def setup_weights(self, weights):
""" Setup weights """
if type(weights) in [int,float]:
weights = np.ones((1,)*len(self.source.shape))*weights
dtype = weights.dtype
# Is kernel already a sparse array ?
if sparse.issparse(weights):
if weights.shape != (self.target.size, self.source.size):
raise ConnectionError, \
'weights matrix shape is wrong relative to source and target'
else:
W = weights.tocoo()
data, row, col = W.data,W.row,W.col
i = (1 - np.isnan(data)).nonzero()
data, row, col = data[i], row[i], col[i]
data = np.where(data, data, np.NaN)
weights = sparse.coo_matrix((data,(row,col)), shape=W.shape)
weights.data = np.nan_to_num(data)
# Else, we need to build it
elif weights.shape != (self.target.size,self.source.size):
if len(weights.shape) == len(self.source.shape):
# If we have a toric connection, weights cannot be greater than source
# in any dimension
if self._toric:
s = np.array(self.source.shape)
w = np.array(weights.shape)
weights = extract(weights, np.minimum(s,w), w//2)
weights = convolution_matrix(self.source, self.target, weights, self._toric)
else:
raise ConnectionError, \
'weights matrix shape is wrong relative to source and target'
self._weights = csr_array(weights, dtype=dtype)
def setup_weights(self, weights):
""" Setup weights """
if type(weights) in [int,float]:
weights = np.ones((1,)*len(self.source.shape))*weights
if weights.shape == (self.target.size, self.source.size):
self._weights = weights
self._mask = 1-np.isnan(self._weights).astype(np.int32)
if self._mask.all():
self._mask = 1
return
if len(weights.shape) != len(weights.shape):
raise ConnectionError, \
'Weights matrix shape is wrong relative to source and target'
# If we have a toric connection, weights cannot be greater than source
# in any dimension
if self._toric:
s = np.array(self.source.shape)
w = np.array(weights.shape)
weights = extract(weights, np.minimum(s,w), w//2)
K = convolution_matrix(self.source, self.target, weights, self._toric)
nz_rows = K.row
nz_cols = K.col
self._weights = K.todense()
self._mask = np.zeros(K.shape)
self._mask[nz_rows, nz_cols] = 1
if self._mask.all(): self._mask = 1
self._weights = np.array(K.todense())
def setup_weights(self, weights):
""" Setup weights """
if type(weights) in [int, float]:
weights = np.ones((1, ) * len(self.source.shape)) * weights
if weights.shape == (self.target.size, self.source.size):
self._weights = weights
self._mask = 1 - np.isnan(self._weights).astype(np.int32)
if self._mask.all():
self._mask = 1
return
if len(weights.shape) != len(weights.shape):
raise ConnectionError, \
'Weights matrix shape is wrong relative to source and target'
# If we have a toric connection, weights cannot be greater than source
# in any dimension
if self._toric:
s = np.array(self.source.shape)
w = np.array(weights.shape)
weights = extract(weights, np.minimum(s, w), w // 2)
K = convolution_matrix(self.source, self.target, weights, self._toric)
nz_rows = K.row
nz_cols = K.col
self._weights = K.todense()
self._mask = np.zeros(K.shape)
self._mask[nz_rows, nz_cols] = 1
if self._mask.all(): self._mask = 1
self._weights = np.array(K.todense())
def setup_weights(self, weights):
""" Setup weights """
if type(weights) in [int, float]:
weights = np.ones((1, ) * len(self.source.shape)) * weights
dtype = weights.dtype
# Is kernel already a sparse array ?
if sparse.issparse(weights):
if weights.shape != (self.target.size, self.source.size):
raise ConnectionError, \
'weights matrix shape is wrong relative to source and target'
else:
W = weights.tocoo()
data, row, col = W.data, W.row, W.col
i = (1 - np.isnan(data)).nonzero()
data, row, col = data[i], row[i], col[i]
data = np.where(data, data, np.NaN)
weights = sparse.coo_matrix((data, (row, col)), shape=W.shape)
weights.data = np.nan_to_num(data)
# Else, we need to build it
elif weights.shape != (self.target.size, self.source.size):
if len(weights.shape) == len(self.source.shape):
# If we have a toric connection, weights cannot be greater than source
# in any dimension
if self._toric:
s = np.array(self.source.shape)
w = np.array(weights.shape)
weights = extract(weights, np.minimum(s, w), w // 2)
weights = convolution_matrix(self.source, self.target, weights,
self._toric)
else:
raise ConnectionError, \
'weights matrix shape is wrong relative to source and target'
self._weights = csr_array(weights, dtype=dtype)