59 lines
2.2 KiB
Python
59 lines
2.2 KiB
Python
from sklearn.neural_network import MLPClassifier
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from sklearn.datasets import load_digits
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import classification_report,confusion_matrix
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import numpy as np
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import matplotlib.pyplot as plt
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digits = load_digits()#载入数据
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x_data = digits.data #数据
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y_data = digits.target #标签
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x_train,x_test,y_train,y_test = train_test_split(x_data,y_data) #分割数据1/4为测试数据，3/4为训练数据
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print(x_data.shape)
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mlp = MLPClassifier(hidden_layer_sizes=(100,50) ,max_iter=500)
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mlp.fit(x_train,y_train)
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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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def pca(dataMat,top):
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# 数据中心化
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newData,meanVal=zeroMean(dataMat)
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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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# 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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# 对特征值从小到大排序
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eigValIndice = np.argsort(eigVals)
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# 最大的n个特征值的下标
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n_eigValIndice = eigValIndice[-1:-(top+1):-1]
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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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# 低维特征空间的数据
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lowDDataMat = newData*n_eigVect
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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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# 返回低维特征空间的数据和重构的矩阵
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return lowDDataMat,reconMat
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lowDDataMat,reconMat = pca(x_data,2)
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# 重构的数据
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x = np.array(lowDDataMat)[:,0]
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y = np.array(lowDDataMat)[:,1]
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plt.scatter(x,y,c='r')
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plt.show()
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predictions = mlp.predict(x_data)
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# 重构的数据
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x = np.array(lowDDataMat)[:,0]
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y = np.array(lowDDataMat)[:,1]
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plt.scatter(x,y,c=y_data)
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plt.show()
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lowDDataMat,reconMat = pca(x_data,3)
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from mpl_toolkits.mplot3d import Axes3D
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x = np.array(lowDDataMat)[:,0]
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y = np.array(lowDDataMat)[:,1]
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z = np.array(lowDDataMat)[:,2]
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ax = plt.figure().add_subplot(111, projection = '3d')
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ax.scatter(x, y, z, c = y_data, s = 10) #点为红色三角形
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plt.show()
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