(self, s1, s2, steps)
return all_s, self.sess.run(self.x_reconstr, {self.s: all_s})
Interpolate between and s1 and s1 samples.
        s1, s2 are np.arrays of size (n_samp_points, 3).
Interpolate between and s1 and s1 samples.
        s1, s2 are np.arrays of size (n_samp_points, 3).
Interpolate between and s1 and s1 samples . s1 s2 are np . arrays of size ( n_samp_points 3 ) .
def interpolate_samples(self, s1, s2, steps):
        """ Interpolate between and s1 and s1 samples.
        s1, s2 are np.arrays of size (n_samp_points, 3).
        """
        s1_matched = self.match_samples(
            np.expand_dims(s1, axis=0), np.expand_dims(s2, axis=0)
        )
        s1_matched = np.squeeze(s1_matched, axis=0)

        all_s = np.zeros([steps + 2] + [len(s1)] + [3])
        for i, alpha in enumerate(np.linspace(0, 1, steps + 2)):
            all_s[i, :, :] = (alpha * s2) + ((1.0 - alpha) * s1_matched)

        return all_s, self.sess.run(self.x_reconstr, {self.s: all_s})
def interpolate_samples ( self , s1 , s2 , steps ) : s1_matched = self . match_samples ( np . expand_dims ( s1 , axis = 0 ) , np . expand_dims ( s2 , axis = 0 ) ) s1_matched = np . squeeze ( s1_matched , axis = 0 ) all_s = np . zeros ( [ steps + 2 ] + [ len ( s1 ) ] + [ 3 ] ) for i , alpha in enumerate ( np . linspace ( 0 , 1 , steps + 2 ) ) : all_s [ i , : , : ] = ( alpha * s2 ) + ( ( 1.0 - alpha ) * s1_matched ) return all_s , self . sess . run ( self . x_reconstr , { self . s : all_s } )