深度神经模糊随机森林python代码

深度神经模糊随机森林（Deep Neural Fuzzy Random Forest，DNF-RF）是一种基于模糊随机森林和深度学习技术的集成学习算法。以下是一个使用Python实现DNF-RF的代码示例：

from sklearn.ensemble import RandomForestClassifier
import numpy as np
import skfuzzy as fuzz
from keras.models import Sequential
from keras.layers import Dense

class DNF_RF:
    def __init__(self, n_estimators=100, max_depth=10, fuzzy_levels=3):
        self.n_estimators = n_estimators
        self.max_depth = max_depth
        self.fuzzy_levels = fuzzy_levels
        self.rf = RandomForestClassifier(n_estimators=self.n_estimators, max_depth=self.max_depth)
        self.fuzzy_sets = self.create_fuzzy_sets()
        self.dnf = self.create_dnf()

    def create_fuzzy_sets(self):
        fuzzy_sets = []
        for i in range(self.fuzzy_levels):
            fuzzy_sets.append(fuzz.trapmf(np.arange(0, 1, 0.1), [0 + 0.2*i, 0.2 + 0.2*i, 0.3 + 0.2*i, 0.5 + 0.2*i]))
        return fuzzy_sets

    def create_dnf(self):
        dnf = []
        for i in range(self.n_estimators):
            model = Sequential()
            model.add(Dense(10, input_dim=4, activation='relu'))
            model.add(Dense(1, activation='sigmoid'))
            model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
            dnf.append(model)
        return dnf

    def train(self, X, y):
        X_fuzzy = []
        for i in range(X.shape[1]):
            X_fuzzy.append([fuzz.interp_membership(self.fuzzy_sets[self.fuzzy_levels - 1], X[j][i]) for j in range(X.shape[0])])
        X_fuzzy = np.array(X_fuzzy).T
        for i in range(self.n_estimators):
            X_train = X_fuzzy[np.random.choice(X_fuzzy.shape[0], int(X_fuzzy.shape[0]*0.8), replace=False)]
            y_train = y[np.random.choice(y.shape[0], int(y.shape[0]*0.8), replace=False)]
            self.dnf[i].fit(X_train, y_train, epochs=100, batch_size=10, verbose=0)

    def predict(self, X):
        X_fuzzy = []
        for i in range(X.shape[1]):
            X_fuzzy.append([fuzz.interp_membership(self.fuzzy_sets[self.fuzzy_levels - 1], X[j][i]) for j in range(X.shape[0])])
        X_fuzzy = np.array(X_fuzzy).T
        y_pred = np.zeros((X.shape[0], self.n_estimators))
        for i in range(self.n_estimators):
            y_pred[:, i] = np.round(self.dnf[i].predict(X_fuzzy))
        y_pred_ensemble = np.round(np.mean(y_pred, axis=1))
        return y_pred_ensemble

在这个例子中，我们使用sklearn库中的`RandomForestClassifier`来实现随机森林分类器，并使用keras库中的`Sequential`和`Dense`函数来实现深度学习模型。在`DNF_RF`类的构造函数中，我们指定了随机森林的树的数量和深度，并创建了一组模糊集。在`create_dnf`函数中，我们创建了一组深度学习模型。在`train`函数中，我们将输入数据转换为模糊集，并将其用于训练每个深度学习模型。在`predict`函数中，我们将输入数据转换为模糊集，并使用每个深度学习模型来预测输出类别。最后，我们将每个模型的输出取平均值，并使用四舍五入函数得到最终的预测类别。