[Scipy-svn] r3647 - in trunk/scipy/sparse: . tests
scipy-svn at scipy.org
scipy-svn at scipy.org
Fri Dec 14 00:44:12 EST 2007
Author: wnbell
Date: 2007-12-13 23:44:09 -0600 (Thu, 13 Dec 2007)
New Revision: 3647
Added:
trunk/scipy/sparse/tests/test_construct.py
trunk/scipy/sparse/tests/test_sparse.py
Modified:
trunk/scipy/sparse/__init__.py
Log:
reorganized unittests
Modified: trunk/scipy/sparse/__init__.py
===================================================================
--- trunk/scipy/sparse/__init__.py 2007-12-14 05:24:17 UTC (rev 3646)
+++ trunk/scipy/sparse/__init__.py 2007-12-14 05:44:09 UTC (rev 3647)
@@ -1,4 +1,4 @@
-"Rudimentary sparse matrix class"
+"Sparse Matrix Support"
from info import __doc__
Added: trunk/scipy/sparse/tests/test_construct.py
===================================================================
--- trunk/scipy/sparse/tests/test_construct.py 2007-12-14 05:24:17 UTC (rev 3646)
+++ trunk/scipy/sparse/tests/test_construct.py 2007-12-14 05:44:09 UTC (rev 3647)
@@ -0,0 +1,95 @@
+"""test sparse matrix construction functions"""
+
+import numpy
+from numpy import array
+from numpy.testing import *
+
+set_package_path()
+from scipy.sparse import csc_matrix, csr_matrix, dok_matrix, coo_matrix, \
+ spidentity, speye, spkron, extract_diagonal, lil_matrix, lil_eye, \
+ lil_diags, spdiags
+from scipy.linsolve import splu
+restore_path()
+
+class TestConstructUtils(NumpyTestCase):
+ def check_identity(self):
+ a = spidentity(3)
+ b = array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype='d')
+ assert_array_equal(a.toarray(), b)
+
+ def check_eye(self):
+ a = speye(2, 3 )
+# print a, a.__repr__
+ b = array([[1, 0, 0], [0, 1, 0]], dtype='d')
+ assert_array_equal(a.toarray(), b)
+
+ a = speye(3, 2)
+# print a, a.__repr__
+ b = array([[1, 0], [0, 1], [0, 0]], dtype='d')
+ assert_array_equal( a.toarray(), b)
+
+ a = speye(3, 3)
+# print a, a.__repr__
+ b = array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype='d')
+ assert_array_equal(a.toarray(), b)
+
+ def check_spkron(self):
+ from numpy import kron
+
+ cases = []
+
+ cases.append(array([[ 0]]))
+ cases.append(array([[-1]]))
+ cases.append(array([[ 4]]))
+ cases.append(array([[10]]))
+ cases.append(array([[0],[0]]))
+ cases.append(array([[0,0]]))
+ cases.append(array([[1,2],[3,4]]))
+ cases.append(array([[0,2],[5,0]]))
+ cases.append(array([[0,2,-6],[8,0,14]]))
+ cases.append(array([[5,4],[0,0],[6,0]]))
+ cases.append(array([[5,4,4],[1,0,0],[6,0,8]]))
+ cases.append(array([[0,1,0,2,0,5,8]]))
+ cases.append(array([[0.5,0.125,0,3.25],[0,2.5,0,0]]))
+
+ for a in cases:
+ for b in cases:
+ result = spkron(csr_matrix(a),csr_matrix(b)).todense()
+ expected = kron(a,b)
+
+ assert_array_equal(result,expected)
+
+ def check_lil_diags(self):
+ assert_array_equal(lil_diags([[1,2,3],[4,5],[6]],
+ [0,1,2],(3,3)).todense(),
+ [[1,4,6],
+ [0,2,5],
+ [0,0,3]])
+
+ assert_array_equal(lil_diags([[6],[4,5],[1,2,3]],
+ [2,1,0],(3,3)).todense(),
+ [[1,4,6],
+ [0,2,5],
+ [0,0,3]])
+
+ assert_array_equal(lil_diags([[6,7,8],[4,5],[1,2,3]],
+ [2,1,0],(3,3)).todense(),
+ [[1,4,6],
+ [0,2,5],
+ [0,0,3]])
+
+ assert_array_equal(lil_diags([[1,2,3],[4,5],[6]],
+ [0,-1,-2],(3,3)).todense(),
+ [[1,0,0],
+ [4,2,0],
+ [6,5,3]])
+
+ assert_array_equal(lil_diags([[6,7,8],[4,5]],
+ [-2,-1],(3,3)).todense(),
+ [[0,0,0],
+ [4,0,0],
+ [6,5,0]])
+
+if __name__ == "__main__":
+ NumpyTest().run()
+
Added: trunk/scipy/sparse/tests/test_sparse.py
===================================================================
--- trunk/scipy/sparse/tests/test_sparse.py 2007-12-14 05:24:17 UTC (rev 3646)
+++ trunk/scipy/sparse/tests/test_sparse.py 2007-12-14 05:44:09 UTC (rev 3647)
@@ -0,0 +1,147 @@
+"""general tests and simple benchmarks for the sparse module"""
+
+import numpy
+from numpy import ones
+
+import random
+from numpy.testing import *
+set_package_path()
+from scipy.sparse import csc_matrix, csr_matrix, dok_matrix, \
+ coo_matrix, lil_matrix, spidentity, spdiags
+from scipy.linsolve import splu
+restore_path()
+
+
+def poisson2d(N,epsilon=1.0):
+ """
+ Return a sparse CSR matrix for the 2d poisson problem
+ with standard 5-point finite difference stencil on a
+ square N-by-N grid.
+ """
+
+ D = (2 + 2*epsilon)*ones(N*N)
+ T = -epsilon * ones(N*N)
+ O = -ones(N*N)
+ T[N-1::N] = 0
+ return spdiags([D,O,T,T,O],[0,-N,-1,1,N],N*N,N*N).tocoo().tocsr() #eliminate explicit zeros
+
+
+import time
+class TestSparseTools(NumpyTestCase):
+ """Simple benchmarks for sparse matrix module"""
+
+ def test_matvec(self,level=5):
+ matrices = []
+ matrices.append(('Identity',spidentity(10**5)))
+ matrices.append(('Poisson5pt', poisson2d(250)))
+ matrices.append(('Poisson5pt', poisson2d(500)))
+ matrices.append(('Poisson5pt', poisson2d(1000)))
+
+ print
+ print ' Sparse Matrix Vector Product'
+ print '=================================================================='
+ print ' type | name | shape | nnz | MFLOPs '
+ print '------------------------------------------------------------------'
+ fmt = ' %3s | %12s | %20s | %8d | %6.1f '
+
+ for name,A in matrices:
+ A = A.tocsr()
+
+ x = ones(A.shape[1],dtype=A.dtype)
+
+ y = A*x #warmup
+
+ start = time.clock()
+ iter = 0
+ while iter < 5 or time.clock() < start + 1:
+ y = A*x
+ iter += 1
+ end = time.clock()
+
+ name = name.center(12)
+ shape = ("%s" % (A.shape,)).center(20)
+ MFLOPs = (2*A.nnz*iter/(end-start))/float(1e6)
+
+ print fmt % (A.format,name,shape,A.nnz,MFLOPs)
+
+ def bench_construction(self,level=5):
+ """build matrices by inserting single values"""
+ matrices = []
+ matrices.append( ('Empty',csr_matrix((10000,10000))) )
+ matrices.append( ('Identity',spidentity(10000)) )
+ matrices.append( ('Poisson5pt', poisson2d(100)) )
+
+ print
+ print ' Sparse Matrix Construction'
+ print '===================================================================='
+ print ' type | name | shape | nnz | time (sec) '
+ print '--------------------------------------------------------------------'
+ fmt = ' %3s | %12s | %20s | %8d | %6.4f '
+
+ for name,A in matrices:
+ A = A.tocoo()
+
+ for format in ['lil','dok']:
+
+ start = time.clock()
+
+ iter = 0
+ while time.clock() < start + 0.1:
+ T = eval(format + '_matrix')(A.shape)
+ for i,j,v in zip(A.row,A.col,A.data):
+ T[i,j] = v
+ iter += 1
+ end = time.clock()
+
+ del T
+ name = name.center(12)
+ shape = ("%s" % (A.shape,)).center(20)
+
+ print fmt % (format,name,shape,A.nnz,(end-start)/float(iter))
+
+
+ def bench_conversion(self,level=5):
+ A = poisson2d(100)
+
+ formats = ['csr','csc','coo','lil','dok']
+
+ print
+ print ' Sparse Matrix Conversion'
+ print '=========================================================='
+ print ' format | tocsr() | tocsc() | tocoo() | tolil() | todok() '
+ print '----------------------------------------------------------'
+
+ for fromfmt in formats:
+ base = getattr(A,'to' + fromfmt)()
+
+ times = []
+
+ for tofmt in formats:
+ try:
+ fn = getattr(base,'to' + tofmt)
+ except:
+ times.append(None)
+ else:
+ x = fn() #warmup
+ start = time.clock()
+ iter = 0
+ while time.clock() < start + 0.2:
+ x = fn()
+ iter += 1
+ end = time.clock()
+ del x
+ times.append( (end - start)/float(iter))
+
+ output = " %3s " % fromfmt
+ for t in times:
+ if t is None:
+ output += '| n/a '
+ else:
+ output += '| %5.1fms ' % (1000*t)
+ print output
+
+
+
+if __name__ == "__main__":
+ NumpyTest().run()
+
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