Matrix::__construct() - Code Metrics - php-ai/php-ml - Measure and Improve Code Quality continuously with Scrutinizer

Matrix::__construct() A
last analyzed 2020-05-15 05:48 UTC

↳ Parent: Matrix

Complexity

Conditions	5
Paths	6

Size

Total Lines	21
Code Lines	12

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	12
dl	0
loc	21
rs	9.5555
c	0
b	0
f	0
cc	5
nc	6
nop	2

<?php

declare(strict_types=1);

namespace Phpml\Math;

use Phpml\Exception\InvalidArgumentException;
use Phpml\Exception\MatrixException;
use Phpml\Math\LinearAlgebra\LUDecomposition;

class Matrix
{
    /**
     * @var array
     */
    private $matrix = [];

    /**
     * @var int
     */
    private $rows;

    /**
     * @var int
     */
    private $columns;

    /**
     * @var float
     */
    private $determinant;

    /**
     * @throws InvalidArgumentException
     */
    public function __construct(array $matrix, bool $validate = true)
    {
        // When a row vector is given
        if (!is_array($matrix[0])) {
            $this->rows = 1;
            $this->columns = count($matrix);
            $matrix = [$matrix];
        } else {
            $this->rows = count($matrix);
            $this->columns = count($matrix[0]);
        }

        if ($validate) {
            for ($i = 0; $i < $this->rows; ++$i) {
                if (count($matrix[$i]) !== $this->columns) {
                    throw new InvalidArgumentException('Matrix dimensions did not match');
                }
            }
        }

        $this->matrix = $matrix;
    }

    public static function fromFlatArray(array $array): self
    {
        $matrix = [];
        foreach ($array as $value) {
            $matrix[] = [$value];
        }

        return new self($matrix);
    }

    public function toArray(): array
    {
        return $this->matrix;
    }

    public function toScalar(): float
    {
        return $this->matrix[0][0];
    }

    public function getRows(): int
    {
        return $this->rows;
    }

    public function getColumns(): int
    {
        return $this->columns;
    }

    /**
     * @throws MatrixException
     */
    public function getColumnValues(int $column): array
    {
        if ($column >= $this->columns) {
            throw new MatrixException('Column out of range');
        }

        return array_column($this->matrix, $column);
    }

    /**
     * @return float|int
     *
     * @throws MatrixException
     */
    public function getDeterminant()
    {
        if ($this->determinant !== null) {
            return $this->determinant;
        }

        if (!$this->isSquare()) {
            throw new MatrixException('Matrix is not square matrix');
        }

        $lu = new LUDecomposition($this);

        return $this->determinant = $lu->det();
    }

    public function isSquare(): bool
    {
        return $this->columns === $this->rows;
    }

    public function transpose(): self
    {
        if ($this->rows === 1) {
            $matrix = array_map(static function ($el): array {
                return [$el];
            }, $this->matrix[0]);
        } else {
            $matrix = array_map(null, ...$this->matrix);
        }

        return new self($matrix, false);
    }

    public function multiply(self $matrix): self
    {
        if ($this->columns !== $matrix->getRows()) {
            throw new InvalidArgumentException('Inconsistent matrix supplied');
        }

        $array1 = $this->toArray();
        $array2 = $matrix->toArray();
        $colCount = $matrix->columns;

        /*
         - To speed-up multiplication, we need to avoid use of array index operator [ ] as much as possible( See #255 for details)
         - A combination of "foreach" and "array_column" works much faster then accessing the array via index operator
        */
        $product = [];
        foreach ($array1 as $row => $rowData) {
            for ($col = 0; $col < $colCount; ++$col) {
                $columnData = array_column($array2, $col);
                $sum = 0;
                foreach ($rowData as $key => $valueData) {
                    $sum += $valueData * $columnData[$key];
                }

                $product[$row][$col] = $sum;
            }
        }

        return new self($product, false);
    }

    /**
     * @param float|int $value
     */
    public function divideByScalar($value): self
    {
        $newMatrix = [];
        for ($i = 0; $i < $this->rows; ++$i) {
            for ($j = 0; $j < $this->columns; ++$j) {
                $newMatrix[$i][$j] = $this->matrix[$i][$j] / $value;
            }
        }

        return new self($newMatrix, false);
    }

    /**
     * @param float|int $value
     */
    public function multiplyByScalar($value): self
    {
        $newMatrix = [];
        for ($i = 0; $i < $this->rows; ++$i) {
            for ($j = 0; $j < $this->columns; ++$j) {
                $newMatrix[$i][$j] = $this->matrix[$i][$j] * $value;
            }
        }

        return new self($newMatrix, false);
    }

    /**
     * Element-wise addition of the matrix with another one
     */
    public function add(self $other): self
    {
        return $this->sum($other);
    }

    /**
     * Element-wise subtracting of another matrix from this one
     */
    public function subtract(self $other): self
    {
        return $this->sum($other, -1);
    }

    public function inverse(): self
    {
        if (!$this->isSquare()) {
            throw new MatrixException('Matrix is not square matrix');
        }

        $LU = new LUDecomposition($this);
        $identity = $this->getIdentity();
        $inverse = $LU->solve($identity);

        return new self($inverse, false);
    }

    public function crossOut(int $row, int $column): self
    {
        $newMatrix = [];
        $r = 0;
        for ($i = 0; $i < $this->rows; ++$i) {
            $c = 0;
            if ($row != $i) {
                for ($j = 0; $j < $this->columns; ++$j) {
                    if ($column != $j) {
                        $newMatrix[$r][$c] = $this->matrix[$i][$j];
                        ++$c;
                    }
                }

                ++$r;
            }
        }

        return new self($newMatrix, false);
    }

    public function isSingular(): bool
    {
        return $this->getDeterminant() == 0;
    }

    /**
     * Frobenius norm (Hilbert–Schmidt norm, Euclidean norm) (‖A‖F)
     * Square root of the sum of the square of all elements.
     *
     * https://en.wikipedia.org/wiki/Matrix_norm#Frobenius_norm
     *
     *          _____________
     *         /ᵐ   ⁿ
     * ‖A‖F = √ Σ   Σ  |aᵢⱼ|²
     *         ᵢ₌₁ ᵢ₌₁
     */
    public function frobeniusNorm(): float
    {
        $squareSum = 0;
        for ($i = 0; $i < $this->rows; ++$i) {
            for ($j = 0; $j < $this->columns; ++$j) {
                $squareSum += $this->matrix[$i][$j] ** 2;
            }
        }

        return $squareSum ** .5;
    }

    /**
     * Returns the transpose of given array
     */
    public static function transposeArray(array $array): array
    {
        return (new self($array, false))->transpose()->toArray();
    }

    /**
     * Returns the dot product of two arrays<br>
     * Matrix::dot(x, y) ==> x.y'
     */
    public static function dot(array $array1, array $array2): array
    {
        $m1 = new self($array1, false);
        $m2 = new self($array2, false);

        return $m1->multiply($m2->transpose())->toArray()[0];
    }

    /**
     * Element-wise addition or substraction depending on the given sign parameter
     */
    private function sum(self $other, int $sign = 1): self
    {
        $a1 = $this->toArray();
        $a2 = $other->toArray();

        $newMatrix = [];
        for ($i = 0; $i < $this->rows; ++$i) {
            for ($k = 0; $k < $this->columns; ++$k) {
                $newMatrix[$i][$k] = $a1[$i][$k] + $sign * $a2[$i][$k];
            }
        }

        return new self($newMatrix, false);
    }

    /**
     * Returns diagonal identity matrix of the same size of this matrix
     */
    private function getIdentity(): self
    {
        $array = array_fill(0, $this->rows, array_fill(0, $this->columns, 0));
        for ($i = 0; $i < $this->rows; ++$i) {
            $array[$i][$i] = 1;
        }

        return new self($array, false);
    }
}


1			<?php
2
3			declare(strict_types=1);
4
5			namespace Phpml\Math;
6
7			use Phpml\Exception\InvalidArgumentException;
8			use Phpml\Exception\MatrixException;
9			use Phpml\Math\LinearAlgebra\LUDecomposition;
10
11			class Matrix
12			{
13			/**
14			* @var array
15			*/
16			private $matrix = [];
17
18			/**
19			* @var int
20			*/
21			private $rows;
22
23			/**
24			* @var int
25			*/
26			private $columns;
27
28			/**
29			* @var float
30			*/
31			private $determinant;
32
33			/**
34			* @throws InvalidArgumentException
35			*/
36			public function __construct(array $matrix, bool $validate = true)
37			{
38			// When a row vector is given
39			if (!is_array($matrix[0])) {
40			$this->rows = 1;
41			$this->columns = count($matrix);
42			$matrix = [$matrix];
43			} else {
44			$this->rows = count($matrix);
45			$this->columns = count($matrix[0]);
46			}
47
48			if ($validate) {
49			for ($i = 0; $i < $this->rows; ++$i) {
50			if (count($matrix[$i]) !== $this->columns) {
51			throw new InvalidArgumentException('Matrix dimensions did not match');
52			}
53			}
54			}
55
56			$this->matrix = $matrix;
57			}
58
59			public static function fromFlatArray(array $array): self
60			{
61			$matrix = [];
62			foreach ($array as $value) {
63			$matrix[] = [$value];
64			}
65
66			return new self($matrix);
67			}
68
69			public function toArray(): array
70			{
71			return $this->matrix;
72			}
73
74			public function toScalar(): float
75			{
76			return $this->matrix[0][0];
77			}
78
79			public function getRows(): int
80			{
81			return $this->rows;
82			}
83
84			public function getColumns(): int
85			{
86			return $this->columns;
87			}
88
89			/**
90			* @throws MatrixException
91			*/
92			public function getColumnValues(int $column): array
93			{
94			if ($column >= $this->columns) {
95			throw new MatrixException('Column out of range');
96			}
97
98			return array_column($this->matrix, $column);
99			}
100
101			/**
102			* @return float\|int
103			*
104			* @throws MatrixException
105			*/
106			public function getDeterminant()
107			{
108			if ($this->determinant !== null) {
109			return $this->determinant;
110			}
111
112			if (!$this->isSquare()) {
113			throw new MatrixException('Matrix is not square matrix');
114			}
115
116			$lu = new LUDecomposition($this);
117
118			return $this->determinant = $lu->det();
119			}
120
121			public function isSquare(): bool
122			{
123			return $this->columns === $this->rows;
124			}
125
126			public function transpose(): self
127			{
128			if ($this->rows === 1) {
129			$matrix = array_map(static function ($el): array {
130			return [$el];
131			}, $this->matrix[0]);
132			} else {
133			$matrix = array_map(null, ...$this->matrix);
134			}
135
136			return new self($matrix, false);
137			}
138
139			public function multiply(self $matrix): self
140			{
141			if ($this->columns !== $matrix->getRows()) {
142			throw new InvalidArgumentException('Inconsistent matrix supplied');
143			}
144
145			$array1 = $this->toArray();
146			$array2 = $matrix->toArray();
147			$colCount = $matrix->columns;
148
149			/*
150			- To speed-up multiplication, we need to avoid use of array index operator [ ] as much as possible( See #255 for details)
151			- A combination of "foreach" and "array_column" works much faster then accessing the array via index operator
152			*/
153			$product = [];
154			foreach ($array1 as $row => $rowData) {
155			for ($col = 0; $col < $colCount; ++$col) {
156			$columnData = array_column($array2, $col);
157			$sum = 0;
158			foreach ($rowData as $key => $valueData) {
159			$sum += $valueData * $columnData[$key];
160			}
161
162			$product[$row][$col] = $sum;
163			}
164			}
165
166			return new self($product, false);
167			}
168
169			/**
170			* @param float\|int $value
171			*/
172			public function divideByScalar($value): self
173			{
174			$newMatrix = [];
175			for ($i = 0; $i < $this->rows; ++$i) {
176			for ($j = 0; $j < $this->columns; ++$j) {
177			$newMatrix[$i][$j] = $this->matrix[$i][$j] / $value;
178			}
179			}
180
181			return new self($newMatrix, false);
182			}
183
184			/**
185			* @param float\|int $value
186			*/
187			public function multiplyByScalar($value): self
188			{
189			$newMatrix = [];
190			for ($i = 0; $i < $this->rows; ++$i) {
191			for ($j = 0; $j < $this->columns; ++$j) {
192			$newMatrix[$i][$j] = $this->matrix[$i][$j] * $value;
193			}
194			}
195
196			return new self($newMatrix, false);
197			}
198
199			/**
200			* Element-wise addition of the matrix with another one
201			*/
202			public function add(self $other): self
203			{
204			return $this->sum($other);
205			}
206
207			/**
208			* Element-wise subtracting of another matrix from this one
209			*/
210			public function subtract(self $other): self
211			{
212			return $this->sum($other, -1);
213			}
214
215			public function inverse(): self
216			{
217			if (!$this->isSquare()) {
218			throw new MatrixException('Matrix is not square matrix');
219			}
220
221			$LU = new LUDecomposition($this);
222			$identity = $this->getIdentity();
223			$inverse = $LU->solve($identity);
224
225			return new self($inverse, false);
226			}
227
228			public function crossOut(int $row, int $column): self
229			{
230			$newMatrix = [];
231			$r = 0;
232			for ($i = 0; $i < $this->rows; ++$i) {
233			$c = 0;
234			if ($row != $i) {
235			for ($j = 0; $j < $this->columns; ++$j) {
236			if ($column != $j) {
237			$newMatrix[$r][$c] = $this->matrix[$i][$j];
238			++$c;
239			}
240			}
241
242			++$r;
243			}
244			}
245
246			return new self($newMatrix, false);
247			}
248
249			public function isSingular(): bool
250			{
251			return $this->getDeterminant() == 0;
252			}
253
254			/**
255			* Frobenius norm (Hilbert–Schmidt norm, Euclidean norm) (‖A‖F)
256			* Square root of the sum of the square of all elements.
257			*
258			* https://en.wikipedia.org/wiki/Matrix_norm#Frobenius_norm
259			*
260			* _____________
261			* /ᵐ ⁿ
262			* ‖A‖F = √ Σ Σ \|aᵢⱼ\|²
263			* ᵢ₌₁ ᵢ₌₁
264			*/
265			public function frobeniusNorm(): float
266			{
267			$squareSum = 0;
268			for ($i = 0; $i < $this->rows; ++$i) {
269			for ($j = 0; $j < $this->columns; ++$j) {
270			$squareSum += $this->matrix[$i][$j] ** 2;
271			}
272			}
273
274			return $squareSum ** .5;
275			}
276
277			/**
278			* Returns the transpose of given array
279			*/
280			public static function transposeArray(array $array): array
281			{
282			return (new self($array, false))->transpose()->toArray();
283			}
284
285			/**
286			* Returns the dot product of two arrays<br>
287			* Matrix::dot(x, y) ==> x.y'
288			*/
289			public static function dot(array $array1, array $array2): array
290			{
291			$m1 = new self($array1, false);
292			$m2 = new self($array2, false);
293
294			return $m1->multiply($m2->transpose())->toArray()[0];
295			}
296
297			/**
298			* Element-wise addition or substraction depending on the given sign parameter
299			*/
300			private function sum(self $other, int $sign = 1): self
301			{
302			$a1 = $this->toArray();
303			$a2 = $other->toArray();
304
305			$newMatrix = [];
306			for ($i = 0; $i < $this->rows; ++$i) {
307			for ($k = 0; $k < $this->columns; ++$k) {
308			$newMatrix[$i][$k] = $a1[$i][$k] + $sign * $a2[$i][$k];
309			}
310			}
311
312			return new self($newMatrix, false);
313			}
314
315			/**
316			* Returns diagonal identity matrix of the same size of this matrix
317			*/
318			private function getIdentity(): self
319			{
320			$array = array_fill(0, $this->rows, array_fill(0, $this->columns, 0));
321			for ($i = 0; $i < $this->rows; ++$i) {
322			$array[$i][$i] = 1;
323			}
324
325			return new self($array, false);
326			}
327			}
328

php-ai / php-ml

Matrix::__construct() A last analyzed 2020-05-15 05:48 UTC

Complexity

Size

Duplication

Importance

Duplication Side-by-Side

Filter issues like

Matrix::__construct() A
last analyzed 2020-05-15 05:48 UTC