def test_dotSum(self):
A = VectorMatrix(Shape(10,10,10))
B = VectorMatrix(Shape(10,10,10))
for idx in range(A.size()):
A.set(idx, (0.1*idx, 0.2*idx, -0.3*idx))
B.set(idx, (-0.4*idx, 0.5*idx, -0.6*idx))
self.assertTrue(abs(A.dotSum(B) - 79880040.0) < 0.1)
def test_add_1(self):
A = VectorMatrix(Shape(100,100,10))
B = VectorMatrix(Shape(100,100,10))
for idx in range(A.size()):
A.set(idx, (1*idx, 2*idx, 3*idx))
B.set(idx, (4*idx, 5*idx, 6*idx))
A.add(B, 2.0)
for idx in range(A.size()):
self.assertEqual(A.get(idx), (9*idx, 12*idx, 15*idx))
def test_matrix_to_numpy_1(self):
A = Matrix(Shape(10, 10, 10))
A.fill(42.0)
B = A.to_numpy()
self.assertEquals((10, 10, 10), B.shape)
self.assertTrue(np.all(B[:, :, :] == 42.0))
def test_vectormatrix_to_numpy_1(self):
A = VectorMatrix(Shape(10, 10, 10))
A.fill((1.0, 2.0, 3.0))
B = A.to_numpy()
self.assertEquals((10, 10, 10, 3), B.shape)
self.assertTrue(np.all(B[:, :, :, 0] == 1.0))
self.assertTrue(np.all(B[:, :, :, 1] == 2.0))
self.assertTrue(np.all(B[:, :, :, 2] == 3.0))
def test_fill(self):
m1 = Matrix(Shape(10, 10, 10))
m1.fill(1.0)
self.assertEqual(m1.get(4, 4, 4), 1.0)
self.assertEqual(m1.uniform_value, 1.0)
self.assertTrue(m1.isUniform())
m1.set(4, 4, 4, 2.0)
self.assertEqual(m1.get(4, 4, 4), 2.0)
self.assertFalse(m1.isUniform())
def test_fill(self):
m1 = VectorMatrix(Shape(10, 10, 10));
m1.fill((1.0, 2.0, 3.0));
self.assertEqual(m1.get(4, 4, 4), (1.0, 2.0, 3.0))
self.assertEqual(m1.uniform_value, (1.0, 2.0, 3.0))
self.assertTrue(m1.isUniform())
m1.set(4, 4, 4, (3.0, 2.0, 1.0))
self.assertEqual(m1.get(4, 4, 4), (3.0, 2.0, 1.0))
self.assertEqual(m1.get(0, 1, 2), (1.0, 2.0, 3.0))
self.assertFalse(m1.isUniform())
def test_matrix_to_numpy_2(self):
def flub(x, y, z):
return x * y * z + 42 + (5 * x) * (2 - y) + 8 * z
A = Matrix(Shape(10, 20, 30))
for x, y, z in itertools.product(range(10), range(20), range(30)):
A.set(x, y, z, flub(x, y, z))
B = A.to_numpy()
self.assertEquals(A.shape, B.shape)
for x, y, z in itertools.product(range(10), range(20), range(30)):
self.assertEquals(flub(x, y, z), B[x, y, z])
def test_vectormatrix_to_numpy_2(self):
def flub(x, y, z):
return x * y * z, 42, (5 * x) * (2 - y) + 8 * z
A = VectorMatrix(Shape(10, 20, 30))
for x, y, z in itertools.product(range(10), range(20), range(30)):
A.set(x, y, z, flub(x, y, z))
B = A.to_numpy()
self.assertEquals(A.shape + (3, ), B.shape)
for x, y, z in itertools.product(range(10), range(20), range(30)):
f = flub(x, y, z)
self.assertEquals(f[0], B[x, y, z, 0])
self.assertEquals(f[1], B[x, y, z, 1])
self.assertEquals(f[2], B[x, y, z, 2])
def test_average(self):
A = VectorMatrix(Shape(10,10,10))
for idx in range(A.size()):
A.set(idx, (idx, 2*idx, 3*idx))
self.assertTrue(A.average() == (999.0/2,2*999.0/2,3*999.0/2))
def test_absMax(self):
A = VectorMatrix(Shape(10,10,10))
for idx in range(A.size()):
A.set(idx, (10,10,10))
A.set(500, (10,20,30))
self.assertTrue(A.absMax() - 37.41657387 < 0.0001)
def test_fill(self):
A = VectorMatrix(Shape(100,100,10))
A.fill((1.0, 2.0, 3.0))
for idx in range(A.size()):
self.assertTrue(A.get(idx) == (1.0, 2.0, 3.0))