def testPredictionDistributionOverlap(self):
""" Test the distribution of predictions with overlapping input SDRs
Here, we intend the classifier to learn the associations:
SDR1 => bucketIdx 0 (30%)
=> bucketIdx 1 (30%)
=> bucketIdx 2 (40%)
SDR2 => bucketIdx 1 (50%)
=> bucketIdx 3 (50%)
SDR1 and SDR2 has 10% overlaps (2 bits out of 20)
The classifier should get the distribution almost right despite the overlap
"""
c = Classifier(0.0005)
# generate 2 SDRs with 2 shared bits
SDR1 = SDR(100)
SDR2 = SDR(100)
SDR1.randomize(.20)
SDR2.setSDR(SDR1)
SDR2.addNoise(.9)
random.seed(42)
for _ in range(5000):
randomNumber = random.random()
if randomNumber < 0.3:
bucketIdx = 0
elif randomNumber < 0.6:
bucketIdx = 1
else:
bucketIdx = 2
c.learn(SDR1, bucketIdx)
randomNumber = random.random()
if randomNumber < 0.5:
bucketIdx = 1
else:
bucketIdx = 3
c.learn(SDR2, bucketIdx)
result1 = c.infer(SDR1)
self.assertAlmostEqual(result1[0], 0.3, places=1)
self.assertAlmostEqual(result1[1], 0.3, places=1)
self.assertAlmostEqual(result1[2], 0.4, places=1)
result2 = c.infer(SDR2)
self.assertAlmostEqual(result2[1], 0.5, places=1)
self.assertAlmostEqual(result2[3], 0.5, places=1)
def testOverlapPattern(self):
classifier = Classifier(alpha=10.0)
inp = SDR(10)
inp.randomize(.2)
classifier.learn(pattern=inp, classification=9)
classifier.learn(pattern=inp, classification=9)
inp.addNoise(.5)
retval = classifier.infer(pattern=inp)
# Since overlap - should be previous with high likelihood
self.assertGreater(retval[9], 0.9)
classifier.learn(pattern=inp, classification=2)
classifier.learn(pattern=inp, classification=2)
# Second example: now new value should be more probable than old
retval = classifier.infer(pattern=inp)
self.assertGreater(retval[2], retval[9])
def testAddNoise(self):
A = SDR((103, ))
B = SDR((103, ))
A.randomize(.1)
B.setSDR(A)
A.addNoise(.5)
assert (A.getOverlap(B) == 5)
# Check different seed makes different results.
A.randomize(.3, 42)
B.randomize(.3, 42)
A.addNoise(.5)
B.addNoise(.5)
assert (A != B)
# Check same seed makes same results.
A.randomize(.3, 42)
B.randomize(.3, 42)
A.addNoise(.5, 42)
B.addNoise(.5, 42)
assert (A == B)
# Check that it returns itself.
C = A.addNoise(.5)
assert (C is A)
run()
# Lesson 3, Trying similar inputs.
print("=" * 70)
print("")
print("")
print("Lesson 3) Similar inputs give similar outputs.")
print(" Now we are changing the input SDR slightly.")
print(" We change a small percentage of 1s to 0s and 0s to 1s.")
print(
" The resulting SDRs are similar, but not identical to the original SDR"
)
print("")
print("Adding 10% noise to the input SDR from the previous run.")
inputSDR.addNoise(0.10)
print("Input " + str(inputSDR))
print("")
run()
print("Notice how the output SDR hardly changed at all.")
print("")
print("")
print("Adding another 20% noise to the input SDR.")
inputSDR.addNoise(0.2)
print("Input " + str(inputSDR))
print("")
run()
print(
"The output SDR now differs considerably from that of the previous output."
)
print("However there are still similarities between the outputs.")
