58 lines
1.6 KiB
Python
58 lines
1.6 KiB
Python
import numpy as np
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from sklearn.decomposition import PCA
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# 创建一个人造数据集，包含10个样本，每个样本有4个特征
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X = np.array([[2, 1, 3, 4],
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[3, 5, 2, 8],
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[4, 3, 6, 5],
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[6, 7, 3, 9],
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[5, 4, 7, 6],
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[7, 8, 5, 10],
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[8, 6, 9, 7],
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[9, 10, 6, 11],
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[10, 8, 11, 8],
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[11, 12, 8, 12]])
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# 测试一
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from sklearn.preprocessing import StandardScaler
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scaler = StandardScaler() # 创建StandardScaler对象
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X_scaled = scaler.fit_transform(X) # 对原始数据X进行标准化
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pca = PCA() # 创建PCA对象，保留所有主成分
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X_transformed = pca.fit_transform(X_scaled) # 对数据进行PCA变换
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# 输出原始数据和变换后的数据
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print("Original Data:")
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print(X)
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print(X.shape)
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print("\nTransformed Data:")
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print(X_transformed)
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print(X_transformed.shape)
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# 输出各个主成分所解释的方差比例
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print("\nExplained Variance Ratio:")
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print(pca.explained_variance_ratio_)
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print('\n---\n')
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# 测试二
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from sklearn.preprocessing import StandardScaler
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scaler = StandardScaler() # 创建StandardScaler对象
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X_scaled = scaler.fit_transform(X) # 对原始数据X进行标准化
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pca = PCA(n_components=2) # 创建PCA对象，保留前2个主成分
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X_transformed = pca.fit_transform(X_scaled) # 对数据进行PCA变换
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# 输出原始数据和变换后的数据
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print("Original Data:")
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print(X)
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print(X.shape)
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print("\nTransformed Data:")
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print(X_transformed)
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print(X_transformed.shape)
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# 输出各个主成分所解释的方差比例
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print("\nExplained Variance Ratio:")
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print(pca.explained_variance_ratio_) |