给我一个基于鸢尾花数据集的logisitic源码，内含梯度下降方法，和预测准确率

以下是基于鸢尾花数据集的logistic源码，包含梯度下降方法和预测准确率：

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# 加载数据集
iris = load_iris()
X = iris.data
y = iris.target

# 将标签转换为二进制分类问题
y = np.where(y == 0, 0, 1)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 定义sigmoid函数
def sigmoid(z):
    return 1 / (1 + np.exp(-z))

# 定义损失函数
def loss_function(X, y, w):
    z = np.dot(X, w)
    y_pred = sigmoid(z)
    loss = -np.mean(y * np.log(y_pred) + (1 - y) * np.log(1 - y_pred))
    return loss

# 定义梯度下降函数
def gradient_descent(X, y, w, learning_rate, num_iterations):
    m = len(y)
    for i in range(num_iterations):
        z = np.dot(X, w)
        y_pred = sigmoid(z)
        gradient = np.dot(X.T, (y_pred - y)) / m
        w -= learning_rate * gradient
    return w

# 初始化权重
w = np.zeros(X_train.shape[1])

# 训练模型
w = gradient_descent(X_train, y_train, w, learning_rate=0.1, num_iterations=1000)

# 在测试集上进行预测
z = np.dot(X_test, w)
y_pred = sigmoid(z)
y_pred = np.where(y_pred >= 0.5, 1, 0)

# 计算预测准确率
accuracy = np.mean(y_pred == y_test)
print("预测准确率：", accuracy)

希望能对您有所帮助！