def runClassification(trainingVocabList, fullData, fullClassVec):
# split into training and test data
trainingData = fullData
trainingClassVec = fullClassVec
TESTINGDATASIZE = 10
testingData = []
actualTestingVec = []
for index in range(0,TESTINGDATASIZE):
import random
i = int(random.uniform(0,len(trainingData)))
testingData.append(trainingData[i])
actualTestingVec.append(trainingClassVec[i])
del(trainingData[i])
del(trainingClassVec[i])
(pC0,pWGivenC0), (pC1,pWGivenC1) = naiveBayes.trainData(trainingVocabList, trainingData, trainingClassVec)
topPC0 = []
topPC1 = []
for testData in testingData:
testDataVector = np.array(naiveBayes.bagOfWordsToVector(trainingVocabList, testData))
pC0GivenData = testDataVector * pWGivenC0 * pC0 + 1
pC1GivenData = testDataVector * pWGivenC1 * pC1 + 1
topPC0 = addUnique(topPC0, getTopN(trainingVocabList, pC0GivenData, 30)) # make a UNIQUE list of the most frequent words
topPC1 = addUnique(topPC1, getTopN(trainingVocabList, pC1GivenData, 30)) # make a UNIQUE list of the most frequent words
return getTopNFromList(topPC0,30), getTopNFromList(topPC1,30)
def runClassification(trainingData, trainingClassVec):
# split training and test data
TESTINGDATASIZE = 10
testingData = []
actualTestingVec = []
for index in range(0,TESTINGDATASIZE):
import random
i = int(random.uniform(0,len(trainingData)))
testingData.append(trainingData[i])
actualTestingVec.append(trainingClassVec[i])
del(trainingData[i])
del(trainingClassVec[i])
trainingVocabList = naiveBayes.createVocabList(trainingData)
(pC0,pWGivenC0), (pC1,pWGivenC1) = naiveBayes.trainData(trainingVocabList, trainingData, trainingClassVec)
predictedTestingVec = []
for testData in testingData:
testDataVector = np.array(naiveBayes.bagOfWordsToVector(trainingVocabList, testData))
pC0GivenData = testDataVector * pWGivenC0 * pC0 + 1
pC1GivenData = testDataVector * pWGivenC1 * pC1 + 1
if sum(np.log(pC0GivenData)) > sum(np.log(pC1GivenData)):
predictedTestingVec.append(0)
else:
predictedTestingVec.append(1)
i = 0
error = 0
misClassified = []
for predicted in predictedTestingVec:
if (actualTestingVec[i] != predicted):
error += 1
misClassified.append(testingData[i])
i += 1
if(DEBUG):
print predictedTestingVec
print actualTestingVec
print 'num errors: %d' % error
print 'misclassified:'
print misClassified
return float(error)/TESTINGDATASIZE