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Loc-scale varaint of Poisson distribution #84

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Jan 22, 2024
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Loc-scale varaint of Poisson distribution #84

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32 changes: 24 additions & 8 deletions jaxampler/_src/rvs/poisson.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,34 +27,50 @@


class Poisson(DiscreteRV):
def __init__(self, lmbda: Numeric | Any, name: Optional[str] = None) -> None:
shape, self._lmbda = jx_cast(lmbda)
def __init__(self, mu: Numeric | Any, loc: Numeric | Any = 0.0, name: Optional[str] = None) -> None:
shape, self._mu, self._loc = jx_cast(mu, loc)
self.check_params()
super().__init__(name=name, shape=shape)

def check_params(self) -> None:
assert jnp.all(self._lmbda > 0.0), "Lambda must be positive"
assert jnp.all(self._mu > 0.0), "Lambda must be positive"

@partial(jit, static_argnums=(0,))
def logpmf_x(self, x: Numeric) -> Numeric:
return jax_poisson.logpmf(x, self._lmbda)
return jax_poisson.logpmf(
k=x,
mu=self._mu,
loc=self._loc,
)

@partial(jit, static_argnums=(0,))
def pmf_x(self, x: Numeric) -> Numeric:
return jax_poisson.pmf(x, self._lmbda)
return jax_poisson.pmf(
k=x,
mu=self._mu,
loc=self._loc,
)

@partial(jit, static_argnums=(0,))
def logcdf_x(self, x: Numeric) -> Numeric:
return jnp.log(self.cdf_x(x))

@partial(jit, static_argnums=(0,))
def cdf_x(self, x: Numeric) -> Numeric:
return jax_poisson.cdf(x, self._lmbda)
return jax_poisson.cdf(
k=x,
mu=self._mu,
loc=self._loc,
)

def rvs(self, shape: tuple[int, ...], key: Optional[Array] = None) -> Array:
if key is None:
key = self.get_key()
new_shape = shape + self._shape
return jax.random.poisson(key, self._lmbda, shape=new_shape)
return jax.random.poisson(key, self._mu, shape=new_shape)

def __repr__(self) -> str:
string = f"Poisson(lmbda={self._lmbda}"
string = f"Poisson(lmbda={self._mu}"
if self._name is not None:
string += f", name={self._name}"
string += ")"
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