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

感谢您的问题，以下是使用Python实现高斯贝叶斯分类的代码，处理鸢尾花数据集：

import csv
import math


def load_csv(filename):
    dataset = []
    with open(filename, 'r') as csvfile:
        csvreader = csv.reader(csvfile)
        for row in csvreader:
            dataset.append([float(x) for x in row])
    return dataset


def split_dataset(dataset, split_ratio):
    train_size = int(len(dataset) * split_ratio)
    train_set = []
    test_set = list(dataset)
    while len(train_set) < train_size:
        index = random.randrange(len(test_set))
        train_set.append(test_set.pop(index))
    return [train_set, test_set]


def separate_by_class(dataset):
    separated = {}
    for i in range(len(dataset)):
        vector = dataset[i]
        if vector[-1] not in separated:
            separated[vector[-1]] = []
        separated[vector[-1]].append(vector)
    return separated


def mean(numbers):
    return sum(numbers) / float(len(numbers))


def stdev(numbers):
    avg = mean(numbers)
    variance = sum([pow(x - avg, 2) for x in numbers]) / float(len(numbers) - 1)
    return math.sqrt(variance)


def summarize(dataset):
    summaries = [(mean(attribute), stdev(attribute)) for attribute in zip(*dataset)]
    del summaries[-1]
    return summaries


def summarize_by_class(dataset):
    separated = separate_by_class(dataset)
    summaries = {}
    for class_value, instances in separated.items():
        summaries[class_value] = summarize(instances)
    return summaries


def calculate_probability(x, mean, stdev):
    exponent = math.exp(-(math.pow(x - mean, 2) / (2 * math.pow(stdev, 2))))
    return (1 / (math.sqrt(2 * math.pi) * stdev)) * exponent


def calculate_class_probabilities(summaries, input_vector):
    probabilities = {}
    for class_value, class_summaries in summaries.items():
        probabilities[class_value] = 1
        for i in range(len(class_summaries)):
            mean, stdev = class_summaries[i]
            x = input_vector[i]
            probabilities[class_value] *= calculate_probability(x, mean, stdev)
    return probabilities


def predict(summaries, input_vector):
    probabilities = calculate_class_probabilities(summaries, input_vector)
    best_label, best_prob = None, -1
    for class_value, probability in probabilities.items():
        if best_label is None or probability > best_prob:
            best_prob = probability
            best_label = class_value
    return best_label


def get_predictions(summaries, test_set):
    predictions = []
    for i in range(len(test_set)):
        result = predict(summaries, test_set[i])
        predictions.append(result)
    return predictions


def get_accuracy(test_set, predictions):
    correct = 0
    for i in range(len(test_set)):
        if test_set[i][-1] == predictions[i]:
            correct += 1
    return (correct / float(len(test_set))) * 100.0


def main():
    filename = 'iris.csv'
    split_ratio = 0.67
    dataset = load_csv(filename)
    training_set, test_set = split_dataset(dataset, split_ratio)
    print(f'Split {len(dataset)} rows into train={len(training_set)} and test={len(test_set)} rows')
    summaries = summarize_by_class(training_set)
    predictions = get_predictions(summaries, test_set)
    accuracy = get_accuracy(test_set, predictions)
    print(f'Accuracy: {accuracy}%')


if __name__ == '__main__':
    main()

这是一个非常简单的实现，使用基于高斯分布的贝叶斯分类器来对鸢尾花数据集进行分类。它将数据集拆分为训练集和测试集，并计算训练集的各项摘要统计信息（均值和标准差）。对于每个测试向量，分类器计算它属于每个类的概率，并选择具有最高概率的类作为预测结果。最后，它计算分类器的准确性，并输出结果。注意，这个实现没有使用任何第三方库，仅使用Python内置的数学函数和csv模块来处理数据集。