Uniform Distribution

stamox.distribution.punif(q: Union[Float, ArrayLike], mini: Union[Float, ArrayLike] = 0.0, maxi: Union[Float, ArrayLike] = 1.0, lower_tail: Bool = True, log_prob: Bool = False, dtype = <class 'jax.numpy.float64'>) -> ArrayLike

Computes the cumulative distribution function of the uniform distribution.

Parameters:

Name	Type	Description	Default
q	Union[Float, ArrayLike]	The value at which to evaluate the CDF.	required
mini	Union[Float, ArrayLike]	The minimum value of the uniform distribution. Defaults to 0.0.	0.0
maxi	Union[Float, ArrayLike]	The maximum value of the uniform distribution. Defaults to 1.0.	1.0
lower_tail	bool	Whether to compute the lower tail of the CDF. Defaults to True.	True
log_prob	bool	Whether to return the log probability. Defaults to False.	False
dtype	jnp.dtype	The dtype of the output. Defaults to jnp.float_.	<class 'jax.numpy.float64'>

Returns:

Type	Description
ArrayLike	The cumulative distribution function of the uniform distribution evaluated at q.

Examples:

>>> punif(0.5)
Array(0.5, dtype=float32, weak_type=True)

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stamox.distribution.qunif(p: Union[Float, ArrayLike], mini: Union[Float, ArrayLike] = 0.0, maxi: Union[Float, ArrayLike] = 1.0, lower_tail: Bool = True, log_prob: Bool = False, dtype = <class 'jax.numpy.float64'>) -> ArrayLike

Computes the quantile function of a uniform distribution.

Parameters:

Name	Type	Description	Default
p	Union[Float, ArrayLike]	Quantiles to compute.	required
mini	Union[Float, Array]	Lower bound of the uniform distribution. Defaults to 0.0.	0.0
maxi	Union[Float, Array]	Upper bound of the uniform distribution. Defaults to 1.0.	1.0
lower_tail	Bool	Whether to compute the lower tail or not. Defaults to True.	True
log_prob	Bool	Whether to compute the log probability or not. Defaults to False.	False
dtype	jnp.dtype	The dtype of the output. Defaults to jnp.float_.	<class 'jax.numpy.float64'>

Returns:

Type	Description
ArrayLike	The quantiles of the uniform distribution.

Examples:

>>> qunif(0.5)
Array(0.5, dtype=float32, weak_type=True)

---

stamox.distribution.dunif(x: Union[Float, ArrayLike], mini: Union[Float, ArrayLike] = 0.0, maxi: Union[Float, ArrayLike] = 1.0, lower_tail: Bool = True, log_prob: Bool = False, dtype = <class 'jax.numpy.float64'>) -> ArrayLike

Calculates the probability density function of a uniform distribution.

Parameters:

Name	Type	Description	Default
x	Union[Float, ArrayLike]	The value or array of values for which to calculate the probability density.	required
mini	Union[Float, Array]	The lower bound of the uniform distribution. Defaults to 0.0.	0.0
maxi	Union[Float, Array]	The upper bound of the uniform distribution. Defaults to 1.0.	1.0
lower_tail	Bool	Whether to calculate the lower tail probability. Defaults to True.	True
log_prob	Bool	Whether to return the log probability. Defaults to False.	False
dtype	jnp.dtype	The dtype of the output. Defaults to jnp.float_.	<class 'jax.numpy.float64'>

Returns:

Type	Description
ArrayLike	The probability density of the given value(s).

Examples:

>>> dunif(0.5)
Array(1., dtype=float32, weak_type=True)

---

stamox.distribution.runif(key: Union[jax.Array, jax._src.prng.PRNGKeyArray], sample_shape: Optional[Sequence[int]] = None, mini: Union[Float, ArrayLike] = 0.0, maxi: Union[Float, ArrayLike] = 1.0, lower_tail: Bool = True, log_prob: Bool = False, dtype = <class 'jax.numpy.float64'>) -> ArrayLike

Generates random numbers from a uniform distribution.

Parameters:

Name	Type	Description	Default
key	Union[jax.Array, jax._src.prng.PRNGKeyArray]	A PRNGKey to use for generating the random numbers.	required
sample_shape	Optional[Sequence[int]]	The shape of the output array.	None
mini	Union[Float, ArrayLike]	The minimum value of the uniform distribution.	0.0
maxi	Union[Float, ArrayLike]	The maximum value of the uniform distribution.	1.0
lower_tail	Bool	Whether to generate values from the lower tail of the distribution.	True
log_prob	Bool	Whether to return the log probability of the generated values.	False
dtype		The dtype of the output. Defaults to jnp.float_.	<class 'jax.numpy.float64'>

Returns:

Type	Description
ArrayLike	An array of random numbers from a uniform distribution.

Examples:

>>> runif(key, sample_shape=(2, 3))
Array([[0.57450044, 0.09968603, 0.7419659 ],
        [0.8941783 , 0.59656656, 0.45325184]], dtype=float32)