def __init__(self,data,bandwidth=None,fixed=True,k=2, p=2,
function='triangular',eps=1.0000001,ids=None):
# handle point_array
if type(data).__name__=='ndarray':
self.data=data
elif type(data).__name__=='list':
self.data=data
else:
raise Exception, 'Unsupported type'
self.k=k+1
self.function=function.lower()
self.fixed=fixed
self.eps=eps
self.kdt=KDTree(self.data)
self.dmat,self.neigh=self.kdt.query(data,k=k+1,p=p)
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)
W.__init__(self,neighbors,weights,ids)
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:
nis = neighbors[i]
weights[i][neighbors[i].index(i)] = 1.0
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,
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,
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, 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 __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 __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,
p=2,
function='triangular',
eps=1.0000001,
ids=None):
# handle point_array
if type(data).__name__ == 'ndarray':
self.data = data
elif type(data).__name__ == 'list':
self.data = data
else:
raise Exception, 'Unsupported type'
self.k = k + 1
self.function = function.lower()
self.fixed = fixed
self.eps = eps
self.kdt = KDTree(self.data)
self.dmat, self.neigh = self.kdt.query(data, k=k + 1, p=p)
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)
W.__init__(self, neighbors, weights, ids)
