partition stringclasses 3
values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1
value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
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train | NoUTurnSamplerDA.generate_sample | Returns a generator type object whose each iteration yields a sample
Parameters
----------
initial_pos: A 1d array like object
Vector representing values of parameter position, the starting
state in markov chain.
num_adapt: int
The number of interati... | pgmpy/sampling/NUTS.py | def generate_sample(self, initial_pos, num_adapt, num_samples, stepsize=None):
"""
Returns a generator type object whose each iteration yields a sample
Parameters
----------
initial_pos: A 1d array like object
Vector representing values of parameter position, the sta... | def generate_sample(self, initial_pos, num_adapt, num_samples, stepsize=None):
"""
Returns a generator type object whose each iteration yields a sample
Parameters
----------
initial_pos: A 1d array like object
Vector representing values of parameter position, the sta... | [
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train | ContinuousFactor.discretize | Discretizes the continuous distribution into discrete
probability masses using various methods.
Parameters
----------
method : A Discretizer Class from pgmpy.discretize
*args, **kwargs:
The parameters to be given to the Discretizer Class.
Returns
--... | pgmpy/factors/continuous/ContinuousFactor.py | def discretize(self, method, *args, **kwargs):
"""
Discretizes the continuous distribution into discrete
probability masses using various methods.
Parameters
----------
method : A Discretizer Class from pgmpy.discretize
*args, **kwargs:
The parameter... | def discretize(self, method, *args, **kwargs):
"""
Discretizes the continuous distribution into discrete
probability masses using various methods.
Parameters
----------
method : A Discretizer Class from pgmpy.discretize
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train | ContinuousFactor.reduce | Reduces the factor to the context of the given variable values.
Parameters
----------
values: list, array-like
A list of tuples of the form (variable_name, variable_value).
inplace: boolean
If inplace=True it will modify the factor itself, else would return
... | pgmpy/factors/continuous/ContinuousFactor.py | def reduce(self, values, inplace=True):
"""
Reduces the factor to the context of the given variable values.
Parameters
----------
values: list, array-like
A list of tuples of the form (variable_name, variable_value).
inplace: boolean
If inplace=T... | def reduce(self, values, inplace=True):
"""
Reduces the factor to the context of the given variable values.
Parameters
----------
values: list, array-like
A list of tuples of the form (variable_name, variable_value).
inplace: boolean
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train | ContinuousFactor.marginalize | Marginalize the factor with respect to the given variables.
Parameters
----------
variables: list, array-like
List of variables with respect to which factor is to be maximized.
inplace: boolean
If inplace=True it will modify the factor itself, else would return
... | pgmpy/factors/continuous/ContinuousFactor.py | def marginalize(self, variables, inplace=True):
"""
Marginalize the factor with respect to the given variables.
Parameters
----------
variables: list, array-like
List of variables with respect to which factor is to be maximized.
inplace: boolean
... | def marginalize(self, variables, inplace=True):
"""
Marginalize the factor with respect to the given variables.
Parameters
----------
variables: list, array-like
List of variables with respect to which factor is to be maximized.
inplace: boolean
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train | ContinuousFactor.normalize | Normalizes the pdf of the continuous factor so that it integrates to
1 over all the variables.
Parameters
----------
inplace: boolean
If inplace=True it will modify the factor itself, else would return
a new factor.
Returns
-------
Contin... | pgmpy/factors/continuous/ContinuousFactor.py | def normalize(self, inplace=True):
"""
Normalizes the pdf of the continuous factor so that it integrates to
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Parameters
----------
inplace: boolean
If inplace=True it will modify the factor itself, else would return
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"""
Normalizes the pdf of the continuous factor so that it integrates to
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If inplace=True it will modify the factor itself, else would return
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train | ContinuousFactor._operate | Gives the ContinuousFactor operation (product or divide) with
the other factor.
Parameters
----------
other: ContinuousFactor
The ContinuousFactor to be multiplied.
operation: String
'product' for multiplication operation and 'divide' for
div... | pgmpy/factors/continuous/ContinuousFactor.py | def _operate(self, other, operation, inplace=True):
"""
Gives the ContinuousFactor operation (product or divide) with
the other factor.
Parameters
----------
other: ContinuousFactor
The ContinuousFactor to be multiplied.
operation: String
... | def _operate(self, other, operation, inplace=True):
"""
Gives the ContinuousFactor operation (product or divide) with
the other factor.
Parameters
----------
other: ContinuousFactor
The ContinuousFactor to be multiplied.
operation: String
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train | ContinuousFactor.divide | Gives the ContinuousFactor divide with the other factor.
Parameters
----------
other: ContinuousFactor
The ContinuousFactor to be divided.
Returns
-------
ContinuousFactor or None:
if inplace=True (default) returns None
... | pgmpy/factors/continuous/ContinuousFactor.py | def divide(self, other, inplace=True):
"""
Gives the ContinuousFactor divide with the other factor.
Parameters
----------
other: ContinuousFactor
The ContinuousFactor to be divided.
Returns
-------
ContinuousFactor or None:
... | def divide(self, other, inplace=True):
"""
Gives the ContinuousFactor divide with the other factor.
Parameters
----------
other: ContinuousFactor
The ContinuousFactor to be divided.
Returns
-------
ContinuousFactor or None:
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train | factor_product | Returns factor product over `args`.
Parameters
----------
args: `BaseFactor` instances.
factors to be multiplied
Returns
-------
BaseFactor: `BaseFactor` representing factor product over all the `BaseFactor` instances in args.
Examples
--------
>>> from pgmpy.factors.discr... | pgmpy/factors/base.py | def factor_product(*args):
"""
Returns factor product over `args`.
Parameters
----------
args: `BaseFactor` instances.
factors to be multiplied
Returns
-------
BaseFactor: `BaseFactor` representing factor product over all the `BaseFactor` instances in args.
Examples
--... | def factor_product(*args):
"""
Returns factor product over `args`.
Parameters
----------
args: `BaseFactor` instances.
factors to be multiplied
Returns
-------
BaseFactor: `BaseFactor` representing factor product over all the `BaseFactor` instances in args.
Examples
--... | [
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train | factor_divide | Returns `DiscreteFactor` representing `phi1 / phi2`.
Parameters
----------
phi1: Factor
The Dividend.
phi2: Factor
The Divisor.
Returns
-------
DiscreteFactor: `DiscreteFactor` representing factor division `phi1 / phi2`.
Examples
--------
>>> from pgmpy.factor... | pgmpy/factors/base.py | def factor_divide(phi1, phi2):
"""
Returns `DiscreteFactor` representing `phi1 / phi2`.
Parameters
----------
phi1: Factor
The Dividend.
phi2: Factor
The Divisor.
Returns
-------
DiscreteFactor: `DiscreteFactor` representing factor division `phi1 / phi2`.
Exam... | def factor_divide(phi1, phi2):
"""
Returns `DiscreteFactor` representing `phi1 / phi2`.
Parameters
----------
phi1: Factor
The Dividend.
phi2: Factor
The Divisor.
Returns
-------
DiscreteFactor: `DiscreteFactor` representing factor division `phi1 / phi2`.
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train | BayesianEstimator.get_parameters | Method to estimate the model parameters (CPDs).
Parameters
----------
prior_type: 'dirichlet', 'BDeu', or 'K2'
string indicting which type of prior to use for the model parameters.
- If 'prior_type' is 'dirichlet', the following must be provided:
'pseudo_... | pgmpy/estimators/BayesianEstimator.py | def get_parameters(self, prior_type='BDeu', equivalent_sample_size=5, pseudo_counts=None):
"""
Method to estimate the model parameters (CPDs).
Parameters
----------
prior_type: 'dirichlet', 'BDeu', or 'K2'
string indicting which type of prior to use for the model par... | def get_parameters(self, prior_type='BDeu', equivalent_sample_size=5, pseudo_counts=None):
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Method to estimate the model parameters (CPDs).
Parameters
----------
prior_type: 'dirichlet', 'BDeu', or 'K2'
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train | BayesianEstimator.estimate_cpd | Method to estimate the CPD for a given variable.
Parameters
----------
node: int, string (any hashable python object)
The name of the variable for which the CPD is to be estimated.
prior_type: 'dirichlet', 'BDeu', 'K2',
string indicting which type of prior to us... | pgmpy/estimators/BayesianEstimator.py | def estimate_cpd(self, node, prior_type='BDeu', pseudo_counts=[], equivalent_sample_size=5):
"""
Method to estimate the CPD for a given variable.
Parameters
----------
node: int, string (any hashable python object)
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Method to estimate the CPD for a given variable.
Parameters
----------
node: int, string (any hashable python object)
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train | BayesianModelSampling.forward_sample | Generates sample(s) from joint distribution of the bayesian network.
Parameters
----------
size: int
size of sample to be generated
return_type: string (dataframe | recarray)
Return type for samples, either of 'dataframe' or 'recarray'.
Defaults to '... | pgmpy/sampling/Sampling.py | def forward_sample(self, size=1, return_type='dataframe'):
"""
Generates sample(s) from joint distribution of the bayesian network.
Parameters
----------
size: int
size of sample to be generated
return_type: string (dataframe | recarray)
Return t... | def forward_sample(self, size=1, return_type='dataframe'):
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Generates sample(s) from joint distribution of the bayesian network.
Parameters
----------
size: int
size of sample to be generated
return_type: string (dataframe | recarray)
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train | BayesianModelSampling.rejection_sample | Generates sample(s) from joint distribution of the bayesian network,
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Parameters
----------
evidence: list of `pgmpy.factor.State` namedtuples
None if no evidence
size: int
size of sample to be generated
return_type: string (dat... | pgmpy/sampling/Sampling.py | def rejection_sample(self, evidence=None, size=1, return_type="dataframe"):
"""
Generates sample(s) from joint distribution of the bayesian network,
given the evidence.
Parameters
----------
evidence: list of `pgmpy.factor.State` namedtuples
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Generates sample(s) from joint distribution of the bayesian network,
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----------
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train | BayesianModelSampling.likelihood_weighted_sample | Generates weighted sample(s) from joint distribution of the bayesian
network, that comply with the given evidence.
'Probabilistic Graphical Model Principles and Techniques', Koller and
Friedman, Algorithm 12.2 pp 493.
Parameters
----------
evidence: list of `pgmpy.factor... | pgmpy/sampling/Sampling.py | def likelihood_weighted_sample(self, evidence=None, size=1, return_type="dataframe"):
"""
Generates weighted sample(s) from joint distribution of the bayesian
network, that comply with the given evidence.
'Probabilistic Graphical Model Principles and Techniques', Koller and
Fried... | def likelihood_weighted_sample(self, evidence=None, size=1, return_type="dataframe"):
"""
Generates weighted sample(s) from joint distribution of the bayesian
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train | GibbsSampling._get_kernel_from_bayesian_model | Computes the Gibbs transition models from a Bayesian Network.
'Probabilistic Graphical Model Principles and Techniques', Koller and
Friedman, Section 12.3.3 pp 512-513.
Parameters:
-----------
model: BayesianModel
The model from which probabilities will be computed. | pgmpy/sampling/Sampling.py | def _get_kernel_from_bayesian_model(self, model):
"""
Computes the Gibbs transition models from a Bayesian Network.
'Probabilistic Graphical Model Principles and Techniques', Koller and
Friedman, Section 12.3.3 pp 512-513.
Parameters:
-----------
model: BayesianM... | def _get_kernel_from_bayesian_model(self, model):
"""
Computes the Gibbs transition models from a Bayesian Network.
'Probabilistic Graphical Model Principles and Techniques', Koller and
Friedman, Section 12.3.3 pp 512-513.
Parameters:
-----------
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train | GibbsSampling._get_kernel_from_markov_model | Computes the Gibbs transition models from a Markov Network.
'Probabilistic Graphical Model Principles and Techniques', Koller and
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Parameters:
-----------
model: MarkovModel
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Computes the Gibbs transition models from a Markov Network.
'Probabilistic Graphical Model Principles and Techniques', Koller and
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Parameters:
-----------
model: MarkovModel
... | def _get_kernel_from_markov_model(self, model):
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Computes the Gibbs transition models from a Markov Network.
'Probabilistic Graphical Model Principles and Techniques', Koller and
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model: MarkovModel
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train | GibbsSampling.sample | Sample from the Markov Chain.
Parameters:
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start_state: dict or array-like iterable
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size: int
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Sample from the Markov Chain.
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start_state: dict or array-like iterable
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Sample from the Markov Chain.
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train | GibbsSampling.generate_sample | Generator version of self.sample
Return Type:
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List of State namedtuples, representing the assignment to all variables of the model.
Examples:
---------
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List of State namedtuples, representing the assignment to all variables of the model.
Examples:
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Generator version of self.sample
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List of State namedtuples, representing the assignment to all variables of the model.
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train | BaseEstimator._collect_state_names | Return a list of states that the variable takes in the data | pgmpy/estimators/base.py | def _collect_state_names(self, variable):
"Return a list of states that the variable takes in the data"
states = sorted(list(self.data.ix[:, variable].dropna().unique()))
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train | BaseEstimator.state_counts | Return counts how often each state of 'variable' occured in the data.
If a list of parents is provided, counting is done conditionally
for each state configuration of the parents.
Parameters
----------
variable: string
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Return counts how often each state of 'variable' occured in the data.
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Return counts how often each state of 'variable' occured in the data.
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train | ParameterEstimator.state_counts | Return counts how often each state of 'variable' occured in the data.
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Return counts how often each state of 'variable' occured in the data.
If the variable has parents, counting is done conditionally
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train | ClusterGraph.add_node | Add a single node to the cluster graph.
Parameters
----------
node: node
A node should be a collection of nodes forming a clique. It can be
a list, set or tuple of nodes
Examples
--------
>>> from pgmpy.models import ClusterGraph
>>> G = ... | pgmpy/models/ClusterGraph.py | def add_node(self, node, **kwargs):
"""
Add a single node to the cluster graph.
Parameters
----------
node: node
A node should be a collection of nodes forming a clique. It can be
a list, set or tuple of nodes
Examples
--------
>>... | def add_node(self, node, **kwargs):
"""
Add a single node to the cluster graph.
Parameters
----------
node: node
A node should be a collection of nodes forming a clique. It can be
a list, set or tuple of nodes
Examples
--------
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train | ClusterGraph.add_nodes_from | Add multiple nodes to the cluster graph.
Parameters
----------
nodes: iterable container
A container of nodes (list, dict, set, etc.).
Examples
--------
>>> from pgmpy.models import ClusterGraph
>>> G = ClusterGraph()
>>> G.add_nodes_from([('... | pgmpy/models/ClusterGraph.py | def add_nodes_from(self, nodes, **kwargs):
"""
Add multiple nodes to the cluster graph.
Parameters
----------
nodes: iterable container
A container of nodes (list, dict, set, etc.).
Examples
--------
>>> from pgmpy.models import ClusterGraph
... | def add_nodes_from(self, nodes, **kwargs):
"""
Add multiple nodes to the cluster graph.
Parameters
----------
nodes: iterable container
A container of nodes (list, dict, set, etc.).
Examples
--------
>>> from pgmpy.models import ClusterGraph
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train | ClusterGraph.add_edge | Add an edge between two clique nodes.
Parameters
----------
u, v: nodes
Nodes can be any list or set or tuple of nodes forming a clique.
Examples
--------
>>> from pgmpy.models import ClusterGraph
>>> G = ClusterGraph()
>>> G.add_nodes_from([... | pgmpy/models/ClusterGraph.py | def add_edge(self, u, v, **kwargs):
"""
Add an edge between two clique nodes.
Parameters
----------
u, v: nodes
Nodes can be any list or set or tuple of nodes forming a clique.
Examples
--------
>>> from pgmpy.models import ClusterGraph
... | def add_edge(self, u, v, **kwargs):
"""
Add an edge between two clique nodes.
Parameters
----------
u, v: nodes
Nodes can be any list or set or tuple of nodes forming a clique.
Examples
--------
>>> from pgmpy.models import ClusterGraph
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train | ClusterGraph.add_factors | Associate a factor to the graph.
See factors class for the order of potential values
Parameters
----------
*factor: pgmpy.factors.factors object
A factor object on any subset of the variables of the model which
is to be associated with the model.
Returns... | pgmpy/models/ClusterGraph.py | def add_factors(self, *factors):
"""
Associate a factor to the graph.
See factors class for the order of potential values
Parameters
----------
*factor: pgmpy.factors.factors object
A factor object on any subset of the variables of the model which
... | def add_factors(self, *factors):
"""
Associate a factor to the graph.
See factors class for the order of potential values
Parameters
----------
*factor: pgmpy.factors.factors object
A factor object on any subset of the variables of the model which
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train | ClusterGraph.get_factors | Return the factors that have been added till now to the graph.
If node is not None, it would return the factor corresponding to the
given node.
Examples
--------
>>> from pgmpy.models import ClusterGraph
>>> from pgmpy.factors.discrete import DiscreteFactor
>>> ... | pgmpy/models/ClusterGraph.py | def get_factors(self, node=None):
"""
Return the factors that have been added till now to the graph.
If node is not None, it would return the factor corresponding to the
given node.
Examples
--------
>>> from pgmpy.models import ClusterGraph
>>> from pgm... | def get_factors(self, node=None):
"""
Return the factors that have been added till now to the graph.
If node is not None, it would return the factor corresponding to the
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Examples
--------
>>> from pgmpy.models import ClusterGraph
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train | ClusterGraph.get_cardinality | Returns the cardinality of the node
Parameters
----------
node: any hashable python object (optional)
The node whose cardinality we want. If node is not specified returns a
dictionary with the given variable as keys and their respective cardinality
as values.... | pgmpy/models/ClusterGraph.py | def get_cardinality(self, node=None):
"""
Returns the cardinality of the node
Parameters
----------
node: any hashable python object (optional)
The node whose cardinality we want. If node is not specified returns a
dictionary with the given variable as ke... | def get_cardinality(self, node=None):
"""
Returns the cardinality of the node
Parameters
----------
node: any hashable python object (optional)
The node whose cardinality we want. If node is not specified returns a
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train | ClusterGraph.get_partition_function | r"""
Returns the partition function for a given undirected graph.
A partition function is defined as
.. math:: \sum_{X}(\prod_{i=1}^{m} \phi_i)
where m is the number of factors present in the graph
and X are all the random variables present.
Examples
--------
... | pgmpy/models/ClusterGraph.py | def get_partition_function(self):
r"""
Returns the partition function for a given undirected graph.
A partition function is defined as
.. math:: \sum_{X}(\prod_{i=1}^{m} \phi_i)
where m is the number of factors present in the graph
and X are all the random variables pr... | def get_partition_function(self):
r"""
Returns the partition function for a given undirected graph.
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.. math:: \sum_{X}(\prod_{i=1}^{m} \phi_i)
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train | ClusterGraph.check_model | Check the model for various errors. This method checks for the following
errors.
* Checks if factors are defined for all the cliques or not.
* Check for running intersection property is not done explicitly over
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* Checks if cardinality i... | pgmpy/models/ClusterGraph.py | def check_model(self):
"""
Check the model for various errors. This method checks for the following
errors.
* Checks if factors are defined for all the cliques or not.
* Check for running intersection property is not done explicitly over
here as it done in the add_edge... | def check_model(self):
"""
Check the model for various errors. This method checks for the following
errors.
* Checks if factors are defined for all the cliques or not.
* Check for running intersection property is not done explicitly over
here as it done in the add_edge... | [
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train | ClusterGraph.copy | Returns a copy of ClusterGraph.
Returns
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ClusterGraph: copy of ClusterGraph
Examples
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>>> from pgmpy.factors.discrete import DiscreteFactor
>>> G = ClusterGraph()
>>> G.add_nodes_from([('a', 'b'), ('b', 'c')])
>>> G.add_edge(('a', '... | pgmpy/models/ClusterGraph.py | def copy(self):
"""
Returns a copy of ClusterGraph.
Returns
-------
ClusterGraph: copy of ClusterGraph
Examples
-------
>>> from pgmpy.factors.discrete import DiscreteFactor
>>> G = ClusterGraph()
>>> G.add_nodes_from([('a', 'b'), ('b', '... | def copy(self):
"""
Returns a copy of ClusterGraph.
Returns
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ClusterGraph: copy of ClusterGraph
Examples
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>>> from pgmpy.factors.discrete import DiscreteFactor
>>> G = ClusterGraph()
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train | Mplp._update_message | This is the message-update method.
Parameters
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sending_cluster: The resulting messages are lambda_{c-->s} from the given
cluster 'c' to all of its intersection_sets 's'.
Here 's' are the elements of intersection_sets_for_cluster_c.
Reference
--... | pgmpy/inference/mplp.py | def _update_message(self, sending_cluster):
"""
This is the message-update method.
Parameters
----------
sending_cluster: The resulting messages are lambda_{c-->s} from the given
cluster 'c' to all of its intersection_sets 's'.
Here 's' are the elements ... | def _update_message(self, sending_cluster):
"""
This is the message-update method.
Parameters
----------
sending_cluster: The resulting messages are lambda_{c-->s} from the given
cluster 'c' to all of its intersection_sets 's'.
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train | Mplp._local_decode | Finds the index of the maximum values for all the single node dual objectives.
Reference:
code presented by Sontag in 2012 here: http://cs.nyu.edu/~dsontag/code/README_v2.html | pgmpy/inference/mplp.py | def _local_decode(self):
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Finds the index of the maximum values for all the single node dual objectives.
Reference:
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"""
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Finds the index of the maximum values for all the single node dual objectives.
Reference:
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train | Mplp._is_converged | This method checks the integrality gap to ensure either:
* we have found a near to exact solution or
* stuck on a local minima.
Parameters
----------
dual_threshold: double
This sets the minimum width between the dual objective decrements. If the ... | pgmpy/inference/mplp.py | def _is_converged(self, dual_threshold=None, integrality_gap_threshold=None):
"""
This method checks the integrality gap to ensure either:
* we have found a near to exact solution or
* stuck on a local minima.
Parameters
----------
dual_threshold: double
... | def _is_converged(self, dual_threshold=None, integrality_gap_threshold=None):
"""
This method checks the integrality gap to ensure either:
* we have found a near to exact solution or
* stuck on a local minima.
Parameters
----------
dual_threshold: double
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train | Mplp.find_triangles | Finds all the triangles present in the given model
Examples
--------
>>> from pgmpy.models import MarkovModel
>>> from pgmpy.factors.discrete import DiscreteFactor
>>> from pgmpy.inference import Mplp
>>> mm = MarkovModel()
>>> mm.add_nodes_from(['x1', 'x2', 'x3'... | pgmpy/inference/mplp.py | def find_triangles(self):
"""
Finds all the triangles present in the given model
Examples
--------
>>> from pgmpy.models import MarkovModel
>>> from pgmpy.factors.discrete import DiscreteFactor
>>> from pgmpy.inference import Mplp
>>> mm = MarkovModel()
... | def find_triangles(self):
"""
Finds all the triangles present in the given model
Examples
--------
>>> from pgmpy.models import MarkovModel
>>> from pgmpy.factors.discrete import DiscreteFactor
>>> from pgmpy.inference import Mplp
>>> mm = MarkovModel()
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train | Mplp._update_triangles | From a set of variables forming a triangle in the model, we form the corresponding Clusters.
These clusters are then appended to the code.
Parameters
----------
triangle_list : list
The list of variables forming the triangles to be updated. It is of the form of
... | pgmpy/inference/mplp.py | def _update_triangles(self, triangles_list):
"""
From a set of variables forming a triangle in the model, we form the corresponding Clusters.
These clusters are then appended to the code.
Parameters
----------
triangle_list : list
The list of vari... | def _update_triangles(self, triangles_list):
"""
From a set of variables forming a triangle in the model, we form the corresponding Clusters.
These clusters are then appended to the code.
Parameters
----------
triangle_list : list
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train | Mplp._get_triplet_scores | Returns the score of each of the triplets found in the current model
Parameters
---------
triangles_list: list
The list of variables forming the triangles to be updated. It is of the form of
[['var_5', 'var_8', 'var_7'], ['var_4', 'var_5', 'var_7'... | pgmpy/inference/mplp.py | def _get_triplet_scores(self, triangles_list):
"""
Returns the score of each of the triplets found in the current model
Parameters
---------
triangles_list: list
The list of variables forming the triangles to be updated. It is of the form of
... | def _get_triplet_scores(self, triangles_list):
"""
Returns the score of each of the triplets found in the current model
Parameters
---------
triangles_list: list
The list of variables forming the triangles to be updated. It is of the form of
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train | Mplp._run_mplp | Updates messages until either Mplp converges or if doesn't converges; halts after no_iterations.
Parameters
--------
no_iterations: integer
Number of maximum iterations that we want MPLP to run. | pgmpy/inference/mplp.py | def _run_mplp(self, no_iterations):
"""
Updates messages until either Mplp converges or if doesn't converges; halts after no_iterations.
Parameters
--------
no_iterations: integer
Number of maximum iterations that we want MPLP to run.
"""
... | def _run_mplp(self, no_iterations):
"""
Updates messages until either Mplp converges or if doesn't converges; halts after no_iterations.
Parameters
--------
no_iterations: integer
Number of maximum iterations that we want MPLP to run.
"""
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train | Mplp._tighten_triplet | This method finds all the triplets that are eligible and adds them iteratively in the bunch of max_triplets
Parameters
----------
max_iterations: integer
Maximum number of times we tighten the relaxation
later_iter: integer
Number of maximum ... | pgmpy/inference/mplp.py | def _tighten_triplet(self, max_iterations, later_iter, max_triplets, prolong):
"""
This method finds all the triplets that are eligible and adds them iteratively in the bunch of max_triplets
Parameters
----------
max_iterations: integer
Maximum number of ... | def _tighten_triplet(self, max_iterations, later_iter, max_triplets, prolong):
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This method finds all the triplets that are eligible and adds them iteratively in the bunch of max_triplets
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----------
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train | Mplp.map_query | MAP query method using Max Product LP method.
This returns the best assignment of the nodes in the form of a dictionary.
Parameters
----------
init_iter: integer
Number of maximum iterations that we want MPLP to run for the first time.
later_iter: integer
... | pgmpy/inference/mplp.py | def map_query(self, init_iter=1000, later_iter=20, dual_threshold=0.0002, integrality_gap_threshold=0.0002,
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train | MarkovModel.add_edge | Add an edge between u and v.
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not already in the graph
Parameters
----------
u,v : nodes
Nodes can be any hashable Python object.
Examples
--------
>>> from pgmpy.models import MarkovM... | pgmpy/models/MarkovModel.py | def add_edge(self, u, v, **kwargs):
"""
Add an edge between u and v.
The nodes u and v will be automatically added if they are
not already in the graph
Parameters
----------
u,v : nodes
Nodes can be any hashable Python object.
Examples
... | def add_edge(self, u, v, **kwargs):
"""
Add an edge between u and v.
The nodes u and v will be automatically added if they are
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----------
u,v : nodes
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train | MarkovModel.add_factors | Associate a factor to the graph.
See factors class for the order of potential values
Parameters
----------
*factor: pgmpy.factors.factors object
A factor object on any subset of the variables of the model which
is to be associated with the model.
Returns... | pgmpy/models/MarkovModel.py | def add_factors(self, *factors):
"""
Associate a factor to the graph.
See factors class for the order of potential values
Parameters
----------
*factor: pgmpy.factors.factors object
A factor object on any subset of the variables of the model which
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Associate a factor to the graph.
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----------
*factor: pgmpy.factors.factors object
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train | MarkovModel.get_factors | Returns all the factors containing the node. If node is not specified
returns all the factors that have been added till now to the graph.
Parameter
---------
node: any hashable python object (optional)
The node whose factor we want. If node is not specified
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"""
Returns all the factors containing the node. If node is not specified
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Parameter
---------
node: any hashable python object (optional)
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Returns all the factors containing the node. If node is not specified
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---------
node: any hashable python object (optional)
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train | MarkovModel.check_model | Check the model for various errors. This method checks for the following
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* Factors are defined for all the random variables.
Returns
-------
check: boolean
Tru... | pgmpy/models/MarkovModel.py | def check_model(self):
"""
Check the model for various errors. This method checks for the following
errors -
* Checks if the cardinalities of all the variables are consistent across all the factors.
* Factors are defined for all the random variables.
Returns
---... | def check_model(self):
"""
Check the model for various errors. This method checks for the following
errors -
* Checks if the cardinalities of all the variables are consistent across all the factors.
* Factors are defined for all the random variables.
Returns
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train | MarkovModel.to_factor_graph | Converts the markov model into factor graph.
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random variables whereas the second type corresponds to factors over
these variables. The graph only contains edges between variables and
factor nodes. Each factor node is as... | pgmpy/models/MarkovModel.py | def to_factor_graph(self):
"""
Converts the markov model into factor graph.
A factor graph contains two types of nodes. One type corresponds to
random variables whereas the second type corresponds to factors over
these variables. The graph only contains edges between variables a... | def to_factor_graph(self):
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Converts the markov model into factor graph.
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train | MarkovModel.triangulate | Triangulate the graph.
If order of deletion is given heuristic algorithm will not be used.
Parameters
----------
heuristic: H1 | H2 | H3 | H4 | H5 | H6
The heuristic algorithm to use to decide the deletion order of
the variables to compute the triangulated graph... | pgmpy/models/MarkovModel.py | def triangulate(self, heuristic='H6', order=None, inplace=False):
"""
Triangulate the graph.
If order of deletion is given heuristic algorithm will not be used.
Parameters
----------
heuristic: H1 | H2 | H3 | H4 | H5 | H6
The heuristic algorithm to use to de... | def triangulate(self, heuristic='H6', order=None, inplace=False):
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Triangulate the graph.
If order of deletion is given heuristic algorithm will not be used.
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----------
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train | MarkovModel.to_junction_tree | Creates a junction tree (or clique tree) for a given markov model.
For a given markov model (H) a junction tree (G) is a graph
1. where each node in G corresponds to a maximal clique in H
2. each sepset in G separates the variables strictly on one side of the
edge to other.
Exa... | pgmpy/models/MarkovModel.py | def to_junction_tree(self):
"""
Creates a junction tree (or clique tree) for a given markov model.
For a given markov model (H) a junction tree (G) is a graph
1. where each node in G corresponds to a maximal clique in H
2. each sepset in G separates the variables strictly on one... | def to_junction_tree(self):
"""
Creates a junction tree (or clique tree) for a given markov model.
For a given markov model (H) a junction tree (G) is a graph
1. where each node in G corresponds to a maximal clique in H
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train | MarkovModel.get_local_independencies | Returns all the local independencies present in the markov model.
Local independencies are the independence assertion in the form of
.. math:: {X \perp W - {X} - MB(X) | MB(X)}
where MB is the markov blanket of all the random variables in X
Parameters
----------
latex: ... | pgmpy/models/MarkovModel.py | def get_local_independencies(self, latex=False):
"""
Returns all the local independencies present in the markov model.
Local independencies are the independence assertion in the form of
.. math:: {X \perp W - {X} - MB(X) | MB(X)}
where MB is the markov blanket of all the random ... | def get_local_independencies(self, latex=False):
"""
Returns all the local independencies present in the markov model.
Local independencies are the independence assertion in the form of
.. math:: {X \perp W - {X} - MB(X) | MB(X)}
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train | MarkovModel.to_bayesian_model | Creates a Bayesian Model which is a minimum I-Map for this markov model.
The ordering of parents may not remain constant. It would depend on the
ordering of variable in the junction tree (which is not constant) all the
time.
Examples
--------
>>> from pgmpy.models impor... | pgmpy/models/MarkovModel.py | def to_bayesian_model(self):
"""
Creates a Bayesian Model which is a minimum I-Map for this markov model.
The ordering of parents may not remain constant. It would depend on the
ordering of variable in the junction tree (which is not constant) all the
time.
Examples
... | def to_bayesian_model(self):
"""
Creates a Bayesian Model which is a minimum I-Map for this markov model.
The ordering of parents may not remain constant. It would depend on the
ordering of variable in the junction tree (which is not constant) all the
time.
Examples
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train | MarkovModel.get_partition_function | Returns the partition function for a given undirected graph.
A partition function is defined as
.. math:: \sum_{X}(\prod_{i=1}^{m} \phi_i)
where m is the number of factors present in the graph
and X are all the random variables present.
Examples
--------
>>> f... | pgmpy/models/MarkovModel.py | def get_partition_function(self):
"""
Returns the partition function for a given undirected graph.
A partition function is defined as
.. math:: \sum_{X}(\prod_{i=1}^{m} \phi_i)
where m is the number of factors present in the graph
and X are all the random variables pre... | def get_partition_function(self):
"""
Returns the partition function for a given undirected graph.
A partition function is defined as
.. math:: \sum_{X}(\prod_{i=1}^{m} \phi_i)
where m is the number of factors present in the graph
and X are all the random variables pre... | [
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train | MarkovModel.copy | Returns a copy of this Markov Model.
Returns
-------
MarkovModel: Copy of this Markov model.
Examples
-------
>>> from pgmpy.factors.discrete import DiscreteFactor
>>> from pgmpy.models import MarkovModel
>>> G = MarkovModel()
>>> G.add_nodes_fro... | pgmpy/models/MarkovModel.py | def copy(self):
"""
Returns a copy of this Markov Model.
Returns
-------
MarkovModel: Copy of this Markov model.
Examples
-------
>>> from pgmpy.factors.discrete import DiscreteFactor
>>> from pgmpy.models import MarkovModel
>>> G = Marko... | def copy(self):
"""
Returns a copy of this Markov Model.
Returns
-------
MarkovModel: Copy of this Markov model.
Examples
-------
>>> from pgmpy.factors.discrete import DiscreteFactor
>>> from pgmpy.models import MarkovModel
>>> G = Marko... | [
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train | GaussianDistribution.pdf | Returns the probability density function(pdf).
Returns
-------
function: The probability density function of the distribution.
Examples
--------
>>> from pgmpy.factors.distributions import GaussianDistribution
>>> dist = GD(variables=['x1', 'x2', 'x3'],
... | pgmpy/factors/distributions/GaussianDistribution.py | def pdf(self):
"""
Returns the probability density function(pdf).
Returns
-------
function: The probability density function of the distribution.
Examples
--------
>>> from pgmpy.factors.distributions import GaussianDistribution
>>> dist = GD(var... | def pdf(self):
"""
Returns the probability density function(pdf).
Returns
-------
function: The probability density function of the distribution.
Examples
--------
>>> from pgmpy.factors.distributions import GaussianDistribution
>>> dist = GD(var... | [
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train | GaussianDistribution.precision_matrix | Returns the precision matrix of the distribution.
Precision is defined as the inverse of the variance. This method returns
the inverse matrix of the covariance.
Examples
--------
>>> import numpy as np
>>> from pgmpy.factors.distributions import GaussianDistribution as ... | pgmpy/factors/distributions/GaussianDistribution.py | def precision_matrix(self):
"""
Returns the precision matrix of the distribution.
Precision is defined as the inverse of the variance. This method returns
the inverse matrix of the covariance.
Examples
--------
>>> import numpy as np
>>> from pgmpy.facto... | def precision_matrix(self):
"""
Returns the precision matrix of the distribution.
Precision is defined as the inverse of the variance. This method returns
the inverse matrix of the covariance.
Examples
--------
>>> import numpy as np
>>> from pgmpy.facto... | [
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train | GaussianDistribution.marginalize | Modifies the distribution with marginalized values.
Parameters
----------
variables: iterator over any hashable object.
List of variables over which marginalization is to be done.
inplace: boolean
If inplace=True it will modify the distribution itself,
... | pgmpy/factors/distributions/GaussianDistribution.py | def marginalize(self, variables, inplace=True):
"""
Modifies the distribution with marginalized values.
Parameters
----------
variables: iterator over any hashable object.
List of variables over which marginalization is to be done.
inplace: boolean
... | def marginalize(self, variables, inplace=True):
"""
Modifies the distribution with marginalized values.
Parameters
----------
variables: iterator over any hashable object.
List of variables over which marginalization is to be done.
inplace: boolean
... | [
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train | GaussianDistribution.reduce | Reduces the distribution to the context of the given variable values.
The formula for the obtained conditional distribution is given by -
For,
.. math:: N(X_j | X_i = x_i) ~ N(mu_{j.i} ; sig_{j.i})
where,
.. math:: mu_{j.i} = mu_j + sig_{j, i} * {sig_{i, i}^{-1}} * (x_i - mu_i... | pgmpy/factors/distributions/GaussianDistribution.py | def reduce(self, values, inplace=True):
"""
Reduces the distribution to the context of the given variable values.
The formula for the obtained conditional distribution is given by -
For,
.. math:: N(X_j | X_i = x_i) ~ N(mu_{j.i} ; sig_{j.i})
where,
.. math:: mu... | def reduce(self, values, inplace=True):
"""
Reduces the distribution to the context of the given variable values.
The formula for the obtained conditional distribution is given by -
For,
.. math:: N(X_j | X_i = x_i) ~ N(mu_{j.i} ; sig_{j.i})
where,
.. math:: mu... | [
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train | GaussianDistribution.normalize | Normalizes the distribution. In case of a Gaussian Distribution the
distribution is always normalized, therefore this method doesn't do
anything and has been implemented only for a consistent API across
distributions. | pgmpy/factors/distributions/GaussianDistribution.py | def normalize(self, inplace=True):
"""
Normalizes the distribution. In case of a Gaussian Distribution the
distribution is always normalized, therefore this method doesn't do
anything and has been implemented only for a consistent API across
distributions.
"""
phi... | def normalize(self, inplace=True):
"""
Normalizes the distribution. In case of a Gaussian Distribution the
distribution is always normalized, therefore this method doesn't do
anything and has been implemented only for a consistent API across
distributions.
"""
phi... | [
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train | GaussianDistribution.copy | Return a copy of the distribution.
Returns
-------
GaussianDistribution: copy of the distribution
Examples
--------
>>> import numpy as np
>>> from pgmpy.factors.distributions import GaussianDistribution as GD
>>> gauss_dis = GD(variables=['x1', 'x2', 'x... | pgmpy/factors/distributions/GaussianDistribution.py | def copy(self):
"""
Return a copy of the distribution.
Returns
-------
GaussianDistribution: copy of the distribution
Examples
--------
>>> import numpy as np
>>> from pgmpy.factors.distributions import GaussianDistribution as GD
>>> gaus... | def copy(self):
"""
Return a copy of the distribution.
Returns
-------
GaussianDistribution: copy of the distribution
Examples
--------
>>> import numpy as np
>>> from pgmpy.factors.distributions import GaussianDistribution as GD
>>> gaus... | [
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train | GaussianDistribution.to_canonical_factor | u"""
Returns an equivalent CanonicalDistribution object.
The formulas for calculating the cannonical factor parameters
for N(μ; Σ) = C(K; h; g) are as follows -
K = sigma^(-1)
h = sigma^(-1) * mu
g = -(0.5) * mu.T * sigma^(-1) * mu -
log((2*pi)^(n/2) * det(s... | pgmpy/factors/distributions/GaussianDistribution.py | def to_canonical_factor(self):
u"""
Returns an equivalent CanonicalDistribution object.
The formulas for calculating the cannonical factor parameters
for N(μ; Σ) = C(K; h; g) are as follows -
K = sigma^(-1)
h = sigma^(-1) * mu
g = -(0.5) * mu.T * sigma^(-1) * mu... | def to_canonical_factor(self):
u"""
Returns an equivalent CanonicalDistribution object.
The formulas for calculating the cannonical factor parameters
for N(μ; Σ) = C(K; h; g) are as follows -
K = sigma^(-1)
h = sigma^(-1) * mu
g = -(0.5) * mu.T * sigma^(-1) * mu... | [
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train | GaussianDistribution.divide | Returns the division of two gaussian distributions.
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Returns the division of two gaussian distributions.
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train | CustomDistribution.discretize | Discretizes the continuous distribution into discrete
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Discretizes the continuous distribution into discrete
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train | CustomDistribution.reduce | Reduces the factor to the context of the given variable values.
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Reduces the factor to the context of the given variable values.
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Reduces the factor to the context of the given variable values.
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train | CustomDistribution.marginalize | Marginalize the distribution with respect to the given variables.
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List of variables to be removed from the marginalized distribution.
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Marginalize the distribution with respect to the given variables.
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List of variables to be removed from the marginalized distribution.
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Marginalize the distribution with respect to the given variables.
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List of variables to be removed from the marginalized distribution.
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train | VariableElimination._variable_elimination | Implementation of a generalized variable elimination.
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variables: list, array-like
variables that are not to be eliminated.
operation: str ('marginalize' | 'maximize')
The operation to do for eliminating the variable.
evidence: dict
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Implementation of a generalized variable elimination.
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variables: list, array-like
variables that are not to be eliminated.
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variables: list, array-like
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train | VariableElimination.query | Parameters
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list of variables for which you want to compute the probability
evidence: dict
a dict key, value pair as {var: state_of_var_observed}
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elimination_order: list
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list of variables for which you want to compute the probability
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a dict key, value pair as {var: state_of_var_observed}
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list of variables for which you want to compute the probability
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train | VariableElimination.max_marginal | Computes the max-marginal over the variables given the evidence.
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variables: list
list of variables over which we want to compute the max-marginal.
evidence: dict
a dict key, value pair as {var: state_of_var_observed}
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Computes the max-marginal over the variables given the evidence.
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variables: list
list of variables over which we want to compute the max-marginal.
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list of variables over which we want to compute the max-marginal.
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train | VariableElimination.map_query | Computes the MAP Query over the variables given the evidence.
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Parameters
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variables: list
list of variables over which we want to compute the max-marginal.
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train | VariableElimination.induced_graph | Returns the induced graph formed by running Variable Elimination on the network.
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elimination_order: list, array like
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train | BeliefPropagation._update_beliefs | This is belief-update method.
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sending_clique: node (as the operation is on junction tree, node should be a tuple)
Node sending the message
recieving_clique: node (as the operation is on junction tree, node should be a tuple)
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operation: str ('marginalize' | 'maximize')
The operation to do for passing messages between nodes.
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Checks whether the calibration has converged or not. At convergence
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operation: str ('marginalize' | 'maximize')
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train | BeliefPropagation._calibrate_junction_tree | Generalized calibration of junction tree or clique using belief propagation. This method can be used for both
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Uses Lauritzen-Spiegelhalter algorithm or belief-update message passing.
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"""
Generalized calibration of junction tree or clique using belief propagation. This method can be used for both
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Uses Lauritzen-Spiegelhalter algorithm or belief-update message passing.
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train | BeliefPropagation._query | This is a generalized query method that can be used for both query and map query.
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variables: list
list of variables for which you want to compute the probability
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"""
This is a generalized query method that can be used for both query and map query.
Parameters
----------
variables: list
list of variables for which you want to compute the probability
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variables: list
list of variables for which you want to compute the probability
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train | BeliefPropagation.query | Query method using belief propagation.
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list of variables for which you want to compute the probability
evidence: dict
a dict key, value pair as {var: state_of_var_observed}
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Query method using belief propagation.
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variables: list
list of variables for which you want to compute the probability
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a dict key, value pair as {var:... | def query(self, variables, evidence=None, joint=True):
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Query method using belief propagation.
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list of variables for which you want to compute the probability
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a dict key, value pair as {var:... | [
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train | BeliefPropagation.map_query | MAP Query method using belief propagation.
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Parameters
----------
variables: list
list of variables for which you want to compute the probability
evidence: dict
... | pgmpy/inference/ExactInference.py | def map_query(self, variables=None, evidence=None):
"""
MAP Query method using belief propagation.
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Parameters
----------
variables: list
list of variabl... | def map_query(self, variables=None, evidence=None):
"""
MAP Query method using belief propagation.
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----------
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train | LinearGaussianCPD.maximum_likelihood_estimator | Fit using MLE method.
Parameters
----------
data: pandas.DataFrame or 2D array
Dataframe of values containing samples from the conditional distribution, (Y|X)
and corresponding X values.
states: All the input states that are jointly gaussian.
Returns
... | pgmpy/factors/continuous/LinearGaussianCPD.py | def maximum_likelihood_estimator(self, data, states):
"""
Fit using MLE method.
Parameters
----------
data: pandas.DataFrame or 2D array
Dataframe of values containing samples from the conditional distribution, (Y|X)
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... | def maximum_likelihood_estimator(self, data, states):
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Fit using MLE method.
Parameters
----------
data: pandas.DataFrame or 2D array
Dataframe of values containing samples from the conditional distribution, (Y|X)
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train | LinearGaussianCPD.fit | Determine βs from data
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Dataframe containing samples from the conditional distribution, p(Y|X)
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completely_samples_only: boolean (True or False)
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train | LinearGaussianCPD.copy | Returns a copy of the distribution.
Returns
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LinearGaussianCPD: copy of the distribution
Examples
--------
>>> from pgmpy.factors.continuous import LinearGaussianCPD
>>> cpd = LinearGaussianCPD('Y', [0.2, -2, 3, 7], 9.6, ['X1', 'X2', 'X3'])
>>> c... | pgmpy/factors/continuous/LinearGaussianCPD.py | def copy(self):
"""
Returns a copy of the distribution.
Returns
-------
LinearGaussianCPD: copy of the distribution
Examples
--------
>>> from pgmpy.factors.continuous import LinearGaussianCPD
>>> cpd = LinearGaussianCPD('Y', [0.2, -2, 3, 7], 9.... | def copy(self):
"""
Returns a copy of the distribution.
Returns
-------
LinearGaussianCPD: copy of the distribution
Examples
--------
>>> from pgmpy.factors.continuous import LinearGaussianCPD
>>> cpd = LinearGaussianCPD('Y', [0.2, -2, 3, 7], 9.... | [
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train | _pipe_segment_with_colons | Return a segment of a horizontal line with optional colons which
indicate column's alignment (as in `pipe` output format). | pgmpy/extern/tabulate.py | def _pipe_segment_with_colons(align, colwidth):
"""Return a segment of a horizontal line with optional colons which
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w = colwidth
if align in ["right", "decimal"]:
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train | _isint | >>> _isint("123")
True
>>> _isint("123.45")
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"""
>>> _isint("123")
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>>> _isint("123.45")
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"""
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train | _afterpoint | Symbols after a decimal point, -1 if the string lacks the decimal point.
>>> _afterpoint("123.45")
2
>>> _afterpoint("1001")
-1
>>> _afterpoint("eggs")
-1
>>> _afterpoint("123e45")
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"""Symbols after a decimal point, -1 if the string lacks the decimal point.
>>> _afterpoint("123.45")
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>>> _afterpoint("1001")
-1
>>> _afterpoint("eggs")
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>>> _afterpoint("123e45")
2
"""
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train | _padleft | Flush right.
>>> _padleft(6, '\u044f\u0439\u0446\u0430') == ' \u044f\u0439\u0446\u0430'
True | pgmpy/extern/tabulate.py | def _padleft(width, s, has_invisible=True):
"""Flush right.
>>> _padleft(6, '\u044f\u0439\u0446\u0430') == ' \u044f\u0439\u0446\u0430'
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"""
iwidth = width + len(s) - len(_strip_invisible(s)) if has_invisible else width
fmt = "{0:>%ds}" % iwidth
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>>> _padleft(6, '\u044f\u0439\u0446\u0430') == ' \u044f\u0439\u0446\u0430'
True
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train | _strip_invisible | Remove invisible ANSI color codes. | pgmpy/extern/tabulate.py | def _strip_invisible(s):
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train | _visible_width | Visible width of a printed string. ANSI color codes are removed.
>>> _visible_width('\x1b[31mhello\x1b[0m'), _visible_width("world")
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train | _column_type | The least generic type all column values are convertible to.
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train | _build_simple_row | Format row according to DataRow format without padding. | pgmpy/extern/tabulate.py | def _build_simple_row(padded_cells, rowfmt):
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train | _build_row | Return a string which represents a row of data cells. | pgmpy/extern/tabulate.py | def _build_row(padded_cells, colwidths, colaligns, rowfmt):
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train | _build_line | Return a string which represents a horizontal line. | pgmpy/extern/tabulate.py | def _build_line(colwidths, colaligns, linefmt):
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train | _format_table | Produce a plain-text representation of the table. | pgmpy/extern/tabulate.py | def _format_table(fmt, headers, rows, colwidths, colaligns):
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train | BaseDiscretizer.get_labels | Returns a list of strings representing the values about
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Default value is the points -
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Returns a list of strings representing the values about
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train | UnbiasedDiscretizer._lim_moment | This method calculates the kth order limiting moment of
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where, pdf is the probability density function and cdf is the
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train | MaximumLikelihoodEstimator.get_parameters | Method to estimate the model parameters (CPDs) using Maximum Likelihood Estimation.
Returns
-------
parameters: list
List of TabularCPDs, one for each variable of the model
Examples
--------
>>> import numpy as np
>>> import pandas as pd
>>> ... | pgmpy/estimators/MLE.py | def get_parameters(self):
"""
Method to estimate the model parameters (CPDs) using Maximum Likelihood Estimation.
Returns
-------
parameters: list
List of TabularCPDs, one for each variable of the model
Examples
--------
>>> import numpy as n... | def get_parameters(self):
"""
Method to estimate the model parameters (CPDs) using Maximum Likelihood Estimation.
Returns
-------
parameters: list
List of TabularCPDs, one for each variable of the model
Examples
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train | MaximumLikelihoodEstimator.estimate_cpd | Method to estimate the CPD for a given variable.
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node: int, string (any hashable python object)
The name of the variable for which the CPD is to be estimated.
Returns
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Method to estimate the CPD for a given variable.
Parameters
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node: int, string (any hashable python object)
The name of the variable for which the CPD is to be estimated.
Returns
-------
CPD: Tabula... | def estimate_cpd(self, node):
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Method to estimate the CPD for a given variable.
Parameters
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node: int, string (any hashable python object)
The name of the variable for which the CPD is to be estimated.
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train | ConstraintBasedEstimator.estimate | Estimates a DAG for the data set, using the PC constraint-based
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train | ConstraintBasedEstimator.estimate_from_independencies | Estimates a DAG from an Independencies()-object or a
decision function for conditional independencies. This requires that
the set of independencies admits a faithful representation (e.g. is a
set of d-seperation for some BN or is closed under the semi-graphoid
axioms). See `build_skeleto... | pgmpy/estimators/ConstraintBasedEstimator.py | def estimate_from_independencies(nodes, independencies):
"""Estimates a DAG from an Independencies()-object or a
decision function for conditional independencies. This requires that
the set of independencies admits a faithful representation (e.g. is a
set of d-seperation for some BN or i... | def estimate_from_independencies(nodes, independencies):
"""Estimates a DAG from an Independencies()-object or a
decision function for conditional independencies. This requires that
the set of independencies admits a faithful representation (e.g. is a
set of d-seperation for some BN or i... | [
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