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‎Book6_Ch02_Python_Codes/Bk6_Ch02_01.ipynb

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‎Book6_Ch03_Python_Codes/Bk6_Ch03_01.ipynb

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‎Book6_Ch03_Python_Codes/Bk6_Ch03_02.ipynb

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‎Book6_Ch04_Python_Codes/Bk6_Ch04_01.ipynb

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‎Book6_Ch04_Python_Codes/Bk6_Ch04_02.ipynb

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‎Book6_Ch04_Python_Codes/Bk6_Ch04_03.ipynb

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‎Book6_Ch04_Python_Codes/Bk6_Ch04_04.ipynb

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‎Book6_Ch04_Python_Codes/Bk6_Ch04_05.ipynb

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‎Book6_Ch04_Python_Codes/Bk6_Ch04_06.ipynb

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‎Book6_Ch04_Python_Codes/Bk6_Ch04_07.ipynb

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‎Book6_Ch04_Python_Codes/Bk6_Ch04_08.ipynb

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‎Book6_Ch05_Python_Codes/Bk6_Ch05_01.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "38ebe56d-5a05-484f-9db6-4d9a9ca4b432",
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"metadata": {},
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"source": [
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"Chapter 05\n",
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"\n",
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"# 欧氏距离\n",
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"Book_6《数据有道》 | 鸢尾花书：从加减乘除到机器学习"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "8fd2e18c-6578-4eb3-a40d-9781282d55fe",
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"metadata": {},
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"outputs": [],
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"source": [
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"from scipy.spatial import distance\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "480dd9e7-27fe-44ea-a5d1-b012d23155fd",
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"metadata": {},
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"outputs": [],
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"source": [
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"x_i = (0, 0, 0) # data point\n",
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"q = (4, 8, 6) # query point"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "41f20fe9-4b51-4d76-8d0b-27390500ee30",
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"metadata": {},
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"outputs": [],
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"source": [
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"# calculate Euclidean distance\n",
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"dst_1 = distance.euclidean(x_i, q)\n",
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"\n",
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"dst_2 = np.linalg.norm(np.array(x_i) - np.array(q))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎Book6_Ch05_Python_Codes/Bk6_Ch05_02.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "68587ff0-cc9d-4a4c-9ca0-df46c20839ba",
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"metadata": {},
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"source": [
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"Chapter 05\n",
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"\n",
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"# 成对欧氏距离\n",
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"Book_6《数据有道》 | 鸢尾花书：从加减乘除到机器学习"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "22d75b52-c37a-437d-8b74-3897be0e5536",
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.metrics.pairwise import euclidean_distances\n",
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"\n",
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"# Sample data points\n",
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"X = [[-5, 0], [4, 3], [3, -4]]\n",
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"\n",
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"# Query point\n",
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"q = [[0, 0]]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "f6e4443f-b711-44d7-805e-e37071f2f83c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Pairwise distances between X and q\n",
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"[[5.]\n",
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" [5.]\n",
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" [5.]]\n"
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]
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}
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],
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"source": [
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"# pairwise distances between rows of X and q\n",
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"dst_pairwise_X_q = euclidean_distances(X, q)\n",
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"print('Pairwise distances between X and q')\n",
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"print(dst_pairwise_X_q)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "d5b436f6-6a6b-42fc-85f0-2f4264125888",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Pairwise distances between X and X\n",
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"[[0. 9.48683298 8.94427191]\n",
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" [9.48683298 0. 7.07106781]\n",
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" [8.94427191 7.07106781 0. ]]\n"
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]
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}
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],
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"source": [
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"# pairwise distances between rows of X and itself\n",
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"dst_pairwise_X_X = euclidean_distances(X, X)\n",
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"print('Pairwise distances between X and X')\n",
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"print(dst_pairwise_X_X)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎Book6_Ch05_Python_Codes/Bk6_Ch05_03.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "5626dae8-008d-441e-bd76-a799a4bb15ba",
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"metadata": {},
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"source": [
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"Chapter 05\n",
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"\n",
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"# 标准欧氏距离\n",
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"Book_6《数据有道》 | 鸢尾花书：从加减乘除到机器学习"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "c42a0dbd-3aec-4d40-8445-20f1540959d2",
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"metadata": {},
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"outputs": [],
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"source": [
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"from scipy.spatial import distance\n",
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"import numpy as np\n",
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"\n",
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"# Variance-covariance matrix\n",
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"SIGMA = np.array([[2, 1], [1, 2]])\n",
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"\n",
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"q = [0, 0]; # query point\n",
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"x_1 = [-3.5, -4]; # data point 1\n",
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"x_2 = [2.75, -1.5]; # data point 1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "fb5fef77-9256-4878-813c-4bdb9bf888c7",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Calculate standardized Euclidean distances\n",
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"\n",
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"d_1 = distance.seuclidean(q, x_1, np.diag(SIGMA))\n",
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"d_2 = distance.seuclidean(q, x_2, np.diag(SIGMA))\n",
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"\n",
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"# Note1: V is an 1-D array of component variances"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎Book6_Ch05_Python_Codes/Bk6_Ch05_04.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "d6ef6479-b2ba-42e9-8e0e-bea7f535a8bc",
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"metadata": {},
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"source": [
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"Chapter 05\n",
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"\n",
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"# 马氏距离\n",
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"Book_6《数据有道》 | 鸢尾花书：从加减乘除到机器学习"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "dfd8a1aa-0e15-4e5d-94d1-c91d022a3f7c",
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"metadata": {},
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"outputs": [],
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"source": [
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"from scipy.spatial import distance\n",
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"import numpy as np\n",
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"from numpy.linalg import inv"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "384bbf81-052d-47f6-9f23-b4d3288fdde4",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Variance-covariance matrix\n",
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"SIGMA = np.array([[2, 1], [1, 2]])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "cdd13e51-5b97-40f1-902b-7773f3f863dd",
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"metadata": {},
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"outputs": [],
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"source": [
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"q = [0, 0]; # query point\n",
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"x_1 = [-3.5, -4]; # data point 1\n",
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"x_2 = [2.75, -1.5]; # data point 1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "f6dfd618-5465-45fa-ac09-088520220d99",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Calculate Mahal distances\n",
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"\n",
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"d_1 = distance.mahalanobis(q, x_1, inv(SIGMA))\n",
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"d_2 = distance.mahalanobis(q, x_2, inv(SIGMA))\n",
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"\n",
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"# Note1: the output of the function is Mahal distance, not Mahal distance squared\n",
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"# Note2: input is the inverse of the covariance matrix"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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