fix: fix copilot comments

Author: iraedeus

experimental_env/analysis/analyze_summarizers/error_summarizer.py

@@ -41,11 +41,11 @@ def calculate(self, results: list[ExperimentDescription]) -> tuple:
         if not errors:
             return 0, 0, 0
 
-        mean = np.sum(errors) / len(errors)
-        standart_deviation = np.sqrt(np.sum([(x - mean) ** 2 for x in errors]) / len(errors))
-        median = errors[len(errors) // 2]
+        mean = np.mean(errors)
+        std = np.std(errors)
+        median = np.median(errors)
 
-        return float(mean), float(standart_deviation), float(median)
+        return float(mean), float(std), float(median)
 
     def analyze_method(self, results: list[ExperimentDescription], method: str):
         mean, deviation, median = self.calculate(results)

pyproject.toml

@@ -4,8 +4,7 @@ version = "0.1.0"
 description = "Parameter estimation of mixture distribution problem solver, based on EM algorithm"
 authors = ["ToxaKaz <[email protected]>", "iraedeus <[email protected]>"]
 keywords = ["EM algorithm", "mixture distribution", "parameter estimation"]
-
-license = "LICENSE"
+license = "MIT"
 readme = "README.md"
 classifiers = [
     "Development Status :: 3 - Alpha",

tests/models/test_beta.py

@@ -48,16 +48,16 @@ def test_params_convert_from_model(self, params):
 
     @given(valid_external_params())
     def test_roundtrip_external_to_internal(self, params):
-        pareto_model = Beta()
-        internal_params = pareto_model.params_convert_to_model(params)
-        recovered_params = pareto_model.params_convert_from_model(internal_params)
+        beta_model = Beta()
+        internal_params = beta_model.params_convert_to_model(params)
+        recovered_params = beta_model.params_convert_from_model(internal_params)
         assert np.allclose(recovered_params, params)
 
     @given(valid_internal_params())
     def test_roundtrip_internal_to_external(self, params):
-        pareto_model = Beta()
-        external_params = pareto_model.params_convert_from_model(params)
-        recovered_params = pareto_model.params_convert_to_model(external_params)
+        beta_model = Beta()
+        external_params = beta_model.params_convert_from_model(params)
+        recovered_params = beta_model.params_convert_to_model(external_params)
         assert np.allclose(recovered_params, params)
 
     @given(st.integers(min_value=1000, max_value=10000), valid_external_params())