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	rector: codingStyle
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		| @@ -30,6 +30,7 @@ class GaussianWeightedSumFactor extends GaussianFactor | ||||
|      * @var array<float[]> $weights | ||||
|      */ | ||||
|     private array $weights = []; | ||||
|  | ||||
|     /** | ||||
|      * @var array<float[]> $weightsSquared | ||||
|      */ | ||||
| @@ -48,7 +49,7 @@ class GaussianWeightedSumFactor extends GaussianFactor | ||||
|         $variableWeightsLength = count($variableWeights); | ||||
|         $this->weights[0] = array_fill(0, count($variableWeights), 0); | ||||
|  | ||||
|         for ($i = 0; $i < $variableWeightsLength; $i++) { | ||||
|         for ($i = 0; $i < $variableWeightsLength; ++$i) { | ||||
|             $weight = &$variableWeights[$i]; | ||||
|             $this->weights[0][$i] = $weight; | ||||
|             $this->weightsSquared[0][$i] = BasicMath::square($weight); | ||||
| @@ -58,7 +59,7 @@ class GaussianWeightedSumFactor extends GaussianFactor | ||||
|  | ||||
|         // 0..n-1 | ||||
|         $this->variableIndexOrdersForWeights[0] = []; | ||||
|         for ($i = 0; $i < ($variablesToSumLength + 1); $i++) { | ||||
|         for ($i = 0; $i < ($variablesToSumLength + 1); ++$i) { | ||||
|             $this->variableIndexOrdersForWeights[0][] = $i; | ||||
|         } | ||||
|  | ||||
| @@ -70,7 +71,7 @@ class GaussianWeightedSumFactor extends GaussianFactor | ||||
|         // By convention, we'll put the v_0 term at the end | ||||
|  | ||||
|         $weightsLength = $variableWeightsLength + 1; | ||||
|         for ($weightsIndex = 1; $weightsIndex < $weightsLength; $weightsIndex++) { | ||||
|         for ($weightsIndex = 1; $weightsIndex < $weightsLength; ++$weightsIndex) { | ||||
|             $currentWeights = \array_fill(0, $variableWeightsLength, 0); | ||||
|  | ||||
|             $variableIndices = \array_fill(0, $variableWeightsLength + 1, 0); | ||||
| @@ -82,7 +83,7 @@ class GaussianWeightedSumFactor extends GaussianFactor | ||||
|             // This is helpful since we skip over one of the spots | ||||
|             $currentDestinationWeightIndex = 0; | ||||
|  | ||||
|             for ($currentWeightSourceIndex = 0; $currentWeightSourceIndex < $variableWeightsLength; $currentWeightSourceIndex++) { | ||||
|             for ($currentWeightSourceIndex = 0; $currentWeightSourceIndex < $variableWeightsLength; ++$currentWeightSourceIndex) { | ||||
|                 if ($currentWeightSourceIndex === $weightsIndex - 1) { | ||||
|                     continue; | ||||
|                 } | ||||
| @@ -98,7 +99,7 @@ class GaussianWeightedSumFactor extends GaussianFactor | ||||
|                 $currentWeightsSquared[$currentDestinationWeightIndex] = $currentWeight * $currentWeight; | ||||
|  | ||||
|                 $variableIndices[$currentDestinationWeightIndex + 1] = $currentWeightSourceIndex + 1; | ||||
|                 $currentDestinationWeightIndex++; | ||||
|                 ++$currentDestinationWeightIndex; | ||||
|             } | ||||
|  | ||||
|             // And the final one | ||||
| @@ -108,6 +109,7 @@ class GaussianWeightedSumFactor extends GaussianFactor | ||||
|                 // HACK: Getting around division by zero | ||||
|                 $finalWeight = 0; | ||||
|             } | ||||
|  | ||||
|             $currentWeights[$currentDestinationWeightIndex] = $finalWeight; | ||||
|             $currentWeightsSquared[$currentDestinationWeightIndex] = BasicMath::square($finalWeight); | ||||
|             $variableIndices[count($variableWeights)] = 0; | ||||
| @@ -135,7 +137,7 @@ class GaussianWeightedSumFactor extends GaussianFactor | ||||
|         $counter = count($vars); | ||||
|  | ||||
|         // We start at 1 since offset 0 has the sum | ||||
|         for ($i = 1; $i < $counter; $i++) { | ||||
|         for ($i = 1; $i < $counter; ++$i) { | ||||
|             $result += GaussianDistribution::logRatioNormalization($vars[$i]->getValue(), $messages[$i]->getValue()); | ||||
|         } | ||||
|  | ||||
| @@ -164,7 +166,7 @@ class GaussianWeightedSumFactor extends GaussianFactor | ||||
|  | ||||
|         $weightsSquaredLength = count($weightsSquared); | ||||
|  | ||||
|         for ($i = 0; $i < $weightsSquaredLength; $i++) { | ||||
|         for ($i = 0; $i < $weightsSquaredLength; ++$i) { | ||||
|             // These flow directly from the paper | ||||
|  | ||||
|             $inverseOfNewPrecisionSum += $weightsSquared[$i] / | ||||
| @@ -221,7 +223,7 @@ class GaussianWeightedSumFactor extends GaussianFactor | ||||
|         // The tricky part here is that we have to put the messages and variables in the same | ||||
|         // order as the weights. Thankfully, the weights and messages share the same index numbers, | ||||
|         // so we just need to make sure they're consistent | ||||
|         for ($i = 0; $i < $counter; $i++) { | ||||
|         for ($i = 0; $i < $counter; ++$i) { | ||||
|             $updatedMessages[] = $allMessages[$indicesToUse[$i]]; | ||||
|             $updatedVariables[] = $allVariables[$indicesToUse[$i]]; | ||||
|         } | ||||
| @@ -245,7 +247,7 @@ class GaussianWeightedSumFactor extends GaussianFactor | ||||
|         $result .= ' = '; | ||||
|  | ||||
|         $totalVars = count($variablesToSum); | ||||
|         for ($i = 0; $i < $totalVars; $i++) { | ||||
|         for ($i = 0; $i < $totalVars; ++$i) { | ||||
|             $isFirst = ($i == 0); | ||||
|  | ||||
|             if ($isFirst && ($weights[$i] < 0)) { | ||||
|   | ||||
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