55 lines
1.5 KiB
Python
55 lines
1.5 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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# 载入数据
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data = np.genfromtxt("data.csv", delimiter=",")
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x_data = data[:,0]
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y_data = data[:,1]
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plt.scatter(x_data,y_data)
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plt.show()
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print(x_data.shape)
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# 数据中心化
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def zeroMean(dataMat):
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# 按列求平均，即各个特征的平均
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meanVal = np.mean(dataMat, axis=0)
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newData = dataMat - meanVal
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return newData, meanVal
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newData,meanVal=zeroMean(data)
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# np.cov用于求协方差矩阵，参数rowvar=0说明数据一行代表一个样本
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covMat = np.cov(newData, rowvar=0)
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# 协方差矩阵
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print(covMat)
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# np.linalg.eig求矩阵的特征值和特征向量
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eigVals, eigVects = np.linalg.eig(np.mat(covMat))
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# 特征值
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print(eigVals)
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# 特征向量
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print(eigVects)
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# 对特征值从小到大排序
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eigValIndice = np.argsort(eigVals)
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print(eigValIndice)
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top = 1
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# 最大的top个特征值的下标
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n_eigValIndice = eigValIndice[-1:-(top+1):-1]
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print(n_eigValIndice)
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# 最大的n个特征值对应的特征向量
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n_eigVect = eigVects[:,n_eigValIndice]
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print(n_eigVect)
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# 低维特征空间的数据
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lowDDataMat = newData*n_eigVect
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print(lowDDataMat)
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# 利用低纬度数据来重构数据
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reconMat = (lowDDataMat*n_eigVect.T) + meanVal
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print(reconMat)
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# 载入数据
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data = np.genfromtxt("data.csv", delimiter=",")
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x_data = data[:,0]
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y_data = data[:,1]
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plt.scatter(x_data,y_data)
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# 重构的数据
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x_data = np.array(reconMat)[:,0]
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y_data = np.array(reconMat)[:,1]
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plt.scatter(x_data,y_data,c='r')
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plt.show()
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