fitness.py 文件源码

def evaluate(self, individual):
        #print(" *** evaluate *** ")

        #model = individual.createNetwork()
        #return random.random(), 

        random.seed(42) 
        # perform KFold crossvalidation 
        kf = KFold(n_splits=3)
        scores = []
        for train, test in kf.split(self.X):   # train, test are indicies 
            X_train, X_test = self.X[train], self.X[test]
            y_train, y_test = self.y[train], self.y[test]

            model = individual.createNetwork()
            model.fit(X_train, y_train,
                      batch_size=Config.batch_size, nb_epoch=Config.epochs, verbose=0)

            yy_test = model.predict(X_test)
            scores.append(error(y_test, yy_test))

        fitness = np.mean(scores)

        return fitness,