def runkNNDigit(percent,trainsize,testsize):
trainingData,trainingLabels,testData,testLabels=extractImages("digit",trainsize,testsize)
featureFunction = featureFuncLib.basicFeatureExtractorDigit_3
trainFeatures = list(map(featureFunction, trainingData))
testFeatures = list(map(featureFunction,testData))
trainFeatures,trainingLabels=xPercent(percent,trainFeatures,trainingLabels)
kvalue = 1
start = time.time()
predict=kNN.predict(trainFeatures,trainingLabels,testFeatures,testLabels,kvalue)
end = time.time()
kNNpredictTimePerImage = abs(end-start) / len(predict)
return percentCorrect(testLabels,predict),kNNpredictTimePerImage
def detect_if_lie(self):
# get detected features
number_of_blinks = self.blink_detector.get_and_reset_number_of_blinks()
number_of_blushing_occurred = self.blushing_detector.get_number_of_blushing_occurred_and_reset(
)
number_of_lip_pursing_occurred = self.pursed_lips_detector.get_and_reset_number_of_lip_pursing(
)
if self.seconds > 0:
number_of_blinks_per_second = number_of_blinks / self.seconds
else:
number_of_blinks_per_second = number_of_blinks
to_predict = [
self.person.average_number_of_blinks, number_of_blinks_per_second,
number_of_lip_pursing_occurred, number_of_blushing_occurred
]
if self.algorithm == "knn":
prediction = kNN.predict([to_predict], DATASET_PATH)
else:
prediction = feedforward_nn.predict([to_predict])
print(prediction)
prediction = np.round(prediction[0])
if prediction == 1:
prediction = "truth"
else:
prediction = "lie"
self.write_to_file(number_of_blinks, number_of_blushing_occurred,
number_of_lip_pursing_occurred,
number_of_blinks_per_second, prediction)
# reset seconds counter
self.seconds = 0
hwLabels = []
trainingFileList = listdir('trainingDigits') #load the training set
m = len(trainingFileList)
trainingMat = zeros((m, 1024))
for i in range(m):
fileNameStr = trainingFileList[i]
fileStr = fileNameStr.split('.')[0] #take off .txt
classNumStr = int(fileStr.split('_')[0])
hwLabels.append(classNumStr)
trainingMat[i, :] = img2vector('trainingDigits/%s' % fileNameStr)
testLabels = []
testFileList = listdir('testDigits') #iterate through the test set
mTest = len(testFileList)
testMat = zeros((mTest, 1024))
for i in range(mTest):
fileNameStr = testFileList[i]
fileStr = fileNameStr.split('.')[0] #take off .txt
classNumStr = int(fileStr.split('_')[0])
testLabels.append(classNumStr)
testMat[i, :] = img2vector('testDigits/%s' % fileNameStr)
return testMat, testLabels, trainingMat, hwLabels
k = 3
testMat, testLabels, trainingMat, hwLabels = loadDataSet()
predictions = kNN.predict(testMat, trainingMat, hwLabels, k)
accuracy = kNN.accuracy(testLabels, predictions)
print("the total number of test obs is: %d" % len(testLabels))
print("\nthe total accuracy rate is: %f" % accuracy)