binf.model package

Submodules

binf.model.errormodels module

This module contains interfaces for error models

class binf.model.errormodels.AbstractErrorModel(name='', **args)

Bases: binf.pdf.AbstractBinfPDF

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 35

_abc_registry = <_weakrefset.WeakSet object>

_check_differentiability(**variables)

Checks whether this object can be differentiated w.r.t. specific variables

_complete_variables(variables)

_complete_variables and _reduce_variables so far only work for classes which both inherit from AbstractBinfNamedCallable and can hold parameters (that is, PDFs and models)

_delete_variable(name)

Removes a variable from the set of registed variables

Parameters:name (str) – name of the variable to be removed

_evaluate(**variables)

In this method, the actual function evaluation takes place

The variables argument holds values for both fixed and unfixed variables; the implementation in this method thus does not depend on whether the set of originally passed variables equals the set of original variables

Parameters:**variables – list of variable name / value pairs

_evaluate_gradient(**variables)

In this method, the actual gradient evaluation takes place

The variables argument holds values for both fixed and unfixed variables; the implementation in this method thus does not depend on whether the set of originally passed variables equals the set of original variables

Parameters:**variables – list of variable name / value pairs

_evaluate_log_prob(**variables)

In this method, the actual evaluation of the log-probability takes place

The variables argument holds values for both fixed and unfixed variables; the implementation in this method thus does not depend on whether the set of originally passed variables equals the set of original variables

Parameters:**variables – list of variable name / value pairs

_get_variables_intersection(test_variables)

Returns the intersection of the variables stored in the argument with the set of currently registered variables

_register(name)

Register a new parameter name.

_register_variable(name, differentiable=False)

Registers a variable so that later on it can be fixed or checked whether it has been passed to the __call__ method

Parameters:

• name (str) – name of the new variable
• differentiable (bool) – True for variables you might at one point want to take the gradient w.r.t. This is probably deprecated.

_set(param, value)

Update the C{value} of C{param}.

_set_original_variables()

Stores the original set of named variables this object takes

_validate(param, value)

Parameter value validation hook. @raise ParameterValueError: on failed validation (value not accepted)

clone()

Returns an exact copy (same parameters / variables…) of this object

Returns:a copy of this object Return type:AbstractBinfPDF

conditional_factory(**fixed_vars)

Makes a copy of this object in which one or several variables are set to specific values (on which the new PDF is ‘conditioned’ on)

The ‘conditioning’ must not be taken literally, as no prior probabilities are taken into account. But that doesn’t bother us, as we’re going to MCMC sampling anyways

Parameters:**variables – keyword arguments containg name / value pairs for variables on which this PDF should be conditioned on Returns:PDF object conditioned on the given values Return type:AbstractBinfPDF

differentiable_variables

Returns the set of currently registered variables this object implements the gradient w.r.t

Returns:set of variables this object can be differentiated w.r.t. Return type:set

estimate(data)

Estimate and load the parameters of the distribution from sample C{data} using the current L{AbstractEstimator} strategy.

@param data: sample values @type data: array

@raise NotImplementedError: when no estimator is available for this

distribution

@raise IncompatibleEstimatorError: when the current estimator is not

compatible with this pdf

estimator

evaluate(x)

Evaluate the probability of observing values C{x}.

@param x: values @type x: array @rtype: array

fix_variables(**fixed_vars)

Sets (‘fixes’) specific variables to values given as keyword arguments by removing these variables from the list of registered variables and registering corresponding parameters to this object.

get_params()

gradient(**variables)

This function will be called from outside to evaluate the gradient of the function (or a related one, e.g., log-probability) represented by this object

Parameters:**variables – list of variable name / value pairs Returns:gradient of the function represented by this object Return type:numpy.ndarray

log_prob(**variables)

Evaluates the log-probability of the PDF represented by this object

Parameters:**variables – list of variable name / value pairs Returns:log-probability Return type:float

name

Returns the name of this object

parameters

Get a list of all distribution parameter names.

random(size=None)

Generate random samples from the probability distribution.

@param size: number of values to sample @type size: int

set_fixed_variables_from_pdf(pdf)

Retrieves fixed variables from another PDF object and fixes the same variables in this object accordingly

Parameters:pdf (AbstractBinfPDF) – PDF object to retrieve variables to fix from

set_params(*values, **named_params)

update_var_param_types(**values)

Updates the dictionary holding the types of registered variables

var_param_types

Empty by default, but for a conditional PDF to be built, one has to add AbstractParameter subclasses suiting the variables.

variables

Returns the set of currently registed variables

Returns:set of currently registered variables Return type:set

binf.model.forwardmodels module

This module contains interfaces for forward models.

class binf.model.forwardmodels.AbstractForwardModel(name, parameters=[])

Bases: binf.model.AbstractModel

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 35

_abc_registry = <_weakrefset.WeakSet object>

_check_differentiability(**variables)

Checks whether this object can be differentiated w.r.t. specific variables

_complete_variables(variables)

_complete_variables and _reduce_variables so far only work for classes which both inherit from AbstractBinfNamedCallable and can hold parameters (that is, PDFs and models)

_delete_variable(name)

Removes a variable from the set of registed variables

Parameters:name (str) – name of the variable to be removed

_evaluate(**variables)

In this method, the actual function evaluation takes place

The variables argument holds values for both fixed and unfixed variables; the implementation in this method thus does not depend on whether the set of originally passed variables equals the set of original variables

Parameters:**variables – list of variable name / value pairs

_evaluate_gradient(**variables)

In this method, the actual gradient evaluation takes place

The variables argument holds values for both fixed and unfixed variables; the implementation in this method thus does not depend on whether the set of originally passed variables equals the set of original variables

Parameters:**variables – list of variable name / value pairs

_evaluate_jacobi_matrix(**model_parameters)

_get_variables_intersection(test_variables)

Returns the intersection of the variables stored in the argument with the set of currently registered variables

_reduce_variables(**variables)

_register(name)

Register a new parameter name.

_register_variable(name, differentiable=False)

Registers a variable so that later on it can be fixed or checked whether it has been passed to the __call__ method

Parameters:

• name (str) – name of the new variable
• differentiable (bool) – True for variables you might at one point want to take the gradient w.r.t. This is probably deprecated.

_set_original_variables()

Stores the original set of named variables this object takes

_set_parameters(copy)

_validate(param, value)

Parameter value validation hook. @raise ParameterValueError: on failed validation (value not accepted)

clone()

data

differentiable_variables

Returns the set of currently registered variables this object implements the gradient w.r.t

Returns:set of variables this object can be differentiated w.r.t. Return type:set

fix_variables(**fixed_vars)

Sets (‘fixes’) specific variables to values given as keyword arguments by removing these variables from the list of registered variables and registering corresponding parameters to this object.

get_params()

gradient(**variables)

This function will be called from outside to evaluate the gradient of the function (or a related one, e.g., log-probability) represented by this object

Parameters:**variables – list of variable name / value pairs Returns:gradient of the function represented by this object Return type:numpy.ndarray

jacobi_matrix(**variables)

name

Returns the name of this object

parameters

Get a list of all distribution parameter names.

set_params(*values, **named_params)

update_var_param_types(**values)

Updates the dictionary holding the types of registered variables

var_param_types

Empty by default, but for a conditional PDF to be built, one has to add AbstractParameter subclasses suiting the variables.

variables

Returns the set of currently registed variables

Returns:set of currently registered variables Return type:set

Module contents

This module contains all “models” occuring in Binf, like the forward models and the error models.

class binf.model.AbstractModel(name, parameters=[])

Bases: binf.AbstractBinfNamedCallable

Some copy & pasting from csb.statistics.pdf.AbstractDensity involved.

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 35

_abc_registry = <_weakrefset.WeakSet object>

_check_differentiability(**variables)

Checks whether this object can be differentiated w.r.t. specific variables

_complete_variables(variables)

_complete_variables and _reduce_variables so far only work for classes which both inherit from AbstractBinfNamedCallable and can hold parameters (that is, PDFs and models)

_delete_variable(name)

Removes a variable from the set of registed variables

Parameters:name (str) – name of the variable to be removed

_evaluate(**variables)

In this method, the actual function evaluation takes place

The variables argument holds values for both fixed and unfixed variables; the implementation in this method thus does not depend on whether the set of originally passed variables equals the set of original variables

Parameters:**variables – list of variable name / value pairs

_evaluate_gradient(**variables)

In this method, the actual gradient evaluation takes place

The variables argument holds values for both fixed and unfixed variables; the implementation in this method thus does not depend on whether the set of originally passed variables equals the set of original variables

Parameters:**variables – list of variable name / value pairs

_get_variables_intersection(test_variables)

Returns the intersection of the variables stored in the argument with the set of currently registered variables

_reduce_variables(**variables)

_register(name)

Register a new parameter name.

_register_variable(name, differentiable=False)

Registers a variable so that later on it can be fixed or checked whether it has been passed to the __call__ method

Parameters:

• name (str) – name of the new variable
• differentiable (bool) – True for variables you might at one point want to take the gradient w.r.t. This is probably deprecated.

_set_original_variables()

Stores the original set of named variables this object takes

_validate(param, value)

Parameter value validation hook. @raise ParameterValueError: on failed validation (value not accepted)

differentiable_variables

Returns the set of currently registered variables this object implements the gradient w.r.t

Returns:set of variables this object can be differentiated w.r.t. Return type:set

fix_variables(**fixed_vars)

Sets (‘fixes’) specific variables to values given as keyword arguments by removing these variables from the list of registered variables and registering corresponding parameters to this object.

get_params()

gradient(**variables)

This function will be called from outside to evaluate the gradient of the function (or a related one, e.g., log-probability) represented by this object

Parameters:**variables – list of variable name / value pairs Returns:gradient of the function represented by this object Return type:numpy.ndarray

name

Returns the name of this object

parameters

Get a list of all distribution parameter names.

set_params(*values, **named_params)

update_var_param_types(**values)

Updates the dictionary holding the types of registered variables

var_param_types

Empty by default, but for a conditional PDF to be built, one has to add AbstractParameter subclasses suiting the variables.

variables

Returns the set of currently registed variables

Returns:set of currently registered variables Return type:set