def __init__(self, data, threshold, p=2, alpha=-1.0, binary=True, ids=None,
            build_sp=True, silent=False):
        """Casting to floats is a work around for a bug in scipy.spatial.
        See detail in pysal issue #126.

        """
        self.p = p
        self.threshold = threshold
        self.binary = binary
        self.alpha = alpha
        self.build_sp = build_sp
        self.silent = silent
        
        if isKDTree(data):
            self.kd = data
            self.data = self.kd.data
        else:
            if self.build_sp:
                try:
                    data = np.asarray(data)
                    if data.dtype.kind != 'f':
                        data = data.astype(float)
                    self.data = data
                    self.kd = KDTree(self.data)
                except:
                    raise ValueError("Could not make array from data")        
            else:
                self.data = data
                self.kd = None       
        self._band()
        neighbors, weights = self._distance_to_W(ids)
        W.__init__(self, neighbors, weights, ids, silent_island_warning=self.silent)

    def __init__(self, data, threshold, p=2, alpha=-1.0, binary=True, ids=None):
        """Casting to floats is a work around for a bug in scipy.spatial.
        See detail in pysal issue #126.

        """
        if issubclass(type(data), scipy.spatial.KDTree):
            self.kd = data
            self.data = self.kd.data
        else:
            try:
                data = np.asarray(data)
                if data.dtype.kind != 'f':
                    data = data.astype(float)
                self.data = data
                self.kd = KDTree(self.data)
            except:
                raise ValueError("Could not make array from data")

        self.p = p
        self.threshold = threshold
        self.binary = binary
        self.alpha = alpha
        self._band()
        neighbors, weights = self._distance_to_W(ids)
        W.__init__(self, neighbors, weights, ids)

class DistanceBand(W):
    """
    Spatial weights based on distance band.

    Parameters
    ----------

    data        : array
                  (n,k) or KDTree where KDtree.data is array (n,k)
                  n observations on k characteristics used to measure
                  distances between the n objects
    threshold  : float
                 distance band
    p          : float
                 Minkowski p-norm distance metric parameter:
                 1<=p<=infinity
                 2: Euclidean distance
                 1: Manhattan distance
    binary     : boolean
                 If true w_{ij}=1 if d_{i,j}<=threshold, otherwise w_{i,j}=0
                 If false wij=dij^{alpha}
    alpha      : float
                 distance decay parameter for weight (default -1.0)
                 if alpha is positive the weights will not decline with
                 distance. If binary is True, alpha is ignored

    ids         : list
                  values to use for keys of the neighbors and weights dicts
    
    build_sp    : boolean
                  True to build sparse distance matrix and false to build dense
                  distance matrix; significant speed gains may be obtained
                  dending on the sparsity of the of distance_matrix and
                  threshold that is applied
    silent      : boolean
                  By default PySAL will print a warning if the
                  dataset contains any disconnected observations or
                  islands. To silence this warning set this
                  parameter to True.

    Attributes
    ----------
    weights : dict
              of neighbor weights keyed by observation id

    neighbors : dict
                of neighbors keyed by observation id

    Examples
    --------

    >>> points=[(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
    >>> wcheck = pysal.W({0: [1, 3], 1: [0, 3], 2: [], 3: [0, 1], 4: [5], 5: [4]})
    WARNING: there is one disconnected observation (no neighbors)
    Island id:  [2]
    >>> w=DistanceBand(points,threshold=11.2)
    WARNING: there is one disconnected observation (no neighbors)
    Island id:  [2]
    >>> pysal.weights.util.neighbor_equality(w, wcheck)
    True
    >>> w=DistanceBand(points,threshold=14.2)
    >>> wcheck = pysal.W({0: [1, 3], 1: [0, 3, 4], 2: [4], 3: [1, 0], 4: [5, 2, 1], 5: [4]})
    >>> pysal.weights.util.neighbor_equality(w, wcheck)
    True



    inverse distance weights

    >>> w=DistanceBand(points,threshold=11.2,binary=False)
    WARNING: there is one disconnected observation (no neighbors)
    Island id:  [2]
    >>> w.weights[0]
    [0.10000000000000001, 0.089442719099991588]
    >>> w.neighbors[0]
    [1, 3]
    >>>

    gravity weights

    >>> w=DistanceBand(points,threshold=11.2,binary=False,alpha=-2.)
    WARNING: there is one disconnected observation (no neighbors)
    Island id:  [2]
    >>> w.weights[0]
    [0.01, 0.0079999999999999984]

    Notes
    -----

    This was initially implemented running scipy 0.8.0dev (in epd 6.1).
    earlier versions of scipy (0.7.0) have a logic bug in scipy/sparse/dok.py
    so serge changed line 221 of that file on sal-dev to fix the logic bug.

    """

    def __init__(self, data, threshold, p=2, alpha=-1.0, binary=True, ids=None,
            build_sp=True, silent=False):
        """Casting to floats is a work around for a bug in scipy.spatial.
        See detail in pysal issue #126.

#.........这里部分代码省略.........

class Kernel(W):
    """
    Spatial weights based on kernel functions.

    Parameters
    ----------

    data        : array
                  (n,k) or KDTree where KDtree.data is array (n,k)
                  n observations on k characteristics used to measure
                  distances between the n objects
    bandwidth   : float
                  or array-like (optional)
                  the bandwidth :math:`h_i` for the kernel.
    fixed       : binary
                  If true then :math:`h_i=h \\forall i`. If false then
                  bandwidth is adaptive across observations.
    k           : int
                  the number of nearest neighbors to use for determining
                  bandwidth. For fixed bandwidth, :math:`h_i=max(dknn) \\forall i`
                  where :math:`dknn` is a vector of k-nearest neighbor
                  distances (the distance to the kth nearest neighbor for each
                  observation).  For adaptive bandwidths, :math:`h_i=dknn_i`
    diagonal    : boolean
                  If true, set diagonal weights = 1.0, if false (default),
                  diagonals weights are set to value according to kernel
                  function.
    function    : {'triangular','uniform','quadratic','quartic','gaussian'}
                  kernel function defined as follows with

                  .. math::

                      z_{i,j} = d_{i,j}/h_i

                  triangular

                  .. math::

                      K(z) = (1 - |z|) \ if |z| \le 1

                  uniform

                  .. math::

                      K(z) = 1/2 \ if |z| \le 1

                  quadratic

                  .. math::

                      K(z) = (3/4)(1-z^2) \ if |z| \le 1

                  quartic

                  .. math::

                      K(z) = (15/16)(1-z^2)^2 \ if |z| \le 1

                  gaussian

                  .. math::

                      K(z) = (2\pi)^{(-1/2)} exp(-z^2 / 2)

    eps         : float
                  adjustment to ensure knn distance range is closed on the
                  knnth observations

    Attributes
    ----------
    weights : dict
              Dictionary keyed by id with a list of weights for each neighbor

    neighbors : dict
                of lists of neighbors keyed by observation id

    bandwidth : array
                array of bandwidths

    Examples
    --------

    >>> points=[(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
    >>> kw=Kernel(points)
    >>> kw.weights[0]
    [1.0, 0.500000049999995, 0.4409830615267465]
    >>> kw.neighbors[0]
    [0, 1, 3]
    >>> kw.bandwidth
    array([[ 20.000002],
           [ 20.000002],
           [ 20.000002],
           [ 20.000002],
           [ 20.000002],
           [ 20.000002]])
    >>> kw15=Kernel(points,bandwidth=15.0)
    >>> kw15[0]
    {0: 1.0, 1: 0.33333333333333337, 3: 0.2546440075000701}
    >>> kw15.neighbors[0]
    [0, 1, 3]
#.........这里部分代码省略.........

class KNN(W):
    """
    Creates nearest neighbor weights matrix based on k nearest
    neighbors.

    Parameters
    ----------
    kdtree      : object
                  PySAL KDTree or ArcKDTree where KDtree.data is array (n,k)
                  n observations on k characteristics used to measure
                  distances between the n objects
    k           : int
                  number of nearest neighbors
    p           : float
                  Minkowski p-norm distance metric parameter:
                  1<=p<=infinity
                  2: Euclidean distance
                  1: Manhattan distance
                  Ignored if the KDTree is an ArcKDTree
    ids         : list
                  identifiers to attach to each observation

    Returns
    -------

    w         : W
                instance
                Weights object with binary weights

    Examples
    --------
    >>> points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
    >>> kd = pysal.cg.kdtree.KDTree(np.array(points))
    >>> wnn2 = pysal.KNN(kd, 2)
    >>> [1,3] == wnn2.neighbors[0]
    True

    ids

    >>> wnn2 = KNN(kd,2)
    >>> wnn2[0]
    {1: 1.0, 3: 1.0}
    >>> wnn2[1]
    {0: 1.0, 3: 1.0}

    now with 1 rather than 0 offset

    >>> wnn2 = KNN(kd, 2, ids=range(1,7))
    >>> wnn2[1]
    {2: 1.0, 4: 1.0}
    >>> wnn2[2]
    {1: 1.0, 4: 1.0}
    >>> 0 in wnn2.neighbors
    False

    Notes
    -----

    Ties between neighbors of equal distance are arbitrarily broken.

    See Also
    --------
    :class:`pysal.weights.W`
    """
    def __init__(self, data, k=2, p=2, ids=None, radius=None, distance_metric='euclidean'):
        if isKDTree(data):
            self.kdtree = data
            self.data = data.data
        else:
            self.data = data
            self.kdtree = KDTree(data, radius=radius, distance_metric=distance_metric)
        self.k = k 
        self.p = p
        this_nnq = self.kdtree.query(self.data, k=k+1, p=p)
        
        to_weight = this_nnq[1]
        if ids is None:
            ids = list(range(to_weight.shape[0]))
        
        neighbors = {}
        for i,row in enumerate(to_weight):
            row = row.tolist()
            row.remove(i)
            row = [ids[j] for j in row]
            focal = ids[i]
            neighbors[focal] = row
        W.__init__(self, neighbors, id_order=ids)
    
    @classmethod
    def from_shapefile(cls, filepath, **kwargs):
        """
        Nearest neighbor weights from a shapefile.

        Parameters
        ----------

        data       : string
                     shapefile containing attribute data.
        k          : int
                     number of nearest neighbors
#.........这里部分代码省略.........

class DistanceBand(W):
    """Spatial weights based on distance band

    Parameters
    ----------

    data        : array (n,k) or KDTree where KDtree.data is array (n,k)
                  n observations on k characteristics used to measure
                  distances between the n objects
    threshold  : float
                 distance band
    p          : float
                 Minkowski p-norm distance metric parameter:
                 1<=p<=infinity
                 2: Euclidean distance
                 1: Manhattan distance
    binary     : binary
                 If true w_{ij}=1 if d_{i,j}<=threshold, otherwise w_{i,j}=0
                 If false wij=dij^{alpha}
    alpha      : float
                 distance decay parameter for weight (default -1.0)
                 if alpha is positive the weights will not decline with
                 distance. If binary is True, alpha is ignored

    Examples
    --------

    >>> points=[(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
    >>> w=DistanceBand(points,threshold=11.2)
    WARNING: there is one disconnected observation (no neighbors)
    Island id:  [2]
    >>> w.weights
    {0: [1, 1], 1: [1, 1], 2: [], 3: [1, 1], 4: [1], 5: [1]}
    >>> w.neighbors
    {0: [1, 3], 1: [0, 3], 2: [], 3: [0, 1], 4: [5], 5: [4]}
    >>> w=DistanceBand(points,threshold=14.2)
    >>> w.weights
    {0: [1, 1], 1: [1, 1, 1], 2: [1], 3: [1, 1], 4: [1, 1, 1], 5: [1]}
    >>> w.neighbors
    {0: [1, 3], 1: [0, 3, 4], 2: [4], 3: [0, 1], 4: [1, 2, 5], 5: [4]}

    inverse distance weights

    >>> w=DistanceBand(points,threshold=11.2,binary=False)
    WARNING: there is one disconnected observation (no neighbors)
    Island id:  [2]
    >>> w.weights[0]
    [0.10000000000000001, 0.089442719099991588]
    >>> w.neighbors[0]
    [1, 3]
    >>>

    gravity weights

    >>> w=DistanceBand(points,threshold=11.2,binary=False,alpha=-2.)
    WARNING: there is one disconnected observation (no neighbors)
    Island id:  [2]
    >>> w.weights[0]
    [0.01, 0.0079999999999999984]

    Notes
    -----

    this was initially implemented running scipy 0.8.0dev (in epd 6.1).
    earlier versions of scipy (0.7.0) have a logic bug in scipy/sparse/dok.py
    so serge changed line 221 of that file on sal-dev to fix the logic bug

    """
    def __init__(self, data, threshold, p=2, alpha=-1.0, binary=True, ids=None):
        """
        Casting to floats is a work around for a bug in scipy.spatial.  See detail in pysal issue #126
        """
        if issubclass(type(data), scipy.spatial.KDTree):
            self.kd = data
            self.data = self.kd.data
        else:
            try:
                data = np.asarray(data)
                if data.dtype.kind != 'f':
                    data = data.astype(float)
                self.data = data
                self.kd = KDTree(self.data)
            except:
                raise ValueError("Could not make array from data")

        self.p = p
        self.threshold = threshold
        self.binary = binary
        self.alpha = alpha
        self._band()
        neighbors, weights = self._distance_to_W(ids)
        W.__init__(self, neighbors, weights, ids)

    def _band(self):
        """
        find all pairs within threshold
        """
        kd = self.kd
        #ns=[kd.query_ball_point(point,self.threshold) for point in self.data]
        ns = kd.query_ball_tree(kd, self.threshold)
#.........这里部分代码省略.........

def knnW(data, k=2, p=2, ids=None, pct_unique=0.25):
    """
    Creates nearest neighbor weights matrix based on k nearest
    neighbors.

    Parameters
    ----------

    data       : array (n,k) or KDTree where KDtree.data is array (n,k)
                 n observations on k characteristics used to measure
                 distances between the n objects
    k          : int
                 number of nearest neighbors
    p          : float
                 Minkowski p-norm distance metric parameter:
                 1<=p<=infinity
                 2: Euclidean distance
                 1: Manhattan distance
    ids        : list
                 identifiers to attach to each observation
    pct_unique : float
                 threshold percentage of unique points in data. Below this
                 threshold tree is built on unique values only

    Returns
    -------

    w         : W instance
                Weights object with binary weights

    Examples
    --------

    >>> x,y=np.indices((5,5))
    >>> x.shape=(25,1)
    >>> y.shape=(25,1)
    >>> data=np.hstack([x,y])
    >>> wnn2=knnW(data,k=2)
    >>> wnn4=knnW(data,k=4)
    >>> set([1,5,6,2]) == set(wnn4.neighbors[0])
    True
    >>> set([0,6,10,1]) == set(wnn4.neighbors[5])
    True
    >>> set([1,5]) == set(wnn2.neighbors[0])
    True
    >>> set([0,6]) == set(wnn2.neighbors[5])
    True
    >>> "%.2f"%wnn2.pct_nonzero
    '0.08'
    >>> wnn4.pct_nonzero
    0.16
    >>> wnn3e=knnW(data,p=2,k=3)
    >>> set([1,5,6]) == set(wnn3e.neighbors[0])
    True
    >>> wnn3m=knnW(data,p=1,k=3)
    >>> a = set([1,5,2])
    >>> b = set([1,5,6])
    >>> c = set([1,5,10])
    >>> w0n = set(wnn3m.neighbors[0])
    >>> a==w0n or b==w0n or c==w0n
    True

    ids

    >>> wnn2 = knnW(data,2)
    >>> wnn2[0]
    {1: 1.0, 5: 1.0}
    >>> wnn2[1]
    {0: 1.0, 2: 1.0}

    now with 1 rather than 0 offset

    >>> wnn2 = knnW(data,2, ids = range(1,26))
    >>> wnn2[1]
    {2: 1.0, 6: 1.0}
    >>> wnn2[2]
    {1: 1.0, 3: 1.0}
    >>> 0 in wnn2.neighbors
    False

    Notes
    -----

    Ties between neighbors of equal distance are arbitrarily broken.

    See Also
    --------
    pysal.weights.W

    """

    if issubclass(type(data), scipy.spatial.KDTree):
        kd = data
        data = kd.data
        nnq = kd.query(data, k=k+1, p=p)
        info = nnq[1]
    elif type(data).__name__ == 'ndarray':
        # check if unique points are a small fraction of all points
        ind =  np.lexsort(data.T)
        u = data[np.concatenate(([True],np.any(data[ind[1:]]!=data[ind[:-1]],axis=1)))]
#.........这里部分代码省略.........

class DistanceBand(W):
    """
    Spatial weights based on distance band.

    Parameters
    ----------

    data        : array
                  (n,k) or KDTree where KDtree.data is array (n,k)
                  n observations on k characteristics used to measure
                  distances between the n objects
    threshold  : float
                 distance band
    p          : float
                 Minkowski p-norm distance metric parameter:
                 1<=p<=infinity
                 2: Euclidean distance
                 1: Manhattan distance
    binary     : boolean
                 If true w_{ij}=1 if d_{i,j}<=threshold, otherwise w_{i,j}=0
                 If false wij=dij^{alpha}
    alpha      : float
                 distance decay parameter for weight (default -1.0)
                 if alpha is positive the weights will not decline with
                 distance. If binary is True, alpha is ignored

    ids         : list
                  values to use for keys of the neighbors and weights dicts

    Examples
    --------

    >>> points=[(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
    >>> w=DistanceBand(points,threshold=11.2)
    WARNING: there is one disconnected observation (no neighbors)
    Island id:  [2]
    >>> w.weights
    {0: [1, 1], 1: [1, 1], 2: [], 3: [1, 1], 4: [1], 5: [1]}
    >>> w.neighbors
    {0: [1, 3], 1: [0, 3], 2: [], 3: [1, 0], 4: [5], 5: [4]}
    >>> w=DistanceBand(points,threshold=14.2)
    >>> w.weights
    {0: [1, 1], 1: [1, 1, 1], 2: [1], 3: [1, 1], 4: [1, 1, 1], 5: [1]}
    >>> w.neighbors
    {0: [1, 3], 1: [0, 3, 4], 2: [4], 3: [1, 0], 4: [5, 1, 2], 5: [4]}

    inverse distance weights

    >>> w=DistanceBand(points,threshold=11.2,binary=False)
    WARNING: there is one disconnected observation (no neighbors)
    Island id:  [2]
    >>> w.weights[0]
    [0.10000000000000001, 0.089442719099991588]
    >>> w.neighbors[0]
    [1, 3]
    >>>

    gravity weights

    >>> w=DistanceBand(points,threshold=11.2,binary=False,alpha=-2.)
    WARNING: there is one disconnected observation (no neighbors)
    Island id:  [2]
    >>> w.weights[0]
    [0.01, 0.0079999999999999984]

    Notes
    -----

    This was initially implemented running scipy 0.8.0dev (in epd 6.1).
    earlier versions of scipy (0.7.0) have a logic bug in scipy/sparse/dok.py
    so serge changed line 221 of that file on sal-dev to fix the logic bug.

    """

    def __init__(self, data, threshold, p=2, alpha=-1.0, binary=True, ids=None):
        """Casting to floats is a work around for a bug in scipy.spatial.
        See detail in pysal issue #126.

        """
        if issubclass(type(data), scipy.spatial.KDTree):
            self.kd = data
            self.data = self.kd.data
        else:
            try:
                data = np.asarray(data)
                if data.dtype.kind != 'f':
                    data = data.astype(float)
                self.data = data
                self.kd = KDTree(self.data)
            except:
                raise ValueError("Could not make array from data")

        self.p = p
        self.threshold = threshold
        self.binary = binary
        self.alpha = alpha
        self._band()
        neighbors, weights = self._distance_to_W(ids)
        W.__init__(self, neighbors, weights, ids)

#.........这里部分代码省略.........

def knnW(data, k=2, p=2, ids=None):
    """
    Creates nearest neighbor weights matrix based on k nearest
    neighbors.

    Parameters
    ----------

    kdtree      : object
                  PySAL KDTree or ArcKDTree where KDtree.data is array (n,k)
                  n observations on k characteristics used to measure
                  distances between the n objects
    k           : int
                  number of nearest neighbors
    p           : float
                  Minkowski p-norm distance metric parameter:
                  1<=p<=infinity
                  2: Euclidean distance
                  1: Manhattan distance
                  Ignored if the KDTree is an ArcKDTree
    ids         : list
                  identifiers to attach to each observation

    Returns
    -------

    w         : W
                instance
                Weights object with binary weights

    Examples
    --------

    >>> points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
    >>> kd = pysal.cg.kdtree.KDTree(np.array(points))
    >>> wnn2 = pysal.knnW(kd, 2)
    >>> [1,3] == wnn2.neighbors[0]
    True

    ids

    >>> wnn2 = knnW(kd,2)
    >>> wnn2[0]
    {1: 1.0, 3: 1.0}
    >>> wnn2[1]
    {0: 1.0, 3: 1.0}

    now with 1 rather than 0 offset

    >>> wnn2 = knnW(kd, 2, ids=range(1,7))
    >>> wnn2[1]
    {2: 1.0, 4: 1.0}
    >>> wnn2[2]
    {1: 1.0, 4: 1.0}
    >>> 0 in wnn2.neighbors
    False

    Notes
    -----

    Ties between neighbors of equal distance are arbitrarily broken.

    See Also
    --------
    pysal.weights.W

    """
    if isKDTree(data):
        kdt = data
        data = kdt.data
    else:
        kdt = KDTree(data)
    nnq = kdt.query(data, k=k+1, p=p)
    info = nnq[1]

    neighbors = {}
    for i, row in enumerate(info):
        row = row.tolist()
        if i in row:
            row.remove(i)
            focal = i
        if ids:
            row = [ ids[j] for j in row]
            focal = ids[i]
        neighbors[focal] = row
    return pysal.weights.W(neighbors,  id_order=ids)

class Kernel(W):
    def __init__(self, data, bandwidth=None, fixed=True, k=2,
                 function='triangular', eps=1.0000001, ids=None,
                 diagonal=False, ncores=1):
        if issubclass(type(data), scipy.spatial.KDTree):
            self.kdt = data
            self.data = self.kdt.data
            data = self.data
        else:
            self.data = data
            self.kdt = KDTree(self.data)
        self.k = k + 1
        self.function = function.lower()
        self.fixed = fixed
        self.eps = eps
        self.ncores = ncores

        if bandwidth:
            try:
                bandwidth = np.array(bandwidth)
                bandwidth.shape = (len(bandwidth), 1)
            except:
                bandwidth = np.ones((len(data), 1), 'float') * bandwidth
            self.bandwidth = bandwidth
        else:
            self._set_bw()

        self._eval_kernel()
        neighbors, weights = self._k_to_W(ids)
        if diagonal:
            for i in neighbors:
                weights[i][neighbors[i].index(i)] = 1.0
        W.__init__(self, neighbors, weights, ids)

    def _k_to_W(self, ids=None):
        allneighbors = {}
        weights = {}
        if ids:
            ids = np.array(ids)
        else:
            ids = np.arange(len(self.data))
        for i, neighbors in enumerate(self.kernel):
            if len(self.neigh[i]) == 0:
                allneighbors[ids[i]] = []
                weights[ids[i]] = []
            else:
                allneighbors[ids[i]] = list(ids[self.neigh[i]])
                weights[ids[i]] = self.kernel[i].tolist()
        return allneighbors, weights

    def _set_bw(self):
        dmat, neigh = self.kdt.query(self.data, k=self.k)
        if self.fixed:
            # use max knn distance as bandwidth
            bandwidth = dmat.max() * self.eps
            n = len(dmat)
            self.bandwidth = np.ones((n, 1), 'float') * bandwidth
        else:
            # use local max knn distance
            self.bandwidth = dmat.max(axis=1) * self.eps
            self.bandwidth.shape = (self.bandwidth.size, 1)
            # identify knn neighbors for each point
            nnq = self.kdt.query(self.data, k=self.k)
            self.neigh = nnq[1]

    def _eval_kernel(self):
        t1 = time.time()
        # get points within bandwidth distance of each point
        kdtbq = self.kdt.query_ball_point
        kdtq = self.kdt.query
        bw = self.bandwidth
        if self.ncores > 1:
            pool = mp.Pool(processes=self.ncores, initializer=loadkd, initargs=(kdtbq,kdtq,bw))
        if not hasattr(self, 'neigh'):
            if self.ncores > 1:
                neighbors = pool.map(bqwrapper,self.data, chunksize = len(self.bandwidth) / self.ncores)
            else:
                neighbors = [kdtbq(self.data[i], r=bwi[0]) for i,
                            bwi in enumerate(self.bandwidth)]
            self.neigh = neighbors
        t2 = time.time()
        print "Ball Point Query took {} seconds.".format(t2 - t1)
        # get distances for neighbors
        bw = self.bandwidth

        #kdtq = self.kdt.query
        z = []
        t1 = time.time()
        if self.ncores > 1:
            iterable = [(i,nids, self.data[i]) for i, nids in enumerate(self.neigh)]
            z = pool.map(qwrapper, iterable)
        else:
            for i, nids in enumerate(self.neigh):
                di, ni = kdtq(self.data[i], k=len(nids))
                zi = np.array([dict(zip(ni, di))[nid] for nid in nids]) / bw[i]
                z.append(zi)
        t2 = time.time()
        print "Local query took: {} seconds".format(t2 - t1)
        zs = z
        # functions follow Anselin and Rey (2010) table 5.4
#.........这里部分代码省略.........

class Kernel(W):
    """Spatial weights based on kernel functions

    Parameters
    ----------

    data        : array (n,k) or KDTree where KDtree.data is array (n,k)
                  n observations on k characteristics used to measure
                  distances between the n objects
    bandwidth   : float or array-like (optional)
                  the bandwidth :math:`h_i` for the kernel.
    fixed       : binary
                  If true then :math:`h_i=h \\forall i`. If false then
                  bandwidth is adaptive across observations.
    k           : int
                  the number of nearest neighbors to use for determining
                  bandwidth. For fixed bandwidth, :math:`h_i=max(dknn) \\forall i`
                  where :math:`dknn` is a vector of k-nearest neighbor
                  distances (the distance to the kth nearest neighbor for each
                  observation).  For adaptive bandwidths, :math:`h_i=dknn_i`
    diagonal    : boolean
                  If true, set diagonal weights = 1.0, if false (default),
                  diagonals weights are set to value according to kernel
                  function.
    function    : string {'triangular','uniform','quadratic','quartic','gaussian'}
                  kernel function defined as follows with

                  .. math::

                      z_{i,j} = d_{i,j}/h_i

                  triangular

                  .. math::

                      K(z) = (1 - |z|) \ if |z| \le 1

                  uniform

                  .. math::

                      K(z) = 1/2 \ if |z| \le 1

                  quadratic

                  .. math::

                      K(z) = (3/4)(1-z^2) \ if |z| \le 1

                  quartic

                  .. math::

                      K(z) = (15/16)(1-z^2)^2 \ if |z| \le 1

                  gaussian

                  .. math::

                      K(z) = (2\pi)^{(-1/2)} exp(-z^2 / 2)

    eps         : float
                  adjustment to ensure knn distance range is closed on the
                  knnth observations

    Examples
    --------

    >>> points=[(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
    >>> kw=Kernel(points)
    >>> kw.weights[0]
    [1.0, 0.500000049999995, 0.4409830615267465]
    >>> kw.neighbors[0]
    [0, 1, 3]
    >>> kw.bandwidth
    array([[ 20.000002],
           [ 20.000002],
           [ 20.000002],
           [ 20.000002],
           [ 20.000002],
           [ 20.000002]])
    >>> kw15=Kernel(points,bandwidth=15.0)
    >>> kw15[0]
    {0: 1.0, 1: 0.33333333333333337, 3: 0.2546440075000701}
    >>> kw15.neighbors[0]
    [0, 1, 3]
    >>> kw15.bandwidth
    array([[ 15.],
           [ 15.],
           [ 15.],
           [ 15.],
           [ 15.],
           [ 15.]])

    Adaptive bandwidths user specified

    >>> bw=[25.0,15.0,25.0,16.0,14.5,25.0]
    >>> kwa=Kernel(points,bandwidth=bw)
    >>> kwa.weights[0]
    [1.0, 0.6, 0.552786404500042, 0.10557280900008403]
#.........这里部分代码省略.........