def var_vs_comp(X, start, stop, step):
print("Making variance v/s components plot. . . ")
components = []
variances = []
d = X.shape[1]
i_cols = np.arange(start, stop, step)
for k in i_cols:
pca = Preprocessing.PCA(X, k=k, whiten=False)
components.append(k)
variances.append(pca.var_retained)
plt.plot(components, variances)
plt.ylabel('variance retained')
plt.xlabel('number of components')
plt.show()
def performPCA(inputDataClass, reduced_columns):
############################################## PCA Visualisation #############################################
# #variance v/s n_components : Fashion MNIST
# start = 10
# stop = 500
# step = 15
# Visualization.var_vs_comp(inputDataClass.Train[:,:-1], start, stop, step)
########################################################### PCA #############################################
##### Our PCA ####
pca = Preprocessing.PCA(inputDataClass.Train[:, :-1],
k=reduced_columns,
whiten=False) ##### Hyperparameter ####
reduced_train = pca.reduce(inputDataClass.Train[:, :-1], True)
inputDataClass.Train = np.hstack(
(reduced_train, inputDataClass.Train[:, -1].reshape(-1, 1)))
print("train_data reduced. YAYAYAYA")
print("Train data reduced to columns = " + str(reduced_train.shape[1]))
reduced_test = pca.reduce(inputDataClass.Test[:, :-1], False)
inputDataClass.Test = np.hstack(
(reduced_test, inputDataClass.Test[:, -1].reshape(-1, 1)))
print("test_data reduced. YAYAYAYA")
print("Test data reduced to columns = " + str(reduced_test.shape[1]))
