def testKernelRegressionTrainAndPredict(self):
n, d = 100, 20
truth = self.rng.normal(size=d)
xs = self.rng.normal(size=(n, d))
ys = jnp.dot(xs, truth)
kernel = lambda x, y: jnp.dot(x, y, precision=lax.Precision.HIGH)
predict = kernel_lsq.train(kernel, xs, ys)
np.testing.assert_allclose(predict(xs), ys, atol=1e-3, rtol=1e-3)
def testKernelRegressionTrainAndPredict(self):
n, d = 100, 20
rng = onp.random.RandomState(0)
truth = rng.randn(d)
xs = rng.randn(n, d)
ys = np.dot(xs, truth)
kernel = lambda x, y: np.dot(x, y)
predict = kernel_lsq.train(kernel, xs, ys)
assert np.allclose(predict(xs), ys, atol=1e-3)
def testKernelRegressionTrainAndPredict(self):
# TODO(frostig): reenable this test.
self.skipTest("Test is broken")
n, d = 100, 20
rng = onp.random.RandomState(0)
truth = rng.randn(d)
xs = rng.randn(n, d)
ys = np.dot(xs, truth)
kernel = lambda x, y: np.dot(x, y)
predict = kernel_lsq.train(kernel, xs, ys)
assert np.allclose(predict(xs), ys, atol=1e-3)
def testKernelRegressionTrainAndPredict(self):
n, d = 100, 20
rng = np.random.RandomState(0)
truth = rng.randn(d)
xs = rng.randn(n, d)
ys = jnp.dot(xs, truth)
kernel = lambda x, y: jnp.dot(x, y)
predict = kernel_lsq.train(kernel, xs, ys)
self.assertAllClose(predict(xs),
ys,
atol=1e-3,
rtol=1e-3,
check_dtypes=False)