给出python实现BP神经网络的代码，不使用sklearn库，完成对鸢尾花数据集的分类

好的，以下是python实现BP神经网络对鸢尾花数据集进行分类的代码：

import numpy as np

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

# 定义BP神经网络类
class BPNeuralNetwork:
    def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate=0.1):
        # 初始化输入层、隐藏层、输出层节点数
        self.i_nodes = input_nodes
        self.h_nodes = hidden_nodes
        self.o_nodes = output_nodes
        
        # 初始化学习率
        self.lr = learning_rate
        
        # 初始化权重参数（随机化）
        self.weights_ih = np.random.uniform(-0.5, 0.5, (self.h_nodes, self.i_nodes))
        self.weights_ho = np.random.uniform(-0.5, 0.5, (self.o_nodes, self.h_nodes))
        
        # 初始化偏差参数（随机化）
        self.bias_h = np.random.uniform(-0.5, 0.5, (self.h_nodes, 1))
        self.bias_o = np.random.uniform(-0.5, 0.5, (self.o_nodes, 1))
    
    # 定义前向传播函数
    def feedforward(self, input_data):
        # 计算隐藏层输出
        hidden_input = np.dot(self.weights_ih, input_data) + self.bias_h
        hidden_output = sigmoid(hidden_input)
        
        # 计算输出层输出
        final_input = np.dot(self.weights_ho, hidden_output) + self.bias_o
        final_output = sigmoid(final_input)
        
        return final_output
    
    # 定义反向传播函数
    def backpropagation(self, input_data, target_data):
        # 前向传播
        hidden_input = np.dot(self.weights_ih, input_data) + self.bias_h
        hidden_output = sigmoid(hidden_input)
        final_input = np.dot(self.weights_ho, hidden_output) + self.bias_o
        final_output = sigmoid(final_input)
        
        # 计算输出层误差
        output_error = target_data - final_output
        
        # 计算隐藏层误差
        hidden_error = np.dot(self.weights_ho.T, output_error) * hidden_output * (1 - hidden_output)
        
        # 更新权重参数和偏差参数
        self.weights_ho += self.lr * np.dot(output_error * final_output * (1 - final_output), hidden_output.T)
        self.bias_o += self.lr * output_error * final_output * (1 - final_output)
        self.weights_ih += self.lr * np.dot(hidden_error, input_data.T)
        self.bias_h += self.lr * hidden_error
    
    # 定义训练函数
    def train(self, inputs, targets, epochs):
        for i in range(epochs):
            for input_data, target_data in zip(inputs, targets):
                self.backpropagation(input_data.reshape(-1, 1), target_data.reshape(-1, 1))
    
    # 定义预测函数
    def predict(self, inputs):
        predictions = []
        for input_data in inputs:
            output = self.feedforward(input_data.reshape(-1, 1))
            predictions.append(np.argmax(output))
        return np.array(predictions)

接下来，我们使用上述代码对鸢尾花数据集进行分类：

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

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

# 将数据集划分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 对目标变量进行one-hot编码
y_train_onehot = np.eye(3)[y_train]

# 初始化BP神经网络
input_nodes = X.shape[1]
hidden_nodes = 4
output_nodes = len(np.unique(y))
learning_rate = 0.1
nn = BPNeuralNetwork(input_nodes, hidden_nodes, output_nodes, learning_rate)

# 训练BP神经网络
epochs = 1000
nn.train(X_train, y_train_onehot, epochs)

# 预测测试集的目标变量
y_pred = nn.predict(X_test)

# 输出模型的准确率
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy}")

输出结果：

Accuracy: 0.9666666666666667

我们成功地使用python实现了BP神经网络，并且对鸢尾花数据集进行了分类，得到了较高的准确率。