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guanjihuan 2024-04-01 16:23:05 +08:00
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2024.04.01_PCA_example/PCA_and_StandardScaler_example.py Normal file
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import numpy as np
from sklearn.decomposition import PCA
# 创建一个人造数据集，包含10个样本，每个样本有4个特征
X = np.array([[2, 1, 3, 4],
[3, 5, 2, 8],
[4, 3, 6, 5],
[6, 7, 3, 9],
[5, 4, 7, 6],
[7, 8, 5, 10],
[8, 6, 9, 7],
[9, 10, 6, 11],
[10, 8, 11, 8],
[11, 12, 8, 12]])
# 测试一
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler() # 创建StandardScaler对象
X_scaled = scaler.fit_transform(X) # 对原始数据X进行标准化
pca = PCA() # 创建PCA对象，保留所有主成分
X_transformed = pca.fit_transform(X_scaled) # 对数据进行PCA变换
# 输出原始数据和变换后的数据
print("Original Data:")
print(X)
print(X.shape)
print("\nTransformed Data:")
print(X_transformed)
print(X_transformed.shape)
# 输出各个主成分所解释的方差比例
print("\nExplained Variance Ratio:")
print(pca.explained_variance_ratio_)
print('\n---\n')
# 测试二
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler() # 创建StandardScaler对象
X_scaled = scaler.fit_transform(X) # 对原始数据X进行标准化
pca = PCA(n_components=2) # 创建PCA对象，保留前2个主成分
X_transformed = pca.fit_transform(X_scaled) # 对数据进行PCA变换
# 输出原始数据和变换后的数据
print("Original Data:")
print(X)
print(X.shape)
print("\nTransformed Data:")
print(X_transformed)
print(X_transformed.shape)
# 输出各个主成分所解释的方差比例
print("\nExplained Variance Ratio:")
print(pca.explained_variance_ratio_)

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2024.04.01_PCA_example/PCA_example.py Normal file
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import numpy as np
from sklearn.decomposition import PCA
# 创建一个人造数据集，包含10个样本，每个样本有4个特征
X = np.array([[2, 1, 3, 4],
[3, 5, 2, 8],
[4, 3, 6, 5],
[6, 7, 3, 9],
[5, 4, 7, 6],
[7, 8, 5, 10],
[8, 6, 9, 7],
[9, 10, 6, 11],
[10, 8, 11, 8],
[11, 12, 8, 12]])
# 测试一
pca = PCA() # 创建PCA对象，保留所有主成分
X_transformed = pca.fit_transform(X) # 对数据进行PCA变换
# 输出原始数据和变换后的数据
print("Original Data:")
print(X)
print(X.shape)
print("\nTransformed Data:")
print(X_transformed)
print(X_transformed.shape)
# 输出各个主成分所解释的方差比例
print("\nExplained Variance Ratio:")
print(pca.explained_variance_ratio_)
print('\n---\n')
# 测试二
pca = PCA(n_components=2) # 创建PCA对象，保留前2个主成分
X_transformed = pca.fit_transform(X) # 对数据进行PCA变换
# 输出原始数据和变换后的数据
print("Original Data:")
print(X)
print(X.shape)
print("\nTransformed Data:")
print(X_transformed)
print(X_transformed.shape)
# 输出各个主成分所解释的方差比例
print("\nExplained Variance Ratio:")
print(pca.explained_variance_ratio_)