def test_sum():
m1 = TensorProductMean(f1)
m2 = TensorProductMean(f2)
# Test equality.
assert m1 + m2 == m1 + m2
assert m1 + m2 == m2 + m1
assert m1 + m2 != ZeroMean() + m2
assert m1 + m2 != m1 + ZeroMean()
def test_function_mean():
m1 = 5 * OneMean() + (lambda x: x**2)
m2 = (lambda x: x**2) + 5 * OneMean()
m3 = (lambda x: x**2) + ZeroMean()
m4 = ZeroMean() + (lambda x: x**2)
x = np.random.randn(10, 1)
yield ok, np.allclose(m1(x), 5 + x**2)
yield ok, np.allclose(m2(x), 5 + x**2)
yield ok, np.allclose(m3(x), x**2)
yield ok, np.allclose(m4(x), x**2)
def my_function(x):
pass
yield eq, str(TensorProductMean(my_function)), 'my_function'
def test_basic_arithmetic():
dispatch = Dispatcher()
@dispatch(Number)
def f1(x):
return np.array([[x**2]])
@dispatch(object)
def f1(x):
return np.sum(x**2, axis=1)[:, None]
@dispatch(Number)
def f2(x):
return np.array([[x**3]])
@dispatch(object)
def f2(x):
return np.sum(x**3, axis=1)[:, None]
m1 = TensorProductMean(f1)
m2 = TensorProductMean(f2)
m3 = ZeroMean()
x1 = np.random.randn(10, 2)
x2 = np.random.randn()
yield ok, np.allclose((m1 * m2)(x1), m1(x1) * m2(x1)), 'prod'
yield ok, np.allclose((m1 * m2)(x2), m1(x2) * m2(x2)), 'prod 2'
yield ok, np.allclose((m1 + m3)(x1), m1(x1) + m3(x1)), 'sum'
yield ok, np.allclose((m1 + m3)(x2), m1(x2) + m3(x2)), 'sum 2'
yield ok, np.allclose((5. * m1)(x1), 5. * m1(x1)), 'prod 3'
yield ok, np.allclose((5. * m1)(x2), 5. * m1(x2)), 'prod 4'
yield ok, np.allclose((5. + m1)(x1), 5. + m1(x1)), 'sum 3'
yield ok, np.allclose((5. + m1)(x2), 5. + m1(x2)), 'sum 4'
def test_tensor_product():
m1 = 5 * OneMean() + (lambda x: x ** 2)
m2 = (lambda x: x ** 2) + 5 * OneMean()
m3 = (lambda x: x ** 2) + ZeroMean()
m4 = ZeroMean() + (lambda x: x ** 2)
x = B.randn(10, 1)
assert np.allclose(m1(x), 5 + x ** 2)
assert np.allclose(m2(x), 5 + x ** 2)
assert np.allclose(m3(x), x ** 2)
assert np.allclose(m4(x), x ** 2)
def my_function(x):
pass
assert str(TensorProductMean(my_function)) == "my_function"
def test_derivative():
yield eq, str(EQ().diff(0)), 'd(0) EQ()'
yield eq, str(EQ().diff(0, 1)), 'd(0, 1) EQ()'
yield eq, str(ZeroKernel().diff(0)), '0'
yield eq, str(OneKernel().diff(0)), '0'
yield eq, str(ZeroMean().diff(0)), '0'
yield eq, str(OneMean().diff(0)), '0'
def test_derivative():
assert str(EQ().diff(0)) == 'd(0) EQ()'
assert str(EQ().diff(0, 1)) == 'd(0, 1) EQ()'
assert str(ZeroKernel().diff(0)) == '0'
assert str(OneKernel().diff(0)) == '0'
assert str(ZeroMean().diff(0)) == '0'
assert str(OneMean().diff(0)) == '0'
def test_basic_arithmetic():
m1 = TensorProductMean(f1)
m2 = TensorProductMean(f2)
m3 = ZeroMean()
x1 = B.randn(10, 2)
x2 = B.randn()
approx((m1 * m2)(x1), m1(x1) * m2(x1))
approx((m1 * m2)(x2), m1(x2) * m2(x2))
approx((m1 + m3)(x1), m1(x1) + m3(x1))
approx((m1 + m3)(x2), m1(x2) + m3(x2))
approx((5.0 * m1)(x1), 5.0 * m1(x1))
approx((5.0 * m1)(x2), 5.0 * m1(x2))
approx((5.0 + m1)(x1), 5.0 + m1(x1))
approx((5.0 + m1)(x2), 5.0 + m1(x2))
def test_shifting():
# Kernels:
yield eq, str(ZeroKernel().shift(5)), '0'
yield eq, str(EQ().shift(5)), 'EQ()'
yield eq, str(Linear().shift(5)), 'Linear() shift 5'
yield eq, str((5 * EQ()).shift(5)), '5 * EQ()'
yield eq, str((5 * Linear()).shift(5)), '(5 * Linear()) shift 5'
# Means:
def mean(x):
return x
m = TensorProductMean(mean)
yield eq, str(ZeroMean().shift(5)), '0'
yield eq, str(m.shift(5)), 'mean shift 5'
yield eq, str(m.shift(5).shift(5)), 'mean shift 10'
yield eq, str((5 * m).shift(5)), '(5 * mean) shift 5'
def test_shifting():
# Kernels:
assert str(ZeroKernel().shift(5)) == '0'
assert str(EQ().shift(5)) == 'EQ()'
assert str(Linear().shift(5)) == 'Linear() shift 5'
assert str((5 * EQ()).shift(5)) == '5 * EQ()'
assert str((5 * Linear()).shift(5)) == '(5 * Linear()) shift 5'
# Means:
def mean(x):
return x
m = TensorProductMean(mean)
assert str(ZeroMean().shift(5)) == '0'
assert str(m.shift(5)) == 'mean shift 5'
assert str(m.shift(5).shift(5)) == 'mean shift 10'
assert str((5 * m).shift(5)) == '(5 * mean) shift 5'
def test_ones_zeros():
c = Cache()
# Nothing to check for kernels: ones and zeros are represented in a
# structured way.
# Test that ones and zeros are cached and that all signatures work.
m = ZeroMean()
yield eq, id(m(np.random.randn(10, 10), c)), \
id(m(np.random.randn(10, 10), c))
yield neq, id(m(np.random.randn(10, 10), c)), \
id(m(np.random.randn(5, 10), c))
yield eq, id(m(1, c)), id(m(1, c))
m = OneMean()
yield eq, id(m(np.random.randn(10, 10), c)), \
id(m(np.random.randn(10, 10), c))
yield neq, id(m(np.random.randn(10, 10), c)), \
id(m(np.random.randn(5, 10), c))
yield eq, id(m(1, c)), id(m(1, c))