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parameterspace.priors.categorical

Categorical

Categorical prior with separate probabilities for each value.

loglikelihood(self, value)

Compute the log PDF (up to an additive constant) of a given value.

Note

Values for the priors are always after the transformation!

Parameters:

Name Type Description Default
value

[description]

 required

Returns:

Type Description

[descriptions]
Source code in parameterspace/priors/categorical.py 
def loglikelihood(self, value):
    return np.log(self.pdf(value))

pdf(self, value)

Probability given the numerical representation(s).

The numerical representation is converted to integers and the corresponding prior_values are returned. If the value exceeds the given range of values, a ValueError is raised.

Only finite values are converted, and the respective probability is computed. NaN and INF will result in a NaN. For example: value = [nan, inf, 0] will result in [NaN, NaN, p(0)].

Source code in parameterspace/priors/categorical.py 
def pdf(self, value):
    """Probability given the numerical representation(s).

    The numerical representation is converted to integers and the corresponding
    prior_values are returned.
    If the value exceeds the given range of values, a ValueError is raised.

    Only finite values are converted, and the respective probability is computed.
    NaN and INF will result in a NaN. For example:
        value = [nan, inf, 0]
    will result in
        [NaN, NaN, p(0)].

    """
    value = np.atleast_1d(value)
    idx = np.isfinite(value)

    integer_value = np.around(len(self.probabilities) * value[idx] - 0.5).astype(
        int
    )
    pdf = np.full(value.shape, np.nan, dtype=float)

    try:
        pdf[idx] = self.probabilities[integer_value]
    except IndexError:
        raise ValueError(
            "Unknown value in the numerical representation for a "
            + "categorical parameter encountered!"
        )
    return pdf.squeeze()

sample(self, num_samples=None, random_state=<module 'numpy.random' from '/home/runner/.cache/pypoetry/virtualenvs/parameterspace-9AYrJA9h-py3.8/lib/python3.8/site-packages/numpy/random/__init__.py'>)

Draw random samples from the prior.

Parameters:

Name Type Description Default
num_samples

[description]

 None

Returns:

Type Description

[descriptions]
Source code in parameterspace/priors/categorical.py 
def sample(self, num_samples=None, random_state=np.random):
    return random_state.choice(
        self.numerical_values, size=num_samples, replace=True, p=self.probabilities
    )