sys.path.append('..')
import numpy as np
import rbm_rm
import rbm_cm
import matplotlib.pyplot as plt
import utils
import Preprocess
mnist_dir = os.path.join(os.environ['DATA_HOME'], 'mnist')
mnist_train_path = os.path.join(mnist_dir, 'MNISTTrainData.npy')
data_rm = np.load(mnist_train_path)
[normed, meanv, stdv] = Preprocess.mean_zero_unit_variance(data_rm)
#Look, I didn't actually use the normalized data because it broke everything
train_rm = data_rm[30000:, :]
valid_rm = data_rm[:30000, :]
data_cm = data_rm.transpose()
train_cm = data_cm[:,30000:]
valid_cm = data_cm[:,:30000]
nHidden = 100
ViewDimensions = (10, 10) # Should multiply to nHidden
TP = rbm_rm.RBMTrainParams()
TP.maxepoch = 15
rm_learner = rbm_rm.GV_RBM(nHidden, train_rm.shape[1])
