使用python写一个有关水质信息的BP网络（8个输入，1个输出，5个隐藏节点），并写一个main实现将训练集投入网络训练，训练后将测试集投入得出预测输出

好的，以下是一个简单的BP神经网络的实现（使用numpy库）：

import numpy as np

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

# sigmoid的导数
def sigmoid_derivative(x):
    return x * (1 - x)

# BP神经网络类
class NeuralNetwork:
    def __init__(self, input_size, hidden_size, output_size):
        # 初始化权重和偏置
        self.weights1 = np.random.rand(input_size, hidden_size)
        self.bias1 = np.zeros((1, hidden_size))
        self.weights2 = np.random.rand(hidden_size, output_size)
        self.bias2 = np.zeros((1, output_size))

    def forward(self, X):
        # 前向传播
        self.z1 = np.dot(X, self.weights1) + self.bias1
        self.a1 = sigmoid(self.z1)
        self.z2 = np.dot(self.a1, self.weights2) + self.bias2
        self.a2 = sigmoid(self.z2)
        return self.a2

    def backward(self, X, y, output):
        # 反向传播
        self.error = output - y
        self.delta2 = self.error * sigmoid_derivative(output)
        self.weights2 -= np.dot(self.a1.T, self.delta2)
        self.bias2 -= np.sum(self.delta2, axis=0)
        self.delta1 = np.dot(self.delta2, self.weights2.T) * sigmoid_derivative(self.a1)
        self.weights1 -= np.dot(X.T, self.delta1)
        self.bias1 -= np.sum(self.delta1, axis=0)

    def train(self, X, y):
        output = self.forward(X)
        self.backward(X, y, output)

    def predict(self, X):
        return self.forward(X)

# 测试数据
X_train = np.array([[1, 2, 3, 4, 5, 6, 7, 8],
                    [2, 3, 4, 5, 6, 7, 8, 9],
                    [3, 4, 5, 6, 7, 8, 9, 10],
                    [4, 5, 6, 7, 8, 9, 10, 11],
                    [5, 6, 7, 8, 9, 10, 11, 12],
                    [6, 7, 8, 9, 10, 11, 12, 13],
                    [7, 8, 9, 10, 11, 12, 13, 14],
                    [8, 9, 10, 11, 12, 13, 14, 15]])

y_train = np.array([[0.2], [0.3], [0.4], [0.5], [0.6], [0.7], [0.8], [0.9]])

X_test = np.array([[9, 10, 11, 12, 13, 14, 15, 16],
                   [10, 11, 12, 13, 14, 15, 16, 17]])

# 初始化神经网络
nn = NeuralNetwork(8, 5, 1)

# 训练神经网络
for i in range(10000):
    nn.train(X_train, y_train)

# 测试神经网络
print(nn.predict(X_test))

这个实现的神经网络有8个输入、5个隐藏节点和1个输出。在main函数中，首先创建了训练集和测试集的numpy数组，然后初始化了神经网络，并对训练集进行了10000次训练。最后，将测试集输入到神经网络中，得到了预测输出。