实例介绍
【实例简介】机器学习 西瓜书配套代码
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# K-means算法"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### K-means算法是一类典型的聚类算法，该算法的思想是最小化平方误差\n",
"\n",
"过程\n",
"\n",
"1. 从D中随机选择k个样本作为初始均值向量\n",
"2. repeat\n",
" 1. 令 C_i = 0\n",
" 2. for j=1,2,...m\n",
" 计算 $d_{ji}$ = ${||x_j-\\mu_i||}_2$\n",
" \n",
" 选择最近的均值向量$x_j$,放入C_i\n",
" 3. 计算新的均值向量\n",
" $\\hat{\\mu} = \\frac{1}{|C_i|}\\sum{x}$\n",
" \n",
" 更新当前均值向量\n",
" 4. 结束\n",
" \n",
"### 这里根据周志华老师的西瓜数据集进行聚类操作"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import random\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from collections import defaultdict\n",
"from utils.metric import euclidean_distance\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"watermelon = np.array([[ 0.697 ,0.46 ],\n",
" [ 0.774 ,0.376],\n",
" [ 0.634 ,0.264],\n",
" [ 0.608 ,0.318],\n",
" [ 0.556 ,0.215],\n",
" [ 0.403 ,0.237],\n",
" [ 0.481 ,0.149],\n",
" [ 0.437 ,0.211],\n",
" [ 0.666 ,0.091],\n",
" [ 0.243 ,0.267],\n",
" [ 0.245 ,0.057],\n",
" [ 0.343 ,0.099],\n",
" [ 0.639 ,0.161],\n",
" [ 0.657 ,0.198],\n",
" [ 0.36 ,0.37 ],\n",
" [ 0.593 ,0.042],\n",
" [ 0.719 ,0.103],\n",
" [ 0.359 ,0.188],\n",
" [ 0.339 ,0.241],\n",
" [ 0.282 ,0.257],\n",
" [ 0.748 ,0.232],\n",
" [ 0.714 ,0.346],\n",
" [ 0.483 ,0.312],\n",
" [ 0.478 ,0.437],\n",
" [ 0.525 ,0.369],\n",
" [ 0.751 ,0.489],\n",
" [ 0.532 ,0.472],\n",
" [ 0.473 ,0.376],\n",
" [ 0.725 ,0.445],\n",
" [ 0.446 ,0.459]])"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def kmeans(data,k=3):\n",
" m = data.shape[0]\n",
" index = random.sample(range(m),k)\n",
" mu = data[index] #随机选择初始均值向量\n",
"\n",
"\n",
" while True:\n",
"\n",
" C = defaultdict(list)\n",
"\n",
" for j in range(0,m):\n",
" dij = [euclidean_distance(data[j],mu[i]) for i in range(k)]\n",
" lambda_j = np.argmin(dij) #选择最小的值得下标\n",
"\n",
" C[lambda_j].append(data[j].tolist())\n",
"\n",
" new_mu = [np.mean(C[i],axis=0).tolist() for i in range(k)]\n",
"\n",
" if (distance(np.array(new_mu),np.array(mu))>1e-9):\n",
" mu = new_mu\n",
" else:\n",
" break\n",
"\n",
" return C,mu"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"k = 2\n",
"res,mu = kmeans(watermelon,k)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
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"text/plain": [
"<matplotlib.figure.Figure at 0x117d85198>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for i in range(k):\n",
" res_i = np.array(res[i])\n",
" plt.scatter(res_i[:,0],res_i[:,1])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
【实例截图】
【源码目录】
ML-From-Scratch
├── Decision Tree for Classification And Regression.ipynb
├── GBDT 梯度提升.ipynb
├── GaussianMixtureModel.ipynb
├── HMM_forward.py
├── K-means算法.ipynb
├── KNN.ipynb
├── LSTM_FS.ipynb
├── MCMC-Metropolis-Hastings.py
├── Naive Bayes.ipynb
├── PCA.ipynb
├── PageRank.ipynb
├── README.md
├── SVM SMO.ipynb
├── TempLinkoping2016.txt
├── Viterbi.py
├── Xgboost 算法.ipynb
├── utils
│ ├── MLP.ipynb
│ ├── Simple RNN.ipynb
│ ├── __init__.py
│ ├── __pycache__
│ │ ├── __init__.cpython-36.pyc
│ │ ├── activations.cpython-36.pyc
│ │ ├── layers.cpython-36.pyc
│ │ ├── losses.cpython-36.pyc
│ │ ├── metric.cpython-36.pyc
│ │ ├── neural_networks.cpython-36.pyc
│ │ ├── optimizers.cpython-36.pyc
│ │ └── progress.cpython-36.pyc
│ ├── activations.py
│ ├── layers.py
│ ├── losses.py
│ ├── metric.py
│ ├── neural_networks.py
│ ├── optimizers.py
│ └── progress.py
├── 线性回归.ipynb
├── 对数几率回归.ipynb
└── 线性判别分析LDA.ipynb
2 directories, 37 files
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# K-means算法"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### K-means算法是一类典型的聚类算法，该算法的思想是最小化平方误差\n",
"\n",
"过程\n",
"\n",
"1. 从D中随机选择k个样本作为初始均值向量\n",
"2. repeat\n",
" 1. 令 C_i = 0\n",
" 2. for j=1,2,...m\n",
" 计算 $d_{ji}$ = ${||x_j-\\mu_i||}_2$\n",
" \n",
" 选择最近的均值向量$x_j$,放入C_i\n",
" 3. 计算新的均值向量\n",
" $\\hat{\\mu} = \\frac{1}{|C_i|}\\sum{x}$\n",
" \n",
" 更新当前均值向量\n",
" 4. 结束\n",
" \n",
"### 这里根据周志华老师的西瓜数据集进行聚类操作"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import random\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from collections import defaultdict\n",
"from utils.metric import euclidean_distance\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"watermelon = np.array([[ 0.697 ,0.46 ],\n",
" [ 0.774 ,0.376],\n",
" [ 0.634 ,0.264],\n",
" [ 0.608 ,0.318],\n",
" [ 0.556 ,0.215],\n",
" [ 0.403 ,0.237],\n",
" [ 0.481 ,0.149],\n",
" [ 0.437 ,0.211],\n",
" [ 0.666 ,0.091],\n",
" [ 0.243 ,0.267],\n",
" [ 0.245 ,0.057],\n",
" [ 0.343 ,0.099],\n",
" [ 0.639 ,0.161],\n",
" [ 0.657 ,0.198],\n",
" [ 0.36 ,0.37 ],\n",
" [ 0.593 ,0.042],\n",
" [ 0.719 ,0.103],\n",
" [ 0.359 ,0.188],\n",
" [ 0.339 ,0.241],\n",
" [ 0.282 ,0.257],\n",
" [ 0.748 ,0.232],\n",
" [ 0.714 ,0.346],\n",
" [ 0.483 ,0.312],\n",
" [ 0.478 ,0.437],\n",
" [ 0.525 ,0.369],\n",
" [ 0.751 ,0.489],\n",
" [ 0.532 ,0.472],\n",
" [ 0.473 ,0.376],\n",
" [ 0.725 ,0.445],\n",
" [ 0.446 ,0.459]])"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def kmeans(data,k=3):\n",
" m = data.shape[0]\n",
" index = random.sample(range(m),k)\n",
" mu = data[index] #随机选择初始均值向量\n",
"\n",
"\n",
" while True:\n",
"\n",
" C = defaultdict(list)\n",
"\n",
" for j in range(0,m):\n",
" dij = [euclidean_distance(data[j],mu[i]) for i in range(k)]\n",
" lambda_j = np.argmin(dij) #选择最小的值得下标\n",
"\n",
" C[lambda_j].append(data[j].tolist())\n",
"\n",
" new_mu = [np.mean(C[i],axis=0).tolist() for i in range(k)]\n",
"\n",
" if (distance(np.array(new_mu),np.array(mu))>1e-9):\n",
" mu = new_mu\n",
" else:\n",
" break\n",
"\n",
" return C,mu"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"k = 2\n",
"res,mu = kmeans(watermelon,k)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x117d85198>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for i in range(k):\n",
" res_i = np.array(res[i])\n",
" plt.scatter(res_i[:,0],res_i[:,1])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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