def test_linear_add_reduce():
ops = LinearOperations()
assert_almost_equal(ops.add_reduce(np.array([0,0,0])), 0)
assert_almost_equal(ops.add_reduce(np.array([0,1,2])), 3)
assert_almost_equal(ops.add_reduce(np.array([1,1,1])), 3)
assert_almost_equal(ops.add_reduce(np.array([-1,0,1])), 0)
assert_almost_equal(ops.add_reduce(np.array([2,0,-2])), 0)
assert_almost_equal(ops.add_reduce(np.array([-1,-1,-1])), -3)
def test_linear_mult():
ops = LinearOperations()
assert_almost_equal(ops.mult(0, 0), 0)
assert_almost_equal(ops.mult(1, 0), 0)
assert_almost_equal(ops.mult(0, 1), 0)
assert_almost_equal(ops.mult(-1, 0), 0)
assert_almost_equal(ops.mult(0, -1), 0)
assert_almost_equal(ops.mult(1, -1), -1)
assert_almost_equal(ops.mult(-1, 1), -1)
assert_almost_equal(ops.mult(1, 1), 1)
assert_almost_equal(ops.mult(-1, -1), 1)
assert_almost_equal(ops.mult(2, 1), 2)
assert_almost_equal(ops.mult(2, 2), 4)
assert_almost_equal(ops.mult(2, -2), -4)
def test_linear_add():
ops = LinearOperations()
assert_almost_equal(ops.add(0, 0), 0)
assert_almost_equal(ops.add(1, 0), 1)
assert_almost_equal(ops.add(0, 1), 1)
assert_almost_equal(ops.add(-1, 0), -1)
assert_almost_equal(ops.add(0, -1), -1)
assert_almost_equal(ops.add(1, -1), 0)
assert_almost_equal(ops.add(-1, 1), 0)
assert_almost_equal(ops.add(1, 1), 2)
assert_almost_equal(ops.add(-1, -1), -2)
def setup_class(self):
self.ops = LinearOperations()
class TestLinear(object):
def setup_class(self):
self.ops = LinearOperations()
def test_add(self):
X = np.array([0, 1, 0, -1, 0, 1, -1, 1, -1])
Y = np.array([0, 0, 1, 0, -1, -1, 1, 1, -1])
Z = np.array([0, 1, 1, -1, -1, 0, 0, 2, -2])
for x, y, z in zip(X, Y, Z):
assert self.ops.add(x, y) == pytest.approx(z)
assert np.allclose(self.ops.add(X, Y), Z)
def test_add_inplace(self):
X = np.array([0, 1, 0, -1, 0, 1, -1, 1, -1])
Y = np.array([0, 0, 1, 0, -1, -1, 1, 1, -1])
Z = np.array([0, 1, 1, -1, -1, 0, 0, 2, -2])
self.ops.add_inplace(X, Y)
assert np.allclose(X, Z)
def test_add_reduce(self):
X = np.array([[0, 0, 0], [0, 1, 2], [1, 1, 1], [-1, 0, 1], [2, 0, -2],
[-1, -1, -1]])
Y = np.array([0, 3, 3, 0, 0, -3])
for x, y in zip(X, Y):
assert self.ops.add_reduce(x) == pytest.approx(y)
def test_mult(self):
X = np.array([0, 1, 0, -1, 0, 1, -1, 1, -1, 2, 2, 2])
Y = np.array([0, 0, 1, 0, -1, -1, 1, 1, -1, 1, 2, -2])
Z = np.array([0, 0, 0, 0, 0, -1, -1, 1, 1, 2, 4, -4])
for x, y, z in zip(X, Y, Z):
assert self.ops.mult(x, y) == pytest.approx(z)
assert np.allclose(self.ops.mult(X, Y), Z)
def test_mult_inplace(self):
X = np.array([0, 1, 0, -1, 0, 1, -1, 1, -1, 2, 2, 2])
Y = np.array([0, 0, 1, 0, -1, -1, 1, 1, -1, 1, 2, -2])
Z = np.array([0, 0, 0, 0, 0, -1, -1, 1, 1, 2, 4, -4])
self.ops.mult_inplace(X, Y)
assert np.allclose(X, Z)
def test_invert(self):
X = np.array([1, 2, -1, 10], dtype=float)
Y = np.array([1, 1 / 2, -1, 1 / 10])
for x, y in zip(X, Y):
assert self.ops.invert(x) == pytest.approx(y)
def test_mult_reduce(self):
prods = [1, 2, 6, 24, 120]
for i, p in enumerate(prods):
assert self.ops.mult_reduce(np.arange(1,
i + 2)) == pytest.approx(p)
def test_normalize(self):
X = np.ones(3)
Y = self.ops.normalize(X)
Y_ = X / 3
assert np.allclose(Y, Y_)
def setUp(self):
self.ops = LinearOperations()
class TestLinear(object):
def setUp(self):
self.ops = LinearOperations()
def test_add(self):
X = np.array([0, 1, 0, -1, 0, 1, -1, 1, -1])
Y = np.array([0, 0, 1, 0, -1, -1, 1, 1, -1])
Z = np.array([0, 1, 1, -1, -1, 0, 0, 2, -2])
for x, y, z in zip(X, Y, Z):
assert_almost_equal(self.ops.add(x, y), z)
npt.assert_allclose(self.ops.add(X, Y), Z)
def test_add_inplace(self):
X = np.array([0, 1, 0, -1, 0, 1, -1, 1, -1])
Y = np.array([0, 0, 1, 0, -1, -1, 1, 1, -1])
Z = np.array([0, 1, 1, -1, -1, 0, 0, 2, -2])
self.ops.add_inplace(X, Y)
npt.assert_allclose(X, Z)
def test_add_reduce(self):
X = np.array([[0, 0, 0],
[0, 1, 2],
[1, 1, 1],
[-1, 0, 1],
[2, 0, -2],
[-1, -1, -1]])
Y = np.array([0, 3, 3, 0, 0, -3])
for x, y in zip(X, Y):
assert_almost_equal(self.ops.add_reduce(x), y)
def test_mult(self):
X = np.array([0, 1, 0, -1, 0, 1, -1, 1, -1, 2, 2, 2])
Y = np.array([0, 0, 1, 0, -1, -1, 1, 1, -1, 1, 2, -2])
Z = np.array([0, 0, 0, 0, 0, -1, -1, 1, 1, 2, 4, -4])
for x, y, z in zip(X, Y, Z):
assert_almost_equal(self.ops.mult(x, y), z)
npt.assert_allclose(self.ops.mult(X, Y), Z)
def test_mult_inplace(self):
X = np.array([0, 1, 0, -1, 0, 1, -1, 1, -1, 2, 2, 2])
Y = np.array([0, 0, 1, 0, -1, -1, 1, 1, -1, 1, 2, -2])
Z = np.array([0, 0, 0, 0, 0, -1, -1, 1, 1, 2, 4, -4])
self.ops.mult_inplace(X, Y)
npt.assert_allclose(X, Z)
def test_invert(self):
X = np.array([1, 2, -1, 10], dtype=float)
Y = np.array([1, 1/2, -1, 1/10])
for x, y in zip(X, Y):
assert_almost_equal(self.ops.invert(x), y)
def test_mult_reduce(self):
prods = [1, 2, 6, 24, 120]
for i, p in enumerate(prods):
assert_almost_equal(self.ops.mult_reduce(np.arange(1, i+2)), p)
def test_normalize(self):
X = np.ones(3)
Y = self.ops.normalize(X)
Y_ = X / 3
npt.assert_allclose(Y, Y_)
def setup_class(self):
self.ops = LinearOperations()
def test_linear_invert():
ops = LinearOperations()
assert_almost_equal(ops.invert(1), 1)
assert_almost_equal(ops.invert(2.0), 1/2)
assert_almost_equal(ops.invert(-1), -1)
assert_almost_equal(ops.invert(10.0), 1/10)