utilities.py 文件源码

def getConfidenceScores(features_train, labels_train, C):
    train_confidence = []
    #confidence scores for training data are computed using K-fold cross validation
    kfold = KFold(features_train.shape[0], n_folds=10)

    for train_index,test_index in kfold:
        X_train, X_test = features_train[train_index], features_train[test_index]
        y_train, y_test = labels_train[train_index], labels_train[test_index]

        #train classifier for the subset of train data
        m = SVM.train(X_train,y_train,c=C,k="linear")

        #predict confidence for test data and append it to list
        conf = m.decision_function(X_test)
        for x in conf:
                train_confidence.append(x)

    return np.array(train_confidence)

#save pos scores