from RBM import RBM
from preprocessing import read_data
from sklearn.preprocessing import LabelEncoder
def sigmoid(x):
return 1/(1+np.exp(-x))
load_saved = True
train_data = np.load('train_data.npy')
if load_saved:
report = np.load("report.npy").item()
rbm = RBM(len(train_data), report["n_hidden"], report["batch_size"])
rbm.W = report["W"]
rbm.hbias = report["hbias"]
rbm.vbias = report["vbias"]
Y = np.argmax(train_data[:,:20], axis=1)
train_data = train_data[:,20:]
X = sigmoid(np.dot(train_data, rbm.W) + rbm.hbias)
#X = train_data
classifier = lr(0.01, solver = 'lbfgs', multi_class='multinomial')
classifier.fit(X, Y)
test_data = np.load('test_data.npy')
test_X = sigmoid(np.dot(test_data, rbm.W) + rbm.hbias)
#test_X = test_data
