def _test(params, n): rv = PointMass(params=params) rv_sample = rv.sample(n) x = rv_sample.eval() x_tf = tf.constant(x, dtype=tf.float32) params = params.eval() assert np.allclose( rv.log_prob(x_tf).eval(), pointmass_logpmf_vec(x, params))
def _test(shape, n): rv = PointMass(shape, params=tf.zeros(shape)+0.5) rv_sample = rv.sample(n) x = rv_sample.eval() x_tf = tf.constant(x, dtype=tf.float32) params = rv.params.eval() for idx in range(shape[0]): assert np.allclose( rv.log_prob_idx((idx, ), x_tf).eval(), pointmass_logpmf_vec(x[:, idx], params[idx]))
def _test(shape, params, n): x = PointMass(shape, params) val_est = tuple(get_dims(x.sample(n))) val_true = (n,) + shape assert val_est == val_true
def _test(self, params, n): x = PointMass(params=params) val_est = x.sample(n).shape.as_list() val_true = n + tf.convert_to_tensor(params).shape.as_list() self.assertEqual(val_est, val_true)
def _test(shape, params, size): x = PointMass(shape, params) val_est = tuple(get_dims(x.sample(size=size))) val_true = (size, ) + shape assert val_est == val_true
def _test(params, n): x = PointMass(params=params) val_est = get_dims(x.sample(n)) val_true = n + get_dims(params) assert val_est == val_true
def _test(shape, params, n): x = PointMass(shape, params) val_est = tuple(get_dims(x.sample(n))) val_true = (n, ) + shape assert val_est == val_true