# Path: pomp/spaces/edgechecker.py
# class EdgeChecker:
#     def feasible(self,interpolator):
#         """ interpolator: a subclass of Interpolator.
#         Returns true if all points along the interpolator are feasible.
#         """
#         raise NotImplementedError()
# 
# Path: pomp/spaces/metric.py
# def euclideanMetric(a,b):
# def L2Metric(a,b):
# def L1Metric(a,b):
# def LinfMetric(a,b):
#     def __init__(self,w):
#     def __call__(self,a,b):
#     def __init__(self,p):
#     def __call__(self,a,b):
# class WeightedEuclideanMetric:
# class LpMetric:
# 
# Path: pomp/structures/randomdict.py
# class RandomDict(object):
#     def __init__(self): # O(1)
#         self.dictionary = {}
#         self.indexdict = {}
#         self.next_index = 0
#         self.removed_indices = None
#         self.len = 0
# 
#     def __len__(self): # might as well include this
#         return self.len
# 
#     def __contains__(self, key):
#         return key in self.dictionary
# 
#     def __getitem__(self, key): # O(1)
#         return self.dictionary[key][1]
# 
#     def __setitem__(self, key, value): # O(1)
#         if key in self.dictionary: # O(1)
#             self.dictionary[key][1] = value # O(1)
#             return
#         if self.removed_indices is None:
#             index = self.next_index
#             self.next_index += 1
#         else:
#             index = self.removed_indices[0]
#             self.removed_indices = self.removed_indices[1]
#         self.dictionary[key] = [index, value] # O(1)
#         self.indexdict[index] = key # O(1)
#         self.len += 1
# 
#     def get(self, key, defaultValue): # O(1)
#         v = self.dictionary.get(key,None)
#         if v == None: return defaultValue
#         return v[1]
# 
#     def setdefault(self,key,defaultValue):
#         if key in self.dictionary: # O(1)
#             return self.dictionary[key][1] # O(1)
#         if self.removed_indices is None:
#             index = self.next_index
#             self.next_index += 1
#         else:
#             index = self.removed_indices[0]
#             self.removed_indices = self.removed_indices[1]
#         self.dictionary[key] = [index, defaultValue] # O(1)
#         self.indexdict[index] = key # O(1)
#         self.len += 1
#         return self.dictionary[key][1]
# 
#     def __delitem__(self, key): # O(1)
#         index = self.dictionary[key][0] # O(1)
#         del self.dictionary[key] # O(1)
#         del self.indexdict[index] # O(1)
#         self.removed_indices = (index, self.removed_indices)
#         self.len -= 1
# 
#     def random_key(self,weight=None):
#         """Randomly select a key in the dictionary.  Unweighted
#         version is O(log(next_index/len)).  Weighted version selects
#         items proportionally to weight(key,value), and runs in time
#         O(len)
#         """
#         if self.len == 0: # which is usually close to O(1)
#             raise KeyError
#         if weight==None:
#             while True:
#                 r = random.randrange(0, self.next_index)
#                 if r in self.indexdict:
#                     return self.indexdict[r]
#         else:
#             weights = {}
#             sumweight = 0.0
#             for k,v in iteritems(self.dictionary):
#                 w = weight(k,v)
#                 weights[k] = w
#                 sumweight += weights[k]
#             u = random.random()*sumweight
#             for k,v in iteritems(self.dictionary):
#                 u -= weights[k]
#                 if u <= 0: return k
#             print("Numerical error in random_key")
#             return iterkeys(self.dictionary).next()


from builtins import range
from six import iteritems
from ..spaces.sampler import *
from ..spaces.costspace import *
from ..spaces.edgechecker import EdgeChecker
from ..spaces import metric
from ..structures.randomdict import RandomDict
from ..structures.nearestneighbors import *
from .profiler import *
from .helpers import *
import time
import itertools
        return c / len(self.projectionBases)
        #account for multiple counting of axes
        #power = (float(len(x))-1)/(3.0*len(self.projectionBases))
        #if len(self.projectionBases) > 1:
        #    print("Correction power",power)
        #    print("Original density",c*len(self.nodes),"Resulting density",(c**power)*len(self.nodes))
        #c = c**power
        #print("Resulting density",c
        #return c*len(self.nodes)
        return c

    def pruneTree(self):
        self.projectionBases = []
        self.projectionHashes = []
        EST.pruneTree(self)
    
    def pickNode(self):
        """Picks a node according to inverse density weighting strategy"""
        index = random.randint(0,len(self.projectionBases)-1)
        basis = self.projectionBases[index]
        bhash = self.projectionHashes[index]
        key = bhash.random_key()
        #key = bhash.random_key(weight=lambda k,x:1.0/len(x))
        #print("Basis",basis,"select",key,"random from points",len(bhash[key]))
        res = random.choice(bhash[key])
        if self.prune(res): return None
        #print("Selected",res.x)
        return res



# Path: pomp/spaces/edgechecker.py
# class EdgeChecker:
#     def feasible(self,interpolator):
#         """ interpolator: a subclass of Interpolator.
#         Returns true if all points along the interpolator are feasible.
#         """
#         raise NotImplementedError()
# 
# Path: pomp/spaces/metric.py
# def euclideanMetric(a,b):
# def L2Metric(a,b):
# def L1Metric(a,b):
# def LinfMetric(a,b):
#     def __init__(self,w):
#     def __call__(self,a,b):
#     def __init__(self,p):
#     def __call__(self,a,b):
# class WeightedEuclideanMetric:
# class LpMetric:
# 
# Path: pomp/structures/randomdict.py
# class RandomDict(object):
#     def __init__(self): # O(1)
#         self.dictionary = {}
#         self.indexdict = {}
#         self.next_index = 0
#         self.removed_indices = None
#         self.len = 0
# 
#     def __len__(self): # might as well include this
#         return self.len
# 
#     def __contains__(self, key):
#         return key in self.dictionary
# 
#     def __getitem__(self, key): # O(1)
#         return self.dictionary[key][1]
# 
#     def __setitem__(self, key, value): # O(1)
#         if key in self.dictionary: # O(1)
#             self.dictionary[key][1] = value # O(1)
#             return
#         if self.removed_indices is None:
#             index = self.next_index
#             self.next_index += 1
#         else:
#             index = self.removed_indices[0]
#             self.removed_indices = self.removed_indices[1]
#         self.dictionary[key] = [index, value] # O(1)
#         self.indexdict[index] = key # O(1)
#         self.len += 1
# 
#     def get(self, key, defaultValue): # O(1)
#         v = self.dictionary.get(key,None)
#         if v == None: return defaultValue
#         return v[1]
# 
#     def setdefault(self,key,defaultValue):
#         if key in self.dictionary: # O(1)
#             return self.dictionary[key][1] # O(1)
#         if self.removed_indices is None:
#             index = self.next_index
#             self.next_index += 1
#         else:
#             index = self.removed_indices[0]
#             self.removed_indices = self.removed_indices[1]
#         self.dictionary[key] = [index, defaultValue] # O(1)
#         self.indexdict[index] = key # O(1)
#         self.len += 1
#         return self.dictionary[key][1]
# 
#     def __delitem__(self, key): # O(1)
#         index = self.dictionary[key][0] # O(1)
#         del self.dictionary[key] # O(1)
#         del self.indexdict[index] # O(1)
#         self.removed_indices = (index, self.removed_indices)
#         self.len -= 1
# 
#     def random_key(self,weight=None):
#         """Randomly select a key in the dictionary.  Unweighted
#         version is O(log(next_index/len)).  Weighted version selects
#         items proportionally to weight(key,value), and runs in time
#         O(len)
#         """
#         if self.len == 0: # which is usually close to O(1)
#             raise KeyError
#         if weight==None:
#             while True:
#                 r = random.randrange(0, self.next_index)
#                 if r in self.indexdict:
#                     return self.indexdict[r]
#         else:
#             weights = {}
#             sumweight = 0.0
#             for k,v in iteritems(self.dictionary):
#                 w = weight(k,v)
#                 weights[k] = w
#                 sumweight += weights[k]
#             u = random.random()*sumweight
#             for k,v in iteritems(self.dictionary):
#                 u -= weights[k]
#                 if u <= 0: return k
#             print("Numerical error in random_key")
#             return iterkeys(self.dictionary).next()


# Path: pomp/planners/kinodynamicplanner.py
from builtins import range
from six import iteritems
from ..spaces.sampler import *
from ..spaces.costspace import *
from ..spaces.edgechecker import EdgeChecker
from ..spaces import metric
from ..structures.randomdict import RandomDict
from ..structures.nearestneighbors import *
from .profiler import *
from .helpers import *
import time
import itertools

        return c / len(self.projectionBases)
        #account for multiple counting of axes
        #power = (float(len(x))-1)/(3.0*len(self.projectionBases))
        #if len(self.projectionBases) > 1:
        #    print("Correction power",power)
        #    print("Original density",c*len(self.nodes),"Resulting density",(c**power)*len(self.nodes))
        #c = c**power
        #print("Resulting density",c
        #return c*len(self.nodes)
        return c

    def pruneTree(self):
        self.projectionBases = []
        self.projectionHashes = []
        EST.pruneTree(self)
    
    def pickNode(self):
        """Picks a node according to inverse density weighting strategy"""
        index = random.randint(0,len(self.projectionBases)-1)
        basis = self.projectionBases[index]
        bhash = self.projectionHashes[index]
        key = bhash.random_key()
        #key = bhash.random_key(weight=lambda k,x:1.0/len(x))
        #print("Basis",basis,"select",key,"random from points",len(bhash[key]))
        res = random.choice(bhash[key])
        if self.prune(res): return None
        #print("Selected",res.x)
        return res



class CostEdgeChecker(EdgeChecker):