JorgeVB/code_unlearning_dataset · Datasets at Hugging Face

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Normalizes the values of factor so that they sum to 1. Parameters ---------- inplace: boolean If inplace=True it will modify the factor itself, else would return a new factor Returns ------- DiscreteFactor or None: if inplace=True (default) retur...
Reduces the factor to the context of given variable values. Parameters ---------- values: list, array-like A list of tuples of the form (variable_name, variable_state). inplace: boolean If inplace=True it will modify the factor itself, else would return ...
DiscreteFactor sum with `phi1`. Parameters ---------- phi1: `DiscreteFactor` instance. DiscreteFactor to be added. inplace: boolean If inplace=True it will modify the factor itself, else would return a new factor. Returns ------- ...
DiscreteFactor division by `phi1`. Parameters ---------- phi1 : `DiscreteFactor` instance The denominator for division. inplace: boolean If inplace=True it will modify the factor itself, else would return a new factor. Returns ------...
Returns a copy of the factor. Returns ------- DiscreteFactor: copy of the factor Examples -------- >>> import numpy as np >>> from pgmpy.factors.discrete import DiscreteFactor >>> phi = DiscreteFactor(['x1', 'x2', 'x3'], [2, 3, 3], np.arange(18)) ...
Generate the string from `__str__` method. Parameters ---------- phi_or_p: 'phi' \| 'p' 'phi': When used for Factors. 'p': When used for CPDs. print_state_names: boolean If True, the user defined state names are displayed. def _str(self,...
Checks whether given parameter is a 1d array like object, and returns a numpy array object def _check_1d_array_object(parameter, name_param): """ Checks whether given parameter is a 1d array like object, and returns a numpy array object """ if isinstance(parameter, (np.ndarray, list, tuple, np.matrix))...
Raises an error when the length of given two arguments is not equal def _check_length_equal(param_1, param_2, name_param_1, name_param_2): """ Raises an error when the length of given two arguments is not equal """ if len(param_1) != len(param_2): raise ValueError("Length of {} must be same as ...
Returns list of variables of the network Examples -------- >>> reader = XMLBIF.XMLBIFReader("xmlbif_test.xml") >>> reader.get_variables() ['light-on', 'bowel-problem', 'dog-out', 'hear-bark', 'family-out'] def get_variables(self): """ Returns list of variables o...
Returns the edges of the network Examples -------- >>> reader = XMLBIF.XMLBIFReader("xmlbif_test.xml") >>> reader.get_edges() [['family-out', 'light-on'], ['family-out', 'dog-out'], ['bowel-problem', 'dog-out'], ['dog-out', 'hear-bark']] def get_edges...
Returns the states of variables present in the network Examples -------- >>> reader = XMLBIF.XMLBIFReader("xmlbif_test.xml") >>> reader.get_states() {'bowel-problem': ['true', 'false'], 'dog-out': ['true', 'false'], 'family-out': ['true', 'false'], 'he...
Returns the parents of the variables present in the network Examples -------- >>> reader = XMLBIF.XMLBIFReader("xmlbif_test.xml") >>> reader.get_parents() {'bowel-problem': [], 'dog-out': ['family-out', 'bowel-problem'], 'family-out': [], 'hear-bark': ...
Returns the CPD of the variables present in the network Examples -------- >>> reader = XMLBIF.XMLBIFReader("xmlbif_test.xml") >>> reader.get_values() {'bowel-problem': array([[ 0.01], [ 0.99]]), 'dog-out': array([[ 0.99, 0.01, 0.97, 0...
Returns the property of the variable Examples -------- >>> reader = XMLBIF.XMLBIFReader("xmlbif_test.xml") >>> reader.get_property() {'bowel-problem': ['position = (190, 69)'], 'dog-out': ['position = (155, 165)'], 'family-out': ['position = (112, 69)'], ...
Add variables to XMLBIF Return ------ dict: dict of type {variable: variable tags} Examples -------- >>> writer = XMLBIFWriter(model) >>> writer.get_variables() {'bowel-problem': <Element VARIABLE at 0x7fe28607dd88>, 'family-out': <Element VARIA...
Add outcome to variables of XMLBIF Return ------ dict: dict of type {variable: outcome tags} Examples -------- >>> writer = XMLBIFWriter(model) >>> writer.get_states() {'dog-out': [<Element OUTCOME at 0x7ffbabfcdec8>, <Element OUTCOME at 0x7ffbabfcdf08>]...
Transform the input state_name into a valid state in XMLBIF. XMLBIF states must start with a letter an only contain letters, numbers and underscores. def _make_valid_state_name(self, state_name): """Transform the input state_name into a valid state in XMLBIF. XMLBIF states must start wi...
Add property to variables in XMLBIF Return ------ dict: dict of type {variable: property tag} Examples -------- >>> writer = XMLBIFWriter(model) >>> writer.get_property() {'light-on': <Element PROPERTY at 0x7f7a2ffac1c8>, 'family-out': <Element ...
Add Definition to XMLBIF Return ------ dict: dict of type {variable: definition tag} Examples -------- >>> writer = XMLBIFWriter(model) >>> writer.get_definition() {'hear-bark': <Element DEFINITION at 0x7f1d48977408>, 'family-out': <Element DEFI...
Add Table to XMLBIF. Return --------------- dict: dict of type {variable: table tag} Examples ------- >>> writer = XMLBIFWriter(model) >>> writer.get_values() {'dog-out': <Element TABLE at 0x7f240726f3c8>, 'light-on': <Element TABLE at 0x7f24072...
Write the xml data into the file. Parameters ---------- filename: Name of the file. Examples ------- >>> writer = XMLBIFWriter(model) >>> writer.write_xmlbif(test_file) def write_xmlbif(self, filename): """ Write the xml data into the file. ...
Returns the marginal distribution over variables. Parameters ---------- variables: string, list, tuple, set, dict Variable or list of variables over which marginal distribution needs to be calculated inplace: Boolean (default True) If Fals...
Check if the Joint Probability Distribution satisfies the given independence condition. Parameters ---------- event1: list random variable whose independence is to be checked. event2: list random variable from which event1 is independent. values: 2D array...
Returns the independent variables in the joint probability distribution. Returns marginally independent variables if condition=None. Returns conditionally independent variables if condition!=None Parameter --------- condition: array_like Random Variable on which ...
Returns Conditional Probability Distribution after setting values to 1. Parameters ---------- values: list or array_like A list of tuples of the form (variable_name, variable_state). The values on which to condition the Joint Probability Distribution. inplace: Bo...
Returns a Bayesian Model which is minimal IMap of the Joint Probability Distribution considering the order of the variables. Parameters ---------- order: array-like The order of the random variables. Examples -------- >>> import numpy as np >...
Checks whether the given BayesianModel is Imap of JointProbabilityDistribution Parameters ----------- model : An instance of BayesianModel Class, for which you want to check the Imap Returns -------- boolean : True if given bayesian model is Imap for Joint P...
Returns the acceptance probability for given new position(position) and momentum def _acceptance_prob(self, position, position_bar, momentum, momentum_bar): """ Returns the acceptance probability for given new position(position) and momentum """ # Parameters to help in evaluating Joint...
Temporary method to fix issue in numpy 0.12 #852 def _get_condition(self, acceptance_prob, a): """ Temporary method to fix issue in numpy 0.12 #852 """ if a == 1: return (acceptance_prob a) > (1/(2a)) else: return (1/(acceptance_prob a)) > (2(-a...
Method for choosing initial value of stepsize References ----------- Matthew D. Hoffman, Andrew Gelman, The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research 15 (2014) 1351-1381 Algorithm 4 : Heuristic for...
Runs a single sampling iteration to return a sample def _sample(self, position, trajectory_length, stepsize, lsteps=None): """ Runs a single sampling iteration to return a sample """ # Resampling momentum momentum = np.reshape(np.random.normal(0, 1, len(position)), position.shap...
Method to return samples using Hamiltonian Monte Carlo Parameters ---------- initial_pos: A 1d array like object Vector representing values of parameter position, the starting state in markov chain. num_samples: int Number of samples to be generated ...
Method returns a generator type object whose each iteration yields a sample using Hamiltonian Monte Carlo Parameters ---------- initial_pos: A 1d array like object Vector representing values of parameter position, the starting state in markov chain. num_...
Run tha adaptation for stepsize for better proposals of position def _adapt_params(self, stepsize, stepsize_bar, h_bar, mu, index_i, alpha, n_alpha=1): """ Run tha adaptation for stepsize for better proposals of position """ gamma = 0.05 # free parameter that controls the amount of shr...
Method returns a generator type object whose each iteration yields a sample using Hamiltonian Monte Carlo Parameters ---------- initial_pos: A 1d array like object Vector representing values of parameter position, the starting state in markov chain. num_...
A list of states example - [('x1', 'easy'), ('x2', 'hard')] Returns ------- True, if arg is a list of states else False. def is_list_of_states(self, arg): """ A list of states example - [('x1', 'easy'), ('x2', 'hard')] Returns ------- Tr...
A list of list of states example - [[('x1', 'easy'), ('x2', 'hard')], [('x1', 'hard'), ('x2', 'medium')]] Returns ------- True, if arg is a list of list of states else False. def is_list_of_list_of_states(self, arg): """ A list of list of states example - [[('x1...
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. weight: int, float (default=None) The weight of the edge...
Check if the given nodes form a clique. Parameters ---------- nodes: list, array-like List of nodes to check if they are a part of any clique. Examples -------- >>> from pgmpy.base import UndirectedGraph >>> G = UndirectedGraph(ebunch=[('A', 'B'), ('...
Set the start state of the Markov Chain. If the start_state is given as a array-like iterable, its contents are reordered in the internal representation. Parameters: ----------- start_state: dict or array-like iterable object Dict (or list) of tuples representing the startin...
Checks if a list representing the state of the variables is valid. def _check_state(self, state): """ Checks if a list representing the state of the variables is valid. """ if not hasattr(state, '__iter__') or isinstance(state, six.string_types): raise ValueError('Start stat...
Add a variable to the model. Parameters: ----------- variable: any hashable python object card: int Representing the cardinality of the variable to be added. Examples: --------- >>> from pgmpy.models import MarkovChain as MC >>> model = MC()...
Add several variables to the model at once. Parameters: ----------- variables: array-like iterable object List of variables to be added. cards: array-like iterable object List of cardinalities of the variables to be added. Examples: --------- ...
Adds a transition model for a particular variable. Parameters: ----------- variable: any hashable python object must be an existing variable of the model. transition_model: dict or 2d array dict representing valid transition probabilities defined for every possib...
Sample from the Markov Chain. Parameters: ----------- start_state: dict or array-like iterable Representing the starting states of the variables. If None is passed, a random start_state is chosen. size: int Number of samples to be generated. Return Type:...
Given an instantiation (partial or complete) of the variables of the model, compute the probability of observing it over multiple windows in a given sample. If 'sample' is not passed as an argument, generate the statistic by sampling from the Markov Chain, starting with a random initial state. ...
Generator version of self.sample Return Type: ------------ List of State namedtuples, representing the assignment to all variables of the model. Examples: --------- >>> from pgmpy.models.MarkovChain import MarkovChain >>> from pgmpy.factors.discrete import State...
Checks if the given markov chain is stationary and checks the steady state probablity values for the state are consistent. Parameters: ----------- tolerance: float represents the diff between actual steady state value and the computed value sample: [State(i,j)] ...
Generates a random state of the Markov Chain. Return Type: ------------ List of namedtuples, representing a random assignment to all variables of the model. Examples: --------- >>> from pgmpy.models import MarkovChain as MC >>> model = MC(['intel', 'diff'], [2, ...
Returns a copy of Markov Chain Model. Return Type: ------------ MarkovChain : Copy of MarkovChain. Examples: --------- >>> from pgmpy.models import MarkovChain >>> from pgmpy.factors.discrete import State >>> model = MarkovChain() >>> model.add_v...
Returns `True` if `assertion` is contained in this `Independencies`-object, otherwise `False`. Parameters ---------- assertion: IndependenceAssertion()-object Examples -------- >>> from pgmpy.independencies import Independencies, IndependenceAssertion >>...
Adds assertions to independencies. Parameters ---------- assertions: Lists or Tuples Each assertion is a list or tuple of variable, independent_of and given. Examples -------- >>> from pgmpy.independencies import Independencies >>> independencies...
Returns a new `Independencies()`-object that additionally contains those `IndependenceAssertions` that are implied by the the current independencies (using with the `semi-graphoid axioms <https://en.wikipedia.org/w/index.php?title=Conditional_independence&oldid=708760689#Rules_of_conditional_independenc...
Returns `True` if the `entailed_independencies` are implied by this `Independencies`-object, otherwise `False`. Entailment is checked using the semi-graphoid axioms. Might be very slow if more than six variables are involved. Parameters ---------- entailed_independencies: Indep...
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 -------- >>> from pgmpy.models import Naive...
Returns a list of all ancestors of all the observed nodes. Parameters ---------- obs_nodes_list: string, list-type name of all the observed nodes def _get_ancestors_of(self, obs_nodes_list): """ Returns a list of all ancestors of all the observed nodes. Par...
Returns all the nodes reachable from start via an active trail. Parameters ---------- start: Graph node observed : List of nodes (optional) If given the active trail would be computed assuming these nodes to be observed. Examples -------- >>> from p...
Returns an instance of Independencies containing the local independencies of each of the variables. Parameters ---------- variables: str or array like variables whose local independencies are to found. Examples -------- >>> from pgmpy.models import ...
Computes the CPD for each node from a given data in the form of a pandas dataframe. If a variable from the data is not present in the model, it adds that node into the model. Parameters ---------- data : pandas DataFrame object A DataFrame object with column names same as th...
Computes a score to measure how well the given `BayesianModel` fits to the data set. (This method relies on the `local_score`-method that is implemented in each subclass.) Parameters ---------- model: `BayesianModel` instance The Bayesian network that is to be scored. Nodes ...
Computes a score that measures how much a \ given variable is \"influenced\" by a given list of potential parents. def local_score(self, variable, parents): "Computes a score that measures how much a \ given variable is \"influenced\" by a given list of potential parents." var_states =...
Generate a cartesian product of input arrays. Parameters ---------- arrays : list of array-like 1-D arrays to form the cartesian product of. out : ndarray Array to place the cartesian product in. Returns ------- out : ndarray 2-D array of shape (M, len(arrays)) cont...
Generate a sample of given size, given a probability mass function. Parameters ---------- values: numpy.array: Array of all possible values that the random variable can take. weights: numpy.array or list of numpy.array: Array(s) representing the PMF of the random variable. size: int: Si...
Generates all subsets of list `l` (as tuples). Example ------- >>> from pgmpy.utils.mathext import powerset >>> list(powerset([1,2,3])) [(), (1,), (2,), (3,), (1,2), (1,3), (2,3), (1,2,3)] def powerset(l): """ Generates all subsets of list `l` (as tuples). Example ------- >>> ...
Remove draft pages from space using datetime.now :param confluence: :param space_key: :param count: :param date_now: :return: int counter def clean_draft_pages_from_space(confluence, space_key, count, date_now): """ Remove draft pages from space using datetime.now :param confluence: ...
Remove all draft pages for all spaces older than DRAFT_DAYS :param days: int :param confluence: :return: def clean_all_draft_pages_from_all_spaces(confluence, days=30): """ Remove all draft pages for all spaces older than DRAFT_DAYS :param days: int :param confluence: :return: """ ...
Provide content by type (page, blog, comment) :param page_id: A string containing the id of the type content container. :param type: :param start: OPTIONAL: The start point of the collection to return. Default: None (0). :param limit: OPTIONAL: how many items should be returned after the...
Returns the list of labels on a piece of Content. :param space: Space key :param title: Title of the page :param start: OPTIONAL: The start point of the collection to return. Default: None (0). :param limit: OPTIONAL: The limit of the number of labels to return, this may be restricted by...
Get page by ID :param page_id: Content ID :param expand: OPTIONAL: expand e.g. history :return: def get_page_by_id(self, page_id, expand=None): """ Get page by ID :param page_id: Content ID :param expand: OPTIONAL: expand e.g. history :return: """...
Returns the list of labels on a piece of Content. :param page_id: A string containing the id of the labels content container. :param prefix: OPTIONAL: The prefixes to filter the labels with {@see Label.Prefix}. Default: None. :param start: OPTIONAL: The start poin...
Provide content by id with status = draft :param page_id: :param status: :return: def get_draft_page_by_id(self, page_id, status='draft'): """ Provide content by id with status = draft :param page_id: :param status: :return: """ url = 'res...
Get all page by label :param label: :param start: OPTIONAL: The start point of the collection to return. Default: None (0). :param limit: OPTIONAL: The limit of the number of pages to return, this may be restricted by fixed system limits. Default: 50 :return: def g...
Get all pages from space :param space: :param start: OPTIONAL: The start point of the collection to return. Default: None (0). :param limit: OPTIONAL: The limit of the number of pages to return, this may be restricted by fixed system limits. Default: 50 :param...
Get list of pages from trash :param space: :param start: OPTIONAL: The start point of the collection to return. Default: None (0). :param limit: OPTIONAL: The limit of the number of pages to return, this may be restricted by fixed system limits. Default: 500 :...
Get list of draft pages from space Use case is cleanup old drafts from Confluence :param space: :param start: OPTIONAL: The start point of the collection to return. Default: None (0). :param limit: OPTIONAL: The limit of the number of pages to return, this may be restricted by ...
Search list of draft pages by space key Use case is cleanup old drafts from Confluence :param space: Space Key :param status: Can be changed :param start: OPTIONAL: The start point of the collection to return. Default: None (0). :param limit: OPTIONAL: The limit of the number of ...
This method removes a page, if it has recursive flag, method removes including child pages :param page_id: :param status: OPTIONAL: type of page :param recursive: OPTIONAL: if True - will recursively delete all children pages too :return: def remove_page(self, page_id, status=None, recu...
Create page from scratch :param space: :param title: :param body: :param parent_id: :param type: :return: def create_page(self, space, title, body, parent_id=None, type='page'): """ Create page from scratch :param space: :param title: ...
Get all spaces with provided limit :param start: OPTIONAL: The start point of the collection to return. Default: None (0). :param limit: OPTIONAL: The limit of the number of pages to return, this may be restricted by fixed system limits. Default: 500 def get_all_spaces(self,...
Add comment into page :param page_id :param text def add_comment(self, page_id, text): """ Add comment into page :param page_id :param text """ data = {'type': 'comment', 'container': {'id': page_id, 'type': 'page', 'status': 'current'}, ...
Attach (upload) a file to a page, if it exists it will update the automatically version the new file and keep the old one. :param title: The page name :type title: ``str`` :param space: The space name :type space: ``str`` :param page_id: The page id to which we would li...
Set a label on the page :param page_id: content_id format :param label: label to add :return: def set_page_label(self, page_id, label): """ Set a label on the page :param page_id: content_id format :param label: label to add :return: """ u...
Remove content history. It works as experimental method :param page_id: :param version_number: version number :return: def remove_content_history(self, page_id, version_number): """ Remove content history. It works as experimental method :param page_id: :param ve...
Remove content history. It works in CLOUD :param page_id: :param version_id: :return: def remove_content_history_in_cloud(self, page_id, version_id): """ Remove content history. It works in CLOUD :param page_id: :param version_id: :return: """ ...
Check has unknown attachment error on page :param page_id: :return: def has_unknown_attachment_error(self, page_id): """ Check has unknown attachment error on page :param page_id: :return: """ unknown_attachment_identifier = 'plugins/servlet/confluence/pl...
Compare content and check is already updated or not :param page_id: Content ID for retrieve storage value :param body: Body for compare it :return: True if the same def is_page_content_is_already_updated(self, page_id, body): """ Compare content and check is already updated or n...
Update page if already exist :param parent_id: :param page_id: :param title: :param body: :param type: :param minor_edit: Indicates whether to notify watchers about changes. If False then notifications will be sent. :return: def update_page(self, pare...
Update page or create a page if it is not exists :param parent_id: :param title: :param body: :return: def update_or_create(self, parent_id, title, body): """ Update page or create a page if it is not exists :param parent_id: :param title: :param ...
Set the page (content) property e.g. add hash parameters :param page_id: content_id format :param data: data should be as json data :return: def set_page_property(self, page_id, data): """ Set the page (content) property e.g. add hash parameters :param page_id: content_i...
Delete the page (content) property e.g. delete key of hash :param page_id: content_id format :param page_property: key of property :return: def delete_page_property(self, page_id, page_property): """ Delete the page (content) property e.g. delete key of hash :param page_...
Get the page (content) property e.g. get key of hash :param page_id: content_id format :param page_property_key: key of property :return: def get_page_property(self, page_id, page_property_key): """ Get the page (content) property e.g. get key of hash :param page_id: con...
Get the page (content) properties :param page_id: content_id format :return: get properties def get_page_properties(self, page_id): """ Get the page (content) properties :param page_id: content_id format :return: get properties """ url = 'rest/api/content...
Provide the ancestors from the page (content) id :param page_id: content_id format :return: get properties def get_page_ancestors(self, page_id): """ Provide the ancestors from the page (content) id :param page_id: content_id format :return: get properties """ ...
Clean caches from cache management e.g. com.gliffy.cache.gon org.hibernate.cache.internal.StandardQueryCache_v5 def clean_package_cache(self, cache_name='com.gliffy.cache.gon'): """ Clean caches from cache management e.g. com.gliffy.cache.gon ...
Get all groups from Confluence User management :param start: OPTIONAL: The start point of the collection to return. Default: None (0). :param limit: OPTIONAL: The limit of the number of groups to return, this may be restricted by fixed system limits. Default: 1000 ...
Get a paginated collection of users in the given group :param group_name :param start: OPTIONAL: The start point of the collection to return. Default: None (0). :param limit: OPTIONAL: The limit of the number of users to return, this may be restricted by fixed system ...
Get information about a space through space key :param space_key: The unique space key name :param expand: OPTIONAL: additional info from description, homepage :return: Returns the space along with its ID def get_space(self, space_key, expand='description.plain,homepage'): """ G...
Get information about a user through username :param username: The user name :param expand: OPTIONAL expand for get status of user. Possible param is "status". Results are "Active, Deactivated" :return: Returns the user details def get_user_details_by_username(self, username, ex...
Get information about a user through user key :param userkey: The user key :param expand: OPTIONAL expand for get status of user. Possible param is "status". Results are "Active, Deactivated" :return: Returns the user details def get_user_details_by_userkey(self, userkey, expand...
Get results from cql search result with all related fields Search for entities in Confluence using the Confluence Query Language (CQL) :param cql: :param start: OPTIONAL: The start point of the collection to return. Default: 0. :param limit: OPTIONAL: The limit of the number of issues to...

Normalizes the values of factor so that they sum to 1. Parameters ---------- inplace: boolean If inplace=True it will modify the factor itself, else would return a new factor Returns ------- DiscreteFactor or None: if inplace=True (default) retur...

Reduces the factor to the context of given variable values. Parameters ---------- values: list, array-like A list of tuples of the form (variable_name, variable_state). inplace: boolean If inplace=True it will modify the factor itself, else would return ...

DiscreteFactor sum with `phi1`. Parameters ---------- phi1: `DiscreteFactor` instance. DiscreteFactor to be added. inplace: boolean If inplace=True it will modify the factor itself, else would return a new factor. Returns ------- ...

DiscreteFactor division by `phi1`. Parameters ---------- phi1 : `DiscreteFactor` instance The denominator for division. inplace: boolean If inplace=True it will modify the factor itself, else would return a new factor. Returns ------...

Returns a copy of the factor. Returns ------- DiscreteFactor: copy of the factor Examples -------- >>> import numpy as np >>> from pgmpy.factors.discrete import DiscreteFactor >>> phi = DiscreteFactor(['x1', 'x2', 'x3'], [2, 3, 3], np.arange(18)) ...

Generate the string from `__str__` method. Parameters ---------- phi_or_p: 'phi' | 'p' 'phi': When used for Factors. 'p': When used for CPDs. print_state_names: boolean If True, the user defined state names are displayed. def _str(self,...

Checks whether given parameter is a 1d array like object, and returns a numpy array object def _check_1d_array_object(parameter, name_param): """ Checks whether given parameter is a 1d array like object, and returns a numpy array object """ if isinstance(parameter, (np.ndarray, list, tuple, np.matrix))...

Raises an error when the length of given two arguments is not equal def _check_length_equal(param_1, param_2, name_param_1, name_param_2): """ Raises an error when the length of given two arguments is not equal """ if len(param_1) != len(param_2): raise ValueError("Length of {} must be same as ...

Returns list of variables of the network Examples -------- >>> reader = XMLBIF.XMLBIFReader("xmlbif_test.xml") >>> reader.get_variables() ['light-on', 'bowel-problem', 'dog-out', 'hear-bark', 'family-out'] def get_variables(self): """ Returns list of variables o...

Returns the edges of the network Examples -------- >>> reader = XMLBIF.XMLBIFReader("xmlbif_test.xml") >>> reader.get_edges() [['family-out', 'light-on'], ['family-out', 'dog-out'], ['bowel-problem', 'dog-out'], ['dog-out', 'hear-bark']] def get_edges...

Returns the states of variables present in the network Examples -------- >>> reader = XMLBIF.XMLBIFReader("xmlbif_test.xml") >>> reader.get_states() {'bowel-problem': ['true', 'false'], 'dog-out': ['true', 'false'], 'family-out': ['true', 'false'], 'he...

Returns the parents of the variables present in the network Examples -------- >>> reader = XMLBIF.XMLBIFReader("xmlbif_test.xml") >>> reader.get_parents() {'bowel-problem': [], 'dog-out': ['family-out', 'bowel-problem'], 'family-out': [], 'hear-bark': ...

Returns the CPD of the variables present in the network Examples -------- >>> reader = XMLBIF.XMLBIFReader("xmlbif_test.xml") >>> reader.get_values() {'bowel-problem': array([[ 0.01], [ 0.99]]), 'dog-out': array([[ 0.99, 0.01, 0.97, 0...

Returns the property of the variable Examples -------- >>> reader = XMLBIF.XMLBIFReader("xmlbif_test.xml") >>> reader.get_property() {'bowel-problem': ['position = (190, 69)'], 'dog-out': ['position = (155, 165)'], 'family-out': ['position = (112, 69)'], ...

Add variables to XMLBIF Return ------ dict: dict of type {variable: variable tags} Examples -------- >>> writer = XMLBIFWriter(model) >>> writer.get_variables() {'bowel-problem': <Element VARIABLE at 0x7fe28607dd88>, 'family-out': <Element VARIA...

Add outcome to variables of XMLBIF Return ------ dict: dict of type {variable: outcome tags} Examples -------- >>> writer = XMLBIFWriter(model) >>> writer.get_states() {'dog-out': [<Element OUTCOME at 0x7ffbabfcdec8>, <Element OUTCOME at 0x7ffbabfcdf08>]...

Transform the input state_name into a valid state in XMLBIF. XMLBIF states must start with a letter an only contain letters, numbers and underscores. def _make_valid_state_name(self, state_name): """Transform the input state_name into a valid state in XMLBIF. XMLBIF states must start wi...

Add property to variables in XMLBIF Return ------ dict: dict of type {variable: property tag} Examples -------- >>> writer = XMLBIFWriter(model) >>> writer.get_property() {'light-on': <Element PROPERTY at 0x7f7a2ffac1c8>, 'family-out': <Element ...

Add Definition to XMLBIF Return ------ dict: dict of type {variable: definition tag} Examples -------- >>> writer = XMLBIFWriter(model) >>> writer.get_definition() {'hear-bark': <Element DEFINITION at 0x7f1d48977408>, 'family-out': <Element DEFI...

Add Table to XMLBIF. Return --------------- dict: dict of type {variable: table tag} Examples ------- >>> writer = XMLBIFWriter(model) >>> writer.get_values() {'dog-out': <Element TABLE at 0x7f240726f3c8>, 'light-on': <Element TABLE at 0x7f24072...

Write the xml data into the file. Parameters ---------- filename: Name of the file. Examples ------- >>> writer = XMLBIFWriter(model) >>> writer.write_xmlbif(test_file) def write_xmlbif(self, filename): """ Write the xml data into the file. ...

Returns the marginal distribution over variables. Parameters ---------- variables: string, list, tuple, set, dict Variable or list of variables over which marginal distribution needs to be calculated inplace: Boolean (default True) If Fals...

Check if the Joint Probability Distribution satisfies the given independence condition. Parameters ---------- event1: list random variable whose independence is to be checked. event2: list random variable from which event1 is independent. values: 2D array...

Returns the independent variables in the joint probability distribution. Returns marginally independent variables if condition=None. Returns conditionally independent variables if condition!=None Parameter --------- condition: array_like Random Variable on which ...

Returns Conditional Probability Distribution after setting values to 1. Parameters ---------- values: list or array_like A list of tuples of the form (variable_name, variable_state). The values on which to condition the Joint Probability Distribution. inplace: Bo...

Returns a Bayesian Model which is minimal IMap of the Joint Probability Distribution considering the order of the variables. Parameters ---------- order: array-like The order of the random variables. Examples -------- >>> import numpy as np >...

Checks whether the given BayesianModel is Imap of JointProbabilityDistribution Parameters ----------- model : An instance of BayesianModel Class, for which you want to check the Imap Returns -------- boolean : True if given bayesian model is Imap for Joint P...

Returns the acceptance probability for given new position(position) and momentum def _acceptance_prob(self, position, position_bar, momentum, momentum_bar): """ Returns the acceptance probability for given new position(position) and momentum """ # Parameters to help in evaluating Joint...

Temporary method to fix issue in numpy 0.12 #852 def _get_condition(self, acceptance_prob, a): """ Temporary method to fix issue in numpy 0.12 #852 """ if a == 1: return (acceptance_prob ** a) > (1/(2**a)) else: return (1/(acceptance_prob ** a)) > (2**(-a...

Method for choosing initial value of stepsize References ----------- Matthew D. Hoffman, Andrew Gelman, The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research 15 (2014) 1351-1381 Algorithm 4 : Heuristic for...

Runs a single sampling iteration to return a sample def _sample(self, position, trajectory_length, stepsize, lsteps=None): """ Runs a single sampling iteration to return a sample """ # Resampling momentum momentum = np.reshape(np.random.normal(0, 1, len(position)), position.shap...

Method to return samples using Hamiltonian Monte Carlo Parameters ---------- initial_pos: A 1d array like object Vector representing values of parameter position, the starting state in markov chain. num_samples: int Number of samples to be generated ...

Method returns a generator type object whose each iteration yields a sample using Hamiltonian Monte Carlo Parameters ---------- initial_pos: A 1d array like object Vector representing values of parameter position, the starting state in markov chain. num_...

Run tha adaptation for stepsize for better proposals of position def _adapt_params(self, stepsize, stepsize_bar, h_bar, mu, index_i, alpha, n_alpha=1): """ Run tha adaptation for stepsize for better proposals of position """ gamma = 0.05 # free parameter that controls the amount of shr...

Method returns a generator type object whose each iteration yields a sample using Hamiltonian Monte Carlo Parameters ---------- initial_pos: A 1d array like object Vector representing values of parameter position, the starting state in markov chain. num_...

A list of states example - [('x1', 'easy'), ('x2', 'hard')] Returns ------- True, if arg is a list of states else False. def is_list_of_states(self, arg): """ A list of states example - [('x1', 'easy'), ('x2', 'hard')] Returns ------- Tr...

A list of list of states example - [[('x1', 'easy'), ('x2', 'hard')], [('x1', 'hard'), ('x2', 'medium')]] Returns ------- True, if arg is a list of list of states else False. def is_list_of_list_of_states(self, arg): """ A list of list of states example - [[('x1...

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. weight: int, float (default=None) The weight of the edge...

Check if the given nodes form a clique. Parameters ---------- nodes: list, array-like List of nodes to check if they are a part of any clique. Examples -------- >>> from pgmpy.base import UndirectedGraph >>> G = UndirectedGraph(ebunch=[('A', 'B'), ('...

Set the start state of the Markov Chain. If the start_state is given as a array-like iterable, its contents are reordered in the internal representation. Parameters: ----------- start_state: dict or array-like iterable object Dict (or list) of tuples representing the startin...

Checks if a list representing the state of the variables is valid. def _check_state(self, state): """ Checks if a list representing the state of the variables is valid. """ if not hasattr(state, '__iter__') or isinstance(state, six.string_types): raise ValueError('Start stat...

Add a variable to the model. Parameters: ----------- variable: any hashable python object card: int Representing the cardinality of the variable to be added. Examples: --------- >>> from pgmpy.models import MarkovChain as MC >>> model = MC()...

Add several variables to the model at once. Parameters: ----------- variables: array-like iterable object List of variables to be added. cards: array-like iterable object List of cardinalities of the variables to be added. Examples: --------- ...

Adds a transition model for a particular variable. Parameters: ----------- variable: any hashable python object must be an existing variable of the model. transition_model: dict or 2d array dict representing valid transition probabilities defined for every possib...

Sample from the Markov Chain. Parameters: ----------- start_state: dict or array-like iterable Representing the starting states of the variables. If None is passed, a random start_state is chosen. size: int Number of samples to be generated. Return Type:...

Given an instantiation (partial or complete) of the variables of the model, compute the probability of observing it over multiple windows in a given sample. If 'sample' is not passed as an argument, generate the statistic by sampling from the Markov Chain, starting with a random initial state. ...

Generator version of self.sample Return Type: ------------ List of State namedtuples, representing the assignment to all variables of the model. Examples: --------- >>> from pgmpy.models.MarkovChain import MarkovChain >>> from pgmpy.factors.discrete import State...

Checks if the given markov chain is stationary and checks the steady state probablity values for the state are consistent. Parameters: ----------- tolerance: float represents the diff between actual steady state value and the computed value sample: [State(i,j)] ...

Generates a random state of the Markov Chain. Return Type: ------------ List of namedtuples, representing a random assignment to all variables of the model. Examples: --------- >>> from pgmpy.models import MarkovChain as MC >>> model = MC(['intel', 'diff'], [2, ...

Returns a copy of Markov Chain Model. Return Type: ------------ MarkovChain : Copy of MarkovChain. Examples: --------- >>> from pgmpy.models import MarkovChain >>> from pgmpy.factors.discrete import State >>> model = MarkovChain() >>> model.add_v...

Returns `True` if `assertion` is contained in this `Independencies`-object, otherwise `False`. Parameters ---------- assertion: IndependenceAssertion()-object Examples -------- >>> from pgmpy.independencies import Independencies, IndependenceAssertion >>...

Adds assertions to independencies. Parameters ---------- assertions: Lists or Tuples Each assertion is a list or tuple of variable, independent_of and given. Examples -------- >>> from pgmpy.independencies import Independencies >>> independencies...

Returns a new `Independencies()`-object that additionally contains those `IndependenceAssertions` that are implied by the the current independencies (using with the `semi-graphoid axioms <https://en.wikipedia.org/w/index.php?title=Conditional_independence&oldid=708760689#Rules_of_conditional_independenc...

Returns `True` if the `entailed_independencies` are implied by this `Independencies`-object, otherwise `False`. Entailment is checked using the semi-graphoid axioms. Might be very slow if more than six variables are involved. Parameters ---------- entailed_independencies: Indep...

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 -------- >>> from pgmpy.models import Naive...

Returns a list of all ancestors of all the observed nodes. Parameters ---------- obs_nodes_list: string, list-type name of all the observed nodes def _get_ancestors_of(self, obs_nodes_list): """ Returns a list of all ancestors of all the observed nodes. Par...

Returns all the nodes reachable from start via an active trail. Parameters ---------- start: Graph node observed : List of nodes (optional) If given the active trail would be computed assuming these nodes to be observed. Examples -------- >>> from p...

Returns an instance of Independencies containing the local independencies of each of the variables. Parameters ---------- variables: str or array like variables whose local independencies are to found. Examples -------- >>> from pgmpy.models import ...

Computes the CPD for each node from a given data in the form of a pandas dataframe. If a variable from the data is not present in the model, it adds that node into the model. Parameters ---------- data : pandas DataFrame object A DataFrame object with column names same as th...

Computes a score to measure how well the given `BayesianModel` fits to the data set. (This method relies on the `local_score`-method that is implemented in each subclass.) Parameters ---------- model: `BayesianModel` instance The Bayesian network that is to be scored. Nodes ...

Computes a score that measures how much a \ given variable is \"influenced\" by a given list of potential parents. def local_score(self, variable, parents): "Computes a score that measures how much a \ given variable is \"influenced\" by a given list of potential parents." var_states =...

Generate a cartesian product of input arrays. Parameters ---------- arrays : list of array-like 1-D arrays to form the cartesian product of. out : ndarray Array to place the cartesian product in. Returns ------- out : ndarray 2-D array of shape (M, len(arrays)) cont...

Generate a sample of given size, given a probability mass function. Parameters ---------- values: numpy.array: Array of all possible values that the random variable can take. weights: numpy.array or list of numpy.array: Array(s) representing the PMF of the random variable. size: int: Si...

Generates all subsets of list `l` (as tuples). Example ------- >>> from pgmpy.utils.mathext import powerset >>> list(powerset([1,2,3])) [(), (1,), (2,), (3,), (1,2), (1,3), (2,3), (1,2,3)] def powerset(l): """ Generates all subsets of list `l` (as tuples). Example ------- >>> ...

Remove draft pages from space using datetime.now :param confluence: :param space_key: :param count: :param date_now: :return: int counter def clean_draft_pages_from_space(confluence, space_key, count, date_now): """ Remove draft pages from space using datetime.now :param confluence: ...

Remove all draft pages for all spaces older than DRAFT_DAYS :param days: int :param confluence: :return: def clean_all_draft_pages_from_all_spaces(confluence, days=30): """ Remove all draft pages for all spaces older than DRAFT_DAYS :param days: int :param confluence: :return: """ ...

Provide content by type (page, blog, comment) :param page_id: A string containing the id of the type content container. :param type: :param start: OPTIONAL: The start point of the collection to return. Default: None (0). :param limit: OPTIONAL: how many items should be returned after the...

Returns the list of labels on a piece of Content. :param space: Space key :param title: Title of the page :param start: OPTIONAL: The start point of the collection to return. Default: None (0). :param limit: OPTIONAL: The limit of the number of labels to return, this may be restricted by...

Get page by ID :param page_id: Content ID :param expand: OPTIONAL: expand e.g. history :return: def get_page_by_id(self, page_id, expand=None): """ Get page by ID :param page_id: Content ID :param expand: OPTIONAL: expand e.g. history :return: """...

Returns the list of labels on a piece of Content. :param page_id: A string containing the id of the labels content container. :param prefix: OPTIONAL: The prefixes to filter the labels with {@see Label.Prefix}. Default: None. :param start: OPTIONAL: The start poin...

Provide content by id with status = draft :param page_id: :param status: :return: def get_draft_page_by_id(self, page_id, status='draft'): """ Provide content by id with status = draft :param page_id: :param status: :return: """ url = 'res...

Get all page by label :param label: :param start: OPTIONAL: The start point of the collection to return. Default: None (0). :param limit: OPTIONAL: The limit of the number of pages to return, this may be restricted by fixed system limits. Default: 50 :return: def g...

Get all pages from space :param space: :param start: OPTIONAL: The start point of the collection to return. Default: None (0). :param limit: OPTIONAL: The limit of the number of pages to return, this may be restricted by fixed system limits. Default: 50 :param...

Get list of pages from trash :param space: :param start: OPTIONAL: The start point of the collection to return. Default: None (0). :param limit: OPTIONAL: The limit of the number of pages to return, this may be restricted by fixed system limits. Default: 500 :...

Get list of draft pages from space Use case is cleanup old drafts from Confluence :param space: :param start: OPTIONAL: The start point of the collection to return. Default: None (0). :param limit: OPTIONAL: The limit of the number of pages to return, this may be restricted by ...

Search list of draft pages by space key Use case is cleanup old drafts from Confluence :param space: Space Key :param status: Can be changed :param start: OPTIONAL: The start point of the collection to return. Default: None (0). :param limit: OPTIONAL: The limit of the number of ...

This method removes a page, if it has recursive flag, method removes including child pages :param page_id: :param status: OPTIONAL: type of page :param recursive: OPTIONAL: if True - will recursively delete all children pages too :return: def remove_page(self, page_id, status=None, recu...

Create page from scratch :param space: :param title: :param body: :param parent_id: :param type: :return: def create_page(self, space, title, body, parent_id=None, type='page'): """ Create page from scratch :param space: :param title: ...

Get all spaces with provided limit :param start: OPTIONAL: The start point of the collection to return. Default: None (0). :param limit: OPTIONAL: The limit of the number of pages to return, this may be restricted by fixed system limits. Default: 500 def get_all_spaces(self,...

Add comment into page :param page_id :param text def add_comment(self, page_id, text): """ Add comment into page :param page_id :param text """ data = {'type': 'comment', 'container': {'id': page_id, 'type': 'page', 'status': 'current'}, ...

Attach (upload) a file to a page, if it exists it will update the automatically version the new file and keep the old one. :param title: The page name :type title: ``str`` :param space: The space name :type space: ``str`` :param page_id: The page id to which we would li...

Set a label on the page :param page_id: content_id format :param label: label to add :return: def set_page_label(self, page_id, label): """ Set a label on the page :param page_id: content_id format :param label: label to add :return: """ u...

Remove content history. It works as experimental method :param page_id: :param version_number: version number :return: def remove_content_history(self, page_id, version_number): """ Remove content history. It works as experimental method :param page_id: :param ve...

Remove content history. It works in CLOUD :param page_id: :param version_id: :return: def remove_content_history_in_cloud(self, page_id, version_id): """ Remove content history. It works in CLOUD :param page_id: :param version_id: :return: """ ...

Check has unknown attachment error on page :param page_id: :return: def has_unknown_attachment_error(self, page_id): """ Check has unknown attachment error on page :param page_id: :return: """ unknown_attachment_identifier = 'plugins/servlet/confluence/pl...

Compare content and check is already updated or not :param page_id: Content ID for retrieve storage value :param body: Body for compare it :return: True if the same def is_page_content_is_already_updated(self, page_id, body): """ Compare content and check is already updated or n...

Update page if already exist :param parent_id: :param page_id: :param title: :param body: :param type: :param minor_edit: Indicates whether to notify watchers about changes. If False then notifications will be sent. :return: def update_page(self, pare...

Update page or create a page if it is not exists :param parent_id: :param title: :param body: :return: def update_or_create(self, parent_id, title, body): """ Update page or create a page if it is not exists :param parent_id: :param title: :param ...

Set the page (content) property e.g. add hash parameters :param page_id: content_id format :param data: data should be as json data :return: def set_page_property(self, page_id, data): """ Set the page (content) property e.g. add hash parameters :param page_id: content_i...

Delete the page (content) property e.g. delete key of hash :param page_id: content_id format :param page_property: key of property :return: def delete_page_property(self, page_id, page_property): """ Delete the page (content) property e.g. delete key of hash :param page_...

Get the page (content) property e.g. get key of hash :param page_id: content_id format :param page_property_key: key of property :return: def get_page_property(self, page_id, page_property_key): """ Get the page (content) property e.g. get key of hash :param page_id: con...

Get the page (content) properties :param page_id: content_id format :return: get properties def get_page_properties(self, page_id): """ Get the page (content) properties :param page_id: content_id format :return: get properties """ url = 'rest/api/content...

Provide the ancestors from the page (content) id :param page_id: content_id format :return: get properties def get_page_ancestors(self, page_id): """ Provide the ancestors from the page (content) id :param page_id: content_id format :return: get properties """ ...

Clean caches from cache management e.g. com.gliffy.cache.gon org.hibernate.cache.internal.StandardQueryCache_v5 def clean_package_cache(self, cache_name='com.gliffy.cache.gon'): """ Clean caches from cache management e.g. com.gliffy.cache.gon ...

Get all groups from Confluence User management :param start: OPTIONAL: The start point of the collection to return. Default: None (0). :param limit: OPTIONAL: The limit of the number of groups to return, this may be restricted by fixed system limits. Default: 1000 ...

Get a paginated collection of users in the given group :param group_name :param start: OPTIONAL: The start point of the collection to return. Default: None (0). :param limit: OPTIONAL: The limit of the number of users to return, this may be restricted by fixed system ...

Get information about a space through space key :param space_key: The unique space key name :param expand: OPTIONAL: additional info from description, homepage :return: Returns the space along with its ID def get_space(self, space_key, expand='description.plain,homepage'): """ G...

Get information about a user through username :param username: The user name :param expand: OPTIONAL expand for get status of user. Possible param is "status". Results are "Active, Deactivated" :return: Returns the user details def get_user_details_by_username(self, username, ex...

Get information about a user through user key :param userkey: The user key :param expand: OPTIONAL expand for get status of user. Possible param is "status". Results are "Active, Deactivated" :return: Returns the user details def get_user_details_by_userkey(self, userkey, expand...

Get results from cql search result with all related fields Search for entities in Confluence using the Confluence Query Language (CQL) :param cql: :param start: OPTIONAL: The start point of the collection to return. Default: 0. :param limit: OPTIONAL: The limit of the number of issues to...