# Path: classifip/representations/binaryTree.py
# class BinaryTree(credalset.CredalSet):
#     class Node:
#         def __init__(self, label=None,proba=None):
#         def __str__(self):
#         def isEmpty(self):
#         def count(self):
#         def splitNode(self, method = "random", label = None):
#     def __init__(self, node=None, label=None,load=None):
#     def build(self, method="random",codes=None,shuffle=False,probas=None):
#         def regular(codes):
#         def genTree(tree,codes,proba_nodes=None): 
#     def save(self,name):
#     def getlowerexpectation(self,function):
#         def lowerExp(NDtree):
#     def getlowerprobability(self, subset):
#     def getupperprobability(self,subset):
#     def toIntervalsProbability(self):
#     def isreachable(self):
#     def isproper(self):
#     def setreachableprobability(self):
#     def printTree(self, _p = 0):
#     def printProba(self):
#         def printP(tree, _p = 0):


from classifip.representations import binaryTree as bt
from scipy.stats import rv_discrete
import pickle, copy
import random
        
        # recursion
        if self.left.node.count() > 1:
            self.left._evalCurrent(testdataset,out.left,**kwargs)
        if self.right.node.count() > 1:
            self.right._evalCurrent(testdataset,out.right,**kwargs)
            
        #return out
    
    def evaluate(self,testdataset,**kwargs):
        """
        Recursively evaluate all local models (see :func:`~classifip.models.nestedDichotomies.evalCurrent`)
        of the entire dichotomy tree for the test dataset.
        
        :param testdataset: list of input features of instances to evaluate
        :type testdataset: list
        
        :param **kwargs: should contain any parameter available to the base 
        classifier.
        
        :returns: a set of probabilistic binary tree
        :rtype: lists of :class:`~classifip.representations.binaryTree.BinaryTree`
        
        .. warning:: no check is performed on the validity of the arguments.
        
        """
        
        if self is not None :
            results = []
            for item in testdataset:

# Path: classifip/representations/binaryTree.py
# class BinaryTree(credalset.CredalSet):
#     class Node:
#         def __init__(self, label=None,proba=None):
#         def __str__(self):
#         def isEmpty(self):
#         def count(self):
#         def splitNode(self, method = "random", label = None):
#     def __init__(self, node=None, label=None,load=None):
#     def build(self, method="random",codes=None,shuffle=False,probas=None):
#         def regular(codes):
#         def genTree(tree,codes,proba_nodes=None): 
#     def save(self,name):
#     def getlowerexpectation(self,function):
#         def lowerExp(NDtree):
#     def getlowerprobability(self, subset):
#     def getupperprobability(self,subset):
#     def toIntervalsProbability(self):
#     def isreachable(self):
#     def isproper(self):
#     def setreachableprobability(self):
#     def printTree(self, _p = 0):
#     def printProba(self):
#         def printP(tree, _p = 0):


# Path: classifip/models/nestedDichotomies.py
from classifip.representations import binaryTree as bt
from scipy.stats import rv_discrete
import pickle, copy
import random

        
        # recursion
        if self.left.node.count() > 1:
            self.left._evalCurrent(testdataset,out.left,**kwargs)
        if self.right.node.count() > 1:
            self.right._evalCurrent(testdataset,out.right,**kwargs)
            
        #return out
    
    def evaluate(self,testdataset,**kwargs):
        """
        Recursively evaluate all local models (see :func:`~classifip.models.nestedDichotomies.evalCurrent`)
        of the entire dichotomy tree for the test dataset.
        
        :param testdataset: list of input features of instances to evaluate
        :type testdataset: list
        
        :param **kwargs: should contain any parameter available to the base 
        classifier.
        
        :returns: a set of probabilistic binary tree
        :rtype: lists of :class:`~classifip.representations.binaryTree.BinaryTree`
        
        .. warning:: no check is performed on the validity of the arguments.
        
        """
        
        if self is not None :
            results = []
            for item in testdataset:

                tree = bt.BinaryTree(label=['initialization'])