# 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
            if isinstance(self.controlSpace,CostControlSpace):
                scale[-1] *= 2.0
            """
            if isinstance(self.controlSpace,CostControlSpace):
                if scale[-1] != 0:
                    scale[-1] = 1
            """
            print("EST projection hash scale",scale,"resolution",self.projectionResolution)
        except Exception:
            scale = [1]*self.controlSpace.configurationSpace().dimension()
            print("EST projection hash scale",scale,"resolution",self.projectionResolution)
        #now enumerate all size-4 subsets of the indices
        d = min(len(indices),4)
        self.projectionBases = []
        self.projectionHashes = []
        for element in itertools.combinations(indices,d):
            self.projectionBases.append([])
            for i in element:
                basis = {i:self.projectionResolution*scale[i]}
                self.projectionBases[-1].append(basis)
            self.projectionHashes.append(RandomDict())
        #add hierarchy
        baseCnt = len(self.projectionHashes)
        for level in range(1,self.projectionHierarchyLevels):
            for b in range(baseCnt):
                basis = []
                for base in self.projectionBases[b]:
                    scalebasis = dict((i,v*(1+level)) for i,v in iteritems(base))
                    basis.append(scalebasis)
                self.projectionBases.append(basis)

# 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

            if isinstance(self.controlSpace,CostControlSpace):
                scale[-1] *= 2.0
            """
            if isinstance(self.controlSpace,CostControlSpace):
                if scale[-1] != 0:
                    scale[-1] = 1
            """
            print("EST projection hash scale",scale,"resolution",self.projectionResolution)
        except Exception:
            scale = [1]*self.controlSpace.configurationSpace().dimension()
            print("EST projection hash scale",scale,"resolution",self.projectionResolution)
        #now enumerate all size-4 subsets of the indices
        d = min(len(indices),4)
        self.projectionBases = []
        self.projectionHashes = []
        for element in itertools.combinations(indices,d):
            self.projectionBases.append([])
            for i in element:
                basis = {i:self.projectionResolution*scale[i]}
                self.projectionBases[-1].append(basis)
            self.projectionHashes.append(RandomDict())
        #add hierarchy
        baseCnt = len(self.projectionHashes)
        for level in range(1,self.projectionHierarchyLevels):
            for b in range(baseCnt):
                basis = []
                for base in self.projectionBases[b]:
                    scalebasis = dict((i,v*(1+level)) for i,v in iteritems(base))
                    basis.append(scalebasis)
                self.projectionBases.append(basis)

                self.projectionHashes.append(RandomDict())