ghostjat /
Np
| Total Complexity | 284 |
| Total Lines | 1391 |
| Duplicated Lines | 0 % |
| Changes | 4 | ||
| Bugs | 3 | Features | 0 |
Complex classes like matrix often do a lot of different things. To break such a class down, we need to identify a cohesive component within that class. A common approach to find such a component is to look for fields/methods that share the same prefixes, or suffixes.
Once you have determined the fields that belong together, you can apply the Extract Class refactoring. If the component makes sense as a sub-class, Extract Subclass is also a candidate, and is often faster.
While breaking up the class, it is a good idea to analyze how other classes use matrix, and based on these observations, apply Extract Interface, too.
| 1 | <?php |
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| 20 | class matrix extends nd { |
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| 21 | |||
| 22 | use ops,linAlgb\linAlg; |
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| 23 | |||
| 24 | /** |
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| 25 | * create empty 2d matrix for given data type |
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| 26 | * @param int $row num of rows |
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| 27 | * @param int $col num of cols |
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| 28 | * @return \Np\matrix |
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| 29 | */ |
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| 30 | public static function factory(int $row, int $col): matrix { |
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| 31 | return new self($row, $col); |
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| 32 | } |
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| 33 | |||
| 34 | /** |
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| 35 | * create 2d matrix using php array |
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| 36 | * @param array $data |
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| 37 | * @return \Np\matrix |
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| 38 | */ |
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| 39 | public static function ar(array $data): matrix { |
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| 40 | if (is_array($data) && is_array($data[0])) { |
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| 41 | $ar = self::factory(count($data), count($data[0])); |
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| 42 | $ar->setData($data); |
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| 43 | unset($data); |
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| 44 | return $ar; |
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| 45 | } else { |
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| 46 | self::_err('given array is not rank-2 or given is not an array'); |
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| 47 | } |
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| 48 | } |
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| 49 | |||
| 50 | /** |
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| 51 | * create one like 2d matrix |
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| 52 | * @param int $row |
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| 53 | * @param int $col |
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| 54 | * @return \Np\matrix |
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| 55 | */ |
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| 56 | public static function ones(int $row, int $col): matrix { |
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| 57 | $ar = self::factory($row, $col); |
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| 58 | for ($i = 0; $i < $ar->ndim; ++$i) { |
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| 59 | $ar->data[$i] = 1; |
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| 60 | } |
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| 61 | return $ar; |
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| 62 | } |
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| 63 | |||
| 64 | /** |
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| 65 | * Create Matrix with random values |
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| 66 | * @param int $row |
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| 67 | * @param int $col |
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| 68 | * @return \Np\matrix |
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| 69 | */ |
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| 70 | public static function randn(int $row, int $col): matrix { |
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| 71 | $ar = self::factory($row, $col); |
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| 72 | $max = getrandmax(); |
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| 73 | for ($i = 0; $i < $ar->ndim; ++$i) { |
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| 74 | $ar->data[$i] = rand() / $max; |
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| 75 | } |
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| 76 | return $ar; |
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| 77 | } |
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| 78 | |||
| 79 | /** |
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| 80 | * Return 2d matrix with uniform values |
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| 81 | * @param int $row |
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| 82 | * @param int $col |
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| 83 | * @return \Np\matrix |
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| 84 | */ |
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| 85 | public static function uniform(int $row, int $col): matrix { |
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| 86 | $ar = self::factory($row, $col); |
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| 87 | $max = getrandmax(); |
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| 88 | for ($i = 0; $i < $ar->ndim; ++$i) { |
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| 89 | $ar->data[$i] = rand(-$max, $max) / $max; |
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| 90 | } |
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| 91 | return $ar; |
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| 92 | } |
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| 93 | |||
| 94 | /** |
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| 95 | * Return a zero matrix with the given dimensions. |
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| 96 | * @param int $row |
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| 97 | * @param int $col |
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| 98 | * @return \Np\matrix |
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| 99 | */ |
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| 100 | public static function zeros(int $row, int $col): matrix { |
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| 101 | $ar = self::factory($row, $col); |
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| 102 | for ($i = 0; $i < $ar->ndim; ++$i) { |
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| 103 | $ar->data[$i] = 0.0; |
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| 104 | } |
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| 105 | return $ar; |
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| 106 | } |
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| 107 | |||
| 108 | /** |
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| 109 | * create a null like 2d matrix |
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| 110 | * @param int $row |
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| 111 | * @param int $col |
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| 112 | * @return \Np\matrix |
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| 113 | */ |
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| 114 | public static function null(int $row, int $col): matrix { |
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| 115 | $ar = self::factory($row, $col); |
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| 116 | for ($i = 0; $i < $ar->ndim; ++$i) { |
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| 117 | $ar->data[$i] = null; |
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| 118 | } |
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| 119 | return $ar; |
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| 120 | } |
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| 121 | |||
| 122 | /** |
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| 123 | * create a 2d matrix with given scalar value |
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| 124 | * @param int $row |
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| 125 | * @param int $col |
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| 126 | * @param int|float $val |
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| 127 | * @return \Np\matrix |
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| 128 | */ |
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| 129 | public static function full(int $row, int $col, int|float $val): matrix { |
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| 130 | $ar = self::factory($row, $col); |
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| 131 | for ($i = 0; $i < $ar->ndim; ++$i) { |
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| 132 | $ar->data[$i] = $val; |
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| 133 | } |
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| 134 | return $ar; |
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| 135 | } |
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| 136 | |||
| 137 | /** |
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| 138 | * create a diagonal 2d matrix with given 1d array; |
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| 139 | * @param array $elements |
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| 140 | * @return \Np\matrix |
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| 141 | */ |
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| 142 | public static function diagonal(array $elements): matrix { |
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| 143 | $n = count($elements); |
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| 144 | $ar = self::factory($n, $n); |
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| 145 | for ($i = 0; $i < $n; ++$i) { |
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| 146 | $ar->data[$i * $n + $i] = $elements[$i]; #for ($j = 0; $j < $n; ++$j) {$i === $j ? $elements[$i] : 0;#} |
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| 147 | } |
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| 148 | return $ar; |
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| 149 | } |
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| 150 | |||
| 151 | /** |
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| 152 | * Generate a m x n matrix with elements from a Poisson distribution. |
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| 153 | * @param int $row |
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| 154 | * @param int $col |
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| 155 | * @param float $lambda |
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| 156 | * @return \Np\matrix |
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| 157 | */ |
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| 158 | public static function poisson(int $row, int $col, float $lambda = 1.0): matrix { |
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| 159 | $ar = self::factory($row, $col); |
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| 160 | $max = getrandmax(); |
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| 161 | $l = exp(-$lambda); |
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| 162 | for ($i = 0; $i < $row; ++$i) { |
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| 163 | for ($j = 0; $j < $col; ++$j) { |
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| 164 | $k = 0; |
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| 165 | $p = 1.0; |
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| 166 | while ($p > $l) { |
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| 167 | ++$k; |
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| 168 | $p = $p * rand() / $max; |
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| 169 | } |
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| 170 | $ar->data[$i * $col + $j] = $k - 1; |
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| 171 | } |
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| 172 | } |
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| 173 | return $ar; |
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| 174 | } |
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| 175 | |||
| 176 | /** |
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| 177 | * Return a standard normally distributed random matrix i.e values |
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| 178 | * between -1 and 1. |
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| 179 | * @param int $row |
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| 180 | * @param int $col |
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| 181 | * @return \Np\matrix |
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| 182 | */ |
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| 183 | public static function gaussian(int $row, int $col): matrix { |
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| 184 | $max = getrandmax(); |
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| 185 | $a = $extras = []; |
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| 186 | |||
| 187 | while (count($a) < $row) { |
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| 188 | $rowA = []; |
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| 189 | |||
| 190 | if (!empty($extras)) { |
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| 191 | $rowA[] = array_pop($extras); |
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| 192 | } |
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| 193 | |||
| 194 | while (count($rowA) < $col) { |
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| 195 | $r = sqrt(-2.0 * log(rand() / $max)); |
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| 196 | |||
| 197 | $phi = rand() / $max * self::TWO_PI; |
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| 198 | |||
| 199 | $rowA[] = $r * sin($phi); |
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| 200 | $rowA[] = $r * cos($phi); |
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| 201 | } |
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| 202 | |||
| 203 | if (count($rowA) > $col) { |
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| 204 | $extras[] = array_pop($rowA); |
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| 205 | } |
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| 206 | |||
| 207 | $a[] = $rowA; |
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| 208 | } |
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| 209 | |||
| 210 | return self::ar($a); |
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| 211 | } |
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| 212 | |||
| 213 | /** |
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| 214 | * create an identity matrix with the given dimensions. |
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| 215 | * @param int $n |
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| 216 | * @return matrix |
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| 217 | * @throws \InvalidArgumentException |
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| 218 | */ |
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| 219 | public static function identity(int $n): matrix { |
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| 220 | if ($n < 1) { |
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| 221 | self::_dimensionaMisMatchErr('dimensionality must be greater than 0 on all axes.'); |
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| 222 | } |
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| 223 | |||
| 224 | $ar = self::factory($n, $n); |
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| 225 | for ($i = 0; $i < $n; ++$i) { |
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| 226 | for ($j = 0; $j < $n; ++$j) { |
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| 227 | $ar->data[$i * $n + $j] = $i === $j ? 1 : 0; |
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| 228 | } |
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| 229 | } |
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| 230 | return $ar; |
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| 231 | } |
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| 232 | |||
| 233 | /** |
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| 234 | * Return a row as vector from the matrix. |
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| 235 | * @param int $index |
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| 236 | * @return \Np\vector |
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| 237 | */ |
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| 238 | public function rowAsVector(int $index): vector { |
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| 239 | $vr = vector::factory($this->col); |
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| 240 | for ($j = 0; $j < $this->col; ++$j) { |
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| 241 | $vr->data[$j] = $this->data[$index * $this->col + $j]; |
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| 242 | } |
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| 243 | return $vr; |
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| 244 | } |
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| 245 | |||
| 246 | /** |
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| 247 | * Return a col as vector from the matrix. |
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| 248 | * @param int $index |
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| 249 | * @return \Np\vector |
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| 250 | */ |
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| 251 | public function colAsVector(int $index): vector { |
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| 252 | $vr = vector::factory($this->row); |
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| 253 | for ($i = 0; $i < $this->row; ++$i) { |
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| 254 | $vr->data[$i] = $this->data[$i * $this->row + $index]; |
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| 255 | } |
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| 256 | return $vr; |
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| 257 | } |
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| 258 | |||
| 259 | /** |
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| 260 | * Return the diagonal elements of a square matrix as a vector. |
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| 261 | * @return \Np\vector |
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| 262 | */ |
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| 263 | public function diagonalAsVector(): vector { |
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| 264 | if ($this->isSquare()) { |
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| 265 | $vr = vector::factory($this->row); |
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| 266 | for ($i = 0; $i < $this->row; ++$i) { |
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| 267 | $vr->data[$i] = $this->getDiagonalVal($i); |
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| 268 | } |
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| 269 | return $vr; |
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| 270 | } |
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| 271 | self::_err('Can not trace of a none square matrix'); |
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| 272 | } |
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| 273 | |||
| 274 | /** |
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| 275 | * Flatten i.e unravel the matrix into a vector. |
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| 276 | * |
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| 277 | * @return \Np\vector |
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| 278 | */ |
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| 279 | public function asVector(): vector { |
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| 280 | $vr = vector::factory($this->ndim); |
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| 281 | for ($i = 0; $i < $this->ndim; ++$i) { |
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| 282 | $vr->data[$i] = $this->data[$i]; |
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| 283 | } |
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| 284 | return $vr; |
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| 285 | } |
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| 286 | |||
| 287 | /** |
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| 288 | * 2D convolution between a matrix ma and kernel kb, with a given stride. |
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| 289 | * @param \Np\matrix $m |
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| 290 | * @param int $stride |
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| 291 | * @return matrix |
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| 292 | */ |
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| 293 | public function convolve(matrix $m, int $stride = 1): matrix { |
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| 294 | return convolve::conv2D($this, $m, $stride); |
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| 295 | } |
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| 296 | |||
| 297 | /** |
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| 298 | * Calculate the determinant of the matrix. |
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| 299 | * @return float |
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| 300 | */ |
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| 301 | public function det(): float { |
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| 302 | if (!$this->isSquare()) { |
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| 303 | self::_err('determinant is undefined for a non square matrix'); |
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| 304 | } |
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| 305 | $lu = $this->lu(); |
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| 306 | $nSwaps = $lu->p()->diagonalAsVector()->subtract($lu->p()->diagonalAsVector()->sum())->col - 1; |
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| 307 | $detP = (-1) ** $nSwaps; |
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| 308 | $detL = $lu->l()->diagonalAsVector()->product(); |
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| 309 | $detU = $lu->u()->diagonalAsVector()->product(); |
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| 310 | unset($lu); |
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| 311 | return ($detP * $detL * $detU); |
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| 312 | } |
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| 313 | |||
| 314 | /** |
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| 315 | * Return the trace of the matrix i.e the sum of all diagonal elements of a square matrix. |
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| 316 | * @return float |
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| 317 | */ |
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| 318 | public function trace(): float { |
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| 319 | if (!$this->isSquare()) { |
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| 320 | self::_err('Error::matrix is not a squared can not Trace!'); |
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| 321 | } |
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| 322 | $trace = 0.0; |
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| 323 | for ($i = 0; $i < $this->row; ++$i) { |
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| 324 | for ($j = 0; $j < $this->col; ++$j) { |
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| 325 | if ($i == $j) { |
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| 326 | $trace += $this->data[$i * $this->col + $i]; |
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| 327 | } |
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| 328 | } |
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| 329 | } |
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| 330 | return $trace; |
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| 331 | } |
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| 332 | |||
| 333 | /** |
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| 334 | * dignoalInterChange |
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| 335 | */ |
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| 336 | public function dignoalInterChange() { |
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| 337 | for ($i = 0; $i < $this->row; ++$i) { |
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| 338 | for ($j = 0; $j < $this->col; ++$j) { |
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| 339 | $tmp = $this->data[$i * $this->col - $j]; |
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| 340 | $this->data[$i * $this->col - $j] = $tmp; |
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| 341 | } |
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| 342 | } |
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| 343 | } |
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| 344 | |||
| 345 | //---------------Arthmetic Opreations----------------------------------- |
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| 346 | |||
| 347 | /** |
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| 348 | * multiply this matrix with another matrix|scalar element-wise |
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| 349 | * Matrix Scalar\Matrix multiplication |
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| 350 | * @param int|float|matrix|vector $m |
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| 351 | * @return matrix|vector |
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| 352 | */ |
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| 353 | public function multiply(int|float|matrix|vector $m): matrix|vector { |
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| 354 | if ($m instanceof self) { |
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| 355 | return $this->multiplyMatrix($m); |
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| 356 | } else if ($m instanceof vector) { |
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| 357 | return $this->multiplyVector($m); |
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| 358 | } else { |
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| 359 | return $this->scale($m); |
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| 360 | } |
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| 361 | } |
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| 362 | |||
| 363 | /** |
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| 364 | * |
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| 365 | * @param \Np\vector $v |
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| 366 | * @return matrix |
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| 367 | */ |
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| 368 | protected function multiplyVector(vector $v): matrix { |
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| 369 | if ($this->checkDimensions($v, $this)) { |
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| 370 | $ar = matrix::factory($this->row, $this->col); |
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| 371 | for ($i = 0; $i < $this->row; ++$i) { |
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| 372 | for ($j = 0; $j < $this->col; ++$j) { |
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| 373 | $ar->data[$i * $this->col + $j] = $v->data[$j] * $this->data[$i * $this->col + $j]; |
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| 374 | } |
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| 375 | } |
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| 376 | return $ar; |
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| 377 | } |
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| 378 | } |
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| 379 | |||
| 380 | /** |
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| 381 | * |
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| 382 | * @param \Np\matrix $m |
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| 383 | * @return matrix |
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| 384 | */ |
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| 385 | protected function multiplyMatrix(matrix $m): matrix { |
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| 386 | if ($this->checkShape($this, $m)) { |
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| 387 | $ar = self::factory($this->row, $this->col); |
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| 388 | for ($i = 0; $i < $this->row; ++$i) { |
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| 389 | for ($j = 0; $j < $this->col; ++$j) { |
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| 390 | $ar->data[$i * $this->col + $j] = $this->data[$i * $this->col + $j] * $m->data[$i * $this->col + $j]; |
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| 391 | } |
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| 392 | } |
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| 393 | return $ar; |
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| 394 | } |
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| 395 | } |
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| 396 | |||
| 397 | /** |
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| 398 | * Sum of two matrix, vector or a scalar to current matrix |
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| 399 | * |
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| 400 | * @param int|float|matrix|vector $m |
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| 401 | * @return matrix |
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| 402 | */ |
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| 403 | public function sum(int|float|matrix|vector $m): matrix { |
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| 404 | if ($m instanceof self) { |
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| 405 | return $this->sumMatrix($m); |
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| 406 | } elseif ($m instanceof vector) { |
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| 407 | return $this->sumVector($m); |
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| 408 | } else { |
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| 409 | return $this->sumScalar($m); |
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| 410 | } |
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| 411 | } |
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| 412 | |||
| 413 | protected function sumScalar(int|float $s): matrix { |
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| 414 | $ar = self::factory($this->row, $this->col); |
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| 415 | for ($i = 0; $i < $this->ndim; ++$i) { |
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| 416 | $ar->data[$i] = $this->data[$i] + $s; |
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| 417 | } |
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| 418 | return $ar; |
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| 419 | } |
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| 420 | |||
| 421 | protected function sumMatrix(matrix $m): matrix { |
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| 422 | if ($this->checkShape($this, $m)) { |
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| 423 | $ar = self::factory($this->row, $this->col); |
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| 424 | for ($i = 0; $i < $this->ndim; ++$i) { |
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| 425 | $ar->data[$i] = $this->data[$i] + $m->data[$i]; |
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| 426 | } |
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| 427 | return $ar; |
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| 428 | } |
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| 429 | } |
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| 430 | |||
| 431 | protected function sumVector(vector $v): matrix { |
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| 432 | if ($this->checkDimensions($v, $this)) { |
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| 433 | $ar = self::factory($this->row, $this->col); |
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| 434 | for ($i = 0; $i < $this->row; ++$i) { |
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| 435 | for ($j = 0; $j < $this->col; ++$j) { |
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| 436 | $ar->data[$i * $this->col + $j] = $v->data[$j] + $this->data[$i * $this->col + $j]; |
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| 437 | } |
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| 438 | } |
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| 439 | return $ar; |
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| 440 | } |
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| 441 | } |
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| 442 | |||
| 443 | /** |
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| 444 | * Sum of Rows of matrix |
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| 445 | * @return vector |
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| 446 | */ |
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| 447 | public function sumRows(): vector { |
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| 448 | $vr = vector::factory($this->row); |
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| 449 | for ($i = 0; $i < $this->row; ++$i) { |
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| 450 | $sum = 0.0; |
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| 451 | for ($j = 0; $j < $this->col; ++$j) { |
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| 452 | $sum += $this->data[$i * $this->col + $j]; |
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| 453 | } |
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| 454 | $vr->data[$i] = $sum; |
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| 455 | } |
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| 456 | return $vr; |
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| 457 | } |
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| 458 | |||
| 459 | /** |
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| 460 | * subtract another matrix, vector or a scalar to this matrix |
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| 461 | * @param int|float|matrix|vector $d matrix|$scalar to subtract this matrix |
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| 462 | * @return \Np\matrix |
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| 463 | */ |
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| 464 | public function subtract(int|float|matrix|vector $d): matrix { |
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| 465 | if ($d instanceof self) { |
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| 466 | return $this->subtractMatrix($d); |
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| 467 | } elseif ($d instanceof vector) { |
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| 468 | return $this->subtractVector($d); |
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| 469 | } else { |
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| 470 | return $this->subtractScalar($d); |
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| 471 | } |
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| 472 | } |
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| 473 | |||
| 474 | protected function subtractScalar(int|float $s): matrix { |
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| 475 | $ar = self::factory($this->row, $this->col); |
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| 476 | for ($i = 0; $i < $this->ndim; ++$i) { |
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| 477 | $ar->data[$i] = $this->data[$i] - $s; |
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| 478 | } |
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| 479 | return $ar; |
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| 480 | } |
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| 481 | |||
| 482 | /** |
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| 483 | * |
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| 484 | * @param matrix $m |
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| 485 | * @return matrix |
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| 486 | */ |
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| 487 | protected function subtractMatrix(matrix $m): matrix { |
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| 488 | if ($this->checkShape($this, $m)) { |
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| 489 | $ar = self::factory($this->row, $this->col); |
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| 490 | for ($i = 0; $i < $this->ndim; ++$i) { |
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| 491 | $ar->data[$i] = $this->data[$i] - $m->data[$i]; |
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| 492 | } |
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| 493 | return $ar; |
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| 494 | } |
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| 495 | } |
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| 496 | |||
| 497 | /** |
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| 498 | * |
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| 499 | * @param vector $v |
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| 500 | * @return matrix |
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| 501 | */ |
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| 502 | protected function subtractVector(vector $v): matrix { |
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| 503 | if ($this->checkDimensions($v, $this)) { |
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| 504 | $ar = self::factory($this->row, $this->col); |
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| 505 | for ($i = 0; $i < $this->row; ++$i) { |
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| 506 | for ($j = 0; $j < $this->col; ++$j) { |
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| 507 | $ar->data[$i * $this->col + $j] = $this->data[$i * $this->col + $j] - $v->data[$j]; |
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| 508 | } |
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| 509 | } |
||
| 510 | return $ar; |
||
| 511 | } |
||
| 512 | } |
||
| 513 | |||
| 514 | /** |
||
| 515 | * |
||
| 516 | * @param vector $v |
||
| 517 | * @return matrix |
||
| 518 | */ |
||
| 519 | public function subtractColumnVector(vector $v): matrix { |
||
| 520 | if ($this->checkDimensions($v, $this)) { |
||
| 521 | $ar = self::factory($this->row, $this->col); |
||
| 522 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 523 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 524 | $ar->data[$i * $this->col + $j] = $this->data[$i * $this->col + $j] - $v->data[$i]; |
||
| 525 | } |
||
| 526 | } |
||
| 527 | return $ar; |
||
| 528 | } |
||
| 529 | } |
||
| 530 | |||
| 531 | /** |
||
| 532 | * Return the division of two elements, element-wise. |
||
| 533 | * @param int|float|matrix $d |
||
| 534 | * @return matrix |
||
| 535 | */ |
||
| 536 | public function divide(int|float|matrix|vector $d): matrix { |
||
| 537 | if ($d instanceof self) { |
||
| 538 | return $this->divideMatrix($d); |
||
| 539 | } elseif ($d instanceof vector) { |
||
| 540 | return $this->divideVector($d); |
||
| 541 | } else { |
||
| 542 | return $this->divideScalar($d); |
||
| 543 | } |
||
| 544 | } |
||
| 545 | |||
| 546 | protected function divideMatrix(matrix $m): matrix { |
||
| 547 | if ($this->checkShape($this, $m)) { |
||
| 548 | $ar = self::factory($this->row, $this->col); |
||
| 549 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 550 | $ar->data[$i] = $this->data[$i] / $m->data[$i]; |
||
| 551 | } |
||
| 552 | return $ar; |
||
| 553 | } |
||
| 554 | } |
||
| 555 | |||
| 556 | protected function divideVector(vector $v): matrix { |
||
| 557 | if ($this->checkDimensions($v, $this)) { |
||
| 558 | $ar = self::factory($this->row, $this->col); |
||
| 559 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 560 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 561 | $ar->data[$i * $this->col + $j] = $this->data[$i * $this->col + $j] / $v->data[$j]; |
||
| 562 | } |
||
| 563 | } |
||
| 564 | return $ar; |
||
| 565 | } |
||
| 566 | } |
||
| 567 | |||
| 568 | protected function divideScalar(int|float $s): matrix { |
||
| 569 | $ar = self::factory($this->row, $this->col); |
||
| 570 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 571 | $ar->data[$i] = $this->data[$i] / $s; |
||
| 572 | } |
||
| 573 | return $ar; |
||
| 574 | } |
||
| 575 | |||
| 576 | /** |
||
| 577 | * |
||
| 578 | * Raise this matrix to the power of the element-wise entry in another matrix. |
||
| 579 | * |
||
| 580 | * @param int|float|matrix $m |
||
| 581 | * @return matrix |
||
| 582 | */ |
||
| 583 | public function pow(int|float|matrix|vector $d): matrix { |
||
| 584 | if ($d instanceof self) { |
||
| 585 | return $this->powMatrix($d); |
||
| 586 | } else if ($d instanceof vector) { |
||
| 587 | return $this->powVector($d); |
||
| 588 | } else { |
||
| 589 | return $this->powScalar($d); |
||
| 590 | } |
||
| 591 | } |
||
| 592 | |||
| 593 | protected function powMatrix(matrix $m): matrix { |
||
| 594 | if ($this->checkShape($this, $m)) { |
||
| 595 | $ar = self::factory($this->row, $this->col); |
||
| 596 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 597 | $ar->data[$i] = $this->data[$i] ** $m->data[$i]; |
||
| 598 | } |
||
| 599 | return $ar; |
||
| 600 | } |
||
| 601 | } |
||
| 602 | |||
| 603 | protected function powVector(vector $v): matrix { |
||
| 604 | if ($this->checkDimensions($v, $this)) { |
||
| 605 | $ar = self::factory($this->row, $this->col); |
||
| 606 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 607 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 608 | $ar->data[$i * $this->col + $j] = $this->data[$i * $this->col + $j] ** $v->data[$j]; |
||
| 609 | } |
||
| 610 | } |
||
| 611 | return $ar; |
||
| 612 | } |
||
| 613 | } |
||
| 614 | |||
| 615 | protected function powScalar(int|float $s): matrix { |
||
| 616 | $ar = $this->copy(); |
||
| 617 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 618 | $ar->data[$i] **= $s; |
||
| 619 | } |
||
| 620 | return $ar; |
||
| 621 | } |
||
| 622 | |||
| 623 | /** |
||
| 624 | * Calculate the modulus i.e remainder of division between this matrix and another matrix. |
||
| 625 | * @param int|float|matrix|vector $d |
||
| 626 | * @return matrix |
||
| 627 | */ |
||
| 628 | public function mod(int|float|matrix|vector $d): matrix { |
||
| 629 | if ($d instanceof self) { |
||
| 630 | $this->modMatrix($d); |
||
| 631 | } else if ($d instanceof vector) { |
||
| 632 | $this->modVector($d); |
||
| 633 | } else { |
||
| 634 | $this->modScalar($d); |
||
| 635 | } |
||
| 636 | } |
||
| 637 | |||
| 638 | protected function modMatrix(matrix $m): matrix { |
||
| 639 | if ($this->checkShape($this, $m)) { |
||
| 640 | $ar = self::factory($this->row, $this->col); |
||
| 641 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 642 | $ar->data[$i] = $this->data[$i] % $m->data[$i]; |
||
| 643 | } |
||
| 644 | return $ar; |
||
| 645 | } |
||
| 646 | } |
||
| 647 | |||
| 648 | protected function modVector(vector $v): matrix { |
||
| 649 | if ($this->checkDimensions($v, $this)) { |
||
| 650 | $ar = self::factory($this->row, $this->col); |
||
| 651 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 652 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 653 | $ar->data[$i * $this->col + $j] = $this->data[$i * $this->col + $j] % $v->data[$j]; |
||
| 654 | } |
||
| 655 | } |
||
| 656 | return $ar; |
||
| 657 | } |
||
| 658 | } |
||
| 659 | |||
| 660 | protected function modScalar(int|float $s): matrix { |
||
| 661 | $ar = $this->copy(); |
||
| 662 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 663 | $ar->data[$i] %= $s; |
||
| 664 | } |
||
| 665 | return $ar; |
||
| 666 | } |
||
| 667 | |||
| 668 | /** |
||
| 669 | * Return the element-wise reciprocal of the matrix. |
||
| 670 | * |
||
| 671 | * @return matrix |
||
| 672 | */ |
||
| 673 | public function reciprocal(): matrix { |
||
| 674 | return self::ones($this->row, $this->col)->divideMatrix($this); |
||
| 675 | } |
||
| 676 | |||
| 677 | /** |
||
| 678 | * Transpose the matrix i.e row become cols and cols become rows. |
||
| 679 | * @return \Np\matrix |
||
| 680 | */ |
||
| 681 | public function transpose(): matrix { |
||
| 682 | $ar = self::factory($this->col, $this->row); |
||
| 683 | for ($i = 0; $i < $ar->row; ++$i) { |
||
| 684 | for ($j = 0; $j < $ar->col; ++$j) { |
||
| 685 | $ar->data[$i * $ar->col + $j] = $this->data[$j * $this->col + $i]; |
||
| 686 | } |
||
| 687 | } |
||
| 688 | return $ar; |
||
| 689 | } |
||
| 690 | |||
| 691 | /** |
||
| 692 | * swap specific values in matrix |
||
| 693 | * @param int $i1 |
||
| 694 | * @param int $i2 |
||
| 695 | */ |
||
| 696 | public function swapValue(int $i1, int $i2) { |
||
| 697 | $tmp = $this->data[$i1]; |
||
| 698 | $this->data[$i1] = $this->data[$i2]; |
||
| 699 | $this->data[$i2] = $tmp; |
||
| 700 | } |
||
| 701 | |||
| 702 | /** |
||
| 703 | * swap specific rows in matrix |
||
| 704 | * @param int $r1 |
||
| 705 | * @param int $r2 |
||
| 706 | */ |
||
| 707 | public function swapRows(int $r1, int $r2) { |
||
| 708 | for ($i = 0; $i < $this->col; ++$i) { |
||
| 709 | $tmp = $this->data[$r1 * $this->col + $i]; |
||
| 710 | $this->data[$r1 * $this->col + $i] = $this->data[$r2 * $this->col + $i]; |
||
| 711 | $this->data[$r2 * $this->col + $i] = $tmp; |
||
| 712 | } |
||
| 713 | } |
||
| 714 | |||
| 715 | /** |
||
| 716 | * swap specific cols in matrix |
||
| 717 | * @param int $c1 |
||
| 718 | * @param int $c2 |
||
| 719 | */ |
||
| 720 | public function swapCols(int $c1, int $c2) { |
||
| 721 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 722 | $tmp = $this->data[$i * $this->row + $c1]; |
||
| 723 | $this->data[$i * $this->row + $c1] = $this->data[$i * $this->row + $c2]; |
||
| 724 | $this->data[$i * $this->row + $c2] = $tmp; |
||
| 725 | } |
||
| 726 | } |
||
| 727 | |||
| 728 | /** |
||
| 729 | * |
||
| 730 | * @param int|float $scalar |
||
| 731 | * @return matrix |
||
| 732 | */ |
||
| 733 | public function scale(int|float $scalar): matrix { |
||
| 734 | if ($scalar == 0) { |
||
| 735 | return self::zeros($this->row, $this->col); |
||
| 736 | } |
||
| 737 | |||
| 738 | $ar = $this->copy(); |
||
| 739 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 740 | $ar->data[$i] *= $scalar; |
||
| 741 | } |
||
| 742 | |||
| 743 | return $ar; |
||
| 744 | } |
||
| 745 | |||
| 746 | /** |
||
| 747 | * scale all the elements of a row |
||
| 748 | * @param int $row |
||
| 749 | * @param int|float $c |
||
| 750 | */ |
||
| 751 | public function scaleRow(int $row, int|float $c) { |
||
| 752 | for ($i = 0; $i < $this->col; ++$i) { |
||
| 753 | $this->data[$row * $this->col + $i] *= $c; |
||
| 754 | } |
||
| 755 | } |
||
| 756 | |||
| 757 | /** |
||
| 758 | * scale all the elements of |
||
| 759 | * @param int $col |
||
| 760 | * @param int|float $c |
||
| 761 | */ |
||
| 762 | public function scaleCol(int $col, int|float $c) { |
||
| 763 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 764 | $this->data[$i * $this->col + $col] *= $c; |
||
| 765 | } |
||
| 766 | } |
||
| 767 | |||
| 768 | /** |
||
| 769 | * Scale digonally |
||
| 770 | * @param int|float $c |
||
| 771 | * @param bool $lDig |
||
| 772 | */ |
||
| 773 | public function scaleDigonalCol(int|float $c, bool $lDig = true) { |
||
| 774 | if ($lDig) { |
||
| 775 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 776 | $this->data[$i * $this->col + $i] *= $c; |
||
| 777 | } |
||
| 778 | } else { |
||
| 779 | for ($i = $this->row; $i > 0; --$i) { |
||
| 780 | $this->data[$i * $this->col - $i] *= $c; |
||
| 781 | } |
||
| 782 | } |
||
| 783 | } |
||
| 784 | |||
| 785 | /** |
||
| 786 | * |
||
| 787 | * @param int $r1 |
||
| 788 | * @param int $r2 |
||
| 789 | * @param float $c |
||
| 790 | */ |
||
| 791 | public function addScaleRow(int $r1, int $r2, float $c) { |
||
| 794 | } |
||
| 795 | } |
||
| 796 | |||
| 797 | /** |
||
| 798 | * Attach given matrix to the left of this matrix. |
||
| 799 | * |
||
| 800 | * @param \Np\matrix $m |
||
| 801 | * @return \Np\matrix |
||
| 802 | */ |
||
| 803 | public function joinLeft(matrix $m): matrix { |
||
| 804 | if ($this->row == $m->row) { |
||
| 805 | $col = $this->col + $m->col; |
||
| 806 | $ar = self::factory($this->row, $col); |
||
| 807 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 808 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 809 | $ar->data[$i * $col + $j] = $this->data[$i * $this->col + $j]; |
||
| 810 | } |
||
| 811 | for ($j = 0; $j < $m->col; ++$j) { |
||
| 812 | $ar->data[$i * $col + ($this->col + $j)] = $m->data[$i * $m->col + $j]; |
||
| 813 | } |
||
| 814 | } |
||
| 815 | return $ar; |
||
| 816 | } |
||
| 817 | self::_err('Error::Invalid size! or DataType!'); |
||
| 818 | } |
||
| 819 | |||
| 820 | /** |
||
| 821 | * Join matrix m to the Right of this matrix. |
||
| 822 | * @param \Np\matrix $m |
||
| 823 | * @return matrix |
||
| 824 | */ |
||
| 825 | public function joinRight(matrix $m): matrix { |
||
| 826 | if ($this->row == $m->row) { |
||
| 827 | self::_err('Error::Invalid size! or DataType!'); |
||
| 828 | } |
||
| 829 | $col = $this->col + $m->col; |
||
| 830 | $ar = self::factory($this->row, $col); |
||
| 831 | for ($i = 0; $i < $m->row; ++$i) { |
||
| 832 | for ($j = 0; $j < $m->col; ++$j) { |
||
| 833 | $ar->data[$i * $col + $j] = $m->data[$i * $m->col + $j]; |
||
| 834 | } |
||
| 835 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 836 | $ar->data[$i * $col + ($this->col + $j)] = $this->data[$i * $this->col + $j]; |
||
| 837 | } |
||
| 838 | } |
||
| 839 | return $ar; |
||
| 840 | } |
||
| 841 | |||
| 842 | /** |
||
| 843 | * Join matrix m Above this matrix. |
||
| 844 | * @param \Np\matrix $m |
||
| 845 | * @return matrix |
||
| 846 | */ |
||
| 847 | public function joinAbove(matrix $m): matrix { |
||
| 862 | } |
||
| 863 | |||
| 864 | /** |
||
| 865 | * Join matrix m below this matrix. |
||
| 866 | * @param \Np\matrix $m |
||
| 867 | * @return matrix |
||
| 868 | */ |
||
| 869 | public function joinBelow(matrix $m): matrix { |
||
| 870 | if ($this->col == $m->col) { |
||
| 871 | $row = $this->row + $m->row; |
||
| 872 | $ar = self::factory($row, $this->col); |
||
| 873 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 874 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 875 | $ar->data[$i * $this->col + $j] = $this->data[$i * $this->col + $j]; |
||
| 876 | } |
||
| 877 | for ($j = 0; $j < $m->col; ++$j) { |
||
| 878 | $ar->data[($i + $m->row) * $m->col + $j] = $m->data[$i * $m->col + $j]; |
||
| 879 | } |
||
| 880 | } |
||
| 881 | return $ar; |
||
| 882 | } |
||
| 883 | self::_err('Error::Invalid size! or DataType!'); |
||
| 884 | } |
||
| 885 | |||
| 886 | /** |
||
| 887 | * |
||
| 888 | * @param int $cols |
||
| 889 | * @return \Np\matrix |
||
| 890 | */ |
||
| 891 | public function diminish_left(int $cols): matrix { |
||
| 892 | $ar = self::factory($this->row, $cols); |
||
| 893 | for ($i = 0; $i < $ar->row; ++$i) { |
||
| 894 | for ($j = 0; $j < $ar->col; ++$j) { |
||
| 895 | $ar->data[$i * $ar->col + $j] = $this->data[$i * $this->col + $j]; |
||
| 896 | } |
||
| 897 | } |
||
| 898 | return $ar; |
||
| 899 | } |
||
| 900 | |||
| 901 | /** |
||
| 902 | * |
||
| 903 | * @param int $cols |
||
| 904 | * @return \Np\matrix |
||
| 905 | */ |
||
| 906 | public function diminish_right(int $cols): matrix { |
||
| 907 | $ar = self::factory($this->row, $cols); |
||
| 908 | for ($i = 0; $i < $ar->row; ++$i) { |
||
| 909 | for ($j = 0; $j < $ar->col; ++$j) { |
||
| 910 | $ar->data[$i * $ar->col + $j] = $this->data[$i * $this->col - $cols + $j]; |
||
| 911 | } |
||
| 912 | } |
||
| 913 | return $ar; |
||
| 914 | } |
||
| 915 | |||
| 916 | /** |
||
| 917 | * Return the index of the maximum element in every row of the matrix. |
||
| 918 | * @return \Np\vector int |
||
| 919 | */ |
||
| 920 | public function argMax(): vector { |
||
| 921 | $v = vector::factory($this->row, vector::INT); |
||
| 922 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 923 | $v->data[$i] = blas::max($this->rowAsVector($i)); |
||
| 924 | } |
||
| 925 | return $v; |
||
| 926 | } |
||
| 927 | |||
| 928 | /** |
||
| 929 | * Return the index of the minimum element in every row of the matrix. |
||
| 930 | * @return \Np\vector int |
||
| 931 | */ |
||
| 932 | public function argMin(): vector { |
||
| 933 | $v = vector::factory($this->row, vector::INT); |
||
| 934 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 935 | $v->data[$i] = blas::min($this->rowAsVector($i)); |
||
| 936 | } |
||
| 937 | |||
| 938 | return $v; |
||
| 939 | } |
||
| 940 | |||
| 941 | /** |
||
| 942 | * Set given data in matrix |
||
| 943 | * @param int|float|array $data |
||
| 944 | * @param bool $dignoal |
||
| 945 | * @return void |
||
| 946 | */ |
||
| 947 | public function setData(int|float|array $data): void { |
||
| 948 | |||
| 949 | if (is_array($data) && is_array($data[0])) { |
||
| 950 | $f = $this->flattenArray($data); |
||
| 951 | foreach ($f as $k => $v) { |
||
| 952 | $this->data[$k] = $v; |
||
| 953 | } |
||
| 954 | } elseif (is_numeric($data)) { |
||
| 955 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 956 | $this->data[$i] = $data; |
||
| 957 | } |
||
| 958 | } elseif (is_array($data) && !is_array($data[0])) { |
||
| 959 | foreach ($data as $i => $v) { |
||
| 960 | $this->data[$i] = $v; |
||
| 961 | } |
||
| 962 | } |
||
| 963 | } |
||
| 964 | |||
| 965 | /** |
||
| 966 | * get the matrix data type |
||
| 967 | * @return int |
||
| 968 | */ |
||
| 969 | public function getDtype(): int { |
||
| 970 | return $this->dtype; |
||
| 971 | } |
||
| 972 | |||
| 973 | /** |
||
| 974 | * get the shape of matrix |
||
| 975 | * @return object |
||
| 976 | */ |
||
| 977 | public function getShape(): object { |
||
| 978 | return (object) ['m' => $this->row, 'n' => $this->col]; |
||
| 979 | } |
||
| 980 | |||
| 981 | /** |
||
| 982 | * get the number of elements in the matrix. |
||
| 983 | * @return int |
||
| 984 | */ |
||
| 985 | public function getSize(): int { |
||
| 986 | return $this->ndim; |
||
| 987 | } |
||
| 988 | |||
| 989 | /** |
||
| 990 | * Is the matrix symmetric i.e. is it equal to its own transpose? |
||
| 991 | * |
||
| 992 | * @return bool |
||
| 993 | */ |
||
| 994 | public function isSymmetric(): bool { |
||
| 995 | if (!$this->isSquare()) { |
||
| 996 | return false; |
||
| 997 | } |
||
| 998 | $ar = $this->transpose(); |
||
| 999 | for ($i = 0; $i < $ar->ndim; ++$i) { |
||
| 1000 | if ($ar->data[$i] != $this->data[$i]) { |
||
| 1001 | unset($ar); |
||
| 1002 | return false; |
||
| 1003 | } |
||
| 1004 | } |
||
| 1005 | unset($ar); |
||
| 1006 | return true; |
||
| 1007 | } |
||
| 1008 | |||
| 1009 | /** |
||
| 1010 | * is matrix squred |
||
| 1011 | * @return bool |
||
| 1012 | */ |
||
| 1013 | public function isSquare(): bool { |
||
| 1014 | if ($this->row === $this->col) { |
||
| 1015 | return true; |
||
| 1016 | } |
||
| 1017 | return false; |
||
| 1018 | } |
||
| 1019 | |||
| 1020 | /** |
||
| 1021 | * |
||
| 1022 | * @param int|float $d |
||
| 1023 | * @return bool |
||
| 1024 | */ |
||
| 1025 | public static function is_zero($d): bool { |
||
| 1026 | if (abs($d) < self::EPSILON) { |
||
| 1027 | return true; |
||
| 1028 | } |
||
| 1029 | return false; |
||
| 1030 | } |
||
| 1031 | |||
| 1032 | /** |
||
| 1033 | * is row zero |
||
| 1034 | * @param int $row |
||
| 1035 | * @return bool |
||
| 1036 | */ |
||
| 1037 | public function is_rowZero(int $row): bool { |
||
| 1038 | for ($i = 0; $i < $this->col; ++$i) { |
||
| 1039 | if ($this->data[$row * $this->col + $i] != 0) { |
||
| 1040 | return false; |
||
| 1041 | } |
||
| 1042 | } |
||
| 1043 | return true; |
||
| 1044 | } |
||
| 1045 | |||
| 1046 | /** |
||
| 1047 | * |
||
| 1048 | * @return bool |
||
| 1049 | */ |
||
| 1050 | public function has_ZeroRow(): bool { |
||
| 1051 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 1052 | if ($this->is_rowZero($i)) { |
||
| 1053 | return true; |
||
| 1054 | } |
||
| 1055 | } |
||
| 1056 | return false; |
||
| 1057 | } |
||
| 1058 | |||
| 1059 | /** |
||
| 1060 | * Return the elements of the matrix in a 2-d array. |
||
| 1061 | * @return array |
||
| 1062 | */ |
||
| 1063 | public function asArray(): array { |
||
| 1064 | $ar = array_fill(0, $this->row, array_fill(0, $this->col, null)); |
||
| 1065 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 1066 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 1067 | $ar[$i][$j] = $this->data[$i * $this->col + $j]; |
||
| 1068 | } |
||
| 1069 | } |
||
| 1070 | return $ar; |
||
| 1071 | } |
||
| 1072 | |||
| 1073 | /** |
||
| 1074 | * get a diagonal value from matrix |
||
| 1075 | * @param int $i |
||
| 1076 | * @return float |
||
| 1077 | */ |
||
| 1078 | public function getDiagonalVal(int $i) { |
||
| 1079 | if ($this->isSquare()) { |
||
| 1080 | return $this->data[$i * $this->row + $i]; |
||
| 1081 | } |
||
| 1082 | } |
||
| 1083 | |||
| 1084 | /** |
||
| 1085 | * Calculate the row echelon form of the matrix. |
||
| 1086 | * Return the reduced matrix. |
||
| 1087 | * |
||
| 1088 | * @return matrix|null |
||
| 1089 | */ |
||
| 1090 | public function ref(): matrix|null { |
||
| 1091 | return ref::factory($this); |
||
| 1092 | } |
||
| 1093 | |||
| 1094 | /** |
||
| 1095 | * Return the lower triangular matrix of the Cholesky decomposition. |
||
| 1096 | * |
||
| 1097 | * @return matrix|null |
||
| 1098 | */ |
||
| 1099 | public function cholesky(): matrix|null { |
||
| 1100 | return cholesky::factory($this); |
||
| 1101 | } |
||
| 1102 | |||
| 1103 | /** |
||
| 1104 | * FIXME-------------- |
||
| 1105 | * RREF |
||
| 1106 | * The reduced row echelon form (RREF) of a matrix. |
||
| 1107 | * @return \Np\matrix |
||
| 1108 | */ |
||
| 1109 | public function rref(): matrix { |
||
| 1110 | return rref::factory($this); |
||
| 1111 | } |
||
| 1112 | |||
| 1113 | /** |
||
| 1114 | * Compute the singular value decomposition of a matrix and |
||
| 1115 | * return an object of the singular values and unitary matrices |
||
| 1116 | * |
||
| 1117 | * @return object (u,s,v) |
||
| 1118 | */ |
||
| 1119 | public function svd(): svd { |
||
| 1120 | return svd::factory($this); |
||
| 1121 | } |
||
| 1122 | |||
| 1123 | /** |
||
| 1124 | * Compute the eigen decomposition of a general matrix. |
||
| 1125 | * return the eigenvalues and eigenvectors as object |
||
| 1126 | * |
||
| 1127 | * @param bool $symmetric |
||
| 1128 | * @return eigen |
||
| 1129 | */ |
||
| 1130 | public function eign(bool $symmetric = false): eigen { |
||
| 1131 | return eigen::factory($this, $symmetric); |
||
| 1132 | } |
||
| 1133 | |||
| 1134 | /** |
||
| 1135 | * |
||
| 1136 | * Compute the LU factorization of matrix. |
||
| 1137 | * return lower, upper, and permutation matrices as object. |
||
| 1138 | * |
||
| 1139 | * @return lu |
||
| 1140 | */ |
||
| 1141 | public function lu(): lu { |
||
| 1142 | return lu::factory($this); |
||
| 1143 | } |
||
| 1144 | |||
| 1145 | /** |
||
| 1146 | * Return the L1 norm of the matrix. |
||
| 1147 | * @return float |
||
| 1148 | */ |
||
| 1149 | public function normL1(): float { |
||
| 1150 | return lapack::lange('l', $this); |
||
| 1151 | } |
||
| 1152 | |||
| 1153 | /** |
||
| 1154 | * Return the L2 norm of the matrix. |
||
| 1155 | * @return float |
||
| 1156 | */ |
||
| 1157 | public function normL2(): float { |
||
| 1158 | return lapack::lange('f', $this); |
||
| 1159 | } |
||
| 1160 | |||
| 1161 | /** |
||
| 1162 | * Return the L1 norm of the matrix. |
||
| 1163 | * @return float |
||
| 1164 | */ |
||
| 1165 | public function normINF(): float { |
||
| 1166 | return lapack::lange('i', $this); |
||
| 1167 | } |
||
| 1168 | |||
| 1169 | /** |
||
| 1170 | * Return the Frobenius norm of the matrix. |
||
| 1171 | * @return float |
||
| 1172 | */ |
||
| 1173 | public function normFrob(): float { |
||
| 1174 | return $this->normL2(); |
||
| 1175 | } |
||
| 1176 | |||
| 1177 | /** |
||
| 1178 | * Compute the means of each row and return them in a vector. |
||
| 1179 | * |
||
| 1180 | * @return vector |
||
| 1181 | */ |
||
| 1182 | public function mean(): vector { |
||
| 1183 | return $this->sumRows()->divide($this->col); |
||
| 1184 | } |
||
| 1185 | |||
| 1186 | /** |
||
| 1187 | * Compute the row variance of the matrix. |
||
| 1188 | * |
||
| 1189 | * @param vector|null $mean |
||
| 1190 | * @return vector |
||
| 1191 | */ |
||
| 1192 | public function variance(vector|null $mean = null): vector { |
||
| 1193 | if (isset($mean)) { |
||
| 1194 | if (!$mean instanceof vector) { |
||
|
introduced
by
|
|||
| 1195 | self::_invalidArgument('mean must be a vector!'); |
||
| 1196 | } |
||
| 1197 | if ($this->row !== $mean->col) { |
||
| 1198 | self::_err('Err:: given mean vector dimensionality mismatched!'); |
||
| 1199 | } |
||
| 1200 | } else { |
||
| 1201 | $mean = $this->mean(); |
||
| 1202 | } |
||
| 1203 | return $this->subtractColumnVector($mean)->square() |
||
| 1204 | ->sumRows()->divide($this->row); |
||
| 1205 | } |
||
| 1206 | |||
| 1207 | /** |
||
| 1208 | * Return the median vector of this matrix. |
||
| 1209 | * @return vector |
||
| 1210 | */ |
||
| 1211 | public function median(): vector { |
||
| 1212 | $mid = intdiv($this->col, 2); |
||
| 1213 | $odd = $this->col % 2 === 1; |
||
| 1214 | $vr = vector::factory($this->row); |
||
| 1215 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 1216 | $a = $this->rowAsVector($i)->sort(); |
||
| 1217 | if ($odd) { |
||
| 1218 | $median = $a->data[$mid]; |
||
| 1219 | } else { |
||
| 1220 | $median = ($a->data[$mid - 1] + $a->data[$mid]) / 2.0; |
||
| 1221 | } |
||
| 1222 | $vr->data[$i] = $median; |
||
| 1223 | } |
||
| 1224 | unset($a); |
||
| 1225 | return $vr; |
||
| 1226 | } |
||
| 1227 | |||
| 1228 | /** |
||
| 1229 | * Compute the covariance matrix. |
||
| 1230 | * |
||
| 1231 | * @param vector|null $mean |
||
| 1232 | * @return matrix |
||
| 1233 | */ |
||
| 1234 | public function covariance(vector|null $mean = null): matrix { |
||
| 1235 | if (isset($mean)) { |
||
| 1236 | if ($mean->col !== $this->row) { |
||
| 1237 | self::_err('Err:: given mean vector dimensionality mismatched!'); |
||
| 1238 | } |
||
| 1239 | } else { |
||
| 1240 | $mean = $this->mean(); |
||
| 1241 | } |
||
| 1242 | |||
| 1243 | $b = $this->subtractColumnVector($mean); |
||
| 1244 | |||
| 1245 | return $b->dot($b->transpose()) |
||
| 1246 | ->divideScalar($this->row); |
||
| 1247 | } |
||
| 1248 | |||
| 1249 | /** |
||
| 1250 | * Square of matrix |
||
| 1251 | * @return matrix |
||
| 1252 | */ |
||
| 1253 | public function square(): matrix { |
||
| 1254 | return $this->multiplyMatrix($this); |
||
| 1255 | } |
||
| 1256 | |||
| 1257 | /** |
||
| 1258 | * |
||
| 1259 | * @param int|float|matrix|vector $d |
||
| 1260 | * @return matrix |
||
| 1261 | */ |
||
| 1262 | public function equal(int|float|matrix|vector $d): matrix { |
||
| 1263 | if ($d instanceof self) { |
||
| 1264 | return $this->equalMatrix($d); |
||
| 1265 | } |
||
| 1266 | if ($d instanceof vector) { |
||
| 1267 | return $this->equalVector($d); |
||
| 1268 | } |
||
| 1269 | return $this->equalScalar($d); |
||
| 1270 | } |
||
| 1271 | |||
| 1272 | protected function equalMatrix(matrix $m): matrix { |
||
| 1273 | if ($this->checkShape($this, $m)) { |
||
| 1274 | $ar = self::factory($this->row, $this->col); |
||
| 1275 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 1276 | $ar->data[$i] = $this->data[$i] == $m->data[$i] ? 1 : 0; |
||
| 1277 | } |
||
| 1278 | return $ar; |
||
| 1279 | } |
||
| 1280 | } |
||
| 1281 | |||
| 1282 | protected function equalVector(vector $v): matrix { |
||
| 1283 | if ($this->checkDimensions($v, $this)) { |
||
| 1284 | $ar = self::factory($this->row, $this->col); |
||
| 1285 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 1286 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 1287 | $ar->data[$i * $this->col + $j] = $this->data[$i * $this->col + $j] == $v->data[$j] ? 1 : 0; |
||
| 1288 | } |
||
| 1289 | } |
||
| 1290 | return $ar; |
||
| 1291 | } |
||
| 1292 | } |
||
| 1293 | |||
| 1294 | protected function equalScalar(int|float $s): matrix { |
||
| 1295 | $ar = self::factory($this->row, $this->col); |
||
| 1296 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 1297 | $ar->data[$i] = $this->data[$i] == $s ? 1 : 0; |
||
| 1298 | } |
||
| 1299 | return $ar; |
||
| 1300 | } |
||
| 1301 | |||
| 1302 | /** |
||
| 1303 | * |
||
| 1304 | * @param int|float|matrix|vector $d |
||
| 1305 | * @return matrix |
||
| 1306 | */ |
||
| 1307 | public function greater(int|float|matrix|vector $d): matrix { |
||
| 1315 | } |
||
| 1316 | |||
| 1317 | protected function greaterMatrix(matrix $m): matrix { |
||
| 1318 | if ($this->checkShape($this, $m)) { |
||
| 1319 | $ar = self::factory($this->row, $this->col); |
||
| 1320 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 1321 | $ar->data[$i] = $this->data[$i] > $m->data[$i] ? 1 : 0; |
||
| 1322 | } |
||
| 1323 | return $ar; |
||
| 1324 | } |
||
| 1325 | } |
||
| 1326 | |||
| 1327 | protected function greaterVector(vector $v): matrix { |
||
| 1328 | if ($this->checkDimensions($v, $this)) { |
||
| 1329 | $ar = self::factory($this->row, $this->col); |
||
| 1330 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 1331 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 1332 | $ar->data[$i * $this->col + $j] = $this->data[$i * $this->col + $j] > $v->data[$j] ? 1 : 0; |
||
| 1333 | } |
||
| 1334 | } |
||
| 1335 | return $ar; |
||
| 1336 | } |
||
| 1337 | } |
||
| 1338 | |||
| 1339 | protected function greaterScalar(int|float $s): matrix { |
||
| 1340 | $ar = self::factory($this->row, $this->col); |
||
| 1341 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 1342 | $ar->data[$i] = $this->data[$i] > $s ? 1 : 0; |
||
| 1343 | } |
||
| 1344 | return $ar; |
||
| 1345 | } |
||
| 1346 | |||
| 1347 | /** |
||
| 1348 | * |
||
| 1349 | * @param int|float|matrix $m |
||
| 1350 | * @return matrix |
||
| 1351 | */ |
||
| 1352 | public function less(int|float|matrix $m): matrix { |
||
| 1366 | } |
||
| 1367 | } |
||
| 1368 | |||
| 1369 | /** |
||
| 1370 | * print the matrix in consol |
||
| 1371 | */ |
||
| 1372 | public function printMatrix() { |
||
| 1373 | echo __CLASS__ . PHP_EOL; |
||
| 1374 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 1375 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 1376 | printf('%lf ', $this->data[$i * $this->col + $j]); |
||
| 1377 | } |
||
| 1378 | echo PHP_EOL; |
||
| 1379 | } |
||
| 1380 | } |
||
| 1381 | |||
| 1382 | public function __toString() { |
||
| 1384 | } |
||
| 1385 | |||
| 1386 | private function flattenArray(array $ar) { |
||
| 1387 | if (is_array($ar) && is_array($ar[0])) { |
||
| 1388 | $a = []; |
||
| 1389 | foreach ($ar as $y => $value) { |
||
| 1390 | foreach ($value as $k => $v) { |
||
| 1391 | $a[] = $v; |
||
| 1392 | } |
||
| 1393 | } |
||
| 1394 | return $a; |
||
| 1395 | } |
||
| 1396 | } |
||
| 1397 | |||
| 1398 | /** |
||
| 1399 | * |
||
| 1400 | * @param int $row |
||
| 1401 | * @param int $col |
||
| 1402 | * @param int $dtype |
||
| 1403 | * @return $this |
||
| 1404 | */ |
||
| 1405 | protected function __construct(public int $row, public int $col, int $dtype = self::DOUBLE) { |
||
| 1411 | } |
||
| 1412 | } |
||
| 1413 |
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