def testMarginalizeBothAxis(self):
a = ExprSymbolic([1], np.array([[1, 0, 0]]))
b = ExprSymbolic([1], np.array([[0, 1, 0]]))
c = ExprSymbolic([2], np.array([[0, 0, 1]]))
expressions = np.array([[a, b, c], [a, b, c]], dtype=np.object)
matrix = ExprMatrix('A', expressions, powers=np.array([1]))
matrix = matrix.Marginalize(axis=None)
self.assertEquals((1, 1), matrix.expressions.shape)
self.assertEquals(3, len(matrix.expressions.item(0, 0).quants))
self.assertEquals([2, 2, 4],
sorted(matrix.expressions.item(0, 0).quants))
def testSameHash(self):
manage.config.MAXPOWER = np.array([10])
a = ExprSymbolic([1], np.array([[1, 0, 0, 0]]))
b = ExprSymbolic([1], np.array([[0, 1, 0, 0]]))
c = ExprSymbolic([1], np.array([[0, 0, 1, 0]]))
d = ExprSymbolic([1], np.array([[0, 0, 0, 1]]))
expressions = np.array([[a, b], [c, d]], dtype=np.object)
matrix = ExprMatrix('A', expressions, powers=np.array([1]))
Q = matrix.Marginalize(axis=0).Marginalize(axis=1)
W = matrix.Marginalize(axis=1).Marginalize(axis=0)
self.assertEquals(Q, W)
def testShapesAfterSumAreCorrec(self):
a = ExprSymbolic([1], np.array([[1, 0, 0]]))
b = ExprSymbolic([1], np.array([[0, 1, 0]]))
expressions = np.array([[a, a, a], [b, b, b]])
matrix = ExprMatrix('M', expressions, powers=np.array([1]))
# Exp matrix test
self.assertEquals((2, 3), matrix.expressions.shape)
m0 = copy.copy(matrix)
m0 = m0.Marginalize(axis=0)
self.assertEquals((1, 3), m0.expressions.shape)
self.assertEquals(a + b, m0.expressions[0, 0])
self.assertEquals(a + b, m0.expressions[0, 1])
m1 = copy.copy(matrix)
m1 = m1.Marginalize(axis=1)
self.assertEquals((2, 1), m1.expressions.shape)
self.assertEquals(a + a + a, m1.expressions[0, 0])
self.assertEquals(b + b + b, m1.expressions[1, 0])
def testAddOne(self):
manage.config.MAXPOWER = np.array([10])
a = ExprSymbolic([5], np.array([[0, 0, 0, 0]]))
expressions = np.array([[a]], dtype=np.object)
matrix = ExprMatrix('A', expressions, powers=np.array([1]))
self.assertTrue(matrix.Add(4).expressions[0, 0].quants.shape[0] == 1)
def testFindLinearCombinationWithMatrix(self):
manage.config.MAXPOWER = np.array([10])
# mat1 mat2 target
# x1 | x1x2 x1 | x1x2 3x1 | 3x1x2
# 2x3 | x2^2 x1 | x3 4x3+x1 | 2x2^2 + x3
mat1_11 = ExprSymbolic([1], np.array([[1, 0, 0]]))
mat1_12 = ExprSymbolic([1], np.array([[1, 1, 0]]))
mat1_21 = ExprSymbolic([2], np.array([[0, 0, 1]]))
mat1_22 = ExprSymbolic([1], np.array([[0, 2, 0]]))
mat2_11 = ExprSymbolic([1], np.array([[1, 0, 0]]))
mat2_12 = ExprSymbolic([1], np.array([[1, 1, 0]]))
mat2_21 = ExprSymbolic([1], np.array([[1, 0, 0]]))
mat2_22 = ExprSymbolic([1], np.array([[0, 0, 1]]))
target_11 = ExprSymbolic([3], np.array([[1, 0, 0]]))
target_12 = ExprSymbolic([3], np.array([[1, 1, 0]]))
target_21 = ExprSymbolic([4, 1], np.array([[0, 0, 1], [1, 0, 0]]))
target_22 = ExprSymbolic([2, 1], np.array([[0, 2, 0], [0, 0, 1]]))
mat1 = ExprMatrix("a",
np.array([[mat1_11, mat1_12], [mat1_21, mat1_22]]),
np.array([1]))
mat2 = ExprMatrix("b",
np.array([[mat2_11, mat2_12], [mat2_21, mat2_22]]),
np.array([1]))
target = ExprMatrix(
"target", np.array([[target_11, target_12], [target_21,
target_22]]),
np.array([1]))
expressions_list = [mat1, mat2]
weights = ExprSymbolic.FindLinearCombination(expressions_list,
target)[0]
self.assertEquals(2, len(weights))
self.assertAlmostEqual(2.0, weights[0])
self.assertAlmostEqual(1.0, weights[1])
def testDiffHash(self):
a = ExprSymbolic([F(2, 7), F(3, 11)], np.array([[1, 0, 0], [0, 1, 0]]))
b = ExprSymbolic([F(2, 13), F(3, 19)], np.array([[1, 0, 0], [0, 1,
0]]))
self.assertNotEquals(a, b)
def testHashIsComputed(self):
a = ExprSymbolic([2, 3], np.array([[1, 2, 3], [4, 5, 6]]))
b = ExprSymbolic([2, 2], np.array([[1, 2, 3], [4, 5, 6]]))
self.assertNotEquals(a, b)