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June 11, 2017 11:06
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This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,100 @@ print(__doc__) import matplotlib.pyplot as plt import csv from sklearn.decomposition import PCA from sklearn.discriminant_analysis import LinearDiscriminantAnalysis def pca_ol(filename): X = [] y = [0, 1, 2] with open(filename) as csvfile: readCSV = csv.reader(csvfile, delimiter=',') for row in readCSV: row = [float(i.replace(",",".")) for i in row] X.append(row) target_names = ["G1", "G2", "G3"] pca = PCA(n_components=2) X_r = pca.fit(X).transform(X) # Percentage of variance explained for each components print('explained variance ratio (first two components): %s' % str(pca.explained_variance_ratio_)) plt.figure() colors = ['navy', 'turquoise', 'darkorange'] lw = 2 print(X_r) for color, i, target_name in zip(colors, [0, 1, 2], target_names): plt.scatter(X_r[i, 0], X_r[i, 1], color=color, alpha=.8, lw=lw, label=target_name) plt.legend(loc='best', shadow=False, scatterpoints=1) plt.show() # pca_ol('tdgma1.csv') # pca_ol('tdgma2.csv') # pca_ol('tdgma3.csv') # pca_ol('tdgma4.csv') pca_ol('tdgma5.csv') print(__doc__) import matplotlib.pyplot as plt import csv from sklearn.decomposition import PCA from sklearn.discriminant_analysis import LinearDiscriminantAnalysis def pca_ol(filename): X = [] y = [0, 1, 2] with open(filename) as csvfile: readCSV = csv.reader(csvfile, delimiter=',') for row in readCSV: row = [float(i.replace(",",".")) for i in row] X.append(row) target_names = ["G1", "G2", "G3"] pca = PCA(n_components=2) X_r = pca.fit(X).transform(X) # Percentage of variance explained for each components print('explained variance ratio (first two components): %s' % str(pca.explained_variance_ratio_)) plt.figure() colors = ['navy', 'turquoise', 'darkorange'] lw = 2 print(X_r) for color, i, target_name in zip(colors, [0, 1, 2], target_names): plt.scatter(X_r[i, 0], X_r[i, 1], color=color, alpha=.8, lw=lw, label=target_name) plt.legend(loc='best', shadow=False, scatterpoints=1) plt.show() # pca_ol('tdgma1.csv') # pca_ol('tdgma2.csv') # pca_ol('tdgma3.csv') # pca_ol('tdgma4.csv') pca_ol('tdgma5.csv')