226340622c3c9f6108d6efbed2f9967e2679802e,mvpa/clfs/glmnet.py,GLMNET,_predict,#GLMNET#Any#,165

Before Change

                                 s=self.__trained_model["beta.pure"].shape[0])
                                 //s=self.__lowest_Cp_step)
                                 
        fit = N.asarray(res["fit"])
        if len(fit.shape) == 0:
            // if we just got 1 sample with a scalar
            fit = fit.reshape( (1,) )
        return fit

After Change

            class_ind = rpy.r.as_vector(class_ind)

            // convert the strings to ints and subtract 1
            class_ind = N.array([int(float(c))-1 for c in class_ind])
            
            // convert to actual labels
            classes = self.__ulabels[class_ind]
            
        if not classes is None:
            // set the values and return none
            self.values = values

Instances