用ANFIS代码预测三分类数据

ANFIS（自适应网络模糊推理系统）是一种基于模糊逻辑和神经网络的混合模型，可以用于回归和分类问题。下面是一个用于预测三分类数据的简单 ANFIS 代码：

import numpy as np
import skfuzzy as fuzz
from sklearn.datasets import make_classification
from sklearn.metrics import accuracy_score

# 生成三分类数据
X, y = make_classification(n_samples=1000, n_features=4, n_classes=3)

# 数据归一化
X_norm = np.zeros_like(X)
for i in range(X.shape[1]):
    X_norm[:, i] = fuzz.interp_membership(X[:, i], X[:, i], [np.min(X[:, i]), np.max(X[:, i])])

# 随机划分训练集和测试集
idx = np.arange(X.shape[0])
np.random.shuffle(idx)
train_idx = idx[:int(0.8 * X.shape[0])]
test_idx = idx[int(0.8 * X.shape[0]):]
X_train, y_train = X_norm[train_idx], y[train_idx]
X_test, y_test = X_norm[test_idx], y[test_idx]

# 定义 ANFIS 模型
class ANFIS:
    def __init__(self, n_input, n_output, n_mf):
        self.n_input = n_input
        self.n_output = n_output
        self.n_mf = n_mf
        self.mf_params = np.zeros((n_input, n_mf))
        self.output_params = np.zeros((n_mf + 1, n_output))

    def membership_function(self, x, params):
        mf = fuzz.trimf(x, params)
        return mf

    def rule_evaluation(self, x):
        rule_out = np.zeros((x.shape[0], self.n_mf ** self.n_input))
        for i in range(x.shape[0]):
            rule = np.zeros((self.n_mf, self.n_input))
            for j in range(self.n_input):
                rule[:, j] = self.membership_function(x[i, j], self.mf_params[j, :])
            rule = np.meshgrid(*rule)
            rule = np.stack(rule, axis=-1)
            rule = rule.reshape((-1, self.n_input))
            rule_out[i, :] = np.min(rule, axis=1)
        return rule_out

    def output_evaluation(self, rule_out):
        output = np.zeros((rule_out.shape[0], self.n_output))
        for i in range(self.n_output):
            output[:, i] = np.sum(rule_out * self.output_params[1:, i], axis=1) + self.output_params[0, i]
        return output

    def fit(self, x, y, lr=0.1, epochs=100):
        for epoch in range(epochs):
            rule_out = self.rule_evaluation(x)
            output = self.output_evaluation(rule_out)
            error = y - output
            delta_output = error
            delta_params = np.zeros_like(self.output_params)
            for i in range(self.n_output):
                delta_params[0, i] = np.sum(delta_output[:, i])
                for j in range(self.n_mf + 1):
                    delta_params[j, i] = np.sum(delta_output[:, i] * rule_out[:, j])
            self.output_params += lr * delta_params / x.shape[0]
            delta_rule_out = np.zeros_like(rule_out)
            for i in range(self.n_input):
                for j in range(self.n_mf):
                    mf = self.membership_function(x[:, i], self.mf_params[i, :])
                    delta_rule_out += delta_output[:, :] * mf[:, j, np.newaxis]
                    self.mf_params[i, j] += lr * np.sum(delta_output[:, :] * rule_out[:, j, np.newaxis] *
                                                         (x[:, i, np.newaxis] - self.mf_params[i, j]), axis=0) / \
                                           np.sum(delta_output[:, :] * rule_out[:, j, np.newaxis], axis=0)
            if epoch % 10 == 0:
                print('Epoch: %d, Train Loss: %.4f' % (epoch, np.mean(np.abs(error))))

    def predict(self, x):
        rule_out = self.rule_evaluation(x)
        output = self.output_evaluation(rule_out)
        return np.argmax(output, axis=1)

# 训练 ANFIS 模型
model = ANFIS(n_input=X_train.shape[1], n_output=3, n_mf=3)
model.fit(X_train, y_train, lr=0.1, epochs=100)

# 在测试集上进行预测
y_pred = model.predict(X_test)
print('Test Accuracy: %.4f' % accuracy_score(y_test, y_pred))

在这个例子中，我们首先生成了一个随机的三分类数据集，然后对数据进行归一化处理。接着，我们将数据随机划分为训练集和测试集，并定义了一个 ANFIS 类来构建模型。在训练过程中，我们使用了批量梯度下降算法来更新 ANFIS 模型的参数，直到达到指定的迭代次数。最后，我们在测试集上进行了预测，并计算了模型的准确率。