def test_GIVEN_one_masked_one_nparray_WHEN_lin_regression_THEN_regression_correct(
self):
stats = StatsAnalyzer(self.data1, self.missing2)
res = stats.linear_regression()
expected_res = [
-5.1404761905, 12.3595238095, -0.4079085869, 5.14561290806
]
actual_res = res[0].grad, res[1].intercept, res[2].r, res[3].stderr
assert_that(np.allclose(actual_res, expected_res))
def test_GIVEN_missing_vals_WHEN_lin_regression_THEN_regression_correct(
self):
stats = StatsAnalyzer(self.missing1, self.missing2)
res = stats.linear_regression()
expected_res = [
1.1920369653, -0.6908343017, 0.999845219, 0.0104877890357
]
actual_res = res[0].grad, res[1].intercept, res[2].r, res[3].stderr
assert_that(np.allclose(actual_res, expected_res))
def test_GIVEN_no_missing_vals_WHEN_lin_regression_THEN_regression_correct(
self):
stats = StatsAnalyzer(self.data1, self.data2)
res = stats.linear_regression()
expected_res = [
0.9912730184, 0.1345076061, 0.997485722, 0.0248994694107
]
actual_res = res[0].grad, res[1].intercept, res[2].r, res[3].stderr
assert_that(np.allclose(actual_res, expected_res))
def test_GIVEN_one_masked_one_nparray_WHEN_lin_regression_THEN_regression_correct(self):
stats = StatsAnalyzer(self.data1, self.missing2)
res = stats.linear_regression()
expected_res = [-5.1404761905, 12.3595238095, -0.4079085869, 5.14561290806]
actual_res = res[0].grad, res[1].intercept, res[2].r, res[3].stderr
assert_that(np.allclose(actual_res, expected_res))
def test_GIVEN_missing_vals_WHEN_lin_regression_THEN_regression_correct(self):
stats = StatsAnalyzer(self.missing1, self.missing2)
res = stats.linear_regression()
expected_res = [1.1920369653, -0.6908343017, 0.999845219, 0.0104877890357]
actual_res = res[0].grad, res[1].intercept, res[2].r, res[3].stderr
assert_that(np.allclose(actual_res, expected_res))
def test_GIVEN_no_missing_vals_WHEN_lin_regression_THEN_regression_correct(self):
stats = StatsAnalyzer(self.data1, self.data2)
res = stats.linear_regression()
expected_res = [0.9912730184, 0.1345076061, 0.997485722, 0.0248994694107]
actual_res = res[0].grad, res[1].intercept, res[2].r, res[3].stderr
assert_that(np.allclose(actual_res, expected_res))
