Did I find the right examples for you? yes no      Crawl my project      Python Jobs

All Samples(7)  |  Call(7)  |  Derive(0)  |  Import(0)

src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngRF.py   AZOrange(Download)
                if  self.useBuiltInMissValHandling:
                    #compute the missing _mask
                    (exampleCvMat, missing_mask) = dataUtilities.Example2CvMat(inExample,self.varNames,self.thisVer,True) 
                else:
                    missing_mask = None
                    ##ecPA
                    # Remove the response variable from the example to be predicted and transfrom the example to a tab sep string 
                    exampleCvMat = dataUtilities.Example2CvMat(examplesImp,self.varNames,self.thisVer)
                    del examplesImp
                if not exampleCvMat:

src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngCvSVM.py   AZOrange(Download)
        if examplesImp: 
            if self.scalizer:
                exToPredict = dataUtilities.Example2CvMat(self.scalizer.scaleEx(examplesImp,True), self.varNames)
                res = self.classifier.predict(exToPredict)
                res = self.scalizer.convertClass(res)
                res = dataUtilities.CvMat2orangeResponse(res,self.classVar)
            else:
                exToPredict = dataUtilities.Example2CvMat(examplesImp,self.varNames)
                res = self.classifier.predict(exToPredict)
                if self.classVar.varType != orange.VarTypes.Continuous and len(self.classVar.values) == 2 and returnDFV:

src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngCvANN.py   AZOrange(Download)
                Nout = len(self.classVar.values)
        out = cv.cvCreateMat(1,Nout,cv.CV_32FC1)
        self.classifier.predict(dataUtilities.Example2CvMat(examplesImp,self.varNames),out)
        #print "OUT = ",out
        #print out,"->",dataUtilities.CvMat2orangeResponse(out,self.classVar,True),":",origExamples[self.classVar.name].value

src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngCvBoost.py   AZOrange(Download)
            return None
 
        out = self.classifier.predict(dataUtilities.Example2CvMat(examplesImp,self.varNames))
        probabilities = None
        DFV = None

src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngCvBayes.py   AZOrange(Download)
        else:
            ex = examplesImp        
        out = self.classifier.predict(dataUtilities.Example2CvMat(ex,self.varNames))
        #print "OUT:",out
        probabilities = None