some update of pca

doc/src/DimRed/Programs/PCAsimple.py

@@ -1,30 +1,35 @@
 import numpy as np
 import pandas as pd
 from IPython.display import display
+import matplotlib.pyplot as plt
+from sklearn.model_selection import  train_test_split 
+from sklearn.preprocessing import MinMaxScaler, StandardScaler
 
 
-n = 100
+n = 10000
 mean = (-1, 2)
 cov = [[4, 2], [2, 2]]
+print(cov)
 X = np.random.multivariate_normal(mean, cov, n)
-print(X)
+print(np.cov(X.T))
 
 df = pd.DataFrame(X)
 # Pandas does the centering for us
 df = df -df.mean()
-display(df)
+correlation_matrix = df.cov()
+print(correlation_matrix)
 
 # we center it ourselves
 X_centered = X - X.mean(axis=0)
-# test that we get the same as Pandas
-print(X_centered-df)
+print(np.cov(X_centered.T))
+#print("test that we get the same as Pandas")
+#print(X_centered-X_train_scaled)
 #Now we do an SVD
 U, s, V = np.linalg.svd(X_centered)
 c1 = V.T[:, 0]
 c2 = V.T[:, 1]
 W2 = V.T[:, :2]
 X2D = X_centered.dot(W2)
-print(X2D)
 #thereafter we do a PCA with Scikit-learn
 from sklearn.decomposition import PCA
 pca = PCA(n_components = 2)
@@ -34,3 +39,5 @@ print(X2D-X2Dsl)
 
 print(pca.components_.T[:, 0])
 
+
+