ghostjat /
Np
| Total Complexity | 307 |
| Total Lines | 1402 |
| Duplicated Lines | 0 % |
| Changes | 2 | ||
| 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 | use ops,linAlg; |
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| 30 | /** |
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| 31 | * create empty 2d matrix for given data type |
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| 32 | * @param int $row num of rows |
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| 33 | * @param int $col num of cols |
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| 34 | * @param int $dtype matrix data type float|double |
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| 35 | * @return \Np\matrix |
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| 36 | */ |
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| 37 | public static function factory(int $row, int $col, int $dtype = self::FLOAT): matrix { |
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| 38 | return new self($row, $col, $dtype); |
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| 39 | } |
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| 40 | |||
| 41 | /** |
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| 42 | * create 2d matrix using php array |
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| 43 | * @param array $data |
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| 44 | * @param int $dtype matrix data type float|double |
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| 45 | * @return \Np\matrix |
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| 46 | */ |
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| 47 | public static function ar(array $data, int $dtype = self::FLOAT): matrix { |
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| 48 | if (is_array($data) && is_array($data[0])) { |
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| 49 | $ar = self::factory(count($data), count($data[0]), $dtype); |
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| 50 | $ar->setData($data); |
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| 51 | unset($data); |
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| 52 | return $ar; |
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| 53 | } else { |
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| 54 | self::_err('given array is not rank-2 or given is not an array'); |
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| 55 | } |
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| 56 | } |
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| 57 | |||
| 58 | /** |
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| 59 | * create one like 2d matrix |
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| 60 | * @param int $row |
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| 61 | * @param int $col |
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| 62 | * @return \Np\matrix |
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| 63 | */ |
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| 64 | public static function ones(int $row, int $col, int $dtype = self::FLOAT): matrix { |
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| 65 | $ar = self::factory($row, $col, $dtype); |
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| 66 | for ($i = 0; $i < $ar->ndim; ++$i) { |
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| 67 | $ar->data[$i] = 1; |
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| 68 | } |
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| 69 | return $ar; |
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| 70 | } |
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| 71 | |||
| 72 | /** |
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| 73 | * Create Matrix with random values |
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| 74 | * @param int $row |
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| 75 | * @param int $col |
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| 76 | * @param int $dtype Float|Double |
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| 77 | * @return \Np\matrix |
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| 78 | */ |
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| 79 | public static function randn(int $row, int $col, int $dtype = self::FLOAT): matrix { |
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| 80 | $ar = self::factory($row, $col, $dtype); |
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| 81 | $max = getrandmax(); |
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| 82 | for ($i = 0; $i < $ar->ndim; ++$i) { |
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| 83 | $ar->data[$i] = rand() / $max; |
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| 84 | } |
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| 85 | return $ar; |
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| 86 | } |
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| 87 | |||
| 88 | /** |
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| 89 | * Return 2d matrix with uniform values |
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| 90 | * @param int $row |
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| 91 | * @param int $col |
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| 92 | * @param int $dtype |
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| 93 | * @return \Np\matrix |
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| 94 | */ |
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| 95 | public static function uniform(int $row, int $col, int $dtype = self::FLOAT): matrix { |
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| 102 | } |
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| 103 | |||
| 104 | /** |
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| 105 | * Return a zero matrix with the given dimensions. |
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| 106 | * @param int $row |
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| 107 | * @param int $col |
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| 108 | * @param int $dtype |
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| 109 | * @return \Np\matrix |
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| 110 | */ |
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| 111 | public static function zeros(int $row, int $col, int $dtype = self::FLOAT): matrix { |
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| 112 | $ar = self::factory($row, $col, $dtype); |
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| 113 | for ($i = 0; $i < $ar->ndim; ++$i) { |
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| 114 | $ar->data[$i] = 0.0; |
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| 115 | } |
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| 116 | return $ar; |
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| 117 | } |
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| 118 | |||
| 119 | /** |
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| 120 | * create a null like 2d matrix |
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| 121 | * @param int $row |
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| 122 | * @param int $col |
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| 123 | * @return \Np\matrix |
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| 124 | */ |
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| 125 | public static function null(int $row, int $col, int $dtype = self::FLOAT): matrix { |
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| 126 | $ar = self::factory($row, $col, $dtype); |
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| 127 | for ($i = 0; $i < $ar->ndim; ++$i) { |
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| 128 | $ar->data[$i] = null; |
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| 129 | } |
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| 130 | return $ar; |
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| 131 | } |
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| 132 | |||
| 133 | /** |
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| 134 | * create a 2d matrix with given scalar value |
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| 135 | * @param int $row |
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| 136 | * @param int $col |
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| 137 | * @param int|float|double $val |
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| 138 | * @return \Np\matrix |
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| 139 | */ |
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| 140 | public static function full(int $row, int $col, $val, int $dtype = self::FLOAT): matrix { |
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| 141 | $ar = self::factory($row, $col, $dtype); |
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| 142 | for ($i = 0; $i < $ar->ndim; ++$i) { |
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| 143 | $ar->data[$i] = $val; |
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| 144 | } |
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| 145 | return $ar; |
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| 146 | } |
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| 147 | |||
| 148 | /** |
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| 149 | * create a diagonal 2d matrix with given 1d array; |
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| 150 | * @param array $elements |
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| 151 | * @return \Np\matrix |
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| 152 | */ |
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| 153 | public static function diagonal(array $elements, int $dtype = self::FLOAT): matrix { |
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| 160 | } |
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| 161 | |||
| 162 | /** |
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| 163 | * Generate a m x n matrix with elements from a Poisson distribution. |
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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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| 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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| 245 | } |
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| 246 | |||
| 247 | /** |
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| 248 | * 2D convolution between a matrix ma and kernel kb, with a given stride. |
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| 249 | * @param \Np\matrix $m |
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| 250 | * @param int $stride |
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| 251 | * @return matrix |
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| 252 | */ |
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| 253 | public function convolve(matrix $m, int $stride = 1): matrix { |
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| 254 | return convolve::conv2D($this, $m, $stride); |
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| 255 | } |
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| 256 | |||
| 257 | /** |
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| 258 | * Calculate the determinant of the matrix. |
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| 259 | * @return float |
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| 260 | */ |
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| 261 | public function det(): float { |
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| 262 | if (!$this->isSquare()) { |
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| 263 | self::_err('determinant is undefined for a non square matrix'); |
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| 264 | } |
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| 265 | $lu = $this->lu(); |
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| 266 | $nSwaps = $lu->p()->diagonalAsVector()->subtract($lu->p()->diagonalAsVector()->sum())->col - 1; |
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| 267 | $detP = (-1) ** $nSwaps; |
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| 268 | $detL = $lu->l()->diagonalAsVector()->product(); |
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| 269 | $detU = $lu->u()->diagonalAsVector()->product(); |
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| 270 | unset($lu); |
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| 271 | return ($detP * $detL * $detU); |
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| 272 | } |
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| 273 | |||
| 274 | /** |
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| 275 | * Return the trace of the matrix i.e the sum of all diagonal elements of a square matrix. |
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| 276 | * @return float |
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| 277 | */ |
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| 278 | public function trace(): float { |
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| 279 | if (!$this->isSquare()) { |
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| 280 | self::_err('Error::matrix is not a squared can not Trace!'); |
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| 281 | } |
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| 282 | $trace = 0.0; |
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| 283 | for ($i = 0; $i < $this->row; ++$i) { |
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| 284 | for ($j = 0; $j < $this->col; ++$j) { |
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| 285 | if ($i == $j) { |
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| 286 | $trace += $this->data[$i * $this->col + $i]; |
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| 287 | } |
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| 288 | } |
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| 289 | } |
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| 290 | return $trace; |
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| 291 | } |
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| 292 | |||
| 293 | /** |
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| 294 | * dignoalInterChange |
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| 295 | */ |
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| 296 | public function dignoalInterChange() { |
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| 297 | for ($i = 0; $i < $this->row; ++$i) { |
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| 298 | for ($j = 0; $j < $this->col; ++$j) { |
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| 299 | $tmp = $this->data[$i * $this->col - $j]; |
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| 300 | $this->data[$i * $this->col - $j] = $tmp; |
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| 301 | } |
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| 302 | } |
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| 303 | } |
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| 304 | |||
| 305 | //---------------Arthmetic Opreations----------------------------------- |
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| 306 | |||
| 307 | /** |
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| 308 | * multiply this matrix with another matrix|scalar element-wise |
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| 309 | * Matrix Scalar\Matrix multiplication |
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| 310 | * @param int|float|matrix|vector $m |
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| 311 | * @return matrix|vector |
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| 312 | */ |
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| 313 | public function multiply(int|float|matrix|vector $m): matrix|vector { |
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| 314 | if ($m instanceof self) { |
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| 315 | return $this->multiplyMatrix($m); |
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| 316 | } else if ($m instanceof vector) { |
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| 317 | return $this->multiplyVector($m); |
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| 318 | } else { |
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| 319 | return $this->scale($m); |
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| 320 | } |
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| 321 | } |
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| 322 | |||
| 323 | /** |
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| 324 | * |
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| 325 | * @param \Np\vector $v |
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| 326 | * @return matrix |
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| 327 | */ |
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| 328 | protected function multiplyVector(vector $v): matrix { |
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| 329 | if ($this->checkDimensions($v, $this) && $this->checkDtype($this, $v)) { |
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| 330 | $ar = matrix::factory($this->row, $this->col, $this->dtype); |
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| 331 | for ($i = 0; $i < $this->row; ++$i) { |
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| 332 | for ($j = 0; $j < $this->col; ++$j) { |
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| 333 | $ar->data[$i * $this->col + $j] = $v->data[$j] * $this->data[$i * $this->col + $j]; |
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| 334 | } |
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| 335 | } |
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| 336 | return $ar; |
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| 337 | } |
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| 338 | } |
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| 339 | |||
| 340 | /** |
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| 341 | * |
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| 342 | * @param \Np\matrix $m |
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| 343 | * @return matrix |
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| 344 | */ |
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| 345 | protected function multiplyMatrix(matrix $m): matrix { |
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| 346 | if ($this->checkDtype($this, $m) && $this->checkShape($this, $m)) { |
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| 347 | $ar = self::factory($this->row, $this->col, $this->dtype); |
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| 348 | for ($i = 0; $i < $this->row; ++$i) { |
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| 349 | for ($j = 0; $j < $this->col; ++$j) { |
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| 350 | $ar->data[$i * $this->col + $j] = $this->data[$i * $this->col + $j] * $m->data[$i * $this->col + $j]; |
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| 351 | } |
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| 352 | } |
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| 353 | return $ar; |
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| 354 | } |
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| 355 | } |
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| 356 | |||
| 357 | /** |
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| 358 | * Sum of Rows of matrix |
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| 359 | * @return vector |
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| 360 | */ |
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| 361 | public function sumRows(): vector { |
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| 362 | $vr = vector::factory($this->row, $this->dtype); |
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| 363 | for ($i = 0; $i < $this->row; ++$i) { |
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| 364 | $sum = 0.0; |
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| 365 | for ($j = 0; $j < $this->col; ++$j) { |
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| 366 | $sum += $this->data[$i * $this->col + $j]; |
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| 367 | } |
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| 368 | $vr->data[$i] = $sum; |
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| 369 | } |
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| 370 | return $vr; |
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| 371 | } |
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| 372 | |||
| 373 | /** |
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| 374 | * Sum of two matrix, vector or a scalar to current matrix |
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| 375 | * |
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| 376 | * @param int|float|matrix|vector $m |
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| 377 | * @return matrix |
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| 378 | */ |
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| 379 | public function sum(int|float|matrix|vector $m): matrix { |
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| 380 | if ($m instanceof self) { |
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| 381 | return $this->sumMatrix($m); |
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| 382 | } elseif ($m instanceof vector) { |
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| 383 | return $this->sumVector($m); |
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| 384 | } else { |
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| 385 | return $this->sumScalar($m); |
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| 386 | } |
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| 387 | } |
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| 388 | |||
| 389 | protected function sumScalar(int|float $s): matrix { |
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| 390 | $ar = self::factory($this->row, $this->col, $this->dtype); |
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| 391 | for ($i = 0; $i < $this->ndim; ++$i) { |
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| 392 | $ar->data[$i] = $this->data[$i] + $s; |
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| 393 | } |
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| 394 | return $ar; |
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| 395 | } |
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| 396 | |||
| 397 | protected function sumMatrix(matrix $m): matrix { |
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| 398 | if ($this->checkShape($this, $m) && $this->checkDtype($this, $m)) { |
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| 399 | $ar = self::factory($this->row, $this->col, $this->dtype); |
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| 400 | for ($i = 0; $i < $this->ndim; ++$i) { |
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| 401 | $ar->data[$i] = $this->data[$i] + $m->data[$i]; |
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| 402 | } |
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| 403 | return $ar; |
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| 404 | } |
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| 405 | } |
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| 406 | |||
| 407 | protected function sumVector(vector $v): matrix { |
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| 408 | if ($this->checkDimensions($v, $this) && $this->checkDtype($this, $v)) { |
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| 409 | $ar = self::factory($this->row, $this->col, $this->dtype); |
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| 410 | for ($i = 0; $i < $this->row; ++$i) { |
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| 411 | for ($j = 0; $j < $this->col; ++$j) { |
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| 412 | $ar->data[$i * $this->col + $j] = $v->data[$j] + $this->data[$i * $this->col + $j]; |
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| 413 | } |
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| 414 | } |
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| 415 | return $ar; |
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| 416 | } |
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| 417 | } |
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| 418 | |||
| 419 | /** |
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| 420 | * subtract another matrix, vector or a scalar to this matrix |
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| 421 | * @param int|float|matrix|vector $d matrix|$scalar to subtract this matrix |
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| 422 | * @return \Np\matrix |
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| 423 | */ |
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| 424 | public function subtract(int|float|matrix|vector $d): matrix { |
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| 425 | if ($d instanceof self) { |
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| 426 | return $this->subtractMatrix($d); |
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| 427 | } elseif ($d instanceof vector) { |
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| 428 | return $this->subtractVector($d); |
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| 429 | } else { |
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| 430 | return $this->subtractScalar($d); |
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| 431 | } |
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| 432 | } |
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| 433 | |||
| 434 | protected function subtractScalar(int|float $s): matrix { |
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| 435 | $ar = self::factory($this->row, $this->col, $this->dtype); |
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| 436 | for ($i = 0; $i < $this->ndim; ++$i) { |
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| 437 | $ar->data[$i] = $this->data[$i] - $s; |
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| 438 | } |
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| 439 | return $ar; |
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| 440 | } |
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| 441 | |||
| 442 | /** |
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| 443 | * |
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| 444 | * @param matrix $m |
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| 445 | * @return matrix |
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| 446 | */ |
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| 447 | protected function subtractMatrix(matrix $m): matrix { |
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| 448 | if ($this->checkShape($this, $m) && $this->checkDtype($this, $m)) { |
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| 449 | $ar = self::factory($this->row, $this->col, $this->dtype); |
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| 450 | for ($i = 0; $i < $this->ndim; ++$i) { |
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| 451 | $ar->data[$i] = $this->data[$i] - $m->data[$i]; |
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| 452 | } |
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| 453 | return $ar; |
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| 454 | } |
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| 455 | } |
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| 456 | |||
| 457 | /** |
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| 458 | * |
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| 459 | * @param vector $v |
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| 460 | * @return matrix |
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| 461 | */ |
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| 462 | protected function subtractVector(vector $v): matrix { |
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| 463 | if ($this->checkDimensions($v, $this) && $this->checkDtype($this,$v)) { |
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| 464 | $ar = self::factory($this->row, $this->col, $this->dtype); |
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| 465 | for ($i = 0; $i < $this->row; ++$i) { |
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| 466 | for ($j = 0; $j < $this->col; ++$j) { |
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| 467 | $ar->data[$i * $this->col + $j] = $this->data[$i * $this->col + $j] - $v->data[$j]; |
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| 468 | } |
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| 469 | } |
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| 470 | return $ar; |
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| 471 | } |
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| 472 | } |
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| 473 | |||
| 474 | /** |
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| 475 | * |
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| 476 | * @param vector $v |
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| 477 | * @return matrix |
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| 478 | */ |
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| 479 | public function subtractColumnVector(vector $v): matrix { |
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| 480 | if ($this->checkDimensions($v, $this) && $this->checkDtype($this, $v)) { |
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| 481 | $ar = self::factory($this->row, $this->col, $this->dtype); |
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| 482 | for ($j = 0; $j < $this->col; ++$j) { |
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| 483 | for ($i = 0; $i < $this->row; ++$i) { |
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| 484 | $ar->data[$i * $this->col + $j] = $this->data[$i * $this->col + $j] - $v->data[$i]; |
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| 485 | } |
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| 486 | } |
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| 487 | return $ar; |
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| 488 | } |
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| 489 | } |
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| 490 | |||
| 491 | /** |
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| 492 | * Return the division of two elements, element-wise. |
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| 493 | * @param int|float|matrix $d |
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| 494 | * @return matrix |
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| 495 | */ |
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| 496 | public function divide(int|float|matrix|vector $d): matrix { |
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| 497 | if ($d instanceof self) { |
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| 498 | return $this->divideMatrix($d); |
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| 499 | } elseif ($d instanceof vector) { |
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| 500 | return $this->divideVector($d); |
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| 501 | } else { |
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| 502 | return $this->divideScalar($d); |
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| 503 | } |
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| 504 | } |
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| 505 | |||
| 506 | protected function divideMatrix(matrix $m): matrix { |
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| 507 | if ($this->checkShape($this, $m) && $this->checkDtype($this, $m)) { |
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| 508 | $ar = self::factory($this->row, $this->col, $this->dtype); |
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| 509 | for ($i = 0; $i < $this->ndim; ++$i) { |
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| 510 | $ar->data[$i] = $this->data[$i] / $m->data[$i]; |
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| 511 | } |
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| 512 | return $ar; |
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| 513 | } |
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| 514 | } |
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| 515 | |||
| 516 | protected function divideVector(vector $v): matrix { |
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| 517 | if ($this->checkDimensions($v, $this) && $this->checkDtype($this, $v)) { |
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| 518 | $ar = self::factory($this->row, $this->col, $this->dtype); |
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| 519 | for ($i = 0; $i < $this->row; ++$i) { |
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| 520 | for ($j = 0; $j < $this->col; ++$j) { |
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| 521 | $ar->data[$i * $this->col + $j] = $this->data[$i * $this->col + $j] / $v->data[$j]; |
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| 522 | } |
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| 523 | } |
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| 524 | return $ar; |
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| 525 | } |
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| 526 | } |
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| 527 | |||
| 528 | protected function divideScalar(int|float $s): matrix { |
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| 529 | $ar = self::factory($this->row, $this->col, $this->dtype); |
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| 530 | for ($i = 0; $i < $this->ndim; ++$i) { |
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| 531 | $ar->data[$i] = $this->data[$i] / $s; |
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| 532 | } |
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| 533 | return $ar; |
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| 534 | } |
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| 535 | |||
| 536 | /** |
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| 537 | * |
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| 538 | * Raise this matrix to the power of the element-wise entry in another matrix. |
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| 539 | * |
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| 540 | * @param int|float|matrix $m |
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| 541 | * @return matrix |
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| 542 | */ |
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| 543 | public function pow(int|float|matrix|vector $d): matrix { |
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| 544 | if ($d instanceof self) { |
||
| 545 | return $this->powMatrix($d); |
||
| 546 | } else if ($d instanceof vector) { |
||
| 547 | return $this->powVector($d); |
||
| 548 | } else { |
||
| 549 | return $this->powScalar($d); |
||
| 550 | } |
||
| 551 | } |
||
| 552 | |||
| 553 | protected function powMatrix(matrix $m): matrix { |
||
| 554 | if ($this->checkShape($this, $m) && $this->checkDtype($this, $m)) { |
||
| 555 | $ar = self::factory($this->row, $this->col, $this->dtype); |
||
| 556 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 557 | $ar->data[$i] = $this->data[$i] ** $m->data[$i]; |
||
| 558 | } |
||
| 559 | return $ar; |
||
| 560 | } |
||
| 561 | } |
||
| 562 | |||
| 563 | protected function powVector(vector $v): matrix { |
||
| 564 | if ($this->checkDimensions($v, $this) && $this->checkDtype($this, $v)) { |
||
| 565 | $ar = self::factory($this->row, $this->col, $this->dtype); |
||
| 566 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 567 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 568 | $ar->data[$i * $this->col + $j] = $this->data[$i * $this->col + $j] ** $v->data[$j]; |
||
| 569 | } |
||
| 570 | } |
||
| 571 | return $ar; |
||
| 572 | } |
||
| 573 | } |
||
| 574 | |||
| 575 | protected function powScalar(int|float $s): matrix { |
||
| 576 | $ar = $this->copy(); |
||
| 577 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 578 | $ar->data[$i] **= $s; |
||
| 579 | } |
||
| 580 | return $ar; |
||
| 581 | } |
||
| 582 | |||
| 583 | /** |
||
| 584 | * Calculate the modulus i.e remainder of division between this matrix and another matrix. |
||
| 585 | * @param int|float|matrix|vector $d |
||
| 586 | * @return matrix |
||
| 587 | */ |
||
| 588 | public function mod(int|float|matrix|vector $d): matrix { |
||
| 589 | if ($d instanceof self) { |
||
| 590 | $this->modMatrix($d); |
||
| 591 | } else if ($d instanceof vector) { |
||
| 592 | $this->modVector($d); |
||
| 593 | } else { |
||
| 594 | $this->modScalar($d); |
||
| 595 | } |
||
| 596 | } |
||
| 597 | |||
| 598 | protected function modMatrix(matrix $m): matrix { |
||
| 599 | if ($this->checkShape($this, $m) && $this->checkDtype($this, $m)) { |
||
| 600 | $ar = self::factory($this->row, $this->col, $this->dtype); |
||
| 601 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 602 | $ar->data[$i] = $this->data[$i] % $m->data[$i]; |
||
| 603 | } |
||
| 604 | return $ar; |
||
| 605 | } |
||
| 606 | } |
||
| 607 | |||
| 608 | protected function modVector(vector $v): matrix { |
||
| 609 | if ($this->checkDimensions($v, $this) && $this->checkDtype($this, $v)) { |
||
| 610 | $ar = self::factory($this->row, $this->col, $this->dtype); |
||
| 611 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 612 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 613 | $ar->data[$i * $this->col + $j] = $this->data[$i * $this->col + $j] % $v->data[$j]; |
||
| 614 | } |
||
| 615 | } |
||
| 616 | return $ar; |
||
| 617 | } |
||
| 618 | } |
||
| 619 | |||
| 620 | protected function modScalar(int|float $s): matrix { |
||
| 621 | $ar = $this->copy(); |
||
| 622 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 623 | $ar->data[$i] %= $s; |
||
| 624 | } |
||
| 625 | return $ar; |
||
| 626 | } |
||
| 627 | |||
| 628 | /** |
||
| 629 | * Return the element-wise reciprocal of the matrix. |
||
| 630 | * |
||
| 631 | * @return matrix |
||
| 632 | */ |
||
| 633 | public function reciprocal(): matrix { |
||
| 634 | return self::ones($this->row, $this->col, $this->dtype)->divideMatrix($this); |
||
| 635 | } |
||
| 636 | |||
| 637 | /** |
||
| 638 | * |
||
| 639 | * @param int|float $d |
||
| 640 | * @return bool |
||
| 641 | */ |
||
| 642 | public static function is_zero($d): bool { |
||
| 643 | if (abs($d) < self::EPSILON) { |
||
| 644 | return true; |
||
| 645 | } |
||
| 646 | return false; |
||
| 647 | } |
||
| 648 | |||
| 649 | /** |
||
| 650 | * is row zero |
||
| 651 | * @param int $row |
||
| 652 | * @return bool |
||
| 653 | */ |
||
| 654 | public function is_rowZero(int $row): bool { |
||
| 655 | for ($i = 0; $i < $this->col; ++$i) { |
||
| 656 | if ($this->data[$row * $this->col + $i] != 0) { |
||
| 657 | return false; |
||
| 658 | } |
||
| 659 | } |
||
| 660 | return true; |
||
| 661 | } |
||
| 662 | |||
| 663 | /** |
||
| 664 | * |
||
| 665 | * @return bool |
||
| 666 | */ |
||
| 667 | public function has_ZeroRow(): bool { |
||
| 668 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 669 | if ($this->is_rowZero($i)) { |
||
| 670 | return true; |
||
| 671 | } |
||
| 672 | } |
||
| 673 | return false; |
||
| 674 | } |
||
| 675 | |||
| 676 | /** |
||
| 677 | * Transpose the matrix i.e row become cols and cols become rows. |
||
| 678 | * @return \Np\matrix |
||
| 679 | */ |
||
| 680 | public function transpose(): matrix { |
||
| 681 | $ar = self::factory($this->col, $this->row, $this->dtype); |
||
| 682 | for ($i = 0; $i < $ar->row; ++$i) { |
||
| 683 | for ($j = 0; $j < $ar->col; ++$j) { |
||
| 684 | $ar->data[$i * $ar->col + $j] = $this->data[$j * $ar->col + $i]; |
||
| 685 | } |
||
| 686 | } |
||
| 687 | return $ar; |
||
| 688 | } |
||
| 689 | |||
| 690 | /** |
||
| 691 | * swap specific values in matrix |
||
| 692 | * @param int $i1 |
||
| 693 | * @param int $i2 |
||
| 694 | */ |
||
| 695 | public function swapValue(int $i1, int $i2) { |
||
| 696 | $tmp = $this->data[$i1]; |
||
| 697 | $this->data[$i1] = $this->data[$i2]; |
||
| 698 | $this->data[$i2] = $tmp; |
||
| 699 | } |
||
| 700 | |||
| 701 | /** |
||
| 702 | * swap specific rows in matrix |
||
| 703 | * @param int $r1 |
||
| 704 | * @param int $r2 |
||
| 705 | */ |
||
| 706 | public function swapRows(int $r1, int $r2) { |
||
| 707 | for ($i = 0; $i < $this->col; ++$i) { |
||
| 708 | $tmp = $this->data[$r1 * $this->col + $i]; |
||
| 709 | $this->data[$r1 * $this->col + $i] = $this->data[$r2 * $this->col + $i]; |
||
| 710 | $this->data[$r2 * $this->col + $i] = $tmp; |
||
| 711 | } |
||
| 712 | } |
||
| 713 | |||
| 714 | /** |
||
| 715 | * swap specific cols in matrix |
||
| 716 | * @param int $c1 |
||
| 717 | * @param int $c2 |
||
| 718 | */ |
||
| 719 | public function swapCols(int $c1, int $c2) { |
||
| 720 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 721 | $tmp = $this->data[$i * $this->row + $c1]; |
||
| 722 | $this->data[$i * $this->row + $c1] = $this->data[$i * $this->row + $c2]; |
||
| 723 | $this->data[$i * $this->row + $c2] = $tmp; |
||
| 724 | } |
||
| 725 | } |
||
| 726 | |||
| 727 | /** |
||
| 728 | * |
||
| 729 | * @param int|float $scalar |
||
| 730 | * @return matrix |
||
| 731 | */ |
||
| 732 | public function scale(int|float $scalar): matrix { |
||
| 733 | if ($scalar == 0) { |
||
| 734 | return self::zeros($this->row, $this->col, $this->dtype); |
||
| 735 | } |
||
| 736 | |||
| 737 | $ar = $this->copy(); |
||
| 738 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 739 | $ar->data[$i] *= $scalar; |
||
| 740 | } |
||
| 741 | |||
| 742 | return $ar; |
||
| 743 | } |
||
| 744 | |||
| 745 | /** |
||
| 746 | * scale all the elements of a row |
||
| 747 | * @param int $row |
||
| 748 | * @param int|float $c |
||
| 749 | */ |
||
| 750 | public function scaleRow(int $row, int|float $c) { |
||
| 751 | for ($i = 0; $i < $this->col; ++$i) { |
||
| 752 | $this->data[$row * $this->col + $i] *= $c; |
||
| 753 | } |
||
| 754 | } |
||
| 755 | |||
| 756 | /** |
||
| 757 | * scale all the elements of |
||
| 758 | * @param int $col |
||
| 759 | * @param int|float $c |
||
| 760 | */ |
||
| 761 | public function scaleCol(int $col, int|float $c) { |
||
| 762 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 763 | $this->data[$i * $this->col + $col] *= $c; |
||
| 764 | } |
||
| 765 | } |
||
| 766 | |||
| 767 | /** |
||
| 768 | * Scale digonally |
||
| 769 | * @param int|float $c |
||
| 770 | * @param bool $lDig |
||
| 771 | */ |
||
| 772 | public function scaleDigonalCol(int|float $c, bool $lDig = true) { |
||
| 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 $c) { |
||
| 792 | for ($i = 0; $i < $this->col; ++$i) { |
||
| 793 | $this->data[$r2 * $this->col + $i] += $this->data[$r1 * $this->col + $i] * $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 != $m->row && !$this->checkDtype($this, $m)) { |
||
| 805 | self::_err('Error::Invalid size! or DataType!'); |
||
| 806 | } |
||
| 807 | $col = $this->col + $m->col; |
||
| 808 | $ar = self::factory($this->row, $col, $this->dtype); |
||
| 809 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 810 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 811 | $ar->data[$i * $col + $j] = $this->data[$i * $this->col + $j]; |
||
| 812 | } |
||
| 813 | for ($j = 0; $j < $m->col; ++$j) { |
||
| 814 | $ar->data[$i * $col + ($this->col + $j)] = $m->data[$i * $m->col + $j]; |
||
| 815 | } |
||
| 816 | } |
||
| 817 | return $ar; |
||
| 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 != $m->row && !$this->checkDtype($this,$m)) { |
||
| 827 | self::_err('Error::Invalid size! or DataType!'); |
||
| 828 | } |
||
| 829 | $col = $this->col + $m->col; |
||
| 830 | $ar = self::factory($this->row, $col, $this->dtype); |
||
| 831 | for ($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 !== $m->col && !$this->checkDtype($this, $m)) { |
||
| 871 | self::_err('Error::Invalid size! or DataType!'); |
||
| 872 | } |
||
| 873 | $row = $this->row + $m->row; |
||
| 874 | $ar = self::factory($row, $this->col, $this->dtype); |
||
| 875 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 876 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 877 | $ar->data[$i * $this->col + $j] = $this->data[$i * $this->col + $j]; |
||
| 878 | } |
||
| 879 | for ($j = 0; $j < $m->col; ++$j) { |
||
| 880 | $ar->data[($i + $m->row) * $m->col + $j] = $m->data[$i * $m->col + $j]; |
||
| 881 | } |
||
| 882 | } |
||
| 883 | return $ar; |
||
| 884 | } |
||
| 885 | |||
| 886 | /** |
||
| 887 | * Calculate the row echelon form of the matrix. |
||
| 888 | * Return the reduced matrix. |
||
| 889 | * |
||
| 890 | * @return matrix|null |
||
| 891 | */ |
||
| 892 | public function ref(): matrix|null { |
||
| 893 | return ref::factory($this); |
||
| 894 | } |
||
| 895 | |||
| 896 | /** |
||
| 897 | * Return the lower triangular matrix of the Cholesky decomposition. |
||
| 898 | * |
||
| 899 | * @return matrix|null |
||
| 900 | */ |
||
| 901 | public function cholesky(): matrix|null { |
||
| 902 | return cholesky::factory($this); |
||
| 903 | } |
||
| 904 | |||
| 905 | /** |
||
| 906 | * FIXME-------------- |
||
| 907 | * RREF |
||
| 908 | * The reduced row echelon form (RREF) of a matrix. |
||
| 909 | * @return \Np\matrix |
||
| 910 | */ |
||
| 911 | public function rref(): matrix { |
||
| 912 | return rref::factory($this); |
||
| 913 | } |
||
| 914 | |||
| 915 | /** |
||
| 916 | * |
||
| 917 | * @param int $cols |
||
| 918 | * @return \Np\matrix |
||
| 919 | */ |
||
| 920 | public function diminish_left(int $cols): matrix { |
||
| 921 | $ar = self::factory($this->row, $cols, $this->dtype); |
||
| 922 | for ($i = 0; $i < $ar->row; ++$i) { |
||
| 923 | for ($j = 0; $j < $ar->col; ++$j) { |
||
| 924 | $ar->data[$i * $ar->col + $j] = $this->data[$i * $this->col + $j]; |
||
| 925 | } |
||
| 926 | } |
||
| 927 | return $ar; |
||
| 928 | } |
||
| 929 | |||
| 930 | /** |
||
| 931 | * |
||
| 932 | * @param int $cols |
||
| 933 | * @return \Np\matrix |
||
| 934 | */ |
||
| 935 | public function diminish_right(int $cols): matrix { |
||
| 936 | $ar = self::factory($this->row, $cols, $this->dtype); |
||
| 937 | for ($i = 0; $i < $ar->row; ++$i) { |
||
| 938 | for ($j = 0; $j < $ar->col; ++$j) { |
||
| 939 | $ar->data[$i * $ar->col + $j] = $this->data[$i * $this->col - $cols + $j]; |
||
| 940 | } |
||
| 941 | } |
||
| 942 | return $ar; |
||
| 943 | } |
||
| 944 | |||
| 945 | /** |
||
| 946 | * Return the index of the maximum element in every row of the matrix. |
||
| 947 | * @return \Np\vector int |
||
| 948 | */ |
||
| 949 | public function argMax(): vector { |
||
| 950 | $v = vector::factory($this->row, vector::INT); |
||
| 951 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 952 | $v->data[$i] = blas::max($this->rowAsVector($i)); |
||
| 953 | } |
||
| 954 | return $v; |
||
| 955 | } |
||
| 956 | |||
| 957 | /** |
||
| 958 | * Return the index of the minimum element in every row of the matrix. |
||
| 959 | * @return \Np\vector int |
||
| 960 | */ |
||
| 961 | public function argMin(): vector { |
||
| 962 | $v = vector::factory($this->row, vector::INT); |
||
| 963 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 964 | $v->data[$i] = blas::min($this->rowAsVector($i)); |
||
| 965 | } |
||
| 966 | |||
| 967 | return $v; |
||
| 968 | } |
||
| 969 | |||
| 970 | /** |
||
| 971 | * Set given data in matrix |
||
| 972 | * @param int|float|array $data |
||
| 973 | * @param bool $dignoal |
||
| 974 | * @return void |
||
| 975 | */ |
||
| 976 | public function setData(int|float|array $data, bool $dignoal = false): void { |
||
| 977 | if ($dignoal == false) { |
||
| 978 | if (is_array($data) && is_array($data[0])) { |
||
| 979 | $f = $this->flattenArray($data); |
||
| 980 | foreach ($f as $k => $v) { |
||
| 981 | $this->data[$k] = $v; |
||
| 982 | } |
||
| 983 | } elseif (is_numeric($data)) { |
||
| 984 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 985 | $this->data[$i] = $data; |
||
| 986 | } |
||
| 987 | } |
||
| 988 | } elseif (is_numeric($data) || is_array($data) && !is_array($data[0])) { |
||
| 989 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 990 | $this->data[$i * $this->col * $i] = $data; |
||
| 991 | } |
||
| 992 | } |
||
| 993 | } |
||
| 994 | |||
| 995 | /** |
||
| 996 | * get the matrix data type |
||
| 997 | * @return int |
||
| 998 | */ |
||
| 999 | public function getDtype():int { |
||
| 1000 | return $this->dtype; |
||
| 1001 | } |
||
| 1002 | |||
| 1003 | /** |
||
| 1004 | * get the shape of matrix |
||
| 1005 | * @return object |
||
| 1006 | */ |
||
| 1007 | public function getShape(): object { |
||
| 1008 | return (object) ['m' => $this->row, 'n' => $this->col]; |
||
| 1009 | } |
||
| 1010 | |||
| 1011 | /** |
||
| 1012 | * get the number of elements in the matrix. |
||
| 1013 | * @return int |
||
| 1014 | */ |
||
| 1015 | public function getSize(): int { |
||
| 1016 | return $this->ndim; |
||
| 1017 | } |
||
| 1018 | |||
| 1019 | /** |
||
| 1020 | * is matrix squred |
||
| 1021 | * @return bool |
||
| 1022 | */ |
||
| 1023 | public function isSquare(): bool { |
||
| 1024 | if ($this->row === $this->col) { |
||
| 1025 | return true; |
||
| 1026 | } |
||
| 1027 | return false; |
||
| 1028 | } |
||
| 1029 | |||
| 1030 | /** |
||
| 1031 | * Return a row as vector from the matrix. |
||
| 1032 | * @param int $index |
||
| 1033 | * @return \Np\vector |
||
| 1034 | */ |
||
| 1035 | public function rowAsVector(int $index): vector { |
||
| 1036 | $vr = vector::factory($this->col, $this->dtype); |
||
| 1037 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 1038 | $vr->data[$j] = $this->data[$index * $this->col + $j]; |
||
| 1039 | } |
||
| 1040 | return $vr; |
||
| 1041 | } |
||
| 1042 | |||
| 1043 | /** |
||
| 1044 | * Return a col as vector from the matrix. |
||
| 1045 | * @param int $index |
||
| 1046 | * @return \Np\vector |
||
| 1047 | */ |
||
| 1048 | public function colAsVector(int $index): vector { |
||
| 1049 | $vr = vector::factory($this->row, $this->dtype); |
||
| 1050 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 1051 | $vr->data[$i] = $this->data[$i * $this->row + $index]; |
||
| 1052 | } |
||
| 1053 | return $vr; |
||
| 1054 | } |
||
| 1055 | |||
| 1056 | /** |
||
| 1057 | * Return the diagonal elements of a square matrix as a vector. |
||
| 1058 | * @return \Np\vector |
||
| 1059 | */ |
||
| 1060 | public function diagonalAsVector(): vector { |
||
| 1061 | if (!$this->isSquare()) { |
||
| 1062 | self::_err('Can not trace of a none square matrix'); |
||
| 1063 | } |
||
| 1064 | $vr = vector::factory($this->row, $this->dtype); |
||
| 1065 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 1066 | $vr->data[$i] = $this->getDiagonalVal($i); |
||
| 1067 | } |
||
| 1068 | return $vr; |
||
| 1069 | } |
||
| 1070 | |||
| 1071 | /** |
||
| 1072 | * Flatten i.e unravel the matrix into a vector. |
||
| 1073 | * |
||
| 1074 | * @return \Np\vector |
||
| 1075 | */ |
||
| 1076 | public function asVector(): vector { |
||
| 1077 | $vr = vector::factory($this->ndim, $this->dtype); |
||
| 1078 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 1079 | $vr->data[$i] = $this->data[$i]; |
||
| 1080 | } |
||
| 1081 | return $vr; |
||
| 1082 | } |
||
| 1083 | |||
| 1084 | /** |
||
| 1085 | * Return the elements of the matrix in a 2-d array. |
||
| 1086 | * @return array |
||
| 1087 | */ |
||
| 1088 | public function asArray(): array { |
||
| 1089 | $ar = array_fill(0, $this->row, array_fill(0, $this->col, null)); |
||
| 1090 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 1091 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 1092 | $ar[$i][$j] = $this->data[$i * $this->col + $j]; |
||
| 1093 | } |
||
| 1094 | } |
||
| 1095 | return $ar; |
||
| 1096 | } |
||
| 1097 | |||
| 1098 | /** |
||
| 1099 | * get a diagonal value from matrix |
||
| 1100 | * @param int $i |
||
| 1101 | * @return float |
||
| 1102 | */ |
||
| 1103 | public function getDiagonalVal(int $i) { |
||
| 1104 | if ($this->isSquare()) { |
||
| 1105 | return $this->data[$i * $this->row + $i]; |
||
| 1106 | } |
||
| 1107 | } |
||
| 1108 | |||
| 1109 | |||
| 1110 | |||
| 1111 | /** |
||
| 1112 | * Compute the singular value decomposition of a matrix and |
||
| 1113 | * return an object of the singular values and unitary matrices |
||
| 1114 | * |
||
| 1115 | * @return object (u,s,v) |
||
| 1116 | */ |
||
| 1117 | public function svd(): svd { |
||
| 1118 | return svd::factory($this); |
||
| 1119 | } |
||
| 1120 | |||
| 1121 | /** |
||
| 1122 | * Compute the eigen decomposition of a general matrix. |
||
| 1123 | * return the eigenvalues and eigenvectors as object |
||
| 1124 | * |
||
| 1125 | * @param bool $symmetric |
||
| 1126 | * @return eigen |
||
| 1127 | */ |
||
| 1128 | public function eign(bool $symmetric = false): eigen { |
||
| 1129 | return eigen::factory($this, $symmetric); |
||
| 1130 | } |
||
| 1131 | |||
| 1132 | /** |
||
| 1133 | * |
||
| 1134 | * Compute the LU factorization of matrix. |
||
| 1135 | * return lower, upper, and permutation matrices as object. |
||
| 1136 | * |
||
| 1137 | * @return lu |
||
| 1138 | */ |
||
| 1139 | public function lu(): lu { |
||
| 1140 | return lu::factory($this); |
||
| 1141 | } |
||
| 1142 | |||
| 1143 | /** |
||
| 1144 | * Return the L1 norm of the matrix. |
||
| 1145 | * @return float |
||
| 1146 | */ |
||
| 1147 | public function normL1(): float { |
||
| 1148 | return lapack::lange('l', $this); |
||
| 1149 | } |
||
| 1150 | |||
| 1151 | /** |
||
| 1152 | * Return the L2 norm of the matrix. |
||
| 1153 | * @return float |
||
| 1154 | */ |
||
| 1155 | public function normL2(): float { |
||
| 1156 | return lapack::lange('f', $this); |
||
| 1157 | } |
||
| 1158 | |||
| 1159 | /** |
||
| 1160 | * Return the L1 norm of the matrix. |
||
| 1161 | * @return float |
||
| 1162 | */ |
||
| 1163 | public function normINF(): float { |
||
| 1164 | return lapack::lange('i', $this); |
||
| 1165 | } |
||
| 1166 | |||
| 1167 | /** |
||
| 1168 | * Return the Frobenius norm of the matrix. |
||
| 1169 | * @return float |
||
| 1170 | */ |
||
| 1171 | public function normFrob(): float { |
||
| 1172 | return $this->normL2(); |
||
| 1173 | } |
||
| 1174 | |||
| 1175 | /** |
||
| 1176 | * Compute the means of each row and return them in a vector. |
||
| 1177 | * |
||
| 1178 | * @return vector |
||
| 1179 | */ |
||
| 1180 | public function mean(): vector { |
||
| 1181 | return $this->sumRows()->divide($this->col); |
||
| 1182 | } |
||
| 1183 | |||
| 1184 | /** |
||
| 1185 | * Compute the row variance of the matrix. |
||
| 1186 | * |
||
| 1187 | * @param vector|null $mean |
||
| 1188 | * @return vector |
||
| 1189 | */ |
||
| 1190 | public function variance(vector|null $mean = null): vector { |
||
| 1191 | if (isset($mean)) { |
||
| 1192 | if (!$mean instanceof vector) { |
||
| 1193 | self::_invalidArgument('mean must be a vector!'); |
||
| 1194 | } |
||
| 1195 | if ($this->row !== $mean->col) { |
||
| 1196 | self::_err('Err:: given mean vector dimensionality mismatched!'); |
||
| 1197 | } |
||
| 1198 | } else { |
||
| 1199 | $mean = $this->mean(); |
||
| 1200 | } |
||
| 1201 | return $this->subtractColumnVector($mean)->square() |
||
| 1202 | ->sumRows()->divide($this->row); |
||
| 1203 | } |
||
| 1204 | |||
| 1205 | /** |
||
| 1206 | * Return the median vector of this matrix. |
||
| 1207 | * @return vector |
||
| 1208 | */ |
||
| 1209 | public function median(): vector { |
||
| 1210 | $mid = intdiv($this->col, 2); |
||
| 1211 | $odd = $this->col % 2 === 1; |
||
| 1212 | $vr = vector::factory($this->row, $this->dtype); |
||
| 1213 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 1214 | $a = $this->rowAsVector($i)->sort(); |
||
| 1215 | if ($odd) { |
||
| 1216 | $median = $a->data[$mid]; |
||
| 1217 | } else { |
||
| 1218 | $median = ($a->data[$mid - 1] + $a->data[$mid]) / 2.0; |
||
| 1219 | } |
||
| 1220 | $vr->data[$i] = $median; |
||
| 1221 | } |
||
| 1222 | unset($a); |
||
| 1223 | return $vr; |
||
| 1224 | } |
||
| 1225 | |||
| 1226 | /** |
||
| 1227 | * Compute the covariance matrix. |
||
| 1228 | * |
||
| 1229 | * @param vector|null $mean |
||
| 1230 | * @return matrix |
||
| 1231 | */ |
||
| 1232 | public function covariance(vector|null $mean = null): matrix { |
||
| 1233 | if (isset($mean)) { |
||
| 1234 | if ($mean->col !== $this->row) { |
||
| 1235 | self::_err('Err:: given mean vector dimensionality mismatched!'); |
||
| 1236 | } |
||
| 1237 | } else { |
||
| 1238 | $mean = $this->mean(); |
||
| 1239 | } |
||
| 1240 | |||
| 1241 | $b = $this->subtractColumnVector($mean); |
||
| 1242 | |||
| 1243 | return $b->dot($b->transpose()) |
||
| 1244 | ->divideScalar($this->row); |
||
| 1245 | } |
||
| 1246 | |||
| 1247 | /** |
||
| 1248 | * Square of matrix |
||
| 1249 | * @return matrix |
||
| 1250 | */ |
||
| 1251 | public function square(): matrix { |
||
| 1252 | return $this->multiplyMatrix($this); |
||
| 1253 | } |
||
| 1254 | |||
| 1255 | /** |
||
| 1256 | * |
||
| 1257 | * @param int|float|matrix|vector $d |
||
| 1258 | * @return matrix |
||
| 1259 | */ |
||
| 1260 | public function equal(int|float|matrix|vector $d): matrix { |
||
| 1261 | if ($d instanceof self) { |
||
| 1262 | return $this->equalMatrix($d); |
||
| 1263 | } elseif ($d instanceof vector) { |
||
| 1264 | return $this->equalVector($d); |
||
| 1265 | } else { |
||
| 1266 | return $this->equalScalar($d); |
||
| 1267 | } |
||
| 1268 | } |
||
| 1269 | |||
| 1270 | protected function equalMatrix(matrix $m): matrix { |
||
| 1271 | if ($this->checkShape($this, $m) && $this->checkDtype($this, $m)) { |
||
| 1272 | $ar = self::factory($this->row, $this->col, $this->dtype); |
||
| 1273 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 1274 | $ar->data[$i] = $this->data[$i] == $m->data[$i] ? 1 : 0; |
||
| 1275 | } |
||
| 1276 | return $ar; |
||
| 1277 | } |
||
| 1278 | } |
||
| 1279 | |||
| 1280 | protected function equalVector(vector $v): matrix { |
||
| 1281 | if ($this->checkDimensions($v, $this) && $this->checkDtype($this, $v)) { |
||
| 1282 | $ar = self::factory($this->row, $this->col, $this->dtype); |
||
| 1283 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 1284 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 1285 | $ar->data[$i * $this->col + $j] = $this->data[$i * $this->col + $j] == $v->data[$j] ? 1 : 0; |
||
| 1286 | } |
||
| 1287 | } |
||
| 1288 | return $ar; |
||
| 1289 | } |
||
| 1290 | } |
||
| 1291 | |||
| 1292 | protected function equalScalar(int|float $s): matrix { |
||
| 1293 | $ar = self::factory($this->row, $this->col, $this->dtype); |
||
| 1294 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 1295 | $ar->data[$i] = $this->data[$i] == $s ? 1 : 0; |
||
| 1296 | } |
||
| 1297 | return $ar; |
||
| 1298 | } |
||
| 1299 | |||
| 1300 | /** |
||
| 1301 | * |
||
| 1302 | * @param int|float|matrix|vector $d |
||
| 1303 | * @return matrix |
||
| 1304 | */ |
||
| 1305 | public function greater(int|float|matrix|vector $d): matrix { |
||
| 1312 | } |
||
| 1313 | } |
||
| 1314 | |||
| 1315 | protected function greaterMatrix(matrix $m): matrix { |
||
| 1316 | if ($this->checkShape($this, $m) && $this->checkDtype($this,$m)) { |
||
| 1317 | $ar = self::factory($this->row, $this->col, $this->dtype); |
||
| 1318 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 1319 | $ar->data[$i] = $this->data[$i] > $m->data[$i] ? 1 : 0; |
||
| 1320 | } |
||
| 1321 | return $ar; |
||
| 1322 | } |
||
| 1323 | } |
||
| 1324 | |||
| 1325 | protected function greaterVector(vector $v): matrix { |
||
| 1326 | if ($this->checkDimensions($v, $this) && $this->checkDtype($this,$v)) { |
||
| 1327 | $ar = self::factory($this->row, $this->col, $this->dtype); |
||
| 1328 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 1329 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 1330 | $ar->data[$i * $this->col + $j] = $this->data[$i * $this->col + $j] > $v->data[$j] ? 1 : 0; |
||
| 1331 | } |
||
| 1332 | } |
||
| 1333 | return $ar; |
||
| 1334 | } |
||
| 1335 | } |
||
| 1336 | |||
| 1337 | protected function greaterScalar(int|float $s): matrix { |
||
| 1338 | $ar = self::factory($this->row, $this->col, $this->dtype); |
||
| 1339 | for ($i = 0; $i < $this->ndim; ++$i) { |
||
| 1340 | $ar->data[$i] = $this->data[$i] > $s ? 1 : 0; |
||
| 1341 | } |
||
| 1342 | return $ar; |
||
| 1343 | } |
||
| 1344 | |||
| 1345 | /** |
||
| 1346 | * |
||
| 1347 | * @param int|float|matrix $m |
||
| 1348 | * @return matrix |
||
| 1349 | */ |
||
| 1350 | public function less(int|float|matrix $m): matrix { |
||
| 1364 | } |
||
| 1365 | } |
||
| 1366 | |||
| 1367 | /** |
||
| 1368 | * Is the matrix symmetric i.e. is it equal to its own transpose? |
||
| 1369 | * |
||
| 1370 | * @return bool |
||
| 1371 | */ |
||
| 1372 | public function isSymmetric(): bool { |
||
| 1373 | if (!$this->isSquare()) { |
||
| 1374 | return false; |
||
| 1375 | } |
||
| 1376 | $ar = $this->transpose(); |
||
| 1377 | for ($i = 0; $i < $ar->ndim; ++$i) { |
||
| 1378 | if ($ar->data[$i] != $this->data[$i]) { |
||
| 1379 | unset($ar); |
||
| 1380 | return false; |
||
| 1381 | } |
||
| 1382 | } |
||
| 1383 | unset($ar); |
||
| 1384 | return true; |
||
| 1385 | } |
||
| 1386 | |||
| 1387 | /** |
||
| 1388 | * print the matrix in consol |
||
| 1389 | */ |
||
| 1390 | public function printMatrix() { |
||
| 1391 | echo __CLASS__ . PHP_EOL; |
||
| 1392 | for ($i = 0; $i < $this->row; ++$i) { |
||
| 1393 | for ($j = 0; $j < $this->col; ++$j) { |
||
| 1394 | printf('%lf ', $this->data[$i * $this->col + $j]); |
||
| 1395 | } |
||
| 1396 | echo PHP_EOL; |
||
| 1397 | } |
||
| 1398 | } |
||
| 1399 | |||
| 1400 | public function __toString() { |
||
| 1402 | } |
||
| 1403 | |||
| 1404 | private function flattenArray(array $ar) { |
||
| 1405 | if (is_array($ar) && is_array($ar[0])) { |
||
| 1406 | $a = []; |
||
| 1407 | foreach ($ar as $y => $value) { |
||
| 1408 | foreach ($value as $k => $v) { |
||
| 1409 | $a[] = $v; |
||
| 1410 | } |
||
| 1411 | } |
||
| 1412 | return $a; |
||
| 1413 | } |
||
| 1414 | } |
||
| 1415 | |||
| 1416 | /** |
||
| 1417 | * |
||
| 1418 | * @param int $row |
||
| 1419 | * @param int $col |
||
| 1420 | * @param int $dtype |
||
| 1421 | * @return $this |
||
| 1422 | */ |
||
| 1423 | protected function __construct(public int $row, public int $col, int $dtype = self::Float) { |
||
| 1429 | } |
||
| 1430 | } |
||
| 1431 |
