kmeans对图像和数据进行分割

kmeans的算法细节就不再细讲了，比较简单。

第一个应用是对波形数据进行分类，总共有三种波，有21个特征用于分类。总共有5000条数据

import numpy as npimport matplotlib.pyplot as pltf = open('data/waveform.csv')context = f.readlines()x = np.zeros((5000, 21))y = np.zeros((5000,))id = 0for line in context:    line = line.replace('\n', '')    array = line.split(',')    x[id] = array[:-1]    y[id] = array[-1]    id += 1plt.scatter(x[:, 1], x[:, 3])plt.show()print(x, '\n')print(y)

这段代码将数据列与标签列进行分离

def kmeans(data,k=3):    def _distance(p1, p2):        """        Return Eclud distance between two points.        p1 = np.array([0,0]), p2 = np.array([1,1]) => 1.414        """        tmp = np.sum((p1-p2)**2)        return np.sqrt(tmp)    def _rand_center(data,k):        """Generate k center within the range of data set."""        n = data.shape[1]             # features        centroids = np.zeros((k, n))  # init with (0,0)....        for i in range(n):            dmin, dmax = np.min(data[:, i]), np.max(data[:, i])            centroids[:, i] = dmin + (dmax - dmin) * np.random.rand(k)        return centroids    def _converged(centroids1, centroids2):        # if centroids not changed, we say 'converged'        set1 = set([tuple(c) for c in centroids1])        set2 = set([tuple(c) for c in centroids2])        return (set1 == set2)    n = data.shape[0]                   # number of entries    centroids = _rand_center(data, k)    label = np.zeros(n, dtype=np.int)   # track the nearest centroid    assement = np.zeros(n)              # for the assement of our model    converged = False    while not converged:        old_centroids = np.copy(centroids)        for i in range(n):            # determine the nearest centroid and track it with label            min_dist, min_index = np.inf, -1            for j in range(k):                dist = _distance(data[i], centroids[j])                if dist < min_dist:                    min_dist, min_index = dist, j                    label[i] = j            assement[i] = _distance(data[i], centroids[label[i]]) ** 2        # update centroid        for m in range(k):            centroids[m] = np.mean(data[label == m], axis=0)        converged = _converged(old_centroids, centroids)    return centroids, label, np.sum(assement)best_assement = np.infbest_centroids = Nonebest_label = None

这段代码是别人写好的kmeans算法，我直接拿过来用了

data0 = data[best_label==0]data1 = data[best_label==1]data2 = data[best_label==2]fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))ax1.scatter(data[:, 0], data[:, 1], c='c', s=30, marker='o')ax2.scatter(data0[:, 0], data0[:, 1], c='r')ax2.scatter(data1[:, 0], data1[:, 1], c='c')ax2.scatter(data2[:, 0], data2[:, 1], c='b')ax2.scatter(centroids[:, 0], centroids[:, 1], c='b', s=120, marker='o')plt.show()

这段代码对数据可视化

x0 = x[best_label==0]x1 = x[best_label==1]x2 = x[best_label==2]n0=n1=n2=0for id in range(5000):    if y[id] == 0:        n0 += 1    if y[id] == 1:        n1 += 1    if y[id] == 2:        n2 += 1y0 = y[best_label==0]y1 = y[best_label==1]y2 = y[best_label==2]m0=m1=m2=0for id in range(len(y0)):    if y0[id] == 0:        m0 += 1for id in range(len(y1)):    if y1[id] == 1:        m1 += 1for id in range(len(y2)):    if y2[id] == 2:        m2 += 1print(m0/n0)print(m1/n1)print(m2/n2)

因为给定了标签，所以可以统计正确率

由于kmeans和初始点的选择有关系，所以我们进行进行10次然后选取最好的实验结果保存

实验证明正确率为50%,20%,30%