python的3d散点图
from sklearn import KMeans
from sklearn.externals import joblib from sklearn import cluster import numpy as np # 生成10*3的矩阵 data = np.random.rand(100,3) print data # 聚类为4类 estimator=KMeans(n_clusters=3) # fit_predict表示拟合+预测,也可以分开写 res=estimator.fit_predict(data) # 预测类别标签结果 lable_pred=estimator.labels_ # 各个类别的聚类中心值 centroids=estimator.cluster_centers_ # 聚类中心均值向量的总和 inertia=estimator.inertia_ print lable_pred print centroids print inertia import numpy as np from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt %matplotlib inline fig = plt.figure() ax = plt.subplot(111,projection = "3d") X = data[:,0] Y = data[:,1] Z = data[:,2] C = [] for i in range(len(data)): if int(lable_pred[i]) == 0: C.append('r') if int(lable_pred[i]) == 1: C.append('b') if int(lable_pred[i]) == 2: C.append('black') ax.scatter(X,Y,Z,c=C) ax.set_xlabel('longitude') ax.set_ylabel('latitude') ax.set_zlabel('deepth') plt.show()