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<?php

namespace PhpOffice\PhpSpreadsheet\Shared\Trend;

use Matrix\Matrix;

// Phpstan and Scrutinizer seem to have legitimate complaints.

// $this->slope is specified where an array is expected in several places.

// But it seems that it should always be float.

// This code is probably not exercised at all in unit tests.

class PolynomialBestFit extends BestFit

{

    /**

     * Algorithm type to use for best-fit

     * (Name of this Trend class).

     */

    protected string $bestFitType = 'polynomial';

    /**

     * Polynomial order.

     */

    protected int $order = 0;

    /**

     * Return the order of this polynomial.

     */

    public function getOrder(): int

    {

        return $this->order;

    }

    /**

     * Return the Y-Value for a specified value of X.

     *

     * @param float $xValue X-Value

     *

     * @return float Y-Value

     */

    public function getValueOfYForX(float $xValue): float

    {

        $retVal = $this->getIntersect();

        $slope = $this->getSlope();

        // Phpstan and Scrutinizer are both correct - getSlope returns float, not array.

        // @phpstan-ignore-next-line

        foreach ($slope as $key => $value) {

            if ($value != 0.0) {

                $retVal += $value * $xValue ** ($key + 1);

            }

        }

        return $retVal;

    }

    /**

     * Return the X-Value for a specified value of Y.

     *

     * @param float $yValue Y-Value

     *

     * @return float X-Value

     */

    public function getValueOfXForY(float $yValue): float

    {

        return ($yValue - $this->getIntersect()) / $this->getSlope();

    }

    /**

     * Return the Equation of the best-fit line.

     *

     * @param int $dp Number of places of decimal precision to display

     */

    public function getEquation(int $dp = 0): string

    {

        $slope = $this->getSlope($dp);

        $intersect = $this->getIntersect($dp);

        $equation = 'Y = ' . $intersect;

        // Phpstan and Scrutinizer are both correct - getSlope returns float, not array.

        // @phpstan-ignore-next-line

        foreach ($slope as $key => $value) {

            if ($value != 0.0) {

                $equation .= ' + ' . $value . ' * X';

                if ($key > 0) {

                    $equation .= '^' . ($key + 1);

                }

            }

        }

        return $equation;

    }

    /**

     * Return the Slope of the line.

     *

     * @param int $dp Number of places of decimal precision to display

     */

    public function getSlope(int $dp = 0): float

    {

        if ($dp != 0) {

            $coefficients = [];

            //* @phpstan-ignore-next-line

            foreach ($this->slope as $coefficient) {

                $coefficients[] = round($coefficient, $dp);

            }

            // @phpstan-ignore-next-line

            return $coefficients;

        }

        return $this->slope;

    }

    public function getCoefficients(int $dp = 0): array

    {

        // Phpstan and Scrutinizer are both correct - getSlope returns float, not array.

        // @phpstan-ignore-next-line

        return array_merge([$this->getIntersect($dp)], $this->getSlope($dp));

    }

    /**

     * Execute the regression and calculate the goodness of fit for a set of X and Y data values.

     *

     * @param int $order Order of Polynomial for this regression

     * @param float[] $yValues The set of Y-values for this regression

     * @param float[] $xValues The set of X-values for this regression

     */

    private function polynomialRegression(int $order, array $yValues, array $xValues): void

    {

        // calculate sums

        $x_sum = array_sum($xValues);

        $y_sum = array_sum($yValues);

        $xx_sum = $xy_sum = $yy_sum = 0;

        for ($i = 0; $i < $this->valueCount; ++$i) {

            $xy_sum += $xValues[$i] * $yValues[$i];

            $xx_sum += $xValues[$i] * $xValues[$i];

            $yy_sum += $yValues[$i] * $yValues[$i];

        }

        /*

         *    This routine uses logic from the PHP port of polyfit version 0.1

         *    written by Michael Bommarito and Paul Meagher

         *

         *    The function fits a polynomial function of order $order through

         *    a series of x-y data points using least squares.

         *

         */

        $A = [];

        $B = [];

        for ($i = 0; $i < $this->valueCount; ++$i) {

            for ($j = 0; $j <= $order; ++$j) {

                $A[$i][$j] = $xValues[$i] ** $j;

            }

        }

        for ($i = 0; $i < $this->valueCount; ++$i) {

            $B[$i] = [$yValues[$i]];

        }

        $matrixA = new Matrix($A);

        $matrixB = new Matrix($B);

        $C = $matrixA->solve($matrixB);

        $coefficients = [];

        for ($i = 0; $i < $C->rows; ++$i) {

            $r = $C->getValue($i + 1, 1); // row and column are origin-1

            if (!is_numeric($r) || abs($r + 0) <= 10 ** (-9)) {

                $r = 0;

            } else {

                $r += 0;

            }

            $coefficients[] = $r;

        }

        $this->intersect = (float) array_shift($coefficients);

        // Phpstan is correct

        //* @phpstan-ignore-next-line

        $this->slope = $coefficients;

        $this->calculateGoodnessOfFit($x_sum, $y_sum, $xx_sum, $yy_sum, $xy_sum, 0, 0, 0);

        foreach ($this->xValues as $xKey => $xValue) {

            $this->yBestFitValues[$xKey] = $this->getValueOfYForX($xValue);

        }

    }

    /**

     * Define the regression and calculate the goodness of fit for a set of X and Y data values.

     *

     * @param int $order Order of Polynomial for this regression

     * @param float[] $yValues The set of Y-values for this regression

     * @param float[] $xValues The set of X-values for this regression

     */

    public function __construct(int $order, array $yValues, array $xValues = [])

    {

        parent::__construct($yValues, $xValues);

        if (!$this->error) {

            if ($order < $this->valueCount) {

                $this->bestFitType .= '_' . $order;

                $this->order = $order;

                $this->polynomialRegression($order, $yValues, $xValues);

                if (($this->getGoodnessOfFit() < 0.0) || ($this->getGoodnessOfFit() > 1.0)) {

                    $this->error = true;

                }

            } else {

                $this->error = true;

            }

        }

    }

}