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


    def _getFeatureIds(self):

After Change

        if self.params.family == "multinomial":
            // get the classes too (they are 1-indexed)
            rpy.set_default_mode(rpy.NO_CONVERSION)
            class_ind = rpy.r.predict(self.__trained_model,
                                      newx=data,
                                      type="class",
                                      s=self.__last_lambda)
            rpy.set_default_mode(rpy.NO_DEFAULT)
            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]

Instances