以随机森林为基分类器构建基于模糊神经网络（FNN）的神经模糊分类器的python代码

以下是一个基于模糊神经网络（FNN）的神经模糊分类器的Python代码：

from sklearn.ensemble import RandomForestClassifier
import numpy as np
import math

class FNN:
    def __init__(self, num_rules, num_features, num_classes):
        self.num_rules = num_rules
        self.num_features = num_features
        self.num_classes = num_classes
        self.rule_weights = np.random.rand(num_rules, num_features)
        self.rule_biases = np.random.rand(num_rules)
        self.class_weights = np.random.rand(num_rules, num_classes)
        self.class_biases = np.random.rand(num_classes)
        
    def _gaussian(self, x, mean, std_dev):
        return math.exp((-1 * ((x - mean)**2)) / (2 * (std_dev**2)))
    
    def _calculate_rule_outputs(self, X):
        rule_outputs = np.zeros((X.shape[0], self.num_rules))
        for i in range(X.shape[0]):
            for j in range(self.num_rules):
                rule_output = 1
                for k in range(self.num_features):
                    rule_output *= self._gaussian(X[i][k], self.rule_weights[j][k], self.rule_biases[j])
                rule_outputs[i][j] = rule_output
        return rule_outputs
    
    def _calculate_class_outputs(self, rule_outputs):
        class_outputs = np.zeros((rule_outputs.shape[0], self.num_classes))
        for i in range(rule_outputs.shape[0]):
            for j in range(self.num_classes):
                class_output = 0
                for k in range(self.num_rules):
                    class_output += self.class_weights[k][j] * rule_outputs[i][k]
                class_output += self.class_biases[j]
                class_outputs[i][j] = class_output
        return class_outputs
    
    def fit(self, X, y):
        rule_outputs = self._calculate_rule_outputs(X)
        self.class_weights = np.dot(np.linalg.pinv(rule_outputs), y)
        self.class_biases = np.mean(y, axis=0)
        
    def predict(self, X):
        rule_outputs = self._calculate_rule_outputs(X)
        class_outputs = self._calculate_class_outputs(rule_outputs)
        return np.argmax(class_outputs, axis=1)

# 使用随机森林为基分类器
rf = RandomForestClassifier(n_estimators=100)

# 加载数据
X_train = np.loadtxt('train_data.csv', delimiter=',')
y_train = np.loadtxt('train_labels.csv', delimiter=',')
X_test = np.loadtxt('test_data.csv', delimiter=',')

# 使用随机森林对训练集进行拟合
rf.fit(X_train, y_train)

# 提取随机森林的叶节点作为FNN的输入
leaf_indices = rf.apply(X_train)

# 使用FNN对训练集进行拟合
fnn = FNN(num_rules=100, num_features=leaf_indices.shape[1], num_classes=np.unique(y_train).shape[0])
fnn.fit(leaf_indices, y_train)

# 对测试集进行预测
test_leaf_indices = rf.apply(X_test)
predictions = fnn.predict(test_leaf_indices)

print(predictions)

在上面的代码中，我们首先使用随机森林对训练集进行拟合，并提取其叶节点作为FNN的输入。然后，我们使用FNN对训练集进行拟合，并使用FNN对测试集进行预测。最后，我们输出预测结果。请注意，这只是一个示例代码，您需要根据您的数据集进行适当的修改。