ghostjat /
Np
| Total Complexity | 352 |
| Total Lines | 1642 |
| Duplicated Lines | 0 % |
| Changes | 1 | ||
| Bugs | 1 | 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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| 27 | class matrix extends nd{ |
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| 28 | |||
| 29 | /** |
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| 30 | * create empty 2d matrix for given data type |
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| 31 | * @param int $row num of rows |
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| 32 | * @param int $col num of cols |
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| 33 | * @param int $dtype matrix data type float|double |
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| 34 | * @return \Np\matrix |
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| 35 | */ |
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| 36 | public static function factory(int $row, int $col, int $dtype = self::FLOAT): matrix { |
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| 37 | return new self($row, $col, $dtype); |
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| 38 | } |
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| 39 | |||
| 40 | /** |
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| 41 | * create 2d matrix using php array |
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| 42 | * @param array $data |
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| 43 | * @param int $dtype matrix data type float|double |
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| 44 | * @return \Np\matrix |
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| 45 | */ |
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| 46 | public static function ar(array $data, int $dtype = self::FLOAT): matrix { |
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| 47 | if (is_array($data) && is_array($data[0])) { |
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| 48 | $ar = self::factory(count($data), count($data[0]), $dtype); |
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| 49 | $ar->setData($data); |
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| 50 | unset($data); |
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| 51 | return $ar; |
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| 52 | } else { |
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| 53 | self::_err('given array is not rank-2 or given is not an array'); |
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| 54 | } |
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| 55 | } |
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| 56 | |||
| 57 | /** |
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| 58 | * create one like 2d matrix |
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| 59 | * @param int $row |
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| 60 | * @param int $col |
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| 61 | * @return \Np\matrix |
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| 62 | */ |
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| 63 | public static function ones(int $row, int $col, int $dtype = self::FLOAT): matrix { |
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| 64 | $ar = self::factory($row, $col, $dtype); |
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| 65 | for ($i = 0; $i < $ar->ndim; ++$i) { |
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| 66 | $ar->data[$i] = 1; |
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| 67 | } |
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| 68 | return $ar; |
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| 69 | } |
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| 70 | |||
| 71 | /** |
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| 72 | * Create Matrix with random values |
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| 73 | * @param int $row |
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| 74 | * @param int $col |
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| 75 | * @param int $dtype Float|Double |
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| 76 | * @return \Np\matrix |
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| 77 | */ |
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| 78 | public static function randn(int $row, int $col, int $dtype = self::FLOAT): matrix { |
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| 79 | $ar = self::factory($row, $col, $dtype); |
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| 80 | $max = getrandmax(); |
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| 81 | for ($i = 0; $i < $ar->ndim; ++$i) { |
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| 82 | $ar->data[$i] = rand() / $max; |
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| 83 | } |
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| 84 | return $ar; |
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| 85 | } |
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| 86 | |||
| 87 | /** |
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| 88 | * Return 2d matrix with uniform values |
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| 89 | * @param int $row |
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| 90 | * @param int $col |
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| 91 | * @param int $dtype |
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| 92 | * @return \Np\matrix |
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| 93 | */ |
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| 94 | public static function uniform(int $row, int $col, int $dtype = self::FLOAT): matrix { |
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| 95 | $ar = self::factory($row, $col, $dtype); |
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| 96 | $max = getrandmax(); |
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| 97 | for ($i = 0; $i < $ar->ndim; ++$i) { |
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| 98 | $ar->data[$i] = rand(-$max, $max) / $max; |
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| 99 | } |
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| 100 | return $ar; |
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| 101 | } |
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| 102 | |||
| 103 | /** |
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| 104 | * Return a zero matrix with the given dimensions. |
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| 105 | * @param int $row |
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| 106 | * @param int $col |
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| 107 | * @param int $dtype |
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| 108 | * @return \Np\matrix |
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| 109 | */ |
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| 110 | public static function zeros(int $row, int $col, int $dtype = self::FLOAT): matrix { |
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| 111 | $ar = self::factory($row, $col, $dtype); |
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| 112 | for ($i = 0; $i < $ar->ndim; ++$i) { |
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| 113 | $ar->data[$i] = 0.0; |
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| 114 | } |
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| 115 | return $ar; |
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| 116 | } |
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| 117 | |||
| 118 | /** |
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| 119 | * create a null like 2d matrix |
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| 120 | * @param int $row |
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| 121 | * @param int $col |
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| 122 | * @return \Np\matrix |
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| 123 | */ |
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| 124 | public static function null(int $row, int $col, int $dtype = self::FLOAT): matrix { |
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| 125 | $ar = self::factory($row, $col, $dtype); |
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| 126 | for ($i = 0; $i < $ar->ndim; ++$i) { |
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| 127 | $ar->data[$i] = null; |
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| 128 | } |
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| 129 | return $ar; |
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| 130 | } |
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| 131 | |||
| 132 | /** |
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| 133 | * create a 2d matrix with given scalar value |
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| 134 | * @param int $row |
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| 135 | * @param int $col |
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| 136 | * @param int|float|double $val |
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| 137 | * @return \Np\matrix |
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| 138 | */ |
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| 139 | public static function full(int $row, int $col, $val, int $dtype = self::FLOAT): matrix { |
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| 140 | $ar = self::factory($row, $col, $dtype); |
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| 141 | for ($i = 0; $i < $ar->ndim; ++$i) { |
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| 142 | $ar->data[$i] = $val; |
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| 143 | } |
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| 144 | return $ar; |
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| 145 | } |
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| 146 | |||
| 147 | /** |
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| 148 | * create a diagonal 2d matrix with given 1d array; |
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| 149 | * @param array $elements |
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| 150 | * @return \Np\matrix |
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| 151 | */ |
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| 152 | public static function diagonal(array $elements, int $dtype = self::FLOAT): matrix { |
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| 153 | $n = count($elements); |
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| 154 | $ar = self::factory($n, $n, $dtype); |
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| 155 | for ($i = 0; $i < $n; ++$i) { |
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| 156 | $ar->data[$i * $n + $i] = $elements[$i]; #for ($j = 0; $j < $n; ++$j) {$i === $j ? $elements[$i] : 0;#} |
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| 157 | } |
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| 158 | return $ar; |
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| 159 | } |
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| 160 | |||
| 161 | /** |
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| 162 | * Generate a m x n matrix with elements from a Poisson distribution. |
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| 163 | * |
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| 164 | * @param int $row |
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| 165 | * @param int $col |
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| 166 | * @param float $lambda |
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| 167 | * @param int $dtype |
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| 168 | * @return \Np\matrix |
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| 169 | */ |
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| 170 | public static function poisson(int $row, int $col, float $lambda = 1.0, int $dtype = self::FLOAT): matrix { |
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| 171 | $ar = self::factory($row, $col, $dtype); |
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| 172 | $max = getrandmax(); |
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| 173 | $l = exp(-$lambda); |
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| 174 | for ($i = 0; $i < $row; ++$i) { |
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| 175 | for ($j = 0; $j < $col; ++$j) { |
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| 176 | $k = 0; |
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| 177 | $p = 1.0; |
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| 178 | while ($p > $l) { |
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| 179 | ++$k; |
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| 180 | $p = $p * rand() / $max; |
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| 181 | } |
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| 182 | $ar->data[$i * $col + $j] = $k - 1; |
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| 183 | } |
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| 184 | } |
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| 185 | return $ar; |
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| 186 | } |
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| 187 | |||
| 188 | /** |
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| 189 | * Return a standard normally distributed random matrix i.e values |
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| 190 | * between -1 and 1. |
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| 191 | * @param int $row |
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| 192 | * @param int $col |
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| 193 | * @param int $dtype Description |
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| 194 | * @return \Np\matrix |
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| 195 | */ |
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| 196 | public static function gaussian(int $row, int $col, int $dtype = self::FLOAT): matrix { |
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| 197 | $max = getrandmax(); |
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| 198 | $a = $extras = []; |
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| 199 | |||
| 200 | while (count($a) < $row) { |
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| 201 | $rowA = []; |
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| 202 | |||
| 203 | if (!empty($extras)) { |
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| 204 | $rowA[] = array_pop($extras); |
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| 205 | } |
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| 206 | |||
| 207 | while (count($rowA) < $col) { |
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| 208 | $r = sqrt(-2.0 * log(rand() / $max)); |
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| 209 | |||
| 210 | $phi = rand() / $max * self::TWO_PI; |
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| 211 | |||
| 212 | $rowA[] = $r * sin($phi); |
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| 213 | $rowA[] = $r * cos($phi); |
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| 214 | } |
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| 215 | |||
| 216 | if (count($rowA) > $col) { |
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| 217 | $extras[] = array_pop($rowA); |
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| 218 | } |
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| 219 | |||
| 220 | $a[] = $rowA; |
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| 221 | } |
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| 222 | |||
| 223 | return self::ar($a, $dtype); |
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| 224 | } |
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| 225 | |||
| 226 | /** |
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| 227 | * create an identity matrix with the given dimensions. |
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| 228 | * @param int $n |
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| 229 | * @param int $dtype |
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| 230 | * @return matrix |
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| 231 | * @throws \InvalidArgumentException |
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| 232 | */ |
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| 233 | public static function identity(int $n, int $dtype = self::FLOAT): matrix { |
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| 234 | if ($n < 1) { |
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| 235 | self::_dimensionaMisMatchErr('dimensionality must be greater than 0 on all axes.'); |
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| 236 | } |
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| 237 | |||
| 238 | $ar = self::factory($n, $n, $dtype); |
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| 239 | for ($i = 0; $i < $n; ++$i) { |
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| 240 | for ($j = 0; $j < $n; ++$j) { |
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| 241 | $ar->data[$i * $n + $j] = $i === $j ? 1 : 0; |
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| 242 | } |
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| 243 | } |
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| 244 | return $ar; |
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| 245 | } |
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| 246 | |||
| 247 | /** |
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| 248 | * Return the element-wise minimum of two matrices. |
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| 249 | * |
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| 250 | * @param \Np\matrix $m |
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| 251 | * @return matrix |
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| 252 | */ |
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| 253 | public function minimum(matrix $m): matrix { |
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| 254 | if ($this->checkShape($this,$m)) { |
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| 255 | $ar = self::factory($this->row, $this->col, $this->dtype); |
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| 256 | for ($i = 0; $i < $this->ndim; ++$i) { |
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| 257 | $ar->data[$i] = min($this->data[$i], $m->data[$i]); |
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| 258 | } |
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| 259 | return $ar; |
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| 260 | } |
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| 261 | } |
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| 262 | |||
| 263 | /** |
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| 264 | * Return the element-wise maximum of two matrices. |
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| 265 | * |
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| 266 | * @param \Np\matrix $m |
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| 267 | * @return matrix |
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| 268 | */ |
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| 269 | public function maximum(matrix $m): matrix { |
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| 270 | if ($this->checkShape($this, $m)) { |
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| 271 | $ar = self::factory($this->row, $this->col, $this->dtype); |
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| 272 | for ($i = 0; $i < $this->ndim; ++$i) { |
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| 273 | $ar->data[$i] = max($this->data[$i], $m->data[$i]); |
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| 274 | } |
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| 275 | return $ar; |
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| 276 | } |
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| 277 | } |
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| 278 | |||
| 279 | /** |
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| 280 | * 2D convolution between a matrix ma and kernel kb, with a given stride. |
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| 281 | * @param \Np\matrix $m |
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| 282 | * @param int $stride |
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| 283 | * @return matrix |
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| 284 | */ |
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| 285 | public function convolve(matrix $m, int $stride = 1): matrix { |
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| 286 | return convolve::conv2D($this, $m, $stride); |
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| 287 | } |
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| 288 | |||
| 289 | /** |
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| 290 | * Calculate the determinant of the matrix. |
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| 291 | * @return float |
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| 292 | */ |
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| 293 | public function det(): float { |
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| 294 | if (!$this->isSquare()) { |
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| 295 | self::_err('determinant is undefined for a non square matrix'); |
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| 296 | } |
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| 297 | $lu = $this->lu(); |
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| 298 | $nSwaps = $lu->p()->diagonalAsVector()->subtract($lu->p()->diagonalAsVector()->sum())->col - 1; |
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| 299 | $detP = (-1) ** $nSwaps; |
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| 300 | $detL = $lu->l()->diagonalAsVector()->product(); |
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| 301 | $detU = $lu->u()->diagonalAsVector()->product(); |
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| 302 | unset($lu); |
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| 303 | return ($detP * $detL * $detU); |
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| 304 | } |
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| 305 | |||
| 306 | /** |
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| 307 | * Return the trace of the matrix i.e the sum of all diagonal elements of a square matrix. |
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| 308 | * @return float |
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| 309 | */ |
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| 310 | public function trace(): float { |
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| 311 | if (!$this->isSquare()) { |
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| 312 | self::_err('Error::matrix is not a squared can not Trace!'); |
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| 313 | } |
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| 314 | $trace = 0.0; |
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| 315 | for ($i = 0; $i < $this->row; ++$i) { |
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| 316 | for ($j = 0; $j < $this->col; ++$j) { |
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| 317 | if ($i == $j) { |
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| 318 | $trace += $this->data[$i * $this->col + $i]; |
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| 319 | } |
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| 320 | } |
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| 321 | } |
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| 322 | return $trace; |
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| 323 | } |
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| 324 | |||
| 325 | /** |
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| 326 | * dignoalInterChange |
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| 327 | */ |
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| 328 | public function dignoalInterChange() { |
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| 329 | for ($i = 0; $i < $this->row; ++$i) { |
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| 330 | for ($j = 0; $j < $this->col; ++$j) { |
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| 331 | $tmp = $this->data[$i * $this->col - $j]; |
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| 332 | $this->data[$i * $this->col - $j] = $tmp; |
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| 333 | } |
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| 334 | } |
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| 335 | } |
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| 336 | |||
| 337 | //----------------Linear Algebra Opreations------------------------------- |
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| 338 | |||
| 339 | /** |
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| 340 | * |
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| 341 | * get dot product of m.m or m.v |
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| 342 | * |
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| 343 | * @param \Np\matrix|\Np\vector $d |
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| 344 | * @return matrix|vector |
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| 345 | */ |
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| 346 | public function dot(matrix|vector $d): matrix|vector { |
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| 347 | if ($d instanceof self) { |
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| 348 | return $this->dotMatrix($d); |
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| 349 | } else { |
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| 350 | return $this->dotVector($d); |
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| 351 | } |
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| 352 | } |
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| 353 | |||
| 354 | /** |
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| 355 | * get matrix & matrix dot product |
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| 356 | * @param \Np\matrix $matrix |
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| 357 | * @return \Np\matrix |
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| 358 | */ |
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| 359 | protected function dotMatrix(matrix $matrix): matrix { |
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| 360 | if ($this->checkDtype($this, $matrix) && $this->checkDimensions($this,$matrix)) { |
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| 361 | $ar = self::factory($this->row, $this->col, $this->dtype); |
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| 362 | blas::gemm($this, $matrix, $ar); |
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| 363 | return $ar; |
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| 364 | } |
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| 365 | } |
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| 366 | |||
| 367 | /** |
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| 368 | * get dot product of matrix & a vector |
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| 369 | * @param \Np\vector $vector |
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| 370 | * @return \Np\vector |
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| 371 | */ |
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| 372 | protected function dotVector(vector $vector): vector { |
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| 373 | if ($this->checkDtype($this, $vector) && $this->checkDimensions($vector, $this)) { |
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| 374 | $mvr = vector::factory($this->col, $this->dtype); |
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| 375 | blas::gemv($this, $vector, $mvr); |
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| 376 | return $mvr; |
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| 377 | } |
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| 378 | } |
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| 379 | |||
| 380 | //---------------Arthmetic Opreations----------------------------------- |
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| 381 | |||
| 382 | /** |
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| 383 | * multiply this matrix with another matrix|scalar element-wise |
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| 384 | * Matrix Scalar\Matrix multiplication |
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| 385 | * @param int|float|matrix|vector $m |
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| 386 | * @return matrix|vector |
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| 387 | */ |
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| 388 | public function multiply(int|float|matrix|vector $m): matrix|vector { |
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| 389 | if ($m instanceof self) { |
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| 390 | return $this->multiplyMatrix($m); |
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| 391 | } else if ($m instanceof vector) { |
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| 392 | return $this->multiplyVector($m); |
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| 393 | } else { |
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| 394 | return $this->scale($m); |
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| 395 | } |
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| 396 | } |
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| 397 | |||
| 398 | /** |
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| 399 | * |
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| 400 | * @param \Np\vector $v |
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| 401 | * @return matrix |
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| 402 | */ |
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| 403 | protected function multiplyVector(vector $v): matrix { |
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| 404 | if ($this->checkDimensions($v, $this) && $this->checkDtype($this, $v)) { |
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| 405 | $ar = matrix::factory($this->row, $this->col, $this->dtype); |
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| 406 | for ($i = 0; $i < $this->row; ++$i) { |
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| 407 | for ($j = 0; $j < $this->col; ++$j) { |
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| 408 | $ar->data[$i * $this->col + $j] = $v->data[$j] * $this->data[$i * $this->col + $j]; |
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| 409 | } |
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| 410 | } |
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| 411 | return $ar; |
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| 412 | } |
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| 413 | } |
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| 414 | |||
| 415 | /** |
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| 416 | * |
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| 417 | * @param \Np\matrix $m |
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| 418 | * @return matrix |
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| 419 | */ |
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| 420 | protected function multiplyMatrix(matrix $m): matrix { |
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| 421 | if ($this->checkDtype($this, $m) && $this->checkShape($this, $m)) { |
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| 422 | $ar = self::factory($this->row, $this->col, $this->dtype); |
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| 423 | for ($i = 0; $i < $this->row; ++$i) { |
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| 424 | for ($j = 0; $j < $this->col; ++$j) { |
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| 425 | $ar->data[$i * $this->col + $j] = $this->data[$i * $this->col + $j] * $m->data[$i * $this->col + $j]; |
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| 426 | } |
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| 427 | } |
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| 428 | return $ar; |
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| 429 | } |
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| 430 | } |
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| 431 | |||
| 432 | /** |
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| 433 | * |
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| 434 | * @param int|float $scalar |
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| 435 | * @return matrix |
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| 436 | */ |
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| 437 | public function scale(int|float $scalar): matrix { |
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| 438 | if ($scalar == 0) { |
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| 439 | return self::zeros($this->row, $this->col, $this->dtype); |
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| 440 | } |
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| 441 | |||
| 442 | $ar = $this->copyMatrix(); |
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| 443 | for ($i = 0; $i < $this->ndim; ++$i) { |
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| 444 | $ar->data[$i] *= $scalar; |
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| 445 | } |
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| 446 | |||
| 447 | return $ar; |
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| 448 | } |
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| 449 | |||
| 450 | /** |
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| 451 | * Sum of Rows of matrix |
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| 452 | * @return vector |
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| 453 | */ |
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| 454 | public function sumRows(): vector { |
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| 455 | $vr = vector::factory($this->row, $this->dtype); |
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| 456 | for ($i = 0; $i < $this->row; ++$i) { |
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| 457 | $sum = 0.0; |
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| 458 | for ($j = 0; $j < $this->col; ++$j) { |
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| 459 | $sum += $this->data[$i * $this->col + $j]; |
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| 460 | } |
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| 461 | $vr->data[$i] = $sum; |
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| 462 | } |
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| 463 | return $vr; |
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| 464 | } |
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| 465 | |||
| 466 | /** |
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| 467 | * Sum of two matrix, vector or a scalar to current matrix |
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| 468 | * |
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| 469 | * @param int|float|matrix|vector $m |
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| 470 | * @return matrix |
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| 471 | */ |
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| 472 | public function sum(int|float|matrix|vector $m): matrix { |
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| 473 | if ($m instanceof self) { |
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| 474 | return $this->sumMatrix($m); |
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| 475 | } elseif ($m instanceof vector) { |
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| 476 | return $this->sumVector($m); |
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| 477 | } else { |
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| 478 | return $this->sumScalar($m); |
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| 479 | } |
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| 480 | } |
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| 481 | |||
| 482 | protected function sumScalar(int|float $s): matrix { |
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| 483 | $ar = self::factory($this->row, $this->col, $this->dtype); |
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| 484 | for ($i = 0; $i < $this->ndim; ++$i) { |
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| 485 | $ar->data[$i] = $this->data[$i] + $s; |
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| 486 | } |
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| 487 | return $ar; |
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| 488 | } |
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| 489 | |||
| 490 | protected function sumMatrix(matrix $m): matrix { |
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| 491 | if ($this->checkShape($this, $m) && $this->checkDtype($this, $m)) { |
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| 492 | $ar = self::factory($this->row, $this->col, $this->dtype); |
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| 493 | for ($i = 0; $i < $this->ndim; ++$i) { |
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| 494 | $ar->data[$i] = $this->data[$i] + $m->data[$i]; |
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| 495 | } |
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| 496 | return $ar; |
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| 497 | } |
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| 498 | } |
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| 499 | |||
| 500 | protected function sumVector(vector $v): matrix { |
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| 501 | if ($this->checkDimensions($v, $this) && $this->checkDtype($this, $v)) { |
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| 502 | $ar = self::factory($this->row, $this->col, $this->dtype); |
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| 503 | for ($i = 0; $i < $this->row; ++$i) { |
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| 504 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 505 | $ar->data[$i * $this->col + $j] = $v->data[$j] + $this->data[$i * $this->col + $j]; |
||
| 506 | } |
||
| 507 | } |
||
| 508 | return $ar; |
||
| 509 | } |
||
| 510 | } |
||
| 511 | |||
| 512 | /** |
||
| 513 | * subtract another matrix, vector or a scalar to this matrix |
||
| 514 | * @param int|float|matrix|vector $d matrix|$scalar to subtract this matrix |
||
| 515 | * @return \Np\matrix |
||
| 516 | */ |
||
| 517 | public function subtract(int|float|matrix|vector $d): matrix { |
||
| 518 | if ($d instanceof self) { |
||
| 519 | return $this->subtractMatrix($d); |
||
| 520 | } elseif ($d instanceof vector) { |
||
| 521 | return $this->subtractVector($d); |
||
| 522 | } else { |
||
| 523 | return $this->subtractScalar($d); |
||
| 524 | } |
||
| 525 | } |
||
| 526 | |||
| 527 | protected function subtractScalar(int|float $s): matrix { |
||
| 528 | $ar = self::factory($this->row, $this->col, $this->dtype); |
||
| 529 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 530 | $ar->data[$i] = $this->data[$i] - $s; |
||
| 531 | } |
||
| 532 | return $ar; |
||
| 533 | } |
||
| 534 | |||
| 535 | /** |
||
| 536 | * |
||
| 537 | * @param matrix $m |
||
| 538 | * @return matrix |
||
| 539 | */ |
||
| 540 | protected function subtractMatrix(matrix $m): matrix { |
||
| 541 | if ($this->checkShape($this, $m) && $this->checkDtype($this, $m)) { |
||
| 542 | $ar = self::factory($this->row, $this->col, $this->dtype); |
||
| 543 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 544 | $ar->data[$i] = $this->data[$i] - $m->data[$i]; |
||
| 545 | } |
||
| 546 | return $ar; |
||
| 547 | } |
||
| 548 | } |
||
| 549 | |||
| 550 | /** |
||
| 551 | * |
||
| 552 | * @param vector $v |
||
| 553 | * @return matrix |
||
| 554 | */ |
||
| 555 | protected function subtractVector(vector $v): matrix { |
||
| 556 | if ($this->checkDimensions($v, $this) && $this->checkDtype($this,$v)) { |
||
| 557 | $ar = self::factory($this->row, $this->col, $this->dtype); |
||
| 558 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 559 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 560 | $ar->data[$i * $this->col + $j] = $this->data[$i * $this->col + $j] - $v->data[$j]; |
||
| 561 | } |
||
| 562 | } |
||
| 563 | return $ar; |
||
| 564 | } |
||
| 565 | } |
||
| 566 | |||
| 567 | /** |
||
| 568 | * |
||
| 569 | * @param vector $v |
||
| 570 | * @return matrix |
||
| 571 | */ |
||
| 572 | public function subtractColumnVector(vector $v): matrix { |
||
| 573 | if ($this->checkDimensions($v, $this) && $this->checkDtype($this, $v)) { |
||
| 574 | $ar = self::factory($this->row, $this->col, $this->dtype); |
||
| 575 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 576 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 577 | $ar->data[$i * $this->col + $j] = $this->data[$i * $this->col + $j] - $v->data[$i]; |
||
| 578 | } |
||
| 579 | } |
||
| 580 | return $ar; |
||
| 581 | } |
||
| 582 | } |
||
| 583 | |||
| 584 | /** |
||
| 585 | * Return the division of two elements, element-wise. |
||
| 586 | * @param int|float|matrix $d |
||
| 587 | * @return matrix |
||
| 588 | */ |
||
| 589 | public function divide(int|float|matrix|vector $d): matrix { |
||
| 590 | if ($d instanceof self) { |
||
| 591 | return $this->divideMatrix($d); |
||
| 592 | } elseif ($d instanceof vector) { |
||
| 593 | return $this->divideVector($d); |
||
| 594 | } else { |
||
| 595 | return $this->divideScalar($d); |
||
| 596 | } |
||
| 597 | } |
||
| 598 | |||
| 599 | protected function divideMatrix(matrix $m): matrix { |
||
| 600 | if ($this->checkShape($this, $m) && $this->checkDtype($this, $m)) { |
||
| 601 | $ar = self::factory($this->row, $this->col, $this->dtype); |
||
| 602 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 603 | $ar->data[$i] = $this->data[$i] / $m->data[$i]; |
||
| 604 | } |
||
| 605 | return $ar; |
||
| 606 | } |
||
| 607 | } |
||
| 608 | |||
| 609 | protected function divideVector(vector $v): matrix { |
||
| 610 | if ($this->checkDimensions($v, $this) && $this->checkDtype($this, $v)) { |
||
| 611 | $ar = self::factory($this->row, $this->col, $this->dtype); |
||
| 612 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 613 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 614 | $ar->data[$i * $this->col + $j] = $this->data[$i * $this->col + $j] / $v->data[$j]; |
||
| 615 | } |
||
| 616 | } |
||
| 617 | return $ar; |
||
| 618 | } |
||
| 619 | } |
||
| 620 | |||
| 621 | protected function divideScalar(int|float $s): matrix { |
||
| 622 | $ar = self::factory($this->row, $this->col, $this->dtype); |
||
| 623 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 624 | $ar->data[$i] = $this->data[$i] / $s; |
||
| 625 | } |
||
| 626 | return $ar; |
||
| 627 | } |
||
| 628 | |||
| 629 | /** |
||
| 630 | * |
||
| 631 | * Raise this matrix to the power of the element-wise entry in another matrix. |
||
| 632 | * |
||
| 633 | * @param int|float|matrix $m |
||
| 634 | * @return matrix |
||
| 635 | */ |
||
| 636 | public function pow(int|float|matrix|vector $d): matrix { |
||
| 637 | if ($d instanceof self) { |
||
| 638 | return $this->powMatrix($d); |
||
| 639 | } else if ($d instanceof vector) { |
||
| 640 | return $this->powVector($d); |
||
| 641 | } else { |
||
| 642 | return $this->powScalar($d); |
||
| 643 | } |
||
| 644 | } |
||
| 645 | |||
| 646 | protected function powMatrix(matrix $m): matrix { |
||
| 647 | if ($this->checkShape($this, $m) && $this->checkDtype($this, $m)) { |
||
| 648 | $ar = self::factory($this->row, $this->col, $this->dtype); |
||
| 649 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 650 | $ar->data[$i] = $this->data[$i] ** $m->data[$i]; |
||
| 651 | } |
||
| 652 | return $ar; |
||
| 653 | } |
||
| 654 | } |
||
| 655 | |||
| 656 | protected function powVector(vector $v): matrix { |
||
| 657 | if ($this->checkDimensions($v, $this) && $this->checkDtype($this, $v)) { |
||
| 658 | $ar = self::factory($this->row, $this->col, $this->dtype); |
||
| 659 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 660 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 661 | $ar->data[$i * $this->col + $j] = $this->data[$i * $this->col + $j] ** $v->data[$j]; |
||
| 662 | } |
||
| 663 | } |
||
| 664 | return $ar; |
||
| 665 | } |
||
| 666 | } |
||
| 667 | |||
| 668 | protected function powScalar(int|float $s): matrix { |
||
| 669 | $ar = $this->copyMatrix(); |
||
| 670 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 671 | $ar->data[$i] **= $s; |
||
| 672 | } |
||
| 673 | return $ar; |
||
| 674 | } |
||
| 675 | |||
| 676 | /** |
||
| 677 | * Calculate the modulus i.e remainder of division between this matrix and another matrix. |
||
| 678 | * @param int|float|matrix|vector $d |
||
| 679 | * @return matrix |
||
| 680 | */ |
||
| 681 | public function mod(int|float|matrix|vector $d): matrix { |
||
| 682 | if ($d instanceof self) { |
||
| 683 | $this->modMatrix($d); |
||
| 684 | } else if ($d instanceof vector) { |
||
| 685 | $this->modVector($d); |
||
| 686 | } else { |
||
| 687 | $this->modScalar($d); |
||
| 688 | } |
||
| 689 | } |
||
| 690 | |||
| 691 | protected function modMatrix(matrix $m): matrix { |
||
| 692 | if ($this->checkShape($this, $m) && $this->checkDtype($this, $m)) { |
||
| 693 | $ar = self::factory($this->row, $this->col, $this->dtype); |
||
| 694 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 695 | $ar->data[$i] = $this->data[$i] % $m->data[$i]; |
||
| 696 | } |
||
| 697 | return $ar; |
||
| 698 | } |
||
| 699 | } |
||
| 700 | |||
| 701 | protected function modVector(vector $v): matrix { |
||
| 702 | if ($this->checkDimensions($v, $this) && $this->checkDtype($this, $v)) { |
||
| 703 | $ar = self::factory($this->row, $this->col, $this->dtype); |
||
| 704 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 705 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 706 | $ar->data[$i * $this->col + $j] = $this->data[$i * $this->col + $j] % $v->data[$j]; |
||
| 707 | } |
||
| 708 | } |
||
| 709 | return $ar; |
||
| 710 | } |
||
| 711 | } |
||
| 712 | |||
| 713 | protected function modScalar(int|float $s): matrix { |
||
| 714 | $ar = $this->copyMatrix(); |
||
| 715 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 716 | $ar->data[$i] %= $s; |
||
| 717 | } |
||
| 718 | return $ar; |
||
| 719 | } |
||
| 720 | |||
| 721 | /** |
||
| 722 | * Return the element-wise reciprocal of the matrix. |
||
| 723 | * |
||
| 724 | * @return matrix |
||
| 725 | */ |
||
| 726 | public function reciprocal(): matrix { |
||
| 727 | return self::ones($this->row, $this->col, $this->dtype)->divideMatrix($this); |
||
| 728 | } |
||
| 729 | |||
| 730 | /** |
||
| 731 | * |
||
| 732 | * @param int|float $d |
||
| 733 | * @return bool |
||
| 734 | */ |
||
| 735 | public static function is_zero($d): bool { |
||
| 736 | if (abs($d) < self::EPSILON) { |
||
| 737 | return true; |
||
| 738 | } |
||
| 739 | return false; |
||
| 740 | } |
||
| 741 | |||
| 742 | /** |
||
| 743 | * is row zero |
||
| 744 | * @param int $row |
||
| 745 | * @return bool |
||
| 746 | */ |
||
| 747 | public function is_rowZero(int $row): bool { |
||
| 748 | for ($i = 0; $i < $this->col; ++$i) { |
||
| 749 | if ($this->data[$row * $this->col + $i] != 0) { |
||
| 750 | return false; |
||
| 751 | } |
||
| 752 | } |
||
| 753 | return true; |
||
| 754 | } |
||
| 755 | |||
| 756 | /** |
||
| 757 | * |
||
| 758 | * @return bool |
||
| 759 | */ |
||
| 760 | public function has_ZeroRow(): bool { |
||
| 761 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 762 | if ($this->is_rowZero($i)) { |
||
| 763 | return true; |
||
| 764 | } |
||
| 765 | } |
||
| 766 | return false; |
||
| 767 | } |
||
| 768 | |||
| 769 | /** |
||
| 770 | * Transpose the matrix i.e row become cols and cols become rows. |
||
| 771 | * @return \Np\matrix |
||
| 772 | */ |
||
| 773 | public function transpose(): matrix { |
||
| 774 | $ar = self::factory($this->col, $this->row, $this->dtype); |
||
| 775 | for ($i = 0; $i < $ar->row; ++$i) { |
||
| 776 | for ($j = 0; $j < $ar->col; ++$j) { |
||
| 777 | $ar->data[$i * $ar->col + $j] = $this->data[$j * $ar->col + $i]; |
||
| 778 | } |
||
| 779 | } |
||
| 780 | return $ar; |
||
| 781 | } |
||
| 782 | |||
| 783 | /** |
||
| 784 | * swap specific values in matrix |
||
| 785 | * @param int $i1 |
||
| 786 | * @param int $i2 |
||
| 787 | */ |
||
| 788 | public function swapValue(int $i1, int $i2) { |
||
| 789 | $tmp = $this->data[$i1]; |
||
| 790 | $this->data[$i1] = $this->data[$i2]; |
||
| 791 | $this->data[$i2] = $tmp; |
||
| 792 | } |
||
| 793 | |||
| 794 | /** |
||
| 795 | * swap specific rows in matrix |
||
| 796 | * @param int $r1 |
||
| 797 | * @param int $r2 |
||
| 798 | */ |
||
| 799 | public function swapRows(int $r1, int $r2) { |
||
| 800 | for ($i = 0; $i < $this->col; ++$i) { |
||
| 801 | $tmp = $this->data[$r1 * $this->col + $i]; |
||
| 802 | $this->data[$r1 * $this->col + $i] = $this->data[$r2 * $this->col + $i]; |
||
| 803 | $this->data[$r2 * $this->col + $i] = $tmp; |
||
| 804 | } |
||
| 805 | } |
||
| 806 | |||
| 807 | /** |
||
| 808 | * swap specific cols in matrix |
||
| 809 | * @param int $c1 |
||
| 810 | * @param int $c2 |
||
| 811 | */ |
||
| 812 | public function swapCols(int $c1, int $c2) { |
||
| 813 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 814 | $tmp = $this->data[$i * $this->row + $c1]; |
||
| 815 | $this->data[$i * $this->row + $c1] = $this->data[$i * $this->row + $c2]; |
||
| 816 | $this->data[$i * $this->row + $c2] = $tmp; |
||
| 817 | } |
||
| 818 | } |
||
| 819 | |||
| 820 | /** |
||
| 821 | * scale all the elements of a row |
||
| 822 | * @param int $row |
||
| 823 | * @param float $c |
||
| 824 | */ |
||
| 825 | public function scaleRow(int $row, float $c) { |
||
| 826 | for ($i = 0; $i < $this->col; ++$i) { |
||
| 827 | $this->data[$row * $this->col + $i] *= $c; |
||
| 828 | } |
||
| 829 | } |
||
| 830 | |||
| 831 | /** |
||
| 832 | * |
||
| 833 | * @param int $r1 |
||
| 834 | * @param int $r2 |
||
| 835 | * @param float $c |
||
| 836 | */ |
||
| 837 | public function addScaleRow(int $r1, int $r2, float $c) { |
||
| 838 | for ($i = 0; $i < $this->col; ++$i) { |
||
| 839 | $this->data[$r2 * $this->col + $i] += $this->data[$r1 * $this->col + $i] * $c; |
||
| 840 | } |
||
| 841 | } |
||
| 842 | |||
| 843 | /** |
||
| 844 | * Attach given matrix to the left of this matrix. |
||
| 845 | * |
||
| 846 | * @param \Np\matrix $m |
||
| 847 | * @return \Np\matrix |
||
| 848 | */ |
||
| 849 | public function joinLeft(matrix $m): matrix { |
||
| 850 | if ($this->row != $m->row && !$this->checkDtype($this, $m)) { |
||
| 851 | self::_err('Error::Invalid size! or DataType!'); |
||
| 852 | } |
||
| 853 | $col = $this->col + $m->col; |
||
| 854 | $ar = self::factory($this->row, $col, $this->dtype); |
||
| 855 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 856 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 857 | $ar->data[$i * $col + $j] = $this->data[$i * $this->col + $j]; |
||
| 858 | } |
||
| 859 | for ($j = 0; $j < $m->col; ++$j) { |
||
| 860 | $ar->data[$i * $col + ($this->col + $j)] = $m->data[$i * $m->col + $j]; |
||
| 861 | } |
||
| 862 | } |
||
| 863 | return $ar; |
||
| 864 | } |
||
| 865 | |||
| 866 | /** |
||
| 867 | * Join matrix m to the Right of this matrix. |
||
| 868 | * @param \Np\matrix $m |
||
| 869 | * @return matrix |
||
| 870 | */ |
||
| 871 | public function joinRight(matrix $m): matrix { |
||
| 872 | if ($this->row != $m->row && !$this->checkDtype($this,$m)) { |
||
| 873 | self::_err('Error::Invalid size! or DataType!'); |
||
| 874 | } |
||
| 875 | $col = $this->col + $m->col; |
||
| 876 | $ar = self::factory($this->row, $col, $this->dtype); |
||
| 877 | for ($i = 0; $i < $m->row; ++$i) { |
||
| 878 | for ($j = 0; $j < $m->col; ++$j) { |
||
| 879 | $ar->data[$i * $col + $j] = $m->data[$i * $m->col + $j]; |
||
| 880 | } |
||
| 881 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 882 | $ar->data[$i * $col + ($this->col + $j)] = $this->data[$i * $this->col + $j]; |
||
| 883 | } |
||
| 884 | } |
||
| 885 | return $ar; |
||
| 886 | } |
||
| 887 | |||
| 888 | /** |
||
| 889 | * Join matrix m Above this matrix. |
||
| 890 | * @param \Np\matrix $m |
||
| 891 | * @return matrix |
||
| 892 | */ |
||
| 893 | public function joinAbove(matrix $m): matrix { |
||
| 908 | } |
||
| 909 | |||
| 910 | /** |
||
| 911 | * Join matrix m below this matrix. |
||
| 912 | * @param \Np\matrix $m |
||
| 913 | * @return matrix |
||
| 914 | */ |
||
| 915 | public function joinBelow(matrix $m): matrix { |
||
| 916 | if ($this->col !== $m->col && !$this->checkDtype($this, $m)) { |
||
| 917 | self::_err('Error::Invalid size! or DataType!'); |
||
| 918 | } |
||
| 919 | $row = $this->row + $m->row; |
||
| 920 | $ar = self::factory($row, $this->col, $this->dtype); |
||
| 921 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 922 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 923 | $ar->data[$i * $this->col + $j] = $this->data[$i * $this->col + $j]; |
||
| 924 | } |
||
| 925 | for ($j = 0; $j < $m->col; ++$j) { |
||
| 926 | $ar->data[($i + $m->row) * $m->col + $j] = $m->data[$i * $m->col + $j]; |
||
| 927 | } |
||
| 928 | } |
||
| 929 | return $ar; |
||
| 930 | } |
||
| 931 | |||
| 932 | /** |
||
| 933 | * Calculate the row echelon form of the matrix. |
||
| 934 | * Return the reduced matrix. |
||
| 935 | * |
||
| 936 | * @return matrix|null |
||
| 937 | */ |
||
| 938 | public function ref(): matrix|null { |
||
| 939 | return ref::factory($this); |
||
| 940 | } |
||
| 941 | |||
| 942 | /** |
||
| 943 | * Return the lower triangular matrix of the Cholesky decomposition. |
||
| 944 | * |
||
| 945 | * @return matrix|null |
||
| 946 | */ |
||
| 947 | public function cholesky(): matrix|null { |
||
| 948 | return cholesky::factory($this); |
||
| 949 | } |
||
| 950 | |||
| 951 | /** |
||
| 952 | * FIXME-------------- |
||
| 953 | * RREF |
||
| 954 | * The reduced row echelon form (RREF) of a matrix. |
||
| 955 | * @return \Np\matrix |
||
| 956 | */ |
||
| 957 | public function rref(): matrix { |
||
| 958 | return rref::factory($this); |
||
| 959 | } |
||
| 960 | |||
| 961 | /** |
||
| 962 | * make copy of the matrix |
||
| 963 | * @return \Np\matrix |
||
| 964 | */ |
||
| 965 | public function copyMatrix(): matrix { |
||
| 966 | return clone $this; |
||
| 967 | } |
||
| 968 | |||
| 969 | /** |
||
| 970 | * |
||
| 971 | * @param int $cols |
||
| 972 | * @return \Np\matrix |
||
| 973 | */ |
||
| 974 | public function diminish_left(int $cols): matrix { |
||
| 975 | $ar = self::factory($this->row, $cols, $this->dtype); |
||
| 976 | for ($i = 0; $i < $ar->row; ++$i) { |
||
| 977 | for ($j = 0; $j < $ar->col; ++$j) { |
||
| 978 | $ar->data[$i * $ar->col + $j] = $this->data[$i * $this->col + $j]; |
||
| 979 | } |
||
| 980 | } |
||
| 981 | return $ar; |
||
| 982 | } |
||
| 983 | |||
| 984 | /** |
||
| 985 | * |
||
| 986 | * @param int $cols |
||
| 987 | * @return \Np\matrix |
||
| 988 | */ |
||
| 989 | public function diminish_right(int $cols): matrix { |
||
| 990 | $ar = self::factory($this->row, $cols, $this->dtype); |
||
| 991 | for ($i = 0; $i < $ar->row; ++$i) { |
||
| 992 | for ($j = 0; $j < $ar->col; ++$j) { |
||
| 993 | $ar->data[$i * $ar->col + $j] = $this->data[$i * $this->col - $cols + $j]; |
||
| 994 | } |
||
| 995 | } |
||
| 996 | return $ar; |
||
| 997 | } |
||
| 998 | |||
| 999 | /** |
||
| 1000 | * Return the index of the maximum element in every row of the matrix. |
||
| 1001 | * @return \Np\vector int |
||
| 1002 | */ |
||
| 1003 | public function argMax(): vector { |
||
| 1004 | $v = vector::factory($this->row, vector::INT); |
||
| 1005 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 1006 | $v->data[$i] = blas::max($this->rowAsVector($i)); |
||
| 1007 | } |
||
| 1008 | return $v; |
||
| 1009 | } |
||
| 1010 | |||
| 1011 | /** |
||
| 1012 | * Return the index of the minimum element in every row of the matrix. |
||
| 1013 | * @return \Np\vector int |
||
| 1014 | */ |
||
| 1015 | public function argMin(): vector { |
||
| 1016 | $v = vector::factory($this->row, vector::INT); |
||
| 1017 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 1018 | $v->data[$i] = blas::min($this->rowAsVector($i)); |
||
| 1019 | } |
||
| 1020 | |||
| 1021 | return $v; |
||
| 1022 | } |
||
| 1023 | |||
| 1024 | /** |
||
| 1025 | * Set given data in matrix |
||
| 1026 | * @param int|float|array $data |
||
| 1027 | * @param bool $dignoal |
||
| 1028 | * @return void |
||
| 1029 | */ |
||
| 1030 | public function setData(int|float|array $data, bool $dignoal = false): void { |
||
| 1031 | if ($dignoal == false) { |
||
| 1032 | if (is_array($data) && is_array($data[0])) { |
||
| 1033 | $f = $this->flattenArray($data); |
||
| 1034 | foreach ($f as $k => $v) { |
||
| 1035 | $this->data[$k] = $v; |
||
| 1036 | } |
||
| 1037 | } elseif (is_numeric($data)) { |
||
| 1038 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 1039 | $this->data[$i] = $data; |
||
| 1040 | } |
||
| 1041 | } |
||
| 1042 | } elseif (is_numeric($data) || is_array($data) && !is_array($data[0])) { |
||
| 1043 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 1044 | $this->data[$i * $this->col * $i] = $data; |
||
| 1045 | } |
||
| 1046 | } |
||
| 1047 | } |
||
| 1048 | |||
| 1049 | /** |
||
| 1050 | * get the shape of matrix |
||
| 1051 | * @return object |
||
| 1052 | */ |
||
| 1053 | public function getShape(): object { |
||
| 1054 | return (object) ['m' => $this->row, 'n' => $this->col]; |
||
| 1055 | } |
||
| 1056 | |||
| 1057 | /** |
||
| 1058 | * get the number of elements in the matrix. |
||
| 1059 | * @return int |
||
| 1060 | */ |
||
| 1061 | public function getSize(): int { |
||
| 1062 | return $this->row * $this->col; |
||
| 1063 | } |
||
| 1064 | |||
| 1065 | /** |
||
| 1066 | * |
||
| 1067 | * @return bool |
||
| 1068 | */ |
||
| 1069 | public function isSquare(): bool { |
||
| 1070 | if ($this->row === $this->col) { |
||
| 1071 | return true; |
||
| 1072 | } |
||
| 1073 | return false; |
||
| 1074 | } |
||
| 1075 | |||
| 1076 | public function getDtype() { |
||
| 1077 | return $this->dtype; |
||
| 1078 | } |
||
| 1079 | |||
| 1080 | /** |
||
| 1081 | * Return a row as vector from the matrix. |
||
| 1082 | * @param int $index |
||
| 1083 | * @return \Np\vector |
||
| 1084 | */ |
||
| 1085 | public function rowAsVector(int $index): vector { |
||
| 1086 | $vr = vector::factory($this->col, $this->dtype); |
||
| 1087 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 1088 | $vr->data[$j] = $this->data[$index * $this->col + $j]; |
||
| 1089 | } |
||
| 1090 | return $vr; |
||
| 1091 | } |
||
| 1092 | |||
| 1093 | /** |
||
| 1094 | * Return a col as vector from the matrix. |
||
| 1095 | * @param int $index |
||
| 1096 | * @return \Np\vector |
||
| 1097 | */ |
||
| 1098 | public function colAsVector(int $index): vector { |
||
| 1099 | $vr = vector::factory($this->row, $this->dtype); |
||
| 1100 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 1101 | $vr->data[$i] = $this->data[$i * $this->row + $index]; |
||
| 1102 | } |
||
| 1103 | return $vr; |
||
| 1104 | } |
||
| 1105 | |||
| 1106 | /** |
||
| 1107 | * Return the diagonal elements of a square matrix as a vector. |
||
| 1108 | * @return \Np\vector |
||
| 1109 | */ |
||
| 1110 | public function diagonalAsVector(): vector { |
||
| 1111 | if (!$this->isSquare()) { |
||
| 1112 | self::_err('Can not trace of a none square matrix'); |
||
| 1113 | } |
||
| 1114 | $vr = vector::factory($this->row, $this->dtype); |
||
| 1115 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 1116 | $vr->data[$i] = $this->getDiagonalVal($i); |
||
| 1117 | } |
||
| 1118 | return $vr; |
||
| 1119 | } |
||
| 1120 | |||
| 1121 | /** |
||
| 1122 | * Flatten i.e unravel the matrix into a vector. |
||
| 1123 | * |
||
| 1124 | * @return \Np\vector |
||
| 1125 | */ |
||
| 1126 | public function asVector(): vector { |
||
| 1127 | $vr = vector::factory($this->ndim, $this->dtype); |
||
| 1128 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 1129 | $vr->data[$i] = $this->data[$i]; |
||
| 1130 | } |
||
| 1131 | return $vr; |
||
| 1132 | } |
||
| 1133 | |||
| 1134 | /** |
||
| 1135 | * Return the elements of the matrix in a 2-d array. |
||
| 1136 | * @return array |
||
| 1137 | */ |
||
| 1138 | public function asArray(): array { |
||
| 1139 | $ar = array_fill(0, $this->row, array_fill(0, $this->col, null)); |
||
| 1140 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 1141 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 1142 | $ar[$i][$j] = $this->data[$i * $this->col + $j]; |
||
| 1143 | } |
||
| 1144 | } |
||
| 1145 | return $ar; |
||
| 1146 | } |
||
| 1147 | |||
| 1148 | /** |
||
| 1149 | * get a diagonal value from matrix |
||
| 1150 | * @param int $i |
||
| 1151 | * @return float |
||
| 1152 | */ |
||
| 1153 | public function getDiagonalVal(int $i) { |
||
| 1154 | if ($this->isSquare()) { |
||
| 1155 | return $this->data[$i * $this->row + $i]; |
||
| 1156 | } |
||
| 1157 | } |
||
| 1158 | |||
| 1159 | /** |
||
| 1160 | * |
||
| 1161 | * Compute the multiplicative inverse of the matrix. |
||
| 1162 | * @return matrix |
||
| 1163 | */ |
||
| 1164 | public function inverse(): matrix { |
||
| 1165 | if (!$this->isSquare()) { |
||
| 1166 | self::_err('Error::invalid Size of matrix!'); |
||
| 1167 | } |
||
| 1168 | $imat = $this->copyMatrix(); |
||
| 1169 | $ipiv = vector::factory($this->row, vector::INT); |
||
| 1170 | $lp = lapack::getrf($imat, $ipiv); |
||
| 1171 | if ($lp != 0) { |
||
| 1172 | return null; |
||
| 1173 | } |
||
| 1174 | $lp = lapack::getri($imat, $ipiv); |
||
| 1175 | if ($lp != 0) { |
||
| 1176 | return null; |
||
| 1177 | } |
||
| 1178 | unset($ipiv); |
||
| 1179 | unset($lp); |
||
| 1180 | return $imat; |
||
| 1181 | } |
||
| 1182 | |||
| 1183 | /** |
||
| 1184 | * Compute the (Moore-Penrose) pseudo inverse of the general matrix. |
||
| 1185 | * @return matrix|null |
||
| 1186 | */ |
||
| 1187 | public function pseudoInverse(): matrix|null { |
||
| 1188 | $k = min($this->row, $this->col); |
||
| 1189 | $s = vector::factory($k, $this->dtype); |
||
| 1190 | $u = self::factory($this->row, $this->row, $this->dtype); |
||
| 1191 | $vt = self::factory($this->col, $this->col, $this->dtype); |
||
| 1192 | $imat = $this->copyMatrix(); |
||
| 1193 | $lp = lapack::gesdd($imat, $s, $u, $vt); |
||
| 1194 | if ($lp != 0) { |
||
| 1195 | return null; |
||
| 1196 | } |
||
| 1197 | for ($i = 0; $i < $k; ++$i) { |
||
| 1198 | blas::scale(1.0 / $s->data[$i], $vt->rowAsVector($i)); |
||
| 1199 | } |
||
| 1200 | unset($imat); |
||
| 1201 | unset($k); |
||
| 1202 | unset($lp); |
||
| 1203 | unset($s); |
||
| 1204 | $mr = self::factory($this->col, $this->row, $this->dtype); |
||
| 1205 | blas::gemm($vt, $u, $mr); |
||
| 1206 | unset($u); |
||
| 1207 | unset($vt); |
||
| 1208 | return $mr; |
||
| 1209 | } |
||
| 1210 | |||
| 1211 | /** |
||
| 1212 | * Compute the singular value decomposition of a matrix and |
||
| 1213 | * return an object of the singular values and unitary matrices |
||
| 1214 | * |
||
| 1215 | * @return object (u,s,v) |
||
| 1216 | */ |
||
| 1217 | public function svd(): svd { |
||
| 1218 | return svd::factory($this); |
||
| 1219 | } |
||
| 1220 | |||
| 1221 | /** |
||
| 1222 | * Compute the eigen decomposition of a general matrix. |
||
| 1223 | * return the eigenvalues and eigenvectors as object |
||
| 1224 | * |
||
| 1225 | * @param bool $symmetric |
||
| 1226 | * @return eigen |
||
| 1227 | */ |
||
| 1228 | public function eign(bool $symmetric = false): eigen { |
||
| 1229 | return eigen::factory($this, $symmetric); |
||
| 1230 | } |
||
| 1231 | |||
| 1232 | /** |
||
| 1233 | * |
||
| 1234 | * Compute the LU factorization of matrix. |
||
| 1235 | * return lower, upper, and permutation matrices as object. |
||
| 1236 | * |
||
| 1237 | * @return lu |
||
| 1238 | */ |
||
| 1239 | public function lu(): lu { |
||
| 1240 | return lu::factory($this); |
||
| 1241 | } |
||
| 1242 | |||
| 1243 | /** |
||
| 1244 | * Return the L1 norm of the matrix. |
||
| 1245 | * @return float |
||
| 1246 | */ |
||
| 1247 | public function normL1(): float { |
||
| 1248 | return lapack::lange('l', $this); |
||
| 1249 | } |
||
| 1250 | |||
| 1251 | /** |
||
| 1252 | * Return the L2 norm of the matrix. |
||
| 1253 | * @return float |
||
| 1254 | */ |
||
| 1255 | public function normL2(): float { |
||
| 1256 | return lapack::lange('f', $this); |
||
| 1257 | } |
||
| 1258 | |||
| 1259 | /** |
||
| 1260 | * Return the L1 norm of the matrix. |
||
| 1261 | * @return float |
||
| 1262 | */ |
||
| 1263 | public function normINF(): float { |
||
| 1264 | return lapack::lange('i', $this); |
||
| 1265 | } |
||
| 1266 | |||
| 1267 | /** |
||
| 1268 | * Return the Frobenius norm of the matrix. |
||
| 1269 | * @return float |
||
| 1270 | */ |
||
| 1271 | public function normFrob(): float { |
||
| 1272 | return $this->normL2(); |
||
| 1273 | } |
||
| 1274 | |||
| 1275 | /** |
||
| 1276 | * Run a function over all of the elements in the matrix. |
||
| 1277 | * @param callable $func |
||
| 1278 | * @return \Np\matrix |
||
| 1279 | */ |
||
| 1280 | public function map(callable $func): matrix { |
||
| 1281 | $ar = self::factory($this->row, $this->col, $this->dtype); |
||
| 1282 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 1283 | $ar->data[$i] = $func($this->data[$i]); |
||
| 1284 | } |
||
| 1285 | return $ar; |
||
| 1286 | } |
||
| 1287 | |||
| 1288 | public function abs(): matrix { |
||
| 1289 | return $this->map('abs'); |
||
| 1290 | } |
||
| 1291 | |||
| 1292 | public function sqrt(): matrix { |
||
| 1293 | return $this->map('sqrt'); |
||
| 1294 | } |
||
| 1295 | |||
| 1296 | public function exp(): matrix { |
||
| 1297 | return $this->map('exp'); |
||
| 1298 | } |
||
| 1299 | |||
| 1300 | public function exp1(): matrix { |
||
| 1301 | return $this->map('exp1'); |
||
| 1302 | } |
||
| 1303 | |||
| 1304 | public function log(float $b = M_E): matrix { |
||
| 1305 | $ar = $this->copyMatrix(); |
||
| 1306 | for ($i = 0; $i < $ar->ndim; ++$i) { |
||
| 1307 | log($ar->data[$i], $b); |
||
| 1308 | } |
||
| 1309 | return $ar; |
||
| 1310 | } |
||
| 1311 | |||
| 1312 | public function log1p(): matrix { |
||
| 1313 | return $this->map('log1p'); |
||
| 1314 | } |
||
| 1315 | |||
| 1316 | public function sin(): matrix { |
||
| 1317 | return $this->map('sin'); |
||
| 1318 | } |
||
| 1319 | |||
| 1320 | public function asin(): matrix { |
||
| 1321 | return $this->map('asin'); |
||
| 1322 | } |
||
| 1323 | |||
| 1324 | public function cos(): matrix { |
||
| 1325 | return $this->map('cos'); |
||
| 1326 | } |
||
| 1327 | |||
| 1328 | public function acos(): matrix { |
||
| 1329 | return $this->map('acos'); |
||
| 1330 | } |
||
| 1331 | |||
| 1332 | public function tan(): matrix { |
||
| 1333 | return $this->map('tan'); |
||
| 1334 | } |
||
| 1335 | |||
| 1336 | public function atan(): matrix { |
||
| 1337 | return $this->map('atan'); |
||
| 1338 | } |
||
| 1339 | |||
| 1340 | public function radToDeg(): matrix { |
||
| 1341 | return $this->map('rad2deg'); |
||
| 1342 | } |
||
| 1343 | |||
| 1344 | public function degToRad(): matrix { |
||
| 1345 | return $this->map('deg2rad'); |
||
| 1346 | } |
||
| 1347 | |||
| 1348 | public function floor(): matrix { |
||
| 1349 | return $this->map('floor'); |
||
| 1350 | } |
||
| 1351 | |||
| 1352 | public function ceil(): matrix { |
||
| 1353 | return $this->map('ceil'); |
||
| 1354 | } |
||
| 1355 | |||
| 1356 | /** |
||
| 1357 | * Compute the means of each row and return them in a vector. |
||
| 1358 | * |
||
| 1359 | * @return vector |
||
| 1360 | */ |
||
| 1361 | public function mean(): vector { |
||
| 1362 | return $this->sumRows()->divide($this->col); |
||
| 1363 | } |
||
| 1364 | |||
| 1365 | /** |
||
| 1366 | * Compute the row variance of the matrix. |
||
| 1367 | * |
||
| 1368 | * @param vector|null $mean |
||
| 1369 | * @return vector |
||
| 1370 | */ |
||
| 1371 | public function variance(vector|null $mean = null): vector { |
||
| 1372 | if (isset($mean)) { |
||
| 1373 | if (!$mean instanceof vector) { |
||
| 1374 | self::_invalidArgument('mean must be a vector!'); |
||
| 1375 | } |
||
| 1376 | if ($this->row !== $mean->col) { |
||
| 1377 | self::_err('Err:: given mean vector dimensionality mismatched!'); |
||
| 1378 | } |
||
| 1379 | } else { |
||
| 1380 | $mean = $this->mean(); |
||
| 1381 | } |
||
| 1382 | return $this->subtractColumnVector($mean)->square() |
||
| 1383 | ->sumRows()->divide($this->row); |
||
| 1384 | } |
||
| 1385 | |||
| 1386 | /** |
||
| 1387 | * Return the median vector of this matrix. |
||
| 1388 | * @return vector |
||
| 1389 | */ |
||
| 1390 | public function median(): vector { |
||
| 1391 | $mid = intdiv($this->col, 2); |
||
| 1392 | $odd = $this->col % 2 === 1; |
||
| 1393 | $vr = vector::factory($this->row, $this->dtype); |
||
| 1394 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 1395 | $a = $this->rowAsVector($i)->sort(); |
||
| 1396 | if ($odd) { |
||
| 1397 | $median = $a->data[$mid]; |
||
| 1398 | } else { |
||
| 1399 | $median = ($a->data[$mid - 1] + $a->data[$mid]) / 2.0; |
||
| 1400 | } |
||
| 1401 | $vr->data[$i] = $median; |
||
| 1402 | } |
||
| 1403 | unset($a); |
||
| 1404 | return $vr; |
||
| 1405 | } |
||
| 1406 | |||
| 1407 | /** |
||
| 1408 | * Compute the covariance matrix. |
||
| 1409 | * |
||
| 1410 | * @param vector|null $mean |
||
| 1411 | * @return matrix |
||
| 1412 | */ |
||
| 1413 | public function covariance(vector|null $mean = null): matrix { |
||
| 1414 | if (isset($mean)) { |
||
| 1415 | if ($mean->col !== $this->row) { |
||
| 1416 | self::_err('Err:: given mean vector dimensionality mismatched!'); |
||
| 1417 | } |
||
| 1418 | } else { |
||
| 1419 | $mean = $this->mean(); |
||
| 1420 | } |
||
| 1421 | |||
| 1422 | $b = $this->subtractColumnVector($mean); |
||
|
1 ignored issue
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|
|||
| 1423 | |||
| 1424 | return $b->dot($b->transpose()) |
||
| 1425 | ->divideScalar($this->row); |
||
| 1426 | } |
||
| 1427 | |||
| 1428 | /** |
||
| 1429 | * Clip the elements in the matrix to be between given minimum and maximum |
||
| 1430 | * and return a new matrix. |
||
| 1431 | * |
||
| 1432 | * @param float $min |
||
| 1433 | * @param float $max |
||
| 1434 | * @return matrix |
||
| 1435 | */ |
||
| 1436 | public function clip(float $min, float $max): matrix { |
||
| 1437 | $ar = self::factory($this->row, $this->col, $this->dtype); |
||
| 1438 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 1439 | if ($this->data[$i] > $max) { |
||
| 1440 | $ar->data[$i] = $max; |
||
| 1441 | continue; |
||
| 1442 | } |
||
| 1443 | if ($this->data[$i] < $min) { |
||
| 1444 | $ar->data[$i] = $min; |
||
| 1445 | continue; |
||
| 1446 | } |
||
| 1447 | $ar->data[$i] = $this->data[$i]; |
||
| 1448 | } |
||
| 1449 | return $ar; |
||
| 1450 | } |
||
| 1451 | |||
| 1452 | /** |
||
| 1453 | * Clip the matrix to be lower bounded by a given minimum. |
||
| 1454 | * @param float $min |
||
| 1455 | * @return matrix |
||
| 1456 | */ |
||
| 1457 | public function clipLower(float $min): matrix { |
||
| 1458 | $ar = self::factory($this->row, $this->col, $this->dtype); |
||
| 1459 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 1460 | if ($this->data[$i] < $min) { |
||
| 1461 | $ar->data[$i] = $min; |
||
| 1462 | continue; |
||
| 1463 | } |
||
| 1464 | $ar->data[$i] = $this->data[$i]; |
||
| 1465 | } |
||
| 1466 | return $ar; |
||
| 1467 | } |
||
| 1468 | |||
| 1469 | /** |
||
| 1470 | * Clip the matrix to be upper bounded by a given maximum. |
||
| 1471 | * |
||
| 1472 | * @param float $max |
||
| 1473 | * @return matrix |
||
| 1474 | */ |
||
| 1475 | public function clipUpper(float $max): matrix { |
||
| 1476 | $ar = self::factory($this->row, $this->col, $this->dtype); |
||
| 1477 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 1478 | if ($this->data[$i] > $max) { |
||
| 1479 | $ar->data[$i] = $max; |
||
| 1480 | continue; |
||
| 1481 | } |
||
| 1482 | $ar->data[$i] = $this->data[$i]; |
||
| 1483 | } |
||
| 1484 | return $ar; |
||
| 1485 | } |
||
| 1486 | |||
| 1487 | /** |
||
| 1488 | * Square of matrix |
||
| 1489 | * @return matrix |
||
| 1490 | */ |
||
| 1491 | public function square(): matrix { |
||
| 1492 | return $this->multiplyMatrix($this); |
||
| 1493 | } |
||
| 1494 | |||
| 1495 | /** |
||
| 1496 | * |
||
| 1497 | * @param int|float|matrix|vector $d |
||
| 1498 | * @return matrix |
||
| 1499 | */ |
||
| 1500 | public function equal(int|float|matrix|vector $d): matrix { |
||
| 1501 | if ($d instanceof self) { |
||
| 1502 | return $this->equalMatrix($d); |
||
| 1503 | } elseif ($d instanceof vector) { |
||
| 1504 | return $this->equalVector($d); |
||
| 1505 | } else { |
||
| 1506 | return $this->equalScalar($d); |
||
| 1507 | } |
||
| 1508 | } |
||
| 1509 | |||
| 1510 | protected function equalMatrix(matrix $m): matrix { |
||
| 1511 | if ($this->checkShape($this, $m) && $this->checkDtype($this, $m)) { |
||
| 1512 | $ar = self::factory($this->row, $this->col, $this->dtype); |
||
| 1513 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 1514 | $ar->data[$i] = $this->data[$i] == $m->data[$i] ? 1 : 0; |
||
| 1515 | } |
||
| 1516 | return $ar; |
||
| 1517 | } |
||
| 1518 | } |
||
| 1519 | |||
| 1520 | protected function equalVector(vector $v): matrix { |
||
| 1521 | if ($this->checkDimensions($v, $this) && $this->checkDtype($this, $v)) { |
||
| 1522 | $ar = self::factory($this->row, $this->col, $this->dtype); |
||
| 1523 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 1524 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 1525 | $ar->data[$i * $this->col + $j] = $this->data[$i * $this->col + $j] == $v->data[$j] ? 1 : 0; |
||
| 1526 | } |
||
| 1527 | } |
||
| 1528 | return $ar; |
||
| 1529 | } |
||
| 1530 | } |
||
| 1531 | |||
| 1532 | protected function equalScalar(int|float $s): matrix { |
||
| 1533 | $ar = self::factory($this->row, $this->col, $this->dtype); |
||
| 1534 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 1535 | $ar->data[$i] = $this->data[$i] == $s ? 1 : 0; |
||
| 1536 | } |
||
| 1537 | return $ar; |
||
| 1538 | } |
||
| 1539 | |||
| 1540 | /** |
||
| 1541 | * |
||
| 1542 | * @param int|float|matrix|vector $d |
||
| 1543 | * @return matrix |
||
| 1544 | */ |
||
| 1545 | public function greater(int|float|matrix|vector $d): matrix { |
||
| 1552 | } |
||
| 1553 | } |
||
| 1554 | |||
| 1555 | protected function greaterMatrix(matrix $m): matrix { |
||
| 1556 | if ($this->checkShape($this, $m) && $this->checkDtype($this,$m)) { |
||
| 1557 | $ar = self::factory($this->row, $this->col, $this->dtype); |
||
| 1558 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 1559 | $ar->data[$i] = $this->data[$i] > $m->data[$i] ? 1 : 0; |
||
| 1560 | } |
||
| 1561 | return $ar; |
||
| 1562 | } |
||
| 1563 | } |
||
| 1564 | |||
| 1565 | protected function greaterVector(vector $v): matrix { |
||
| 1566 | if ($this->checkDimensions($v, $this) && $this->checkDtype($this,$v)) { |
||
| 1567 | $ar = self::factory($this->row, $this->col, $this->dtype); |
||
| 1568 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 1569 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 1570 | $ar->data[$i * $this->col + $j] = $this->data[$i * $this->col + $j] > $v->data[$j] ? 1 : 0; |
||
| 1571 | } |
||
| 1572 | } |
||
| 1573 | return $ar; |
||
| 1574 | } |
||
| 1575 | } |
||
| 1576 | |||
| 1577 | protected function greaterScalar(int|float $s): matrix { |
||
| 1578 | $ar = self::factory($this->row, $this->col, $this->dtype); |
||
| 1579 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 1580 | $ar->data[$i] = $this->data[$i] > $s ? 1 : 0; |
||
| 1581 | } |
||
| 1582 | return $ar; |
||
| 1583 | } |
||
| 1584 | |||
| 1585 | /** |
||
| 1586 | * |
||
| 1587 | * @param int|float|matrix $m |
||
| 1588 | * @return matrix |
||
| 1589 | */ |
||
| 1590 | public function less(int|float|matrix $m): matrix { |
||
| 1604 | } |
||
| 1605 | } |
||
| 1606 | |||
| 1607 | /** |
||
| 1608 | * Is the matrix symmetric i.e. is it equal to its own transpose? |
||
| 1609 | * |
||
| 1610 | * @return bool |
||
| 1611 | */ |
||
| 1612 | public function isSymmetric(): bool { |
||
| 1613 | if (!$this->isSquare()) { |
||
| 1614 | return false; |
||
| 1615 | } |
||
| 1616 | $ar = $this->transpose(); |
||
| 1617 | for ($i = 0; $i < $ar->ndim; ++$i) { |
||
| 1618 | if ($ar->data[$i] != $this->data[$i]) { |
||
| 1619 | unset($ar); |
||
| 1620 | return false; |
||
| 1621 | } |
||
| 1622 | } |
||
| 1623 | unset($ar); |
||
| 1624 | return true; |
||
| 1625 | } |
||
| 1626 | |||
| 1627 | /** |
||
| 1628 | * print the matrix in consol |
||
| 1629 | */ |
||
| 1630 | public function printMatrix() { |
||
| 1631 | echo __CLASS__ . PHP_EOL; |
||
| 1632 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 1633 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 1634 | printf('%lf ', $this->data[$i * $this->col + $j]); |
||
| 1635 | } |
||
| 1636 | echo PHP_EOL; |
||
| 1637 | } |
||
| 1638 | } |
||
| 1639 | |||
| 1640 | public function __toString() { |
||
| 1642 | } |
||
| 1643 | |||
| 1644 | protected function flattenArray(array $ar) { |
||
| 1645 | if (is_array($ar) && is_array($ar[0])) { |
||
| 1646 | $a = []; |
||
| 1647 | foreach ($ar as $y => $value) { |
||
| 1648 | foreach ($value as $k => $v) { |
||
| 1649 | $a[] = $v; |
||
| 1650 | } |
||
| 1651 | } |
||
| 1652 | return $a; |
||
| 1653 | } |
||
| 1654 | } |
||
| 1655 | |||
| 1656 | /** |
||
| 1657 | * |
||
| 1658 | * @param int $row |
||
| 1659 | * @param int $col |
||
| 1660 | * @param int $dtype |
||
| 1661 | * @return $this |
||
| 1662 | */ |
||
| 1663 | protected function __construct(public int $row, public int $col, int $dtype = self::Float) { |
||
| 1664 | if ($this->row < 1 || $this->col < 1) { |
||
| 1665 | self::_invalidArgument('* To create Numphp/Matrix row & col must be greater than 0!, Op Failed! * '); |
||
| 1666 | } |
||
| 1669 | } |
||
| 1670 | } |
||
| 1671 |
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