def _maybe_add_intercept(self, X): from dask_glm.utils import add_intercept if self.fit_intercept: return add_intercept(X) else: return X
def test_add_intercept_dask(): X = da.from_array(np.zeros((4, 4)), chunks=(2, 4)) result = utils.add_intercept(X) expected = da.from_array(np.array([ [0, 0, 0, 0, 1], [0, 0, 0, 0, 1], [0, 0, 0, 0, 1], [0, 0, 0, 0, 1], ], dtype=X.dtype), chunks=2) assert_eq(result, expected)
def test_add_intercept(): X = np.zeros((4, 4)) result = utils.add_intercept(X) expected = np.array([ [0, 0, 0, 0, 1], [0, 0, 0, 0, 1], [0, 0, 0, 0, 1], [0, 0, 0, 0, 1], ], dtype=X.dtype) assert_eq(result, expected)
def test_add_intercept_sparse(): X = sparse.COO(np.zeros((4, 4))) result = utils.add_intercept(X) expected = sparse.COO(np.array([ [0, 0, 0, 0, 1], [0, 0, 0, 0, 1], [0, 0, 0, 0, 1], [0, 0, 0, 0, 1], ], dtype=X.dtype)) assert (result == expected).all()
def _check_array(self, X): if self.fit_intercept: X = add_intercept(X) return check_array(X)
def _check_array(self, X): if self.fit_intercept: X = add_intercept(X) return check_array(X, accept_unknown_chunks=True)