def test_solve(self):
import random
spA = [
Sparsity.dense(1,1)
]
for n in range(2,5):
spA+= [
Sparsity.diag(n),
Sparsity.dense(n,n),
Sparsity.lower(n),
Sparsity.lower(n).T,
Sparsity.banded(n,1),
diagcat(*[Sparsity.diag(n),Sparsity.dense(n,n)]),
diagcat(*[Sparsity.diag(n),Sparsity.lower(n)]),
diagcat(*[Sparsity.diag(n),Sparsity.lower(n).T]),
diagcat(*[Sparsity.lower(n),Sparsity.lower(n).T]),
Sparsity.diag(n)+Sparsity.rowcol([0],[n-1],n,n),
Sparsity.diag(n)+Sparsity.rowcol([0,n-1],[n-1,0],n,n),
Sparsity.diag(n)+Sparsity.triplet(n,n,[0],[n-1]),
Sparsity.diag(n)+Sparsity.triplet(n,n,[0,n-1],[n-1,0]),
]
for sA in spA:
random.seed(1)
a = DM(sA,[random.random() for i in range(sA.nnz())])
A = SX.sym("a",a.sparsity())
for sB in [ Sparsity.dense(a.size1(),1), vertcat(Sparsity.dense(1,1),Sparsity(a.size1()-1,1)),Sparsity.lower(a.size1()),Sparsity.lower(a.size1()).T]:
b = DM(sB,[random.random() for i in range(sB.nnz())])
B = SX.sym("B",b.sparsity())
C = solve(A,B)
f = Function("f", [A,B],[C])
c = f(a,b)
c_ref = DM(linalg.solve(a,b))
c_ref = sparsify(c_ref)
print sA.dim(), sB.dim()
try:
self.checkarray(c,c_ref)
self.assertTrue(min((IM.ones(c_ref.sparsity())-IM.ones(c.sparsity())).nonzeros())==0)
except Exception as e:
c.print_dense()
print "sol:"
c.sparsity().spy()
print "ref:"
c_ref.sparsity().spy()
c_ref.print_dense()
a.sparsity().sanity_check()
a.print_dense()
raise e
def test_sparsityindex(self):
self.message("sparsity indexing")
nza = set([ (0,0),
(0,1),
(2,0),
(2,3),
(3,3),
(2,4),
(3,1),
(4,1)])
a = Sparsity(5,5)
for i in nza:
a.add_nz(i[0],i[1])
b = MX.sym("b",a)
self.assertRaises(Exception,lambda: b[Sparsity.diag(3)])
d = Sparsity.diag(5)
c = b[d]
self.assertTrue(c.sparsity()==d)
f = Function('f', [b],[c])
fin = DM(b.sparsity(),list(range(1,len(nza)+1)))
f_out = f(fin)
self.checkarray(DM(f_out.nonzeros()),DM([1,0,0,7,0]),"sparsity index")
def test_sparsityindex(self):
self.message("sparsity indexing")
nza = set([ (0,0),
(0,1),
(2,0),
(2,3),
(3,3),
(2,4),
(3,1),
(4,1)])
a = Sparsity(5,5)
for i in nza:
a.addNZ(i[0],i[1])
b = MX.sym("b",a)
self.assertRaises(Exception,lambda: b[Sparsity.diag(3)])
d = Sparsity.diag(5)
c = b[d]
self.assertTrue(c.sparsity()==d)
f = MXFunction([b],[c])
f.init()
f.setInput(range(1,len(nza)+1))
f.evaluate()
self.checkarray(DMatrix(f.getOutput().nonzeros()),DMatrix([1,0,0,7,0]),"sparsity index")
def test_sparsityindex(self):
self.message("sparsity indexing")
nza = set([ (0,0),
(0,1),
(2,0),
(2,3),
(3,3),
(2,4),
(3,1),
(4,1)])
a = CRSSparsity(5,5)
for i in nza:
a.getNZ(i[0],i[1])
b = msym("b",a)
self.assertRaises(Exception,lambda: b[sp_diag(3)])
d = sp_diag(5)
c = b[d]
self.assertTrue(c.sparsity()==d)
f = MXFunction([b],[c])
f.init()
f.input().set(range(1,len(nza)+1))
f.evaluate()
self.checkarray(DMatrix(f.output().data()),DMatrix([1,0,0,7,0]),"sparsity index")
def test_sparsityindex(self):
self.message("sparsity indexing")
nza = set([ (0,0),
(0,1),
(2,0),
(2,3),
(3,3),
(2,4),
(3,1),
(4,1)])
a = CRSSparsity(5,5)
for i in nza:
a.getNZ(i[0],i[1])
b = ssym("b",a)
self.assertRaises(Exception,lambda: b[sp_diag(3)])
d = sp_diag(5)
c = b[d]
self.assertTrue(c.sparsity()==d)
f = SXFunction([b],[c])
f.init()
f.input().set(range(1,len(nza)+1))
f.evaluate()
self.checkarray(DMatrix(f.output().data()),DMatrix([1,0,0,7,0]),"sparsity index")
self.assertTrue(f.output().data()[1]==0)
def test_sparsityindex(self):
self.message("sparsity indexing")
nza = set([(0, 0), (0, 1), (2, 0), (2, 3), (3, 3), (2, 4), (3, 1),
(4, 1)])
a = Sparsity(5, 5)
for i in nza:
a.addNZ(i[0], i[1])
b = SX.sym("b", a)
self.assertRaises(Exception, lambda: b[Sparsity.diag(3)])
d = Sparsity.diag(5)
c = b[d]
self.assertTrue(c.sparsity() == d)
f = SXFunction('f', [b], [c])
f.setInput(range(1, len(nza) + 1))
f.evaluate()
self.checkarray(DMatrix(f.getOutput().nonzeros()),
DMatrix([1, 0, 0, 7, 0]), "sparsity index")
self.assertTrue(f.getOutput().nonzeros()[1] == 0)
def test_sparsityindex(self):
self.message("sparsity indexing")
nza = set([ (0,0),
(0,1),
(2,0),
(2,3),
(3,3),
(2,4),
(3,1),
(4,1)])
a = Sparsity(5,5)
for i in nza:
a.add_nz(i[0],i[1])
b = MX.sym("b",a)
self.assertRaises(Exception,lambda: b[Sparsity.diag(3)])
d = Sparsity.diag(5)
c = b[d]
self.assertTrue(c.sparsity()==d)
f = Function('f', [b],[c])
fin = DM(b.sparsity(),list(range(1,len(nza)+1)))
f_out = f(fin)
self.checkarray(DM(f_out.nonzeros()),DM([1,0,0,7,0]),"sparsity index")
def test_solve(self):
import random
spA = [
Sparsity.dense(1,1)
]
for n in range(2,5):
spA+= [
Sparsity.diag(n),
Sparsity.dense(n,n),
Sparsity.tril(n),
Sparsity.tril(n).T,
Sparsity.banded(n,1),
blkdiag([Sparsity.diag(n),Sparsity.dense(n,n)]),
blkdiag([Sparsity.diag(n),Sparsity.tril(n)]),
blkdiag([Sparsity.diag(n),Sparsity.tril(n).T]),
blkdiag([Sparsity.tril(n),Sparsity.tril(n).T]),
Sparsity.diag(n)+Sparsity.rowcol([0],[n-1],n,n),
Sparsity.diag(n)+Sparsity.rowcol([0,n-1],[n-1,0],n,n),
Sparsity.diag(n)+Sparsity.triplet(n,n,[0],[n-1]),
Sparsity.diag(n)+Sparsity.triplet(n,n,[0,n-1],[n-1,0]),
]
for sA in spA:
random.seed(1)
a = DMatrix(sA,[random.random() for i in range(sA.size())])
A = SX.sym("a",a.sparsity())
for sB in [ Sparsity.dense(a.size1(),1), vertcat([Sparsity.dense(1,1),Sparsity.sparse(a.size1()-1,1)]),Sparsity.tril(a.size1()),Sparsity.tril(a.size1()).T]:
b = DMatrix(sB,[random.random() for i in range(sB.size())])
B = SX.sym("B",b.sparsity())
C = solve(A,B)
f = SXFunction([A,B],[C])
f.init()
f.setInput(a,0)
f.setInput(b,1)
f.evaluate()
c_ref = DMatrix(linalg.solve(a,b))
c_ref = sparse(c_ref)
c = f.getOutput()
print sA.dimString(), sB.dimString()
try:
self.checkarray(c,c_ref)
self.assertTrue(min(IMatrix(c_ref.sparsity(),1)-IMatrix(c.sparsity(),1))==0)
except Exception as e:
c.printDense()
print "sol:"
c.sparsity().spy()
print "ref:"
c_ref.sparsity().spy()
c_ref.printDense()
a.sparsity().sanityCheck()
a.printDense()
raise e
def test_solve(self):
import random
spA = [Sparsity.dense(1, 1)]
for n in range(2, 5):
spA += [
Sparsity.diag(n),
Sparsity.dense(n, n),
Sparsity.tril(n),
Sparsity.tril(n).T,
Sparsity.banded(n, 1),
blkdiag([Sparsity.diag(n),
Sparsity.dense(n, n)]),
blkdiag([Sparsity.diag(n), Sparsity.tril(n)]),
blkdiag([Sparsity.diag(n),
Sparsity.tril(n).T]),
blkdiag([Sparsity.tril(n),
Sparsity.tril(n).T]),
Sparsity.diag(n) + Sparsity.rowcol([0], [n - 1], n, n),
Sparsity.diag(n) +
Sparsity.rowcol([0, n - 1], [n - 1, 0], n, n),
Sparsity.diag(n) + Sparsity.triplet(n, n, [0], [n - 1]),
Sparsity.diag(n) +
Sparsity.triplet(n, n, [0, n - 1], [n - 1, 0]),
]
for sA in spA:
random.seed(1)
a = DMatrix(sA, [random.random() for i in range(sA.size())])
A = SX.sym("a", a.sparsity())
for sB in [
Sparsity.dense(a.size1(), 1),
vertcat([
Sparsity.dense(1, 1),
Sparsity.sparse(a.size1() - 1, 1)
]),
Sparsity.tril(a.size1()),
Sparsity.tril(a.size1()).T
]:
b = DMatrix(sB, [random.random() for i in range(sB.size())])
B = SX.sym("B", b.sparsity())
C = solve(A, B)
f = SXFunction([A, B], [C])
f.init()
f.setInput(a, 0)
f.setInput(b, 1)
f.evaluate()
c_ref = DMatrix(linalg.solve(a, b))
c_ref = sparse(c_ref)
c = f.getOutput()
print sA.dimString(), sB.dimString()
try:
self.checkarray(c, c_ref)
self.assertTrue(
min(
IMatrix(c_ref.sparsity(), 1) -
IMatrix(c.sparsity(), 1)) == 0)
except Exception as e:
c.printDense()
print "sol:"
c.sparsity().spy()
print "ref:"
c_ref.sparsity().spy()
c_ref.printDense()
a.sparsity().sanityCheck()
a.printDense()
raise e
def test_solve(self):
import random
spA = [Sparsity.dense(1, 1)]
for n in range(2, 5):
spA += [
Sparsity.diag(n),
Sparsity.dense(n, n),
Sparsity.lower(n),
Sparsity.lower(n).T,
Sparsity.banded(n, 1),
diagcat(
*[Sparsity.diag(n), Sparsity.dense(n, n)]),
diagcat(
*[Sparsity.diag(n), Sparsity.lower(n)]),
diagcat(
*[Sparsity.diag(n), Sparsity.lower(n).T]),
diagcat(*[Sparsity.lower(n),
Sparsity.lower(n).T]),
Sparsity.diag(n) + Sparsity.rowcol([0], [n - 1], n, n),
Sparsity.diag(n) +
Sparsity.rowcol([0, n - 1], [n - 1, 0], n, n),
Sparsity.diag(n) + Sparsity.triplet(n, n, [0], [n - 1]),
Sparsity.diag(n) +
Sparsity.triplet(n, n, [0, n - 1], [n - 1, 0]),
]
for sA in spA:
random.seed(1)
a = DM(sA, [random.random() for i in range(sA.nnz())])
A = SX.sym("a", a.sparsity())
for sB in [
Sparsity.dense(a.size1(), 1),
vertcat(Sparsity.dense(1, 1), Sparsity(a.size1() - 1, 1)),
Sparsity.lower(a.size1()),
Sparsity.lower(a.size1()).T
]:
b = DM(sB, [random.random() for i in range(sB.nnz())])
B = SX.sym("B", b.sparsity())
C = solve(A, B)
f = Function("f", [A, B], [C])
c = f(a, b)
c_ref = DM(linalg.solve(a, b))
c_ref = sparsify(c_ref)
print sA.dim(), sB.dim()
try:
self.checkarray(c, c_ref)
self.assertTrue(
min((IM.ones(c_ref.sparsity()) -
IM.ones(c.sparsity())).nonzeros()) == 0)
except Exception as e:
c.print_dense()
print "sol:"
c.sparsity().spy()
print "ref:"
c_ref.sparsity().spy()
c_ref.print_dense()
a.sparsity().sanity_check()
a.print_dense()
raise e