请帮我写一份模糊神经网络的代码

以下是一个简单的模糊神经网络的 Python 代码：

import numpy as np

class FuzzyNeuralNetwork:
    def __init__(self, num_inputs, num_outputs, num_rules):
        self.num_inputs = num_inputs
        self.num_outputs = num_outputs
        self.num_rules = num_rules
        
        self.input_mfs = []
        self.output_mfs = []
        self.rules = []
        
        for i in range(num_inputs):
            self.input_mfs.append(self.create_mfs())
        
        for i in range(num_outputs):
            self.output_mfs.append(self.create_mfs())
        
        for i in range(num_rules):
            rule = []
            for j in range(num_inputs):
                rule.append(np.random.randint(len(self.input_mfs[j])))
            rule.append(np.random.randint(len(self.output_mfs)))
            self.rules.append(rule)
    
    def create_mfs(self):
        mfs = []
        mfs.append(('low', np.random.uniform(0, 1)))
        mfs.append(('medium', np.random.uniform(0, 1)))
        mfs.append(('high', np.random.uniform(0, 1)))
        return mfs
    
    def fuzzify_inputs(self, inputs):
        fuzzified_inputs = []
        for i in range(self.num_inputs):
            mfs = self.input_mfs[i]
            fuzzified_input = []
            for j in range(len(mfs)):
                fuzzified_input.append(self.membership_function(inputs[i], mfs[j]))
            fuzzified_inputs.append(fuzzified_input)
        return fuzzified_inputs
    
    def membership_function(self, x, mf):
        if mf[0] == 'low':
            return max(0, min(1, (mf[1] - x) / (mf[1] - mf[2])))
        elif mf[0] == 'medium':
            return max(0, min(1, 2 * (x - mf[0]) / (mf[2] - mf[0])))
        elif mf[0] == 'high':
            return max(0, min(1, (x - mf[1]) / (mf[0] - mf[1])))

    def infer(self, fuzzified_inputs):
        outputs = np.zeros(self.num_outputs)
        for i in range(self.num_rules):
            rule = self.rules[i]
            weight = 1
            for j in range(self.num_inputs):
                weight *= fuzzified_inputs[j][rule[j]]
            outputs[rule[-1]] += weight
        return outputs
    
    def defuzzify_outputs(self, outputs):
        defuzzified_outputs = []
        for i in range(self.num_outputs):
            mfs = self.output_mfs[i]
            defuzzified_output = 0
            for j in range(len(mfs)):
                defuzzified_output += self.inverse_membership_function(outputs[i], mfs[j])
            defuzzified_outputs.append(defuzzified_output / len(mfs))
        return defuzzified_outputs
    
    def inverse_membership_function(self, y, mf):
        if mf[0] == 'low':
            return mf[1] - y * (mf[1] - mf[2])
        elif mf[0] == 'medium':
            return mf[0] + y * (mf[2] - mf[0]) / 2
        elif mf[0] == 'high':
            return mf[1] + y * (mf[0] - mf[1])

这个模糊神经网络包含了模糊化、推理和去模糊化三个步骤。在初始化时，我们需要指定输入、输出和规则的数量。然后，我们随机生成输入和输出的隶属函数以及规则。在推理时，我们首先将输入模糊化，然后根据规则计算每个输出的权重，最后将输出去模糊化。