def test_parametric_rep():
"""
For fixed representations: test successful kernel function use, using
varying number of features.
Ensure get expected result. Test normalization, ensure expected result.
"""
for normalization in [False, True]: # verify everything with/out norm
kernel = gaussian_kernel
domain = InfiniteTrackCartPole.InfTrackCartPole() #2 continuous dims
discretization = 20 # not used
num = 1 # number of basis functions to use IN EACH DIMENSION
resolution_min = 1
resolution_max = 5
rep = RandomLocalBases(domain,
kernel,
num,
resolution_min,
resolution_max,
seed=1,
normalization=normalization,
discretization=discretization)
assert rep.features_num == num # in reality, theres one in each dim.
# Center lies within statespace limits
assert np.all(domain.statespace_limits[:, 0] <= rep.centers[0])
assert np.all(rep.centers[0] <= domain.statespace_limits[:, 1])
# width lies within resolution bounds
statespace_range = domain.statespace_limits[:,
1] - domain.statespace_limits[:,
0]
assert np.all(statespace_range / resolution_max <=
rep.widths[0]) # widths[0] has `state_space_dims` cols
assert np.all(rep.widths[0] <= statespace_range / resolution_min)
phiVecOrigin = rep.phi(np.array([0, 0], dtype=np.float),
terminal=False)
assert np.all(phiVecOrigin >= 0) # nonnegative feat func values
# feature func only dependent on own dimension
phiVec2 = rep.phi(np.array([0, 1], dtype=np.float), terminal=False)
if normalization:
assert sum(phiVecOrigin) == 1
assert sum(phiVec2) == 1
def test_parametric_rep():
"""
For fixed representations: test successful kernel function use, using
varying number of features.
Ensure get expected result. Test normalization, ensure expected result.
"""
for normalization in [False, True]: # verify everything with/out norm
kernel = gaussian_kernel
domain = InfiniteTrackCartPole.InfTrackCartPole() #2 continuous dims
discretization = 20 # not used
num = 1 # number of basis functions to use IN EACH DIMENSION
resolution_min=1
resolution_max=5
rep = RandomLocalBases(domain, kernel, num, resolution_min,
resolution_max,
seed=1, normalization=normalization,
discretization=discretization)
assert rep.features_num == num # in reality, theres one in each dim.
# Center lies within statespace limits
assert np.all(domain.statespace_limits[:,0] <= rep.centers[0])
assert np.all(rep.centers[0] <= domain.statespace_limits[:,1])
# width lies within resolution bounds
statespace_range = domain.statespace_limits[:, 1] - domain.statespace_limits[:, 0]
assert np.all(statespace_range / resolution_max <= rep.widths[0]) # widths[0] has `state_space_dims` cols
assert np.all(rep.widths[0] <= statespace_range / resolution_min)
phiVecOrigin = rep.phi(np.array([0,0], dtype=np.float), terminal=False)
assert np.all(phiVecOrigin >= 0) # nonnegative feat func values
# feature func only dependent on own dimension
phiVec2 = rep.phi(np.array([0,1], dtype=np.float), terminal=False)
if normalization:
assert sum(phiVecOrigin) == 1
assert sum(phiVec2) == 1
