# -*- coding: utf-8 -*- """ Created on Fri Jul 3 18:23:49 2020 @author: cheetah023 """ import numpy as np import scipy.io as sci import matplotlib.pyplot as plt from skimage import io #函数定义 def findClosestCentroids(X, centroids): #样本数量 m,n = X.shape #形心数量 centroids = np.reshape(centroids,[len(centroids),n]) K = centroids.shape[0] idx = np.zeros([m,1]) for i in range(0,m): distance_t = np.sum((X[i,:] - centroids[0,:]) ** 2) j_t = 0 for j in range(1,K): distance = np.sum((X[i,:] - centroids[j,:]) ** 2) if distance < distance_t: distance_t = distance j_t = j idx[i] = j_t return idx def computeCentroids(X, idx, K): m,n = X.shape centroids = np.zeros([K,n]) for i in range(0,K): count = 0 for j in range(0,m): if idx[j] == i: centroids[i,:] += X[j,:] count += 1 centroids[i,:] /= count return centroids def plotDataPoints(X, idx, K): m = X.shape[0] color = ['b','r','y','g','m','c','k','w'] for i in range(0,m): plt.scatter(X[i,0],X[i,1],c=color[int(idx[i,0])]) def plotProgresskMeans(X, centroids, idx, K): plotDataPoints(X, idx, K) for i in range(0,K): plt.plot(centroids[:,0+2*i],centroids[:,1+2*i],'kx--',markersize=8) def runkMeans(X, initial_centroids, max_iters, plot_progress): m,n = X.shape idx = np.zeros([m,1]) K = len(initial_centroids) initial_centroids = np.reshape(initial_centroids,[K,n]) centroids = initial_centroids #将每次迭代的形心点放在一行 #每次迭代centroids_history增加一行 centroids_history = centroids.flatten() if plot_progress: plt.figure() for i in range(0,max_iters): idx = findClosestCentroids(X, centroids) centroids = computeCentroids(X, idx, K) centroids_history = np.row_stack([centroids_history,centroids.flatten()]) if plot_progress: plotProgresskMeans(X, centroids_history, idx, K) return centroids,idx def kMeansInitCentroids(X, K): idx = np.random.choice(X.shape[0],K) centroids = X[idx,:] return centroids #Part 1: Find Closest Centroids data = sci.loadmat('ex7data2.mat') #print('data.keys',data.keys()) X = data['X'] print('X:',X.shape) K = 3 initial_centroids = [[3, 3], [6, 2], [8, 5]] idx = findClosestCentroids(X, initial_centroids) print('Closest centroids for the first 3 examples:',idx[0:3] + 1) print('(the closest centroids should be 1, 3, 2 respectively)') #Part 2: Compute Means centroids = computeCentroids(X, idx, K) print('Centroids computed after initial finding of closest centroids:') print(centroids) print('(the centroids should be') print('[ 2.428301 3.157924 ]') print('[ 5.813503 2.633656 ]') print('[ 7.119387 3.616684 ]') #Part 3: K-Means Clustering K = 3 max_iters = 10 initial_centroids = [[3, 3], [6, 2], [8, 5]] [centroids, idx] = runkMeans(X, initial_centroids, max_iters, True) #Part 4: K-Means Clustering on Pixels data = io.imread('bird_small.png') print('data:',data.shape) m,n,k = data.shape data = data / 255 X = np.reshape(data,[-1,3]) K = 16; max_iters = 10 initial_centroids = kMeansInitCentroids(X, K) [centroids, idx] = runkMeans(X, initial_centroids, max_iters, False) #Part 5: Image Compression idx = findClosestCentroids(X, centroids) X_recovered = np.zeros_like(X) for i in range(0,m*n): X_recovered[i] = centroids[int(idx[i,0])] X_recovered = X_recovered.reshape([m,n,k]) fig,axes = plt.subplots(nrows=1,ncols=2) axes[0].imshow(data) axes[0].set_title('original') axes[1].imshow(X_recovered) axes[1].set_title('Compressed, with %d colors.'.format(K))
运行结果：
X: (300, 2) Closest centroids for the first 3 examples: [[1.] [3.] [2.]] (the closest centroids should be 1, 3, 2 respectively) Centroids computed after initial finding of closest centroids: [[2.42830111 3.15792418] [5.81350331 2.63365645] [7.11938687 3.6166844 ]] (the centroids should be [ 2.428301 3.157924 ] [ 5.813503 2.633656 ] [ 7.119387 3.616684 ] data: (128, 128, 3)
总结：
1、画centroids形心的移动轨迹时按照octave的流程来画不太方便，所以增加了centroids_history 来保存每次迭代的形心坐标，画起来舒服些
