distributions.py 文件源码

def _gaussian_pdf(self, x, mixings, sigma, mean):
        """ Wrapper for Gaussian PDF """
        variance = tf.square(sigma)
        output_size = tf.cast(tf.shape(mean)[1], tf.float32)
        # Left: 1/sqrt(pi * 2 * variance) [N, K]
        left = tf.reciprocal(tf.pow(2*np.pi, output_size/2.0) *
                             tf.pow(sigma, output_size))
        # Exponent: e^[-(x-mu)^2/(2var)]. [N, K]
        right = tf.exp(-tf.divide(tf.square(x - mean), 2 * variance))
        return tf.multiply(left, right)