MoLaplaceNADE.py 文件源码

def sample(self, n):
        W = self.W.get_value()
        V_alpha = self.V_alpha.get_value()
        b_alpha = self.b_alpha.get_value()
        V_mu = self.V_mu.get_value()
        b_mu = self.b_mu.get_value()
        V_sigma = self.V_sigma.get_value()
        b_sigma = self.b_sigma.get_value()
        activation_rescaling = self.activation_rescaling.get_value()
        samples = np.zeros((self.n_visible, n))
        for s in xrange(n):
            a = np.zeros((self.n_hidden,))  # H
            for i in xrange(self.n_visible):
                if i == 0:
                    a = W[i, :]
                else:
                    a = a + W[i, :] * samples[i - 1, s]
                h = self.parameters["nonlinearity"].get_numpy_f()(a * activation_rescaling[i])
                alpha = Utils.nnet.softmax(np.dot(h, V_alpha[i]) + b_alpha[i])  # C
                Mu = np.dot(h, V_mu[i]) + b_mu[i]  # C
                # Sigma = np.minimum(np.exp(np.dot(h, V_sigma[i]) + b_sigma[i]), 1)
                Sigma = np.exp(np.dot(h, V_sigma[i]) + b_sigma[i])
                comp = Utils.nnet.random_component(alpha)
                samples[i, s] = np.random.laplace(Mu[comp], Sigma[comp])
        return samples