GRNN预测模型Python实现

下面给出GRNN（General Regression Neural Network）神经网络的Python实现代码：

import numpy as np

class GRNN:
    def __init__(self, sigma):
        self.sigma = sigma
    
    def gaussian_kernel(self, x, y):
        diff = x - y
        return np.exp(-np.dot(diff, diff) / (2 * self.sigma ** 2))
    
    def fit(self, X, y):
        self.X_train = X
        self.y_train = y
    
    def predict(self, X):
        y_pred = []
        for i in range(X.shape[0]):
            numerator = 0
            denominator = 0
            for j in range(self.X_train.shape[0]):
                kernel = self.gaussian_kernel(X[i], self.X_train[j])
                numerator += kernel * self.y_train[j]
                denominator += kernel
            y_pred.append(numerator / denominator)
        return np.array(y_pred)

使用示例：

import numpy as np
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from grnn import GRNN

# 生成样本数据
X, y = make_regression(n_samples=100, n_features=10, noise=0.1, random_state=42)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 创建GRNN模型
grnn = GRNN(sigma=1.0)

# 训练模型
grnn.fit(X_train, y_train)

# 预测测试集结果
y_pred = grnn.predict(X_test)

# 计算均方误差
mse = mean_squared_error(y_test, y_pred)
print("均方误差:", mse)

这里使用了Scikit-learn库的`make_regression`函数生成100个样本，每个样本包含10个特征，使用GRNN模型进行回归预测，并计算均方误差作为评估指标。