1. Go to this page and download the library: Download rubix/iris 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 / iris example snippets
use Rubix\ML\Datasets\Labeled;
use Rubix\ML\Extractors\CSV;
use Rubix\ML\Transformers\FloatTypeConverter;
$training = Labeled::fromIterator(new CSV('dataset.csv', header: true))
->apply(new FloatTypeConverter());
$testing = $training->randomize()->take(10);
use Rubix\ML\Classifiers\KNearestNeighbors;
$estimator = new KNearestNeighbors(5);
$estimator->train($training);
$predictions = $estimator->predict($testing);
use Rubix\ML\CrossValidation\Metrics\Accuracy;
$metric = new Accuracy();
$score = $metric->score($predictions, $testing->labels());
use Rubix\ML\Datasets\Labeled;
use Rubix\ML\Extractors\CSV;
use Rubix\ML\Transformers\FloatTypeConverter;
use Rubix\ML\Classifiers\KNearestNeighbors;
use Rubix\ML\CrossValidation\Metrics\Accuracy;
$training = Labeled::fromIterator(new CSV('dataset.csv', header: true))
->apply(new FloatTypeConverter());
$testing = $training->randomize()->take(10);
$estimator = new KNearestNeighbors(5);
$estimator->train($training);
$predictions = $estimator->predict($testing);
$metric = new Accuracy();
$score = $metric->score($predictions, $testing->labels());
use Rubix\ML\Loggers\Screen;
use Rubix\ML\Datasets\Labeled;
use Rubix\ML\Extractors\CSV;
use Rubix\ML\Transformers\FloatTypeConverter;
use Rubix\ML\Transformers\TSNE;
$logger = new Screen();
$dataset = Labeled::fromIterator(new CSV('dataset.csv', header: true))
->apply(new FloatTypeConverter());
$embedder = new TSNE(2, 100.0, perplexity: 10, exaggeration: 6.0, epochs: 1000);
$embedder->setLogger($logger);
$dataset->apply($embedder)->exportTo(new CSV('embeddings.csv'));
sh
php train.php
sh
php train.php
sh
php explore.php
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