def fit(self,A,a):
        # find k-NN by brute force
        d = A.T.dot(a).flatten()  # distance = dot product
        nn = d.argsort()[-1:-1+self.k:-1]
        # fit the model to selected features only
        self.model.fit(A[:,nn],a)
        # set our weights for the selected "features" i.e. items
        self.coef_ = np.zeros(A.shape[1])
        self.coef_[nn] = self.model.coef_
dataset/ETHPy150Open Mendeley/mrec/mrec/item_similarity/slim.py/NNFeatureSelectingSGDRegressor.fit/operator `-` -> `+`