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chore: remove build warnings #311

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Merged
williambdean merged 2 commits into from
Feb 15, 2026
Merged

chore: remove build warnings #311

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66dc68a
address helper module
williambdean Feb 15, 2026
4ea46fc
remove warnings
williambdean Feb 15, 2026
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60 changes: 47 additions & 13 deletions conjugate/distributions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -631,7 +631,9 @@ def from_inverse_gamma(
def inverse_gamma(self) -> InverseGamma:
return InverseGamma(alpha=self.alpha, beta=self.beta)

def sample_variance(self, size: int, random_state=None) -> NUMERIC:
def sample_variance(
self, size: int, random_state: np.random.RandomState | None = None
) -> NUMERIC:
"""Sample variance from the inverse gamma distribution.

Args:
Expand All @@ -644,11 +646,15 @@ def sample_variance(self, size: int, random_state=None) -> NUMERIC:
"""
return self.inverse_gamma.dist.rvs(size=size, random_state=random_state)

def _sample_beta_1d(self, variance, size: int, random_state=None) -> NUMERIC:
def _sample_beta_1d(
self, variance, size: int, random_state: np.random.RandomState | None = None
) -> NUMERIC:
sigma = (variance / self.nu) ** 0.5
return stats.norm(self.mu, sigma).rvs(size=size, random_state=random_state)

def _sample_beta_nd(self, variance, size: int, random_state=None) -> NUMERIC:
def _sample_beta_nd(
self, variance, size: int, random_state: np.random.RandomState | None = None
) -> NUMERIC:
variance = (self.delta_inverse[None, ...].T * variance).T
return np.stack(
[
Expand All @@ -663,7 +669,7 @@ def sample_mean(
self,
size: int,
return_variance: bool = False,
random_state=None,
random_state: np.random.RandomState | None = None,
) -> NUMERIC | tuple[NUMERIC, NUMERIC]:
"""Sample the mean from the normal distribution.

Expand All @@ -681,7 +687,10 @@ def sample_mean(
)

def sample_beta(
self, size: int, return_variance: bool = False, random_state=None
self,
size: int,
return_variance: bool = False,
random_state: np.random.RandomState | None = None,
) -> NUMERIC | tuple[NUMERIC, NUMERIC]:
"""Sample beta from the normal distribution.

Expand Down Expand Up @@ -809,7 +818,11 @@ class GammaKnownRateProportional:
c: NUMERIC

def approx_log_likelihood(
self, alpha: NUMERIC, beta: NUMERIC, ln=np.log, gammaln=gammaln
self,
alpha: NUMERIC,
beta: NUMERIC,
ln: Callable = np.log,
gammaln: Callable = gammaln,
) -> NUMERIC:
"""Approximate log likelihood.

Expand Down Expand Up @@ -848,7 +861,11 @@ class GammaProportional:
s: NUMERIC

def approx_log_likelihood(
self, alpha: NUMERIC, beta: NUMERIC, ln=np.log, gammaln=gammaln
self,
alpha: NUMERIC,
beta: NUMERIC,
ln: Callable = np.log,
gammaln: Callable = gammaln,
) -> NUMERIC:
"""Approximate log likelihood.

Expand Down Expand Up @@ -886,7 +903,11 @@ class BetaProportional:
k: NUMERIC

def approx_log_likelihood(
self, alpha: NUMERIC, beta: NUMERIC, ln=np.log, gammaln=gammaln
self,
alpha: NUMERIC,
beta: NUMERIC,
ln: Callable = np.log,
gammaln: Callable = gammaln,
) -> NUMERIC:
"""Approximate log likelihood.

Expand Down Expand Up @@ -946,7 +967,13 @@ class VonMisesKnownConcentration:
a: NUMERIC
b: NUMERIC

def log_likelihood(self, mu: NUMERIC, cos=np.cos, ln=np.log, i0=i0) -> NUMERIC:
def log_likelihood(
self,
mu: NUMERIC,
cos: Callable = np.cos,
ln: Callable = np.log,
i0: Callable = i0,
) -> NUMERIC:
"""Approximate log likelihood.

Args:
Expand Down Expand Up @@ -976,7 +1003,9 @@ class VonMisesKnownDirectionProportional:
c: NUMERIC
r: NUMERIC

def approx_log_likelihood(self, kappa: NUMERIC, ln=np.log, i0=i0) -> NUMERIC:
def approx_log_likelihood(
self, kappa: NUMERIC, ln: Callable = np.log, i0: Callable = i0
) -> NUMERIC:
"""Approximate log likelihood.

Args:
Expand Down Expand Up @@ -1058,7 +1087,9 @@ class NormalGamma:
def gamma(self) -> Gamma:
return Gamma(alpha=self.alpha, beta=self.beta)

def sample_variance(self, size: int, random_state=None) -> NUMERIC:
def sample_variance(
self, size: int, random_state: np.random.RandomState | None = None
) -> NUMERIC:
"""Sample precision from gamma distribution and invert.

Args:
Expand All @@ -1077,7 +1108,7 @@ def sample_mean(
self,
size: int,
return_variance: bool = False,
random_state=None,
random_state: np.random.RandomState | None = None,
) -> NUMERIC | tuple[NUMERIC, NUMERIC]:
"""Sample mean from the normal distribution.

Expand All @@ -1095,7 +1126,10 @@ def sample_mean(
)

def sample_beta(
self, size: int, return_variance: bool = False, random_state=None
self,
size: int,
return_variance: bool = False,
random_state: np.random.RandomState | None = None,
) -> NUMERIC | tuple[NUMERIC, NUMERIC]:
"""Sample beta from the normal distribution.

Expand Down
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