def normalize(x):
    return np.hstack((x[:, 0][:, None], (x[:, 1:] - X_mean) / (X_std)))

X = np.array([normalize(x).astype(theano.config.floatX) for x in X])
y = np.array([x[1:] for x in X])
X = np.array([x[:-1] for x in X])

train_end = int(.8 * len(X))

random_state = np.random.RandomState(1999)
minibatch_size = 20
n_hid = 300
rnn_dim = 1200


train_itr = list_iterator([X, y], minibatch_size, axis=1, make_mask=True,
                          stop_index=train_end)
X_mb, X_mb_mask, y_mb, y_mb_mask = next(train_itr)
train_itr.reset()

datasets_list = [X_mb, X_mb_mask, y_mb, y_mb_mask]
names_list = ["X", "X_mask", "y", "y_mask"]
graph = OrderedDict()
X_sym, X_mask_sym, y_sym, y_mask_sym = add_datasets_to_graph(
    datasets_list, names_list, graph)

l1 = relu_layer([X_sym], graph, 'l1', proj_dim=n_hid,
                random_state=random_state)
h = lstm_recurrent_layer([l1], X_mask_sym, rnn_dim, graph, 'l1_rec',
                         random_state=random_state)
l2 = relu_layer([h], graph, 'l2', proj_dim=n_hid,
                random_state=random_state)
X_std = np.sqrt(X_sqr - X_mean ** 2)


def normalize(x):
    return np.hstack((x[:, 0][:, None], (x[:, 1:] - X_mean) / (X_std)))


def unnormalize(x):
    return np.hstack((x[:, 0][:, None], (x[:, 1:] * X_std) + X_mean))

X = np.array([normalize(x).astype(theano.config.floatX) for x in X])
y = np.array([x[1:] for x in X])
X = np.array([x[:-1] for x in X])

minibatch_size = 20  # Size must match size in training, same for above preproc
train_itr = list_iterator([X, y], minibatch_size, axis=1, make_mask=True,
                          stop_index=1000)
X_mb, X_mb_mask, y_mb, y_mb_mask = next(train_itr)

running_mb = X_mb[:1] * 0
running_mask = X_mb_mask[:1] * 0


def gen_sample(rval, random_state, idx=-1):
    # binary
    # coeffs
    # mus
    # log_sigmas
    # corr
    binary, coeffs, mus, sigmas, corr = rval
    binary = binary[idx, :, 0]
    coeffs = coeffs[idx, :, :]
def normalize(x):
    return np.hstack((x[:, 0][:, None], (x[:, 1:] - X_mean) / (X_std)))


def unnormalize(x):
    return np.hstack((x[:, 0][:, None], (x[:, 1:] * X_std) + X_mean))


X = np.array([normalize(x).astype(theano.config.floatX) for x in X])
y = np.array([x[1:] for x in X])
X = np.array([x[:-1] for x in X])

minibatch_size = 20  # Size must match size in training, same for above preproc
train_itr = list_iterator([X, y],
                          minibatch_size,
                          axis=1,
                          make_mask=True,
                          stop_index=1000)
X_mb, X_mb_mask, y_mb, y_mb_mask = next(train_itr)

running_mb = X_mb[:1] * 0
running_mask = X_mb_mask[:1] * 0


def gen_sample(rval, random_state, idx=-1):
    # binary
    # coeffs
    # mus
    # log_sigmas
    # corr
    binary, coeffs, mus, sigmas, corr = rval