refactor: Rename member functions algorithm to compute

Author: Ангелина

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/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

@@ -1,28 +1,28 @@
-from src.algorithms.semiparam_algorithms.nm_semi_param_algorithms.g_estimation_convolution import (
+from src.procedures.semiparametric.nm_semi_param_algorithms.g_estimation_convolution import (
     NMSemiParametricGEstimation,
 )
-from src.algorithms.semiparam_algorithms.nm_semi_param_algorithms.sigma_estimation_eigenvalue_based import (
+from src.procedures.semiparametric.nm_semi_param_algorithms.sigma_estimation_eigenvalue_based import (
     SemiParametricMeanSigmaEstimationEigenvalueBased,
 )
-from src.algorithms.semiparam_algorithms.nm_semi_param_algorithms.sigma_estimation_empirical import (
+from src.procedures.semiparametric.nm_semi_param_algorithms.sigma_estimation_empirical import (
     SemiParametricMeanSigmaEstimationEmpirical,
 )
-from src.algorithms.semiparam_algorithms.nv_semi_param_algorithms.g_estimation_given_mu import (
+from src.procedures.semiparametric.nv_semi_param_algorithms.g_estimation_given_mu import (
     SemiParametricNVEstimation,
 )
-from src.algorithms.semiparam_algorithms.nv_semi_param_algorithms.g_estimation_post_widder import (
+from src.procedures.semiparametric.nv_semi_param_algorithms.g_estimation_post_widder import (
     NVSemiParametricGEstimationPostWidder,
 )
-from src.algorithms.semiparam_algorithms.nvm_semi_param_algorithms.g_estimation_given_mu import (
+from src.procedures.semiparametric.nvm_semi_param_algorithms.g_estimation_given_mu import (
     SemiParametricGEstimationGivenMu,
 )
-from src.algorithms.semiparam_algorithms.nvm_semi_param_algorithms.g_estimation_given_mu_rqmc_based import (
+from src.procedures.semiparametric.nvm_semi_param_algorithms.g_estimation_given_mu_rqmc_based import (
     SemiParametricGEstimationGivenMuRQMCBased,
 )
-from src.algorithms.semiparam_algorithms.nvm_semi_param_algorithms.g_estimation_post_widder import (
+from src.procedures.semiparametric.nvm_semi_param_algorithms.g_estimation_post_widder import (
     SemiParametricGEstimationPostWidder,
 )
-from src.algorithms.semiparam_algorithms.nvm_semi_param_algorithms.mu_estimation import SemiParametricMuEstimation
+from src.procedures.semiparametric.nvm_semi_param_algorithms.mu_estimation import (SemiParametricMuEstimation)
 from src.register.algorithm_purpose import AlgorithmPurpose
 from src.register.register import Registry
 

src/procedures/semiparametric/nm_semi_param_algorithms/g_estimation_convolution.py

@@ -93,7 +93,7 @@ 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: _typing.NDArray[np.float64]) -> 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/procedures/semiparametric/nm_semi_param_algorithms/sigma_estimation_eigenvalue_based.py

@@ -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/procedures/semiparametric/nm_semi_param_algorithms/sigma_estimation_empirical.py

@@ -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/procedures/semiparametric/nv_semi_param_algorithms/g_estimation_given_mu.py

@@ -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: _typing.NDArray[np.float64]) -> EstimateResult:
         y_data = [self.compute_integrals_for_x(x) for x in self.x_data]
         return EstimateResult(list_value=y_data, success=True)

src/procedures/semiparametric/nv_semi_param_algorithms/g_estimation_post_widder.py

@@ -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._typing.NDArray) -> EstimateResult:
         """Estimate g(x)
 
         Args: