def train(readFromFile=True, path="./training3/"): """ train the image processor :return: True if the training succeeds, false otherwise. """ for i in range(7): ratioKnns.append(LetterKnnClassifier()) print ratioKnns fs = listdir(path) for f in fs: s = f.split(".") if len(s) < 2: continue s = f.split("_") if len(s) < 2: continue c1 = const.getConstantFromString(s[0]) c2 = cc.getColorFromId(c1) if c2 is not None: ratioKnns[c2].loadTrainingImage(path + f, c1) print "LetterClass. " + str(c2) + ":" + str( cc.getColorFromNumber(c2)) + " trained: " + f + " as " + str( const.getStringFromNumber(c1)) knn.populateData() knn.trainModel() for r in ratioKnns: r.trainModel() print "Completed training" print "------------------"
def train(readFromFile=True, path="./training3/"): """ train the image processor :return: True if the training succeeds, false otherwise. """ for i in range(7): ratioKnns.append(LetterKnnClassifier()) print ratioKnns fs = listdir(path) for f in fs: s = f.split(".") if len(s) < 2: continue s = f.split("_") if len(s) < 2: continue c1 = const.getConstantFromString(s[0]) c2 = cc.getColorFromId(c1) if c2 is not None: ratioKnns[c2].loadTrainingImage(path + f, c1) print "LetterClass. " + str(c2) + ":" + str( cc.getColorFromNumber(c2)) + " trained: " + f + " as " + str(const.getStringFromNumber(c1)) knn.populateData() knn.trainModel() for r in ratioKnns: r.trainModel() print "Completed training" print "------------------"
for f in fs: s = f.split(".") if len(s) < 2: continue s = f.split("_") if len(s) < 2: continue c1 = const.getConstantFromString(s[0]) c2 = cc.getColorFromId(c1) if c2 is not None: ratioKnns[c2].loadTrainingImage(path+f, c1) print "LetterClass. "+str(c2)+":"+str(cc.getColorFromNumber(c2))+" trained: "+f+ " as "+str(const.getStringFromNumber(c1)) knn.populateData() knn.trainModel() for r in ratioKnns: r.trainModel() print "done training" print len(knn.train) print [len(r.train) for r in ratioKnns] print "starting test:"
for f in fs: s = f.split(".") if len(s) < 2: continue s = f.split("_") if len(s) < 2: continue c1 = const.getConstantFromString(s[0]) c2 = cc.getColorFromId(c1) if c2 is not None: ratioKnns[c2].loadTrainingImage(path + f, c1) print "LetterClass. " + str(c2) + ":" + str(cc.getColorFromNumber(c2)) + " trained: " + f + " as " + str( const.getStringFromNumber(c1) ) knn.populateData() knn.trainModel() for r in ratioKnns: r.trainModel() print "done training" print len(knn.train) print [len(r.train) for r in ratioKnns]