Sci-Kit学习Kmeans和PCA降维

我有一个数据集,2M行×7列,具有不同的家庭功耗测量值,每个测量的日期.

• 日期,
• Global_active_power,
• Global_reactive_power,
• 电压,
• Global_intensity,
• Sub_metering_1,
• Sub_metering_2,
• Sub_metering_3

我将我的数据集放入pandas数据框中,选择除日期列之外的所有列,然后执行交叉验证拆分.

import pandas as pd
from sklearn.cross_validation import train_test_split

data = pd.read_csv('household_power_consumption.txt', delimiter=';')
power_consumption = data.iloc[0:, 2:9].dropna()
pc_toarray = power_consumption.values
hpc_fit, hpc_fit1 = train_test_split(pc_toarray, train_size=.01)
power_consumption.head()

我使用K-means分类,然后显示PCA降维.

from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import numpy as np
from sklearn.decomposition import PCA

hpc = PCA(n_components=2).fit_transform(hpc_fit)
k_means = KMeans()
k_means.fit(hpc)

x_min, x_max = hpc[:, 0].min() - 5, hpc[:, 0].max() - 1
y_min, y_max = hpc[:, 1].min(), hpc[:, 1].max() + 5 …