Наивный байесовский «с нуля» в python с результатом «Процесс завершен с кодом выхода 0»

Question

Я новичок в PyCharm, и я нашел два кода онлайн по методам классификации, используя наивную байесовскую классификацию. в этом коде нет ошибкино я вижу результат, хотя я использую print(). Я использую библиотеку данных радужной оболочки. и это мой код

import csv
import math
import random
import pandas as pd
from sklearn import datasets

def loadCsv(filename):
    #lines = csv.reader(open(r'E:\KULIAH\TUGAS AKHIR\MachineLearning\kananniih.csv'))
    lines = datasets.load_iris()
    print(lines)
    dataset = list(lines)
    for i in range(len(dataset)):
        dataset[i] = [float(x) for x in dataset[i]]
        return dataset;

#spliit dataa
def splitDataset(dataset, splitRatio):
    trainSize = int(len(dataset) * splitRatio)
    trainSet = []
    copy = list(dataset)
    while len(trainSet) < trainSize:
        index = random.randrange(len(copy))
        trainSet.append(copy.pop(index))
        return [trainSet, copy]

#dikumpulkan berdasar kelas
def separateByClass(dataset):
    separated = {}
    for i in range(len(dataset)):
        vector = dataset[i]
        if (vector[-1] not in separated):
            separated[vector[-1]] = []
            separated[vector[-1]].append(vector)
            return separated

#hitung mean
def mean(numbers):
    return sum(numbers)/float(len(numbers))

#hitung standard deviasi
def stdev(numbers):
    avg = mean(numbers)
    variance = sum([pow(x-avg,2) for x in numbers])/float(len(numbers)-1)
    return math.sqrt(variance)

#hitung jumlah dataset
def summarize(dataset):
    summaries = [(mean(attribute), stdev(attribute)) for attribute in zip(*dataset)]
    del summaries[-1]
    return summaries

#hitung atribut tiap kelas
def summarizeByClass(dataset):
    separated = separateByClass(dataset)
    summaries = {}
    for classValue, instances in separated.items():
        summaries[classValue] = summarize(instances)
        return summaries

#hitung Gaussian PDF
def calculateProbability(x, mean, stdev):
    exponent = math.exp(-(math.pow(x-mean,2)/(2*math.pow(stdev,2))))
    return (1/(math.sqrt(2*math.pi)*stdev))*exponent

#hitung probabilitas kelas
def calculateClassProbabilities(summaries, inputVector):
    probabilities = {}
    for classValue, classSummaries in summaries.items():
        probabilities[classValue] = 1
        for i in range(len(classSummaries)):
            mean, stdev = classSummaries[i]
            x = inputVector[i]
            probabilities[classValue] *= calculateProbability(x, mean, stdev)
            return probabilities

#make prediction
def predict(summaries, inputVector):
    probabilities = calculateClassProbabilities(summaries, inputVector)
    bestLabel, bestProb = None, -1
    for classValue, probability in probabilities.items():
        if bestLabel is None or probability > bestProb:
            bestProb = probability
            bestLabel = classValue
            return bestLabel

#make prediction
def getPredictions(summaries, testSet):
    predictions = []
    for i in range(len(testSet)):
        result = predict(summaries, testSet[i])
        predictions.append(result)
        return predictions

#get accurancy
def getAccuracy(testSet, predictions):
    correct = 0
    for i in range(len(testSet)):
        if testSet[i][-1] == predictions[i]:
            correct += 1
            return (correct / float(len(testSet))) * 100.0

def main():
    filename = datasets.load_iris()
    splitRatio = 0.67
    dataset = loadCsv(filename)
    print(dataset)
    trainingSet, testSet = splitDataset(dataset, splitRatio)
    print(('Split {0} rows into train={1} and test={2} rows').format(len(dataset), len(trainingSet),len(testSet)))
    # prepare model
    summaries = summarizeByClass(trainingSet)
    # test model
    predictions = getPredictions(summaries, testSet)
    accuracy = getAccuracy(testSet, predictions)
    print(('Accuracy: {0}%').format(accuracy))
    main()

Не могли бы вы, ребята, помочь мне, пожалуйста? Очень ценю это! С уважением, Элия