From d8382576424c9eb71137b3da58d865cce89076ed Mon Sep 17 00:00:00 2001
From: guanjihuan <guanjihuan@163.com>
Date: Mon, 1 Apr 2024 16:23:05 +0800
Subject: [PATCH] update

---
 .../PCA_and_StandardScaler_example.py         | 58 +++++++++++++++++++
 2024.04.01_PCA_example/PCA_example.py         | 50 ++++++++++++++++
 2 files changed, 108 insertions(+)
 create mode 100644 2024.04.01_PCA_example/PCA_and_StandardScaler_example.py
 create mode 100644 2024.04.01_PCA_example/PCA_example.py

diff --git a/2024.04.01_PCA_example/PCA_and_StandardScaler_example.py b/2024.04.01_PCA_example/PCA_and_StandardScaler_example.py
new file mode 100644
index 0000000..aaaf093
--- /dev/null
+++ b/2024.04.01_PCA_example/PCA_and_StandardScaler_example.py
@@ -0,0 +1,58 @@
+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_)
\ No newline at end of file
diff --git a/2024.04.01_PCA_example/PCA_example.py b/2024.04.01_PCA_example/PCA_example.py
new file mode 100644
index 0000000..bcc1c58
--- /dev/null
+++ b/2024.04.01_PCA_example/PCA_example.py
@@ -0,0 +1,50 @@
+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_)
\ No newline at end of file