added some examples

doc/src/DimRed/DimRed.do.txt

@@ -948,7 +948,45 @@ pca.components_.T[:, 0].
 Another very useful piece of information is the explained variance ratio of each principal component,
 available via the $explained\_variance\_ratio$ variable. It indicates the proportion of the dataset’s
 variance that lies along the axis of each principal component. 
-More material to come here.
+
+!split
+===== Back to the Cancer Data =====
+We can now repeat the above but applied to real data, in this case our breat cancer data.
+!bc pycod
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import  train_test_split 
+from sklearn.datasets import load_breast_cancer
+from sklearn.linear_model import LogisticRegression
+cancer = load_breast_cancer()
+
+X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)
+print(X_train.shape)
+print(X_test.shape)
+
+logreg = LogisticRegression()
+logreg.fit(X_train, y_train)
+print("Test set accuracy from Logistic Regression: {:.2f}".format(logreg.score(X_test,y_test)))
+
+from sklearn.preprocessing import MinMaxScaler, StandardScaler
+scaler = StandardScaler()
+scaler.fit(X_train)
+X_train_scaled = scaler.transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+logreg.fit(X_train_scaled, y_train)
+print("Test set accuracy scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
+
+#thereafter we do a PCA with Scikit-learn
+from sklearn.decomposition import PCA
+pca = PCA(n_components = 2)
+X2D_train = pca.fit_transform(X_train_scaled)
+X2D_test = pca.fit_transform(X_test_scaled)
+
+logreg.fit(X2D_train,y_train)
+print("Test set accuracy scaled data: {:.2f}".format(logreg.score(X2D_test,y_test)))
+!ec
+
 
 !split
 ===== More on the PCA =====

doc/src/DimRed/PCAcancer.py

@@ -14,7 +14,6 @@ correlation_matrix = cancerpd.corr().round(1)
 # use the heatmap function from seaborn to plot the correlation matrix
 # annot = True to print the values inside the square
 sns.heatmap(data=correlation_matrix, annot=True)
-plt.show()
 EigValues, EigVectors = np.linalg.eig(correlation_matrix)
 print(EigValues)
 
@@ -35,6 +34,11 @@ X_test_scaled = scaler.transform(X_test)
 logreg.fit(X_train_scaled, y_train)
 print("Test set accuracy scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
 
+#thereafter we do a PCA with Scikit-learn
+from sklearn.decomposition import PCA
+pca = PCA(n_components = 2)
+X2D_train = pca.fit_transform(X_train_scaled)
+X2D_test = pca.fit_transform(X_test_scaled)
 
-
-
+logreg.fit(X2D_train,y_train)
+print("Test set accuracy scaled data: {:.2f}".format(logreg.score(X2D_test,y_test)))