Parameters#

class Parameters(**kwargs)[source]#

Bases: object

A class representing a set of parameters. It takes keyword arguments representing the parameters with their values and wraps them into a python object.

Parameters:

**kwargsdict: Keyword arguments representing the parameters.

Returns:

Parameters: A Parameters object, where each attributes is one of the keyword arguments provided for the function wrapped by the Value class.

Examples

>>> from cpm.generators import Parameters
>>> parameters = Parameters(a=0.5, b=0.5, c=0.5)
>>> parameters['a']
0.1
>>> parameters.a
0.1
>>> parameters()
{'a': 0.1, 'b': 0.2, 'c': 0.5}

The Parameters class can also provide a prior.

>>> x = Parameters(
>>>    a=Value(value=0.1, lower=0, upper=1, prior="normal", args={"mean": 0.5, "sd": 0.1}),
>>>    b=0.5,
>>>    weights=Value(value=[0.1, 0.2, 0.3], lower=0, upper=1, prior=None),
>>> )

>>> x.prior(log=True)
-6.5854290732499186

We can also sample new parameter values from the prior distributions. >>> x.sample() {‘a’: 0.4670755733417274, ‘b’: 0.30116207009111917}

update(**kwargs)[source]#

Update the parameters with new values.

Parameters:

**kwargsdict: Keyword arguments representing the parameters.

keys()[source]#

Return a list of all the keys in the parameters dictionary.

Returns:

keyslist: A list of all the keys in the parameters dictionary.

bounds()[source]#

Returns a tuple with lower (first element) and upper (second element) bounds for parameters with defined priors.

Returns:

lower, upper: tuples: A tuple of lower and upper parameter bounds

PDF(log=False)[source]#

Return the prior probability density of the parameter values.

Returns:

float: The summed probability density of the parameter set under the prior distribution for each parameter. If log is True, the log probability is returned. When the PDF is 0, it returns the smallest positive float value. If log is True and the log probability is negative infinity, it returns the most negative float value.

update_prior(**kwargs)[source]#

Update the prior distribution of all parameters.

Parameters:

**kwargsdict: Keyword arguments representing the parameters of the prior distribution. For each parameter, the keyword should be the name of the parameter and the value should be a dictionary of the parameters of the prior distribution. If you are unsure what the names are for the parameters of the prior distribution, you can check the scipy.stats documentation or the prior.kwds attribute.

sample(size=1, jump=False)[source]#

Sample and update parameter values from their prior distribution.

Returns:

sampledict: A dictionary of the sampled parameters.

free()[source]#

Return a dictionary of all parameters with a prior distribution.

Returns:

freedict: A dictionary of all parameters with a prior distribution.

export()[source]#

Return all freely-varying parameters and their accompanying lower and upper bounds, and prior distributions.

Returns:

pandas.DataFrame: A table of all freely-varying parameters and their accompanying lower and upper bounds, and prior distributions. Each row is a parameter, and the columns are the lower bound, upper bound, prior distribution, and the parameters of the prior distribution.