def test_lwr_linear(self):
"""Test LWLR on random linear models of dimensions from 1 to 20.
It should return exact results, give of take floating point imprecisions."""
for cpp in [True, False]:
if cpp:
learners.enable_fastlearners(silent_fail=False)
else:
learners.disable_fastlearners()
for _ in range(20):
n = random.randint(1, 20)
m = random.randint(1, 5)
f = random_linear(n, m)
cfg = {'m_channels' : [learners.Channel('x_{}'.format(i), (0.0, 1.0))
for i in range(n)],
's_channels' : [learners.Channel('y_{}'.format(i), (0.0, 1.0))
for i in range(m)],
'm_uniformize': True,
'sigma' : 1.0}
for learner in [learners.LWLRLearner(cfg), learners.ESLWLRLearner(cfg)]:
for _ in range(2*n):
x = np.random.rand(n)
y = f(x)
learner.update(tools.to_signal(x, cfg['m_channels']),
tools.to_signal(y, cfg['s_channels']))
for _ in range(10):
x = np.random.rand(n).ravel()
y = f(x)
yp = learner.predict(tools.to_signal(x, cfg['m_channels']))
yp = tools.to_vector(yp, cfg['s_channels'])
self.assertTrue(np.allclose(y, yp, rtol = 1e-5, atol = 1e-5))
def test_lwr_linear(self):
"""Test LWLR on random linear models of dimensions from 1 to 20.
It should return exact results, give of take floating point imprecisions."""
for cpp in [True, False]:
if cpp:
learners.enable_fastlearners(silent_fail=False)
else:
learners.disable_fastlearners()
for _ in range(20):
n = random.randint(1, 20)
m = random.randint(1, 5)
f = random_linear(n, m)
cfg = {
'm_channels': [
learners.Channel('x_{}'.format(i), (0.0, 1.0))
for i in range(n)
],
's_channels': [
learners.Channel('y_{}'.format(i), (0.0, 1.0))
for i in range(m)
],
'm_uniformize':
True,
'sigma':
1.0
}
for learner in [
learners.LWLRLearner(cfg),
learners.ESLWLRLearner(cfg)
]:
for _ in range(2 * n):
x = np.random.rand(n)
y = f(x)
learner.update(tools.to_signal(x, cfg['m_channels']),
tools.to_signal(y, cfg['s_channels']))
for _ in range(10):
x = np.random.rand(n).ravel()
y = f(x)
yp = learner.predict(
tools.to_signal(x, cfg['m_channels']))
yp = tools.to_vector(yp, cfg['s_channels'])
self.assertTrue(
np.allclose(y, yp, rtol=1e-5, atol=1e-5))
def bench_lwlr_linear_online(cpp, K=1000):
"""Test LWLR on random linear models of dimensions from 1 to 20.
It should return exact results, give of take floating point imprecisions."""
random.seed(0)
np.random.seed(0)
if cpp:
learners.enable_fastlearners(silent_fail=False)
else:
learners.disable_fastlearners()
for i in range(K):
n = random.randint(1, 20)
m = random.randint(1, 5)
f = random_linear(n, m)
cfg = {'m_channels' : [learners.Channel('x_{}'.format(i), (0.0, 1.0))
for i in range(n)],
's_channels' : [learners.Channel('y_{}'.format(i), (0.0, 1.0))
for i in range(m)],
'm_uniformize': True,
'sigma' : 1.0}
for learner in [learners.LWLRLearner(cfg), learners.ESLWLRLearner(cfg)]:
for i in range(50):
x = np.random.rand(n)
y = f(x)
learner.update(learners.tools.to_signal(x, cfg['m_channels']),
learners.tools.to_signal(y, cfg['s_channels']))
for i in range(950):
x = np.random.rand(n)
y = f(x)
learner.update(learners.tools.to_signal(x, cfg['m_channels']),
learners.tools.to_signal(y, cfg['s_channels']))
x = np.random.rand(n).ravel()
y = f(x)
yp = learner.predict(learners.tools.to_signal(x, cfg['m_channels']))
yp = learners.tools.to_vector(yp, cfg['s_channels'])
assert np.allclose(y, yp, rtol = 1e-5, atol = 1e-5)