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BayesianNetworknameparentpomegranate.BayesianNetworkView.MemoryViewpomegranate/BayesianNetwork.pyx<stringsource>__reduce____name____dict___is_coroutinesendthrowclose: AGraphASCIIAll dimensions preceding dimension %d must be indexed and not slicedAssertionErrorBaseGeneratorBayesianNetwork.__get__.<locals>.genexprBayesianNetwork.__reduce_cython__BayesianNetwork.__setstate_cython__BayesianNetwork._gibbsBayesianNetwork._rejectionBayesianNetwork.bakeBayesianNetwork.fitBayesianNetwork.from_dictBayesianNetwork.from_samplesBayesianNetwork.from_structureBayesianNetwork.from_summariesBayesianNetwork.log_probabilityBayesianNetwork.marginalBayesianNetwork.plotBayesianNetwork.predictBayesianNetwork.predict_probaBayesianNetwork.sampleBayesianNetwork.sample (line 736)BayesianNetwork.summarizeBayesianNetwork.to_dictBuffer view does not expose stridesCan only create a buffer that is contiguous in memory.Cannot assign to read-only memoryviewCannot create writable memory view from read-only memoryviewCannot index with type 'Cannot transpose memoryview with indirect dimensionsCannot use both a constraint graph and Cannot use the Chow-Liu algorithm with inclusion constraints.Cannot use the Chow-Liu algorithm with exclusion constraints.Chow-Liu tree learning does not current support missing valuesDataGeneratorDiGraphDimension %d is not directEllipsisEmpty shape tuple for cython.arrayFactorGraphGImportErrorIncompatible checksums (0x%x vs (0x82a3537, 0x6ae9995, 0xb068931) = (name))IndexErrorIndex out of bounds (axis %d)Indirect dimensions not supportedInvalid algorithm type passed in. Must be one of 'chow-liu', 'exact', 'exact-dp', 'greedy'Invalid mode, expected 'c' or 'fortran', got Invalid shape in axis Maximum iteration limit. Make sure the state configuration hinted at by evidence is reasonably reachable for this network or lower min_probMemoryError<MemoryView of %r at 0x%x><MemoryView of %r object>Model does not contain a state named '{}'NamedTemporaryFileOOut of bounds on buffer access (axis ParallelParentGraphParentGraph.__reduce_cython__ParentGraph.__setstate_cython__ParentGraph.calculate_valuePickleErrorPriorityQueueSample does not have the same number of dimensions as the model {} {}Sample the network, optionally given some evidences
		Use rejection to condition on non marginal nodes

		Parameters
		----------
		n : int, optional
				The number of samples to generate. Defaults to 1.
		evidences : list of dict, optional
				Evidence to set constant while samples are generated.

		algorithm: : str, one of 'gibbs', 'rejection' optional. default 'rejection'
			Rejection sampling successively sample each node given its parents evidence. When evidences are given on
			non-root nodes, only draws that are compatible with evidence nodes are not rejected. Rejection sampling is a good
			option when evidences nodes are not far from the root nodes or when given evidence is likely. Rare evidences
			lead to a high rate of rejected samples, thus to significant slow down of the sampling.
			Gibbs sampling scheme is a Markov Chain Monte Carlo (MCMC) technique designed to speed up the sampling. It
			builds conditional probability of state transition of each nodes given its neighbours in its markov blanket.
			Works well with a lot of evidences in the network, even when they are far from the root nodes. Drawback :
			convergence is only guaranteed when there is a non null probability path between states. If the posterior
			consists of isolated islands of high probability, Gibbs sampling will stay stuck in one the island
			and will never transition to the others. Successive samples will have high correlation.

		min_prob : float <1 optional. If algorithm == "rejection"
			stop iterations when  Sum P(X|Evidence) < min_prob. generated samples for a given evidence will be
			incomplete (<n)

		initial_state : dict, optional
			initial state used by the Gibbs sampler.
			Default is to use the maximum joint-probability values, calculated with self.predict().
			The default should be optimal.

		scan_order: str, one 'topological','random' optional. If algorithm == "gibbs"
			Scan order or the gibbs sampler. Indicate in which order nodes are sampled. Topological order is good for
			chain like networks (lots of successive nodes). Random order yield better results with more connected
			 networks. Default : 'random'.


		burnin : int, optional. If algorithm == "Gibbs"
			Number of sample to discard at the begining of the sampling. Default is 0.

		random_state : seed or seeded numpy instance (for gibbs, only seed)

		Returns
		-------
		a nested list of sampled states of shape [n*len(evidences),len(nodes)]

		Examples
		--------
		>>> network.sample(evidence = [{'HLML': '2'},{'HLML': '2','TYPL':'1'},{'NBPI': '02','TYPL':'1'}])

		SequenceState '{}' does not have key '{}'State '{}' does not match with keys provided.Step may not be zero (axis %d)Total Time (s): {:.4f}TypeErrorUnable to convert item to objectValueErrorX_countX_intX must be a 2D array of shape (n_samples, n_variables) or a list of lists or a list of dictionaries.X_subsetX_subset_ptrXjXk_?*)aabcaddadd_edgeadd_nodeadd_nodesadd_statesalgorithmall_statesall_states_viewallocate_buffer and anyargsargsortarrayasarrayastypeasyncio.coroutinesaxisbbackendbake.<locals>.genexprbatchbatch_sizebatchesbest_scorebest_structurebest_variableburnincardinalitiescardinalitycase_check_nancheck_random_statechildchild_setchild_setschow-liu__class__class__class_getitem__cline_in_tracebackclosedclscolcol_dictcol_dict_invcol_idxscol_lencol_ncollectionscolorcolscolumn_idxscolumns_idxscolumns_idxs_dictcombinationscomponentconcatenateconstraint_graph<contiguous and direct><contiguous and indirect>countcpdcpds_cpdscurrent_statecurrent_state_viewcycledd_mappingdatadata_generatordecoded_statesdefaultdictdelayeddeletedirecteddisablediscrete_chow_liu_tree.<locals>.genexprdiscrete_exact_a_star.<locals>.genexprdiscrete_exact_component.<locals>.genexprdiscrete_exact_dp.<locals>.genexprdiscrete_exact_slap.<locals>.genexprdiscrete_exact_with_constraints.<locals>.genexprdiscrete_exact_with_constraints_task.<locals>.genexprdistdistributiondistributionsdotdrawdtype_is_objecteedgesemptyempty_likeenableencode__enter__entryenumerateerrorev_for_nameevidenceexactexact-dp__exit__expf_mappingfa_mapping-fgfilefilenamefiltered_entryfit.<locals>.genexprflagsfloat32float64forced edge inclusions.from_samples.<locals>.genexprfrom_valuesggcgenerate_parent_graph.<locals>.genexprgetget_edge_data__getstate__gibbs (got got differing extents in dimension grandchildgraphgraph_dictgraphsgreedyhiididxidx_imageimg__import__imreadimshowin_st_nanin_st_predinclude_parentsindexinertiainf_initializingint32ioisenabledisnanissubsetititemsitemsize <= 0 for cython.arrayiter_itertoolsjoblib-jointjointjoint_countkeykeyskeys_kwargsllast_layerlast_layer_childrenlayerlayer_childrenljlklocal_structurelog_probability.<locals>.genexprlogpmm_mapping__main__marg_jmarg_kmatplotlibmatplotlib.imagematplotlib.pyplotmaxmax_keysmin_probmin_scoremin_xmin_ymlemodmod_imodalitiesmodalities_dictmodalities_intmodalities_typemodality_dictmodemodelmultiprocessingmust bake model before computing probabilitymust bake model before computing marginalmust bake model before summarizing datamust bake model before using forward-backward algorithmmust bake model before using imputemust have matplotlib and pygraphviz installed for visualizationmutual_infonn_columnsn_cpdn_modn_parentsn_staten_stepnanndminnetworkx__new__new_child_setnew_variables__next__no default __reduce__ due to non-trivial __cinit__nodenode_dictnode_idxnode_idx_dictnode_namenode_posnodesnumpy.core.multiarray failed to importnumpy.core.umath failed to importnxoobjobjectoffonesopenorder_graphosout_setoutfileoutside_parentsppackparallelparallelize_functionparametersparent_entryparent_graphparent_setsparent_subsetparentspathpdfpgpicklepng.pomegranate.tmppoppredict_proba.<locals>.genexprprev_entryprintprobprob_tmpprob_tmp_viewprob_viewprobaprobsproductprogpushpygraphvizpyplot__pyx_PickleError__pyx_checksum__pyx_result__pyx_state__pyx_type__pyx_vtable__rrandomrandom_staterangeredreduce_dataset__reduce_ex__registerrejectionremoverootroundsafeguardsamplesscan_orderscan_order_is_randomscoresearchsortedseedseen_entriesseen_variablesself.distributions_ptr,self.parent_count,self.parent_idxs cannot be converted to a Python object for picklingself.m,self.parents cannot be converted to a Python object for pickling__setstate__shortest_pathshufflesort_idxsortersource__spec__startstatestate_dictstate_namesstate_orderstate_subsetstate_subset_viewstatesstepstop<strided and direct><strided and direct or indirect><strided and indirect>strongly_connected_componentsstructsubsetsubset_subset_and_outsidesumsystargettasktasks__test__tfthreadingtimeto_jsonto_valuestopo_ordertopologicaltopological_sorttotal_time_spenttraining_start_timeuunable to allocate array data.unable to allocate shape and strides.unionunpackunseen_variablesunvisitedupdateutilsvvalvaluevaluesvariable_setverboseversion_infovisitedww_sumweightweights_ptrwherewritexxjxky_yy_hatzeroszipsend'plotDraw this model's graph using pygraphviz and matplotlib.
		
		If no filename, it uses pygraphviz to write a temporary png file,
		and matplotlib to `imshow()` it. If using jupyter or IPython, enable
		`%matplotlib inline` and this will immediately display your graph.
		Otherwise, per usual matplotlib convention, you have to issue a 
		`plt.show()` or `matplotlib.pyplot.show()` to open a window with the 
		image.
		    
		TODO: Use svg or pdf for original image. Jupyter and IPython can render SVG
		directly, e.g. `from IPython.display import SVG` and `SVG(filename=...)`. 

		Parameters
		----------
		filename : str, optional
			Filename for saving the .pdf graph. Default is None
			
		Returns
		-------
		None
		bakeFinalize the topology of the model.

		Assign a numerical index to every state and create the underlying arrays
		corresponding to the states and edges between the states. This method
		must be called before any of the probability-calculating methods. This
		includes converting conditional probability tables into joint probability
		tables and creating a list of both marginal and table nodes.

		Parameters
		----------
		None

		Returns
		-------
		None
		log_probabilityReturn the log probability of samples under the Bayesian network.

		The log probability is just the sum of the log probabilities under each of
		the components. The log probability of a sample under the graph A -> B is
		just P(A)*P(B|A). This will return a vector of log probabilities, one for each
		sample.

		Parameters
		----------
		X : array-like, shape (n_samples, n_dim)
			The sample is a vector of points where each dimension represents the
			same variable as added to the graph originally. It doesn't matter what
			the connections between these variables are, just that they are all
			ordered the same.

		check_input : bool, optional
			Check to make sure that the observed symbol is a valid symbol for that
			distribution to produce. Default is True.

		n_jobs : int
			The number of jobs to use to parallelize, either the number of threads
			or the number of processes to use. -1 means use all available resources.
			Default is 1.

		Returns
		-------
		logp : numpy.ndarray or double
			The log probability of the samples if many, or the single log probability.
		marginalReturn the marginal probabilities of each variable in the graph.

		This is equivalent to a pass of belief propagation on a graph where
		no data has been given. This will calculate the probability of each
		variable being in each possible emission when nothing is known.

		Parameters
		----------
		None

		Returns
		-------
		marginals : array-like, shape (n_nodes)
			An array of univariate distribution objects showing the marginal
			probabilities of that variable.
		predictPredict missing values of a data matrix using MLE.

		Impute the missing values of a data matrix using the maximally likely
		predictions according to the forward-backward algorithm. Run each
		sample through the algorithm (predict_proba) and replace missing values
		with the maximally likely predicted emission.

		Parameters
		----------
		X : array-like, shape (n_samples, n_nodes)
			Data matrix to impute. Missing values must be either None (if lists)
			or np.nan (if numpy.ndarray). Will fill in these values with the
			maximally likely ones.

		max_iterations : int, optional
			Number of iterations to run loopy belief propagation for. Default
			is 100.

		check_input : bool, optional
			Check to make sure that the observed symbol is a valid symbol for that
			distribution to produce. Default is True.


		n_jobs : int
			The number of jobs to use to parallelize, either the number of threads
			or the number of processes to use. -1 means use all available resources.
			Default is 1.

		Returns
		-------
		y_hat : numpy.ndarray, shape (n_samples, n_nodes)
			This is the data matrix with the missing values imputed.
		predict_probaReturns the probabilities of each variable in the graph given evidence.

		This calculates the marginal probability distributions for each state given
		the evidence provided through loopy belief propagation. Loopy belief
		propagation is an approximate algorithm which is exact for certain graph
		structures.

		Parameters
		----------
		X : dict or array-like, shape <= n_nodes
			The evidence supplied to the graph. This can either be a dictionary
			with keys being state names and values being the observed values
			(either the emissions or a distribution over the emissions) or an
			array with the values being ordered according to the nodes incorporation
			in the graph (the order fed into .add_states/add_nodes) and None for
			variables which are unknown. It can also be vectorized, so a list of
			dictionaries can be passed in where each dictionary is a single sample,
			or a list of lists where each list is a single sample, both formatted
			as mentioned before.

		max_iterations : int, optional
			The number of iterations with which to do loopy belief propagation.
			Usually requires only 1. Default is 100.

		check_input : bool, optional
			Check to make sure that the observed symbol is a valid symbol for that
			distribution to produce. Default is True.

		n_jobs : int, optional
			The number of threads to use when parallelizing the job. This
			parameter is passed directly into joblib. Default is 1, indicating
			no parallelism.

		Returns
		-------
		y_hat : array-like, shape (n_samples, n_nodes)
			An array of univariate distribution objects showing the probabilities
			of each variable.
		fitFit the model to data using MLE estimates.

		Fit the model to the data by updating each of the components of the model,
		which are univariate or multivariate distributions. This uses a simple
		MLE estimate to update the distributions according to their summarize or
		fit methods.

		This is a wrapper for the summarize and from_summaries methods.

		Parameters
		----------
		X : array-like or generator, shape (n_samples, n_nodes)
			The data to train on, where each row is a sample and each column
			corresponds to the associated variable.

		weights : array-like, shape (n_nodes), optional
			The weight of each sample as a positive double. Default is None.

		inertia : double, optional
			The inertia for updating the distributions, passed along to the
			distribution method. Default is 0.0.

		pseudocount : double, optional
			A pseudocount to add to the emission of each distribution. This
			effectively smoothes the states to prevent 0. probability symbols
			if they don't happen to occur in the data. Only effects hidden
			Markov models defined over discrete distributions. Default is 0.

		verbose : bool, optional
			Whether or not to print out improvement information over
			iterations. Only required if doing semisupervised learning.
			Default is False.

		n_jobs : int
			The number of jobs to use to parallelize, either the number of threads
			or the number of processes to use. -1 means use all available resources.
			Default is 1.

		Returns
		-------
		self : BayesianNetwork
			The fit Bayesian network object with updated model parameters.
		sampleSample the network, optionally given some evidences
		Use rejection to condition on non marginal nodes

		Parameters
		----------
		n : int, optional
				The number of samples to generate. Defaults to 1.
		evidences : list of dict, optional
				Evidence to set constant while samples are generated.

		algorithm: : str, one of 'gibbs', 'rejection' optional. default 'rejection'
			Rejection sampling successively sample each node given its parents evidence. When evidences are given on
			non-root nodes, only draws that are compatible with evidence nodes are not rejected. Rejection sampling is a good
			option when evidences nodes are not far from the root nodes or when given evidence is likely. Rare evidences
			lead to a high rate of rejected samples, thus to significant slow down of the sampling.
			Gibbs sampling scheme is a Markov Chain Monte Carlo (MCMC) technique designed to speed up the sampling. It
			builds conditional probability of state transition of each nodes given its neighbours in its markov blanket.
			Works well with a lot of evidences in the network, even when they are far from the root nodes. Drawback :
			convergence is only guaranteed when there is a non null probability path between states. If the posterior
			consists of isolated islands of high probability, Gibbs sampling will stay stuck in one the island
			and will never transition to the others. Successive samples will have high correlation.

		min_prob : float <1 optional. If algorithm == "rejection"
			stop iterations when  Sum P(X|Evidence) < min_prob. generated samples for a given evidence will be
			incomplete (<n)

		initial_state : dict, optional
			initial state used by the Gibbs sampler.
			Default is to use the maximum joint-probability values, calculated with self.predict().
			The default should be optimal.

		scan_order: str, one 'topological','random' optional. If algorithm == "gibbs"
			Scan order or the gibbs sampler. Indicate in which order nodes are sampled. Topological order is good for
			chain like networks (lots of successive nodes). Random order yield better results with more connected
			 networks. Default : 'random'.


		burnin : int, optional. If algorithm == "Gibbs"
			Number of sample to discard at the begining of the sampling. Default is 0.

		random_state : seed or seeded numpy instance (for gibbs, only seed)

		Returns
		-------
		a nested list of sampled states of shape [n*len(evidences),len(nodes)]

		Examples
		--------
		>>> network.sample(evidence = [{'HLML': '2'},{'HLML': '2','TYPL':'1'},{'NBPI': '02','TYPL':'1'}])

		_rejectionSample the network, optionally given some evidences
		Use rejection to condition on non marginal nodes

		Parameters
		----------
		n : int, optional
				The number of samples to generate. Defaults to 1.
		evidences : list of dict, optional
				Evidence to set constant while samples are generated.
		min_prob : stop iterations when  Sum P(X|Evidence) < min_prob. generated samples for a given evidence will be
				incomplete (<n)
		Returns
		-------
		a nested list of sampled states

		_gibbs
		Draw samples from the bayesian network given evidences.
		Evidences can be given for any nodes (root or not).

		This will return sample of size <n> for each of the given evidence in <evidences>

		Node sampling order is shuffled each iteration

		Parameters
		----------
		n : int
			The number of sample to draw for each evidence  each sample as a positive double. Default is None.

		evidences : list [{<state_name>:<state_value>}]
			The data to train on, where each row is a sample and each column
			corresponds to the associated variable. Default [{}]

		initial_state : dict, optional
			Initial state used by the sampler.
			Default is to use the maximum joint-probability values, calculated with self.predict().
			The default should be optimal.
			
		scan_order: str, optional ['topological','random',]
			scan order or the gibbs sampler. Indicate in which order nodes are sampled. Default : 'topological'.


		burnin : int, optional
			Number of sample to discard at the begining of the sampling. Default is 0.

		seed : seed to be applied to srand

		pseudocount : double, optional
			A pseudocount to add to the emission of each distribution. This
			effectively smoothes the states to prevent 0. probability symbols
			if they don't happen to occur in the data. Only effects hidden
			Markov models defined over discrete distributions. Default is 0.

		Returns
		-------
		array : samples (n*len(evidences),n_state)
			sample drawn from the bayesian network
		summarizeSummarize a batch of data and store the sufficient statistics.

		This will partition the dataset into columns which belong to their
		appropriate distribution. If the distribution has parents, then multiple
		columns are sent to the distribution. This relies mostly on the summarize
		function of the underlying distribution.

		Parameters
		----------
		X : array-like, shape (n_samples, n_nodes)
			The data to train on, where each row is a sample and each column
			corresponds to the associated variable.

		weights : array-like, shape (n_nodes), optional
			The weight of each sample as a positive double. Default is None.

		Returns
		-------
		None
		from_summariesUse MLE on the stored sufficient statistics to train the model.

		This uses MLE estimates on the stored sufficient statistics to train
		the model.

		Parameters
		----------
		inertia : double, optional
			The inertia for updating the distributions, passed along to the
			distribution method. Default is 0.0.

		pseudocount : double, optional
			A pseudocount to add to the emission of each distribution. This
			effectively smoothes the states to prevent 0. probability symbols
			if they don't happen to occur in the data. Default is 0.

		Returns
		-------
		None
		to_dictfrom_dictfrom_structureReturn a Bayesian network from a predefined structure.

		Pass in the structure of the network as a tuple of tuples and get a fit
		network in return. The tuple should contain n tuples, with one for each
		node in the graph. Each inner tuple should be of the parents for that
		node. For example, a three node graph where both node 0 and 1 have node
		2 as a parent would be specified as ((2,), (2,), ()).

		Parameters
		----------
		X : array-like, shape (n_samples, n_nodes)
			The data to fit the structure too, where each row is a sample and each column
			corresponds to the associated variable.

		structure : tuple of tuples
			The parents for each node in the graph. If a node has no parents,
			then do not specify any parents.

		weights : array-like, shape (n_nodes), optional
			The weight of each sample as a positive double. Default is None.

		pseudocount : double, optional
			A pseudocount to add to the emission of each distribution. This
			effectively smoothes the states to prevent 0. probability symbols
			if they don't happen to occur in the data. Default is 0.

		name : str, optional
			The name of the model. Default is None.

		state_names : array-like, shape (n_nodes), optional
			A list of meaningful names to be applied to nodes

		keys : list
			A list of sets where each set is the keys present in that column.
			If there are d columns in the data set then this list should have
			d sets and each set should have at least two keys in it.

		Returns
		-------
		model : BayesianNetwork
			A Bayesian network with the specified structure.
		from_samplesLearn the structure of the network from data.

		There are currently two types of approaches implemented. The first,
		the Chow-Liu algorithm, finds a tree-like structure from symmetric
		mutual-information scores given a root node (the `root` parameter).
		The second type searches through structures and returns the structure
		that maximizes the following objective function:

			P(D|M) + penalty * |M|

		where P(D|M) is the probability of the data given the found model,
		penalty is a user-specified parameters, and |M| is the number of
		parameters in the model. When this penalty is log2(|D|) / 2
		(the default) where |D| is the weight sum of the examples, this is
		equivalent to the minimum description length (MDL).

		There are currently three ways that the learned structure can be
		controlled. The first is to increase the penalty term to increase
		sparsity. The second is to pass in a specified list of edges that
		must exist (`include_edges`) or cannot exist (`exclude_edges`). Lastly,
		a constraint graph can be specified where each node in the graph is a
		set of variables being modeled and the edges in the graph indicate
		which sets of variables can be parents to which other sets of
		variables (and where a self-loop is the normal structure learning step).

		Parameters
		----------
		X : array-like or generator, shape (n_samples, n_nodes)
			The data to fit the structure too, where each row is a sample and
			each column corresponds to the associated variable.

		weights : array-like, shape (n_samples), optional
			The weight of each sample as a positive double. Default is None.

		algorithm : str, one of 'chow-liu', 'greedy', 'exact', 'exact-dp' optional
			The algorithm to use for learning the Bayesian network. Default is
			'greedy' that greedily attempts to find the best structure, and
			frequently can identify the optimal structure. 'exact' uses DP/A*
			to find the optimal Bayesian network, and 'exact-dp' tries to find
			the shortest path on the entire order lattice, which is more memory
			and computationally expensive. 'exact' and 'exact-dp' should give
			identical results, with 'exact-dp' remaining an option mostly for
			debugging reasons. 'chow-liu' will return the optimal tree-like
			structure for the Bayesian network, which is a very fast
			approximation but not always the best network.

		max_parents : int, optional
			The maximum number of parents a node can have. If used, this means
			using the k-learn procedure. Can drastically speed up algorithms.
			If -1, no max on parents. Default is -1.

		penalty : float or None, optional
			The weighting of the model complexity term in the objective function.
			Increasing this value will encourage sparsity whereas setting the value
			to 0 will result in an unregularized structure. Default is
			log2(|D|) / 2 where |D| is the sum of the weights of the data.

		root : int, optional
			For algorithms which require a single root ('chow-liu'), this is the
			root for which all edges point away from. User may specify which
			column to use as the root. Default is the first column.

		constraint_graph : networkx.DiGraph or None, optional
			A directed graph showing valid parent sets for each variable. Each
			node is a set of variables, and edges represent which variables can
			be valid parents of those variables. The naive structure learning
			task is just all variables in a single node with a self edge,
			meaning that you know nothing about

		include_edges : list or None, optional
			A list of (parent, child) tuples that are edges which must be 
			present in the found structure. Default is None.

		exclude_edges : list or None, optional
			A list of (parent, child) tuples that are edges which cannot be
			present in the found structure. Default is None.

		pseudocount : double, optional
			A pseudocount to add to the emission of each distribution. This
			effectively smoothes the states to prevent 0. probability symbols
			if they don't happen to occur in the data. Default is 0.

		state_names : array-like, shape (n_nodes), optional
			A list of meaningful names to be applied to nodes

		name : str, optional
			The name of the model. Default is None.

		reduce_dataset : bool, optional
			Given the discrete nature of these datasets, frequently a user
			will pass in a dataset that has many identical samples. It is time
			consuming to go through these redundant samples and a far more
			efficient use of time to simply calculate a new dataset comprised
			of the subset of unique observed samples weighted by the number of
			times they occur in the dataset. This typically will speed up all
			algorithms, including when using a constraint graph. Default is
			True.

		keys : list, optional
			A list of sets where each set is the keys present in that column.
			If there are d columns in the data set then this list should have
			d sets and each set should have at least two keys in it. Default
			is None.

		low_memory : bool or None, optional
			Whether to use a low-memory version of the search algorithm. This
			option only affects algorithm="greedy" and algorithm="exact". 
			Although the low-memory version of both the greedy and exact
			algorithms will use less memory, it will also significantly slow
			down the exact algorithm. However, setting this to True will also
			significantly speed up the greedy algorithm. Setting this value to
			None will enable it when algorithm="greedy" and disable it otherwise.
			Default is None.

		n_jobs : int, optional
			The number of threads to use when learning the structure of the
			network. If a constraint graph is provided, this will parallelize
			the tasks as directed by the constraint graph. If one is not
			provided it will parallelize the building of the parent graphs.
			Both cases will provide large speed gains.

		Returns
		-------
		model : BayesianNetwork
			The learned BayesianNetwork.
		__reduce_cython____setstate_cython__tempfilepltappendkindicesgenexprlogp_arrayffncheck_inputselfmax_iterationsjevidencesinitial_state00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899structureidxskeymapcalculate_valuememviewshapeformat.copycfortranbasendimitemsizesizenumpydtypeDistributionDiscreteDistributioncollections.abc__pyx_unpickle_Enum_check_inputEnsure that the keys in the sample are valid keys.

	Go through each variable in the sample and make sure that the observed
	symbol is a valid key according to the model. Raise an error if the
	symbol is not a key valid key according to the model.

	Parameters
	----------
	X : dict or array-like
		The observed sample.

	states : list
		A list of states ordered by the columns in the sample.

	Returns
	-------
	None
	discrete_chow_liu_treeX_ndarrayweights_ndarraykey_count_ndarraydiscrete_exact_dp
	Find the optimal graph over a set of variables with no other knowledge.

	This is the naive dynamic programming structure learning task where the
	optimal graph is identified from a set of variables using an order graph
	and parent graphs. This can be used either when no constraint graph is
	provided or for a SCC which is made up of a node containing a self-loop.
	This is a reference implementation that uses the naive shortest path
	algorithm over the entire order graph. The 'exact' option uses the A* path
	in order to avoid considering the full order graph.

	Parameters
	----------
	X : numpy.ndarray, shape=(n, d)
		The data to fit the structure too, where each row is a sample and
		each column corresponds to the associated variable.

	weights : numpy.ndarray, shape=(n,)
		The weight of each sample as a positive double. Default is None.

	key_count : numpy.ndarray, shape=(d,)
		The number of unique keys in each column.

	include_edges : list or None
		A set of (parent, child) tuples where each tuple is an edge that
		must exist in the found structure.

	exclude_edges : list or None
		A set of (parent, child) tuples where each tuple is an edge that
		cannot exist in the found structure.

	penalty : float or None, optional
		The weighting of the model complexity term in the objective function.
		Increasing this value will encourage sparsity whereas setting the value
		to 0 will result in an unregularized structure. Default is
		log2(|D|) / 2 where |D| is the sum of the weights of the data.

	max_parents : int
		The maximum number of parents a node can have. If used, this means
		using the k-learn procedure. Can drastically speed up algorithms.
		If -1, no max on parents. Default is -1.

	n_jobs : int
		The number of threads to use when learning the structure of the
		network. This parallelizes the creation of the parent graphs.

	Returns
	-------
	structure : tuple, shape=(d,)
		The parents for each variable in this SCC
	Xweightskey_countinclude_edgesexclude_edgespseudocountpenaltymax_parentsvariablediscrete_exact_a_star
	Find the optimal graph over a set of variables with no other knowledge.

	This is the naive dynamic programming structure learning task where the
	optimal graph is identified from a set of variables using an order graph
	and parent graphs. This can be used either when no constraint graph is
	provided or for a SCC which is made up of a node containing a self-loop.
	It uses DP/A* in order to find the optimal graph without considering all
	possible topological sorts. A greedy version of the algorithm can be used
	that massively reduces both the computational and memory cost while frequently
	producing the optimal graph.

	Parameters
	----------
	X : numpy.ndarray, shape=(n, d)
		The data to fit the structure too, where each row is a sample and
		each column corresponds to the associated variable.

	weights : numpy.ndarray, shape=(n,)
		The weight of each sample as a positive double. Default is None.

	key_count : numpy.ndarray, shape=(d,)
		The number of unique keys in each column.

	include_edges : list or None
		A list of (parent, child) tuples where each tuple corresponds to an
		edge that must exist in the found structure.

	exclude_edges : list or None
		A list of (parent, child) tuples where each tuple corresponds to an
		edge that cannot exist in the found structure.

	pseudocount : double
		A pseudocount to add to each possibility.

	penalty : float or None, optional
		The weighting of the model complexity term in the objective function.
		Increasing this value will encourage sparsity whereas setting the value
		to 0 will result in an unregularized structure. Default is
		log2(|D|) / 2 where |D| is the sum of the weights of the data.

	max_parents : int
		The maximum number of parents a node can have. If used, this means
		using the k-learn procedure. Can drastically speed up algorithms.
		If -1, no max on parents. Default is -1.

	low_memory : bool or None, optional
		Whether to use a low-memory version of the search algorithm. This
		option only affects algorithm="greedy" and algorithm="exact". 
		Although the low-memory version of both the greedy and exact
		algorithms will use less memory, it will also significantly slow
		down the exact algorithm. However, setting this to True will also
		significantly speed up the greedy algorithm. Setting this value to
		None will enable it when algorithm="greedy" and disable it otherwise.
		Default is None.

	n_jobs : int
		The number of threads to use when learning the structure of the
		network. This parallelizes the creation of the parent graphs.

	Returns
	-------
	structure : tuple, shape=(d,)
		The parents for each variable in this SCC
	parent_graphsother_variablesvariablesdiscrete_greedyFind the optimal graph over a set of variables with no other knowledge.

	Parameters
	----------
	X : numpy.ndarray, shape=(n, d)
		The data to fit the structure too, where each row is a sample and
		each column corresponds to the associated variable.

	weights : numpy.ndarray, shape=(n,)
		The weight of each sample as a positive double. Default is None.

	key_count : numpy.ndarray, shape=(d,)
		The number of unique keys in each column.

	include_edges : list or None
		A list of (parent, child) tuples where each tuple corresponds to an
		edge that must exist in the found structure.

	exclude_edges : list or None
		A list of (parent, child) tuples where each tuple corresponds to an
		edge that cannot exist in the found structure.

	pseudocount : double
		A pseudocount to add to each possibility.

	penalty : float or None, optional
		The weighting of the model complexity term in the objective function.
		Increasing this value will encourage sparsity whereas setting the value
		to 0 will result in an unregularized structure. Default is
		log2(|D|) / 2 where |D| is the sum of the weights of the data.

	max_parents : int
		The maximum number of parents a node can have. If used, this means
		using the k-learn procedure. Can drastically speed up algorithms.
		If -1, no max on parents. Default is -1.

	low_memory : bool or None, optional
		Whether to use a low-memory version of the search algorithm. This
		option only affects algorithm="greedy" and algorithm="exact". 
		Although the low-memory version of both the greedy and exact
		algorithms will use less memory, it will also significantly slow
		down the exact algorithm. However, setting this to True will also
		significantly speed up the greedy algorithm. Setting this value to
		None will enable it when algorithm="greedy" and disable it otherwise.
		Default is None.

	n_jobs : int
		The number of threads to use when learning the structure of the
		network. This parallelizes the creation of the parent graphs.

	Returns
	-------
	structure : tuple, shape=(d,)
		The parents for each variable in this SCC
	discrete_exact_with_constraintsThis returns the optimal Bayesian network given a set of constraints.

	This function controls the process of learning the Bayesian network by
	taking in a constraint graph, identifying the strongly connected
	components (SCCs) and solving each one using the appropriate algorithm.
	This is mostly an internal function.

	Parameters
	----------
	X : numpy.ndarray, shape=(n, d)
		The data to fit the structure too, where each row is a sample and
		each column corresponds to the associated variable.

	weights : numpy.ndarray, shape=(n,)
		The weight of each sample as a positive double. Default is None.

	key_count : numpy.ndarray, shape=(d,)
		The number of unique keys in each column.

	include_edges : list or None
		A set of (parent, child) tuples where each tuple is an edge that
		must exist in the found structure.

	exclude_edges : list or None
		A set of (parent, child) tuples where each tuple is an edge that
		cannot exist in the found structure.

	pseudocount : double
		A pseudocount to add to each possibility.

	penalty : float or None, optional
		The weighting of the model complexity term in the objective function.
		Increasing this value will encourage sparsity whereas setting the value
		to 0 will result in an unregularized structure. Default is
		log2(|D|) / 2 where |D| is the sum of the weights of the data.

	max_parents : int
		The maximum number of parents a node can have. If used, this means
		using the k-learn procedure. Can drastically speed up algorithms.
		If -1, no max on parents. Default is -1.

	constraint_graph : networkx.DiGraph
		A directed graph showing valid parent sets for each variable. Each
		node is a set of variables, and edges represent which variables can
		be valid parents of those variables. The naive structure learning
		task is just all variables in a single node with a self edge,
		meaning that you know nothing about

	low_memory : bool or None, optional
		Whether to use a low-memory version of the search algorithm. This
		option only affects algorithm="greedy" and algorithm="exact". 
		Although the low-memory version of both the greedy and exact
		algorithms will use less memory, it will also significantly slow
		down the exact algorithm. However, setting this to True will also
		significantly speed up the greedy algorithm. Setting this value to
		None will enable it when algorithm="greedy" and disable it otherwise.
		Default is None.

	n_jobs : int
		The number of threads to use when learning the structure of the
		network. This parallelized both the creation of the parent
		graphs for each variable and the solving of the SCCs. -1 means
		use all available resources. Default is 1, meaning no parallelism.

	Returns
	-------
	structure : tuple, shape=(d,)
		The parents for each variable in the network.
	local_structureslow_memoryn_jobsdiscrete_exact_with_constraints_taskThis is a wrapper for the function to be parallelized by joblib.

	This function takes in a single task as an id and a set of parents and
	children and calls the appropriate function. This is mostly a wrapper for
	joblib to parallelize.

	Parameters
	----------
	X : numpy.ndarray, shape=(n, d)
		The data to fit the structure too, where each row is a sample and
		each column corresponds to the associated variable.

	weights : numpy.ndarray, shape=(n,)
		The weight of each sample as a positive double. Default is None.

	key_count : numpy.ndarray, shape=(d,)
		The number of unique keys in each column.

	include_edges : list or None
		A set of (parent, child) tuples where each tuple is an edge that
		must exist in the found structure.

	exclude_edges : list or None
		A set of (parent, child) tuples where each tuple is an edge that
		cannot exist in the found structure.

	pseudocount : double
		A pseudocount to add to each possibility.

	penalty : float or None, optional
		The weighting of the model complexity term in the objective function.
		Increasing this value will encourage sparsity whereas setting the value
		to 0 will result in an unregularized structure. Default is
		log2(|D|) / 2 where |D| is the sum of the weights of the data.

	max_parents : int
		The maximum number of parents a node can have. If used, this means
		using the k-learn procedure. Can drastically speed up algorithms.
		If -1, no max on parents. Default is -1.

	task : tuple
		A 3-tuple containing the id, the set of parents and the set of children
		to learn a component of the Bayesian network over. The cases represent
		a SCC of the following:

			0 - Self loop and no parents
			1 - Self loop and parents
			2 - Parents and no self loop
			3 - Multiple nodes

	low_memory : bool or None, optional
		Whether to use a low-memory version of the search algorithm. This
		option only affects algorithm="greedy" and algorithm="exact". 
		Although the low-memory version of both the greedy and exact
		algorithms will use less memory, it will also significantly slow
		down the exact algorithm. However, setting this to True will also
		significantly speed up the greedy algorithm. Setting this value to
		None will enable it when algorithm="greedy" and disable it otherwise.
		Default is None.

	n_jobs : int
		The number of threads to use when learning the structure of the
		network. This parallelizes the creation of the parent graphs
		for each task or the finding of best parents in case 2.

	Returns
	-------
	structure : tuple, shape=(d,)
		The parents for each variable in this SCC
	exclude_parentsbest_parentsdiscrete_exact_slap
	Find the optimal graph in a node with a Self Loop And Parents (SLAP).

	Instead of just performing exact BNSL over the set of all parents and
	removing the offending edges there are efficiencies that can be gained
	by considering the structure. In particular, parents not coming from the
	main node do not need to be considered in the order graph but simply
	added to each entry after creation of the order graph. This is because
	those variables occur earlier in the topological ordering but it doesn't
	matter how they occur otherwise. Parent graphs must be defined over all
	variables however.

	Parameters
	----------
	X : numpy.ndarray, shape=(n, d)
		The data to fit the structure too, where each row is a sample and
		each column corresponds to the associated variable.

	weights : numpy.ndarray, shape=(n,)
		The weight of each sample as a positive double. Default is None.

	key_count : numpy.ndarray, shape=(d,)
		The number of unique keys in each column.

	include_edges : list or None
		A list of (parent, child) tuples where each tuple corresponds to an
		edge that must exist in the found structure.

	exclude_edges : list or None
		A list of (parent, child) tuples where each tuple corresponds to an
		edge that cannot exist in the found structure.

	pseudocount : double
		A pseudocount to add to each possibility.

	penalty : float or None, optional
		The weighting of the model complexity term in the objective function.
		Increasing this value will encourage sparsity whereas setting the value
		to 0 will result in an unregularized structure. Default is
		log2(|D|) / 2 where |D| is the sum of the weights of the data.

	max_parents : int
		The maximum number of parents a node can have. If used, this means
		using the k-learn procedure. Can drastically speed up algorithms.
		If -1, no max on parents. Default is -1.

	n_jobs : int
		The number of threads to use when learning the structure of the
		network. This parallelizes the creation of the parent graphs.

	Returns
	-------
	structure : tuple, shape=(d,)
		The parents for each variable in this SCC
	childrendiscrete_exact_componentFind the optimal graph over a multi-node component of the constaint graph.

	The general algorithm in this case is to begin with each variable and add
	all possible single children for that entry recursively until completion.
	This will result in a far sparser order graph than before. In addition, one
	can eliminate entries from the parent graphs that contain invalid parents
	as they are a fast of computational time.

	Parameters
	----------
	X : numpy.ndarray, shape=(n, d)
		The data to fit the structure too, where each row is a sample and
		each column corresponds to the associated variable.

	weights : numpy.ndarray, shape=(n,)
		The weight of each sample as a positive double. Default is None.

	include_edges : list or None
		A list of (parent, child) tuples where each tuple corresponds to an
		edge that must exist in the found structure.

	exclude_edges : list or None
		A list of (parent, child) tuples where each tuple corresponds to an
		edge that cannot exist in the found structure.

	key_count : numpy.ndarray, shape=(d,)
		The number of unique keys in each column.

	pseudocount : double
		A pseudocount to add to each possibility.

	penalty : float or None, optional
		The weighting of the model complexity term in the objective function.
		Increasing this value will encourage sparsity whereas setting the value
		to 0 will result in an unregularized structure. Default is
		log2(|D|) / 2 where |D| is the sum of the weights of the data.

	max_parents : int
		The maximum number of parents a node can have. If used, this means
		using the k-learn procedure. Can drastically speed up algorithms.
		If -1, no max on parents. Default is -1.

	n_jobs : int
		The number of threads to use when learning the structure of the
		network. This parallelizes the creation of the parent graphs.

	Returns
	-------
	structure : tuple, shape=(d,)
		The parents for each variable in this SCC
	parent_setgenerate_parent_graph
	Generate a parent graph for a single variable over its parents.

	This will generate the parent graph for a single parents given the data.
	A parent graph is the dynamically generated best parent set and respective
	score for each combination of parent variables. For example, if we are
	generating a parent graph for x1 over x2, x3, and x4, we may calculate that
	having x2 as a parent is better than x2,x3 and so store the value
	of x2 in the node for x2,x3.

	Parameters
	----------
	X : numpy.ndarray, shape=(n, d)
		The data to fit the structure too, where each row is a sample and
		each column corresponds to the associated variable.

	weights : numpy.ndarray, shape=(n,)
		The weight of each sample as a positive double. Default is None.

	key_count : numpy.ndarray, shape=(d,)
		The number of unique keys in each column.

	i : int
		The column index to build the parent graph for.

	include_edges : list or None
		A list of (parent, child) tuples where each tuple corresponds to an
		edge that must exist in the found structure.

	exclude_edges : list or None
		A list of (parent, child) tuples where each tuple corresponds to an
		edge that cannot exist in the found structure.

	pseudocount : double
		A pseudocount to add to each possibility.

	penalty : float or None, optional
		The weighting of the model complexity term in the objective function.
		Increasing this value will encourage sparsity whereas setting the value
		to 0 will result in an unregularized structure. Default is
		log2(|D|) / 2 where |D| is the sum of the weights of the data.

	max_parents : int
		The maximum number of parents a node can have. If used, this means
		using the k-learn procedure. Can drastically speed up algorithms.
		If -1, no max on parents. Default is -1.

	parent_set : tuple, default ()
		The variables which are possible parents for this variable. If nothing
		is passed in then it defaults to all other variables, as one would
		expect in the naive case. This allows for cases where we want to build
		a parent graph over only a subset of the variables.

	Returns
	-------
	structure : tuple, shape=(d,)
		The parents for each variable in this SCC
	

INITloader__loader__origin__file____package__submodule_search_locations__path__Interpreter change detected - this module can only be loaded into one interpreter per process.Module 'BayesianNetwork' has already been imported. Re-initialisation is not supported.builtinscython_runtime__builtins__init pomegranate.BayesianNetworkcompile time Python version %d.%d of module '%.100s' %s runtime version %d.%ddoes not match_cython_3_0_10Shared Cython type %.200s is not a type objectShared Cython type %.200s has the wrong size, try recompiling_cython_3_0_10.cython_function_or_method<cyfunction %U at %p>unbound method %.200S() needs an argumentkeywords must be strings%.200s() takes no arguments (%zd given)%.200s() takes exactly one argument (%zd given)Bad call flags for CyFunction%.200s() takes no keyword arguments__module__func_doc__doc__func_name__qualname__func_dictfunc_globals__globals__func_closure__closure__func_code__code__func_defaults__defaults____kwdefaults____annotations____name__ must be set to a string object__qualname__ must be set to a string objectfunction's dictionary may not be deletedsetting function's dictionary to a non-dict__defaults__ must be set to a tuple objectchanges to cyfunction.__defaults__ will not currently affect the values used in function calls__kwdefaults__ must be set to a dict objectchanges to cyfunction.__kwdefaults__ will not currently affect the values used in function calls__annotations__ must be set to a dict object_cython_3_0_10.generatorgenerator already executingcan't send non-None value to a just-started generatorsend(arg) -> send 'arg' into generator,
return next yielded value or raise StopIteration.throw(typ[,val[,tb]]) -> raise exception in generator,
return next yielded value or raise StopIteration.close() -> raise GeneratorExit inside generator.'%.200s' object has no attribute '%U' while calling a Python objectNULL result without error in PyObject_Callgenerator ignored GeneratorExitraise: arg 3 must be a traceback or Noneinstance exception may not have a separate valuecalling %R should have returned an instance of BaseException, not %Rraise: exception class must be a subclass of BaseExceptiongi_runninggi_yieldfromobject being iterated by 'yield from', or Nonegi_codename of the generatorqualified name of the generatorgi_frameFrame of the generatornumpy.core.multiarray failed to import (auto-generated because you didn't call 'numpy.import_array()' after cimporting numpy; use '<void>numpy._import_array' to disable if you are certain you don't need it).numpy.core._multiarray_umath_ARRAY_API_ARRAY_API is not PyCapsule object_ARRAY_API is NULL pointermodule compiled against ABI version 0x%x but this version of numpy is 0x%xmodule compiled against API version 0x%x but this version of numpy is 0x%x . Check the section C-API incompatibility at the Troubleshooting ImportError section at https://numpy.org/devdocs/user/troubleshooting-importerror.html#c-api-incompatibility for indications on how to solve this problem .FATAL: module compiled as unknown endianFATAL: module compiled as little endian, but detected different endianness at runtimepomegranate.baseGraphModel%.200s.%.200s is not a type object%.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject%s.%s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObjectinvalid vtable found for imported typepomegranate.BayesianNetwork.BayesianNetwork._log_probabilitypomegranate.BayesianNetwork.BayesianNetwork.cpd_prodBuffer has wrong number of dimensions (expected %d, got %d)Item size of buffer (%zd byte%s) does not match size of '%s' (%zd byte%s)buffer dtypeBig-endian buffer not supported on little-endian compilerBuffer acquisition: Expected '{' after 'T'Buffer dtype mismatch, expected %s%s%s but got %sBuffer dtype mismatch, expected '%s' but got %s in '%s.%s''bool''char''signed char''unsigned char''short''unsigned short''int''unsigned int''long''unsigned long''long long''unsigned long long''complex float''float''complex double''double''complex long double''long double'a structPython objecta pointera stringunparsable format stringExpected a dimension of size %zu, got %zuExpected %d dimensions, got %dBuffer dtype mismatch; next field is at offset %zd but %zd expectedPython does not define a standard format string size for long double ('g')..Unexpected format string character: '%c'Cannot handle repeated arrays in format stringExpected a dimension of size %zu, got %dExpected a comma in format string, got '%c'Expected %d dimension(s), got %dUnexpected end of format string, expected ')'Does not understand character buffer dtype format string ('%c')double_tpomegranate.BayesianNetwork.BayesianNetwork.cpd_prod_marginalpomegranate.BayesianNetwork.BayesianNetworkA Bayesian Network Model.

	A Bayesian network is a directed graph where nodes represent variables, edges
	represent conditional dependencies of the children on their parents, and the
	lack of an edge represents a conditional independence.

	Parameters
	----------
	name : str, optional
		The name of the model. Default is None

	Attributes
	----------
	states : list, shape (n_states,)
		A list of all the state objects in the model

	graph : networkx.DiGraph
		The underlying graph object.

	Example
	-------
	>>> from pomegranate import *
	>>> d1 = DiscreteDistribution({'A': 0.2, 'B': 0.8})
	>>> d2 = ConditionalProbabilityTable([['A', 'A', 0.1],
										 ['A', 'B', 0.9],
										 ['B', 'A', 0.4],
										 ['B', 'B', 0.6]], [d1])
	>>> s1 = Node( d1, name="s1" )
	>>> s2 = Node( d2, name="s2" )
	>>> model = BayesianNetwork()
	>>> model.add_nodes(s1, s2)
	>>> model.add_edge(s1, s2)
	>>> model.bake()
	>>> print(model.log_probability([['A', 'B']]))
	-1.71479842809
	>>> print(model.predict_proba({'s2' : 'A'}))
    [{
        "class" :"Distribution",
        "dtype" :"str",
        "name" :"DiscreteDistribution",
        "parameters" :[
            {
                "A" :0.05882352941176483,
                "B" :0.9411764705882352
            }
        ],
        "frozen" :false
    }
    'A']
    >>> print(model.predict([[None, 'A']]))
	[array(['B', 'A'], dtype=object)]
	pomegranate.BayesianNetwork.BayesianNetwork.plot%.200s() keywords must be strings%s() got an unexpected keyword argument '%U'%s() got multiple values for keyword argument '%U'at leastat mostexactly%.200s() takes %.8s %zd positional argument%.1s (%zd given)'NoneType' object is not iterabletoo many values to unpack (expected %zd)need more than %zd value%.1s to unpacklocal variable '%s' referenced before assignmentobject of type 'NoneType' has no len()float division'NoneType' object has no attribute '%.30s'pomegranate.BayesianNetwork.BayesianNetwork.bakeMissing type objectCannot convert %.200s to %.200spomegranate.BayesianNetwork.BayesianNetwork.bake.genexpr.0'NoneType' object is not subscriptablefree variable '%s' referenced before assignment in enclosing scopecannot fit '%.200s' into an index-sized integer'%.200s' object is not subscriptablecan't convert negative value to size_tintan integer is required__int__ returned non-int (type %.200s).  The ability to return an instance of a strict subclass of int is deprecated, and may be removed in a future version of Python.__%.4s__ returned non-%.4s (type %.200s)value too large to convert to intpomegranate.BayesianNetwork.BayesianNetwork.log_probabilitypomegranate.BayesianNetwork.BayesianNetwork.log_probability.genexprpomegranate.BayesianNetwork.BayesianNetwork.marginalpomegranate.BayesianNetwork.BayesianNetwork.predictpomegranate.BayesianNetwork.BayesianNetwork.predict_probapomegranate.BayesianNetwork.BayesianNetwork.predict_proba.genexprpomegranate.BayesianNetwork.BayesianNetwork.fitpomegranate.BayesianNetwork.BayesianNetwork.fit.genexprpomegranate.BayesianNetwork.BayesianNetwork.samplefunctiondictionary changed size during iterationpomegranate.BayesianNetwork.BayesianNetwork._rejectionCythonUnboundCMethodpomegranate.BayesianNetwork.BayesianNetwork._gibbsArgument '%.200s' has incorrect type (expected %.200s, got %.200s)Index out of bounds (axis 0)value too large to convert to unsigned intcan't convert negative value to unsigned intItem size of buffer (%zu byte%s) does not match size of '%s' (%zu byte%s)View.MemoryView.memoryview_cwrapperBuffer is not indirectly contiguous in dimension %d.Buffer and memoryview are not contiguous in the same dimension.C-contiguous buffer is not contiguous in dimension %dC-contiguous buffer is not indirect in dimension %dBuffer exposes suboffsets but no stridesBuffer not compatible with direct access in dimension %d.Buffer is not indirectly accessible in dimension %d.memviewslice is already initialized!double'%.200s' object does not support slice %.10sassignmentdeletionOut of bounds on buffer access (axis %d)Acquisition count is %d (line %d)__pyx_fatalerrorView.MemoryView.slice_memviewsliceView.MemoryView._err_dimView.MemoryView.memoryview_copy_contentsView.MemoryView._err_extentsjoin() result is too long for a Python stringView.MemoryView.copy_data_to_tempView.MemoryView._err_no_memoryView.MemoryView.transpose_memsliceView.MemoryView._errpomegranate.BayesianNetwork.BayesianNetwork.summarizepomegranate.BayesianNetwork.BayesianNetwork.from_summariespomegranate.BayesianNetwork.BayesianNetwork.to_dictpomegranate.BayesianNetwork.BayesianNetwork.from_dictpomegranate.BayesianNetwork.BayesianNetwork.from_structurepomegranate.BayesianNetwork.BayesianNetwork.from_samplespomegranate.BayesianNetwork.BayesianNetwork.from_samples.genexprpomegranate.BayesianNetwork.BayesianNetwork.__reduce_cython__pomegranate.BayesianNetwork.BayesianNetwork.__setstate_cython__pomegranate.BayesianNetwork.BayesianNetwork.structure.__get__pomegranate.BayesianNetwork.BayesianNetwork.structure.__get__.genexprlistpomegranate.BayesianNetwork.BayesianNetwork.idxs.__set__Expected %s, got %.200spomegranate.BayesianNetwork.BayesianNetwork.keymap.__set__base class '%.200s' is not a heap typeextension type '%.200s' has no __dict__ slot, but base type '%.200s' has: either add 'cdef dict __dict__' to the extension type or add '__slots__ = [...]' to the base typemultiple bases have vtable conflict: '%.200s' and '%.200s'Unable to initialize pickling for %.200spomegranate.BayesianNetwork.ParentGraph
	Generate a parent graph for a single variable over its parents.

	This will generate the parent graph for a single parents given the data.
	A parent graph is the dynamically generated best parent set and respective
	score for each combination of parent variables. For example, if we are
	generating a parent graph for x1 over x2, x3, and x4, we may calculate that
	having x2 as a parent is better than x2,x3 and so store the value
	of x2 in the node for x2,x3.

	Parameters
	----------
	X : numpy.ndarray, shape=(n, d)
		The data to fit the structure too, where each row is a sample and
		each column corresponds to the associated variable.

	weights : numpy.ndarray, shape=(n,)
		The weight of each sample as a positive double. Default is None.

	key_count : numpy.ndarray, shape=(d,)
		The number of unique keys in each column.

	include_parents : tuple
		A set of parents that this node must have.

	exclude_parents : tuple
		A set of parents that this node cannot have.

	pseudocount : double
		A pseudocount to add to each possibility.

	penalty : float or None, optional
		The weighting of the model complexity term in the objective function.
		Increasing this value will encourage sparsity whereas setting the value
		to 0 will result in an unregularized structure. Default is
		log2(|D|) / 2 where |D| is the sum of the weights of the data.

	max_parents : int
		The maximum number of parents a node can have. If used, this means
		using the k-learn procedure. Can drastically speed up algorithms.
		If -1, no max on parents. Default is -1.

	low_memory : bool or None, optional
		Whether to use a low-memory version of the search algorithm. This
		option only affects algorithm="greedy" and algorithm="exact". 
		Although the low-memory version of both the greedy and exact
		algorithms will use less memory, it will also significantly slow
		down the exact algorithm. However, setting this to True will also
		significantly speed up the greedy algorithm. Setting this value to
		None will enable it when algorithm="greedy" and disable it otherwise.
		Default is None.

	Returns
	-------
	structure : tuple, shape=(d,)
		The parents for each variable in this SCC
	pomegranate.BayesianNetwork.ParentGraph.__len__pomegranate.BayesianNetwork.ParentGraph.__getitem__set changed size during iteration'%.200s' object is unsliceablepomegranate.BayesianNetwork.ParentGraph.calculate_valueinteger division or modulo by zeropomegranate.BayesianNetwork.discrete_score_nodepomegranate.BayesianNetwork.ParentGraph.__reduce_cython__pomegranate.BayesianNetwork.ParentGraph.__setstate_cython____init__pomegranate.BayesianNetwork.ParentGraph.__init__pomegranate.BayesianNetwork.__pyx_scope_struct____get__pomegranate.BayesianNetwork.__pyx_scope_struct_1_genexprpomegranate.BayesianNetwork.__pyx_scope_struct_2_bakepomegranate.BayesianNetwork.__pyx_scope_struct_3_genexprpomegranate.BayesianNetwork.__pyx_scope_struct_4_log_probabilitypomegranate.BayesianNetwork.__pyx_scope_struct_5_genexprpomegranate.BayesianNetwork.__pyx_scope_struct_6_predict_probapomegranate.BayesianNetwork.__pyx_scope_struct_7_genexprpomegranate.BayesianNetwork.__pyx_scope_struct_8_fitpomegranate.BayesianNetwork.__pyx_scope_struct_9_genexprpomegranate.BayesianNetwork.__pyx_scope_struct_10_genexprpomegranate.BayesianNetwork.__pyx_scope_struct_11_genexprpomegranate.BayesianNetwork.__pyx_scope_struct_12_discrete_exact_dppomegranate.BayesianNetwork.__pyx_scope_struct_13_genexprpomegranate.BayesianNetwork.__pyx_scope_struct_14_genexprpomegranate.BayesianNetwork.__pyx_scope_struct_15_genexprpomegranate.BayesianNetwork.__pyx_scope_struct_16_discrete_exact_a_starpomegranate.BayesianNetwork.__pyx_scope_struct_17_genexprpomegranate.BayesianNetwork.__pyx_scope_struct_18_genexprpomegranate.BayesianNetwork.__pyx_scope_struct_19_genexprpomegranate.BayesianNetwork.__pyx_scope_struct_20_discrete_exact_with_constraintspomegranate.BayesianNetwork.__pyx_scope_struct_21_genexprpomegranate.BayesianNetwork.__pyx_scope_struct_22_genexprpomegranate.BayesianNetwork.__pyx_scope_struct_23_discrete_exact_with_constraints_taskpomegranate.BayesianNetwork.__pyx_scope_struct_24_genexprpomegranate.BayesianNetwork.__pyx_scope_struct_25_discrete_exact_slappomegranate.BayesianNetwork.__pyx_scope_struct_26_genexprpomegranate.BayesianNetwork.__pyx_scope_struct_27_genexprpomegranate.BayesianNetwork.__pyx_scope_struct_28_genexprpomegranate.BayesianNetwork.__pyx_scope_struct_29_genexprpomegranate.BayesianNetwork.__pyx_scope_struct_30_discrete_exact_componentpomegranate.BayesianNetwork.__pyx_scope_struct_31_genexprpomegranate.BayesianNetwork.__pyx_scope_struct_32_genexprpomegranate.BayesianNetwork.__pyx_scope_struct_33_genexprpomegranate.BayesianNetwork.__pyx_scope_struct_34_generate_parent_graphpomegranate.BayesianNetwork.__pyx_scope_struct_35_genexprView.MemoryView.array.get_memviewpomegranate.BayesianNetwork.arrayView.MemoryView.array.__getitem__Subscript deletion not supported by %.200sView.MemoryView.array.__setitem__View.MemoryView.array.__getattr__PyObject_GetBuffer: view==NULL argument is obsoleteView.MemoryView.array.__getbuffer____getattr__View.MemoryView.array.__reduce_cython__View.MemoryView.array.__setstate_cython__View.MemoryView.array.memview.__get____cinit__View.MemoryView.array.__cinit__Argument '%.200s' must not be Nonebytesexpected bytes, NoneType foundvalue too large to perform divisionView.MemoryView._allocate_bufferpomegranate.BayesianNetwork.Enum__repr__View.MemoryView.Enum.__reduce_cython__View.MemoryView.Enum.__setstate_cython__tupleView.MemoryView.__pyx_unpickle_Enum__set_statehasattr(): attribute name must be stringView.MemoryView.Enum.__init__View.MemoryView.memoryview.get_item_pointerView.MemoryView.pybuffer_indexView.MemoryView.memoryview.is_sliceView.MemoryView.memoryview.setitem_slice_assignmentView.MemoryView.get_slice_from_memviewView.MemoryView.memoryview.setitem_slice_assign_scalarView.MemoryView.assert_direct_dimensionsView.MemoryView.memoryview.setitem_indexedView.MemoryView.memoryview.convert_item_to_objectNo module named '%U'byte string is too long'NoneType' is not iterableView.MemoryView.memoryview.assign_item_from_objectpomegranate.BayesianNetwork.memoryviewView.MemoryView.memoryview.__repr__View.MemoryView.memoryview.__getitem__View.MemoryView._unellipsifymemviewsliceobjView.MemoryView.memview_sliceView.MemoryView.memoryview_fromsliceView.MemoryView.memoryview.__setitem__View.MemoryView.memoryview.__str__View.MemoryView.memoryview.__getbuffer__is_c_contigis_f_contigcopy_fortranView.MemoryView.memoryview.is_c_contigView.MemoryView.memoryview.is_f_contigView.MemoryView.memoryview.copyCannot copy memoryview slice with indirect dimensions (axis %d)View.MemoryView.array_cwrapperView.MemoryView.memoryview_copy_from_sliceView.MemoryView.memoryview.copy_fortranView.MemoryView.memoryview.__reduce_cython__View.MemoryView.memoryview.__setstate_cython__TstridessuboffsetsnbytesView.MemoryView.memoryview.T.__get__View.MemoryView.memoryview_copyView.MemoryView.memoryview.base.__get__View.MemoryView.memoryview.shape.__get__View.MemoryView.memoryview.strides.__get__View.MemoryView.memoryview.suboffsets.__get__View.MemoryView.memoryview.ndim.__get__View.MemoryView.memoryview.itemsize.__get__View.MemoryView.memoryview.nbytes.__get__View.MemoryView.memoryview.size.__get__View.MemoryView.memoryview.__cinit__View.MemoryView._memoryviewslice.convert_item_to_objectView.MemoryView._memoryviewslice.assign_item_from_objectpomegranate.BayesianNetwork._memoryviewsliceInternal class for passing memoryview slices to PythonView.MemoryView._memoryviewslice.__reduce_cython__View.MemoryView._memoryviewslice.__setstate_cython__typeflatiterbroadcastndarraygenericnumberintegersignedintegerunsignedintegerinexactfloatingcomplexfloatingflexiblecharacterufuncModelStatepomegranate.distributions.distributionsMultivariateDistributionpomegranate.distributions.DiscreteDistributionpomegranate.distributions.ConditionalProbabilityTableConditionalProbabilityTablepomegranate.utils_logdouble (double)_log2choose_onePyObject *(__Pyx_memviewslice, int)__pyx_capi__%.200s does not export expected C function %.200sC function %.200s.%.200s has wrong signature (expected %.500s, got %.500s)Cython module failed to register with collections.abc modulebackports_abcif _cython_generator_type is not None:
    try: Generator = _module.Generator
    except AttributeError: pass
    else: Generator.register(_cython_generator_type)
if _cython_coroutine_type is not None:
    try: Coroutine = _module.Coroutine
    except AttributeError: pass
    else: Coroutine.register(_cython_coroutine_type)
_cython_coroutine_type_cython_generator_type_moduleCython module failed to patch module with custom type%s (%s:%d)pomegranate/BayesianNetwork.cView.MemoryView.__pyx_unpickle_Enumcannot import name %Sname '%U' is not defined%.200s() needs an argumentpomegranate.BayesianNetwork._check_inputpomegranate.BayesianNetwork.discrete_chow_liu_treepomegranate.BayesianNetwork.discrete_chow_liu_tree.genexprpomegranate.BayesianNetwork.discrete_exact_dppomegranate.BayesianNetwork.discrete_exact_dp.genexprpomegranate.BayesianNetwork.discrete_exact_a_starpomegranate.BayesianNetwork.discrete_exact_a_star.genexprpomegranate.BayesianNetwork.discrete_greedypomegranate.BayesianNetwork.discrete_exact_with_constraintspomegranate.BayesianNetwork.discrete_exact_with_constraints.genexprpomegranate.BayesianNetwork.discrete_exact_with_constraints_taskpomegranate.BayesianNetwork.discrete_exact_with_constraints_task.genexprpomegranate.BayesianNetwork.discrete_find_best_parentspomegranate.BayesianNetwork.discrete_exact_slappomegranate.BayesianNetwork.discrete_exact_slap.genexprpomegranate.BayesianNetwork.discrete_exact_componentpomegranate.BayesianNetwork.discrete_exact_component.genexprpomegranate.BayesianNetwork.generate_parent_graphpomegranate.BayesianNetwork.generate_parent_graph.genexpr
	@@

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