<s> '''<STR_LIT>''' <EOL> import math <EOL> import types <EOL> from functools import wraps <EOL> from numbers import Number <EOL> from collections import Counter <EOL> from itertools import product as xproduct <EOL> from StringIO import StringIO <EOL> from bayesian . graph import Graph , Node , connect <EOL> from bayesian . gaussian import make_gaussian_cdf <EOL> from bayesian . gaussian import marginalize_joint <EOL> from bayesian . gaussian import joint_to_conditional , conditional_to_joint <EOL> from bayesian . gaussian import CovarianceMatrix , MeansVector <EOL> from bayesian . linear_algebra import zeros , Matrix <EOL> from bayesian . utils import get_args <EOL> from bayesian . utils import get_original_factors <EOL> from bayesian . exceptions import VariableNotInGraphError <EOL> from bayesian . linear_algebra import Matrix <EOL> def gaussian ( mu , sigma ) : <EOL> def gaussianize ( f ) : <EOL> @ wraps ( f ) <EOL> def gaussianized ( * args ) : <EOL> x = args [ <NUM_LIT:0> ] <EOL> return <NUM_LIT:1> / ( sigma * ( <NUM_LIT:2> * math . pi ) ** <NUM_LIT:0.5> ) * math . exp ( ( - ( x - mu ) ** <NUM_LIT:2> ) / ( <NUM_LIT:2> * sigma ** <NUM_LIT:2> ) ) <EOL> gaussianized . mean = mu <EOL> gaussianized . std_dev = sigma <EOL> gaussianized . variance = sigma ** <NUM_LIT:2> <EOL> gaussianized . cdf = make_gaussian_cdf ( mu , sigma ) <EOL> gaussianized . argspec = get_args ( f ) <EOL> gaussianized . entropy = types . MethodType ( <EOL> lambda x : <NUM_LIT:0.5> * math . log ( <NUM_LIT:2> * math . pi * math . e * x . variance ) , <EOL> gaussianized ) <EOL> return gaussianized <EOL> return gaussianize <EOL> def conditional_gaussian ( mu , sigma , betas ) : <EOL> def conditional_gaussianize ( f ) : <EOL> @ wraps ( f ) <EOL> def conditional_gaussianized ( * args , ** kwds ) : <EOL> '''<STR_LIT>''' <EOL> x = zeros ( ( len ( args ) , <NUM_LIT:1> ) ) <EOL> for i , a in enumerate ( args ) : <EOL> x [ i , <NUM_LIT:0> ] = a <EOL> sigma = conditional_gaussianized . covariance_matrix <EOL> mu = conditional_gaussianized . joint_mu <EOL> return <NUM_LIT:1> / ( <NUM_LIT:2> * math . pi * sigma . det ( ) ) ** <NUM_LIT:0.5> * math . exp ( - <NUM_LIT:0.5> * ( ( x - mu ) . T * sigma . I * ( x - mu ) ) [ <NUM_LIT:0> , <NUM_LIT:0> ] ) <EOL> conditional_gaussianized . mean = mu <EOL> conditional_gaussianized . std_dev = sigma <EOL> conditional_gaussianized . variance = sigma ** <NUM_LIT:2> <EOL> conditional_gaussianized . raw_betas = betas <EOL> conditional_gaussianized . argspec = get_args ( f ) <EOL> conditional_gaussianized . entropy = types . MethodType ( <EOL> lambda x : len ( x . joint_mu ) / <NUM_LIT:2> * ( <NUM_LIT:1> + math . log ( <NUM_LIT:2> * math . pi ) ) + <NUM_LIT:0.5> * math . log ( x . covariance_matrix . det ( ) ) , conditional_gaussianized ) <EOL> return conditional_gaussianized <EOL> return conditional_gaussianize <EOL> class GBNNode ( Node ) : <EOL> def __init__ ( self , factor ) : <EOL> super ( GBNNode , self ) . __init__ ( factor . __name__ ) <EOL> self . func = factor <EOL> self . argspec = get_args ( factor ) <EOL> def __repr__ ( self ) : <EOL> return '<STR_LIT>' % ( <EOL> self . name , <EOL> self . argspec ) <EOL> @ property <EOL> def variance ( self ) : <EOL> return self . func . variance <EOL> class GaussianBayesianGraph ( Graph ) : <EOL> def __init__ ( self , nodes , name = None ) : <EOL> self . nodes = nodes <EOL> self . name = name <EOL> ordered = self . get_topological_sort ( ) <EOL> for i , node in enumerate ( ordered ) : <EOL> node . index = i <EOL> def get_joint_parameters ( self ) : <EOL> '''<STR_LIT>''' <EOL> ordered = self . get_topological_sort ( ) <EOL> mu_x = Matrix ( [ [ ordered [ <NUM_LIT:0> ] . func . mean ] ] ) <EOL> sigma_x = Matrix ( [ [ ordered [ <NUM_LIT:0> ] . func . variance ] ] ) <EOL> for node in ordered [ <NUM_LIT:1> : ] : <EOL> beta_0 = node . func . mean <EOL> beta = zeros ( ( node . index , <NUM_LIT:1> ) ) <EOL> total = <NUM_LIT:0> <EOL> for parent in node . parents : <EOL> beta [ parent . index , <NUM_LIT:0> ] = node . func . betas [ parent . variable_name ] <EOL> sigma_c = node . func . variance <EOL> mu_x , sigma_x = conditional_to_joint ( <EOL> mu_x , sigma_x , beta_0 , beta , sigma_c ) <EOL> names = [ n . variable_name for n in ordered ] <EOL> mu_x . set_names ( names ) <EOL> sigma_x . set_names ( names ) <EOL> return mu_x , sigma_x <EOL> def query ( self , ** kwds ) : <EOL> invalid_vars = [ v for v in kwds . keys ( ) if v not in self . nodes ] <EOL> if invalid_vars : <EOL> raise VariableNotInGraphError ( invalid_vars ) <EOL> mu , sigma = self . get_joint_parameters ( ) <EOL> result = dict ( ) <EOL> result [ '<STR_LIT>' ] = kwds <EOL> for k , v in kwds . items ( ) : <EOL> x = MeansVector ( [ [ v ] ] , names = [ k ] ) <EOL> sigma_yy , sigma_yx , sigma_xy , sigma_xx = ( <EOL> sigma . split ( k ) ) <EOL> mu_y , mu_x = mu . split ( k ) <EOL> mu_y_given_x = MeansVector ( <EOL> ( mu_y + sigma_yx * sigma_xx . I * ( x - mu_x ) ) . rows , <EOL> names = mu_y . name_ordering ) <EOL> sigma_y_given_x = CovarianceMatrix ( <EOL> ( sigma_yy - sigma_yx * sigma_xx . I * sigma_xy ) . rows , <EOL> names = sigma_yy . name_ordering ) <EOL> sigma = sigma_y_given_x <EOL> mu = mu_y_given_x <EOL> result [ '<STR_LIT>' ] = dict ( mu = mu , sigma = sigma ) <EOL> return result <EOL> def q ( self , ** kwds ) : <EOL> '''<STR_LIT>''' <EOL> result = self . query ( ** kwds ) <EOL> mu = result [ '<STR_LIT>' ] [ '<STR_LIT>' ] <EOL> sigma = result [ '<STR_LIT>' ] [ '<STR_LIT>' ] <EOL> evidence = result [ '<STR_LIT>' ] <EOL> print '<STR_LIT>' % str ( evidence ) <EOL> print '<STR_LIT>' <EOL> print sigma <EOL> print '<STR_LIT>' <EOL> print mu <EOL> def discover_sample_ordering ( self ) : <EOL> return discover_sample_ordering ( self ) <EOL> def get_graphviz_source ( self ) : <EOL> fh = StringIO ( ) <EOL> fh . write ( '<STR_LIT>' ) <EOL> fh . write ( '<STR_LIT>' ) <EOL> edges = set ( ) <EOL> for node in sorted ( self . nodes . values ( ) , key = lambda x : x . name ) : <EOL> fh . write ( '<STR_LIT>' % node . name ) <EOL> for child in node . children : <EOL> edge = ( node . name , child . name ) <EOL> edges . add ( edge ) <EOL> for source , target in sorted ( edges , key = lambda x : ( x [ <NUM_LIT:0> ] , x [ <NUM_LIT:1> ] ) ) : <EOL> fh . write ( '<STR_LIT>' % ( source , target ) ) <EOL> fh . write ( '<STR_LIT>' ) <EOL> return fh . getvalue ( ) <EOL> def build_gbn ( * args , ** kwds ) : <EOL> '''<STR_LIT>''' <EOL> variables = set ( ) <EOL> name = kwds . get ( '<STR_LIT:name>' ) <EOL> variable_nodes = dict ( ) <EOL> factor_nodes = dict ( ) <EOL> if isinstance ( args [ <NUM_LIT:0> ] , list ) : <EOL> args = args [ <NUM_LIT:0> ] <EOL> for factor in args : <EOL> factor_args = get_args ( factor ) <EOL> variables . update ( factor_args ) <EOL> node = GBNNode ( factor ) <EOL> factor_nodes [ factor . __name__ ] = node <EOL> original_factors = get_original_factors ( factor_nodes . values ( ) ) <EOL> for var_name , factor in original_factors . items ( ) : <EOL> factor . variable_name = var_name <EOL> for factor_node in factor_nodes . values ( ) : <EOL> factor_args = get_args ( factor_node ) <EOL> parents = [ original_factors [ arg ] for arg in <EOL> factor_args if original_factors [ arg ] != factor_node ] <EOL> for parent in parents : <EOL> connect ( parent , factor_node ) <EOL> for factor_node in factor_nodes . values ( ) : <EOL> if hasattr ( factor_node . func , '<STR_LIT>' ) : <EOL> if isinstance ( factor_node . func . raw_betas , Number ) : <EOL> assert len ( get_args ( factor_node ) ) == <NUM_LIT:2> <EOL> betas = dict ( ) <EOL> for arg in get_args ( factor_node ) : <EOL> if arg != factor_node . variable_name : <EOL> betas [ arg ] = factor_node . func . raw_betas <EOL> factor_node . func . betas = betas <EOL> else : <EOL> factor_node . func . betas = factor_node . func . raw_betas <EOL> gbn = GaussianBayesianGraph ( original_factors , name = name ) <EOL> sorted = gbn . get_topological_sort ( ) <EOL> joint_mu , joint_sigma = gbn . get_joint_parameters ( ) <EOL> for node in sorted : <EOL> if hasattr ( node . func , '<STR_LIT>' ) : <EOL> names = [ n . variable_name for n in node . parents ] + [ node . variable_name ] <EOL> node . func . joint_mu = MeansVector . zeros ( ( len ( names ) , <NUM_LIT:1> ) , names = names ) <EOL> for name in names : <EOL> node . func . joint_mu [ name ] = joint_mu [ name ] [ <NUM_LIT:0> , <NUM_LIT:0> ] <EOL> node . func . covariance_matrix = CovarianceMatrix . zeros ( <EOL> ( len ( names ) , len ( names ) ) , names ) <EOL> for row , col in xproduct ( names , names ) : <EOL> node . func . covariance_matrix [ row , col ] = joint_sigma [ row , col ] <EOL> return gbn <EOL> def build_graph ( * args , ** kwds ) : <EOL> '''<STR_LIT>''' <EOL> return build_gbn ( * args , ** kwds ) </s>
