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PHP实现elm

2026-01-29 10:12:50PHP

PHP实现ELM（极限学习机）

ELM（Extreme Learning Machine）是一种单隐层前馈神经网络算法，具有训练速度快、泛化性能好的特点。以下是PHP实现ELM的关键步骤和代码示例。

核心步骤

随机初始化权重和偏置 ELM的输入层到隐层的权重和偏置是随机生成的，通常使用均匀分布或正态分布。

PHP实现elm

function randomWeights($inputSize, $hiddenSize) {
    $weights = [];
    for ($i = 0; $i < $hiddenSize; $i++) {
        $row = [];
        for ($j = 0; $j < $inputSize; $j++) {
            $row[] = (mt_rand() / mt_getrandmax()) * 2 - 1; // [-1, 1]均匀分布
        }
        $weights[] = $row;
    }
    return $weights;
}

function randomBiases($hiddenSize) {
    $biases = [];
    for ($i = 0; $i < $hiddenSize; $i++) {
        $biases[] = (mt_rand() / mt_getrandmax()) * 2 - 1;
    }
    return $biases;
}

隐层激活函数计算 常用的激活函数包括sigmoid、ReLU等。以sigmoid为例：

PHP实现elm

function sigmoid($x) {
    return 1.0 / (1.0 + exp(-$x));
}

function hiddenLayerOutput($input, $weights, $biases) {
    $hiddenOutput = [];
    for ($i = 0; $i < count($weights); $i++) {
        $sum = 0;
        for ($j = 0; $j < count($weights[$i]); $j++) {
            $sum += $input[$j] * $weights[$i][$j];
        }
        $hiddenOutput[] = sigmoid($sum + $biases[$i]);
    }
    return $hiddenOutput;
}

输出权重计算 通过最小二乘法求解输出权重，需要矩阵运算支持。PHP可使用外部库如php-matrix：

require_once 'vendor/autoload.php';
use Matrix\Matrix;

function calculateOutputWeights($hiddenOutputs, $targets) {
    $H = new Matrix($hiddenOutputs);
    $T = new Matrix($targets);
    $Ht = $H->transpose();
    $HtH = $Ht->multiply($H);
    $invHtH = $HtH->inverse();
    $HtT = $Ht->multiply($T);
    $beta = $invHtH->multiply($HtT);
    return $beta->toArray();
}

完整示例

class ELM {
    private $inputSize;
    private $hiddenSize;
    private $outputSize;
    private $weights;
    private $biases;
    private $beta;

    public function __construct($inputSize, $hiddenSize, $outputSize) {
        $this->inputSize = $inputSize;
        $this->hiddenSize = $hiddenSize;
        $this->outputSize = $outputSize;
        $this->weights = $this->randomWeights($inputSize, $hiddenSize);
        $this->biases = $this->randomBiases($hiddenSize);
    }

    public function train($inputs, $targets) {
        $hiddenOutputs = [];
        foreach ($inputs as $input) {
            $hiddenOutputs[] = $this->hiddenLayerOutput($input);
        }
        $this->beta = $this->calculateOutputWeights($hiddenOutputs, $targets);
    }

    public function predict($input) {
        $hiddenOutput = $this->hiddenLayerOutput($input);
        $output = [];
        for ($i = 0; $i < $this->outputSize; $i++) {
            $sum = 0;
            for ($j = 0; $j < $this->hiddenSize; $j++) {
                $sum += $hiddenOutput[$j] * $this->beta[$j][$i];
            }
            $output[] = $sum;
        }
        return $output;
    }
}