<?php

namespace MathPHP\Statistics\Regression;

use MathPHP\Exception;
use MathPHP\Functions\Map\Single;

/**
 * Use the Lineweaver-Burk method to fit an equation of the form
 *       V * x
 * y = ----------
 *       K + x
 *
 * The equation is linearized and fit using Least Squares
 */
class LineweaverBurk extends ParametricRegression
{
    use Models\MichaelisMenten;
    use Methods\LeastSquares;

    /**
     * Calculate the regression parameters by least squares on linearized data
     * y⁻¹ = K * V⁻¹ * x⁻¹ + V⁻¹
     *
     * @throws Exception\BadDataException
     * @throws Exception\IncorrectTypeException
     * @throws Exception\MatrixException
     * @throws Exception\MathException
     */
    public function calculate()
    {
        // Linearize the relationship by taking the inverse of both x and y
        $x’ = Single::pow($this->xs, -1);
        $y’ = Single::pow($this->ys, -1);

        // Perform Least Squares Fit
        $linearized_parameters = $this->leastSquares($y’, $x’)->getColumn(0);

        // Translate the linearized parameters back.
        $V = 1 / $linearized_parameters[0];
        $K = $linearized_parameters[1] * $V;

        $this->parameters = [$V, $K];
    }

    /**
     * Evaluate the regression equation at x
     * Uses the instance model's evaluateModel method.
     *
     * @param  float $x
     *
     * @return float
     */
    public function evaluate(float $x): float
    {
        return $this->evaluateModel($x, $this->parameters);
    }
}