Create a NumPy clone for php using php class

Is it possible to create a numpy clone for php using php class? If yes, create one class named np and functions with parameters. Functions name: np.array(), np.zeros()

Asked by: Mr. Hyphen
php

Answers:

It is possible to create a basic version of a numpy-like library in PHP using classes and functions.

class np {
    public static function array(...$elements) {
        return $elements;
    }

    public static function zeros($shape) {
        if (is_array($shape)) {
            $zeros = [];
            foreach ($shape as $dim) {
                $zeros[] = array_fill(0, $dim, 0);
            }
            return $zeros;
        } else {
            return [];
        }
    }

    public static function arange($start, $stop, $step = 1) {
        $range = [];
        for ($i = $start; $i < $stop; $i += $step) {
            $range[] = $i;
        }
        return $range;
    }

    public static function eye($rows, $cols = null, $diagonalValue = 1) {
        if ($cols === null) {
            $cols = $rows;
        }
        $eye = [];
        for ($i = 0; $i < $rows; $i++) {
            $row = array_fill(0, $cols, 0);
            $row[$i] = $diagonalValue;
            $eye[] = $row;
        }
        return $eye;
    }

    public static function linspace($start, $stop, $num = 50) {
        $linspace = [];
        $step = ($stop - $start) / ($num - 1);
        for ($i = 0; $i < $num; $i++) {
            $linspace[] = $start + $i * $step;
        }
        return $linspace;
    }

    public static function random($shape, $min = 0, $max = 1) {
        if (is_array($shape)) {
            $random = [];
            foreach ($shape as $dim) {
                $row = [];
                for ($i = 0; $i < $dim; $i++) {
                    $row[] = mt_rand($min, $max);
                }
                $random[] = $row;
            }
            return $random;
        } else {
            return mt_rand($min, $max);
        }
    }
}

// Usage
$array = np::array(1, 2, 3, 4, 5);
print_r($array);

$zeros = np::zeros([3, 2]);
print_r($zeros);

$arange = np::arange(0, 10, 2);
print_r($arange);

$eye = np::eye(3);
print_r($eye);

$linspace = np::linspace(0, 1, 5);
print_r($linspace);

$random = np::random([2, 3], 0, 10);
print_r($random);
Answered by: BinaryNinja 
class np {
    // ... Previous functions ...

    public static function max($array) {
        return max($array);
    }

    public static function min($array) {
        return min($array);
    }

    public static function argmax($array) {
        return array_search(max($array), $array);
    }

    public static function argmin($array) {
        return array_search(min($array), $array);
    }

    public static function sqrt($array) {
        $result = [];
        foreach ($array as $element) {
            $result[] = sqrt($element);
        }
        return $result;
    }

    public static function reshape($array, $newShape) {
        $totalElements = array_product($newShape);
        if (count($array) !== $totalElements) {
            return false; // Invalid reshape
        }
        $reshaped = [];
        $index = 0;
        foreach ($newShape as $dim) {
            $row = [];
            for ($i = 0; $i < $dim; $i++) {
                $row[] = $array[$index++];
            }
            $reshaped[] = $row;
        }
        return $reshaped;
    }

    public static function get($array, $indices) {
        $result = [];
        foreach ($indices as $index) {
            $result[] = $array[$index];
        }
        return $result;
    }

    public static function slice($array, $start, $end) {
        return array_slice($array, $start, $end - $start);
    }
}

// Usage
$array = np::array(1, 2, 3, 4, 5);
$max = np::max($array);
$min = np::min($array);
$argmax = np::argmax($array);
$argmin = np::argmin($array);
$sqrt = np::sqrt($array);
$reshaped = np::reshape($array, [2, 3]);
$value = np::get($array, [1, 3]);
$slice = np::slice($array, 1, 4);

echo "Max: $max\n";
echo "Min: $min\n";
echo "Argmax: $argmax\n";
echo "Argmin: $argmin\n";
echo "Sqrt: ";
print_r($sqrt);
echo "Reshaped: ";
print_r($reshaped);
echo "Value: ";
print_r($value);
echo "Slice: ";
print_r($slice);
Answered by: ScriptGenius 
class np {
    // ... Previous functions ...

    public static function sum($array) {
        return array_sum($array);
    }

    public static function mean($array) {
        $count = count($array);
        if ($count === 0) {
            return null;
        }
        return array_sum($array) / $count;
    }

    public static function std($array) {
        $mean = self::mean($array);
        $squaredDifferences = [];
        foreach ($array as $element) {
            $squaredDifferences[] = pow($element - $mean, 2);
        }
        $variance = array_sum($squaredDifferences) / count($array);
        return sqrt($variance);
    }

    public static function dot($array1, $array2) {
        $result = 0;
        $count = count($array1);
        for ($i = 0; $i < $count; $i++) {
            $result += $array1[$i] * $array2[$i];
        }
        return $result;
    }

    public static function matmul($matrix1, $matrix2) {
        $result = [];
        $rows1 = count($matrix1);
        $cols1 = count($matrix1[0]);
        $cols2 = count($matrix2[0]);

        for ($i = 0; $i < $rows1; $i++) {
            $row = [];
            for ($j = 0; $j < $cols2; $j++) {
                $value = 0;
                for ($k = 0; $k < $cols1; $k++) {
                    $value += $matrix1[$i][$k] * $matrix2[$k][$j];
                }
                $row[] = $value;
            }
            $result[] = $row;
        }

        return $result;
    }
}

// Usage
$array = np::array(1, 2, 3, 4, 5);
$sum = np::sum($array);
$mean = np::mean($array);
$std = np::std($array);

$dotProduct = np::dot([1, 2, 3], [4, 5, 6]);

$matrix1 = np::array([1, 2], [3, 4]);
$matrix2 = np::array([5, 6], [7, 8]);
$matrixProduct = np::matmul($matrix1, $matrix2);

echo "Sum: $sum\n";
echo "Mean: $mean\n";
echo "Standard Deviation: $std\n";
echo "Dot Product: $dotProduct\n";
echo "Matrix Product: ";
print_r($matrixProduct);
Answered by: Wouadud Al Reon

I can provide optimized versions of the functions to improve performance. However, please keep in mind that these optimizations might make the code more complex and might not cover all edge cases.

class np {
    public static function array(...$elements) {
        return $elements;
    }

    public static function zeros($shape) {
        if (is_array($shape)) {
            return array_fill(0, array_product($shape), 0);
        } else {
            return [];
        }
    }

    public static function arange($start, $stop, $step = 1) {
        return range($start, $stop - 1, $step);
    }

    public static function eye($rows, $cols = null, $diagonalValue = 1) {
        if ($cols === null) {
            $cols = $rows;
        }
        $eye = array_fill(0, $rows, array_fill(0, $cols, 0));
        for ($i = 0; $i < min($rows, $cols); $i++) {
            $eye[$i][$i] = $diagonalValue;
        }
        return $eye;
    }

    public static function linspace($start, $stop, $num = 50) {
        $step = ($stop - $start) / max($num - 1, 1);
        return array_map(function ($i) use ($start, $step) {
            return $start + $i * $step;
        }, range(0, $num - 1));
    }

    public static function random($shape, $min = 0, $max = 1) {
        if (!is_array($shape)) {
            return mt_rand($min, $max);
        }
        return array_map(function ($dim) use ($min, $max) {
            return array_map(function () use ($min, $max) {
                return mt_rand($min, $max);
            }, array_fill(0, $dim, null));
        }, $shape);
    }

    // ... More functions ...

    public static function sum($array) {
        return array_sum($array);
    }

    public static function mean($array) {
        return count($array) > 0 ? array_sum($array) / count($array) : null;
    }

    public static function std($array) {
        $mean = self::mean($array);
        $squaredDifferences = array_map(function ($element) use ($mean) {
            return pow($element - $mean, 2);
        }, $array);
        $variance = array_sum($squaredDifferences) / count($array);
        return sqrt($variance);
    }

    public static function dot($array1, $array2) {
        return array_sum(array_map(function ($a, $b) {
            return $a * $b;
        }, $array1, $array2));
    }

    public static function matmul($matrix1, $matrix2) {
        $rows1 = count($matrix1);
        $cols1 = count($matrix1[0]);
        $cols2 = count($matrix2[0]);
        $result = [];
        for ($i = 0; $i < $rows1; $i++) {
            $row = [];
            for ($j = 0; $j < $cols2; $j++) {
                $value = 0;
                for ($k = 0; $k < $cols1; $k++) {
                    $value += $matrix1[$i][$k] * $matrix2[$k][$j];
                }
                $row[] = $value;
            }
            $result[] = $row;
        }
        return $result;
    }
}

// Usage
$array = np::array(1, 2, 3, 4, 5);
$sum = np::sum($array);
$mean = np::mean($array);
$std = np::std($array);

$dotProduct = np::dot([1, 2, 3], [4, 5, 6]);

$matrix1 = np::array([1, 2], [3, 4]);
$matrix2 = np::array([5, 6], [7, 8]);
$matrixProduct = np::matmul($matrix1, $matrix2);

echo "Sum: $sum\n";
echo "Mean: $mean\n";
echo "Standard Deviation: $std\n";
echo "Dot Product: $dotProduct\n";
echo "Matrix Product: ";
print_r($matrixProduct);

These optimizations focus on reducing the number of loops and improving code readability. However, the performance gains might vary depending on the input data and the PHP interpreter's optimizations.

Answered by: Reon

Answer:

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