Update test_mixing_normal.py

Author: plidan123

tests/generators/nm_generator/test_mixing_normal.py

@@ -16,38 +16,38 @@ class TestMixingNormal:
     )
     def test_classic_generate_variance_0(self, mixing_variance: float, expected_variance: float) -> None:
         mixture = NormalMeanMixtures("classical", alpha=0, beta=mixing_variance**0.5, gamma=1, distribution=norm)
-        sample = self.generator.classical_generate(mixture, self.test_mixture_size)
+        sample = self.generator.generate(mixture, self.test_mixture_size)
         actual_variance = ndimage.variance(sample)
         assert actual_variance == pytest.approx(expected_variance, 0.1)
 
     @pytest.mark.parametrize("beta", np.random.uniform(0, 100, size=50))
     def test_classic_generate_variance_1(self, beta: float) -> None:
         expected_variance = beta**2 + 1
         mixture = NormalMeanMixtures("classical", alpha=0, beta=beta, gamma=1, distribution=norm)
-        sample = self.generator.classical_generate(mixture, self.test_mixture_size)
+        sample = self.generator.generate(mixture, self.test_mixture_size)
         actual_variance = ndimage.variance(sample)
         assert actual_variance == pytest.approx(expected_variance, 0.1)
 
     @pytest.mark.parametrize("beta, gamma", np.random.uniform(0, 100, size=(50, 2)))
     def test_classic_generate_variance_2(self, beta: float, gamma: float) -> None:
         expected_variance = beta**2 + gamma**2
         mixture = NormalMeanMixtures("classical", alpha=0, beta=beta, gamma=gamma, distribution=norm)
-        sample = self.generator.classical_generate(mixture, self.test_mixture_size)
+        sample = self.generator.generate(mixture, self.test_mixture_size)
         actual_variance = ndimage.variance(sample)
         assert actual_variance == pytest.approx(expected_variance, 0.1)
 
     @pytest.mark.parametrize("beta, gamma", np.random.uniform(0, 10, size=(50, 2)))
     def test_classic_generate_mean(self, beta: float, gamma: float) -> None:
         expected_mean = 0
         mixture = NormalMeanMixtures("classical", alpha=0, beta=beta, gamma=gamma, distribution=norm)
-        sample = self.generator.classical_generate(mixture, self.test_mixture_size)
+        sample = self.generator.generate(mixture, self.test_mixture_size)
         actual_mean = np.mean(np.array(sample))
         assert abs(actual_mean - expected_mean) < 1
 
     @pytest.mark.parametrize("expected_size", np.random.randint(0, 100, size=50))
     def test_classic_generate_size(self, expected_size: int) -> None:
         mixture = NormalMeanMixtures("classical", alpha=0, beta=1, gamma=1, distribution=norm)
-        sample = self.generator.classical_generate(mixture, expected_size)
+        sample = self.generator.generate(mixture, expected_size)
         actual_size = np.size(sample)
         assert actual_size == expected_size
 
@@ -56,37 +56,37 @@ def test_classic_generate_size(self, expected_size: int) -> None:
     )
     def test_canonical_generate_variance_0(self, mixing_variance: float, expected_variance: float) -> None:
         mixture = NormalMeanMixtures("canonical", sigma=1, distribution=norm(0, mixing_variance**0.5))
-        sample = self.generator.canonical_generate(mixture, self.test_mixture_size)
+        sample = self.generator.generate(mixture, self.test_mixture_size)
         actual_variance = ndimage.variance(sample)
         assert actual_variance == pytest.approx(expected_variance, 0.1)
 
     @pytest.mark.parametrize("sigma", np.random.uniform(0, 100, size=50))
     def test_canonical_generate_variance_1(self, sigma: float) -> None:
         expected_variance = sigma**2 + 1
         mixture = NormalMeanMixtures("canonical", sigma=sigma, distribution=norm)
-        sample = self.generator.canonical_generate(mixture, self.test_mixture_size)
+        sample = self.generator.generate(mixture, self.test_mixture_size)
         actual_variance = ndimage.variance(sample)
         assert actual_variance == pytest.approx(expected_variance, 0.1)
 
     @pytest.mark.parametrize("mixing_variance, sigma", np.random.uniform(0, 100, size=(50, 2)))
     def test_canonical_generate_variance_2(self, mixing_variance: float, sigma: float) -> None:
         expected_variance = mixing_variance + sigma**2
         mixture = NormalMeanMixtures("canonical", sigma=sigma, distribution=norm(0, mixing_variance**0.5))
-        sample = self.generator.canonical_generate(mixture, self.test_mixture_size)
+        sample = self.generator.generate(mixture, self.test_mixture_size)
         actual_variance = ndimage.variance(sample)
         assert actual_variance == pytest.approx(expected_variance, 0.1)
 
     @pytest.mark.parametrize("sigma", np.random.uniform(0, 10, size=50))
     def test_canonical_generate_mean(self, sigma: float) -> None:
         expected_mean = 0
         mixture = NormalMeanMixtures("canonical", sigma=sigma, distribution=norm)
-        sample = self.generator.canonical_generate(mixture, self.test_mixture_size)
+        sample = self.generator.generate(mixture, self.test_mixture_size)
         actual_mean = np.mean(np.array(sample))
         assert abs(actual_mean - expected_mean) < 1
 
     @pytest.mark.parametrize("expected_size", [*np.random.randint(0, 100, size=50), 0, 1, 1000000])
     def test_canonical_generate_size(self, expected_size: int) -> None:
         mixture = NormalMeanMixtures("canonical", sigma=1, distribution=norm)
-        sample = self.generator.canonical_generate(mixture, expected_size)
+        sample = self.generator.generate(mixture, expected_size)
         actual_size = np.size(sample)
         assert actual_size == expected_size