def testCompute(self):
""" Check that there are no errors in call to compute. """
inputs = SDR(100).randomize(.05)
active = SDR(100)
sp = SP(inputs.dimensions, active.dimensions, stimulusThreshold=1)
sp.compute(inputs, True, active)
assert (active.getSum() > 0)
def testNan(self):
gc = GridCellEncoder(size=200,
sparsity=.25,
periods=[6, 8.5, 12, 17, 24],
seed=42)
zero = SDR(gc.dimensions)
zero.randomize(.25)
gc.encode([3, float('nan')], zero)
assert (zero.getSum() == 0)
def testNaNs(self):
p = ScalarEncoderParameters()
p.size = 100
p.activeBits = 10
p.minimum = 0
p.maximum = 100
enc = ScalarEncoder(p)
sdr = SDR( 100 )
enc.encode( float("nan"), sdr )
assert( sdr.getSum() == 0 )
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 testSetSDR(self):
A = SDR((103, ))
B = SDR((103, ))
A.sparse = [66]
B.setSDR(A)
assert (B.dense[66] == 1)
assert (B.getSum() == 1)
B.dense[77] = 1
B.dense = B.dense
A.setSDR(B)
assert (set(A.sparse) == set((66, 77)))
# Test wrong dimensions assigned
C = SDR((2, 4, 5, 1, 1, 1, 1, 3))
C.randomize(.5)
try:
A.setSDR(C)
except RuntimeError:
pass
else:
self.fail()
# Check return value.
D = A.setSDR(B)
assert (D is A)
def testMirroring(self):
A = SDR(100)
A.randomize(.05)
Ax10 = SDR(100 * 10)
Ax10.concatenate([A] * 10)
assert (Ax10.getSum() == 100 * 10 * .05)
def testGetSum(self):
A = SDR((103, ))
assert (A.getSum() == 0)
A.dense = np.ones(A.size)
assert (A.getSum() == 103)
