<?php

namespace Rubix\ML\Tests\NeuralNet\Layers;

use Tensor\Matrix;
use Rubix\ML\Deferred;
use Rubix\ML\NeuralNet\Layers\Layer;
use Rubix\ML\NeuralNet\Layers\Output;
use Rubix\ML\NeuralNet\Layers\Continuous;
use Rubix\ML\NeuralNet\Optimizers\Stochastic;
use Rubix\ML\NeuralNet\CostFunctions\LeastSquares;
use PHPUnit\Framework\TestCase;

/**
 * @group Layers
 * @covers \Rubix\ML\NeuralNet\Layers\Continuous
 */
class ContinuousTest extends TestCase
{
    protected const RANDOM_SEED = 0;

    /**
     * @var \Tensor\Matrix
     */
    protected $input;

    /**
     * @var (int|float)[]
     */
    protected $labels;

    /**
     * @var \Rubix\ML\NeuralNet\Optimizers\Optimizer
     */
    protected $optimizer;

    /**
     * @var \Rubix\ML\NeuralNet\Layers\Continuous
     */
    protected $layer;

    /**
     * @before
     */
    protected function setUp() : void
    {
        $this->input = Matrix::quick([
            [2.5, 0.0, -6.0],
        ]);

        $this->labels = [0.0, -2.5, 90];

        $this->optimizer = new Stochastic(0.001);

        $this->layer = new Continuous(new LeastSquares());

        srand(self::RANDOM_SEED);
    }

    /**
     * @test
     */
    public function build() : void
    {
        $this->assertInstanceOf(Continuous::class, $this->layer);
        $this->assertInstanceOf(Output::class, $this->layer);
        $this->assertInstanceOf(Layer::class, $this->layer);
    }

    /**
     * @test
     */
    public function initializeForwardBackInfer() : void
    {
        $this->layer->initialize(1);

        $this->assertEquals(1, $this->layer->width());

        $expected = [
            [2.5, 0.0, -6.0],
        ];

        $forward = $this->layer->forward($this->input);

        $this->assertInstanceOf(Matrix::class, $forward);
        $this->assertEquals($expected, $forward->asArray());

        [$computation, $loss] = $this->layer->back($this->labels, $this->optimizer);

        $this->assertInstanceOf(Deferred::class, $computation);
        $this->assertIsFloat($loss);

        $gradient = $computation->compute();

        $expected = [
            [0.8333333333333334, 0.8333333333333334, -32.0],
        ];

        $this->assertInstanceOf(Matrix::class, $gradient);
        $this->assertEquals($expected, $gradient->asArray());

        $expected = [
            [2.5, 0.0, -6.0],
        ];

        $infer = $this->layer->infer($this->input);

        $this->assertInstanceOf(Matrix::class, $infer);
        $this->assertEquals($expected, $infer->asArray());
    }
}
