修改代码使得该代码可以求GaussianNB 高斯朴素贝叶斯模型的准确率，精确率，召回率，F1分数的各个各列除以最大值以及各列减Min除以(Max-Min)

为了修改代码来计算每个指标相对于最大值和最小值的规范化版本，你可以这样做：

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.datasets import make_blobs
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

def normalize_scores(scores, max_val=None, min_val=None):
    if max_val is None:
        max_val = scores.max(axis=0)
    if min_val is None:
        min_val = scores.min(axis=0)

    normalized_scores = (scores - min_val) / (max_val - min_val)
    return normalized_scores

# 加载数据并进行处理
[X, Y] = LoadData('Iris')
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2)
model = GaussianNB()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

# 计算原始指标
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred, average='weighted')
recall = recall_score(y_test, y_pred, average='weighted')
f1 = f1_score(y_test, y_pred, average='weighted')

# 定义最大值和最小值变量，假设我们已经计算过一次
original_scores = np.array([accuracy, precision, recall, f1])
max_values = original_scores.max(axis=0)
min_values = original_scores.min(axis=0)

# 对指标进行规范化
normalized_scores = normalize_scores(np.array([accuracy, precision, recall, f1]), max_values, min_values)

print(f"原始指标：\n{'Accuracy':<15} {accuracy:<10}\n"
      f"Precision:     {precision:<15} {original_scores[1]:<10}\n"
      f"Recall:       {recall:<15} {original_scores[2]:<10}\n"
      f"F1 Score:     {f1:<15} {original_scores[3]:<10}")

print(f"规范化指标（除以最大值）：\n"
      f"Accuracy:      {normalized_scores[0]:<15} {normalized_scores[0]:<10}\n"
      f"Precision:     {normalized_scores[1]:<15} {normalized_scores[1]:<10}\n"
      f"Recall:       {normalized_scores[2]:<15} {normalized_scores[2]:<10}\n"
      f"F1 Score:     {normalized_scores[3]:<15} {normalized_scores[3]:<10}")

print(f"规范化指标（减去最小值，除以Max-Min）：\n"
      f"Accuracy:      {normalized_scores[0]:<15} {normalized_scores[0]:<10}\n"
      f"Precision:     {normalized_scores[1]:<15} {normalized_scores[1]:<10}\n"
      f"Recall:       {normalized_scores[2]:<15} {normalized_scores[2]:<10}\n"
      f"F1 Score:     {normalized_scores[3]:<15} {normalized_scores[3]:<10}")