PHP code example of rubix / har

1. Go to this page and download the library: Download rubix/har 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/ */

rubix / har example snippets


use Rubix\ML\Datasets\Labeled;
use Rubix\ML\Extractors\NDJSON;

$dataset = Labeled::fromIterator(new NDJSON('train.ndjson'));


use Rubix\ML\PersistentModel;
use Rubix\ML\Pipeline;
use Rubix\ML\Transformers\GaussianRandomProjector;
use Rubix\ML\Transformers\ZScaleStandardizer;
use Rubix\ML\Classifiers\SoftmaxClassifier;
use Rubix\ML\NeuralNet\Optimizers\Momentum;
use Rubix\ML\Persisters\Filesystem;

$estimator = new PersistentModel(
    new Pipeline([
        new GaussianRandomProjector(110),
        new ZScaleStandardizer(),
    ], new SoftmaxClassifier(256, new Momentum(0.001))),
    new Filesystem('har.rbx')
);


use Rubix\ML\Loggers\Screen;

$estimator->setLogger(new Screen());


$estimator->train($dataset);


use Rubix\ML\Extractors\CSV;

$extractor = new CSV('progress.csv', true);

$extractor->export($estimator->steps());


$estimator->save();


use Rubix\ML\Datasets\Labeled;
use Rubix\ML\Extractors\NDJSON;

$dataset = Labeled::fromIterator(new NDJSON('test.ndjson'));


use Rubix\ML\PersistentModel;
use Rubix\ML\Persisters\Filesystem;

$estimator = PersistentModel::load(new Filesystem('har.rbx'));


$predictions = $estimator->predict($dataset);


use Rubix\ML\CrossValidation\Reports\AggregateReport;
use Rubix\ML\CrossValidation\Reports\MulticlassBreakdown;
use Rubix\ML\CrossValidation\Reports\ConfusionMatrix;

$report = new AggregateReport([
    new MulticlassBreakdown(),
    new ConfusionMatrix(),
]);


use Rubix\ML\Persisters\Filesystem;

$results = $report->generate($predictions, $dataset->labels());

echo $results;

$results->toJSON()->saveTo(new Filesystem('report.json'));


$ php validate.php

sh
$ php train.php