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 isKDTree(data):
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 __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, bandwidth=None, fixed=True, k=2,
function='triangular', eps=1.0000001, ids=None,
diagonal=False):
if isKDTree(data):
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 __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)
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)
def __init__(self,
data,
bandwidth=None,
fixed=True,
k=2,
function='triangular',
eps=1.0000001,
ids=None,
diagonal=False):
if isKDTree(data):
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 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)
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)
