Python listToOrangeContinuousの例

コード例 #1

ファイル: regLearner.py プロジェクト: dwww/ui_sem1

def linReg(trainD, trainC, testD ):
    orangeData = funkcije.listToOrangeContinuous(trainD+testD, trainC+[0]*len(testD))
    ind = [1]*len(trainD)+[0]*len(testD)
    orangeTrainD = orangeData.select_ref(ind,1)
    orangeTestD = orangeData.select_ref(ind,0)
    learner = Orange.regression.linear.LinearRegressionLearner()
    classifier = learner(orangeTrainD)
    return [classifier(ins).value for ins in orangeTestD]

コード例 #2

ファイル: regLearner.py プロジェクト: dwww/ui_sem1

def lasso(trainD, trainC, testD ):
    orangeData = funkcije.listToOrangeContinuous(trainD+testD, trainC+[0]*len(testD))
    ind = [1]*len(trainD)+[0]*len(testD)
    orangeTrainD = orangeData.select_ref(ind,1)
    orangeTestD = orangeData.select_ref(ind,0)
    learner = Orange.regression.lasso.LassoRegressionLearner(lasso_lambda=1, n_boot=100, n_perm=100)
    classifier = learner(orangeTrainD)
    return [classifier(ins).value for ins in orangeTestD]

コード例 #3

ファイル: regLearner.py プロジェクト: dwww/ui_sem1

def linReg(trainD, trainC, testD):
    orangeData = funkcije.listToOrangeContinuous(trainD + testD,
                                                 trainC + [0] * len(testD))
    ind = [1] * len(trainD) + [0] * len(testD)
    orangeTrainD = orangeData.select_ref(ind, 1)
    orangeTestD = orangeData.select_ref(ind, 0)
    learner = Orange.regression.linear.LinearRegressionLearner()
    classifier = learner(orangeTrainD)
    return [classifier(ins).value for ins in orangeTestD]

コード例 #4

ファイル: regLearner.py プロジェクト: dwww/ui_sem1

def lasso(trainD, trainC, testD):
    orangeData = funkcije.listToOrangeContinuous(trainD + testD,
                                                 trainC + [0] * len(testD))
    ind = [1] * len(trainD) + [0] * len(testD)
    orangeTrainD = orangeData.select_ref(ind, 1)
    orangeTestD = orangeData.select_ref(ind, 0)
    learner = Orange.regression.lasso.LassoRegressionLearner(lasso_lambda=1,
                                                             n_boot=100,
                                                             n_perm=100)
    classifier = learner(orangeTrainD)
    return [classifier(ins).value for ins in orangeTestD]