# 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



class ControlSelector:
    """A function that produces a control u that tries to steer a
    state x to another state xdesired."""
    def select(self,x,xdesired):
        raise NotImplementedError()


class RandomControlSelector(ControlSelector):
    """A ControlSelector that randomly samples numSamples controls
    and finds the one that is closest to the destination, according
    to a given metric."""
    def __init__(self,controlSpace,metric,numSamples):
        self.controlSpace = controlSpace
        self.metric = metric
        self.numSamples = numSamples

    def select(self,x,xdesired):
        ubest = None
        #do we want to eliminate extensions that do not improve the metric
        #from the starting configuration?  experiments suggest no 5/6/2015
        #dbest = self.metric(x,xdesired)
        dbest = infty
        U = self.controlSpace.controlSet(x)
        if self.numSamples == 1:
            u = U.sample()
            return u if U.contains(u) else None
        for iters in range(self.numSamples):

# 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




class ControlSelector:
    """A function that produces a control u that tries to steer a
    state x to another state xdesired."""
    def select(self,x,xdesired):
        raise NotImplementedError()


class RandomControlSelector(ControlSelector):
    """A ControlSelector that randomly samples numSamples controls
    and finds the one that is closest to the destination, according
    to a given metric."""
    def __init__(self,controlSpace,metric,numSamples):
        self.controlSpace = controlSpace
        self.metric = metric
        self.numSamples = numSamples

    def select(self,x,xdesired):
        ubest = None
        #do we want to eliminate extensions that do not improve the metric
        #from the starting configuration?  experiments suggest no 5/6/2015
        #dbest = self.metric(x,xdesired)
        dbest = infty
        U = self.controlSpace.controlSet(x)
        if self.numSamples == 1:
            u = U.sample()
            return u if U.contains(u) else None
        for iters in range(self.numSamples):

            u = U.sample()