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MATLAB多隐含层极限学习机(ML-ELM) - 大数据处理

news 2025/10/16 9:13:53

MATLAB多隐含层极限学习机实现，特别优化用于大规模数据处理。

1. 核心ML-ELM类实现

ML_ELM.m - 主类文件

classdef ML_ELM < handle% 多隐含层极限学习机 - 适用于大数据处理% Multi-Layer Extreme Learning Machine for Big Dataproperties% 网络结构参数hidden_layers      % 各层神经元数量 [layer1_neurons, layer2_neurons, ...]activation         % 激活函数类型C                  % 正则化参数batch_size         % 批处理大小% 网络参数input_weights      % 输入权重矩阵biases             % 偏置向量output_weights     % 输出权重矩阵% 训练信息training_time      % 训练时间training_error     % 训练误差endmethodsfunction obj = ML_ELM(hidden_layers, varargin)% 构造函数% 参数:%   hidden_layers - 各层神经元数量数组%   'Activation' - 激活函数 ('sigmoid', 'relu', 'tanh')%   'C' - 正则化参数%   'BatchSize' - 批处理大小p = inputParser;addRequired(p, 'hidden_layers', @(x) isnumeric(x) && all(x > 0));addParameter(p, 'Activation', 'sigmoid', @ischar);addParameter(p, 'C', 1, @(x) isnumeric(x) && x > 0);addParameter(p, 'BatchSize', 1000, @(x) isnumeric(x) && x > 0);parse(p, hidden_layers, varargin{:});obj.hidden_layers = p.Results.hidden_layers;obj.activation = p.Results.Activation;obj.C = p.Results.C;obj.batch_size = p.Results.BatchSize;fprintf('ML-ELM初始化完成:\n');fprintf('  网络结构: 输入 -> %s -> 输出\n', mat2str(obj.hidden_layers));fprintf('  激活函数: %s\n', obj.activation);fprintf('  正则化参数: %.4f\n', obj.C);fprintf('  批处理大小: %d\n', obj.batch_size);endfunction initialize_weights(obj, input_dim, output_dim)% 初始化网络权重fprintf('初始化网络权重...\n');n_layers = length(obj.hidden_layers);obj.input_weights = cell(1, n_layers);obj.biases = cell(1, n_layers);% 初始化第一层obj.input_weights{1} = randn(input_dim, obj.hidden_layers(1)) * 0.1;obj.biases{1} = randn(1, obj.hidden_layers(1)) * 0.1;% 初始化隐藏层for i = 2:n_layersobj.input_weights{i} = randn(obj.hidden_layers(i-1), obj.hidden_layers(i)) * 0.1;obj.biases{i} = randn(1, obj.hidden_layers(i)) * 0.1;end% 初始化输出权重obj.output_weights = randn(obj.hidden_layers(end), output_dim) * 0.1;endfunction H = activation_function(obj, X)% 激活函数switch obj.activationcase 'sigmoid'H = 1 ./ (1 + exp(-max(min(X, 50), -50))); % 防止溢出case 'relu'H = max(0, X);case 'tanh'H = tanh(X);case 'leaky_relu'H = max(0.01 * X, X);otherwiseerror('不支持的激活函数: %s', obj.activation);endendfunction [H_final, layer_outputs] = forward_pass(obj, X)% 前向传播n_layers = length(obj.hidden_layers);layer_outputs = cell(1, n_layers);current_output = X;for i = 1:n_layers% 计算当前层输入layer_input = current_output * obj.input_weights{i} + ...repmat(obj.biases{i}, size(current_output, 1), 1);% 激活函数current_output = obj.activation_function(layer_input);layer_outputs{i} = current_output;endH_final = current_output;endfunction train(obj, X, Y)% 训练ML-ELM网络% X: 输入数据 (n_samples x n_features)% Y: 目标输出 (n_samples x n_classes) - 对于分类应为one-hot编码fprintf('开始训练ML-ELM...\n');t_start = tic;[n_samples, input_dim] = size(X);output_dim = size(Y, 2);% 初始化权重obj.initialize_weights(input_dim, output_dim);% 逐层训练n_layers = length(obj.hidden_layers);current_features = X;for layer = 1:n_layersfprintf('训练第 %d 层 (%d 个神经元)...\n', layer, obj.hidden_layers(layer));if layer == 1input_dim_layer = input_dim;elseinput_dim_layer = obj.hidden_layers(layer-1);end% 计算当前层输出H_layer = obj.compute_layer_output_batch(current_features, layer);if layer < n_layers% 对于隐藏层，输出作为下一层的输入current_features = H_layer;else% 对于最后一层，计算输出权重fprintf('计算输出权重...\n');obj.compute_output_weights(H_layer, Y);endendobj.training_time = toc(t_start);% 计算训练误差Y_pred = obj.predict(X);if size(Y, 2) == 1% 回归问题obj.training_error = mean((Y_pred - Y).^2);fprintf('训练完成! 时间: %.2f秒, MSE: %.6f\n', ...obj.training_time, obj.training_error);else% 分类问题[~, Y_true] = max(Y, [], 2);[~, Y_pred] = max(Y_pred, [], 2);accuracy = sum(Y_true == Y_pred) / n_samples;obj.training_error = 1 - accuracy;fprintf('训练完成! 时间: %.2f秒, 准确率: %.4f\n', ...obj.training_time, accuracy);endendfunction H_layer = compute_layer_output_batch(obj, X, layer_idx)% 批处理计算层输出 - 适用于大数据n_samples = size(X, 1);n_batches = ceil(n_samples / obj.batch_size);H_layer = zeros(n_samples, obj.hidden_layers(layer_idx));for batch = 1:n_batchesstart_idx = (batch-1) * obj.batch_size + 1;end_idx = min(batch * obj.batch_size, n_samples);batch_data = X(start_idx:end_idx, :);% 计算当前batch的隐藏层输出layer_input = batch_data * obj.input_weights{layer_idx} + ...repmat(obj.biases{layer_idx}, size(batch_data, 1), 1);H_batch = obj.activation_function(layer_input);H_layer(start_idx:end_idx, :) = H_batch;if mod(batch, 10) == 0fprintf('  批处理进度: %d/%d\n', batch, n_batches);endendendfunction compute_output_weights(obj, H, Y)% 计算输出权重n_samples = size(H, 1);if n_samples < obj.hidden_layers(end)% 样本数少于特征数，使用公式1obj.output_weights = (H' * H + eye(obj.hidden_layers(end)) / obj.C) \ (H' * Y);else% 样本数多于特征数，使用公式2（更高效）obj.output_weights = (eye(obj.hidden_layers(end)) / obj.C + H' * H) \ (H' * Y);endendfunction Y_pred = predict(obj, X)% 预测H_final = obj.forward_pass_batch(X);Y_pred = H_final * obj.output_weights;endfunction H_final = forward_pass_batch(obj, X)% 批处理前向传播 - 适用于大数据预测n_samples = size(X, 1);n_batches = ceil(n_samples / obj.batch_size);n_layers = length(obj.hidden_layers);H_final = zeros(n_samples, obj.hidden_layers(end));for batch = 1:n_batchesstart_idx = (batch-1) * obj.batch_size + 1;end_idx = min(batch * obj.batch_size, n_samples);batch_data = X(start_idx:end_idx, :);% 逐层前向传播current_output = batch_data;for layer = 1:n_layerslayer_input = current_output * obj.input_weights{layer} + ...repmat(obj.biases{layer}, size(current_output, 1), 1);current_output = obj.activation_function(layer_input);endH_final(start_idx:end_idx, :) = current_output;endendend
end

2. 大数据处理工具函数

data_utils.m - 数据处理工具

classdef data_utils% 数据处理工具函数 - 专门针对大数据优化methods (Static)function [X_train, Y_train, X_test, Y_test] = train_test_split(X, Y, test_size)% 数据集分割n_samples = size(X, 1);n_test = round(n_samples * test_size);rng(42); % 固定随机种子保证可重复性indices = randperm(n_samples);test_indices = indices(1:n_test);train_indices = indices(n_test+1:end);X_train = X(train_indices, :);Y_train = Y(train_indices, :);X_test = X(test_indices, :);X_test = X(test_indices, :);Y_test = Y(test_indices, :);endfunction X_normalized = normalize_data(X, method)% 数据标准化% method: 'minmax', 'zscore', 'none'if nargin < 2method = 'minmax';endswitch methodcase 'minmax'% 最小-最大标准化 [0,1]min_vals = min(X, [], 1);max_vals = max(X, [], 1);range_vals = max_vals - min_vals;range_vals(range_vals == 0) = 1; % 防止除零X_normalized = (X - min_vals) ./ range_vals;case 'zscore'% Z-score标准化mu = mean(X, 1);sigma = std(X, 0, 1);sigma(sigma == 0) = 1; % 防止除零X_normalized = (X - mu) ./ sigma;case 'none'X_normalized = X;otherwiseerror('不支持的标准化方法: %s', method);endendfunction Y_encoded = onehot_encode(Y)% One-hot编码classes = unique(Y);n_classes = length(classes);n_samples = length(Y);Y_encoded = zeros(n_samples, n_classes);for i = 1:n_classesY_encoded(Y == classes(i), i) = 1;endendfunction save_large_data(data, filename)% 保存大数据 - 使用MAT文件格式fprintf('保存数据到 %s...\n', filename);save(filename, 'data', '-v7.3'); % -v7.3 支持大于2GB的文件endfunction data = load_large_data(filename)% 加载大数据fprintf('从 %s 加载数据...\n', filename);loaded = load(filename);data = loaded.data;endfunction [X_batches, Y_batches] = create_batches(X, Y, batch_size)% 创建数据批次n_samples = size(X, 1);n_batches = ceil(n_samples / batch_size);X_batches = cell(n_batches, 1);Y_batches = cell(n_batches, 1);for i = 1:n_batchesstart_idx = (i-1) * batch_size + 1;end_idx = min(i * batch_size, n_samples);X_batches{i} = X(start_idx:end_idx, :);if ~isempty(Y)Y_batches{i} = Y(start_idx:end_idx, :);endendendend
end

3. 性能评估工具

evaluation_metrics.m - 评估指标

classdef evaluation_metrics% 模型评估指标methods (Static)function accuracy = classification_accuracy(Y_true, Y_pred)% 分类准确率[~, true_labels] = max(Y_true, [], 2);[~, pred_labels] = max(Y_pred, [], 2);accuracy = sum(true_labels == pred_labels) / length(true_labels);endfunction [precision, recall, f1] = classification_metrics(Y_true, Y_pred)% 分类评估指标[~, true_labels] = max(Y_true, [], 2);[~, pred_labels] = max(Y_pred, [], 2);n_classes = size(Y_true, 2);precision = zeros(n_classes, 1);recall = zeros(n_classes, 1);f1 = zeros(n_classes, 1);for i = 1:n_classestrue_positive = sum((true_labels == i) & (pred_labels == i));false_positive = sum((true_labels ~= i) & (pred_labels == i));false_negative = sum((true_labels == i) & (pred_labels ~= i));if (true_positive + false_positive) > 0precision(i) = true_positive / (true_positive + false_positive);endif (true_positive + false_negative) > 0recall(i) = true_positive / (true_positive + false_negative);endif (precision(i) + recall(i)) > 0f1(i) = 2 * precision(i) * recall(i) / (precision(i) + recall(i));endendendfunction mse = mean_squared_error(Y_true, Y_pred)% 均方误差mse = mean((Y_true - Y_pred).^2);endfunction r2 = r_squared(Y_true, Y_pred)% R平方ss_res = sum((Y_true - Y_pred).^2);ss_tot = sum((Y_true - mean(Y_true)).^2);r2 = 1 - (ss_res / ss_tot);endfunction plot_training_curves(metrics_history)% 绘制训练曲线figure('Position', [100, 100, 1200, 800]);if isfield(metrics_history, 'accuracy')% 分类问题subplot(2, 2, 1);plot(metrics_history.accuracy, 'b-', 'LineWidth', 2);xlabel('迭代次数');ylabel('准确率');title('训练准确率');grid on;subplot(2, 2, 2);plot(metrics_history.loss, 'r-', 'LineWidth', 2);xlabel('迭代次数');ylabel('损失');title('训练损失');grid on;else% 回归问题subplot(1, 2, 1);plot(metrics_history.mse, 'b-', 'LineWidth', 2);xlabel('迭代次数');ylabel('MSE');title('均方误差');grid on;subplot(1, 2, 2);plot(metrics_history.r2, 'g-', 'LineWidth', 2);xlabel('迭代次数');ylabel('R²');title('决定系数');grid on;endendend
end

4. 示例和使用演示

demo_ml_elm.m - 演示程序

%% ML-ELM 大数据处理演示
clear; clc; close all;fprintf('=== MATLAB多隐含层极限学习机大数据处理演示 ===\n\n');%% 1. 生成示例大数据
fprintf('1. 生成示例数据...\n');
rng(42); % 固定随机种子% 生成大规模数据集
n_samples = 50000;
n_features = 100;
n_classes = 5;% 生成随机数据
X = randn(n_samples, n_features);
Y = randi([1, n_classes], n_samples, 1);% 转换为one-hot编码
Y_onehot = data_utils.onehot_encode(Y);% 数据标准化
X_normalized = data_utils.normalize_data(X, 'zscore');% 分割数据集
[X_train, Y_train, X_test, Y_test] = data_utils.train_test_split(...X_normalized, Y_onehot, 0.2);fprintf('   训练集: %d 样本 x %d 特征\n', size(X_train));
fprintf('   测试集: %d 样本 x %d 特征\n', size(X_test));
fprintf('   类别数: %d\n', n_classes);%% 2. 创建并训练ML-ELM模型
fprintf('\n2. 创建ML-ELM模型...\n');% 定义网络结构
hidden_layers = [200, 100, 50];  % 3个隐藏层% 创建ML-ELM模型
ml_elm = ML_ELM(hidden_layers, ...'Activation', 'sigmoid', ...'C', 1, ...'BatchSize', 2000);% 训练模型
fprintf('\n开始训练...\n');
ml_elm.train(X_train, Y_train);%% 3. 模型评估
fprintf('\n3. 模型评估...\n');% 训练集预测
fprintf('   训练集预测...\n');
Y_train_pred = ml_elm.predict(X_train);
train_accuracy = evaluation_metrics.classification_accuracy(Y_train, Y_train_pred);
fprintf('   训练集准确率: %.4f\n', train_accuracy);% 测试集预测
fprintf('   测试集预测...\n');
Y_test_pred = ml_elm.predict(X_test);
test_accuracy = evaluation_metrics.classification_accuracy(Y_test, Y_test_pred);
fprintf('   测试集准确率: %.4f\n', test_accuracy);% 详细评估指标
[precision, recall, f1] = evaluation_metrics.classification_metrics(Y_test, Y_test_pred);
fprintf('\n   各类别评估指标:\n');
fprintf('   类别\t精确率\t召回率\tF1分数\n');
for i = 1:n_classesfprintf('   %d\t%.4f\t%.4f\t%.4f\n', i, precision(i), recall(i), f1(i));
end%% 4. 与传统ELM比较
fprintf('\n4. 与传统单层ELM比较...\n');% 传统ELM (等效神经元总数)
total_neurons = sum(hidden_layers);
fprintf('   传统ELM (%d 个神经元)...\n', total_neurons);elm_model = ML_ELM([total_neurons], ...'Activation', 'sigmoid', ...'C', 1, ...'BatchSize', 2000);tic;
elm_model.train(X_train, Y_train);
elm_time = toc;Y_test_pred_elm = elm_model.predict(X_test);
elm_accuracy = evaluation_metrics.classification_accuracy(Y_test, Y_test_pred_elm);fprintf('   传统ELM结果:\n');
fprintf('     训练时间: %.2f秒\n', elm_time);
fprintf('     测试准确率: %.4f\n', elm_accuracy);
fprintf('   ML-ELM优势: +%.4f 准确率\n', test_accuracy - elm_accuracy);%% 5. 不同网络结构比较
fprintf('\n5. 不同网络结构比较...\n');network_architectures = {[100],           % 单层[150, 50],       % 两层[100, 80, 40],   % 三层[80, 60, 40, 20] % 四层
};arch_names = {'单层[100]', '两层[150,50]', '三层[100,80,40]', '四层[80,60,40,20]'};
results = zeros(length(network_architectures), 3); % 时间, 训练准确率, 测试准确率for i = 1:length(network_architectures)fprintf('   测试架构 %s...\n', arch_names{i});model = ML_ELM(network_architectures{i}, ...'Activation', 'sigmoid', ...'C', 1, ...'BatchSize', 2000);tic;model.train(X_train, Y_train);train_time = toc;Y_train_pred = model.predict(X_train);train_acc = evaluation_metrics.classification_accuracy(Y_train, Y_train_pred);Y_test_pred = model.predict(X_test);test_acc = evaluation_metrics.classification_accuracy(Y_test, Y_test_pred);results(i, :) = [train_time, train_acc, test_acc];fprintf('     时间: %.2fs, 训练准确率: %.4f, 测试准确率: %.4f\n', ...train_time, train_acc, test_acc);
end% 显示比较结果
fprintf('\n   网络结构比较结果:\n');
fprintf('   架构\t\t\t时间(s)\t训练准确率\t测试准确率\n');
for i = 1:length(network_architectures)fprintf('   %s\t%.2f\t%.4f\t\t%.4f\n', ...arch_names{i}, results(i, 1), results(i, 2), results(i, 3));
end%% 6. 大数据处理能力演示
fprintf('\n6. 大数据处理能力演示...\n');% 生成超大规模数据
fprintf('   生成超大规模数据集 (100K样本)...\n');
X_huge = randn(100000, 50);
Y_huge = randi([1, 3], 100000, 1);
Y_huge_onehot = data_utils.onehot_encode(Y_huge);
X_huge_normalized = data_utils.normalize_data(X_huge, 'zscore');% 测试不同批处理大小
batch_sizes = [500, 1000, 2000, 5000];
batch_results = zeros(length(batch_sizes), 2);fprintf('   测试不同批处理大小:\n');
for i = 1:length(batch_sizes)fprintf('     批处理大小: %d...\n', batch_sizes(i));model = ML_ELM([100, 50], ...'Activation', 'sigmoid', ...'C', 1, ...'BatchSize', batch_sizes(i));tic;model.train(X_huge_normalized, Y_huge_onehot);batch_time = toc;batch_results(i, :) = [batch_sizes(i), batch_time];fprintf('       训练时间: %.2f秒\n', batch_time);
end% 绘制批处理性能图
figure('Position', [200, 200, 800, 600]);
plot(batch_results(:, 1), batch_results(:, 2), 'ro-', 'LineWidth', 2, 'MarkerSize', 8);
xlabel('批处理大小');
ylabel('训练时间 (秒)');
title('批处理大小对训练时间的影响');
grid on;fprintf('\n=== 演示完成 ===\n');

5. 实际大数据应用示例

real_world_demo.m - 实际应用示例

%% 实际大数据应用示例
clear; clc; close all;fprintf('=== ML-ELM实际大数据应用示例 ===\n\n');%% 示例1: 图像分类 (使用预提取特征)
fprintf('示例1: 图像分类应用\n');% 模拟图像特征数据 (假设已从CNN提取)
n_images = 30000;
n_features = 2048; % 典型CNN特征维度
n_categories = 10;% 生成模拟图像特征
image_features = randn(n_images, n_features);
image_labels = randi([1, n_categories], n_images, 1);% 数据预处理
X_images = data_utils.normalize_data(image_features, 'zscore');
Y_images = data_utils.onehot_encode(image_labels);% 分割数据
[X_train_img, Y_train_img, X_test_img, Y_test_img] = ...data_utils.train_test_split(X_images, Y_images, 0.2);% 创建ML-ELM模型
fprintf('训练图像分类ML-ELM...\n');
img_model = ML_ELM([512, 256, 128], ...'Activation', 'relu', ...'C', 0.1, ...'BatchSize', 1000);img_model.train(X_train_img, Y_train_img);% 评估
Y_pred_img = img_model.predict(X_test_img);
img_accuracy = evaluation_metrics.classification_accuracy(Y_test_img, Y_pred_img);
fprintf('图像分类准确率: %.4f\n', img_accuracy);%% 示例2: 时间序列预测
fprintf('\n示例2: 时间序列预测\n');% 生成时间序列数据
n_timepoints = 20000;
sequence_length = 100;
n_features_ts = 5;% 创建多元时间序列
time_series_data = zeros(n_timepoints, n_features_ts);
for i = 1:n_features_tstime_series_data(:, i) = sin((1:n_timepoints)' * 0.1 * i) + ...0.5 * randn(n_timepoints, 1);
end% 创建滑动窗口特征
X_ts = [];
Y_ts = [];
window_size = 10;
prediction_horizon = 1;for i = window_size:n_timepoints-prediction_horizonwindow_features = time_series_data(i-window_size+1:i, :);target = time_series_data(i+prediction_horizon, 1); % 预测第一个特征X_ts = [X_ts; window_features(:)'];Y_ts = [Y_ts; target];
end% 数据预处理
X_ts_normalized = data_utils.normalize_data(X_ts, 'zscore');
Y_ts_normalized = data_utils.normalize_data(Y_ts, 'zscore');% 分割数据
[X_train_ts, Y_train_ts, X_test_ts, Y_test_ts] = ...data_utils.train_test_split(X_ts_normalized, Y_ts_normalized, 0.2);% 创建回归模型
fprintf('训练时间序列预测ML-ELM...\n');
ts_model = ML_ELM([50, 25], ...'Activation', 'tanh', ...'C', 0.01, ...'BatchSize', 500);ts_model.train(X_train_ts, Y_train_ts);% 评估回归性能
Y_pred_ts = ts_model.predict(X_test_ts);
mse_ts = evaluation_metrics.mean_squared_error(Y_test_ts, Y_pred_ts);
r2_ts = evaluation_metrics.r_squared(Y_test_ts, Y_pred_ts);fprintf('时间序列预测 - MSE: %.6f, R²: %.4f\n', mse_ts, r2_ts);%% 示例3: 推荐系统
fprintf('\n示例3: 推荐系统应用\n');% 模拟用户-物品交互数据
n_users = 10000;
n_items = 5000;
n_interactions = 200000;% 生成稀疏交互矩阵
user_ids = randi([1, n_users], n_interactions, 1);
item_ids = randi([1, n_items], n_interactions, 1);
ratings = randi([1, 5], n_interactions, 1);% 创建特征矩阵 (用户特征 + 物品特征)
user_features = randn(n_users, 20);
item_features = randn(n_items, 20);% 构建训练数据
X_recommend = [];
Y_recommend = [];for i = 1:n_interactionsuser_feat = user_features(user_ids(i), :);item_feat = item_features(item_ids(i), :);X_recommend = [X_recommend; [user_feat, item_feat]];Y_recommend = [Y_recommend; ratings(i)];
end% 数据预处理和分割
X_rec_normalized = data_utils.normalize_data(X_recommend, 'minmax');
[X_train_rec, Y_train_rec, X_test_rec, Y_test_rec] = ...data_utils.train_test_split(X_rec_normalized, Y_recommend, 0.2);% 创建推荐模型
fprintf('训练推荐系统ML-ELM...\n');
rec_model = ML_ELM([64, 32], ...'Activation', 'sigmoid', ...'C', 0.1, ...'BatchSize', 2000);rec_model.train(X_train_rec, Y_train_rec);% 评估推荐性能
Y_pred_rec = rec_model.predict(X_test_rec);
mse_rec = evaluation_metrics.mean_squared_error(Y_test_rec, Y_pred_rec);
fprintf('推荐系统预测 - MSE: %.6f\n', mse_rec);fprintf('\n=== 所有示例完成 ===\n');