给出python实现高斯贝叶斯分类的代码，处理鸢尾花数据集，不调用sklearn库，实现结果可视化

好的，我会遵守您的要求。现在可以开始回答您的问题了。

下面是 Python 实现高斯朴素贝叶斯分类算法的代码，用于处理鸢尾花数据集，并可以实现结果的可视化：

import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm

# 加载鸢尾花数据集
iris_data = np.loadtxt('iris.data', delimiter=',', usecols=[0, 1, 2, 3])
iris_labels = np.loadtxt('iris.data', delimiter=',', usecols=[4], dtype='str')

# 数据集划分为训练集和测试集
train_data = np.concatenate([iris_data[0:40, :], iris_data[50:90, :], iris_data[100:140, :]])
train_labels = np.concatenate([iris_labels[0:40], iris_labels[50:90], iris_labels[100:140]])
test_data = np.concatenate([iris_data[40:50, :], iris_data[90:100, :], iris_data[140:150, :]])
test_labels = np.concatenate([iris_labels[40:50], iris_labels[90:100], iris_labels[140:150]])

# 按照类别分割训练数据
train_data_by_class = [train_data[train_labels == label] for label in np.unique(train_labels)]
train_labels_by_class = [train_labels[train_labels == label] for label in np.unique(train_labels)]

# 计算每个类别的先验概率
class_prior_probs = [len(label) / float(len(train_labels)) for label in train_labels_by_class]

# 计算每个类别中每个特征的均值和方差
class_mean_var = []
for class_data in train_data_by_class:
    class_mean_var.append([(np.mean(feature), np.var(feature)) for feature in class_data.T])

# 定义高斯分布函数
def gaussian(x, mean, var):
    return norm.pdf(x, mean, np.sqrt(var))

# 通过计算后验概率，预测测试数据的分类
def predict_class(test_data):
    posteriors = []
    for i in range(len(np.unique(train_labels))):
        class_posterior = class_prior_probs[i]
        for feature, class_mean_var_ in zip(test_data, class_mean_var[i]):
            class_posterior *= gaussian(feature, class_mean_var_[0], class_mean_var_[1])
        posteriors.append(class_posterior)
    return np.argmax(posteriors)

# 对测试数据进行分类并保存结果
predicted_labels = []
for test_sample in test_data:
    predicted_labels.append(predict_class(test_sample))

# 计算分类准确率
accuracy = np.mean(predicted_labels == test_labels)
print('Classification accuracy:', accuracy)

# 可视化分类结果
colors = {'Iris-setosa': 'r', 'Iris-versicolor': 'g', 'Iris-virginica': 'b'}
markers = {'Iris-setosa': 'o', 'Iris-versicolor': 's', 'Iris-virginica': '^'}
for i in range(len(test_labels)):
    plt.scatter(test_data[i, 0], test_data[i, 1], color=colors[test_labels[i]],
                marker=markers[test_labels[i]])
    plt.scatter(test_data[i, 0], test_data[i, 1], color=colors[predicted_labels[i]],
                marker='x')
plt.xlabel('Sepal length')
plt.ylabel('Sepal width')
plt.show()

以上就是用于实现高斯朴素贝叶斯分类器的代码，带有鸢尾花数据集的处理及分类结果的可视化。