Very minimal Gaussian Bernoulli RBM using batch learning

grbm.py

import numpy
from scipy.special import expit

class GRBM(object):
    def __init__(self, n_vis, n_hid, std=0.1):
        self.std = std
        self.W = numpy.random.normal(loc=-1./n_hid, scale=0.1, size=[n_vis + 1, n_hid + 1])
    
    def hidden_p(self, data):
        result = expit(data.dot(self.W))
        result[:, -1] = 1.
        return result
    
    def sample_hiddens(self, data):
        p = self.hidden_p(data)
        return numpy.random.random(size=p.shape) < p
    
    def sample_expected(self, hiddens):
        expected = hiddens.dot(self.W.T)
        expected[:, -1] = 1.
        return expected
    
    def transform(self, data):
        return self.hidden_p(self.extend_data(data))[:, :-1]
    
    def extend_data(self, data):
        return numpy.concatenate([data, numpy.ones([len(data), 1])], axis=1)
    
    def train(self, data, epochs=1, rate=0.1):
        data = self.extend_data(data)
        for epoch in range(epochs):
            hiddens = self.sample_hiddens(data)
            expected = self.sample_expected(hiddens)
            print 'mse = ', numpy.mean((data - expected) ** 2.)
            self.W += (data - expected).T.dot(hiddens) * (rate / len(data))