def testSparsifyVector(self):
classifier = KNNClassifier(distanceMethod="norm", distanceNorm=2.0)
inputPattern = np.array([0, 1, 3, 7, 11], dtype=np.int32)
# Each of the 4 tests correspond with the each decisional branch in the
# sparsifyVector method
#
# tests: if not self.relativeThreshold:
outputPattern = classifier._sparsifyVector(inputPattern, doWinners=True)
self.assertTrue(np.array_equal(np.array([0, 1, 3, 7, 11], dtype=np.int32),
outputPattern))
# tests: elif self.sparseThreshold > 0:
classifier = KNNClassifier(distanceMethod="norm", distanceNorm=2.0,
relativeThreshold=True, sparseThreshold=.2)
outputPattern = classifier._sparsifyVector(inputPattern, doWinners=True)
self.assertTrue(np.array_equal(np.array([0, 0, 3, 7, 11], dtype=np.int32),
outputPattern))
# tests: if doWinners:
classifier = KNNClassifier(distanceMethod="norm", distanceNorm=2.0,
relativeThreshold=True, sparseThreshold=.2, numWinners=2)
outputPattern = classifier._sparsifyVector(inputPattern, doWinners=True)
self.assertTrue(np.array_equal(np.array([0, 0, 0, 0, 0], dtype=np.int32),
outputPattern))
# tests: Do binarization
classifier = KNNClassifier(distanceMethod="norm", distanceNorm=2.0,
relativeThreshold=True, sparseThreshold=.2, doBinarization=True)
outputPattern = classifier._sparsifyVector(inputPattern, doWinners=True)
self.assertTrue(np.array_equal(np.array(
[0., 0., 1., 1., 1.], dtype=np.float32), outputPattern))
