def test_ls_fit_b(): log_prop_t = test_log_prop_vals() b_matrix_t = np.matrix([[1,2], [1,1]]).T b_inv = ols_matrix(b_matrix_t) ls_fit_FA_t = np.dot(b_inv, np.matrix(log_prop_t)) npt.assert_equal(ls_fit_FA_t, mf.ls_fit_b(log_prop_t, unique_b_t)) return ls_fit_FA_t
def test_ls_fit_b(): log_prop_t = test_log_prop_vals() b_matrix_t = np.matrix([[1,2], [1,1]]).T b_inv = ols_matrix(b_matrix_t) ls_fit_FA_t = np.dot(b_inv, np.matrix(log_prop_t)) for idx in np.arange(len(ls_fit_FA_t)): npt.assert_equal(abs(ls_fit_FA_t[idx]-mf.ls_fit_b(log_prop_t, unique_b_t)[idx])<0.001, 1) return ls_fit_FA_t