def test_cached_kernel(self):
nchunks = 5
n = 50 * nchunks
d = Dataset(np.random.randn(n, 132))
d.sa.chunks = np.random.randint(nchunks, size=n)
# We'll compare against an Rbf just because it has a parameter to change
rk = npK.RbfKernel(sigma=1.5)
# Assure two kernels are independent for this test
ck = CachedKernel(kernel=npK.RbfKernel(sigma=1.5))
ck.compute(d) # Initial cache of all data
self.assertTrue(ck._recomputed,
'CachedKernel was not initially computed')
# Try some splitting
for chunk in [d[d.sa.chunks == i] for i in range(nchunks)]:
rk.compute(chunk)
ck.compute(chunk)
self.kernel_equiv(rk, ck) #, accuracy=1e-12)
self.failIf(ck._recomputed,
"CachedKernel incorrectly recomputed it's kernel")
# Test what happens when a parameter changes
ck.params.sigma = 3.5
ck.compute(d)
self.assertTrue(ck._recomputed,
"CachedKernel doesn't recompute on kernel change")
rk.params.sigma = 3.5
rk.compute(d)
self.assertTrue(np.all(rk._k == ck._k),
'Cached and rbf kernels disagree after kernel change')
# Now test handling new data
d2 = Dataset(np.random.randn(32, 43))
ck.compute(d2)
self.assertTrue(
ck._recomputed,
"CachedKernel did not automatically recompute new data")
ck.compute(d)
self.assertTrue(ck._recomputed,
"CachedKernel did not recompute old data which had\n" + \
"previously been computed, but had the cache overriden")
def test_rbf_sg(self):
d1 = np.random.randn(105, 32)
d2 = np.random.randn(41, 32)
sk = sgK.RbfSGKernel()
nk = npK.RbfKernel()
sigmavals = np.logspace(-2, 5, num=10)
for s in sigmavals:
sk.params.sigma = s
nk.params.sigma = s
sk.compute(d1, d2)
nk.compute(d1, d2)
self.kernel_equiv(nk, sk)