PHP code example of mcordingley / regression

1. Go to this page and download the library: Download mcordingley/regression library. Choose the download type require.

2. Extract the ZIP file and open the index.php.

3. Add this code to the index.php. 
    
        
<?php
require_once('vendor/autoload.php');

/* Start to develop here. Best regards https://php-download.com/ */

mcordingley / regression example snippets


use MCordingley\Regression\Algorithm\LeastSquares;
use MCordingley\Regression\Observations;
use MCordingley\Regression\Predictor\Linear;

$observations = new Observations;

// Load the data
foreach ($data as $datum) {
    // Note addition of a constant for the first feature.
    $observations->add(array_merge([1.0], $datum->features), $datum->outcome);
}

$algorithm = new LeastSquares;
$coefficients = $algorithm->regress($observations);

$predictor = new Linear($coefficients);
$predictedOutcome = $predictor->predict(array_merge([1.0], $hypotheticalFeatures));


use MCordingley\Regression\StatisticsGatherer\Linear;

$gatherer = new Linear($observations, $coefficients, $predictor);

$gatherer->getFStatistic(); // etc.


use MCordingley\Regression\Algorithm\GradientDescent\Batch;
use MCordingley\Regression\Algorithm\GradientDescent\Schedule\Adam;
use MCordingley\Regression\Algorithm\GradientDescent\Gradient\Logistic as LogisticGradient;
use MCordingley\Regression\Algorithm\GradientDescent\StoppingCriteria\GradientNorm;
use MCordingley\Regression\Observations;
use MCordingley\Regression\Predictor\Logistic as LogisticPredictor;

$algorithm = new Batch(new LogisticGradient, new Adam, new GradientNorm);
$coefficients = $algorithm->regress(Observations::fromArray($features, $outcomes));

$predictor = new LogisticPredictor($coefficients);
$predictedOutcomeProbability = $predictor->predict($novelFeatures);