def test_scipy_sparse(self) -> None:
"""Test scipy sparse matrices."""
# Constants.
A = numpy.arange(8).reshape((4, 2))
A = sp.csc_matrix(A)
A = sp.eye(2).tocsc()
key = (slice(0, 1, None), slice(None, None, None))
Aidx = intf.index(A, (slice(0, 2, None), slice(None, None, None)))
Aidx = intf.index(Aidx, key)
self.assertEqual(Aidx.shape, (1, 2))
self.assertEqual(Aidx[0, 0], 1)
self.assertEqual(Aidx[0, 1], 0)
# Linear ops.
var = Variable((4, 2))
A = numpy.arange(8).reshape((4, 2))
A = sp.csc_matrix(A)
B = sp.hstack([A, A])
self.assertExpression(var + A, (4, 2))
self.assertExpression(A + var, (4, 2))
self.assertExpression(B @ var, (4, 2))
self.assertExpression(var - A, (4, 2))
self.assertExpression(A - A - var, (4, 2))
if PY35:
self.assertExpression(var.__rmatmul__(B), (4, 2))
class TestMatrices(unittest.TestCase):
""" Unit tests for testing different forms of matrices as constants. """
def assertExpression(self, expr, shape):
"""Asserts that expr is an Expression with dimension shape.
"""
assert isinstance(expr, Expression) or isinstance(expr, Constraint)
self.assertEqual(expr.shape, shape)
def setUp(self):
self.a = Variable(name='a')
self.b = Variable(name='b')
self.c = Variable(name='c')
self.x = Variable(2, name='x')
self.y = Variable(3, name='y')
self.z = Variable(2, name='z')
self.A = Variable((2, 2), name='A')
self.B = Variable((2, 2), name='B')
self.C = Variable((3, 2), name='C')
# Test numpy arrays
def test_numpy_arrays(self):
# Vector
v = numpy.arange(2)
self.assertExpression(self.x + v, (2, ))
self.assertExpression(v + self.x, (2, ))
self.assertExpression(self.x - v, (2, ))
self.assertExpression(v - self.x, (2, ))
self.assertExpression(self.x <= v, (2, ))
self.assertExpression(v <= self.x, (2, ))
self.assertExpression(self.x == v, (2, ))
self.assertExpression(v == self.x, (2, ))
# Matrix
A = numpy.arange(8).reshape((4, 2))
self.assertExpression(A * self.x, (4, ))
if PY35:
self.assertExpression(self.x.__rmatmul__(A), (4, ))
# PSD inequalities.
A = numpy.ones((2, 2))
self.assertExpression(A << self.A, (2, 2))
self.assertExpression(A >> self.A, (2, 2))
# Test numpy matrices
def test_numpy_matrices(self):
# Vector
v = numpy.arange(2)
self.assertExpression(self.x + v, (2, ))
self.assertExpression(v + v + self.x, (2, ))
self.assertExpression(self.x - v, (2, ))
self.assertExpression(v - v - self.x, (2, ))
self.assertExpression(self.x <= v, (2, ))
self.assertExpression(v <= self.x, (2, ))
self.assertExpression(self.x == v, (2, ))
self.assertExpression(v == self.x, (2, ))
# Matrix
A = numpy.matrix(numpy.arange(8).reshape((4, 2)))
self.assertExpression(A * self.x, (4, ))
self.assertExpression((A.T * A) * self.x, (2, ))
if PY35:
self.assertExpression(self.x.__rmatmul__(A), (4, ))
# PSD inequalities.
A = numpy.matrix(numpy.ones((2, 2)))
self.assertExpression(A << self.A, (2, 2))
self.assertExpression(A >> self.A, (2, 2))
def test_numpy_scalars(self):
"""Test numpy scalars."""
v = numpy.float64(2.0)
self.assertExpression(self.x + v, (2, ))
self.assertExpression(v + self.x, (2, ))
self.assertExpression(v * self.x, (2, ))
self.assertExpression(self.x - v, (2, ))
self.assertExpression(v - v - self.x, (2, ))
self.assertExpression(self.x <= v, (2, ))
self.assertExpression(v <= self.x, (2, ))
self.assertExpression(self.x == v, (2, ))
self.assertExpression(v == self.x, (2, ))
# PSD inequalities.
self.assertExpression(v << self.A, (2, 2))
self.assertExpression(v >> self.A, (2, 2))
# def test_cvxopt_matrices(self):
# """Test cvxopt dense matrices.
# """
# # Vector
# v = cvxopt.matrix( numpy.arange(2).reshape((2,1)) )
# self.assertExpression(self.x + v, (2,1))
# self.assertExpression(v + v + self.x, (2,1))
# self.assertExpression(self.x - v, (2,1))
# self.assertExpression(v - v - self.x, (2,1))
# self.assertExpression(self.x <= v, (2,1))
# self.assertExpression(v <= self.x, (2,1))
# self.assertExpression(self.x == v, (2,1))
# self.assertExpression(v == self.x, (2,1))
# # Matrix
# A = cvxopt.matrix( numpy.arange(8).reshape((4,2)) )
# self.assertExpression(A*self.x, (4,1))
# self.assertExpression( (A.T*A) * self.x, (2,1))
# # Test cvxopt sparse matrices.
# def test_cvxopt_sparse(self):
# m = 100
# n = 20
# mu = cvxopt.exp(cvxopt.normal(m))
# F = cvxopt.normal(m, n)
# D = cvxopt.spdiag(cvxopt.uniform(m))
# x = Variable(m)
# exp = square(norm2(D*x))
def test_scipy_sparse(self):
"""Test scipy sparse matrices."""
# Constants.
A = numpy.matrix(numpy.arange(8).reshape((4, 2)))
A = sp.csc_matrix(A)
A = sp.eye(2).tocsc()
key = (slice(0, 1, None), slice(None, None, None))
Aidx = intf.index(A, (slice(0, 2, None), slice(None, None, None)))
Aidx = intf.index(Aidx, key)
self.assertEqual(Aidx.shape, (1, 2))
self.assertEqual(Aidx[0, 0], 1)
self.assertEqual(Aidx[0, 1], 0)
# Linear ops.
var = Variable((4, 2))
A = numpy.matrix(numpy.arange(8).reshape((4, 2)))
A = sp.csc_matrix(A)
B = sp.hstack([A, A])
self.assertExpression(var + A, (4, 2))
self.assertExpression(A + var, (4, 2))
self.assertExpression(B * var, (4, 2))
self.assertExpression(var - A, (4, 2))
self.assertExpression(A - A - var, (4, 2))
if PY35:
self.assertExpression(var.__rmatmul__(B), (4, 2))
