def _init_model(self, x):
n_features = x.shape[1]
self.converged_ = False
self.n_iter_ = 0
self._z = np.zeros(n_features)
# u has one row per each row-block in x
self._u = ds.zeros((x._n_blocks[0], n_features), (1, x._reg_shape[1]))
def test_full(self):
""" Tests full functions """
x = ds.zeros((10, 10), (3, 7), dtype=int)
x_np = np.zeros((10, 10), dtype=int)
self.assertTrue(_validate_array(x))
self.assertTrue(_equal_arrays(x.collect(), x_np))
x = ds.full((11, 11), (3, 5), 15, dtype=float)
x_np = np.full((11, 11), 15, dtype=float)
self.assertTrue(_validate_array(x))
self.assertTrue(_equal_arrays(x.collect(), x_np))
def empty_state(self):
bs = self._hilbert_size // max_par
self._state = ds.zeros(shape=(1, self._hilbert_size),
block_size=(1, bs),
dtype=np.complex64)
return