def test_lab_cache():
c = Cache()
x1, x2 = np.random.randn(10, 10), np.random.randn(10, 10)
x2 = np.random.randn(10, 10)
yield eq, id(c.pw_dists(x1, x2)), id(c.pw_dists(x1, x2))
yield neq, id(c.pw_dists(x1, x1)), id(c.pw_dists(x1, x2))
yield eq, id(c.matmul(x1, x2, tr_a=True)), id(c.matmul(x1, x2, tr_a=True))
yield neq, id(c.matmul(x1, x2, tr_a=True)), id(c.matmul(x1, x2))
def test_construction():
k = EQ()
x = np.random.randn(10, 1)
c = Cache()
yield k, x
yield k, x, c
yield k, x, x
yield k, x, x, c
yield k, Observed(x)
yield k, Observed(x), c
yield k, Observed(x), Observed(x)
yield k, Observed(x), Observed(x), c
yield k, x, Observed(x)
yield k, x, Observed(x), c
yield k, Observed(x), x
yield k, Observed(x), x, c
yield k.elwise, x
yield k.elwise, x, c
yield k.elwise, x, x
yield k.elwise, x, x, c
yield k.elwise, Observed(x)
yield k.elwise, Observed(x), c
yield k.elwise, Observed(x), Observed(x)
yield k.elwise, Observed(x), Observed(x), c
yield k.elwise, x, Observed(x)
yield k.elwise, x, Observed(x), c
yield k.elwise, Observed(x), x
yield k.elwise, Observed(x), x, c
def test_construction():
model = Graph()
p = GP(EQ(), graph=model)
x = np.random.randn(10, 1)
c = Cache()
yield p.mean, x
yield p.mean, p(x)
yield p.mean, x, c
yield p.mean, p(x), c
yield p.kernel, x
yield p.kernel, p(x)
yield p.kernel, x, c
yield p.kernel, p(x), c
yield p.kernel, x, x
yield p.kernel, p(x), x
yield p.kernel, x, p(x)
yield p.kernel, p(x), p(x)
yield p.kernel, x, x, c
yield p.kernel, p(x), x, c
yield p.kernel, x, p(x), c
yield p.kernel, p(x), p(x), c
yield p.kernel.elwise, x
yield p.kernel.elwise, p(x)
yield p.kernel.elwise, x, c
yield p.kernel.elwise, p(x), c
yield p.kernel.elwise, x, x
yield p.kernel.elwise, p(x), x
yield p.kernel.elwise, x, p(x)
yield p.kernel.elwise, p(x), p(x)
yield p.kernel.elwise, x, x, c
yield p.kernel.elwise, p(x), x, c
yield p.kernel.elwise, x, p(x), c
yield p.kernel.elwise, p(x), p(x), c
# Test resolution of kernel and mean.
k = EQ()
m = TensorProductMean(lambda x: x ** 2)
yield assert_instance, GP(k, graph=model).mean, ZeroMean
yield assert_instance, GP(k, 5, graph=model).mean, ScaledMean
yield assert_instance, GP(k, 1, graph=model).mean, OneMean
yield assert_instance, GP(k, 0, graph=model).mean, ZeroMean
yield assert_instance, GP(k, m, graph=model).mean, TensorProductMean
yield assert_instance, GP(k, graph=model).kernel, EQ
yield assert_instance, GP(5, graph=model).kernel, ScaledKernel
yield assert_instance, GP(1, graph=model).kernel, OneKernel
yield assert_instance, GP(0, graph=model).kernel, ZeroKernel
# Test construction of finite-dimensional distribution.
d = GP(k, m, graph=model)(x)
yield assert_allclose, d.var, k(x)
yield assert_allclose, d.mean, m(x)
def test_construction():
m = TensorProductMean(lambda x: x**2)
x = np.random.randn(10, 1)
c = Cache()
yield m, x
yield m, x, c
yield m, Observed(x)
yield m, Observed(x), c
def test_cache_performance():
c = Cache()
k1 = EQ()
k2 = EQ()
x = np.linspace(0, 1, 2000)
dur1, y1 = benchmark(k1, (x, c), n=1, get_output=True)
dur2, y2 = benchmark(k1, (x, c), n=1, get_output=True)
dur3, y3 = benchmark(k2, (x, c), n=1, get_output=True)
yield assert_allclose, y1, y2
yield assert_allclose, y1, y3
# Test performance of call cache.
yield le, dur2, dur1 / 500
# Test performance of LAB cache.
yield le, dur3, dur1 / 20
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))
def test_elwise_cache_aliasing():
c = Cache()
k = EQ()
x1 = np.random.randn(10, 2)
x2 = np.random.randn(10, 2)
yield assert_allclose, k.elwise(x1, x2, c), Kernel.elwise(k, x1, x2, c)