def obtain_test_data(): ''' Obtain testing data for the classifier''' featureList = [] tweets = [] with open('../data/preprocessedTesting.data','rb') as f: reader = csv.reader(f, delimiter='\t') l = list(reader) for row in l: sentiment = row[2] tweet = row[3] tweet = tweet[:-2] # ignoring hashcounts featureVector = featureExtraction.getFeatureVector(tweet) # featureList.extend(featureVector) tweets.append((featureVector, sentiment)) with open('../data/preprocessedTraining.data','rb') as f: reader = csv.reader(f, delimiter='\t') l = list(reader) for row in l: sentiment = row[2] tweet = row[3] tweet = tweet[:-2] # ignoring hashcounts featureVector = featureExtraction.getFeatureVector(tweet) featureList.extend(featureVector) # Remove featureList duplicates featureList = list(set(featureList)) result = featureExtraction.getFeatureListAndLabels(tweets, featureList) return result
def obtain_test_data(): ''' Obtain testing data for the classifier''' featureList = [] tweets = [] with open('../data/preprocessedTesting.data', 'rb') as f: reader = csv.reader(f, delimiter='\t') l = list(reader) for row in l: sentiment = row[2] tweet = row[3] tweet = tweet[:-2] # ignoring hashcounts featureVector = featureExtraction.getFeatureVector(tweet) # featureList.extend(featureVector) tweets.append((featureVector, sentiment)) with open('../data/preprocessedTraining.data', 'rb') as f: reader = csv.reader(f, delimiter='\t') l = list(reader) for row in l: sentiment = row[2] tweet = row[3] tweet = tweet[:-2] # ignoring hashcounts featureVector = featureExtraction.getFeatureVector(tweet) featureList.extend(featureVector) # Remove featureList duplicates featureList = list(set(featureList)) result = featureExtraction.getFeatureListAndLabels(tweets, featureList) return result
def getFeatures(folder): features = [] with open(os.path.join(folder,"calibration.csv")) as csvfile: reader = csv.reader(csvfile) low = [ float(x) for x in reader.next()] high = [ float(x) for x in reader.next()] green = fe.colourFilter(tuple(low),tuple(high)) low = [ float(x) for x in reader.next()] high = [ float(x) for x in reader.next()] blue = fe.colourFilter(tuple(low),tuple(high)) for f in os.listdir(folder): if os.path.splitext(f)[1] == ".ppm": imbgr = cv2.imread(os.path.join(folder,f)) hull = green.getColourHull(imbgr) features.append(fe.getFeatureVector(hull,['central'])) hull = blue.getColourHull(imbgr) features.append(fe.getFeatureVector(hull,['central'])) return features
# if temp<minimumValue: # minimumValue=temp isLog = True featureMatrix = [] labelMatrix = [] featureVector = [] for index in csvData.keys(): print(len(csvData[index])) if isLog: featureLog = './' + index + '_feature.csv' fl = open(featureLog, 'w') if True: actionLength = len(csvData[index]) for i in range(actionLength): featureVector = getFeatureVector(csvData[index][i]) # if len(featureVector)!=21: # print('wrong length -- discarded') # continue # print(len(featureVector)) featureMatrix.append(featureVector) labelMatrix.append(labelSwitch(index)) if isLog: for item in featureVector: fl.write(str(item)) fl.write(',') fl.write(str(index)) fl.write('\n') # if index=='fi': # print(len(featureVector))
# f = open(path, 'w') # for i in range(len(signalSegment[2])): # f.write(str(signalSegment[0][i])) # f.write(',') # f.write(str(signalSegment[1][i])) # f.write(',') # f.write(str(signalSegment[2][i])) # f.write('\n') # f.close() # f = open(path, 'a') # fl = open(featureLog, 'a') # pl = open(predictionLog, 'a') ### SVM featureVector = feature.getFeatureVector(signalSegment) featureArray = np.array(featureVector) featureArray = featureArray.reshape((1,len(featureVector))) featureArray = tools.standardise(featureArray,meanValue,stdValue) svmOutput = model.predict(featureArray) print(rr.printOutput(svmOutput)) f=open('result.txt','w') f.write(str(svmOutput)) f.close() ### ANN: '''featureVector = feature.getFeatureVector(signalSegment) print(len(featureVector)) featureArray = np.array(featureVector) featureArray = featureArray.reshape((1,len(featureVector)))
labels = pickle.load(modelfile) running = deque([]) with open(sys.argv[1]) as csvfile: reader = csv.reader(csvfile) low = [ float(x) for x in reader.next()] high = [ float(x) for x in reader.next()] green = fe.colourFilter(tuple(low),tuple(high)) while 1: try: imbgr = fe.get_video() hull = green.getColourHull(imbgr) if len(hull): features = fe.getFeatureVector(hull,['central']) imbgrclass = model.predict([features]) imbgrclass = imbgrclass[0] if len(running) < 10: sign = imbgrclass running.append(sign) else: _ = running.popleft() running.append(imbgrclass) sign = median(running) sign = int(round(sign)) letter = labels[sign]