# coding: GBK
import KNNPredict
import Training
import configuration
if __name__ == '__main__':
testFileVectorFile = open('E:\\TextClassificationData\\test_content.txt',
'w')
testFileVector = None
print 'began to get testFileVector'
testFileVector = KNNPredict.getDocVector(KNNPredict.test_files_to_words,
Training.featureVector)
print 'finished getting testFileVector'
print 'began to predicting by knn method'
result, prediction = KNNPredict.KNN(KNNPredict.training_doc_vector,
testFileVector,
configuration.top_k_number)
true_positive, predicted, accuracy = KNNPredict.get_accuracy(prediction)
print 'finished predicting'
testFileVectorFile.write('%d %d accuracy:%.6lf' %
(true_positive, predicted, accuracy) + '\n')
for doc in result:
testFileVectorFile.write(
str(doc).encode('gbk') + ' ' + str(prediction[doc]).encode('gbk') +
'\n')
vector = result[doc]
for neighbor in vector:
testFileVectorFile.write(' ' + neighbor[0].encode('gbk') + '\n')
# coding: GBK
import KNNPredict
import Training
import configuration
if __name__ == '__main__':
testFileVectorFile = open('E:\\TextClassificationData\\test_content.txt', 'w')
testFileVector = None
print 'began to get testFileVector'
testFileVector = KNNPredict.getDocVector(KNNPredict.test_files_to_words, Training.featureVector)
print 'finished getting testFileVector'
print 'began to predicting by knn method'
result, prediction = KNNPredict.KNN(KNNPredict.training_doc_vector, testFileVector, configuration.top_k_number)
true_positive, predicted, accuracy = KNNPredict.get_accuracy(prediction)
print 'finished predicting'
testFileVectorFile.write('%d %d accuracy:%.6lf'%(true_positive, predicted, accuracy)+'\n')
for doc in result:
testFileVectorFile.write(str(doc).encode('gbk')+' '+str(prediction[doc]).encode('gbk')+'\n')
vector = result[doc]
for neighbor in vector:
testFileVectorFile.write(' '+neighbor[0].encode('gbk')+'\n')
testFileVectorFile.write(' '+str(neighbor[1])+'\n')
testFileVectorFile.close()
