e93def343b5add2be72076be7526b043c81807b2,pymc3/distributions/distribution.py,,generate_samples,#Any#,279

Before Change

                                        broadcast_shape,
                                        repeat_shape + prefix_shape,
                                        *args, **kwargs)
            if broadcast_shape == (1,) and not prefix_shape == ():
                samples = np.reshape(samples, repeat_shape + prefix_shape)
        else:
            samples = replicate_samples(generator,
                                        broadcast_shape,
                                        prefix_shape,

After Change

    else:
        prefix_shape = tuple(dist_shape)

    repeat_shape = infer_shape(size)

    if broadcast_shape == (1,) and prefix_shape == ():
        if size is not None:
            samples = generator(size=size, *args, **kwargs)

Instances