Global refactoring

README.md

@@ -120,7 +120,7 @@ Computes given integral with the limits of integration being 0 and 1 using Rando
 
 ### Usage:
 ```Python
-from src.algorithms.support_algorithms.rqmc import RQMC
+from src.procedures.support.rqmc import RQMC
 rqmc = RQMC(lambda x: x**3 - x**2 + 1, error_tolerance=1e-5)
 ```
 So `rqmc()[0]` is estimated integral value and `rqmc()[1]` is current error tolerance.

jupiter_examples/nm_sigma_estimation_comparison.ipynb

@@ -250,7 +250,7 @@
     "    \"\"\"\n",
     "    generator = NMGenerator()\n",
     "    mixture = NormalMeanMixtures(\"canonical\", sigma=real_sigma, distribution=distribution)\n",
-    "    return generator.canonical_generate(mixture, sample_len)\n",
+    "    return generator.generate(mixture, sample_len)\n",
     "\n",
     "def estimate_sigma_eigenvalue_based(sample, real_sigma, search_area, a, b):\n",
     "    sample_len = len(sample)\n",

src/estimators/abstract_estimator.py

@@ -2,7 +2,7 @@
 
 from numpy import _typing
 
-from src.algorithms import ALGORITHM_REGISTRY
+from src.procedures import ALGORITHM_REGISTRY
 from src.estimators.estimate_result import EstimateResult
 from src.register.algorithm_purpose import AlgorithmPurpose
 
@@ -14,7 +14,7 @@ class AbstractEstimator:
         algorithm_name: A string indicating chosen algorithm.
         params: A dictionary of algorithm parameters.
         estimate_result: Estimation result.
-        _registry: Registry that contains classes of all algorithms.
+        _registry: Registry that contains classes of all procedures.
         _purpose: Defines purpose of algorithm, one of the registry key.
     """
 
@@ -47,7 +47,7 @@ def set_params(self, algorithm_name: str, params: dict | None = None) -> Self:
         return self
 
     def get_available_algorithms(self) -> list[str]:
-        """Get all algorithms that can be used for current estimator class"""
+        """Get all procedures that can be used for current estimator class"""
         return [key[0] for key in self._registry.register_of_names.keys() if key[1] == self._purpose]
 
     def estimate(self, sample: _typing.ArrayLike) -> EstimateResult:

src/estimators/semiparametric/abstract_semiparametric_estimator.py

@@ -4,7 +4,7 @@
 from src.estimators.estimate_result import EstimateResult
 
 
-class AbstractSemiParametricEstimator(AbstractEstimator):
+class AbstractSemiparEstim(AbstractEstimator):
     def __init__(self, algorithm_name: str, params: dict | None = None) -> None:
         super().__init__(algorithm_name, params)
 

src/estimators/semiparametric/nm_semiparametric_estimator.py

@@ -1,11 +1,11 @@
 from numpy import _typing
 
 from src.estimators.estimate_result import EstimateResult
-from src.estimators.semiparametric.abstract_semiparametric_estimator import AbstractSemiParametricEstimator
+from src.estimators.semiparametric.abstract_semiparametric_estimator import AbstractSemiparEstim
 from src.register.algorithm_purpose import AlgorithmPurpose
 
 
-class NMSemiParametricEstimator(AbstractSemiParametricEstimator):
+class NMSemiparEstim(AbstractSemiparEstim):
     def __init__(self, algorithm_name: str, params: dict | None = None) -> None:
         super().__init__(algorithm_name, params)
         self._purpose = AlgorithmPurpose.NM_SEMIPARAMETRIC

src/estimators/semiparametric/nmv_semiparametric_estimator.py

@@ -1,11 +1,11 @@
 from numpy import _typing
 
 from src.estimators.estimate_result import EstimateResult
-from src.estimators.semiparametric.abstract_semiparametric_estimator import AbstractSemiParametricEstimator
+from src.estimators.semiparametric.abstract_semiparametric_estimator import AbstractSemiparEstim
 from src.register.algorithm_purpose import AlgorithmPurpose
 
 
-class NMVSemiParametricEstimator(AbstractSemiParametricEstimator):
+class NMVSemiparEstim(AbstractSemiparEstim):
     def __init__(self, algorithm_name: str, params: dict | None = None) -> None:
         super().__init__(algorithm_name, params)
         self._purpose = AlgorithmPurpose.NMV_SEMIPARAMETRIC

src/estimators/semiparametric/nv_semiparametric_estimator.py

@@ -1,11 +1,11 @@
 from numpy import _typing
 
 from src.estimators.estimate_result import EstimateResult
-from src.estimators.semiparametric.abstract_semiparametric_estimator import AbstractSemiParametricEstimator
+from src.estimators.semiparametric.abstract_semiparametric_estimator import AbstractSemiparEstim
 from src.register.algorithm_purpose import AlgorithmPurpose
 
 
-class NVSemiParametricEstimator(AbstractSemiParametricEstimator):
+class NVSemiparEstim(AbstractSemiparEstim):
     def __init__(self, algorithm_name: str, params: dict | None = None) -> None:
         super().__init__(algorithm_name, params)
         self._purpose = AlgorithmPurpose.NV_SEMIPARAMETRIC

src/generators/nm_generator.py

@@ -9,7 +9,7 @@
 class NMGenerator(AbstractGenerator):
 
     @staticmethod
-    def classical_generate(mixture: AbstractMixtures, size: int) -> tpg.NDArray:
+    def generate(mixture: AbstractMixtures, size: int) -> tpg.NDArray:
         """Generate a sample of given size. Classical form of NMM
 
         Args:
@@ -27,25 +27,6 @@ def classical_generate(mixture: AbstractMixtures, size: int) -> tpg.NDArray:
             raise ValueError("Mixture must be NormalMeanMixtures")
         mixing_values = mixture.params.distribution.rvs(size=size)
         normal_values = scipy.stats.norm.rvs(size=size)
+        if mixture.mixture_form == "canonical":
+            return mixing_values + mixture.params.sigma * normal_values
         return mixture.params.alpha + mixture.params.beta * mixing_values + mixture.params.gamma * normal_values
-
-    @staticmethod
-    def canonical_generate(mixture: AbstractMixtures, size: int) -> tpg.NDArray:
-        """Generate a sample of given size. Canonical form of NMM
-
-        Args:
-            mixture: Normal Mean Mixture
-            size: length of sample
-
-        Returns: sample of given size
-
-        Raises:
-            ValueError: If mixture is not a Normal Mean Mixture
-
-        """
-
-        if not isinstance(mixture, NormalMeanMixtures):
-            raise ValueError("Mixture must be NormalMeanMixtures")
-        mixing_values = mixture.params.distribution.rvs(size=size)
-        normal_values = scipy.stats.norm.rvs(size=size)
-        return mixing_values + mixture.params.sigma * normal_values

src/generators/nmv_generator.py

@@ -9,7 +9,7 @@
 class NMVGenerator(AbstractGenerator):
 
     @staticmethod
-    def classical_generate(mixture: AbstractMixtures, size: int) -> tpg.NDArray:
+    def generate(mixture: AbstractMixtures, size: int) -> tpg.NDArray:
         """Generate a sample of given size. Classical form of NMVM
 
         Args:
@@ -27,29 +27,10 @@ def classical_generate(mixture: AbstractMixtures, size: int) -> tpg.NDArray:
             raise ValueError("Mixture must be NormalMeanMixtures")
         mixing_values = mixture.params.distribution.rvs(size=size)
         normal_values = scipy.stats.norm.rvs(size=size)
+        if mixture.mixture_form == "canonical":
+            return mixture.params.alpha + mixture.params.mu * mixing_values + (mixing_values ** 0.5) * normal_values
         return (
             mixture.params.alpha
             + mixture.params.beta * mixing_values
             + mixture.params.gamma * (mixing_values**0.5) * normal_values
-        )
-
-    @staticmethod
-    def canonical_generate(mixture: AbstractMixtures, size: int) -> tpg.NDArray:
-        """Generate a sample of given size. Canonical form of NMVM
-
-        Args:
-            mixture: Normal Mean Variance Mixtures
-            size: length of sample
-
-        Returns: sample of given size
-
-        Raises:
-            ValueError: If mixture type is not Normal Mean Variance Mixtures
-
-        """
-
-        if not isinstance(mixture, NormalMeanVarianceMixtures):
-            raise ValueError("Mixture must be NormalMeanMixtures")
-        mixing_values = mixture.params.distribution.rvs(size=size)
-        normal_values = scipy.stats.norm.rvs(size=size)
-        return mixture.params.alpha + mixture.params.mu * mixing_values + (mixing_values**0.5) * normal_values
+        )

src/generators/nv_generator.py

@@ -9,7 +9,7 @@
 class NVGenerator(AbstractGenerator):
 
     @staticmethod
-    def classical_generate(mixture: AbstractMixtures, size: int) -> tpg.NDArray:
+    def generate(mixture: AbstractMixtures, size: int) -> tpg.NDArray:
         """Generate a sample of given size. Classical form of NVM
 
         Args:
@@ -27,25 +27,6 @@ def classical_generate(mixture: AbstractMixtures, size: int) -> tpg.NDArray:
             raise ValueError("Mixture must be NormalMeanMixtures")
         mixing_values = mixture.params.distribution.rvs(size=size)
         normal_values = scipy.stats.norm.rvs(size=size)
-        return mixture.params.alpha + mixture.params.gamma * (mixing_values**0.5) * normal_values
-
-    @staticmethod
-    def canonical_generate(mixture: AbstractMixtures, size: int) -> tpg.NDArray:
-        """Generate a sample of given size. Canonical form of NVM
-
-        Args:
-            mixture: Normal Variance Mixtures
-            size: length of sample
-
-        Returns: sample of given size
-
-        Raises:
-            ValueError: If mixture type is not Normal Variance Mixtures
-
-        """
-
-        if not isinstance(mixture, NormalVarianceMixtures):
-            raise ValueError("Mixture must be NormalMeanMixtures")
-        mixing_values = mixture.params.distribution.rvs(size=size)
-        normal_values = scipy.stats.norm.rvs(size=size)
-        return mixture.params.alpha + (mixing_values**0.5) * normal_values
+        if mixture.mixture_form == "canonical":
+            return mixture.params.alpha + (mixing_values ** 0.5) * normal_values
+        return mixture.params.alpha + mixture.params.gamma * (mixing_values**0.5) * normal_values