def runTestPCAKNN(self, short = 0):
LOGGER.info('\nTesting PCA/k-NN classifier')
LOGGER.info('Mode=%s', short)
numDims = 10
numClasses = 10
k = 10
numPatternsPerClass = 100
numPatterns = int(.9 * numClasses * numPatternsPerClass)
numTests = numClasses * numPatternsPerClass - numPatterns
numSVDSamples = int(.1 * numPatterns)
keep = 1
train_data, train_class, test_data, test_class = \
pca_knn_data.generate(numDims, numClasses, k, numPatternsPerClass,
numPatterns, numTests, numSVDSamples, keep)
pca_knn = KNNClassifier(k=k,numSVDSamples=numSVDSamples,
numSVDDims=keep)
knn = KNNClassifier(k=k)
LOGGER.info('Training PCA k-NN')
for i in range(numPatterns):
knn.learn(train_data[i], train_class[i])
pca_knn.learn(train_data[i], train_class[i])
LOGGER.info('Testing PCA k-NN')
numWinnerFailures = 0
numInferenceFailures = 0
numDistFailures = 0
numAbsErrors = 0
for i in range(numTests):
winner, inference, dist, categoryDist = knn.infer(test_data[i])
pca_winner, pca_inference, pca_dist, pca_categoryDist \
= pca_knn.infer(test_data[i])
if winner != test_class[i]:
numAbsErrors += 1
if pca_winner != winner:
numWinnerFailures += 1
if (numpy.abs(pca_inference - inference) > 1e-4).any():
numInferenceFailures += 1
if (numpy.abs(pca_dist - dist) > 1e-4).any():
numDistFailures += 1
s0 = 100*float(numTests - numAbsErrors) / float(numTests)
s1 = 100*float(numTests - numWinnerFailures) / float(numTests)
s2 = 100*float(numTests - numInferenceFailures) / float(numTests)
s3 = 100*float(numTests - numDistFailures) / float(numTests)
LOGGER.info('PCA/k-NN success rate=%s%s', s0, '%')
LOGGER.info('Winner success=%s%s', s1, '%')
LOGGER.info('Inference success=%s%s', s2, '%')
LOGGER.info('Distance success=%s%s', s3, '%')
self.assertEqual(s1, 100.0,
"PCA/k-NN test failed")
def runTestPCAKNN(self, short=0):
LOGGER.info('\nTesting PCA/k-NN classifier')
LOGGER.info('Mode=%s', short)
numDims = 10
numClasses = 10
k = 10
numPatternsPerClass = 100
numPatterns = int(.9 * numClasses * numPatternsPerClass)
numTests = numClasses * numPatternsPerClass - numPatterns
numSVDSamples = int(.1 * numPatterns)
keep = 1
train_data, train_class, test_data, test_class = \
pca_knn_data.generate(numDims, numClasses, k, numPatternsPerClass,
numPatterns, numTests, numSVDSamples, keep)
pca_knn = KNNClassifier(k=k,
numSVDSamples=numSVDSamples,
numSVDDims=keep)
knn = KNNClassifier(k=k)
LOGGER.info('Training PCA k-NN')
for i in range(numPatterns):
knn.learn(train_data[i], train_class[i])
pca_knn.learn(train_data[i], train_class[i])
LOGGER.info('Testing PCA k-NN')
numWinnerFailures = 0
numInferenceFailures = 0
numDistFailures = 0
numAbsErrors = 0
for i in range(numTests):
winner, inference, dist, categoryDist = knn.infer(test_data[i])
pca_winner, pca_inference, pca_dist, pca_categoryDist \
= pca_knn.infer(test_data[i])
if winner != test_class[i]:
numAbsErrors += 1
if pca_winner != winner:
numWinnerFailures += 1
if (numpy.abs(pca_inference - inference) > 1e-4).any():
numInferenceFailures += 1
if (numpy.abs(pca_dist - dist) > 1e-4).any():
numDistFailures += 1
s0 = 100 * float(numTests - numAbsErrors) / float(numTests)
s1 = 100 * float(numTests - numWinnerFailures) / float(numTests)
s2 = 100 * float(numTests - numInferenceFailures) / float(numTests)
s3 = 100 * float(numTests - numDistFailures) / float(numTests)
LOGGER.info('PCA/k-NN success rate=%s%s', s0, '%')
LOGGER.info('Winner success=%s%s', s1, '%')
LOGGER.info('Inference success=%s%s', s2, '%')
LOGGER.info('Distance success=%s%s', s3, '%')
self.assertEqual(s1, 100.0, "PCA/k-NN test failed")