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parameterspace.parameters.integer

IntegerParameter

Integer parameter that can take every integer value in a given range.

check_numerical_value(self, numerical_value) inherited

Check if the numerical representation of the value is valid.

Source code in parameterspace/parameters/integer.py 
def check_numerical_value(self, numerical_value):
    """Check if the numerical representation of the value is valid."""
    return (
        self.transformed_bounds[0] <= numerical_value <= self.transformed_bounds[1]
    )

check_value(self, value)

Check if value is valid.

Source code in parameterspace/parameters/integer.py 
def check_value(self, value):
    """Check if value is valid."""
    return self.bounds[0] <= value <= self.bounds[1] and value == int(value)

get_numerical_bounds(self) inherited

Translate the provided bounds into the numerical representation.

Source code in parameterspace/parameters/integer.py 
def get_numerical_bounds(self):
    """Translate the provided bounds into the numerical representation."""
    return self.transformed_bounds

loglikelihood(self, value) inherited

Compute the loglikelihood based on the prior.

Source code in parameterspace/parameters/integer.py 
def loglikelihood(self, value):
    """Compute the loglikelihood based on the prior."""
    return self.loglikelihood_numerical_value(self._transformation(value))

loglikelihood_numerical_value(self, numerical_value) inherited

Compute the loglikelihood based on the prior.

Source code in parameterspace/parameters/integer.py 
def loglikelihood_numerical_value(self, numerical_value):
    """Compute the loglikelihood based on the prior."""
    return self._prior.loglikelihood(numerical_value) + np.log(
        self._transformation.jacobian_factor(numerical_value)
    )

num2val(self, numerical_value)

Translate the numerical representation into the actual value.

Source code in parameterspace/parameters/integer.py 
def num2val(self, numerical_value):
    """Translate the numerical representation into the actual value."""
    return int(super().num2val(numerical_value))

pdf_numerical_value(self, numerical_value) inherited

Compute the PDF based on the prior.

Source code in parameterspace/parameters/integer.py 
def pdf_numerical_value(self, numerical_value):
    """Compute the PDF based on the prior."""
    return self._prior.pdf(numerical_value) * self._transformation.jacobian_factor(
        numerical_value
    )

sample_numerical_values(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'>) inherited

Generate random values based on the prior, but in the transformed space.

Source code in parameterspace/parameters/integer.py 
def sample_numerical_values(self, num_samples=None, random_state=np.random):
    """Generate random values based on the prior, but in the transformed space."""
    return self._prior.sample(num_samples, random_state=random_state)

sample_values(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'>) inherited

Generate randomly sampled values based on the prior.

Source code in parameterspace/parameters/integer.py 
def sample_values(self, num_samples=None, random_state=np.random):
    """Generate randomly sampled values based on the prior."""
    numerical_samples = self.sample_numerical_values(num_samples, random_state)
    if num_samples is None:
        return self._transformation.inverse(numerical_samples)
    return [
        self._transformation.inverse(num_value) for num_value in numerical_samples
    ]

val2num(self, value) inherited

Translate a value into its numerical representation (incl. normalization).

Source code in parameterspace/parameters/integer.py 
def val2num(self, value):
    """Translate a value into its numerical representation (incl. normalization)."""
    if value is None:
        return self._inactive_numerical_value
    return self._transformation(value)