import numpy as np
import pandas as pd
import os

class Network(object):
    def __init__(self, sizes):
        self.num_layers = len(sizes)
        self.sizes = sizes
        self.biases = [np.random.randn(y, 1) for y in sizes[1:]]
        self.weights = [np.random.randn(y, x) for x, y in zip(sizes[:-1], sizes[1:])]
    
    def feedforward(self, a):
        for b, w in zip(self.biases, self.weights):
            a = sigmoid(np.dot(w, a) + b)
        return a
    
    def train(self, training_data, epochs, eta):
        n = len(training_data)
        for j in range(epochs):
            np.random.shuffle(training_data)
            for x, y in training_data:
                self.update_network(x, y, eta)
            # print(f"Epoch {j} complete")  # 注释掉每个 epoch 完成后的打印
    
    def update_network(self, x, y, eta):
        nabla_b, nabla_w = self.backprop(x, y)
        self.weights = [w - eta * nw for w, nw in zip(self.weights, nabla_w)]
        self.biases = [b - eta * nb for b, nb in zip(self.biases, nabla_b)]
    
    def evaluate(self, test_data):
        test_results = [(np.argmax(self.feedforward(x)), np.argmax(y)) for (x, y) in test_data]
        return sum(int(x == y) for (x, y) in test_results)

    def backprop(self, x, y):
        nabla_b = [np.zeros(b.shape) for b in self.biases]
        nabla_w = [np.zeros(w.shape) for w in self.weights]
        activation = x
        activations = [x]
        zs = []
        for b, w in zip(self.biases, self.weights):
            z = np.dot(w, activation) + b
            zs.append(z)
            activation = sigmoid(z)
            activations.append(activation)
        delta = self.cost_derivative(activations[-1], y) * sigmoid_prime(zs[-1])
        nabla_b[-1] = delta
        nabla_w[-1] = np.dot(delta, activations[-2].transpose())
        for l in range(2, self.num_layers):
            z = zs[-l]
            sp = sigmoid_prime(z)
            delta = np.dot(self.weights[-l + 1].transpose(), delta) * sp
            nabla_b[-l] = delta
            nabla_w[-l] = np.dot(delta, activations[-l - 1].transpose())
        return (nabla_b, nabla_w)
    
    def cost_derivative(self, output_activations, y):
        return (output_activations - y)
    
def sigmoid(z):
    return 1.0 / (1.0 + np.exp(-z))

def sigmoid_prime(z):
    return sigmoid(z) * (1 - sigmoid(z))

def k_fold_cross_validation(dataset, k):
    np.random.shuffle(dataset)
    fold_size = len(dataset) // k
    for i in range(k):
        validation_data = dataset[i * fold_size:(i + 1) * fold_size]
        training_data = dataset[:i * fold_size] + dataset[(i + 1) * fold_size:]
        yield training_data, validation_data

def load_and_preprocess_data(file_path):
    data = pd.read_excel(file_path, header=None)
    features = data.iloc[:, :-1]
    features = (features - features.mean()) / features.std()
    labels = data.iloc[:, -1]
    if labels.dtype == object or np.issubdtype(labels.dtype, np.integer):
        unique_labels = labels.unique()
        label_mapping = {label: idx for idx, label in enumerate(unique_labels)}
        labels = labels.map(label_mapping)
    label_vectors = np.zeros((labels.size, len(unique_labels)))
    for i, label in enumerate(labels):
        label_vectors[i, label] = 1
    dataset = list(zip([np.reshape(x, (len(x), 1)) for x in features.to_numpy()], [np.reshape(y, (len(y), 1)) for y in label_vectors]))
    return dataset

folder_path = 'C:\\Users\\tt235\\Desktop\\Code\\code\\代码复现\\算法学习测试数据集'
files = [f for f in os.listdir(folder_path) if f.endswith('.xls')]

for file in files:
    file_path = os.path.join(folder_path, file)
    dataset = load_and_preprocess_data(file_path)
    for iteration in range(10):  # 外层循环，进行十次交叉验证
        accuracies = []  # 存储当前次交叉验证的所有准确率
        for i, (train_data, validation_data) in enumerate(k_fold_cross_validation(dataset, 10)):
            input_size = len(train_data[0][0])
            output_size = len(train_data[0][1])
            net = Network([input_size, 30, output_size])
            net.train(train_data, 30, 3.0)  # 根据需要调整 epochs 和学习率
            validation_accuracy = net.evaluate(validation_data)
            accuracies.append(validation_accuracy / len(validation_data))
        average_accuracy = (sum(accuracies) / len(accuracies)) * 100
        print(f"\nIteration {iteration + 1} Training on file: {file}")
        print(f"Average Validation Accuracy: {average_accuracy:.2f}%")