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ] @@ -91,7 +91,7 @@ "D_i = 40 # Input dimensions\n", "D_k = 100 # Hidden dimensions\n", "D_o = 10 # Output dimensions\n", - "# TO DO:\n", + "# TODO:\n", "# Define a model with two hidden layers of size 100\n", "# And ReLU activations between them\n", "# Replace this line (see Figure 7.8 of book for help):\n", @@ -99,7 +99,7 @@ "\n", "\n", "def weights_init(layer_in):\n", - " # TO DO:\n", + " # TODO:\n", " # Initialize the parameters with He initialization\n", " # Replace this line (see figure 7.8 of book for help)\n", " print(\"Initializing layer\")\n", @@ -208,7 +208,7 @@ "id": "q-yT6re6GZS4" }, "source": [ - "**TO DO**\n", + "**TODO**\n", "\n", "Play with the model -- try changing the number of layers, hidden units, learning rate, batch size, momentum or anything else you like. See if you can improve the test results.\n", "\n", diff --git a/Notebooks/Chap08/8_2_Bias_Variance_Trade_Off.ipynb b/Notebooks/Chap08/8_2_Bias_Variance_Trade_Off.ipynb index d75c770..207d5ba 100644 --- a/Notebooks/Chap08/8_2_Bias_Variance_Trade_Off.ipynb +++ b/Notebooks/Chap08/8_2_Bias_Variance_Trade_Off.ipynb @@ -32,7 +32,7 @@ "\n", "This notebook investigates the bias-variance trade-off for the toy model used throughout chapter 8 and reproduces the bias/variance trade off curves seen in figure 8.9.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ], diff --git a/Notebooks/Chap08/8_3_Double_Descent.ipynb b/Notebooks/Chap08/8_3_Double_Descent.ipynb index d75e15f..b588f8a 100644 --- a/Notebooks/Chap08/8_3_Double_Descent.ipynb +++ b/Notebooks/Chap08/8_3_Double_Descent.ipynb @@ -36,7 +36,7 @@ "\n", "It uses the MNIST-1D database which can be found at https://github.com/greydanus/mnist1d\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ], @@ -217,7 +217,7 @@ "source": [ "The following code produces the double descent curve by training the model with different numbers of hidden units and plotting the test error.\n", "\n", - "TO DO:\n", + "TODO:\n", "\n", "*Before* you run the code, and considering that there are 4000 training examples predict:
\n", "\n", diff --git a/Notebooks/Chap08/8_4_High_Dimensional_Spaces.ipynb b/Notebooks/Chap08/8_4_High_Dimensional_Spaces.ipynb index f9f914d..c03622b 100644 --- a/Notebooks/Chap08/8_4_High_Dimensional_Spaces.ipynb +++ b/Notebooks/Chap08/8_4_High_Dimensional_Spaces.ipynb @@ -33,7 +33,7 @@ "\n", "This notebook investigates the strange properties of high-dimensional spaces as discussed in the notes at the end of chapter 8.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ], diff --git a/Notebooks/Chap09/9_1_L2_Regularization.ipynb b/Notebooks/Chap09/9_1_L2_Regularization.ipynb index dd3481f..573f010 100644 --- a/Notebooks/Chap09/9_1_L2_Regularization.ipynb +++ b/Notebooks/Chap09/9_1_L2_Regularization.ipynb @@ -33,7 +33,7 @@ "\n", "This notebook investigates adding L2 regularization to the loss function for the Gabor model as in figure 9.1.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n" ], diff --git a/Notebooks/Chap09/9_2_Implicit_Regularization.ipynb b/Notebooks/Chap09/9_2_Implicit_Regularization.ipynb index af91cbe..4f1e11c 100644 --- a/Notebooks/Chap09/9_2_Implicit_Regularization.ipynb +++ b/Notebooks/Chap09/9_2_Implicit_Regularization.ipynb @@ -32,7 +32,7 @@ "\n", "This notebook investigates how the finite step sizes in gradient descent cause the trajectory to deviate and how this can be explained by adding an implicit regularization term. It recreates figure 9.3 from the book.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n" ], diff --git a/Notebooks/Chap09/9_3_Ensembling.ipynb b/Notebooks/Chap09/9_3_Ensembling.ipynb index be9b343..272e7fe 100644 --- a/Notebooks/Chap09/9_3_Ensembling.ipynb +++ b/Notebooks/Chap09/9_3_Ensembling.ipynb @@ -33,7 +33,7 @@ "\n", "This notebook investigates how ensembling can improve the performance of models. We'll work with the simplified neural network model (figure 8.4 of book) which we can fit in closed form, and so we can eliminate any errors due to not finding the global maximum.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n" ], diff --git a/Notebooks/Chap09/9_4_Bayesian_Approach.ipynb b/Notebooks/Chap09/9_4_Bayesian_Approach.ipynb index ef7c3b8..5187721 100644 --- a/Notebooks/Chap09/9_4_Bayesian_Approach.ipynb +++ b/Notebooks/Chap09/9_4_Bayesian_Approach.ipynb @@ -20,7 +20,7 @@ "\n", "This notebook investigates the Bayesian approach to model fitting and reproduces figure 9.11 from the book.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n" ] diff --git a/Notebooks/Chap09/9_5_Augmentation.ipynb b/Notebooks/Chap09/9_5_Augmentation.ipynb index bd0c607..4cfea5f 100644 --- a/Notebooks/Chap09/9_5_Augmentation.ipynb +++ b/Notebooks/Chap09/9_5_Augmentation.ipynb @@ -32,7 +32,7 @@ "\n", "This notebook investigates data augmentation for the MNIST-1D model.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n" ], diff --git a/Notebooks/Chap10/10_1_1D_Convolution.ipynb b/Notebooks/Chap10/10_1_1D_Convolution.ipynb index 173fda0..df99035 100644 --- a/Notebooks/Chap10/10_1_1D_Convolution.ipynb +++ b/Notebooks/Chap10/10_1_1D_Convolution.ipynb @@ -33,7 +33,7 @@ "\n", "This notebook investigates 1D convolutional layers.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n" ], diff --git a/Notebooks/Chap10/10_2_Convolution_for_MNIST_1D.ipynb b/Notebooks/Chap10/10_2_Convolution_for_MNIST_1D.ipynb index c9d02a6..60fbad3 100644 --- a/Notebooks/Chap10/10_2_Convolution_for_MNIST_1D.ipynb +++ b/Notebooks/Chap10/10_2_Convolution_for_MNIST_1D.ipynb @@ -33,7 +33,7 @@ "\n", "This notebook investigates a 1D convolutional network for MNIST-1D as in figure 10.7 and 10.8a.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n", "\n" diff --git a/Notebooks/Chap10/10_3_2D_Convolution.ipynb b/Notebooks/Chap10/10_3_2D_Convolution.ipynb index c304cc0..9e115c0 100644 --- a/Notebooks/Chap10/10_3_2D_Convolution.ipynb +++ b/Notebooks/Chap10/10_3_2D_Convolution.ipynb @@ -33,7 +33,7 @@ "\n", "This notebook investigates the 2D convolution operation. It asks you to hand code the convolution so we can be sure that we are computing the same thing as in PyTorch. The next notebook uses the convolutional layers in PyTorch directly.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ], diff --git a/Notebooks/Chap10/10_4_Downsampling_and_Upsampling.ipynb b/Notebooks/Chap10/10_4_Downsampling_and_Upsampling.ipynb index 3e3d96b..0241ec6 100644 --- a/Notebooks/Chap10/10_4_Downsampling_and_Upsampling.ipynb +++ b/Notebooks/Chap10/10_4_Downsampling_and_Upsampling.ipynb @@ -33,7 +33,7 @@ "\n", "This notebook investigates the upsampling and downsampling methods discussed in section 10.4 of the book.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n" ], diff --git a/Notebooks/Chap10/10_5_Convolution_For_MNIST.ipynb b/Notebooks/Chap10/10_5_Convolution_For_MNIST.ipynb index fb5b32b..40f7c0f 100644 --- a/Notebooks/Chap10/10_5_Convolution_For_MNIST.ipynb +++ b/Notebooks/Chap10/10_5_Convolution_For_MNIST.ipynb @@ -35,7 +35,7 @@ "\n", "The code is adapted from https://nextjournal.com/gkoehler/pytorch-mnist\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n" ], diff --git a/Notebooks/Chap11/11_1_Shattered_Gradients.ipynb b/Notebooks/Chap11/11_1_Shattered_Gradients.ipynb index bf6ae98..578ac68 100644 --- a/Notebooks/Chap11/11_1_Shattered_Gradients.ipynb +++ b/Notebooks/Chap11/11_1_Shattered_Gradients.ipynb @@ -33,7 +33,7 @@ "\n", "This notebook investigates the phenomenon of shattered gradients as discussed in section 11.1.1. It replicates some of the experiments in [Balduzzi et al. (2017)](https://arxiv.org/abs/1702.08591).\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ], diff --git a/Notebooks/Chap11/11_2_Residual_Networks.ipynb b/Notebooks/Chap11/11_2_Residual_Networks.ipynb index 51a67f2..8327cad 100644 --- a/Notebooks/Chap11/11_2_Residual_Networks.ipynb +++ b/Notebooks/Chap11/11_2_Residual_Networks.ipynb @@ -33,7 +33,7 @@ "\n", "This notebook adapts the networks for MNIST1D to use residual connections.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n", "\n" diff --git a/Notebooks/Chap11/11_3_Batch_Normalization.ipynb b/Notebooks/Chap11/11_3_Batch_Normalization.ipynb index 0309676..fbe1c22 100644 --- a/Notebooks/Chap11/11_3_Batch_Normalization.ipynb +++ b/Notebooks/Chap11/11_3_Batch_Normalization.ipynb @@ -33,7 +33,7 @@ "\n", "This notebook investigates the use of batch normalization in residual networks.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n", "\n" diff --git a/Notebooks/Chap12/12_1_Self_Attention.ipynb b/Notebooks/Chap12/12_1_Self_Attention.ipynb index eb584b8..0938c71 100644 --- a/Notebooks/Chap12/12_1_Self_Attention.ipynb +++ b/Notebooks/Chap12/12_1_Self_Attention.ipynb @@ -33,7 +33,7 @@ "\n", "This notebook builds a self-attention mechanism from scratch, as discussed in section 12.2 of the book.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n", "\n" diff --git a/Notebooks/Chap12/12_2_Multihead_Self_Attention.ipynb b/Notebooks/Chap12/12_2_Multihead_Self_Attention.ipynb index 219a8d0..1b8df06 100644 --- a/Notebooks/Chap12/12_2_Multihead_Self_Attention.ipynb +++ b/Notebooks/Chap12/12_2_Multihead_Self_Attention.ipynb @@ -32,7 +32,7 @@ "\n", "This notebook builds a multihead self-attention mechanism as in figure 12.6\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n", "\n" diff --git a/Notebooks/Chap12/12_3_Tokenization.ipynb b/Notebooks/Chap12/12_3_Tokenization.ipynb index 67c5cfc..4ab060b 100644 --- a/Notebooks/Chap12/12_3_Tokenization.ipynb +++ b/Notebooks/Chap12/12_3_Tokenization.ipynb @@ -33,7 +33,7 @@ "\n", "This notebook builds set of tokens from a text string as in figure 12.8 of the book.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "I adapted this code from *SOMEWHERE*. If anyone recognizes it, can you let me know and I will give the proper attribution or rewrite if the license is not permissive.\n", "\n", diff --git a/Notebooks/Chap12/12_4_Decoding_Strategies.ipynb b/Notebooks/Chap12/12_4_Decoding_Strategies.ipynb index 6d7a388..46eb9f4 100644 --- a/Notebooks/Chap12/12_4_Decoding_Strategies.ipynb +++ b/Notebooks/Chap12/12_4_Decoding_Strategies.ipynb @@ -33,7 +33,7 @@ "\n", "This practical investigates neural decoding from transformer models. \n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ], diff --git a/Notebooks/Chap13/13_1_Graph_Representation.ipynb b/Notebooks/Chap13/13_1_Graph_Representation.ipynb index 11d7179..34a5a69 100644 --- a/Notebooks/Chap13/13_1_Graph_Representation.ipynb +++ b/Notebooks/Chap13/13_1_Graph_Representation.ipynb @@ -33,7 +33,7 @@ "\n", "This notebook investigates representing graphs with matrices as illustrated in figure 13.4 from the book.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n", "\n" diff --git a/Notebooks/Chap13/13_2_Graph_Classification.ipynb b/Notebooks/Chap13/13_2_Graph_Classification.ipynb index ea0229f..890160c 100644 --- a/Notebooks/Chap13/13_2_Graph_Classification.ipynb +++ b/Notebooks/Chap13/13_2_Graph_Classification.ipynb @@ -32,7 +32,7 @@ "\n", "This notebook investigates representing graphs with matrices as illustrated in figure 13.4 from the book.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ], diff --git a/Notebooks/Chap13/13_3_Neighborhood_Sampling.ipynb b/Notebooks/Chap13/13_3_Neighborhood_Sampling.ipynb index 3a0bc94..650a7c5 100644 --- a/Notebooks/Chap13/13_3_Neighborhood_Sampling.ipynb +++ b/Notebooks/Chap13/13_3_Neighborhood_Sampling.ipynb @@ -33,7 +33,7 @@ "\n", "This notebook investigates neighborhood sampling of graphs as in figure 13.10 from the book.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ], diff --git a/Notebooks/Chap13/13_4_Graph_Attention_Networks.ipynb b/Notebooks/Chap13/13_4_Graph_Attention_Networks.ipynb index a67e829..7086248 100644 --- a/Notebooks/Chap13/13_4_Graph_Attention_Networks.ipynb +++ b/Notebooks/Chap13/13_4_Graph_Attention_Networks.ipynb @@ -33,7 +33,7 @@ "\n", "This notebook builds a graph attention mechanism from scratch, as discussed in section 13.8.6 of the book and illustrated in figure 13.12c\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n", "\n" diff --git a/Notebooks/Chap15/15_1_GAN_Toy_Example.ipynb b/Notebooks/Chap15/15_1_GAN_Toy_Example.ipynb index c8b7895..a9fcc8b 100644 --- a/Notebooks/Chap15/15_1_GAN_Toy_Example.ipynb +++ b/Notebooks/Chap15/15_1_GAN_Toy_Example.ipynb @@ -32,7 +32,7 @@ "\n", "This notebook investigates the GAN toy example as illustrated in figure 15.1 in the book.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ], diff --git a/Notebooks/Chap15/15_2_Wasserstein_Distance.ipynb b/Notebooks/Chap15/15_2_Wasserstein_Distance.ipynb index 9384399..25cac8a 100644 --- a/Notebooks/Chap15/15_2_Wasserstein_Distance.ipynb +++ b/Notebooks/Chap15/15_2_Wasserstein_Distance.ipynb @@ -32,7 +32,7 @@ "\n", "This notebook investigates the GAN toy example as illustrated in figure 15.1 in the book.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ], diff --git a/Notebooks/Chap16/16_1_1D_Normalizing_Flows.ipynb b/Notebooks/Chap16/16_1_1D_Normalizing_Flows.ipynb index f0e7fe2..cbe5ff5 100644 --- a/Notebooks/Chap16/16_1_1D_Normalizing_Flows.ipynb +++ b/Notebooks/Chap16/16_1_1D_Normalizing_Flows.ipynb @@ -33,7 +33,7 @@ "\n", "This notebook investigates a 1D normalizing flows example similar to that illustrated in figures 16.1 to 16.3 in the book.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ], diff --git a/Notebooks/Chap16/16_2_Autoregressive_Flows.ipynb b/Notebooks/Chap16/16_2_Autoregressive_Flows.ipynb index 3f3b521..4583455 100644 --- a/Notebooks/Chap16/16_2_Autoregressive_Flows.ipynb +++ b/Notebooks/Chap16/16_2_Autoregressive_Flows.ipynb @@ -33,7 +33,7 @@ "\n", "This notebook investigates a 1D normalizing flows example similar to that illustrated in figure 16.7 in the book.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ], diff --git a/Notebooks/Chap16/16_3_Contraction_Mappings.ipynb b/Notebooks/Chap16/16_3_Contraction_Mappings.ipynb index 632bfcf..b97f755 100644 --- a/Notebooks/Chap16/16_3_Contraction_Mappings.ipynb +++ b/Notebooks/Chap16/16_3_Contraction_Mappings.ipynb @@ -22,7 +22,7 @@ "\n", "This notebook investigates a 1D normalizing flows example similar to that illustrated in figure 16.9 in the book.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ] diff --git a/Notebooks/Chap17/17_1_Latent_Variable_Models.ipynb b/Notebooks/Chap17/17_1_Latent_Variable_Models.ipynb index a8cba0a..ac6d2f9 100644 --- a/Notebooks/Chap17/17_1_Latent_Variable_Models.ipynb +++ b/Notebooks/Chap17/17_1_Latent_Variable_Models.ipynb @@ -22,7 +22,7 @@ "\n", "This notebook investigates a non-linear latent variable model similar to that in figures 17.2 and 17.3 of the book.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ] diff --git a/Notebooks/Chap17/17_2_Reparameterization_Trick.ipynb b/Notebooks/Chap17/17_2_Reparameterization_Trick.ipynb index da9cc22..2fa0429 100644 --- a/Notebooks/Chap17/17_2_Reparameterization_Trick.ipynb +++ b/Notebooks/Chap17/17_2_Reparameterization_Trick.ipynb @@ -20,7 +20,7 @@ "\n", "This notebook investigates the reparameterization trick as described in section 17.7 of the book.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ] diff --git a/Notebooks/Chap17/17_3_Importance_Sampling.ipynb b/Notebooks/Chap17/17_3_Importance_Sampling.ipynb index 92caf63..da920b4 100644 --- a/Notebooks/Chap17/17_3_Importance_Sampling.ipynb +++ b/Notebooks/Chap17/17_3_Importance_Sampling.ipynb @@ -22,7 +22,7 @@ "\n", "This notebook investigates importance sampling as described in section 17.8.1 of the book.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ] diff --git a/Notebooks/Chap18/18_1_Diffusion_Encoder.ipynb b/Notebooks/Chap18/18_1_Diffusion_Encoder.ipynb index 4824fcf..7b8028a 100644 --- a/Notebooks/Chap18/18_1_Diffusion_Encoder.ipynb +++ b/Notebooks/Chap18/18_1_Diffusion_Encoder.ipynb @@ -20,7 +20,7 @@ "\n", "This notebook investigates the diffusion encoder as described in section 18.2 of the book.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ] diff --git a/Notebooks/Chap18/18_2_1D_Diffusion_Model.ipynb b/Notebooks/Chap18/18_2_1D_Diffusion_Model.ipynb index 42b7c65..c59e652 100644 --- a/Notebooks/Chap18/18_2_1D_Diffusion_Model.ipynb +++ b/Notebooks/Chap18/18_2_1D_Diffusion_Model.ipynb @@ -22,7 +22,7 @@ "\n", "This notebook investigates the diffusion encoder as described in section 18.3 and 18.4 of the book.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ] diff --git a/Notebooks/Chap18/18_3_Reparameterized_Model.ipynb b/Notebooks/Chap18/18_3_Reparameterized_Model.ipynb index eac07b7..02e56a5 100644 --- a/Notebooks/Chap18/18_3_Reparameterized_Model.ipynb +++ b/Notebooks/Chap18/18_3_Reparameterized_Model.ipynb @@ -22,7 +22,7 @@ "\n", "This notebook investigates the reparameterized model as described in section 18.5 of the book and implements algorithms 18.1 and 18.2.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ] diff --git a/Notebooks/Chap18/18_4_Families_of_Diffusion_Models.ipynb b/Notebooks/Chap18/18_4_Families_of_Diffusion_Models.ipynb index b01d7ff..335e848 100644 --- a/Notebooks/Chap18/18_4_Families_of_Diffusion_Models.ipynb +++ b/Notebooks/Chap18/18_4_Families_of_Diffusion_Models.ipynb @@ -22,7 +22,7 @@ "\n", "This notebook investigates the reparameterized model as described in section 18.5 of the book and computers the results shown in figure 18.10c-f. These models are based on the paper \"Denoising diffusion implicit models\" which can be found [here](https://arxiv.org/pdf/2010.02502.pdf).\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ] diff --git a/Notebooks/Chap19/19_1_Markov_Decision_Processes.ipynb b/Notebooks/Chap19/19_1_Markov_Decision_Processes.ipynb index ddac7cc..76b2854 100644 --- a/Notebooks/Chap19/19_1_Markov_Decision_Processes.ipynb +++ b/Notebooks/Chap19/19_1_Markov_Decision_Processes.ipynb @@ -33,7 +33,7 @@ "\n", "This notebook investigates Markov decision processes as described in section 19.1 of the book.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ], diff --git a/Notebooks/Chap19/19_2_Dynamic_Programming.ipynb b/Notebooks/Chap19/19_2_Dynamic_Programming.ipynb index 62a1275..844c376 100644 --- a/Notebooks/Chap19/19_2_Dynamic_Programming.ipynb +++ b/Notebooks/Chap19/19_2_Dynamic_Programming.ipynb @@ -33,7 +33,7 @@ "\n", "This notebook investigates the dynamic programming approach to tabular reinforcement learning as described in figure 19.10 of the book.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ], diff --git a/Notebooks/Chap19/19_3_Monte_Carlo_Methods.ipynb b/Notebooks/Chap19/19_3_Monte_Carlo_Methods.ipynb index 0f42d3d..22468ce 100644 --- a/Notebooks/Chap19/19_3_Monte_Carlo_Methods.ipynb +++ b/Notebooks/Chap19/19_3_Monte_Carlo_Methods.ipynb @@ -22,7 +22,7 @@ "\n", "NOTE! There is a mistake in Figure 19.11 in the first printing of the book, so check the errata to avoid becoming confused. Apologies!\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n", "\n", diff --git a/Notebooks/Chap19/19_4_Temporal_Difference_Methods.ipynb b/Notebooks/Chap19/19_4_Temporal_Difference_Methods.ipynb index d85443d..8a98c79 100644 --- a/Notebooks/Chap19/19_4_Temporal_Difference_Methods.ipynb +++ b/Notebooks/Chap19/19_4_Temporal_Difference_Methods.ipynb @@ -20,7 +20,7 @@ "\n", "This notebook investigates temporal difference methods for tabular reinforcement learning as described in section 19.3.3 of the book\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n", "\n", diff --git a/Notebooks/Chap19/19_5_Control_Variates.ipynb b/Notebooks/Chap19/19_5_Control_Variates.ipynb index 5af4f99..1ce945a 100644 --- a/Notebooks/Chap19/19_5_Control_Variates.ipynb +++ b/Notebooks/Chap19/19_5_Control_Variates.ipynb @@ -34,7 +34,7 @@ "This notebook investigates the method of control variates as described in figure 19.16\n", "\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ], diff --git a/Notebooks/Chap20/20_1_Random_Data.ipynb b/Notebooks/Chap20/20_1_Random_Data.ipynb index d930a6d..0689209 100644 --- a/Notebooks/Chap20/20_1_Random_Data.ipynb +++ b/Notebooks/Chap20/20_1_Random_Data.ipynb @@ -33,7 +33,7 @@ "\n", "This notebook investigates training the network with random data, as illustrated in figure 20.1.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n", "\n" diff --git a/Notebooks/Chap20/20_2_Full_Batch_Gradient_Descent.ipynb b/Notebooks/Chap20/20_2_Full_Batch_Gradient_Descent.ipynb index 72d8f7e..672d827 100644 --- a/Notebooks/Chap20/20_2_Full_Batch_Gradient_Descent.ipynb +++ b/Notebooks/Chap20/20_2_Full_Batch_Gradient_Descent.ipynb @@ -33,7 +33,7 @@ "\n", "This notebook investigates training a network with full batch gradient descent as in figure 20.2. There is also a version (notebook takes a long time to run), but this didn't speed it up much for me. If you run out of CoLab time, you'll need to download the Python file and run locally.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ], diff --git a/Notebooks/Chap20/20_2_Full_Batch_Gradient_Descent_GPU.ipynb b/Notebooks/Chap20/20_2_Full_Batch_Gradient_Descent_GPU.ipynb index a1f558d..b9bb56d 100644 --- a/Notebooks/Chap20/20_2_Full_Batch_Gradient_Descent_GPU.ipynb +++ b/Notebooks/Chap20/20_2_Full_Batch_Gradient_Descent_GPU.ipynb @@ -35,7 +35,7 @@ "\n", "This notebook investigates training a network with full batch gradient descent as in figure 20.2. This is the GPU version (notebook takes a long time to run). If you are using Colab then you need to go change the runtime type to GPU on the Runtime menu. Even then, you may run out of time. If that's the case, you'll need to download the Python file and run locally.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n", "\n" diff --git a/Notebooks/Chap20/20_3_Lottery_Tickets.ipynb b/Notebooks/Chap20/20_3_Lottery_Tickets.ipynb index aafad0f..0134a53 100644 --- a/Notebooks/Chap20/20_3_Lottery_Tickets.ipynb +++ b/Notebooks/Chap20/20_3_Lottery_Tickets.ipynb @@ -32,7 +32,7 @@ "\n", "This notebook investigates the phenomenon of lottery tickets as discussed in section 20.2.7. This notebook is highly derivative of the MNIST-1D code hosted by Sam Greydanus at https://github.com/greydanus/mnist1d. \n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions." ] diff --git a/Notebooks/Chap20/20_4_Adversarial_Attacks.ipynb b/Notebooks/Chap20/20_4_Adversarial_Attacks.ipynb index eea7371..efd73a0 100644 --- a/Notebooks/Chap20/20_4_Adversarial_Attacks.ipynb +++ b/Notebooks/Chap20/20_4_Adversarial_Attacks.ipynb @@ -33,7 +33,7 @@ "\n", "This notebook builds uses the network for classification of MNIST from Notebook 10.5. The code is adapted from https://nextjournal.com/gkoehler/pytorch-mnist, and uses the fast gradient sign attack of [Goodfellow et al. (2015)](https://arxiv.org/abs/1412.6572). Having trained, the network, we search for adversarial examples -- inputs which look very similar to class A, but are mistakenly classified as class B. We do this by starting with a correctly classified example and perturbing it according to the gradients of the network so that the output changes.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n" ], diff --git a/Notebooks/Chap21/21_1_Bias_Mitigation.ipynb b/Notebooks/Chap21/21_1_Bias_Mitigation.ipynb index 8dbcc8b..63b2c92 100644 --- a/Notebooks/Chap21/21_1_Bias_Mitigation.ipynb +++ b/Notebooks/Chap21/21_1_Bias_Mitigation.ipynb @@ -22,7 +22,7 @@ "\n", "This notebook investigates a post-processing method for bias mitigation (see figure 21.2 in the book). It based on this [blog](https://www.borealisai.com/research-blogs/tutorial1-bias-and-fairness-ai/) that I wrote for Borealis AI in 2019, which itself was derived from [this blog](https://research.google.com/bigpicture/attacking-discrimination-in-ml/) by Wattenberg, Viégas, and Hardt.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n" ] diff --git a/Notebooks/Chap21/21_2_Explainability.ipynb b/Notebooks/Chap21/21_2_Explainability.ipynb index e64cc41..fcdd580 100644 --- a/Notebooks/Chap21/21_2_Explainability.ipynb +++ b/Notebooks/Chap21/21_2_Explainability.ipynb @@ -33,7 +33,7 @@ "\n", "This notebook investigates the LIME explainability method as depicted in figure 21.3 of the book.\n", "\n", - "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n", "\n", "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions.\n" ], From 95549683c4ef99e48bbf500fc32341f4e03b43b8 Mon Sep 17 00:00:00 2001 From: udlbook <110402648+udlbook@users.noreply.github.com> Date: Tue, 11 Feb 2025 15:13:30 -0500 Subject: [PATCH 18/29] Created using Colab --- Blogs/BorealisODENumerical.ipynb | 432 +++++++++++++++++++++++++++++++ 1 file changed, 432 insertions(+) create mode 100644 Blogs/BorealisODENumerical.ipynb diff --git a/Blogs/BorealisODENumerical.ipynb b/Blogs/BorealisODENumerical.ipynb new file mode 100644 index 0000000..57096a0 --- /dev/null +++ b/Blogs/BorealisODENumerical.ipynb @@ -0,0 +1,432 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JXsO7ce7oqeq" + }, + "source": [ + "# Numerical methods for ODEs\n", + "\n", + "This blog contains code that accompanies the RBC Borealis blog on numerical methods for ODEs. Contact udlbookmail@gmail.com if you find any problems." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AnvAKtP_oqes" + }, + "source": [ + "Import relevant libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "UF-gJyZggyrl" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "szWLVrSSoqet" + }, + "source": [ + "Define the ODE that we will be experimenting with." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "NkrGZLL6iM3P" + }, + "outputs": [], + "source": [ + "# The ODE that we will experiment with\n", + "def ode_lin_homog(t,x):\n", + " return 0.5 * x ;\n", + "\n", + "# The derivative of the ODE function with respect to x (needed for Taylor's method)\n", + "def ode_lin_homog_deriv_x(t,x):\n", + " return 0.5 ;\n", + "\n", + "# The derivative of the ODE function with respect to t (needed for Taylor's method)\n", + "def ode_lin_homog_deriv_t(t,x):\n", + " return 0.0 ;\n", + "\n", + "# The closed form solution (so we can measure the error)\n", + "def ode_lin_homog_soln(t,C=0.5):\n", + " return C * np.exp(0.5 * t) ;" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "In1C9wZkoqet" + }, + "source": [ + "This is a generic method that runs the numerical methods. It takes the initial conditions ($t_0$, $x_0$), the final time $t_1$ and the step size $h$. It also takes the ODE function itself and its derivatives (only used for Taylor's method). Finally, the parameter \"step_function\" is the method used to update (e.g., Euler's methods, Runge-Kutte 4-step)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "VZfZDJAfmyrf" + }, + "outputs": [], + "source": [ + "def run_numerical(x_0, t_0, t_1, h, ode_func, ode_func_deriv_x, ode_func_deriv_t, ode_soln, step_function):\n", + " x = [x_0]\n", + " t = [t_0]\n", + " while (t[-1] <= t_1):\n", + " x = x+[step_function(x[-1],t[-1],h, ode_func, ode_func_deriv_x, ode_func_deriv_t)]\n", + " t = t + [t[-1]+h]\n", + "\n", + " # Returns x,y plot plus total numerical error at last point.\n", + " return t, x, np.abs(ode_soln(t[-1])-x[-1])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Vfkc3-_7oqet" + }, + "source": [ + "Run the numerical method with step sizes of 2.0, 1.0, 0.5, 0.25, 0.125, 0.0675 and plot the results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "1tyGbMZhoqeu" + }, + "outputs": [], + "source": [ + "def run_and_plot(ode, ode_deriv_x, ode_deriv_t, ode_solution, step_function):\n", + " # Specify the grid of points to draw the ODE\n", + " t = np.arange(0.04, 4.0, 0.2)\n", + " x = np.arange(0.04, 4.0, 0.2)\n", + " T, X = np.meshgrid(t,x)\n", + "\n", + " # ODE equation at these grid points (used to draw quiver-plot)\n", + " dx = ode(T,X)\n", + " dt = np.ones(dx.shape)\n", + "\n", + " # The ground truth solution\n", + " t2= np.arange(0,10,0.1)\n", + " x2 = ode_solution(t2)\n", + "\n", + " #####################################x_0, t_0, t_1, h #################################################\n", + " t_sim1,x_sim1,error1 = run_numerical(0.5, 0.0, 4.0, 2.0000, ode, ode_deriv_x, ode_deriv_t, ode_solution, step_function)\n", + " t_sim2,x_sim2,error2 = run_numerical(0.5, 0.0, 4.0, 1.0000, ode, ode_deriv_x, ode_deriv_t, ode_solution, step_function)\n", + " t_sim3,x_sim3,error3 = run_numerical(0.5, 0.0, 4.0, 0.5000, ode, ode_deriv_x, ode_deriv_t, ode_solution, step_function)\n", + " t_sim4,x_sim4,error4 = run_numerical(0.5, 0.0, 4.0, 0.2500, ode, ode_deriv_x, ode_deriv_t, ode_solution, step_function)\n", + " t_sim5,x_sim5,error5 = run_numerical(0.5, 0.0, 4.0, 0.1250, ode, ode_deriv_x, ode_deriv_t, ode_solution, step_function)\n", + " t_sim6,x_sim6,error6 = run_numerical(0.5, 0.0, 4.0, 0.0675, ode, ode_deriv_x, ode_deriv_t, ode_solution, step_function)\n", + "\n", + " # Plot the ODE and ground truth solution\n", + " fig,ax = plt.subplots()\n", + " ax.quiver(T,X,dt,dx, scale=35.0)\n", + " ax.plot(t2,x2,'r-')\n", + "\n", + " # Plot the numerical approximations\n", + " ax.plot(t_sim1,x_sim1,'.-',markeredgecolor='#773c23ff',markerfacecolor='#d18362', color='#d18362', markersize=10)\n", + " ax.plot(t_sim2,x_sim2,'.-',markeredgecolor='#773c23ff',markerfacecolor='#d18362', color='#d18362', markersize=10)\n", + " ax.plot(t_sim3,x_sim3,'.-',markeredgecolor='#773c23ff',markerfacecolor='#d18362', color='#d18362', markersize=10)\n", + " ax.plot(t_sim4,x_sim4,'.-',markeredgecolor='#773c23ff',markerfacecolor='#d18362', color='#d18362', markersize=10)\n", + " ax.plot(t_sim5,x_sim5,'.-',markeredgecolor='#773c23ff',markerfacecolor='#d18362', color='#d18362', markersize=10)\n", + " ax.plot(t_sim6,x_sim6,'.-',markeredgecolor='#773c23ff',markerfacecolor='#d18362', color='#d18362', markersize=10)\n", + "\n", + " ax.set_aspect('equal')\n", + " ax.set_xlim(0,4)\n", + " ax.set_ylim(0,4)\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JYrq8QIwvOIy" + }, + "source": [ + "# Euler Method\n", + "\n", + "Define the Euler method and set up functions for plotting." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "N73xMnCukVVX" + }, + "outputs": [], + "source": [ + "def euler_step(x_0, t_0, h, ode_func, ode_func_deriv_x=None, ode_func_deriv_t=None):\n", + " return x_0 + h * ode_func(t_0, x_0) ;" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4B1_PGEcsZ9H" + }, + "outputs": [], + "source": [ + "run_and_plot(ode_lin_homog, None, None, ode_lin_homog_soln, euler_step)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FfwNihtkvJeX" + }, + "source": [ + "# Heun's Method" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "srHfNDcDxI1o" + }, + "outputs": [], + "source": [ + "def heun_step(x_0, t_0, h, ode_func, ode_func_deriv_x=None, ode_func_deriv_t=None):\n", + " f_x0_t0 = ode_func(t_0, x_0)\n", + " return x_0 + h/2 * ( f_x0_t0 + ode_func(t_0+h, x_0+h*f_x0_t0)) ;" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "WOApHz9xoqev" + }, + "outputs": [], + "source": [ + "run_and_plot(ode_lin_homog, None, None, ode_lin_homog_soln, heun_step)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0XSzzFDIvRhm" + }, + "source": [ + "# Modified Euler method" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "fSXprgVJ5Yep" + }, + "outputs": [], + "source": [ + "def modified_euler_step(x_0, t_0, h, ode_func, ode_func_deriv_x=None, ode_func_deriv_t=None):\n", + " f_x0_t0 = ode_func(t_0, x_0)\n", + " return x_0 + h * ode_func(t_0+h/2, x_0+ h * f_x0_t0/2) ;" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "8LKSrCD2oqev" + }, + "outputs": [], + "source": [ + "run_and_plot(ode_lin_homog, None, None, ode_lin_homog_soln, modified_euler_step)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yp8ZBpwooqev" + }, + "source": [ + "# Second order Taylor's method" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "NtBBgzWLoqev" + }, + "outputs": [], + "source": [ + "def taylor_2nd_order(x_0, t_0, h, ode_func, ode_func_deriv_x, ode_func_deriv_t):\n", + " f1 = ode_func(t_0, x_0)\n", + " return x_0 + h * ode_func(t_0, x_0) + (h*h/2) * (ode_func_deriv_x(t_0,x_0) * ode_func(t_0, x_0) + ode_func_deriv_t(t_0, x_0))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ioeeIohUoqev" + }, + "outputs": [], + "source": [ + "run_and_plot(ode_lin_homog, ode_lin_homog_deriv_x, ode_lin_homog_deriv_t, ode_lin_homog_soln, taylor_2nd_order)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WcuhV5lL1zAJ" + }, + "source": [ + "# Fourth Order Runge Kutta" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0NZN81Bpwu56" + }, + "outputs": [], + "source": [ + "def runge_kutta_4_step(x_0, t_0, h, ode_func, ode_func_deriv_x=None, ode_func_deriv_t=None):\n", + " f1 = ode_func(t_0, x_0)\n", + " f2 = ode_func(t_0+h/2,x_0+f1 * h/2)\n", + " f3 = ode_func(t_0+h/2,x_0+f2 * h/2)\n", + " f4 = ode_func(t_0+h, x_0+ f3*h)\n", + " return x_0 + (h/6) * (f1 + 2*f2 + 2*f3+f4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "K-OxE9E6oqew" + }, + "outputs": [], + "source": [ + "run_and_plot(ode_lin_homog, None, None, ode_lin_homog_soln, runge_kutta_4_step)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7JifxBhhoqew" + }, + "source": [ + "# Plot the error as a function of step size" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ZoEpmlCfsi9P" + }, + "outputs": [], + "source": [ + "# Run systematically with a number of different step sizes and store errors for each\n", + "def get_errors(ode, ode_deriv_x, ode_deriv_t, ode_solution, step_function):\n", + " # Choose the step size h to divide the plotting interval into 1,2,4,8... segments.\n", + " # The plots in the article add a few more smaller step sizes, but this takes a while to compute.\n", + " # Add them back in if you want the full plot.\n", + " all_h = (1./np.array([1,2,4,8,16,32,64,128,256,512,1024,2048,4096])).tolist()\n", + " all_err = []\n", + "\n", + " for i in range(len(all_h)):\n", + " t_sim,x_sim,err = run_numerical(0.5, 0.0, 4.0, all_h[i], ode, ode_deriv_x, ode_deriv_t, ode_solution, step_function)\n", + " all_err = all_err + [err]\n", + "\n", + " return all_h, all_err" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "X0O0KK47xF28" + }, + "outputs": [], + "source": [ + "# Plot the errors\n", + "all_h, all_err_euler = get_errors(ode_lin_homog, ode_lin_homog_deriv_x, ode_lin_homog_deriv_t, ode_lin_homog_soln, euler_step)\n", + "all_h, all_err_heun = get_errors(ode_lin_homog, ode_lin_homog_deriv_x, ode_lin_homog_deriv_t, ode_lin_homog_soln, heun_step)\n", + "all_h, all_err_mod_euler = get_errors(ode_lin_homog, ode_lin_homog_deriv_x, ode_lin_homog_deriv_t, ode_lin_homog_soln, modified_euler_step)\n", + "all_h, all_err_taylor = get_errors(ode_lin_homog, ode_lin_homog_deriv_x, ode_lin_homog_deriv_t, ode_lin_homog_soln, taylor_2nd_order)\n", + "all_h, all_err_rk = get_errors(ode_lin_homog, ode_lin_homog_deriv_x, ode_lin_homog_deriv_t, ode_lin_homog_soln, runge_kutta_4_step)\n", + "\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.loglog(all_h, all_err_euler,'ro-')\n", + "ax.loglog(all_h, all_err_heun,'bo-')\n", + "ax.loglog(all_h, all_err_mod_euler,'go-')\n", + "ax.loglog(all_h, all_err_taylor,'co-')\n", + "ax.loglog(all_h, all_err_rk,'mo-')\n", + "ax.set_ylim(1e-13,1e1)\n", + "ax.set_xlim(1e-6,1e1)\n", + "ax.set_aspect(0.5)\n", + "ax.set_xlabel('Step size, $h$')\n", + "ax.set_ylabel('Error')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BttOqpeo9MsJ" + }, + "source": [ + "Note that for this ODE, the Heun, Modified Euler and Taylor methods provide EXACTLY the same updates, and so the error curves for all three are identical (subject to difference is numerical rounding errors). This is not in general the case, although the general trend would be the same for each." + ] + } + ], + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file From 49d74b66a99000fd1bef6ad515146d485f8ba401 Mon Sep 17 00:00:00 2001 From: udlbook <110402648+udlbook@users.noreply.github.com> Date: Sun, 16 Feb 2025 10:25:23 -0500 Subject: [PATCH 19/29] Created using Colab --- Notebooks/Chap07/7_2_Backpropagation.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Notebooks/Chap07/7_2_Backpropagation.ipynb b/Notebooks/Chap07/7_2_Backpropagation.ipynb index 796d8a7..40975f9 100644 --- a/Notebooks/Chap07/7_2_Backpropagation.ipynb +++ b/Notebooks/Chap07/7_2_Backpropagation.ipynb @@ -253,7 +253,7 @@ " # REPLACE THIS LINE\n", " all_dl_dbiases[layer] = np.zeros_like(all_biases[layer])\n", "\n", - " # TODO Calculate the derivatives of the loss with respect to the weights at layer from all_dl_df[layer] and all_h[layer] (eq 7.22)\n", + " # TODO Calculate the derivatives of the loss with respect to the weights at layer from all_dl_df[layer] and all_h[layer] (eq 7.23)\n", " # Don't forget to use np.matmul\n", " # REPLACE THIS LINE\n", " all_dl_dweights[layer] = np.zeros_like(all_weights[layer])\n", From 2cca6dec75bb1f4a60e0b7f7d2e1196c0875f92a Mon Sep 17 00:00:00 2001 From: Fred Hsu Date: Thu, 27 Feb 2025 15:39:46 -0800 Subject: [PATCH 20/29] Update 7_2_Backpropagation.ipynb to fix equation references Some off by one errors in the equation references. --- Notebooks/Chap07/7_2_Backpropagation.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/Notebooks/Chap07/7_2_Backpropagation.ipynb b/Notebooks/Chap07/7_2_Backpropagation.ipynb index 40975f9..272e269 100644 --- a/Notebooks/Chap07/7_2_Backpropagation.ipynb +++ b/Notebooks/Chap07/7_2_Backpropagation.ipynb @@ -248,7 +248,7 @@ "\n", " # Now work backwards through the network\n", " for layer in range(K,-1,-1):\n", - " # TODO Calculate the derivatives of the loss with respect to the biases at layer from all_dl_df[layer]. (eq 7.21)\n", + " # TODO Calculate the derivatives of the loss with respect to the biases at layer from all_dl_df[layer]. (eq 7.22)\n", " # NOTE! To take a copy of matrix X, use Z=np.array(X)\n", " # REPLACE THIS LINE\n", " all_dl_dbiases[layer] = np.zeros_like(all_biases[layer])\n", @@ -258,13 +258,13 @@ " # REPLACE THIS LINE\n", " all_dl_dweights[layer] = np.zeros_like(all_weights[layer])\n", "\n", - " # TODO: calculate the derivatives of the loss with respect to the activations from weight and derivatives of next preactivations (second part of last line of eq 7.24)\n", + " # TODO: calculate the derivatives of the loss with respect to the activations from weight and derivatives of next preactivations (second part of last line of eq 7.25)\n", " # REPLACE THIS LINE\n", " all_dl_dh[layer] = np.zeros_like(all_h[layer])\n", "\n", "\n", " if layer > 0:\n", - " # TODO Calculate the derivatives of the loss with respect to the pre-activation f (use derivative of ReLu function, first part of last line of eq. 7.24)\n", + " # TODO Calculate the derivatives of the loss with respect to the pre-activation f (use derivative of ReLu function, first part of last line of eq. 7.25)\n", " # REPLACE THIS LINE\n", " all_dl_df[layer-1] = np.zeros_like(all_f[layer-1])\n", "\n", @@ -352,4 +352,4 @@ "outputs": [] } ] -} \ No newline at end of file +} From 6c99c6b7eb2347570eaf18c245eece147e4b67c8 Mon Sep 17 00:00:00 2001 From: udlbook <110402648+udlbook@users.noreply.github.com> Date: Tue, 4 Mar 2025 14:31:39 -0500 Subject: [PATCH 21/29] Created using Colab --- .../Chap17/17_3_Importance_Sampling.ipynb | 34 ++++++------------- 1 file changed, 11 insertions(+), 23 deletions(-) diff --git a/Notebooks/Chap17/17_3_Importance_Sampling.ipynb b/Notebooks/Chap17/17_3_Importance_Sampling.ipynb index da920b4..402460f 100644 --- a/Notebooks/Chap17/17_3_Importance_Sampling.ipynb +++ b/Notebooks/Chap17/17_3_Importance_Sampling.ipynb @@ -1,18 +1,16 @@ { "cells": [ { - "attachments": {}, "cell_type": "markdown", "metadata": { - "colab_type": "text", - "id": "view-in-github" + "id": "view-in-github", + "colab_type": "text" }, "source": [ "\"Open" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "t9vk9Elugvmi" @@ -40,7 +38,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "f7a6xqKjkmvT" @@ -126,7 +123,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "Jr4UPcqmnXCS" @@ -166,8 +162,8 @@ "mean_all = np.zeros_like(n_sample_all)\n", "variance_all = np.zeros_like(n_sample_all)\n", "for i in range(len(n_sample_all)):\n", - " print(\"Computing mean and variance for expectation with %d samples\"%(n_sample_all[i]))\n", - " mean_all[i],variance_all[i] = compute_mean_variance(n_sample_all[i])" + " mean_all[i],variance_all[i] = compute_mean_variance(n_sample_all[i])\n", + " print(\"No samples: \", n_sample_all[i], \", Mean: \", mean_all[i], \", Variance: \", variance_all[i])" ] }, { @@ -189,7 +185,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "XTUpxFlSuOl7" @@ -199,7 +194,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "6hxsl3Pxo1TT" @@ -234,7 +228,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "G9Xxo0OJsIqD" @@ -283,7 +276,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "2sVDqP0BvxqM" @@ -313,8 +305,8 @@ "mean_all2 = np.zeros_like(n_sample_all)\n", "variance_all2 = np.zeros_like(n_sample_all)\n", "for i in range(len(n_sample_all)):\n", - " print(\"Computing variance for expectation with %d samples\"%(n_sample_all[i]))\n", - " mean_all2[i], variance_all2[i] = compute_mean_variance2(n_sample_all[i])" + " mean_all2[i], variance_all2[i] = compute_mean_variance2(n_sample_all[i])\n", + " print(\"No samples: \", n_sample_all[i], \", Mean: \", mean_all2[i], \", Variance: \", variance_all2[i])" ] }, { @@ -348,7 +340,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "EtBP6NeLwZqz" @@ -360,7 +351,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "_wuF-NoQu1--" @@ -432,8 +422,8 @@ "mean_all2b = np.zeros_like(n_sample_all)\n", "variance_all2b = np.zeros_like(n_sample_all)\n", "for i in range(len(n_sample_all)):\n", - " print(\"Computing variance for expectation with %d samples\"%(n_sample_all[i]))\n", - " mean_all2b[i], variance_all2b[i] = compute_mean_variance2b(n_sample_all[i])" + " mean_all2b[i], variance_all2b[i] = compute_mean_variance2b(n_sample_all[i])\n", + " print(\"No samples: \", n_sample_all[i], \", Mean: \", mean_all2b[i], \", Variance: \", variance_all2b[i])" ] }, { @@ -478,7 +468,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "y8rgge9MNiOc" @@ -490,9 +479,8 @@ ], "metadata": { "colab": { - "authorship_tag": "ABX9TyNecz9/CDOggPSmy1LjT/Dv", - "include_colab_link": true, - "provenance": [] + "provenance": [], + "include_colab_link": true }, "kernelspec": { "display_name": "Python 3", @@ -504,4 +492,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} +} \ No newline at end of file From 2c6e1cb9f89119aecc875c29cf8f2bac04e501bd Mon Sep 17 00:00:00 2001 From: udlbook <110402648+udlbook@users.noreply.github.com> Date: Tue, 4 Mar 2025 16:32:31 -0500 Subject: [PATCH 22/29] Created using Colab --- Trees/SAT_Exhaustive_Answers.ipynb | 250 +++++++++++++++++++++++++++++ 1 file changed, 250 insertions(+) create mode 100644 Trees/SAT_Exhaustive_Answers.ipynb diff --git a/Trees/SAT_Exhaustive_Answers.ipynb b/Trees/SAT_Exhaustive_Answers.ipynb new file mode 100644 index 0000000..a71f255 --- /dev/null +++ b/Trees/SAT_Exhaustive_Answers.ipynb @@ -0,0 +1,250 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyM5J6fIsz12gt3/KyELcoXi", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "" + ], + "metadata": { + "id": "9Qgup23vwdll" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Boolean logic formulae and exhaustive SAT algorithms\n", + "\n", + "The purpose of this Python notebook experiment with an exhuastive algorithm for SAT solving.\n", + "\n", + "You can save a local copy of this notebook in your Google account and work through it in Colab (recommended) or you can download the notebook and run it locally using Jupyter notebook or similar. \n", + "\n", + "Contact me at iclimbtreesmail@gmail.com if you find any mistakes or have any suggestions." + ], + "metadata": { + "id": "uORlKyPv02ge" + } + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "bbF6SE_F0tU8" + }, + "outputs": [], + "source": [ + "# Math library\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Boolean operators\n", + "\n", + "First, let's write some functions for the five Boolean operators discussed in the unit. " + ], + "metadata": { + "id": "8z_AvPoU6zck" + } + }, + { + "cell_type": "code", + "source": [ + "def or_op(x_1, x_2):\n", + " return x_1 or x_2 ;\n", + "\n", + "def and_op(x_1, x_2):\n", + " return x_1 and x_2\n", + "\n", + "def imp_op(x_1, x_2):\n", + " return not x_1 or x_2\n", + "\n", + "def equiv_op(x_1, x_2):\n", + " return x_1==x_2\n", + "\n", + "def not_op(x_1):\n", + " return not x_1" + ], + "metadata": { + "id": "ogfjSL857Dlo" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Example Boolean Logic Formula\n", + "\n", + "Now let's define and implement a Boolean logic formula. We'll use the one from the text.\n", + "\n", + "$$\\phi:= \\bigl(x_{1}\\Rightarrow (\\lnot x_{2}\\land x_{3})\\bigr) \\land \\bigl(x_{2} \\Leftrightarrow (\\lnot x_{3} \\lor x_{1})\\bigr).$$" + ], + "metadata": { + "id": "e3a1Ps1D8kUH" + } + }, + { + "cell_type": "code", + "source": [ + "def phi(x_1, x_2, x_3):\n", + " phi_val = and_op(imp_op(x_1, and_op(not_op(x_2), x_3)),equiv_op(x_2, or_op(not_op(x_3), x_1)))\n", + " print(\"phi(\",x_1,\",\",x_2,\",\",x_3,\")=\",phi_val)\n", + " return phi_val" + ], + "metadata": { + "id": "yBPbxHTL8xOl" + }, + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Exhaustive SAT\n", + "\n", + "Now let's implement an exhaustive SAT solver. For each possible combination of $x_1$, $x_2$, and $x_3$, we evaluate the Boolean logic formula $\\phi$. If it evaluates to **true** for any of these combinations then the function is **SAT**. Otherwise, it is **UNSAT**." + ], + "metadata": { + "id": "OeRG0RB-9HP4" + } + }, + { + "cell_type": "code", + "source": [ + "def exhaustive_3(phi):\n", + " for t in range (np.power(2,3)):\n", + " x_1 = (t % 2) >= 1\n", + " x_2 = (t % 4) >= 2\n", + " x_3 = (t % 8) >= 4\n", + " phi_val = phi(x_1, x_2, x_3)\n", + " if phi_val:\n", + " print(\"SAT\")\n", + " return\n", + " print(\"UNSAT\")" + ], + "metadata": { + "id": "QBj3Ap3t9CVO" + }, + "execution_count": 14, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "exhaustive_3(phi)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "FJGoau6C_fWP", + "outputId": "a841116e-8e8a-400f-a560-b52c670e6b0a" + }, + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "phi( False , False , False )= False\n", + "phi( True , False , False )= False\n", + "phi( False , True , False )= True\n", + "SAT\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "How about if we add another constraint (by logically ANDing another term)?\n", + "\n", + "$$\\phi_1:= \\bigl(x_{1}\\Rightarrow (\\lnot x_{2}\\land x_{3})\\bigr) \\land \\bigl(x_{2} \\Leftrightarrow (\\lnot x_{3} \\lor x_{1})\\bigr)\\land\\lnot \\bigl(\\lnot x_3 \\Rightarrow x_2\\bigr).$$" + ], + "metadata": { + "id": "nnHxv9IHB185" + } + }, + { + "cell_type": "code", + "source": [ + "def phi_1(x_1, x_2, x_3):\n", + " phi_val = and_op(and_op(imp_op(x_1, and_op(not_op(x_2), x_3)),equiv_op(x_2, or_op(not_op(x_3), x_1))),not_op(imp_op(not_op(x_3), x_2)))\n", + " print(\"phi(\",x_1,\",\",x_2,\",\",x_3,\")=\",phi_val)\n", + " return phi_val\n" + ], + "metadata": { + "id": "T13sNtofBuLs" + }, + "execution_count": 24, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "exhaustive_3(phi_1)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qCm_ZdIgCv_P", + "outputId": "f1cbb656-9f4d-4b68-b347-876ada5fb926" + }, + "execution_count": 25, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "phi( False , False , False )= False\n", + "phi( True , False , False )= False\n", + "phi( False , True , False )= False\n", + "phi( True , True , False )= False\n", + "phi( False , False , True )= False\n", + "phi( True , False , True )= False\n", + "phi( False , True , True )= False\n", + "phi( True , True , True )= False\n", + "UNSAT\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "You should fined that this is **UNSAT**. We have added too many constraints and the three terms that are **AND**ed together are never simultaneously true." + ], + "metadata": { + "id": "nlsUlJQfDB14" + } + } + ] +} \ No newline at end of file From 41e8262f2053a8673759164efc421261acd5a3e6 Mon Sep 17 00:00:00 2001 From: udlbook <110402648+udlbook@users.noreply.github.com> Date: Tue, 4 Mar 2025 16:39:17 -0500 Subject: [PATCH 23/29] Created using Colab --- Trees/SAT_Exhaustive.ipynb | 248 +++++++++++++++++++++++++++++++++++++ 1 file changed, 248 insertions(+) create mode 100644 Trees/SAT_Exhaustive.ipynb diff --git a/Trees/SAT_Exhaustive.ipynb b/Trees/SAT_Exhaustive.ipynb new file mode 100644 index 0000000..a1adffc --- /dev/null +++ b/Trees/SAT_Exhaustive.ipynb @@ -0,0 +1,248 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyMp2p2z+xKwT972x7JpBEo5", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "" + ], + "metadata": { + "id": "9Qgup23vwdll" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Boolean logic formulae and exhaustive SAT algorithms\n", + "\n", + "The purpose of this Python notebook experiment with an exhuastive algorithm for SAT solving.\n", + "\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and write code to complete the functions.\n", + "\n", + "You can save a local copy of this notebook in your Google account and work through it in Colab (recommended) or you can download the notebook and run it locally using Jupyter notebook or similar. If you are using CoLab, we recommend that turn off AI autocomplete (under cog icon in top-right corner), which will give you the answers and defeat the purpose of the exercise.\n", + "\n", + "A fully working version of this notebook with the complete answers can be found [here](https://colab.research.google.com/github/udlbook/udlbook/blob/main/Trees/SAT_Exhaustive_Answers.ipynb).\n", + "\n", + "Contact me at iclimbtreesmail@gmail.com if you find any mistakes or have any suggestions." + ], + "metadata": { + "id": "uORlKyPv02ge" + } + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "bbF6SE_F0tU8" + }, + "outputs": [], + "source": [ + "# Math library\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Boolean operators\n", + "\n", + "First, let's write some functions for the five Boolean operators discussed in the unit. " + ], + "metadata": { + "id": "8z_AvPoU6zck" + } + }, + { + "cell_type": "code", + "source": [ + "def or_op(x_1, x_2):\n", + " # TODO: write this function\n", + " return False ;\n", + "\n", + "def and_op(x_1, x_2):\n", + " # TODO: write this function\n", + " return False\n", + "\n", + "def imp_op(x_1, x_2):\n", + " # TODO: write this function\n", + " return False\n", + "\n", + "def equiv_op(x_1, x_2):\n", + " # TODO: write this function\n", + " return False\n", + "\n", + "def not_op(x_1):\n", + " # TODO: write this function\n", + " return False" + ], + "metadata": { + "id": "ogfjSL857Dlo" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Example Boolean Logic Formula\n", + "\n", + "Now let's define and implement a Boolean logic formula. We'll use the one from the text.\n", + "\n", + "$$\\phi:= \\bigl(x_{1}\\Rightarrow (\\lnot x_{2}\\land x_{3})\\bigr) \\land \\bigl(x_{2} \\Leftrightarrow (\\lnot x_{3} \\lor x_{1})\\bigr).$$" + ], + "metadata": { + "id": "e3a1Ps1D8kUH" + } + }, + { + "cell_type": "code", + "source": [ + "def phi(x_1, x_2, x_3):\n", + " # TODO: write this function\n", + " # Replace the line below\n", + " phi_val = False\n", + " print(\"phi(\",x_1,\",\",x_2,\",\",x_3,\")=\",phi_val)\n", + " return phi_val" + ], + "metadata": { + "id": "yBPbxHTL8xOl" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Exhaustive SAT\n", + "\n", + "Now let's implement an exhaustive SAT solver. For each possible combination of $x_1$, $x_2$, and $x_3$, we evaluate the Boolean logic formula $\\phi$. If it evaluates to **true** for any of these combinations then the function is **SAT**. Otherwise, it is **UNSAT**." + ], + "metadata": { + "id": "OeRG0RB-9HP4" + } + }, + { + "cell_type": "code", + "source": [ + "def exhaustive_3(phi):\n", + " # TODO: write this function\n", + " # You can use three loops or do this in a cleverer way if you know how\n", + " # Replace this line\n", + "\n", + " print(\"UNSAT\")" + ], + "metadata": { + "id": "QBj3Ap3t9CVO" + }, + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "exhaustive_3(phi)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "FJGoau6C_fWP", + "outputId": "589979b2-857c-4ff9-f358-d93a008184a1" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "UNSAT\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "How about if we add another constraint (by logically ANDing another term)?\n", + "\n", + "$$\\phi_1:= \\bigl(x_{1}\\Rightarrow (\\lnot x_{2}\\land x_{3})\\bigr) \\land \\bigl(x_{2} \\Leftrightarrow (\\lnot x_{3} \\lor x_{1})\\bigr)\\land\\lnot \\bigl(\\lnot x_3 \\Rightarrow x_2\\bigr).$$" + ], + "metadata": { + "id": "nnHxv9IHB185" + } + }, + { + "cell_type": "code", + "source": [ + "def phi_1(x_1, x_2, x_3):\n", + " # TODO: write this function\n", + " # Replace this line\n", + " phi_val = False\n", + " print(\"phi(\",x_1,\",\",x_2,\",\",x_3,\")=\",phi_val)\n", + " return phi_val\n" + ], + "metadata": { + "id": "T13sNtofBuLs" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "exhaustive_3(phi_1)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qCm_ZdIgCv_P", + "outputId": "642ca068-8c3a-4071-9d3c-e62de00c7f37" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "UNSAT\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "You should fined that this is **UNSAT**. We have added too many constraints and the three terms that are **AND**ed together are never simultaneously true." + ], + "metadata": { + "id": "nlsUlJQfDB14" + } + } + ] +} \ No newline at end of file From 03ebe5a039d94b7df24fd9ff48d529ac26d6531a Mon Sep 17 00:00:00 2001 From: ullizen Date: Sat, 8 Mar 2025 10:52:03 +0100 Subject: [PATCH 24/29] Update 4_2_Clipping_functions.ipynb --- Notebooks/Chap04/4_2_Clipping_functions.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Notebooks/Chap04/4_2_Clipping_functions.ipynb b/Notebooks/Chap04/4_2_Clipping_functions.ipynb index 87ecab2..e271244 100644 --- a/Notebooks/Chap04/4_2_Clipping_functions.ipynb +++ b/Notebooks/Chap04/4_2_Clipping_functions.ipynb @@ -169,7 +169,7 @@ { "cell_type": "code", "source": [ - "# Define parameters (note first dimension of theta and phi is padded to make indices match\n", + "# Define parameters (note first dimension of theta and psi is padded to make indices match\n", "# notation in book)\n", "theta = np.zeros([4,2])\n", "psi = np.zeros([4,4])\n", From 16afbcdf83e66855f61e1dc2e837e912c690c7ea Mon Sep 17 00:00:00 2001 From: udlbook <110402648+udlbook@users.noreply.github.com> Date: Mon, 24 Mar 2025 15:35:15 -0400 Subject: [PATCH 25/29] Created using Colab --- Notebooks/Chap19/19_2_Dynamic_Programming.ipynb | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/Notebooks/Chap19/19_2_Dynamic_Programming.ipynb b/Notebooks/Chap19/19_2_Dynamic_Programming.ipynb index 844c376..f326f6b 100644 --- a/Notebooks/Chap19/19_2_Dynamic_Programming.ipynb +++ b/Notebooks/Chap19/19_2_Dynamic_Programming.ipynb @@ -4,7 +4,6 @@ "metadata": { "colab": { "provenance": [], - "authorship_tag": "ABX9TyOlD6kmCxX3SKKuh3oJikKA", "include_colab_link": true }, "kernelspec": { @@ -406,6 +405,10 @@ " state_values_new[state] = 3.0\n", " break\n", "\n", + " # TODO -- Write this function (from equation 19.11, but bear in mind policy is deterministic here)\n", + " # Replace this line\n", + " state_values_new[state] = 0\n", + "\n", " return state_values_new\n", "\n", "# Greedily choose the action that maximizes the value for each state.\n", @@ -527,4 +530,4 @@ } } ] -} +} \ No newline at end of file From 0b41646bf39436feef85fa310b5ff81e31ff7673 Mon Sep 17 00:00:00 2001 From: udlbook <110402648+udlbook@users.noreply.github.com> Date: Thu, 27 Mar 2025 12:57:57 -0400 Subject: [PATCH 26/29] Add files via upload --- UDL_Answer_Booklet_Students.pdf | Bin 1795499 -> 1797084 bytes UDL_Errata.pdf | Bin 1250693 -> 1262597 bytes 2 files changed, 0 insertions(+), 0 deletions(-) diff --git a/UDL_Answer_Booklet_Students.pdf b/UDL_Answer_Booklet_Students.pdf index 1cf566a6b266970afe3cc806df526a4b0612b80a..962ec34a20ca93186cf438b0a9c51180ccb07e22 100644 GIT binary patch delta 57876 zcmZttWl&y0vo#C{cPF@9xVyW%ySux)>=4}L!rdhh2<{f#Aq01q;KB9HeeUyBeW&XA zv#VyRr}wO$nwp;JwW=qa)|Z^@s8l7Tnb?@Q5UF|}ir*1=*x5m>AQy9cL_t9ii=v&g zm6^Mpzm+A3?OzMT#>2|tbiwcN`og2g=1>*YG%>iOj 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ddd6bf91494e69c79ee26852290795184dae1afc Mon Sep 17 00:00:00 2001 From: udlbook <110402648+udlbook@users.noreply.github.com> Date: Thu, 27 Mar 2025 17:47:29 -0400 Subject: [PATCH 27/29] Created using Colab --- Trees/SAT_Tseitin_Answers.ipynb | 310 ++++++++++++++++++++++++++++++++ 1 file changed, 310 insertions(+) create mode 100644 Trees/SAT_Tseitin_Answers.ipynb diff --git a/Trees/SAT_Tseitin_Answers.ipynb b/Trees/SAT_Tseitin_Answers.ipynb new file mode 100644 index 0000000..2138421 --- /dev/null +++ b/Trees/SAT_Tseitin_Answers.ipynb @@ -0,0 +1,310 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyMPpuwCJTE40oMgkTfDWCrM", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "" + ], + "metadata": { + "id": "9Qgup23vwdll" + } + }, + { + "cell_type": "markdown", + "source": [ + "# The Tseitin transformation\n", + "\n", + "The purpose of this Python notebook is to check that the Tseitin transformation example in the text is correct. Once we have the formula in conjunctive normal form, we can use the Python SAT library Z3 to solve SAT problems.\n", + "\n", + "You can save a local copy of this notebook in your Google account and work through it in Colab (recommended) or you can download the notebook and run it locally using Jupyter notebook or similar.\n", + "\n", + "Contact me at iclimbtreesmail@gmail.com if you find any mistakes or have any suggestions." + ], + "metadata": { + "id": "uORlKyPv02ge" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bbF6SE_F0tU8" + }, + "outputs": [], + "source": [ + "# Math library\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Boolean operators\n", + "\n", + "First, let's write some functions for the five Boolean operators discussed in the unit. " + ], + "metadata": { + "id": "8z_AvPoU6zck" + } + }, + { + "cell_type": "code", + "source": [ + "def or_op(x_1, x_2):\n", + " return x_1 or x_2 ;\n", + "\n", + "def and_op(x_1, x_2):\n", + " return x_1 and x_2\n", + "\n", + "def imp_op(x_1, x_2):\n", + " return not x_1 or x_2\n", + "\n", + "def equiv_op(x_1, x_2):\n", + " return x_1==x_2\n", + "\n", + "def not_op(x_1):\n", + " return not x_1" + ], + "metadata": { + "id": "ogfjSL857Dlo" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Example Boolean Logic Formula\n", + "\n", + "Now let's define and implement a Boolean logic formula. We'll use the one from the Tseitin transormation example in the text.\n", + "\n", + "$$\\phi:= ((x_{1} \\lor x_{2}) \\Leftrightarrow x_{3}) \\Rightarrow (\\lnot x_{4}).$$" + ], + "metadata": { + "id": "e3a1Ps1D8kUH" + } + }, + { + "cell_type": "code", + "source": [ + "def phi(x_1, x_2, x_3, x_4):\n", + " phi_val = imp_op(equiv_op(or_op(x_1, x_2), x_3), not_op(x_4))\n", + " return phi_val" + ], + "metadata": { + "id": "yBPbxHTL8xOl" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Exhaustive SAT\n", + "\n", + "Now let's implement an exhaustive SAT solver. For each possible combination of $x_1$, $x_2$, and $x_3$, we evaluate the Boolean logic formula $\\phi$. If it evaluates to **true** for any of these combinations then the function is **SAT**. Otherwise, it is **UNSAT**." + ], + "metadata": { + "id": "OeRG0RB-9HP4" + } + }, + { + "cell_type": "code", + "source": [ + "def exhaustive_3(phi):\n", + " is_sat = False\n", + " for t in range (np.power(2,4)):\n", + " x_1 = (t % 2) >= 1\n", + " x_2 = (t % 4) >= 2\n", + " x_3 = (t % 8) >= 4\n", + " x_4 = (t % 16) >= 8\n", + " this_phi = phi(x_1, x_2, x_3,x_4)\n", + " print(\"phi(\",x_1,\",\",x_2,\",\",x_3,\",\",x_4,\")=\",this_phi)\n", + "\n", + " is_sat = is_sat or this_phi\n", + "\n", + " if is_sat:\n", + " print(\"Phi is SAT\")\n", + " else:\n", + " print(\"Phi is UNSAT\")" + ], + "metadata": { + "id": "QBj3Ap3t9CVO" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "exhaustive_3(phi)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "FJGoau6C_fWP", + "outputId": "b3ace7c5-b08a-4959-9318-774d909ef95c" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "phi( False , False , False , False )= True\n", + "phi( True , False , False , False )= True\n", + "phi( False , True , False , False )= True\n", + "phi( True , True , False , False )= True\n", + "phi( False , False , True , False )= True\n", + "phi( True , False , True , False )= True\n", + "phi( False , True , True , False )= True\n", + "phi( True , True , True , False )= True\n", + "phi( False , False , False , True )= False\n", + "phi( True , False , False , True )= True\n", + "phi( False , True , False , True )= True\n", + "phi( True , True , False , True )= True\n", + "phi( False , False , True , True )= True\n", + "phi( True , False , True , True )= False\n", + "phi( False , True , True , True )= False\n", + "phi( True , True , True , True )= False\n", + "Phi is SAT\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "This is the same formula expressed in conjunctive normal form (see main text). The variables $y_1, y_2, y_3, y_4$ are extra terms that were needed to do this conversion. Their final values do not matter.\n", + "\n", + "$$\\begin{align}\\phi_2&:= y_{4} \\land (y_4\\lor y_2) \\land (y_4 \\lor \\lnot y_3) \\land (\\lnot y_4 \\lor \\lnot y_2 \\lor y_3)\\nonumber \\\\\n", + "&\\hspace{0.4cm}\\land (y_3 \\lor x_4) \\land (\\lnot y_3 \\lor \\lnot x_4)\\nonumber\\\\\n", + "& \\hspace{0.4cm}\\land (\\lnot y_2 \\lor \\lnot y_1 \\lor x_3)\\land (\\lnot y_2 \\lor y_1 \\lor \\lnot x_3) \\land (y_2 \\lor \\lnot y_1 \\lor \\lnot x_3) \\land (y_2\\lor y_1\\lor x_3)\\nonumber \\\\&\n", + "\\hspace{0.4cm}\\land (y_1\\lor \\lnot x_1) \\land (y_1 \\lor \\lnot x_2) \\land (\\lnot y_1 \\lor x_1 \\lor x_2). \\end{align}$$" + ], + "metadata": { + "id": "MSTx8YXlmycP" + } + }, + { + "cell_type": "code", + "source": [ + "def phi_2(x_1, x_2, x_3, x_4, y_1, y_2, y_3, y_4):\n", + " return y_4 and (y_4 or y_2) and (y_4 or not y_3) and (not y_4 or not y_2 or y_3) \\\n", + " and (y_3 or x_4) and (not y_3 or not x_4) and (not y_2 or not y_1 or x_3) and (not y_2 or y_1 or not x_3) \\\n", + " and (y_2 or not y_1 or not x_3) and (y_2 or y_1 or x_3) and (y_1 or not x_1) and (y_1 or not x_2) and (not y_1 or x_1 or x_2)" + ], + "metadata": { + "id": "z7wZRlrjnpK9" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def exhaustive_8(phi):\n", + " is_sat = False\n", + " for t in range (np.power(2,4)):\n", + " x_1 = (t % 2) >= 1\n", + " x_2 = (t % 4) >= 2\n", + " x_3 = (t % 8) >= 4\n", + " x_4 = (t % 16) >= 8\n", + " this_phi = False ;\n", + " for t2 in range (np.power(2,4)):\n", + " y_1 = (t2 % 2) >= 1\n", + " y_2 = (t2 % 4) >= 2\n", + " y_3 = (t2 % 8) >= 4\n", + " y_4 = (t2 % 16) >= 8\n", + " this_phi = this_phi or phi_2(x_1, x_2, x_3, x_4, y_1, y_2, y_3, y_4)\n", + " print(\"phi(\",x_1,\",\",x_2,\",\",x_3,\",\",x_4,\")=\",this_phi)\n", + " is_sat = is_sat or this_phi\n", + "\n", + " if is_sat:\n", + " print(\"Phi is SAT\")\n", + " else:\n", + " print(\"Phi is UNSAT\")" + ], + "metadata": { + "id": "YZNCyXCGmiiG" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "exhaustive_8(phi_2)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qCm_ZdIgCv_P", + "outputId": "49c3beb1-4a59-4bc8-a69b-61802e3986ed" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "phi( False , False , False , False )= True\n", + "phi( True , False , False , False )= True\n", + "phi( False , True , False , False )= True\n", + "phi( True , True , False , False )= True\n", + "phi( False , False , True , False )= True\n", + "phi( True , False , True , False )= True\n", + "phi( False , True , True , False )= True\n", + "phi( True , True , True , False )= True\n", + "phi( False , False , False , True )= False\n", + "phi( True , False , False , True )= True\n", + "phi( False , True , False , True )= True\n", + "phi( True , True , False , True )= True\n", + "phi( False , False , True , True )= True\n", + "phi( True , False , True , True )= False\n", + "phi( False , True , True , True )= False\n", + "phi( True , True , True , True )= False\n", + "Phi is SAT\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "You can see that the truth tables are the same for both the original and Tseitin transformed formulae. The two formulations are equivalent. Notice thought that we pay a cost for this simplification in that we have to add four more variables $y_1, y_2, y_3, y_4$ and so potentially have to iterate over 16 times the number of combinations." + ], + "metadata": { + "id": "iyIYwgwvhUXO" + } + } + ] +} \ No newline at end of file From a637eec8880a27ab9f4f8abe2af02aae352f0321 Mon Sep 17 00:00:00 2001 From: udlbook <110402648+udlbook@users.noreply.github.com> Date: Thu, 27 Mar 2025 17:52:22 -0400 Subject: [PATCH 28/29] Created using Colab --- Trees/SAT_Tseitin.ipynb | 251 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 251 insertions(+) create mode 100644 Trees/SAT_Tseitin.ipynb diff --git a/Trees/SAT_Tseitin.ipynb b/Trees/SAT_Tseitin.ipynb new file mode 100644 index 0000000..fc279b8 --- /dev/null +++ b/Trees/SAT_Tseitin.ipynb @@ -0,0 +1,251 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyO30jtLgfo4lSQLEOV3NJw3", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "" + ], + "metadata": { + "id": "9Qgup23vwdll" + } + }, + { + "cell_type": "markdown", + "source": [ + "# The Tseitin transformation\n", + "\n", + "The purpose of this Python notebook is to check that the Tseitin transformation example in the text is correct. Once we have the formula in conjunctive normal form, we can use the Python SAT library Z3 to solve SAT problems.\n", + "\n", + "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and write code to complete the functions.\n", + "\n", + "You can save a local copy of this notebook in your Google account and work through it in Colab (recommended) or you can download the notebook and run it locally using Jupyter notebook or similar. If you are using CoLab, we recommend that turn off AI autocomplete (under cog icon in top-right corner), which will give you the answers and defeat the purpose of the exercise.\n", + "\n", + "A fully working version of this notebook with the complete answers can be found [here](https://colab.research.google.com/github/udlbook/udlbook/blob/main/Trees/SAT_Tseitin_Answers.ipynb).\n", + "\n", + "Contact me at iclimbtreesmail@gmail.com if you find any mistakes or have any suggestions." + ], + "metadata": { + "id": "uORlKyPv02ge" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bbF6SE_F0tU8" + }, + "outputs": [], + "source": [ + "# Math library\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Boolean operators\n", + "\n", + "First, let's write some functions for the five Boolean operators discussed in the unit. " + ], + "metadata": { + "id": "8z_AvPoU6zck" + } + }, + { + "cell_type": "code", + "source": [ + "def or_op(x_1, x_2):\n", + " return x_1 or x_2 ;\n", + "\n", + "def and_op(x_1, x_2):\n", + " return x_1 and x_2\n", + "\n", + "def imp_op(x_1, x_2):\n", + " return not x_1 or x_2\n", + "\n", + "def equiv_op(x_1, x_2):\n", + " return x_1==x_2\n", + "\n", + "def not_op(x_1):\n", + " return not x_1" + ], + "metadata": { + "id": "ogfjSL857Dlo" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Example Boolean Logic Formula\n", + "\n", + "Now let's define and implement a Boolean logic formula. We'll use the one from the Tseitin transormation example in the text.\n", + "\n", + "$$\\phi:= ((x_{1} \\lor x_{2}) \\Leftrightarrow x_{3}) \\Rightarrow (\\lnot x_{4}).$$" + ], + "metadata": { + "id": "e3a1Ps1D8kUH" + } + }, + { + "cell_type": "code", + "source": [ + "def phi(x_1, x_2, x_3, x_4):\n", + " phi_val = imp_op(equiv_op(or_op(x_1, x_2), x_3), not_op(x_4))\n", + " return phi_val" + ], + "metadata": { + "id": "yBPbxHTL8xOl" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Exhaustive SAT\n", + "\n", + "Now let's implement an exhaustive SAT solver. For each possible combination of $x_1$, $x_2$, and $x_3$, we evaluate the Boolean logic formula $\\phi$. If it evaluates to **true** for any of these combinations then the function is **SAT**. Otherwise, it is **UNSAT**." + ], + "metadata": { + "id": "OeRG0RB-9HP4" + } + }, + { + "cell_type": "code", + "source": [ + "def exhaustive_3(phi):\n", + " is_sat = False\n", + " for t in range (np.power(2,4)):\n", + " x_1 = (t % 2) >= 1\n", + " x_2 = (t % 4) >= 2\n", + " x_3 = (t % 8) >= 4\n", + " x_4 = (t % 16) >= 8\n", + " this_phi = phi(x_1, x_2, x_3,x_4)\n", + " print(\"phi(\",x_1,\",\",x_2,\",\",x_3,\",\",x_4,\")=\",this_phi)\n", + "\n", + " is_sat = is_sat or this_phi\n", + "\n", + " if is_sat:\n", + " print(\"Phi is SAT\")\n", + " else:\n", + " print(\"Phi is UNSAT\")" + ], + "metadata": { + "id": "QBj3Ap3t9CVO" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "exhaustive_3(phi)" + ], + "metadata": { + "id": "FJGoau6C_fWP" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "This is the same formula expressed in conjunctive normal form (see main text). The variables $y_1, y_2, y_3, y_4$ are extra terms that were needed to do this conversion. Their final values do not matter.\n", + "\n", + "$$\\begin{align}\\phi_2&:= y_{4} \\land (y_4\\lor y_2) \\land (y_4 \\lor \\lnot y_3) \\land (\\lnot y_4 \\lor \\lnot y_2 \\lor y_3)\\nonumber \\\\\n", + "&\\hspace{0.4cm}\\land (y_3 \\lor x_4) \\land (\\lnot y_3 \\lor \\lnot x_4)\\nonumber\\\\\n", + "& \\hspace{0.4cm}\\land (\\lnot y_2 \\lor \\lnot y_1 \\lor x_3)\\land (\\lnot y_2 \\lor y_1 \\lor \\lnot x_3) \\land (y_2 \\lor \\lnot y_1 \\lor \\lnot x_3) \\land (y_2\\lor y_1\\lor x_3)\\nonumber \\\\&\n", + "\\hspace{0.4cm}\\land (y_1\\lor \\lnot x_1) \\land (y_1 \\lor \\lnot x_2) \\land (\\lnot y_1 \\lor x_1 \\lor x_2). \\end{align}$$" + ], + "metadata": { + "id": "MSTx8YXlmycP" + } + }, + { + "cell_type": "code", + "source": [ + "def phi_2(x_1, x_2, x_3, x_4, y_1, y_2, y_3, y_4):\n", + " return y_4 and (y_4 or y_2) and (y_4 or not y_3) and (not y_4 or not y_2 or y_3) \\\n", + " and (y_3 or x_4) and (not y_3 or not x_4) and (not y_2 or not y_1 or x_3) and (not y_2 or y_1 or not x_3) \\\n", + " and (y_2 or not y_1 or not x_3) and (y_2 or y_1 or x_3) and (y_1 or not x_1) and (y_1 or not x_2) and (not y_1 or x_1 or x_2)" + ], + "metadata": { + "id": "z7wZRlrjnpK9" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def exhaustive_8(phi):\n", + " is_sat = False\n", + "\n", + " # TODO Complete this routine to test whether phi is SAT\n", + " # You now need to iterate over 8 variables (x1,x2,x3,x4,y1,y2,y3,y4)\n", + " # However, the values of y1,y2,y3,y4 don't matter.\n", + " # A given configuration of x_1, x_2, x_3, x_4 is satisfiable if the formula\n", + " # returns true for ANY of the 16 possible values of y_1, y_2, y_3, y_4\n", + " # Print out the results in the same format as above\n", + "\n", + " if is_sat:\n", + " print(\"Phi is SAT\")\n", + " else:\n", + " print(\"Phi is UNSAT\")" + ], + "metadata": { + "id": "YZNCyXCGmiiG" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "exhaustive_8(phi_2)" + ], + "metadata": { + "id": "qCm_ZdIgCv_P" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "You should see that the truth tables are the same for both the original and Tseitin transformed formulae. The two formulations are equivalent. Notice thought that we pay a cost for this simplification in that we have to add four more variables $y_1, y_2, y_3, y_4$ and so potentially have to iterate over 16 times the number of combinations." + ], + "metadata": { + "id": "iyIYwgwvhUXO" + } + } + ] +} \ No newline at end of file From f88127c0d2b192f776d03cd40b3aa9e6a8257ac0 Mon Sep 17 00:00:00 2001 From: udlbook <110402648+udlbook@users.noreply.github.com> Date: Thu, 27 Mar 2025 17:56:09 -0400 Subject: [PATCH 29/29] Created using Colab --- Trees/SAT_Tseitin.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Trees/SAT_Tseitin.ipynb b/Trees/SAT_Tseitin.ipynb index fc279b8..aad361f 100644 --- a/Trees/SAT_Tseitin.ipynb +++ b/Trees/SAT_Tseitin.ipynb @@ -4,7 +4,7 @@ "metadata": { "colab": { "provenance": [], - "authorship_tag": "ABX9TyO30jtLgfo4lSQLEOV3NJw3", + "authorship_tag": "ABX9TyMFurTLUbk5qbN9tTBjrI+h", "include_colab_link": true }, "kernelspec": { @@ -40,7 +40,7 @@ "source": [ "# The Tseitin transformation\n", "\n", - "The purpose of this Python notebook is to check that the Tseitin transformation example in the text is correct. Once we have the formula in conjunctive normal form, we can use the Python SAT library Z3 to solve SAT problems.\n", + "The purpose of this Python notebook is to check that the Tseitin transformation example in the text is correct. \n", "\n", "Work through the cells below, running each cell in turn. In various places you will see the words \"TODO\". Follow the instructions at these places and write code to complete the functions.\n", "\n",