本文整理汇总了Python中pysal.common.KDTree.query方法的典型用法代码示例。如果您正苦于以下问题：Python KDTree.query方法的具体用法？Python KDTree.query怎么用？Python KDTree.query使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pysal.common.KDTree的用法示例。
在下文中一共展示了KDTree.query方法的6个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: Kernel
# 需要导入模块: from pysal.common import KDTree [as 别名]
# 或者: from pysal.common.KDTree import query [as 别名]
#.........这里部分代码省略.........
:class:`pysal.weights.Kernel`
:class:`pysal.weights.W`
"""
return cls(array, **kwargs)
@classmethod
def from_dataframe(cls, df, geom_col='geometry', ids=None, **kwargs):
"""
Make Kernel weights from a dataframe.
Parameters
----------
df : pandas.dataframe
a dataframe with a geometry column that can be used to
construct a W object
geom_col : string
column name of the geometry stored in df
ids : string or iterable
if string, the column name of the indices from the dataframe
if iterable, a list of ids to use for the W
if None, df.index is used.
See Also
--------
:class:`pysal.weights.Kernel`
:class:`pysal.weights.W`
"""
pts = get_points_array(df[geom_col])
if ids is None:
ids = df.index.tolist()
elif isinstance(ids, str):
ids = df[ids].tolist()
return cls(pts, ids=ids, **kwargs)
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):
# get points within bandwidth distance of each point
if not hasattr(self, 'neigh'):
kdtq = self.kdt.query_ball_point
neighbors = [kdtq(self.data[i], r=bwi[0]) for i,
bwi in enumerate(self.bandwidth)]
self.neigh = neighbors
# get distances for neighbors
bw = self.bandwidth
kdtq = self.kdt.query
z = []
for i, nids in enumerate(self.neigh):
di, ni = kdtq(self.data[i], k=len(nids))
if not isinstance(di, np.ndarray):
di = np.asarray([di] * len(nids))
ni = np.asarray([ni] * len(nids))
zi = np.array([dict(zip(ni, di))[nid] for nid in nids]) / bw[i]
z.append(zi)
zs = z
# functions follow Anselin and Rey (2010) table 5.4
if self.function == 'triangular':
self.kernel = [1 - zi for zi in zs]
elif self.function == 'uniform':
self.kernel = [np.ones(zi.shape) * 0.5 for zi in zs]
elif self.function == 'quadratic':
self.kernel = [(3. / 4) * (1 - zi ** 2) for zi in zs]
elif self.function == 'quartic':
self.kernel = [(15. / 16) * (1 - zi ** 2) ** 2 for zi in zs]
elif self.function == 'gaussian':
c = np.pi * 2
c = c ** (-0.5)
self.kernel = [c * np.exp(-(zi ** 2) / 2.) for zi in zs]
else:
print('Unsupported kernel function', self.function)示例2: KNN
# 需要导入模块: from pysal.common import KDTree [as 别名]
# 或者: from pysal.common.KDTree import query [as 别名]
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.........
示例3: knnW
# 需要导入模块: from pysal.common import KDTree [as 别名]
# 或者: from pysal.common.KDTree import query [as 别名]
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.........
示例4: knnW
# 需要导入模块: from pysal.common import KDTree [as 别名]
# 或者: from pysal.common.KDTree import query [as 别名]
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)示例5: Kernel
# 需要导入模块: from pysal.common import KDTree [as 别名]
# 或者: from pysal.common.KDTree import query [as 别名]
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.........
示例6: Kernel
# 需要导入模块: from pysal.common import KDTree [as 别名]
# 或者: from pysal.common.KDTree import query [as 别名]
#.........这里部分代码省略.........
>>> kq.weights
{0: [0.3989422804014327, 0.35206533556593145, 0.3412334260702758], 1: [0.35206533556593145, 0.3989422804014327, 0.2419707487162134, 0.3412334260702758, 0.31069657591175387], 2: [0.2419707487162134, 0.3989422804014327, 0.31069657591175387], 3: [0.3412334260702758, 0.3412334260702758, 0.3989422804014327, 0.3011374490937829, 0.26575287272131043], 4: [0.31069657591175387, 0.31069657591175387, 0.3011374490937829, 0.3989422804014327, 0.35206533556593145], 5: [0.26575287272131043, 0.35206533556593145, 0.3989422804014327]}
>>> kqd = Kernel(points, function='gaussian', diagonal=True)
>>> kqd.weights
{0: [1.0, 0.35206533556593145, 0.3412334260702758], 1: [0.35206533556593145, 1.0, 0.2419707487162134, 0.3412334260702758, 0.31069657591175387], 2: [0.2419707487162134, 1.0, 0.31069657591175387], 3: [0.3412334260702758, 0.3412334260702758, 1.0, 0.3011374490937829, 0.26575287272131043], 4: [0.31069657591175387, 0.31069657591175387, 0.3011374490937829, 1.0, 0.35206533556593145], 5: [0.26575287272131043, 0.35206533556593145, 1.0]}
"""
def __init__(
self, data, bandwidth=None, fixed=True, k=2, function="triangular", eps=1.0000001, ids=None, diagonal=False
):
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
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):
# get points within bandwidth distance of each point
if not hasattr(self, "neigh"):
kdtq = self.kdt.query_ball_point
neighbors = [kdtq(self.data[i], r=bwi[0]) for i, bwi in enumerate(self.bandwidth)]
self.neigh = neighbors
# get distances for neighbors
bw = self.bandwidth
kdtq = self.kdt.query
z = []
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)
zs = z
# functions follow Anselin and Rey (2010) table 5.4
if self.function == "triangular":
self.kernel = [1 - zi for zi in zs]
elif self.function == "uniform":
self.kernel = [np.ones(zi.shape) * 0.5 for zi in zs]
elif self.function == "quadratic":
self.kernel = [(3.0 / 4) * (1 - zi ** 2) for zi in zs]
elif self.function == "quartic":
self.kernel = [(15.0 / 16) * (1 - zi ** 2) ** 2 for zi in zs]
elif self.function == "gaussian":
c = np.pi * 2
c = c ** (-0.5)
self.kernel = [c * np.exp(-(zi ** 2) / 2.0) for zi in zs]
else:
print "Unsupported kernel function", self.function