122f4ae6f400f0c43f3fc6a85481264809e678f5,GPflow/kernels.py,PeriodicKernel,K,#PeriodicKernel#Any#Any#,249

Before Change

        f2= tf.reshape(X2, tf.pack([1, tf.shape(X2)[0], tf.shape(X2)[1]]) )
        r = np.pi * (f-f2) / self.period
        
        dist = tf.exp(-0.5*tf.reduce_sum(tf.square(tf.sin(r)/self.lengthscales),tf.rank(r)-1))

        return self.variance * dist
    
    

After Change

            X2 = X

        // Introduce dummy dimension so we can use broadcasting
        f = tf.expand_dims(X, 1)  // now N x 1 x D
        f2 = tf.expand_dims(X2, 0)  // now 1 x M x D

        r = np.pi * (f-f2) / self.period

Instances