逻辑斯谛回归python 实现鸢尾花分类不导库

好的，我会为您解答。针对您的问题，逻辑斯谛回归实现鸢尾花分类的代码如下：

import csv

# 读取数据
with open('iris.csv', 'r') as csvfile:
    reader = csv.reader(csvfile)
    rows = [row for row in reader]

x_data = []  # 特征值
y_data = []  # 预测目标

# 将数据转换为数值类型
for row in rows:
    x_data.append(list(map(float, row[:-1])))
    y_data.append([int(row[-1])])

# 定义模型参数
theta = [[0], [0], [0], [0], [0]]

# 定义sigmoid函数
import math

def sigmoid(z):
    return 1 / (1 + math.exp(-z))

# 计算模型预测值
def predict(x, theta):
    z = sum([x[i] * theta[i] for i in range(len(x))])
    return sigmoid(z)

# 定义模型损失函数
def cost(x_data, y_data, theta):
    m = len(x_data)
    total_cost = 0
    for i in range(m):
        h = predict(x_data[i], theta)
        total_cost += (-y_data[i][0] * math.log(h) - (1 - y_data[i][0]) * math.log(1 - h))
    return total_cost / m

# 计算模型梯度值
def gradient(x_data, y_data, theta):
    m = len(x_data)
    grad = [0] * len(theta)
    for i in range(m):
        h = predict(x_data[i], theta)
        for j in range(len(grad)):
            grad[j] += (h - y_data[i][0]) * x_data[i][j]
    for j in range(len(grad)):
        grad[j] /= m
    return grad

# 模型训练
alpha = 0.1
iter_num = 1000
for i in range(iter_num):
    grad = gradient(x_data, y_data, theta)
    for j in range(len(theta)):
        theta[j] -= alpha * grad[j]
    print('iteration: %d cost: %f' % (i, cost(x_data, y_data, theta)))

# 输出模型参数
print('theta:', theta)

注：需准备一个名为 iris.csv 的数据文件，示例数据如下：

5.1,3.5,1.4,0.2,0
4.9,3.0,1.4,0.2,0
4.7,3.2,1.3,0.2,0
4.6,3.1,1.5,0.2,0

该代码实现了逻辑斯谛回归模型对鸢尾花数据集进行分类，不使用任何第三方库。