机器学习-kmeans的使用

import numpy as np

import pandas as pd

import matplotlib

from matplotlib import pyplot as plt

%matplotlib inline

matplotlib.rcParams['font.sans-serif'] = ['SimHei']

data = pd.read_csv('./010-data_multivar.csv',header=None)

#拆分数据

dataset_X,dataset_y = data.iloc[:,:-],data.iloc[:,-]

# print(dataset_X.head())

dataset_X = dataset_X.values

dataset_y = dataset_y.values

无标签数据集可视化，将第一列feature作为X，第二列feature作为y

def visual_2D_dataset_dist(dataset):

    '''将二维数据集dataset显示在散点图中'''

    assert dataset.shape[]==,'only support dataset with 2 features'

    plt.figure()

    X=dataset[:,]

    Y=dataset[:,]

    plt.scatter(X,Y,marker='v',c='g',label='dataset')

    X_min,X_max=np.min(X)-,np.max(X)+

    Y_min,Y_max=np.min(Y)-,np.max(Y)+

    plt.title('dataset distribution')

    plt.xlim(X_min,X_max)

    plt.ylim(Y_min,Y_max)

    plt.xlabel('feature_0')

    plt.ylabel('feature_1')

    plt.legend()

visual_2D_dataset_dist(dataset_X)

构造 kmeans

from sklearn.cluster import KMeans

#init

kmeans = KMeans(init='k-means++',n_clusters=4,n_init=5)

kmeans.fit(dataset_X)

将dataset_X聚类效果可视化

def visual_kmeans_effect(k_means,dataset):

    assert dataset.shape[1]==2,'only support dataset with 2 features'

    X=dataset[:,0]

    Y=dataset[:,1]

    X_min,X_max=np.min(X)-1,np.max(X)+1

    Y_min,Y_max=np.min(Y)-1,np.max(Y)+1

    # meshgrid 生成网格点坐标矩阵

    X_values,Y_values=np.meshgrid(np.arange(X_min,X_max,0.01),

                                  np.arange(Y_min,Y_max,0.01))

    # 预测网格点的标记

    predict_labels=k_means.predict(np.c_[X_values.ravel(),Y_values.ravel()])

    predict_labels=predict_labels.reshape(X_values.shape)

    plt.figure()

    plt.imshow(predict_labels,interpolation='nearest',

               extent=(X_values.min(),X_values.max(),

                       Y_values.min(),Y_values.max()),

               cmap=plt.cm.Paired,

               aspect='auto',

               origin='lower')

    # 将数据集绘制到图表中

    plt.scatter(X,Y,marker='v',facecolors='none',edgecolors='k',s=30)

    # 将中心点绘制到图中

    centroids=k_means.cluster_centers_

    plt.scatter(centroids[:,0],centroids[:,1],marker='o',

                s=100,linewidths=2,color='k',zorder=5,facecolors='b')

    plt.title('K-Means effect graph')

    plt.xlim(X_min,X_max)

    plt.ylim(Y_min,Y_max)

    plt.xlabel('feature_0')

    plt.ylabel('feature_1')

    plt.show()

visual_kmeans_effect(kmeans,dataset_X)

 

# 鸢尾花聚类

from sklearn.datasets import load_iris

datairis = load_iris()

dataset = datairis.data

from sklearn.cluster import KMeans

#init

kmeans = KMeans(init='k-means++',n_clusters=,n_init=)

kmeans.fit(dataset)

print(kmeans.labels_) #去除标签

print(datairis)