def test_RBM(train_X, train_Y, test_X, test_Y):
# Create and train RBM
rbm = RBM(28 * 28, 500)
rbm.fit(train_X)
rbm.get_filters()
train_X = np.array([rbm.inference(x) for x in train_X])
test_X = np.array([rbm.inference(x) for x in test_X])
logreg = LogisticRegression(max_iter=10)
logreg.fit(train_X, train_Y)
predict_Y = logreg.predict(train_X)
print("Accuracy on training data")
print(accuracy_score(train_Y, predict_Y))
predict_Y = logreg.predict(test_X)
print("Accuracy on test data")
print(accuracy_score(test_Y, predict_Y))
print("Epoch: {}, Reconstruction Error: {}".format(num, ReconErr))
for num in range(Epoch):
batch_xs = rbm1.passThrough(MA_data)
new_w, new_hb, new_vb, ReconErr = rbm2.train(batch_xs)
print("Epoch: {}, Reconstruction Error: {}".format(num, ReconErr))
for num in range(Epoch):
batch_xs = rbm1.passThrough(MA_data)
batch_xs1 = rbm2.passThrough(batch_xs)
new_w, new_hb, new_vb, ReconErr = rbm3.train(batch_xs1)
print("Epoch: {}, Reconstruction Error: {}".format(num, ReconErr))
temp1 = rbm1.passThrough(MA_data)
temp2 = rbm2.passThrough(temp1)
temp3 = rbm3.inference(temp2)
temp4 = rbm2.passBack(temp3)
REC = rbm1.passBack(temp4)
print(np.shape(REC))
RMSE = np.sqrt(
np.mean(np.square(MA_data - REC), axis=0) /
float(np.shape(MA_data)[0]))
print("Shape of RMSE: {}, RMSE {}".format(np.shape(RMSE), RMSE))
ReconRank = rankdata(RMSE)
with open('FeatureRank.csv', 'wb') as csvfile:
spamwriter = csv.writer(csvfile,
delimiter=',',
quotechar='|',
quoting=csv.QUOTE_MINIMAL)