.gitignore

@@ -1,4 +1,4 @@
-.*/
+.vscode
 vendor
 .*.cache/
 *.phar

phpbench.json

@@ -1,10 +1,6 @@
 {
     "runner.bootstrap": "vendor/autoload.php",
     "runner.path": "benchmark/",
-    "runner.php_disable_ini": true,
-    "runner.retry_threshold": 5,
-    "runner.iterations": 10,
-    "storage.xml_storage_path": "output/benchmarking/",
     "report.outputs": {
         "build-artifact": {
             "renderer": "html",
@@ -12,5 +8,4 @@
             "title": "Benchmarking"
         }
    }
-   
 }

src/Numerics/BasicMath.php

@@ -15,11 +15,11 @@ class BasicMath
     /**
      * Squares the input (x^2 = x * x)
      *
-     * @param $x Value to square (x)
+     * @param float $x Value to square (x)
      *
      * @return float The squared value (x^2)
      */
-    public static function square(float $x): float
+    public static function square($x): float
     {
         return $x * $x;
     }

src/Numerics/DiagonalMatrix.php

@@ -12,11 +12,13 @@ class DiagonalMatrix extends Matrix
     public function __construct(array $diagonalValues)
     {
         $diagonalCount = count($diagonalValues);
+        $rowCount = $diagonalCount;
+        $colCount = $rowCount;
 
-        parent::__construct($diagonalCount, $diagonalCount);
+        parent::__construct($rowCount, $colCount);
 
-        for ($currentRow = 0; $currentRow < $diagonalCount; $currentRow++) {
+        for ($currentRow = 0; $currentRow < $rowCount; $currentRow++) {
-            for ($currentCol = 0; $currentCol < $diagonalCount; $currentCol++) {
+            for ($currentCol = 0; $currentCol < $colCount; $currentCol++) {
                 if ($currentRow === $currentCol) {
                     $this->setValue($currentRow, $currentCol, $diagonalValues[$currentRow]);
                 } else {

src/Numerics/GaussianDistribution.php

@@ -12,13 +12,6 @@ namespace DNW\Skills\Numerics;
  */
 class GaussianDistribution implements \Stringable
 {
-    //sqrt(2*pi)
-    //from https://www.wolframalpha.com/input?i=sqrt%282*pi%29
-    private const M_SQRT_2_PI = 2.5066282746310005024157652848110452530069867406099383166299235763;
-    
-    //log(sqrt(2*pi))
-    //From https://www.wolframalpha.com/input?i=log%28sqrt%282*pi%29%29
-    private const M_LOG_SQRT_2_PI = 0.9189385332046727417803297364056176398613974736377834128171515404;
     // precision and precisionMean are used because they make multiplying and dividing simpler
     // (the the accompanying math paper for more details)
     private float $precision;
@@ -69,7 +62,7 @@ class GaussianDistribution implements \Stringable
     public function getNormalizationConstant(): float
     {
         // Great derivation of this is at http://www.astro.psu.edu/~mce/A451_2/A451/downloads/notes0.pdf
-        return 1.0 / (self::M_SQRT_2_PI * $this->standardDeviation);
+        return 1.0 / (sqrt(2 * M_PI) * $this->standardDeviation);
     }
 
     public static function fromPrecisionMean(float $precisionMean, float $precision): self
@@ -122,7 +115,9 @@ class GaussianDistribution implements \Stringable
         $varianceSum = $left->variance + $right->variance;
         $meanDifference = $left->mean - $right->mean;
 
-        return -self::M_LOG_SQRT_2_PI - (log($varianceSum) / 2.0) - (BasicMath::square($meanDifference) / (2.0 * $varianceSum));
+        $logSqrt2Pi = log(sqrt(2 * M_PI));
+
+        return -$logSqrt2Pi - (log($varianceSum) / 2.0) - (BasicMath::square($meanDifference) / (2.0 * $varianceSum));
     }
 
     public static function divide(GaussianDistribution $numerator, GaussianDistribution $denominator): self
@@ -142,7 +137,9 @@ class GaussianDistribution implements \Stringable
         $varianceDifference = $denominator->variance - $numerator->variance;
         $meanDifference = $numerator->mean - $denominator->mean;
 
-        return log($denominator->variance) + self::M_LOG_SQRT_2_PI - log($varianceDifference) / 2.0 +
+        $logSqrt2Pi = log(sqrt(2 * M_PI));
+
+        return log($denominator->variance) + $logSqrt2Pi - log($varianceDifference) / 2.0 +
         BasicMath::square($meanDifference) / (2 * $varianceDifference);
     }
 
@@ -153,7 +150,7 @@ class GaussianDistribution implements \Stringable
         // P(x) = ------------------- * e
         //        stdDev * sqrt(2*pi)
 
-        $multiplier = 1.0 / ($standardDeviation * self::M_SQRT_2_PI);
+        $multiplier = 1.0 / ($standardDeviation * sqrt(2 * M_PI));
         $expPart = exp((-1.0 * BasicMath::square($x - $mean)) / (2 * BasicMath::square($standardDeviation)));
 
         return $multiplier * $expPart;
@@ -161,7 +158,8 @@ class GaussianDistribution implements \Stringable
 
     public static function cumulativeTo(float $x, float $mean = 0.0, float $standardDeviation = 1.0): float
     {
-        $result = GaussianDistribution::errorFunctionCumulativeTo(-M_SQRT1_2 * $x);
+        $invsqrt2 = -0.707106781186547524400844362104;
+        $result = GaussianDistribution::errorFunctionCumulativeTo($invsqrt2 * $x);
 
         return 0.5 * $result;
     }
@@ -233,11 +231,11 @@ class GaussianDistribution implements \Stringable
 
         $pp = ($p < 1.0) ? $p : 2 - $p;
         $t = sqrt(-2 * log($pp / 2.0)); // Initial guess
-        $x = -M_SQRT1_2 * ((2.30753 + $t * 0.27061) / (1.0 + $t * (0.99229 + $t * 0.04481)) - $t);
+        $x = -0.70711 * ((2.30753 + $t * 0.27061) / (1.0 + $t * (0.99229 + $t * 0.04481)) - $t);
 
         for ($j = 0; $j < 2; $j++) {
             $err = GaussianDistribution::errorFunctionCumulativeTo($x) - $pp;
-            $x += $err / (M_2_SQRTPI * exp(-BasicMath::square($x)) - $x * $err); // Halley
+            $x += $err / (1.12837916709551257 * exp(-BasicMath::square($x)) - $x * $err); // Halley
         }
 
         return ($p < 1.0) ? $x : -$x;
@@ -246,7 +244,7 @@ class GaussianDistribution implements \Stringable
     public static function inverseCumulativeTo(float $x, float $mean = 0.0, float $standardDeviation = 1.0): float
     {
         // From numerical recipes, page 320
-        return $mean - M_SQRT2 * $standardDeviation * GaussianDistribution::inverseErrorFunctionCumulativeTo(2 * $x);
+        return $mean - sqrt(2) * $standardDeviation * GaussianDistribution::inverseErrorFunctionCumulativeTo(2 * $x);
     }
 
     public function __toString(): string

src/Numerics/SquareMatrix.php

@@ -8,17 +8,18 @@ class SquareMatrix extends Matrix
 {
     public function __construct(float|int ...$allValues)
     {
-        $size = (int)sqrt(count($allValues));
+        $rows = (int)sqrt(count($allValues));
+        $cols = $rows;
 
         $matrixData = [];
         $allValuesIndex = 0;
 
-        for ($currentRow = 0; $currentRow < $size; $currentRow++) {
+        for ($currentRow = 0; $currentRow < $rows; $currentRow++) {
-            for ($currentColumn = 0; $currentColumn < $size; $currentColumn++) {
+            for ($currentColumn = 0; $currentColumn < $cols; $currentColumn++) {
                 $matrixData[$currentRow][$currentColumn] = $allValues[$allValuesIndex++];
             }
         }
 
-        parent::__construct($size, $size, $matrixData);
+        parent::__construct($rows, $cols, $matrixData);
     }
 }

src/TrueSkill/DrawMargin.php

@@ -18,6 +18,6 @@ final class DrawMargin
         //
         // margin = inversecdf((draw probability + 1)/2) * sqrt(n1+n2) * beta
         // n1 and n2 are the number of players on each team
-        return GaussianDistribution::inverseCumulativeTo(.5 * ($drawProbability + 1), 0, 1) * M_SQRT2 * $beta;
+        return GaussianDistribution::inverseCumulativeTo(.5 * ($drawProbability + 1), 0, 1) * sqrt(1 + 1) * $beta;
     }
 }

src/TrueSkill/Factors/GaussianWeightedSumFactor.php

@@ -181,6 +181,7 @@ class GaussianWeightedSumFactor extends GaussianFactor
         }
 
         $newPrecision = 1.0 / $inverseOfNewPrecisionSum;
+        $anotherNewPrecision = 1.0 / $anotherInverseOfNewPrecisionSum;
 
         $newPrecisionMean = $newPrecision * $weightedMeanSum;