def testSparsity(self):
test_cases = [
(0.5, 0.5),
(0.1, 0.9),
(0.25, 0.3),
(0.5, 0.5, 0.5),
(0.95, 0.95, 0.95),
(0.10, 0.10, 0.60),
(0.0, 1.0, 1.0),
(0.5, 0.5, 0.5, 0.5),
(0.11, 0.20, 0.05, 0.04, 0.03, 0.01, 0.01, 0.02, 0.02, 0.02),
]
size = 10000
seed = 99
X = SDR(size)
for sparsities in test_cases:
sdrs = []
for S in sparsities:
inp = SDR(size)
inp.randomize(S, seed)
seed += 1
sdrs.append(inp)
X.union(sdrs)
mean_sparsity = np.product(list(1 - s for s in sparsities))
assert (X.getSparsity() >= (2. / 3.) * (1 - mean_sparsity))
assert (X.getSparsity() <= (4. / 3.) * (1 - mean_sparsity))
def testExampleUsage(self):
A = SDR(10)
B = SDR(10)
U = SDR(A.dimensions)
A.sparse = [0, 1, 2, 3]
B.sparse = [2, 3, 4, 5]
U.union(A, B)
assert (set(U.sparse) == set([0, 1, 2, 3, 4, 5]))
def testInPlace(self):
A = SDR(1000)
B = SDR(1000)
A.randomize(.50)
B.randomize(.50)
A.union(A, B)
assert (A.getSparsity() >= .75 - .05 and A.getSparsity() <= .75 + .05)
A.union(B.randomize(.50), A.randomize(.50))
assert (A.getSparsity() >= .75 - .05 and A.getSparsity() <= .75 + .05)
def testReturn(self):
A = SDR(10).randomize(.5)
B = SDR(10).randomize(.5)
X = SDR(A.dimensions)
Y = X.union(A, B)
assert (X is Y)
