1ec4287799a1ccfc019f564f3ce0de468de6e313,ParamSklearn/implementations/ProjLogit.py,ProjLogit,predict_proba,#ProjLogit#Any#,74

Before Change

    def predict_proba(self, X):
        if self.w == None:
            return np.zeros(X.shape[0])
        testx = np.hstack([np.ones((X.shape[0], 1)), X])
        pred = np.dot(testx, self.w)
        //print(pred)

After Change

        return np.argmax(res, axis = 1)
    
    def predict_proba(self, X):
        if self.w0 == None:
            raise NotImplementedError
        testx = np.hstack([np.ones((X.shape[0], 1)), X])
        pred = np.dot(testx, self.w0)
        for ws, w in zip(self.ws_all, self.w_all):
            res = np.hstack([pred, np.power(pred, 2) / 2., np.power(pred, 3) / 6., np.power(pred, 4) / 24.])
            p_res = proj_simplex(np.dot(res, ws))
            pred = p_res + np.dot(testx, w)
        return proj_simplex(pred)
    
    def predict_log_proba(self, X):

Instances