feat(families): added logpdf-characteristicks for builtins and tests for them

Author: MyznikovFD

src/pysatl_core/families/builtins/continuous/exponential.py

@@ -162,6 +162,28 @@ def char_func(parameters: Parametrization, t: NumericArray) -> ComplexArray:
         )
         return cast(ComplexArray, result)
 
+    def lpdf(parameters: Parametrization, x: NumericArray) -> NumericArray:
+        """
+        Logarithm of the probability density function for exponential distribution.
+
+        Parameters
+        ----------
+        parameters : Parametrization
+        Distribution parameters object with field:
+            - lambda_: float (rate parameter)
+        x : NumericArray
+            Points at which to evaluate the log-probability density function
+
+        Returns
+        -------
+        NumericArray
+            Log-probability density values at points x.
+            For x < 0 returns -np.inf.
+        """
+        parameters = cast(_Rate, parameters)
+        lambda_ = parameters.lambda_
+        return np.where(x >= 0, np.log(lambda_) - lambda_ * x, -np.inf)
+
     def mean_func(parameters: Parametrization) -> float:
         """Mean of exponential distribution."""
         parameters = cast(_Rate, parameters)
@@ -210,6 +232,7 @@ def _support(_: Parametrization) -> ContinuousSupport:
             CharacteristicName.CDF: cdf,
             CharacteristicName.PPF: ppf,
             CharacteristicName.CF: char_func,
+            CharacteristicName.LPDF: lpdf,
             CharacteristicName.MEAN: mean_func,
             CharacteristicName.VAR: var_func,
             CharacteristicName.SKEW: skew_func,

src/pysatl_core/families/builtins/continuous/normal.py

@@ -165,6 +165,34 @@ def char_func(parameters: Parametrization, t: NumericArray) -> ComplexArray:
         mu = parameters.mu
         return cast(ComplexArray, np.exp(1j * mu * t - 0.5 * (sigma**2) * (t**2)))
 
+    def lpdf(parameters: Parametrization, x: NumericArray) -> NumericArray:
+        """
+        Logarithm of the probability density function for normal distribution.
+
+        Parameters
+        ----------
+        parameters : Parametrization
+            Distribution parameters object with fields:
+            - mu: float (mean)
+            - sigma: float (standard deviation)
+        x : NumericArray
+            Points at which to evaluate the log-probability density function
+
+        Returns
+        -------
+        NumericArray
+            Log-probability density values at points x
+        """
+        parameters = cast(_MeanStd, parameters)
+        sigma = parameters.sigma
+        mu = parameters.mu
+
+        # log pdf = -0.5*log(2π) - log(σ) - (x-μ)²/(2σ²)
+        return cast(
+            NumericArray,
+            -0.5 * np.log(2 * np.pi) - np.log(sigma) - ((x - mu) ** 2) / (2 * sigma**2),
+        )
+
     def mean_func(parameters: Parametrization) -> float:
         """Mean of normal distribution."""
         parameters = cast(_MeanStd, parameters)
@@ -213,6 +241,7 @@ def _support(_: Parametrization) -> ContinuousSupport:
             CharacteristicName.CDF: cdf,
             CharacteristicName.PPF: ppf,
             CharacteristicName.CF: char_func,
+            CharacteristicName.LPDF: lpdf,
             CharacteristicName.MEAN: mean_func,
             CharacteristicName.VAR: var_func,
             CharacteristicName.SKEW: skew_func,

src/pysatl_core/families/builtins/continuous/uniform.py

@@ -188,6 +188,30 @@ def char_func(parameters: Parametrization, t: NumericArray) -> ComplexArray:
 
         return cast(ComplexArray, sinc_val * np.exp(1j * center * t_arr))
 
+    def lpdf(parameters: Parametrization, x: NumericArray) -> NumericArray:
+        """
+        Logarithm of the probability density function for uniform distribution.
+
+        Parameters
+        ----------
+        parameters : Parametrization
+            Distribution parameters object with fields:
+            - lower_bound: float (lower bound)
+            - upper_bound: float (upper bound)
+        x : NumericArray
+            Points at which to evaluate the log-probability density function
+
+        Returns
+        -------
+        NumericArray
+            Log-probability density values at points x
+            For x outside [lower_bound, upper_bound] returns -np.inf
+        """
+        parameters = cast(_Standard, parameters)
+        a = parameters.lower_bound
+        b = parameters.upper_bound
+        return np.where((x >= a) & (x <= b), -np.log(b - a), -np.inf)
+
     def mean_func(parameters: Parametrization) -> float:
         """Mean of uniform distribution."""
         parameters = cast(_Standard, parameters)
@@ -245,6 +269,7 @@ def _support(parameters: Parametrization) -> ContinuousSupport:
             CharacteristicName.CDF: cdf,
             CharacteristicName.PPF: ppf,
             CharacteristicName.CF: char_func,
+            CharacteristicName.LPDF: lpdf,
             CharacteristicName.MEAN: mean_func,
             CharacteristicName.VAR: var_func,
             CharacteristicName.SKEW: skew_func,

src/pysatl_core/types.py

@@ -285,6 +285,7 @@ class CharacteristicName(StrEnum):
     CDF = "cdf"
     PPF = "ppf"
     PMF = "pmf"
+    LPDF = "lpdf"  # unimplemented in graph yet
     CF = "cf"  # unimplemented in graph yet
     SF = "sf"  # unimplemented in graph yet
     MEAN = "mean"  # unimplemented in graph yet

tests/unit/families/builtins/continuous/test_exponential.py

@@ -121,6 +121,7 @@ def test_analytical_computations_availability(self):
             CharacteristicName.CDF,
             CharacteristicName.PPF,
             CharacteristicName.CF,
+            CharacteristicName.LPDF,
             CharacteristicName.MEAN,
             CharacteristicName.VAR,
             CharacteristicName.SKEW,
@@ -131,14 +132,30 @@ def test_analytical_computations_availability(self):
     @pytest.mark.parametrize(
         "char_name, test_data, scipy_func, scipy_kwargs",
         [
-            (CharacteristicName.PDF, [-1.0, 0.0, 1.0, 2.0, 3.0, 4.0], expon.pdf, {"scale": 2.0}),
-            (CharacteristicName.CDF, [-1.0, 0.0, 1.0, 2.0, 3.0, 4.0], expon.cdf, {"scale": 2.0}),
+            (
+                CharacteristicName.PDF,
+                [-1.0, 0.0, 1.0, 2.0, 3.0, 4.0],
+                expon.pdf,
+                {"scale": 2.0},
+            ),
+            (
+                CharacteristicName.CDF,
+                [-1.0, 0.0, 1.0, 2.0, 3.0, 4.0],
+                expon.cdf,
+                {"scale": 2.0},
+            ),
             (
                 CharacteristicName.PPF,
                 [0.001, 0.01, 0.1, 0.25, 0.5, 0.75, 0.9, 0.99, 0.999],
                 expon.ppf,
                 {"scale": 2.0},
             ),
+            (
+                CharacteristicName.LPDF,
+                [-1.0, 0.0, 1.0, 2.0, 3.0, 4.0],
+                expon.logpdf,
+                {"scale": 2.0},
+            ),
         ],
     )
     def test_array_input_for_characteristics(self, char_name, test_data, scipy_func, scipy_kwargs):

tests/unit/families/builtins/continuous/test_normal.py

@@ -149,6 +149,7 @@ def test_analytical_computations_availability(self):
             CharacteristicName.CDF,
             CharacteristicName.PPF,
             CharacteristicName.CF,
+            CharacteristicName.LPDF,
             CharacteristicName.MEAN,
             CharacteristicName.VAR,
             CharacteristicName.SKEW,
@@ -177,6 +178,12 @@ def test_analytical_computations_availability(self):
                 norm.ppf,
                 {"loc": 2.0, "scale": 1.5},
             ),
+            (
+                CharacteristicName.LPDF,
+                [-1.0, 0.0, 1.0, 2.0, 3.0, 4.0],
+                norm.logpdf,
+                {"loc": 2.0, "scale": 1.5},
+            ),
         ],
     )
     def test_array_input_for_characteristics(self, char_name, test_data, scipy_func, scipy_kwargs):

tests/unit/families/builtins/continuous/test_uniform.py

@@ -148,6 +148,7 @@ def test_analytical_computations_availability(self):
             CharacteristicName.CDF,
             CharacteristicName.PPF,
             CharacteristicName.CF,
+            CharacteristicName.LPDF,
             CharacteristicName.MEAN,
             CharacteristicName.VAR,
             CharacteristicName.SKEW,
@@ -176,6 +177,12 @@ def test_analytical_computations_availability(self):
                 uniform.ppf,
                 {"loc": 2.0, "scale": 3.0},
             ),
+            (
+                CharacteristicName.LPDF,
+                [1.0, 2.0, 2.5, 3.0, 4.0, 5.0, 6.0],
+                uniform.logpdf,
+                {"loc": 2.0, "scale": 3.0},
+            ),
         ],
     )
     def test_array_input_for_characteristics(self, char_name, test_data, scipy_func, scipy_kwargs):