sklearn LDA降維算法

LDA(Linear Discriminant Analysis)線性判斷別分析，可以用于降維和分類。其基本思想是類內散度盡可能小，類間散度盡可能大，是一種經典的監督式降維/分類技術。

sklearn代碼實現

#coding=utf-8import pandas as pd import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn import datasets from sklearn.discriminant_analysis import LinearDiscriminantAnalysis import numpy as npdef main():iris = datasets.load_iris() #典型分類數據模型#這里我們數據統一用pandas處理data = pd.DataFrame(iris.data, columns=iris.feature_names)data['class'] = iris.target#這里只取兩類 # data = data[data['class']!=2]#為了可視化方便，這里取兩個屬性為例X = data[data.columns.drop('class')]Y = data['class']#劃分數據集X_train, X_test, Y_train, Y_test =train_test_split(X, Y)lda = LinearDiscriminantAnalysis(n_components=2)lda.fit(X_train, Y_train)#顯示訓練結果print lda.means_ #中心點print lda.score(X_test, Y_test) #score是指分類的正確率print lda.scalings_ #score是指分類的正確率X_2d = lda.transform(X) #現在已經降到二維X_2d=np.dot(X-lda.xbar_,lda.scalings_)#對于二維數據，我們做個可視化#區域劃分lda.fit(X_2d,Y)h = 0.02x_min, x_max = X_2d[:, 0].min() - 1, X_2d[:, 0].max() + 1y_min, y_max = X_2d[:, 1].min() - 1, X_2d[:, 1].max() + 1xx, yy = np.meshgrid(np.arange(x_min, x_max, h),np.arange(y_min, y_max, h))Z = lda.predict(np.c_[xx.ravel(), yy.ravel()])Z = Z.reshape(xx.shape)plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)#做出原來的散點圖class1_x = X_2d[Y==0,0]class1_y = X_2d[Y==0,1]l1 = plt.scatter(class1_x,class1_y,color='b',label=iris.target_names[0])class1_x = X_2d[Y==1,0]class1_y = X_2d[Y==1,1]l2 = plt.scatter(class1_x,class1_y,color='y',label=iris.target_names[1])class1_x = X_2d[Y==2,0]class1_y = X_2d[Y==2,1]l3 = plt.scatter(class1_x,class1_y,color='r',label=iris.target_names[2])plt.legend(handles = [l1, l2, l3], loc = 'best')plt.grid(True)plt.show()if __name__ == '__main__':main()

測試結果

Means: #各類的中心點 [[ 5.00810811 3.41891892 1.44594595 0.23513514][ 6.06410256 2.80769231 4.32564103 1.33589744][ 6.61666667 2.97222222 5.63055556 2.02777778]] Score: #對于測試集的正確率 0.973684210526 Scalings: [[ 1.19870893 0.76465114][ 1.20339741 -2.46937995][-2.55937543 0.42562073][-2.77824826 -2.4470865 ]] Xbar: [ 5.89285714 3.0625 3.79375 1.19464286] #X'=np.dot(X-lda.xbar_,lda.scalings_)默認的線性變化方程

轉載于:https://www.cnblogs.com/fanghao/p/7523897.html

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