Add testing and result ss

Principal Component Analysis (Dimensionality Reduction)/PCA.py

@@ -13,7 +13,7 @@ def fit(self, X):
 		evalues, evectors = np.linalg.eig(covariance)
 		evectors = evectors.T
 		indexes_maxi_k = np.argsort(evalues)[ : : -1]
-		evectors = evector[indexes_maxi_k]
+		evectors = evectors[indexes_maxi_k]
 		self.final_components = evectors[0 : self.reduced_to_k]
 
 	def transform(self, X):

Principal Component Analysis (Dimensionality Reduction)/pca_testing.py

@@ -0,0 +1,27 @@
+import numpy as np 
+import matplotlib.pyplot as plt 
+from sklearn import datasets
+from PCA import PCA
+
+pre_data = datasets.load_iris()
+X = pre_data.data 
+Y = pre_data.target
+
+pca = PCA(2)
+pca.fit(X)
+X_transformed = pca.transform(X)
+
+print("Dimensions of X is " + str(X.shape))
+
+print("Dimensions of X_transformed is" + str(X_transformed.shape))
+
+print(Y)
+
+x1 = X_transformed[: ,0]
+x2 = X_transformed[: ,1]
+
+plt.xlabel("Component 1")
+plt.ylabel("Component 2")
+plt.scatter(x1, x2, alpha = 1, cmap = plt.cm.get_cmap('Dark2', 3), c = Y, edgecolor = "red")
+plt.colorbar()
+plt.show()