def DTfunc(arg1, arg2, arg3=None): #encode tree-depths if "balance.scale" in arg1: depth = 3 if "nursery" in arg1: depth = 7 if "led" in arg1: depth = 7 if "synthetic.social" in arg1: depth = 8 #Read training file Rf = ReadTrFile.ReadTrFile(arg1) #Read Testing file Tf = ReadTeFile.ReadTeFile(arg2) #Initializer the Trainer class Train = Training.Training(depth, Rf.D, Rf.dictfile, Rf.attributedict, Rf.labeldict, Rf.avcdict) #Generate Decision Tree trlabeldict, dtroot = Train.GenDT() #Initialize Tester Test = Testing.Testing(dtroot, Tf.dictfile) #Perform Decision Tree based Testing prlabel = Test.parsetestfile() #Calculate and Print Confusion Matrix cm = utilities.CalcConMat(Tf.dictfile, prlabel, Rf.labeldict) #Calculate quality of classifer for report if arg3 == True: QualityCalc.QualityCalc(cm)
def RTfunc(arg1, arg2, arg3=None): #encode num-trees if "balance.scale" in arg1: numtrees = 150 numatt = 2 depth = 2 databag = 1 if "nursery" in arg1: numtrees = 100 numatt = 8 depth = 7 databag = 1 if "led" in arg1: numtrees = 65 numatt = 7 depth = 7 databag = 1 if "synthetic.social" in arg1: numtrees = 135 numatt = 12 depth = 8 databag = 1 #Read Training file Rf = ReadTrFile.ReadTrFile(arg1) #Read Testing file Tf = ReadTeFile.ReadTeFile(arg2) #Build DT's and test them prlabel = {} for x in range(0, numtrees): Dr = [] size = len(Rf.D) for y in range(0, size): #Sampling with replacement if databag == 1: index = random.randint(0, size - 1) else: index = y Dr.append(Rf.D[index]) Train = Training.Training(depth, Dr, Rf.dictfile, Rf.attributedict) dtroot = Train.GenDTRF(numatt) Test = Testing.Testing(dtroot, Tf.dictfile, prlabel) prlabel = Test.parsetestfile() #Calculate Confusion Matrix for Random forest results cm = utilities.CalcConMat(Tf.dictfile, prlabel, Rf.labeldict) #Calculate quality if arg3 == True: QualityCalc.QualityCalc(cm)
def main(): arguments = parse_arguments() if arguments.test: Testing.Testing().run_tests() else: # process input file and store the events read events = utility.process_input_data(arguments.input_file) # create moving average class with event and window size moving_average = MovingAverage.MovingAverage(events, arguments.window) # perform moving average time calculation average_times = moving_average.moving_average() # for output in average_times: utility.log_average_time(arguments.input_file, arguments.print, average_times, arguments.output_file)
import Testing import Training # ///////////////////////////////////////////////////////////////// x = float(input("Enter 1 To train Or 2 To Test : ")) if x == 1: train = Training.Training() train.training() else: c = Testing.Testing() c.testing()
plt.title("Report") iteration = [] dt, dl, td = Utils.Utils(dt, dl, td).orderData(3) seper = 5 testArgs = range(seper) w_per = [[], [], [], [], []] w_pa = [[], [], [], [], []] w_svm = [[], [], [], [], []] tester = [Training, Training, Training, Training, Training] for tr in [0]: for i in testArgs: i = int(i) iteration.append(i) w_per[i], w_pa[i], w_svm[i] = Training.Training(dt, dl, params).train(i) tester[i] = Testing.Testing(dt, dl, w_per[i], w_pa[i], w_svm[i]) if len(sys.argv) == 3: # debug mode t1, t2, t3 = tester[i].testStatistic(i) succRateinPER.append(t1) succRateinPA.append(t2) succRateinSVM.append(t3) print("succeeds rate: per:", t1, " pa:", t2, " svm:", t3) if len(sys.argv) > 3: # submit mode Testing.Testing.testerSubmit(tester, td) # tester.test(td) else: plt.plot(testArgs, succRateinSVM, label="SVM {}".format(tr)) succRateinSVM = [] plt.plot(testArgs, succRateinPA, label="PA {} ".format(tr)) succRateinPA = []
"\n") # @Kmeans centroids = Kmeans(x_train, 11) #print("\nCluster Centroids after Kmeans: \n", centroids) # PCA pcaValues = PCA(x_train, 5) print("\nPCA with 5 features: \n") random_forest_predictions = RandomForest(x_train, y_train, x_test) print("Accuracy of Random Forest: ", Accuracy(y_test, random_forest_predictions)) predicitons = SklearnSupervisedLearning(x_train, y_train, x_test) test = Testing(predicitons, x_test, y_test, True) test.run() SklearnVotingClassifier(x_train, y_train, x_test) testVoting = Testing([SklearnVotingClassifier(x_train, y_train, x_test)], x_test, y_test, True) testVoting.run() endTime = timeit.default_timer() print("Total Project time ", endTime - startTime) tuned_parameters = [{ 'kernel': ['rbf'], 'gamma': [1e-3], 'C': [1, 10, 100, 500, 1000] }, { 'kernel': ['linear'],
# -*- coding: utf-8 -*- from Testing import * t = Testing() t.init() t.setTime("22h02m0s") t.setRef(1, "2h31m49s", "89º15'51''", "22h04m20s", "0º27'09''", "36º49'17''") t.setRef(2, "18h36m56s", "38º47'03''", "22h05m07s", "78º10'04''", "70º05'19''") #t.goto1("100","160") t.goto("18h03m48s", "24º23'00''", "22h06m52s") #t.goto("13h17m55s", "8º29'04''", "22h07m51s")