diff --git a/doc/pub/DimRed/html/._DimRed-bs007.html b/doc/pub/DimRed/html/._DimRed-bs007.html
index 4005925d3..0334cf680 100644
--- a/doc/pub/DimRed/html/._DimRed-bs007.html
+++ b/doc/pub/DimRed/html/._DimRed-bs007.html
@@ -182,6 +182,11 @@ correlation_matrix = cancerpd# annot = True to print the values inside the square
sns.heatmap(data=correlation_matrix, annot=True)
plt.show()
+
+#print eigvalues of correlation matrix
+EigValues, EigVectors = np.linalg.eig(correlation_matrix)
+print(EigValues)
+
#split into train and test and then scale thereafter
X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)
print(X_train.shape)
diff --git a/doc/pub/DimRed/html/DimRed-reveal.html b/doc/pub/DimRed/html/DimRed-reveal.html
index c8b2d434c..9fa26b993 100644
--- a/doc/pub/DimRed/html/DimRed-reveal.html
+++ b/doc/pub/DimRed/html/DimRed-reveal.html
@@ -459,6 +459,11 @@ correlation_matrix = cancerpd.corr().round(1
# annot = True to print the values inside the square
sns.heatmap(data=correlation_matrix, annot=True)
plt.show()
+
+#print eigvalues of correlation matrix
+EigValues, EigVectors = np.linalg.eig(correlation_matrix)
+print(EigValues)
+
#split into train and test and then scale thereafter
X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)
print(X_train.shape)
diff --git a/doc/pub/DimRed/html/DimRed-solarized.html b/doc/pub/DimRed/html/DimRed-solarized.html
index bca2cf49d..6b682f7ea 100644
--- a/doc/pub/DimRed/html/DimRed-solarized.html
+++ b/doc/pub/DimRed/html/DimRed-solarized.html
@@ -442,6 +442,11 @@ correlation_matrix = cancerpd.corr().round(1
# annot = True to print the values inside the square
sns.heatmap(data=correlation_matrix, annot=True)
plt.show()
+
+#print eigvalues of correlation matrix
+EigValues, EigVectors = np.linalg.eig(correlation_matrix)
+print(EigValues)
+
#split into train and test and then scale thereafter
X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)
print(X_train.shape)
diff --git a/doc/pub/DimRed/html/DimRed.html b/doc/pub/DimRed/html/DimRed.html
index 53607192d..5b3216924 100644
--- a/doc/pub/DimRed/html/DimRed.html
+++ b/doc/pub/DimRed/html/DimRed.html
@@ -447,6 +447,11 @@ correlation_matrix = cancerpd# annot = True to print the values inside the square
sns.heatmap(data=correlation_matrix, annot=True)
plt.show()
+
+#print eigvalues of correlation matrix
+EigValues, EigVectors = np.linalg.eig(correlation_matrix)
+print(EigValues)
+
#split into train and test and then scale thereafter
X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)
print(X_train.shape)
diff --git a/doc/pub/DimRed/ipynb/DimRed.ipynb b/doc/pub/DimRed/ipynb/DimRed.ipynb
index 8bf7e3023..f9d4059e1 100644
--- a/doc/pub/DimRed/ipynb/DimRed.ipynb
+++ b/doc/pub/DimRed/ipynb/DimRed.ipynb
@@ -330,6 +330,11 @@
"# annot = True to print the values inside the square\n",
"sns.heatmap(data=correlation_matrix, annot=True)\n",
"plt.show()\n",
+ "\n",
+ "#print eigvalues of correlation matrix\n",
+ "EigValues, EigVectors = np.linalg.eig(correlation_matrix)\n",
+ "print(EigValues)\n",
+ "\n",
"#split into train and test and then scale thereafter\n",
"X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)\n",
"print(X_train.shape)\n",
diff --git a/doc/pub/DimRed/ipynb/ipynb-DimRed-src.tar.gz b/doc/pub/DimRed/ipynb/ipynb-DimRed-src.tar.gz
index 927742fa6..2d3fa6507 100644
Binary files a/doc/pub/DimRed/ipynb/ipynb-DimRed-src.tar.gz and b/doc/pub/DimRed/ipynb/ipynb-DimRed-src.tar.gz differ
diff --git a/doc/pub/DimRed/pdf/DimRed-minted.pdf b/doc/pub/DimRed/pdf/DimRed-minted.pdf
index 906b00f9f..6bbfbdce6 100644
Binary files a/doc/pub/DimRed/pdf/DimRed-minted.pdf and b/doc/pub/DimRed/pdf/DimRed-minted.pdf differ
diff --git a/doc/src/DimRed/DimRed.do.txt b/doc/src/DimRed/DimRed.do.txt
index 26cdddd72..536a45c0f 100644
--- a/doc/src/DimRed/DimRed.do.txt
+++ b/doc/src/DimRed/DimRed.do.txt
@@ -287,6 +287,11 @@ correlation_matrix = cancerpd.corr().round(1)
# annot = True to print the values inside the square
sns.heatmap(data=correlation_matrix, annot=True)
plt.show()
+
+#print eigvalues of correlation matrix
+EigValues, EigVectors = np.linalg.eig(correlation_matrix)
+print(EigValues)
+
#split into train and test and then scale thereafter
X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)
print(X_train.shape)
diff --git a/doc/src/DimRed/PCAcancer.py b/doc/src/DimRed/PCAcancer.py
index b4983907e..c0b2f2219 100644
--- a/doc/src/DimRed/PCAcancer.py
+++ b/doc/src/DimRed/PCAcancer.py
@@ -8,22 +8,6 @@ import pandas as pd
cancerpd = pd.DataFrame(cancer.data, columns=cancer.feature_names)
-fig, axes = plt.subplots(15,2,figsize=(10,20))
-malignant = cancer.data[cancer.target == 0]
-benign = cancer.data[cancer.target == 1]
-ax = axes.ravel()
-
-for i in range(30):
- _, bins = np.histogram(cancer.data[:,i], bins =50)
- ax[i].hist(malignant[:,i], bins = bins, alpha = 0.5)
- ax[i].hist(benign[:,i], bins = bins, alpha = 0.5)
- ax[i].set_title(cancer.feature_names[i])
- ax[i].set_yticks(())
-ax[0].set_xlabel("Feature magnitude")
-ax[0].set_ylabel("Frequency")
-ax[0].legend(["Malignant", "Benign"], loc ="best")
-fig.tight_layout()
-plt.show()
import seaborn as sns
correlation_matrix = cancerpd.corr().round(1)
@@ -31,6 +15,9 @@ correlation_matrix = cancerpd.corr().round(1)
# 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)
+
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)