zeopix / MachineLearning

src/MachineLearning/Domain/Model/Value/VectorValue.php

Doc Comments +1 added lines, -1 removed lines

@@ -18,7 +18,7 @@
     }
 
     /**
-     * @return \float[]
+     * @return double[]
      */
     public function getValue()
     {

Spacing +2 added lines, -2 removed lines

@@ -32,8 +32,8 @@
     {
         $result = 0;
         $columns = count($this->value);
-        for ($i=0; $i<$columns; $i++) {
-            $result += $this->value[$i]*$value->getValue()[$i];
+        for ($i = 0; $i < $columns; $i++) {
+            $result += $this->value[$i] * $value->getValue()[$i];
         }
         return $result;
     }

src/MachineLearning/Domain/Model/Value/ScalarValue.php

Spacing +1 added lines, -1 removed lines

@@ -30,7 +30,7 @@
      */
     public function scalar(ValueInterface $value)
     {
-        return $value->getValue()*$this->getValue();
+        return $value->getValue() * $this->getValue();
     }
 
 

src/MachineLearning/Domain/Hypothesis/Linear.php

Spacing +2 added lines, -2 removed lines

@@ -15,7 +15,7 @@ 
      */
     public function calculate(ValueInterface $coefficient, ValueInterface $variable)
     {
-        if (! $variable instanceof VectorValue) {
+        if (!$variable instanceof VectorValue) {
             throw new WrongVariableForHypothesisException();
         }
 
@@ -30,7 +30,7 @@ 
      */
     public function derivative(ValueInterface $coefficient, ValueInterface $variable, $partialVariable)
     {
-        if (! $variable instanceof VectorValue) {
+        if (!$variable instanceof VectorValue) {
             throw new WrongVariableForHypothesisException();
         }
 

src/MachineLearning/Application/Normalization/MeanScaleNormalization.php

Spacing +5 added lines, -5 removed lines

@@ -24,10 +24,10 @@ 
         $rawValue = $value->getValue();
 
         $numberColumns = count($rawValue);
-        for ($i=0; $i<$numberColumns; $i++) {
+        for ($i = 0; $i < $numberColumns; $i++) {
             $rawValue[$i] =
                 (
-                    $rawValue[$i] - $rawCoefficient[static::COEFFICIENT_AVERAGE][$i]
+                    $rawValue[$i]-$rawCoefficient[static::COEFFICIENT_AVERAGE][$i]
                 )
                 / $rawCoefficient[static::COEFFICIENT_RANGE][$i]
             ;
@@ -62,8 +62,8 @@ 
 
         foreach ($featuresSum as $i => $featureSum) {
             $featuresAverage[$i] = $featureSum / $numberRows;
-            $featuresRange[$i] = ($featuresMaximumValue[$i] - $featuresMinimumValue[$i]) > 0 ?
-                ($featuresMaximumValue[$i] - $featuresMinimumValue[$i])
+            $featuresRange[$i] = ($featuresMaximumValue[$i]-$featuresMinimumValue[$i]) > 0 ?
+                ($featuresMaximumValue[$i]-$featuresMinimumValue[$i])
                 : 1;
         }
 
@@ -92,7 +92,7 @@ 
                 if (!isset($featuresMinimumValue[$i]) || $featuresMinimumValue[$i] > $features[$i]) {
                     $featuresMinimumValue[$i] = $features[$i];
                 }
-                $featuresSum[$i] = $featuresSum[$i] + $features[$i];
+                $featuresSum[$i] = $featuresSum[$i]+$features[$i];
             }
         }
         return array($featuresMaximumValue, $featuresMinimumValue, $featuresSum);

src/MachineLearning/Application/Algorithm/GradientDescendent.php

Spacing +11 added lines, -11 removed lines

@@ -72,7 +72,7 @@ 
         $coefficientVector[0] = 0;
         $incrementVector = array_fill(0, $features+1, 0);
 
-        while(!$convergence && !$divergence) {
+        while (!$convergence && !$divergence) {
             list($convergence, $divergence, $coefficientVector) = $this->doStep(
                 $dataset,
                 $coefficientVector,
@@ -100,20 +100,20 @@ 
     protected function doStep(Dataset $dataset, $coefficientVector, $features, $total, $incrementVector)
     {
         $coefficient = new VectorValue($coefficientVector);
-        $costVector = array_fill(0, $features + 1, 0);
+        $costVector = array_fill(0, $features+1, 0);
 
         foreach ($dataset as $result) {
             $costVector = $this->calculateStepCost($features, $coefficient, $result, $costVector);
         }
 
-        for ($j = 0; $j < $features + 1; $j++) {
+        for ($j = 0; $j < $features+1; $j++) {
             $incrementVector[$j] = $this->learningRate * -(1 / ($total)) * $costVector[$j];
         }
 
-        $convergence = (bool)(abs(array_sum($incrementVector)) < $this->convergenceCriteria);
-        $divergence = (bool)(abs(array_sum($incrementVector)) > $this->divergenceCriteria);
+        $convergence = (bool) (abs(array_sum($incrementVector)) < $this->convergenceCriteria);
+        $divergence = (bool) (abs(array_sum($incrementVector)) > $this->divergenceCriteria);
 
-        for ($j = 0; $j < $features + 1; $j++) {
+        for ($j = 0; $j < $features+1; $j++) {
             $coefficientVector[$j] += $incrementVector[$j];
         }
         return array($convergence, $divergence, $coefficientVector);
@@ -128,20 +128,20 @@ 
     protected function calculateStepCost($features, ValueInterface $coefficient, Result $result, array $costVector)
     {
         $firstOrderIncrement = (
-            (float)$this->hypothesis->calculate(
+            (float) $this->hypothesis->calculate(
                 $coefficient,
                 $result->getIndependentVariable()
             )->getValue()
-            - (float)$result->getDependentVariable()->getValue()
+            - (float) $result->getDependentVariable()->getValue()
         );
         $costVector[0] += $firstOrderIncrement;
 
-        for ($j = 1; $j < $features + 1; $j++) {
+        for ($j = 1; $j < $features+1; $j++) {
             $costVector[$j] += $firstOrderIncrement
-                * (float)$this->hypothesis->derivative(
+                * (float) $this->hypothesis->derivative(
                     $coefficient,
                     $result->getIndependentVariable(),
-                    $j - 1
+                    $j-1
                 )->getValue();
         }
         return $costVector;