fix: renaming of statistical procedures

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
 

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/mixtures/nm_mixture.py

@@ -7,8 +7,8 @@
 from scipy.stats import norm, rv_continuous
 from scipy.stats.distributions import rv_frozen
 
-from src.algorithms.support_algorithms.log_rqmc import LogRQMC
-from src.algorithms.support_algorithms.rqmc import RQMC
+from src.procedures.support.log_rqmc import LogRQMC
+from src.procedures.support.rqmc import RQMC
 from src.mixtures.abstract_mixture import AbstractMixtures
 
 

src/mixtures/nmv_mixture.py

@@ -7,8 +7,8 @@
 from scipy.stats import geninvgauss, norm, rv_continuous
 from scipy.stats.distributions import rv_frozen
 
-from src.algorithms.support_algorithms.log_rqmc import LogRQMC
-from src.algorithms.support_algorithms.rqmc import RQMC
+from src.procedures.support.log_rqmc import LogRQMC
+from src.procedures.support.rqmc import RQMC
 from src.mixtures.abstract_mixture import AbstractMixtures
 
 

src/mixtures/nv_mixture.py

@@ -7,8 +7,8 @@
 from scipy.stats import norm, rv_continuous
 from scipy.stats.distributions import rv_frozen
 
-from src.algorithms.support_algorithms.log_rqmc import LogRQMC
-from src.algorithms.support_algorithms.rqmc import RQMC
+from src.procedures.support.log_rqmc import LogRQMC
+from src.procedures.support.rqmc import RQMC
 from src.mixtures.abstract_mixture import AbstractMixtures
 
 

src/procedures/__init__.py

@@ -0,0 +1,46 @@
+from src.procedures.semiparametric.nm_semi_param_algorithms.g_estimation_convolution import (
+    NMEstimationDensityInvMT,
+)
+from src.procedures.semiparametric.nm_semi_param_algorithms.sigma_estimation_eigenvalue_based import (
+    NMEstimationSigmaEigenvals,
+)
+from src.procedures.semiparametric.nm_semi_param_algorithms.sigma_estimation_empirical import (
+    NMEstimationSigmaLaplace,
+)
+from src.procedures.semiparametric.nv_semi_param_algorithms.g_estimation_given_mu import (
+    NVEstimationDensityInvMT,
+)
+from src.procedures.semiparametric.nv_semi_param_algorithms.g_estimation_post_widder import (
+    NMVEstimationDensityPW,
+)
+from src.procedures.semiparametric.nvm_semi_param_algorithms.g_estimation_given_mu import (
+    NMVEstimationDensityInvMTquad,
+)
+from src.procedures.semiparametric.nvm_semi_param_algorithms.g_estimation_given_mu_rqmc_based import (
+    NMVEstimationDensityInvMTquadRQMCBased,
+)
+from src.procedures.semiparametric.nvm_semi_param_algorithms.g_estimation_post_widder import (
+    SemiParametricGEstimationPostWidder,
+)
+from src.procedures.semiparametric.nvm_semi_param_algorithms.mu_estimation import (NMVEstimationMu)
+from src.register.algorithm_purpose import AlgorithmPurpose
+from src.register.register import Registry
+
+ALGORITHM_REGISTRY: Registry = Registry()
+ALGORITHM_REGISTRY.register("mu_estimation", AlgorithmPurpose.NMV_SEMIPARAMETRIC)(NMVEstimationMu)
+ALGORITHM_REGISTRY.register("density_estim_inv_mellin_quad_int", AlgorithmPurpose.NMV_SEMIPARAMETRIC)(
+    NMVEstimationDensityInvMTquad
+)
+ALGORITHM_REGISTRY.register("density_estim_deconv", AlgorithmPurpose.NM_SEMIPARAMETRIC)(NMEstimationDensityInvMT)
+ALGORITHM_REGISTRY.register("sigma_estimation_eigenvalue", AlgorithmPurpose.NM_SEMIPARAMETRIC)(
+    NMEstimationSigmaEigenvals
+)
+ALGORITHM_REGISTRY.register("sigma_estimation_laplace", AlgorithmPurpose.NM_SEMIPARAMETRIC)(
+    NMEstimationSigmaLaplace
+)
+ALGORITHM_REGISTRY.register("density_estim_inv_mellin_rqmc_int", AlgorithmPurpose.NMV_SEMIPARAMETRIC)(
+    NMVEstimationDensityInvMTquadRQMCBased
+)
+ALGORITHM_REGISTRY.register("density_estim_post_widder", AlgorithmPurpose.NMV_SEMIPARAMETRIC)(
+    SemiParametricGEstimationPostWidder
+)

src/algorithms/semiparam_algorithms/nm_semi_param_algorithms/g_estimation_convolution.py → src/procedures/semiparametric/nm_semi_param_algorithms/g_estimation_convolution.py

@@ -11,8 +11,7 @@
 BOHMAN_DELTA_DEFAULT_VALUE: float = 0.0001
 X_DATA_DEFAULT_VALUE: List[float] = [1.0]
 
-
-class NMSemiParametricGEstimation:
+class NMEstimationDensityInvMT:
     """Estimation of mixing density function g (xi density function) of NM mixture
     represented in canonical form Y = xi + sigma*N.
 
@@ -34,8 +33,8 @@ class ParamsAnnotation(TypedDict, total=False):
         bohman_n: int
         bohman_delta: float
 
-    def __init__(self, sample: Optional[_typing.NDArray[np.float64]] = None, **kwargs: Unpack[ParamsAnnotation]):
-        self.sample: _typing.NDArray[np.float64] = np.array([]) if sample is None else sample
+    def __init__(self, sample: Optional[np.ndarray] = None, **kwargs: Unpack[ParamsAnnotation]):
+        self.sample: np.ndarray = np.array([]) if sample is None else sample
         self.n: int = len(self.sample)
         (
             self.x_data,
@@ -93,7 +92,7 @@ def cdf(self, X: np.ndarray) -> np.ndarray:
 
             return F_x.real
 
-    def algorithm(self, sample: _typing.NDArray[np.float64]) -> EstimateResult:
+    def compute(self, sample: np.ndarray) -> EstimateResult:
         inv = self.BohmanA(N=self.bohman_n, delta=self.bohman_delta)
         inv.fit(self.characteristic_function_xi)
         x_data_array = np.array(self.x_data, dtype=np.float64)

src/algorithms/semiparam_algorithms/nm_semi_param_algorithms/sigma_estimation_eigenvalue_based.py → src/procedures/semiparametric/nm_semi_param_algorithms/sigma_estimation_eigenvalue_based.py

@@ -14,7 +14,7 @@
 PARAMETER_KEYS = ["l", "k", "eps", "search_area", "search_density"]
 
 
-class SemiParametricMeanSigmaEstimationEigenvalueBased:
+class NMEstimationSigmaEigenvals:
     """Estimation of sigma parameter of NM mixture represented in canonical form Y = xi + sigma*N.
 
     Args:
@@ -86,7 +86,7 @@ def _build_matrix(self, tau: float) -> np.ndarray:
                 matrix[i, j] = self._alpha(t[i] - t[j], tau)
         return matrix
 
-    def algorithm(self, sample: np.ndarray) -> EstimateResult:
+    def compute(self, sample: np.ndarray) -> EstimateResult:
         """Estimate sigma.
 
         Args:

src/algorithms/semiparam_algorithms/nm_semi_param_algorithms/sigma_estimation_empirical.py → src/procedures/semiparametric/nm_semi_param_algorithms/sigma_estimation_empirical.py

@@ -9,7 +9,7 @@
 PARAMETER_KEYS = ["t"]
 
 
-class SemiParametricMeanSigmaEstimationEmpirical:
+class NMEstimationSigmaLaplace:
     """Estimation of sigma parameter of NM mixture represented in canonical form Y = xi + sigma*N.
 
     Args:
@@ -51,7 +51,7 @@ def _validate_kwargs(**kwargs: ParamsAnnotation) -> float:
             raise ValueError("Expected a positive float as parameter `t`.")
         return t
 
-    def algorithm(self, sample: np.ndarray) -> EstimateResult:
+    def compute(self, sample: np.ndarray) -> EstimateResult:
         """Estimate the sigma parameter.
 
         Args:

src/algorithms/semiparam_algorithms/nv_semi_param_algorithms/g_estimation_given_mu.py → src/procedures/semiparametric/nv_semi_param_algorithms/g_estimation_given_mu.py

@@ -30,7 +30,7 @@ def v_sequence_default_value(n: float) -> float:
 INTEGRATION_LIMIT_DEFAULT_VALUE: int = 50
 
 
-class SemiParametricNVEstimation:
+class NVEstimationDensityInvMT:
     """Estimation of mixing density function g (xi density function) of NV mixture represented in canonical form Y =
     alpha + sqrt(xi)*N, where alpha = 0 and mu = 0.
 
@@ -49,13 +49,13 @@ class ParamsAnnotation(TypedDict, total=False):
         integration_tolerance: float
         integration_limit: int
 
-    def __init__(self, sample: Optional[_typing.NDArray[np.float64]] = None, **kwargs: Unpack[ParamsAnnotation]):
+    def __init__(self, sample: Optional[np.ndarray] = None, **kwargs: Unpack[ParamsAnnotation]):
         self.x_powers: Dict[float, np.ndarray] = {}
         self.second_u_integrals: np.ndarray
         self.first_u_integrals: np.ndarray
         self.gamma_grid: np.ndarray
         self.v_grid: np.ndarray
-        self.sample: _typing.NDArray[np.float64] = np.array([]) if sample is None else sample
+        self.sample: np.ndarray = np.array([]) if sample is None else sample
         self.n: int = len(self.sample)
         (
             self.gmm,
@@ -173,6 +173,6 @@ def compute_integrals_for_x(self, x: float) -> float:
         total = np.sum(first_integral + second_integral) / self.denominator
         return max(0.0, total.real)
 
-    def algorithm(self, sample: _typing.NDArray[np.float64]) -> EstimateResult:
+    def compute(self, sample: np.ndarray) -> EstimateResult:
         y_data = [self.compute_integrals_for_x(x) for x in self.x_data]
         return EstimateResult(list_value=y_data, success=True)

src/algorithms/semiparam_algorithms/nv_semi_param_algorithms/g_estimation_post_widder.py → src/procedures/semiparametric/nv_semi_param_algorithms/g_estimation_post_widder.py

@@ -13,7 +13,7 @@
 X_DATA_DEFAULT_VALUE = [1.0]
 
 
-class NVSemiParametricGEstimationPostWidder:
+class NMVEstimationDensityPW:
     """Estimation of mixing density function g (xi density function) of NVM mixture represented in classical form Y =
     alpha + sigma*sqrt(xi)*N, where alpha = 0 and mu, sigma are given.
 
@@ -77,7 +77,7 @@ def p_x_estimation(self, x: float) -> float:
         )
         return result.real
 
-    def algorithm(self, sample: np._typing.NDArray) -> EstimateResult:
+    def compute(self, sample: np.ndarray) -> EstimateResult:
         """Estimate g(x)
 
         Args:

src/algorithms/semiparam_algorithms/nvm_semi_param_algorithms/g_estimation_given_mu.py → src/procedures/semiparametric/nvm_semi_param_algorithms/g_estimation_given_mu.py

@@ -31,7 +31,7 @@ def v_sequence_default_value(n: float) -> float:
 INTEGRATION_LIMIT_DEFAULT_VALUE: int = 50
 
 
-class SemiParametricGEstimationGivenMu:
+class NMVEstimationDensityInvMTquad:
     """Estimation of mixing density function g (xi density function) of NVM mixture represented in canonical form Y =
     alpha + mu*xi + sqrt(xi)*N, where alpha = 0 and mu is given.
 
@@ -51,13 +51,13 @@ class ParamsAnnotation(TypedDict, total=False):
         integration_tolerance: float
         integration_limit: int
 
-    def __init__(self, sample: Optional[_typing.NDArray[np.float64]] = None, **kwargs: Unpack[ParamsAnnotation]):
+    def __init__(self, sample: Optional[np.ndarray] = None, **kwargs: Unpack[ParamsAnnotation]):
         self.x_powers: Dict[float, np.ndarray] = {}
         self.second_u_integrals: np.ndarray
         self.first_u_integrals: np.ndarray
         self.gamma_grid: np.ndarray
         self.v_grid: np.ndarray
-        self.sample: _typing.NDArray[np.float64] = np.array([]) if sample is None else sample
+        self.sample: np.ndarray= np.array([]) if sample is None else sample
         self.n: int = len(self.sample)
         (
             self.mu,
@@ -179,6 +179,6 @@ def compute_integrals_for_x(self, x: float) -> float:
         total = np.sum(first_integral + second_integral) / self.denominator
         return max(0.0, total.real)
 
-    def algorithm(self, sample: _typing.NDArray[np.float64]) -> EstimateResult:
+    def compute(self, sample: np.ndarray) -> EstimateResult:
         y_data = [self.compute_integrals_for_x(x) for x in self.x_data]
         return EstimateResult(list_value=y_data, success=True)

src/algorithms/semiparam_algorithms/nvm_semi_param_algorithms/g_estimation_given_mu_rqmc_based.py → src/procedures/semiparametric/nvm_semi_param_algorithms/g_estimation_given_mu_rqmc_based.py

@@ -7,7 +7,7 @@
 from scipy.integrate import quad_vec
 from scipy.special import gamma
 
-from src.algorithms.support_algorithms.rqmc import RQMC
+from src.procedures.support.rqmc import RQMC
 from src.estimators.estimate_result import EstimateResult
 
 MU_DEFAULT_VALUE = 1.0
@@ -32,7 +32,7 @@ def v_sequence_default_value(n: float) -> float:
 INTEGRATION_LIMIT_DEFAULT_VALUE: int = 50
 
 
-class SemiParametricGEstimationGivenMuRQMCBased:
+class NMVEstimationDensityInvMTquadRQMCBased:
     """Estimation of mixing density function g (xi density function) of NVM mixture represented in canonical form Y =
     alpha + mu*xi + sqrt(xi)*N, where alpha = 0 and mu is given.
 
@@ -52,13 +52,13 @@ class ParamsAnnotation(TypedDict, total=False):
         integration_tolerance: float
         integration_limit: int
 
-    def __init__(self, sample: Optional[_typing.NDArray[np.float64]] = None, **kwargs: Unpack[ParamsAnnotation]):
+    def __init__(self, sample: Optional[np.ndarray] = None, **kwargs: Unpack[ParamsAnnotation]):
         self.x_powers: Dict[float, np.ndarray] = {}
         self.second_u_integrals: np.ndarray
         self.first_u_integrals: np.ndarray
         self.gamma_grid: np.ndarray
         self.v_grid: np.ndarray
-        self.sample: _typing.NDArray[np.float64] = np.array([]) if sample is None else sample
+        self.sample: np.ndarray = np.array([]) if sample is None else sample
         self.n: int = len(self.sample)
         (
             self.mu,
@@ -171,6 +171,6 @@ def compute_integrals_for_x(self, x: float) -> float:
         total = (first_integral + second_integral) / self.denominator
         return max(0.0, total.real)
 
-    def algorithm(self, sample: np._typing.NDArray) -> EstimateResult:
+    def compute(self, sample: np.ndarray) -> EstimateResult:
         y_data = [self.compute_integrals_for_x(x) for x in self.x_data]
         return EstimateResult(list_value=y_data, success=True)

src/algorithms/semiparam_algorithms/nvm_semi_param_algorithms/g_estimation_post_widder.py → src/procedures/semiparametric/nvm_semi_param_algorithms/g_estimation_post_widder.py

@@ -80,7 +80,7 @@ def p_x_estimation(self, x: float) -> float:
         )
         return result.real
 
-    def algorithm(self, sample: np._typing.NDArray) -> EstimateResult:
+    def compute(self, sample: np._typing.NDArray) -> EstimateResult:
         """Estimate g(x)
 
         Args: