[Scipy-svn] r2540 - in trunk/Lib: linsolve linsolve/umfpack sparse sparse/tests
scipy-svn at scipy.org
scipy-svn at scipy.org
Thu Jan 11 20:25:05 EST 2007
Author: timl
Date: 2007-01-11 19:24:56 -0600 (Thu, 11 Jan 2007)
New Revision: 2540
Modified:
trunk/Lib/linsolve/linsolve.py
trunk/Lib/linsolve/umfpack/umfpack.py
trunk/Lib/sparse/sparse.py
trunk/Lib/sparse/tests/test_sparse.py
Log:
rename .index to .indices in cs matrices
Modified: trunk/Lib/linsolve/linsolve.py
===================================================================
--- trunk/Lib/linsolve/linsolve.py 2007-01-12 01:16:24 UTC (rev 2539)
+++ trunk/Lib/linsolve/linsolve.py 2007-01-12 01:24:56 UTC (rev 2540)
@@ -87,7 +87,7 @@
else:
mat, csc = _toCS_superLU( A )
- index0 = mat.index
+ index0 = mat.indices
ftype, lastel, data, index1 = mat.ftype, mat.nnz, mat.data, mat.indptr
gssv = eval('_superlu.' + ftype + 'gssv')
b = asarray(b, dtype=data.dtype)
@@ -112,7 +112,7 @@
csc = A.tocsc()
gstrf = eval('_superlu.' + csc.ftype + 'gstrf')
- return gstrf(N, csc.nnz, csc.data, csc.index, csc.indptr, permc_spec,
+ return gstrf(N, csc.nnz, csc.data, csc.indices, csc.indptr, permc_spec,
diag_pivot_thresh, drop_tol, relax, panel_size)
def _testme():
Modified: trunk/Lib/linsolve/umfpack/umfpack.py
===================================================================
--- trunk/Lib/linsolve/umfpack/umfpack.py 2007-01-12 01:16:24 UTC (rev 2539)
+++ trunk/Lib/linsolve/umfpack/umfpack.py 2007-01-12 01:24:56 UTC (rev 2540)
@@ -317,10 +317,10 @@
def _getIndx( self, mtx ):
if sp.isspmatrix_csc( mtx ):
- indx = mtx.index
+ indx = mtx.indices
self.isCSR = 0
elif sp.isspmatrix_csr( mtx ):
- indx = mtx.index
+ indx = mtx.indices
self.isCSR = 1
else:
raise TypeError, 'must be a CSC/CSR matrix (is %s)' % mtx.__class__
Modified: trunk/Lib/sparse/sparse.py
===================================================================
--- trunk/Lib/sparse/sparse.py 2007-01-12 01:16:24 UTC (rev 2539)
+++ trunk/Lib/sparse/sparse.py 2007-01-12 01:24:56 UTC (rev 2540)
@@ -498,9 +498,9 @@
raise ValueError, "inconsistent shapes"
other = self._tothis(other)
indptr, ind, data = fn(self.shape[0], self.shape[1], \
- self.indptr, self.index, \
+ self.indptr, self.indices, \
self.data, other.indptr, \
- other.index, other.data)
+ other.indices, other.data)
return self.__class__((data, ind, indptr), self.shape)
elif isdense(other):
# Convert this matrix to a dense matrix and add them
@@ -558,9 +558,9 @@
if (other.shape != self.shape):
raise ValueError, "inconsistent shapes"
indptr, ind, data = fn(self.shape[0], self.shape[1], \
- self.indptr, self.index, \
+ self.indptr, self.indices, \
self.data, other.indptr, \
- other.index, other.data)
+ other.indices, other.data)
return self.__class__((data, ind, indptr), (self.shape[0], other.shape[1]))
else:
raise TypeError, "unsupported type for sparse matrix power"
@@ -573,9 +573,9 @@
if (K1 != K2):
raise ValueError, "shape mismatch error"
other = self._tothis(other)
- indptr, ind, data = fn(M, N, self.indptr, self.index, \
+ indptr, ind, data = fn(M, N, self.indptr, self.indices, \
self.data, other.indptr, \
- other.index, other.data)
+ other.indices, other.data)
return self.__class__((data, ind, indptr), (M, N))
elif isdense(other):
# This is SLOW! We need a more efficient implementation
@@ -592,7 +592,7 @@
# raise ValueError, "dimension mismatch"
oth = numpy.ravel(other)
y = fn(self.shape[0], self.shape[1], \
- self.indptr, self.index, self.data, oth)
+ self.indptr, self.indices, self.data, oth)
if isinstance(other, matrix):
y = asmatrix(y)
# If 'other' was an (nx1) column vector, transpose the result
@@ -616,7 +616,7 @@
else:
cd = self.data
oth = numpy.ravel(other)
- y = fn(shape0, shape1, self.indptr, self.index, cd, oth)
+ y = fn(shape0, shape1, self.indptr, self.indices, cd, oth)
if isinstance(other, matrix):
y = asmatrix(y)
# In the (unlikely) event that this matrix is 1x1 and 'other'
@@ -634,14 +634,14 @@
def _tocoo(self, fn):
rows, cols, data = fn(self.shape[0], self.shape[1], \
- self.indptr, self.index, self.data)
+ self.indptr, self.indices, self.data)
return coo_matrix((data, (rows, cols)), self.shape)
def copy(self):
new = self.__class__(self.shape, nzmax=self.nzmax, dtype=self.dtype)
new.data = self.data.copy()
- new.index = self.index.copy()
+ new.indices = self.indices.copy()
new.indptr = self.indptr.copy()
new._check()
return new
@@ -658,10 +658,10 @@
indices = []
for ind in xrange(self.indptr[i], self.indptr[i+1]):
- if self.index[ind] >= start and self.index[ind] < stop:
+ if self.indices[ind] >= start and self.indices[ind] < stop:
indices.append(ind)
- index = self.index[indices] - start
+ index = self.indices[indices] - start
data = self.data[indices]
indptr = numpy.array([0, len(indices)])
return self.__class__((data, index, indptr), dims=dims, \
@@ -672,11 +672,11 @@
M, N = self.shape
if copy:
data = self.data.copy()
- index = self.index.copy()
+ index = self.indices.copy()
indptr = self.indptr.copy()
else:
data = self.data
- index = self.index
+ index = self.indices
indptr = self.indptr
return cls((data,index,indptr),(N,M))
@@ -685,11 +685,11 @@
new = self.__class__(self.shape, nzmax=self.nzmax, dtype=self.dtype)
if copy:
new.data = self.data.conj().copy()
- new.index = self.index.conj().copy()
+ new.indices = self.indices.conj().copy()
new.indptr = self.indptr.conj().copy()
else:
new.data = self.data.conj()
- new.index = self.index.conj()
+ new.indices = self.indices.conj()
new.indptr = self.indptr.conj()
new._check()
return new
@@ -699,7 +699,7 @@
"""
if inplace:
sparsetools.ensure_sorted_indices(shape0, shape1,
- self.indptr, self.index,
+ self.indptr, self.indices,
self.data )
else:
return self._toother()._toother()
@@ -743,7 +743,7 @@
s = s*1.0
if (rank(s) == 2):
self.shape = s.shape
- self.indptr, self.index, self.data = densetocsr(s.shape[1], \
+ self.indptr, self.indices, self.data = densetocsr(s.shape[1], \
s.shape[0], \
s.T)
else:
@@ -756,23 +756,23 @@
self.shape = s.shape
if copy:
self.data = s.data.copy()
- self.index = s.index.copy()
+ self.indices = s.indices.copy()
self.indptr = s.indptr.copy()
else:
self.data = s.data
- self.index = s.index
+ self.indices = s.indices
self.indptr = s.indptr
elif isinstance(s, csr_matrix):
self.shape = s.shape
- self.indptr, self.index, self.data = csrtocsc(s.shape[0],
+ self.indptr, self.indices, self.data = csrtocsc(s.shape[0],
s.shape[1],
s.indptr,
- s.index,
+ s.indices,
s.data)
else:
temp = s.tocsc()
self.data = temp.data
- self.index = temp.index
+ self.indices = temp.indices
self.indptr = temp.indptr
self.shape = temp.shape
elif type(arg1) == tuple:
@@ -781,7 +781,7 @@
# It's a tuple of matrix dimensions (M, N)
M, N = arg1
self.data = zeros((nzmax,), self.dtype)
- self.index = zeros((nzmax,), intc)
+ self.indices = zeros((nzmax,), intc)
self.indptr = zeros((N+1,), intc)
self.shape = (M, N)
else:
@@ -797,11 +797,11 @@
self.dtype = getdtype(dtype, s)
if copy:
self.data = array(s)
- self.index = array(rowind)
+ self.indices = array(rowind)
self.indptr = array(indptr, dtype=intc)
else:
self.data = asarray(s)
- self.index = asarray(rowind)
+ self.indices = asarray(rowind)
self.indptr = asarray(indptr, dtype=intc)
except:
raise ValueError, "unrecognized form for csc_matrix constructor"
@@ -813,7 +813,7 @@
dtype=self.dtype).tocsc()
self.shape = temp.shape
self.data = temp.data
- self.index = temp.index
+ self.indices = temp.indices
self.indptr = temp.indptr
else:
raise ValueError, "unrecognized form for csc_matrix constructor"
@@ -831,8 +831,8 @@
raise TypeError, "dimensions not understood"
else:
M = N = None
- if len(self.index) > 0:
- M = max(oldM, M, int(amax(self.index)) + 1)
+ if len(self.indices) > 0:
+ M = max(oldM, M, int(amax(self.indices)) + 1)
else:
# Matrix is completely empty
M = max(oldM, M)
@@ -846,8 +846,8 @@
M, N = self.shape
nnz = self.indptr[-1]
- nzmax = len(self.index)
- if (rank(self.data) != 1) or (rank(self.index) != 1) or \
+ nzmax = len(self.indices)
+ if (rank(self.data) != 1) or (rank(self.indices) != 1) or \
(rank(self.indptr) != 1):
raise ValueError, "data, rowind, and indptr arrays "\
"should be rank 1"
@@ -857,14 +857,14 @@
raise ValueError, "index pointer should be of of size N+1"
if (nzmax < nnz):
raise ValueError, "nzmax must not be less than nnz"
- if (nnz>0) and (amax(self.index[:nnz]) >= M):
+ if (nnz>0) and (amax(self.indices[:nnz]) >= M):
raise ValueError, "row values must be < M"
- if (self.indptr[-1] > len(self.index)):
+ if (self.indptr[-1] > len(self.indices)):
raise ValueError, \
"Last value of index list should be less than "\
"the size of data list"
- if (self.index.dtype != numpy.intc):
- self.index = self.index.astype(numpy.intc)
+ if (self.indices.dtype != numpy.intc):
+ self.indices = self.indices.astype(numpy.intc)
if (self.indptr.dtype != numpy.intc):
self.indptr = self.indptr.astype(numpy.intc)
@@ -882,7 +882,7 @@
if attr == 'rowind':
warnings.warn("rowind attribute no longer in use. Use .indices instead",
DeprecationWarning)
- return self.index
+ return self.indices
else:
return _cs_matrix.__getattr__(self, attr)
@@ -898,9 +898,9 @@
if (ocs.shape != self.shape):
raise ValueError, "inconsistent shapes"
indptr, rowind, data = cscplcsc(self.shape[0], self.shape[1], \
- self.indptr, self.index, \
+ self.indptr, self.indices, \
self.data, ocs.indptr, \
- ocs.index, ocs.data)
+ ocs.indices, ocs.data)
return csc_matrix((data, rowind, indptr), self.shape)
elif isdense(other):
# Convert this matrix to a dense matrix and add them.
@@ -939,7 +939,7 @@
else:
return out.sum()
else:
- index = self.index
+ index = self.indices
out = zeros(m, dtype=self.dtype)
# Loop over non-zeros
for k in xrange(self.nnz):
@@ -965,7 +965,7 @@
raise IndexError, "csc_matrix supports slices only of a single"\
" column"
elif isinstance(row, slice):
- return self._getcolslice(row, col)
+ return self._getslice(row, col)
M, N = self.shape
if (row < 0):
row = M + row
@@ -974,7 +974,7 @@
if not (0<=row<M) or not (0<=col<N):
raise IndexError, "index out of bounds"
#this was implemented in fortran before - is there a noticable performance advangate?
- indxs = numpy.where(row == self.index[self.indptr[col]:self.indptr[col+1]])
+ indxs = numpy.where(row == self.indices[self.indptr[col]:self.indptr[col+1]])
if len(indxs[0]) == 0:
return 0
else:
@@ -1007,21 +1007,21 @@
M = row+1
self.shape = (M, N)
- indxs = numpy.where(row == self.index[self.indptr[col]:self.indptr[col+1]])
+ indxs = numpy.where(row == self.indices[self.indptr[col]:self.indptr[col+1]])
if len(indxs[0]) == 0:
#value not present
nzmax = self.nzmax
if (nzmax < self.nnz+1): # need more room
alloc = max(1, self.allocsize)
self.data = resize1d(self.data, nzmax + alloc)
- self.index = resize1d(self.index, nzmax + alloc)
+ self.indices = resize1d(self.indices, nzmax + alloc)
newindex = self.indptr[col]
self.data[newindex+1:] = self.data[newindex:-1]
- self.index[newindex+1:] = self.index[newindex:-1]
+ self.indices[newindex+1:] = self.indices[newindex:-1]
self.data[newindex] = val
- self.index[newindex] = row
+ self.indices[newindex] = row
self.indptr[col+1:] += 1
elif len(indxs[0]) == 1:
@@ -1035,6 +1035,9 @@
# We should allow slices here!
raise IndexError, "invalid index"
+ def _getslice(self, i, myslice):
+ return self._getcolslice(i, myslice)
+
def _getcolslice(self, myslice, j):
"""Returns a view of the elements [myslice.start:myslice.stop, j].
"""
@@ -1042,7 +1045,7 @@
return _cs_matrix._get_slice(self, j, start, stop, stride, (stop - start, 1))
def rowcol(self, ind):
- row = self.index[ind]
+ row = self.indices[ind]
col = searchsorted(self.indptr, ind+1)-1
return (row, col)
@@ -1054,7 +1057,7 @@
def tocsr(self):
indptr, colind, data = csctocsr(self.shape[0], self.shape[1], \
- self.indptr, self.index, self.data)
+ self.indptr, self.indices, self.data)
return csr_matrix((data, colind, indptr), self.shape)
def _toother(self):
@@ -1076,7 +1079,7 @@
return
self.nnz = nnz
self.data = self.data[:nnz]
- self.index = self.index[:nnz]
+ self.indices = self.indices[:nnz]
self.nzmax = nnz
self._check()
@@ -1118,7 +1121,7 @@
if rank(arg1) == 2:
s = arg1
ocsc = csc_matrix(transpose(s))
- self.index = ocsc.index
+ self.indices = ocsc.indices
self.indptr = ocsc.indptr
self.data = ocsc.data
self.shape = (ocsc.shape[1], ocsc.shape[0])
@@ -1132,11 +1135,11 @@
self.shape = s.shape
if copy:
self.data = s.data.copy()
- self.index = s.index.copy()
+ self.indices = s.indices.copy()
self.indptr = s.indptr.copy()
else:
self.data = s.data
- self.index = s.index
+ self.indices = s.indices
self.indptr = s.indptr
else:
try:
@@ -1144,7 +1147,7 @@
except AttributeError:
temp = csr_matrix(s.tocsc())
self.data = temp.data
- self.index = temp.index
+ self.indices = temp.indices
self.indptr = temp.indptr
self.shape = temp.shape
elif type(arg1) == tuple:
@@ -1153,7 +1156,7 @@
M, N = arg1
self.dtype = getdtype(dtype, default=float)
self.data = zeros((nzmax,), self.dtype)
- self.index = zeros((nzmax,), intc)
+ self.indices = zeros((nzmax,), intc)
self.indptr = zeros((M+1,), intc)
self.shape = (M, N)
else:
@@ -1172,11 +1175,11 @@
self.dtype = getdtype(dtype, s)
if copy:
self.data = array(s, dtype=self.dtype)
- self.index = array(colind)
+ self.indices = array(colind)
self.indptr = array(indptr, dtype=intc)
else:
self.data = asarray(s, dtype=self.dtype)
- self.index = asarray(colind)
+ self.indices = asarray(colind)
self.indptr = asarray(indptr, dtype=intc)
else:
# (data, ij) format
@@ -1186,7 +1189,7 @@
dtype=self.dtype).tocsr()
self.shape = temp.shape
self.data = temp.data
- self.index = temp.index
+ self.indices = temp.indices
self.indptr = temp.indptr
else:
raise ValueError, "unrecognized form for csr_matrix constructor"
@@ -1205,8 +1208,8 @@
else:
M = N = None
M = max(0, oldM, M, len(self.indptr) - 1)
- if len(self.index) > 0:
- N = max(oldN, N, int(amax(self.index)) + 1)
+ if len(self.indices) > 0:
+ N = max(oldN, N, int(amax(self.indices)) + 1)
else:
# Matrix is completely empty
N = max(oldN, N)
@@ -1219,8 +1222,8 @@
M, N = self.shape
nnz = self.indptr[-1]
- nzmax = len(self.index)
- if (rank(self.data) != 1) or (rank(self.index) != 1) or \
+ nzmax = len(self.indices)
+ if (rank(self.data) != 1) or (rank(self.indices) != 1) or \
(rank(self.indptr) != 1):
raise ValueError, "data, colind, and indptr arrays "\
"should be rank 1"
@@ -1228,14 +1231,14 @@
raise ValueError, "data and row list should have same length"
if (len(self.indptr) != M+1):
raise ValueError, "index pointer should be of length #rows + 1"
- if (nnz>0) and (amax(self.index[:nnz]) >= N):
+ if (nnz>0) and (amax(self.indices[:nnz]) >= N):
raise ValueError, "column-values must be < N"
if (nnz > nzmax):
raise ValueError, \
"last value of index list should be less than "\
"the size of data list"
- if (self.index.dtype != numpy.intc):
- self.index = self.index.astype(numpy.intc)
+ if (self.indices.dtype != numpy.intc):
+ self.indices = self.indices.astype(numpy.intc)
if (self.indptr.dtype != numpy.intc):
self.indptr = self.indptr.astype(numpy.intc)
@@ -1252,7 +1255,7 @@
if attr == 'colind':
warnings.warn("colind attribute no longer in use. Use .indices instead",
DeprecationWarning)
- return self.index
+ return self.indices
else:
return _cs_matrix.__getattr__(self, attr)
@@ -1286,7 +1289,7 @@
else:
return out.sum()
else:
- index = self.index
+ index = self.indices
out = zeros(n, dtype=self.dtype)
# Loop over non-zeros
for k in xrange(self.nnz):
@@ -1312,7 +1315,7 @@
raise IndexError, "csr_matrix supports slices only of a single"\
" row"
elif isinstance(col, slice):
- return self._getrowslice(row, col)
+ return self._getslice(row, col)
M, N = self.shape
if (row < 0):
row = M + row
@@ -1320,8 +1323,8 @@
col = N + col
if not (0<=row<M) or not (0<=col<N):
raise IndexError, "index out of bounds"
-
- indxs = numpy.where(col == self.index[self.indptr[row]:self.indptr[row+1]])
+ #this was implemented in fortran before - is there a noticable performance advangate?
+ indxs = numpy.where(col == self.indices[self.indptr[row]:self.indptr[row+1]])
if len(indxs[0]) == 0:
return 0
else:
@@ -1330,8 +1333,10 @@
return self[key, :]
else:
raise IndexError, "invalid index"
+
+ def _getslice(self, i, myslice):
+ return self._getrowslice(i, myslice)
-
def _getrowslice(self, i, myslice):
"""Returns a view of the elements [i, myslice.start:myslice.stop].
"""
@@ -1357,21 +1362,21 @@
N = col+1
self.shape = (M, N)
- indxs = numpy.where(col == self.index[self.indptr[row]:self.indptr[row+1]])
+ indxs = numpy.where(col == self.indices[self.indptr[row]:self.indptr[row+1]])
if len(indxs[0]) == 0:
#value not present
nzmax = self.nzmax
if (nzmax < self.nnz+1): # need more room
alloc = max(1, self.allocsize)
self.data = resize1d(self.data, nzmax + alloc)
- self.index = resize1d(self.index, nzmax + alloc)
+ self.indices = resize1d(self.indices, nzmax + alloc)
newindex = self.indptr[row]
self.data[newindex+1:] = self.data[newindex:-1]
- self.index[newindex+1:] = self.index[newindex:-1]
+ self.indices[newindex+1:] = self.indices[newindex:-1]
self.data[newindex] = val
- self.index[newindex] = col
+ self.indices[newindex] = col
self.indptr[row+1:] += 1
elif len(indxs[0]) == 1:
@@ -1386,7 +1391,7 @@
raise IndexError, "invalid index"
def rowcol(self, ind):
- col = self.index[ind]
+ col = self.indices[ind]
row = searchsorted(self.indptr, ind+1)-1
return (row, col)
@@ -1398,7 +1403,7 @@
def tocsc(self):
indptr, rowind, data = csrtocsc(self.shape[0], self.shape[1], \
- self.indptr, self.index, self.data)
+ self.indptr, self.indices, self.data)
return csc_matrix((data, rowind, indptr), self.shape)
def _toother(self):
@@ -1409,7 +1414,7 @@
def toarray(self):
data = numpy.zeros(self.shape, self.data.dtype)
- csrtodense(self.shape[0], self.shape[1], self.indptr, self.index,
+ csrtodense(self.shape[0], self.shape[1], self.indptr, self.indices,
self.data, data)
return data
@@ -1423,7 +1428,7 @@
raise RuntimeError, "should never have nnz > nzmax"
return
self.data = self.data[:nnz]
- self.index = self.index[:nnz]
+ self.indices = self.indices[:nnz]
self.nzmax = nnz
self._check()
@@ -2435,7 +2440,7 @@
if x.shape != (1, self.shape[1]):
raise ValueError, "sparse matrix source must be (1 x n)"
- self.rows[i] = x.index.tolist()
+ self.rows[i] = x.indices.tolist()
self.data[i] = x.data.tolist()
# This should be generalized to other shapes than an entire
# row.
Modified: trunk/Lib/sparse/tests/test_sparse.py
===================================================================
--- trunk/Lib/sparse/tests/test_sparse.py 2007-01-12 01:16:24 UTC (rev 2539)
+++ trunk/Lib/sparse/tests/test_sparse.py 2007-01-12 01:24:56 UTC (rev 2540)
@@ -470,7 +470,7 @@
[0,2,0]],'d')
bsp = csr_matrix(b)
assert_array_almost_equal(bsp.data,[4,3,1,2])
- assert_array_equal(bsp.index,[1,0,2,1])
+ assert_array_equal(bsp.indices,[1,0,2,1])
assert_array_equal(bsp.indptr,[0,1,3,4])
assert_equal(bsp.getnnz(),4)
assert_equal(bsp.getformat(),'csr')
@@ -481,7 +481,7 @@
b[3,4] = 5
bsp = csr_matrix(b)
assert_array_almost_equal(bsp.data,[5])
- assert_array_equal(bsp.index,[4])
+ assert_array_equal(bsp.indices,[4])
assert_array_equal(bsp.indptr,[0,0,0,0,1,1,1])
assert_array_almost_equal(bsp.todense(),b)
@@ -491,7 +491,7 @@
[3,0]],'d')
bsp = csr_matrix(b)
assert_array_almost_equal(bsp.data,[1,2,3])
- assert_array_equal(bsp.index,[0,1,0])
+ assert_array_equal(bsp.indices,[0,1,0])
assert_array_equal(bsp.indptr,[0,1,2,3])
assert_array_almost_equal(bsp.todense(),b)
@@ -523,7 +523,7 @@
print 'in\n', asp
asp.ensure_sorted_indices( inplace = True )
print 'out\n', asp
- assert_array_equal(asp.index,[1, 2, 7, 4, 5])
+ assert_array_equal(asp.indices,[1, 2, 7, 4, 5])
for ir in range( asp.shape[0] ):
for ic in range( asp.shape[1] ):
assert_equal( asp[ir, ic], bsp[ir, ic] )
@@ -535,7 +535,7 @@
b = matrix([[1,0,0],[3,0,1],[0,2,0]],'d')
bsp = csc_matrix(b)
assert_array_almost_equal(bsp.data,[1,3,2,1])
- assert_array_equal(bsp.index,[0,1,2,1])
+ assert_array_equal(bsp.indices,[0,1,2,1])
assert_array_equal(bsp.indptr,[0,2,3,4])
assert_equal(bsp.getnnz(),4)
assert_equal(bsp.getformat(),'csc')
@@ -545,14 +545,14 @@
b[2,4] = 5
bsp = csc_matrix(b)
assert_array_almost_equal(bsp.data,[5])
- assert_array_equal(bsp.index,[2])
+ assert_array_equal(bsp.indices,[2])
assert_array_equal(bsp.indptr,[0,0,0,0,0,1,1])
def check_constructor3(self):
b = matrix([[1,0],[0,2],[3,0]],'d')
bsp = csc_matrix(b)
assert_array_almost_equal(bsp.data,[1,3,2])
- assert_array_equal(bsp.index,[0,2,1])
+ assert_array_equal(bsp.indices,[0,2,1])
assert_array_equal(bsp.indptr,[0,2,3])
def check_empty(self):
@@ -582,7 +582,7 @@
print 'in\n', asp
asp.ensure_sorted_indices( inplace = True )
print 'out\n', asp
- assert_array_equal(asp.index,[1, 2, 7, 4, 5])
+ assert_array_equal(asp.indices,[1, 2, 7, 4, 5])
for ir in range( asp.shape[0] ):
for ic in range( asp.shape[1] ):
assert_equal( asp[ir, ic], bsp[ir, ic] )
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