Python NBModelの例

コード例 #1

ファイル: botacc.py プロジェクト: kldywc/botnetproject

def callSuitable(mlalgo, v2):
    """use and evaluate the selected Machine Learning algorithm"""
    global X, Y, XT, YT

    if mlalgo == 'Decision Tree':
        model = models.DTModel(X, Y, XT, YT, v2)
        model.start()
    elif mlalgo == 'Naive Bayes':
        model = models.NBModel(X, Y, XT, YT, v2)
        model.start()
    elif mlalgo == 'SVM':
        model = models.SVMModel(X, Y, XT, YT, v2)
        model.start()
    else:
        model = models.KNNModel(X, Y, XT, YT, v2)
        model.start()

コード例 #2

ファイル: gui_develop.py プロジェクト: tulkot/Botnet-Detection-Technique-Using-Machine-Learning-Classifiers

def callSuitable(mlalgo, v2):
    #evaluate the selected Machine Learning algorithm
    global X, Y, XT, YT

    if mlalgo == 'Decision Tree':
        model = models.DTModel(X, Y, XT, YT, v2)
        model.start()
    elif mlalgo == 'Naive Bayes':
        model = models.NBModel(X, Y, XT, YT, v2)
        model.start()
    elif mlalgo == 'SVM':
        model = models.SVMModel(X, Y, XT, YT, v2)
        model.start()
    elif mlalgo == 'K Nearest Neighbours':
        model = models.KNNModel(X, Y, XT, YT, v2)
        model.start()
    elif mlalgo == 'Logistic Regression':
        model = models.LogModel(X, Y, XT, YT, v2)
        model.start()
    else:
        model = models.ANNModel(X, Y, XT, YT, v2)
        model.start()

コード例 #3

def loadModel(modelName, fileName=None):
    """load the modelName ML model and test the accuracy"""
    global X, Y, XT, YT
    mlalgo = modelName
    if mlalgo == 'Decision Tree':
        model = models.DTModel(X, Y, XT, YT)
        model.start()
    elif mlalgo == 'Naive Bayes':
        model = models.NBModel(X, Y, XT, YT)
        model.start()
    elif mlalgo == 'SVM':
        model = models.SVMModel(X, Y, XT, YT)
        model.start()
    elif mlalgo == 'K Nearest Neighbours':
        model = models.KNNModel(X, Y, XT, YT)
        model.start()
    elif mlalgo == 'Logistic Regression':
        model = models.LogModel(X, Y, XT, YT)
        model.start()
    else:
        model = models.ANNModel(X, Y, XT, YT)
        model.start()