[Scipy-svn] r3830 - trunk/scipy/sparse
scipy-svn at scipy.org
scipy-svn at scipy.org
Mon Jan 14 14:29:19 EST 2008
Author: wnbell
Date: 2008-01-14 13:29:12 -0600 (Mon, 14 Jan 2008)
New Revision: 3830
Modified:
trunk/scipy/sparse/bsr.py
trunk/scipy/sparse/construct.py
trunk/scipy/sparse/coo.py
trunk/scipy/sparse/csc.py
trunk/scipy/sparse/csr.py
trunk/scipy/sparse/dia.py
trunk/scipy/sparse/info.py
Log:
fixed docstrings to pass doctests
Modified: trunk/scipy/sparse/bsr.py
===================================================================
--- trunk/scipy/sparse/bsr.py 2008-01-14 12:58:10 UTC (rev 3829)
+++ trunk/scipy/sparse/bsr.py 2008-01-14 19:29:12 UTC (rev 3830)
@@ -62,22 +62,22 @@
>>> from scipy.sparse import *
>>> from scipy import *
- >>> bsr_matrix( (3,4), dtype='i' ).todense()
+ >>> bsr_matrix( (3,4), dtype='int32' ).todense()
matrix([[0, 0, 0, 0],
[0, 0, 0, 0],
- [0, 0, 0, 0]])
+ [0, 0, 0, 0]], dtype=int32)
- >>> row = array([0,0,1,2,2,2])
- >>> col = array([0,2,2,0,1,2])
- >>> data = kron([1,2,3,4,5,6])
+ >>> row = array([0,0,1,2,2,2])
+ >>> col = array([0,2,2,0,1,2])
+ >>> data = array([1,2,3,4,5,6])
>>> bsr_matrix( (data,(row,col)), shape=(3,3) ).todense()
matrix([[1, 0, 2],
[0, 0, 3],
[4, 5, 6]])
- >>> indptr = array([0,2,3,6])
+ >>> indptr = array([0,2,3,6])
>>> indices = array([0,2,2,0,1,2])
- >>> data = array([1,2,3,4,5,6]).repeat(4).reshape(6,2,2)
+ >>> data = array([1,2,3,4,5,6]).repeat(4).reshape(6,2,2)
>>> bsr_matrix( (data,indices,indptr), shape=(6,6) ).todense()
matrix([[1, 1, 0, 0, 2, 2],
[1, 1, 0, 0, 2, 2],
@@ -121,6 +121,7 @@
elif len(arg1) == 2:
# (data,(row,col)) format
+ from coo import coo_matrix
self._set_self( coo_matrix(arg1).tobsr(blocksize=blocksize) )
elif len(arg1) == 3:
Modified: trunk/scipy/sparse/construct.py
===================================================================
--- trunk/scipy/sparse/construct.py 2008-01-14 12:58:10 UTC (rev 3829)
+++ trunk/scipy/sparse/construct.py 2008-01-14 19:29:12 UTC (rev 3830)
@@ -98,21 +98,19 @@
>>> A = csr_matrix(array([[0,2],[5,0]]))
>>> B = csr_matrix(array([[1,2],[3,4]]))
>>> spkron(A,B).todense()
- matrix([[ 0., 0., 2., 4.],
- [ 0., 0., 6., 8.],
- [ 5., 10., 0., 0.],
- [ 15., 20., 0., 0.]])
+ matrix([[ 0, 0, 2, 4],
+ [ 0, 0, 6, 8],
+ [ 5, 10, 0, 0],
+ [15, 20, 0, 0]])
>>> spkron(A,[[1,2],[3,4]]).todense()
- matrix([[ 0., 0., 2., 4.],
- [ 0., 0., 6., 8.],
- [ 5., 10., 0., 0.],
- [ 15., 20., 0., 0.]])
+ matrix([[ 0, 0, 2, 4],
+ [ 0, 0, 6, 8],
+ [ 5, 10, 0, 0],
+ [15, 20, 0, 0]])
"""
- #TODO optimize for small dense B and CSR A -> BSR
B = coo_matrix(B)
-
if (format is None or format == "bsr") and 2*B.nnz >= B.shape[0] * B.shape[1]:
#B is fairly dense, use BSR
Modified: trunk/scipy/sparse/coo.py
===================================================================
--- trunk/scipy/sparse/coo.py 2008-01-14 12:58:10 UTC (rev 3829)
+++ trunk/scipy/sparse/coo.py 2008-01-14 19:29:12 UTC (rev 3830)
@@ -70,10 +70,10 @@
>>> from scipy.sparse import *
>>> from scipy import *
- >>> coo_matrix( (3,4), dtype='i' ).todense()
+ >>> coo_matrix( (3,4), dtype='int32' ).todense()
matrix([[0, 0, 0, 0],
[0, 0, 0, 0],
- [0, 0, 0, 0]])
+ [0, 0, 0, 0]], dtype=int32)
>>> row = array([0,3,1,0])
>>> col = array([0,3,1,2])
@@ -84,7 +84,7 @@
[0, 0, 0, 0],
[0, 0, 0, 5]])
- >>> print "example with duplicates"
+ >>> # example with duplicates
>>> row = array([0,0,1,3,1,0,0])
>>> col = array([0,2,1,3,1,0,0])
>>> data = array([1,1,1,1,1,1,1])
Modified: trunk/scipy/sparse/csc.py
===================================================================
--- trunk/scipy/sparse/csc.py 2008-01-14 12:58:10 UTC (rev 3829)
+++ trunk/scipy/sparse/csc.py 2008-01-14 19:29:12 UTC (rev 3830)
@@ -63,26 +63,26 @@
>>> from scipy.sparse import *
>>> from scipy import *
- >>> csc_matrix( (3,4), dtype='i' ).todense()
+ >>> csc_matrix( (3,4), dtype='int32' ).todense()
matrix([[0, 0, 0, 0],
[0, 0, 0, 0],
- [0, 0, 0, 0]])
+ [0, 0, 0, 0]], dtype=int32)
- >>> row = array([0,0,1,2,2,2])
- >>> col = array([0,2,2,0,1,2])
+ >>> row = array([0,2,2,0,1,2])
+ >>> col = array([0,0,1,2,2,2])
>>> data = array([1,2,3,4,5,6])
>>> csc_matrix( (data,(row,col)), shape=(3,3) ).todense()
- matrix([[1, 0, 2],
- [0, 0, 3],
- [4, 5, 6]])
+ matrix([[1, 0, 4],
+ [0, 0, 5],
+ [2, 3, 6]])
>>> indptr = array([0,2,3,6])
>>> indices = array([0,2,2,0,1,2])
- >>> data = array([1,4,6,2,3,5])
+ >>> data = array([1,2,3,4,5,6])
>>> csc_matrix( (data,indices,indptr), shape=(3,3) ).todense()
- matrix([[1, 0, 2],
- [0, 0, 3],
- [4, 5, 6]])
+ matrix([[1, 0, 4],
+ [0, 0, 5],
+ [2, 3, 6]])
"""
Modified: trunk/scipy/sparse/csr.py
===================================================================
--- trunk/scipy/sparse/csr.py 2008-01-14 12:58:10 UTC (rev 3829)
+++ trunk/scipy/sparse/csr.py 2008-01-14 19:29:12 UTC (rev 3830)
@@ -65,10 +65,10 @@
>>> from scipy.sparse import *
>>> from scipy import *
- >>> csr_matrix( (3,4), dtype='i' ).todense()
+ >>> csr_matrix( (3,4), dtype='int32' ).todense()
matrix([[0, 0, 0, 0],
[0, 0, 0, 0],
- [0, 0, 0, 0]])
+ [0, 0, 0, 0]], dtype=int32)
>>> row = array([0,0,1,2,2,2])
>>> col = array([0,2,2,0,1,2])
Modified: trunk/scipy/sparse/dia.py
===================================================================
--- trunk/scipy/sparse/dia.py 2008-01-14 12:58:10 UTC (rev 3829)
+++ trunk/scipy/sparse/dia.py 2008-01-14 19:29:12 UTC (rev 3830)
@@ -32,13 +32,12 @@
Examples
========
-
>>> from scipy.sparse import *
>>> from scipy import *
- >>> dia_matrix( (3,4), dtype='i').todense()
+ >>> dia_matrix( (3,4), dtype='int32').todense()
matrix([[0, 0, 0, 0],
[0, 0, 0, 0],
- [0, 0, 0, 0]])
+ [0, 0, 0, 0]], dtype=int32)
>>> data = array([[1,2,3,4]]).repeat(3,axis=0)
>>> diags = array([0,-1,2])
Modified: trunk/scipy/sparse/info.py
===================================================================
--- trunk/scipy/sparse/info.py 2008-01-14 12:58:10 UTC (rev 3829)
+++ trunk/scipy/sparse/info.py 2008-01-14 19:29:12 UTC (rev 3830)
@@ -41,22 +41,22 @@
>>> A[1, 100:200] = A[0, :100]
>>> A.setdiag(rand(1000))
- Now convert it to CSR format and solve (A A^T) x = b for x:
+ Now convert it to CSR format and solve A x = b for x:
>>> A = A.tocsr()
>>> b = rand(1000)
- >>> x = linsolve.spsolve(A * A.T, b)
+ >>> x = linsolve.spsolve(A, b)
Convert it to a dense matrix and solve, and check that the result
is the same:
- >>> A_ = A.todense()
- >>> x_ = linalg.solve(A_ * A_.T, b)
- >>> err = linalg.norm(x-x_)
+ >>> x_ = linalg.solve(A.todense(), b)
- Now we can print the error norm with:
+ Now we can compute norm of the error with:
- >>> print "Norm error =", err
+ >>> err = linalg.norm(x-x_)
+ >>> err < 1e-10
+ True
It should be small :)
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