def __getitem__(self, key):
def asindices(x):
try:
x = np.asarray(x, dtype=np.intc)
except:
raise IndexError('invalid index')
else:
return x
def check_bounds(indices,N):
max_indx = indices.max()
if max_indx >= N:
raise IndexError('index (%d) out of range' % max_indx)
min_indx = indices.min()
if min_indx < -N:
raise IndexError('index (%d) out of range' % (N + min_indx))
return (min_indx,max_indx)
def extractor(indices,N):
"""Return a sparse matrix P so that P*self implements
slicing of the form self[[1,2,3],:]
"""
indices = asindices(indices)
(min_indx,max_indx) = check_bounds(indices,N)
if min_indx < 0:
indices = indices.copy()
indices[indices < 0] += N
indptr = np.arange(len(indices) + 1, dtype=np.intc)
data = np.ones(len(indices), dtype=self.dtype)
shape = (len(indices),N)
return csr_matrix((data,indices,indptr), shape=shape)
if isinstance(key, tuple):
row = key[0]
col = key[1]
if isintlike(row):
#[1,??]
if isintlike(col):
return self._get_single_element(row, col) #[i,j]
elif isinstance(col, slice):
return self._get_row_slice(row, col) #[i,1:2]
else:
P = extractor(col,self.shape[1]).T #[i,[1,2]]
return self[row,:]*P
elif isinstance(row, slice):
#[1:2,??]
if isintlike(col) or isinstance(col, slice):
return self._get_submatrix(row, col) #[1:2,j]
else:
P = extractor(col,self.shape[1]).T #[1:2,[1,2]]
return self[row,:]*P
else:
#[[1,2],??] or [[[1],[2]],??]
if isintlike(col) or isinstance(col,slice):
P = extractor(row, self.shape[0]) #[[1,2],j] or [[1,2],1:2]
return (P*self)[:,col]
else:
row = asindices(row)
col = asindices(col)
if len(row.shape) == 1:
if len(row) != len(col): #[[1,2],[1,2]]
raise IndexError('number of row and column indices differ')
check_bounds(row, self.shape[0])
check_bounds(col, self.shape[1])
num_samples = len(row)
val = np.empty(num_samples, dtype=self.dtype)
csr_sample_values(self.shape[0], self.shape[1],
self.indptr, self.indices, self.data,
num_samples, row, col, val)
#val = []
#for i,j in zip(row,col):
# val.append(self._get_single_element(i,j))
return np.asmatrix(val)
elif len(row.shape) == 2:
row = np.ravel(row) #[[[1],[2]],[1,2]]
P = extractor(row, self.shape[0])
return (P*self)[:,col]
else:
raise NotImplementedError('unsupported indexing')
elif isintlike(key) or isinstance(key,slice):
return self[key,:] #[i] or [1:2]
else:
return self[asindices(key),:] #[[1,2]]
def __getitem__(self, key):
def asindices(x):
try:
x = np.asarray(x, dtype=np.intc)
except:
raise IndexError('invalid index')
else:
return x
def check_bounds(indices, N):
max_indx = indices.max()
if max_indx >= N:
raise IndexError('index (%d) out of range' % max_indx)
min_indx = indices.min()
if min_indx < -N:
raise IndexError('index (%d) out of range' % (N + min_indx))
return (min_indx, max_indx)
def extractor(indices, N):
"""Return a sparse matrix P so that P*self implements
slicing of the form self[[1,2,3],:]
"""
indices = asindices(indices)
(min_indx, max_indx) = check_bounds(indices, N)
if min_indx < 0:
indices = indices.copy()
indices[indices < 0] += N
indptr = np.arange(len(indices) + 1, dtype=np.intc)
data = np.ones(len(indices), dtype=self.dtype)
shape = (len(indices), N)
return csr_matrix((data, indices, indptr), shape=shape)
if isinstance(key, tuple):
row = key[0]
col = key[1]
if isintlike(row):
#[1,??]
if isintlike(col):
return self._get_single_element(row, col) #[i,j]
elif isinstance(col, slice):
return self._get_row_slice(row, col) #[i,1:2]
else:
P = extractor(col, self.shape[1]).T #[i,[1,2]]
return self[row, :] * P
elif isinstance(row, slice):
#[1:2,??]
if isintlike(col) or isinstance(col, slice):
return self._get_submatrix(row, col) #[1:2,j]
else:
P = extractor(col, self.shape[1]).T #[1:2,[1,2]]
return self[row, :] * P
else:
#[[1,2],??] or [[[1],[2]],??]
if isintlike(col) or isinstance(col, slice):
P = extractor(row,
self.shape[0]) #[[1,2],j] or [[1,2],1:2]
return (P * self)[:, col]
else:
row = asindices(row)
col = asindices(col)
if len(row.shape) == 1:
if len(row) != len(col): #[[1,2],[1,2]]
raise IndexError(
'number of row and column indices differ')
check_bounds(row, self.shape[0])
check_bounds(col, self.shape[1])
num_samples = len(row)
val = np.empty(num_samples, dtype=self.dtype)
csr_sample_values(self.shape[0], self.shape[1],
self.indptr, self.indices, self.data,
num_samples, row, col, val)
#val = []
#for i,j in zip(row,col):
# val.append(self._get_single_element(i,j))
return np.asmatrix(val)
elif len(row.shape) == 2:
row = np.ravel(row) #[[[1],[2]],[1,2]]
P = extractor(row, self.shape[0])
return (P * self)[:, col]
else:
raise NotImplementedError('unsupported indexing')
elif isintlike(key) or isinstance(key, slice):
return self[key, :] #[i] or [1:2]
else:
return self[asindices(key), :] #[[1,2]]