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, 0.25, 0.33, 0.5, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98),
]
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.intersection(sdrs)
mean_sparsity = np.product(sparsities)
assert (X.getSparsity() >= (2. / 3.) * mean_sparsity)
assert (X.getSparsity() <= (4. / 3.) * mean_sparsity)
def testExampleUsage(self):
A = SDR(10)
B = SDR(10)
X = SDR(A.dimensions)
A.sparse = [0, 1, 2, 3]
B.sparse = [2, 3, 4, 5]
X.intersection(A, B)
assert (set(X.sparse) == set([2, 3]))
def testInPlace(self):
A = SDR(1000)
B = SDR(1000)
A.randomize(1.00)
B.randomize(.50)
A.intersection(A, B)
assert (A.getSparsity() == .5)
A.randomize(1.00)
B.randomize(.50)
A.intersection(B, A)
assert (A.getSparsity() == .5)
def calculateRawAnomaly(activeCols, predictiveCols ):
"""
:param activeColSDR: SDR with active columns
:param predictiveColsSDR: SDR with predictive columns, means columns where some of the cells are predictive
:return: Raw anomaly score in range <0.0, 1.0>
"""
if activeCols.dimensions != predictiveCols.dimensions:
raise ValueError("activeColumns must have same dimension as predictiveCellsSDR!")
if activeCols.getSum() != 0:
intersect = SDR(activeCols.dimensions)
intersect.intersection(activeCols, predictiveCols)
rawAnomaly = (activeCols.getSum() - intersect.getSum()) / activeCols.getSum()
if rawAnomaly<0 or rawAnomaly>1.0:
raise ValueError("rawAnomaly out of bounds! <0.0, 1.0>")
else:
rawAnomaly = 0
return rawAnomaly
def testReturn(self):
A = SDR(10).randomize(.5)
B = SDR(10).randomize(.5)
X = SDR(A.dimensions)
Y = X.intersection(A, B)
assert (X is Y)
