Add (log)uniform

Author: tupui

pyproject.toml

@@ -99,3 +99,4 @@ testpaths = [
 
 [tool.ruff.lint.per-file-ignores]
 "**/__init__.py" = ["F403", "F405"]
+"**/normal.py" = ["F821"]

src/skstats/log_uniform.py

@@ -0,0 +1,84 @@
+import numpy as np
+from numpy import inf
+
+from scipy import special
+from scipy.stats._distribution_infrastructure import (
+    ContinuousDistribution,
+    _RealDomain,
+    _RealParameter,
+    _Parameterization,
+)
+
+
+def _log_diff(log_p, log_q):
+    return special.logsumexp([log_p, log_q + np.pi * 1j], axis=0)
+
+
+class _LogUniform(ContinuousDistribution):
+    r"""Log-uniform distribution.
+
+    The probability density function of the log-uniform distribution is:
+
+    .. math::
+
+        f(x; a, b) = \frac{1}
+                          {x (\log(b) - \log(a))}
+
+    If :math:`\log(X)` is a random variable that follows a uniform distribution
+    between :math:`\log(a)` and :math:`\log(b)`, then :math:`X` is log-uniformly
+    distributed with shape parameters :math:`a` and :math:`b`.
+
+    """
+
+    _a_domain = _RealDomain(endpoints=(0, inf))
+    _b_domain = _RealDomain(endpoints=("a", inf))
+    _log_a_domain = _RealDomain(endpoints=(-inf, inf))
+    _log_b_domain = _RealDomain(endpoints=("log_a", inf))
+    _x_support = _RealDomain(endpoints=("a", "b"), inclusive=(True, True))
+
+    _a_param = _RealParameter("a", domain=_a_domain, typical=(1e-3, 0.9))
+    _b_param = _RealParameter("b", domain=_b_domain, typical=(1.1, 1e3))
+    _log_a_param = _RealParameter(
+        "log_a", symbol=r"\log(a)", domain=_log_a_domain, typical=(-3, -0.1)
+    )
+    _log_b_param = _RealParameter(
+        "log_b", symbol=r"\log(b)", domain=_log_b_domain, typical=(0.1, 3)
+    )
+    _x_param = _RealParameter("x", domain=_x_support, typical=("a", "b"))
+
+    _b_domain.define_parameters(_a_param)
+    _log_b_domain.define_parameters(_log_a_param)
+    _x_support.define_parameters(_a_param, _b_param)
+
+    _parameterizations = [
+        _Parameterization(_log_a_param, _log_b_param),
+        _Parameterization(_a_param, _b_param),
+    ]
+    _variable = _x_param
+
+    def __init__(self, *, a=None, b=None, log_a=None, log_b=None, **kwargs):
+        super().__init__(a=a, b=b, log_a=log_a, log_b=log_b, **kwargs)
+
+    def _process_parameters(self, a=None, b=None, log_a=None, log_b=None, **kwargs):
+        a = np.exp(log_a) if a is None else a
+        b = np.exp(log_b) if b is None else b
+        log_a = np.log(a) if log_a is None else log_a
+        log_b = np.log(b) if log_b is None else log_b
+        kwargs.update(dict(a=a, b=b, log_a=log_a, log_b=log_b))
+        return kwargs
+
+    # def _logpdf_formula(self, x, *, log_a, log_b, **kwargs):
+    #     return -np.log(x) - np.log(log_b - log_a)
+
+    def _pdf_formula(self, x, *, log_a, log_b, **kwargs):
+        return ((log_b - log_a) * x) ** -1
+
+    # def _cdf_formula(self, x, *, log_a, log_b, **kwargs):
+    #     return (np.log(x) - log_a)/(log_b - log_a)
+
+    def _moment_raw_formula(self, order, log_a, log_b, **kwargs):
+        if order == 0:
+            return self._one
+        t1 = self._one / (log_b - log_a) / order
+        t2 = np.real(np.exp(_log_diff(order * log_b, order * log_a)))
+        return t1 * t2

src/skstats/uniform.py

@@ -0,0 +1,53 @@
+import numpy as np
+from numpy import inf
+
+from scipy.stats._distribution_infrastructure import (
+    ContinuousDistribution,
+    _RealDomain,
+    _RealParameter,
+    _Parameterization,
+)
+
+
+class Uniform(ContinuousDistribution):
+    r"""Uniform distribution.
+
+    The probability density function of the uniform distribution is:
+
+    .. math::
+
+        f(x; a, b) = \frac{1}
+                          {b - a}
+
+    """
+
+    _a_domain = _RealDomain(endpoints=(-inf, inf))
+    _b_domain = _RealDomain(endpoints=("a", inf))
+    _x_support = _RealDomain(endpoints=("a", "b"), inclusive=(False, False))
+
+    _a_param = _RealParameter("a", domain=_a_domain, typical=(1e-3, 0.9))
+    _b_param = _RealParameter("b", domain=_b_domain, typical=(1.1, 1e3))
+    _x_param = _RealParameter("x", domain=_x_support, typical=("a", "b"))
+
+    _b_domain.define_parameters(_a_param)
+    _x_support.define_parameters(_a_param, _b_param)
+
+    _parameterizations = [_Parameterization(_a_param, _b_param)]
+    _variable = _x_param
+
+    def __init__(self, *, a=None, b=None, **kwargs):
+        super().__init__(a=a, b=b, **kwargs)
+
+    def _process_parameters(self, a=None, b=None, ab=None, **kwargs):
+        ab = b - a
+        kwargs.update(dict(a=a, b=b, ab=ab))
+        return kwargs
+
+    def _pdf_formula(self, x, *, ab, **kwargs):
+        return np.full(x.shape, 1 / ab)
+
+    def _icdf_formula(self, x, a, b, ab, **kwargs):
+        return a + ab * x
+
+    def _mode_formula(self, *, a, b, ab, **kwargs):
+        return a + 0.5 * ab