def GetAnswer(self, event):
# The list used to get all the ratio of string similarity.
ratio = []
# Get the classifier.
classifier = SVM.DoSVM()
# Get the vectoricer.
vectorizer = KeywordProcessor.DataTransformer()[1]
# Get the PCA transformer.
pcaTransform = PCADataProcessor.PCADataProcessor()[1]
# Get the users' question.
testData = self.text_user.GetValue()
# Used to store the users' question.
example = []
# Split the users' question.
seg_list = jieba.cut_for_search(testData)
# Store the users's question.
example.append(" ".join(seg_list))
# Transformed users' question into the matrix.
transDataExample = vectorizer.transform(example)
# Store the new matrix.
dataMatrixExample = transDataExample.toarray()
# Compress the matrix.
newExample = pcaTransform.transform(dataMatrixExample)
# Get the prediction value.
result = classifier.predict(newExample)
# Get the question and the answer from the corresponding cluster.
Questions = sheetForQuestion.col_values(result[0])
Answers = sheetForAnswer.col_values(result[0])
# Do the string similarity to get the answer.
for item in Questions:
ratio.append(
difflib.SequenceMatcher(None, testData, item).quick_ratio())
# Ouput the answer.
wx.MessageBox(Answers[ratio.index(max(ratio))])
def CVTestingTraining():
# Get the users' operations.
operation = int(
input(
"Please choose an operation('1' for CV set, '2' for Testing set): "
))
# Get all the value of the column.
for index in range(1, 121):
data.append(sheet.row(index)[operation].value)
# Store the new data which has been cut.
cutData = []
# Store the prediction value.
prediction = []
# Get the classifier.
classifier = SVM.DoSVM()
# Get the vectoricer.
vectorizer = KeywordProcessor.DataTransformer()[1]
# Get the PCA transformer.
pcaTransform = PCADataProcessor.PCADataProcessor()[1]
# Use the jieba to split all the string.
for index in range(0, 120):
# Split the string.
seg_list = jieba.cut_for_search(data[index])
# Restore the splited string into the data.
cutData.append(" ".join(seg_list))
# Get the transformed data.
transformData = vectorizer.transform(cutData)
# Get the data matrix.
dataMatrix = transformData.toarray()
# Do the PCA.
newData = pcaTransform.transform(dataMatrix)
# Get the prediction.
prediction.append(classifier.predict(newData)[0])
# Clear the list.
cutData.clear()
# Return the prediction.
return prediction
# Test the function.
#print(CVTestingTraining())
