PCA.py

M = np.mean(train.T, axis=1)
C= M - train
V=np.cov(C.T)

eigenValues, eigenVectors =eig(V)
k=2
idx = eigenValues.argsort()[-k:][::-1]
eigenValues = eigenValues[idx]
eigenVectors = eigenVectors[:,idx]

P = eigenVectors.T.dot(C.T)
PrincipalComponents=P.T



#from sklearn.decomposition import PCA

#pca = PCA(n_components=2)

#principalComponents = pca.fit_transform(train)



plt.scatter(PrincipalComponents[:,0], PrincipalComponents[:,1], color=['red'], marker= '*', s=7)