使用协方差矩阵的特征向量PCA来处理数据降维

      取2维特征,方便图形展示

      import matplotlib.pyplot as plt
      from sklearn.decomposition import PCA
      from sklearn.datasets import load_iris
      
      data = load_iris()
      y = data.target
      X = data.data
      pca = PCA(n_components=2)
      reduced_X = pca.fit_transform(X)
      
      red_x, red_y = [], []
      blue_x, blue_y = [], []
      green_x, green_y = [], []
      for i in range(len(reduced_X)):
          if y[i] == 0:
              red_x.append(reduced_X[i][0])
              red_y.append(reduced_X[i][1])
          elif y[i] == 1:
              blue_x.append(reduced_X[i][0])
              blue_y.append(reduced_X[i][1])
          else:
              green_x.append(reduced_X[i][0])
              green_y.append(reduced_X[i][1])
      plt.scatter(red_x, red_y, c=r, marker=x)
      plt.scatter(blue_x, blue_y, c=b, marker=D)
      plt.scatter(green_x, green_y, c=g, marker=.)
      plt.show()

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