diff --git a/README.md b/README.md
index bab758c..b17ac72 100644
--- a/README.md
+++ b/README.md
@@ -88,8 +88,9 @@ Example scripts are available in a separate repository [php-ai/php-ml-examples](
* [Token Count Vectorizer](http://php-ml.readthedocs.io/en/latest/machine-learning/feature-extraction/token-count-vectorizer/)
* [Tf-idf Transformer](http://php-ml.readthedocs.io/en/latest/machine-learning/feature-extraction/tf-idf-transformer/)
* Dimensionality Reduction
- * PCA
+ * PCA (Principal Component Analysis)
* Kernel PCA
+ * LDA (Linear Discriminant Analysis)
* Datasets
* [Array](http://php-ml.readthedocs.io/en/latest/machine-learning/datasets/array-dataset/)
* [CSV](http://php-ml.readthedocs.io/en/latest/machine-learning/datasets/csv-dataset/)
diff --git a/src/Phpml/DimensionReduction/EigenTransformerBase.php b/src/Phpml/DimensionReduction/EigenTransformerBase.php
new file mode 100644
index 0000000..b399002
--- /dev/null
+++ b/src/Phpml/DimensionReduction/EigenTransformerBase.php
@@ -0,0 +1,98 @@
+getRealEigenvalues();
+ $eigVects= $eig->getEigenvectors();
+
+ $totalEigVal = array_sum($eigVals);
+ // Sort eigenvalues in descending order
+ arsort($eigVals);
+
+ $explainedVar = 0.0;
+ $vectors = [];
+ $values = [];
+ foreach ($eigVals as $i => $eigVal) {
+ $explainedVar += $eigVal / $totalEigVal;
+ $vectors[] = $eigVects[$i];
+ $values[] = $eigVal;
+
+ if ($this->numFeatures !== null) {
+ if (count($vectors) == $this->numFeatures) {
+ break;
+ }
+ } else {
+ if ($explainedVar >= $this->totalVariance) {
+ break;
+ }
+ }
+ }
+
+ $this->eigValues = $values;
+ $this->eigVectors = $vectors;
+ }
+
+ /**
+ * Returns the reduced data
+ *
+ * @param array $data
+ *
+ * @return array
+ */
+ protected function reduce(array $data)
+ {
+ $m1 = new Matrix($data);
+ $m2 = new Matrix($this->eigVectors);
+
+ return $m1->multiply($m2->transpose())->toArray();
+ }
+}
diff --git a/src/Phpml/DimensionReduction/KernelPCA.php b/src/Phpml/DimensionReduction/KernelPCA.php
index 86070c7..51fb17a 100644
--- a/src/Phpml/DimensionReduction/KernelPCA.php
+++ b/src/Phpml/DimensionReduction/KernelPCA.php
@@ -86,7 +86,7 @@ class KernelPCA extends PCA
$matrix = $this->calculateKernelMatrix($this->data, $numRows);
$matrix = $this->centerMatrix($matrix, $numRows);
- list($this->eigValues, $this->eigVectors) = $this->eigenDecomposition($matrix, $numRows);
+ $this->eigenDecomposition($matrix, $numRows);
$this->fit = true;
diff --git a/src/Phpml/DimensionReduction/LDA.php b/src/Phpml/DimensionReduction/LDA.php
new file mode 100644
index 0000000..28f34d6
--- /dev/null
+++ b/src/Phpml/DimensionReduction/LDA.php
@@ -0,0 +1,247 @@
+
+ * The algorithm can be initialized by speciyfing
+ * either with the totalVariance(a value between 0.1 and 0.99)
+ * or numFeatures (number of features in the dataset) to be preserved.
+ *
+ * @param float|null $totalVariance Total explained variance to be preserved
+ * @param int|null $numFeatures Number of features to be preserved
+ *
+ * @throws \Exception
+ */
+ public function __construct($totalVariance = null, $numFeatures = null)
+ {
+ if ($totalVariance !== null && ($totalVariance < 0.1 || $totalVariance > 0.99)) {
+ throw new \Exception("Total variance can be a value between 0.1 and 0.99");
+ }
+ if ($numFeatures !== null && $numFeatures <= 0) {
+ throw new \Exception("Number of features to be preserved should be greater than 0");
+ }
+ if ($totalVariance !== null && $numFeatures !== null) {
+ throw new \Exception("Either totalVariance or numFeatures should be specified in order to run the algorithm");
+ }
+
+ if ($numFeatures !== null) {
+ $this->numFeatures = $numFeatures;
+ }
+ if ($totalVariance !== null) {
+ $this->totalVariance = $totalVariance;
+ }
+ }
+
+ /**
+ * Trains the algorithm to transform the given data to a lower dimensional space.
+ *
+ * @param array $data
+ * @param array $classes
+ *
+ * @return array
+ */
+ public function fit(array $data, array $classes) : array
+ {
+ $this->labels = $this->getLabels($classes);
+ $this->means = $this->calculateMeans($data, $classes);
+
+ $sW = $this->calculateClassVar($data, $classes);
+ $sB = $this->calculateClassCov();
+
+ $S = $sW->inverse()->multiply($sB);
+ $this->eigenDecomposition($S->toArray());
+
+ $this->fit = true;
+
+ return $this->reduce($data);
+ }
+
+ /**
+ * Returns unique labels in the dataset
+ *
+ * @param array $classes
+ *
+ * @return array
+ */
+ protected function getLabels(array $classes): array
+ {
+ $counts = array_count_values($classes);
+
+ return array_keys($counts);
+ }
+
+
+ /**
+ * Calculates mean of each column for each class and returns
+ * n by m matrix where n is number of labels and m is number of columns
+ *
+ * @param type $data
+ * @param type $classes
+ *
+ * @return array
+ */
+ protected function calculateMeans($data, $classes) : array
+ {
+ $means = [];
+ $counts= [];
+ $overallMean = array_fill(0, count($data[0]), 0.0);
+
+ foreach ($data as $index => $row) {
+ $label = array_search($classes[$index], $this->labels);
+
+ foreach ($row as $col => $val) {
+ if (! isset($means[$label][$col])) {
+ $means[$label][$col] = 0.0;
+ }
+ $means[$label][$col] += $val;
+ $overallMean[$col] += $val;
+ }
+
+ if (! isset($counts[$label])) {
+ $counts[$label] = 0;
+ }
+ $counts[$label]++;
+ }
+
+ foreach ($means as $index => $row) {
+ foreach ($row as $col => $sum) {
+ $means[$index][$col] = $sum / $counts[$index];
+ }
+ }
+
+ // Calculate overall mean of the dataset for each column
+ $numElements = array_sum($counts);
+ $map = function ($el) use ($numElements) {
+ return $el / $numElements;
+ };
+ $this->overallMean = array_map($map, $overallMean);
+ $this->counts = $counts;
+
+ return $means;
+ }
+
+
+ /**
+ * Returns in-class scatter matrix for each class, which
+ * is a n by m matrix where n is number of classes and
+ * m is number of columns
+ *
+ * @param array $data
+ * @param array $classes
+ *
+ * @return Matrix
+ */
+ protected function calculateClassVar($data, $classes)
+ {
+ // s is an n (number of classes) by m (number of column) matrix
+ $s = array_fill(0, count($data[0]), array_fill(0, count($data[0]), 0));
+ $sW = new Matrix($s, false);
+
+ foreach ($data as $index => $row) {
+ $label = array_search($classes[$index], $this->labels);
+ $means = $this->means[$label];
+
+ $row = $this->calculateVar($row, $means);
+
+ $sW = $sW->add($row);
+ }
+
+ return $sW;
+ }
+
+ /**
+ * Returns between-class scatter matrix for each class, which
+ * is an n by m matrix where n is number of classes and
+ * m is number of columns
+ *
+ * @return Matrix
+ */
+ protected function calculateClassCov()
+ {
+ // s is an n (number of classes) by m (number of column) matrix
+ $s = array_fill(0, count($this->overallMean), array_fill(0, count($this->overallMean), 0));
+ $sB = new Matrix($s, false);
+
+ foreach ($this->means as $index => $classMeans) {
+ $row = $this->calculateVar($classMeans, $this->overallMean);
+ $N = $this->counts[$index];
+ $sB = $sB->add($row->multiplyByScalar($N));
+ }
+
+ return $sB;
+ }
+
+ /**
+ * Returns the result of the calculation (x - m)T.(x - m)
+ *
+ * @param array $row
+ * @param array $means
+ *
+ * @return Matrix
+ */
+ protected function calculateVar(array $row, array $means)
+ {
+ $x = new Matrix($row, false);
+ $m = new Matrix($means, false);
+ $diff = $x->subtract($m);
+
+ return $diff->transpose()->multiply($diff);
+ }
+
+ /**
+ * Transforms the given sample to a lower dimensional vector by using
+ * the eigenVectors obtained in the last run of fit
.
+ *
+ * @param array $sample
+ *
+ * @return array
+ */
+ public function transform(array $sample)
+ {
+ if (!$this->fit) {
+ throw new \Exception("LDA has not been fitted with respect to original dataset, please run LDA::fit() first");
+ }
+
+ if (! is_array($sample[0])) {
+ $sample = [$sample];
+ }
+
+ return $this->reduce($sample);
+ }
+}
diff --git a/src/Phpml/DimensionReduction/PCA.php b/src/Phpml/DimensionReduction/PCA.php
index 422dae4..db2110d 100644
--- a/src/Phpml/DimensionReduction/PCA.php
+++ b/src/Phpml/DimensionReduction/PCA.php
@@ -4,27 +4,12 @@ declare(strict_types=1);
namespace Phpml\DimensionReduction;
-use Phpml\Math\LinearAlgebra\EigenvalueDecomposition;
use Phpml\Math\Statistic\Covariance;
use Phpml\Math\Statistic\Mean;
use Phpml\Math\Matrix;
-class PCA
+class PCA extends EigenTransformerBase
{
- /**
- * Total variance to be conserved after the reduction
- *
- * @var float
- */
- public $totalVariance = 0.9;
-
- /**
- * Number of features to be preserved after the reduction
- *
- * @var int
- */
- public $numFeatures = null;
-
/**
* Temporary storage for mean values for each dimension in given data
*
@@ -32,20 +17,6 @@ class PCA
*/
protected $means = [];
- /**
- * Eigenvectors of the covariance matrix
- *
- * @var array
- */
- protected $eigVectors = [];
-
- /**
- * Top eigenValues of the covariance matrix
- *
- * @var type
- */
- protected $eigValues = [];
-
/**
* @var bool
*/
@@ -100,7 +71,7 @@ class PCA
$covMatrix = Covariance::covarianceMatrix($data, array_fill(0, $n, 0));
- list($this->eigValues, $this->eigVectors) = $this->eigenDecomposition($covMatrix, $n);
+ $this->eigenDecomposition($covMatrix);
$this->fit = true;
@@ -146,63 +117,6 @@ class PCA
return $data;
}
- /**
- * Calculates eigenValues and eigenVectors of the given matrix. Returns
- * top eigenVectors along with the largest eigenValues. The total explained variance
- * of these eigenVectors will be no less than desired $totalVariance value
- *
- * @param array $matrix
- * @param int $n
- *
- * @return array
- */
- protected function eigenDecomposition(array $matrix, int $n)
- {
- $eig = new EigenvalueDecomposition($matrix);
- $eigVals = $eig->getRealEigenvalues();
- $eigVects= $eig->getEigenvectors();
-
- $totalEigVal = array_sum($eigVals);
- // Sort eigenvalues in descending order
- arsort($eigVals);
-
- $explainedVar = 0.0;
- $vectors = [];
- $values = [];
- foreach ($eigVals as $i => $eigVal) {
- $explainedVar += $eigVal / $totalEigVal;
- $vectors[] = $eigVects[$i];
- $values[] = $eigVal;
-
- if ($this->numFeatures !== null) {
- if (count($vectors) == $this->numFeatures) {
- break;
- }
- } else {
- if ($explainedVar >= $this->totalVariance) {
- break;
- }
- }
- }
-
- return [$values, $vectors];
- }
-
- /**
- * Returns the reduced data
- *
- * @param array $data
- *
- * @return array
- */
- protected function reduce(array $data)
- {
- $m1 = new Matrix($data);
- $m2 = new Matrix($this->eigVectors);
-
- return $m1->multiply($m2->transpose())->toArray();
- }
-
/**
* Transforms the given sample to a lower dimensional vector by using
* the eigenVectors obtained in the last run of fit
.
diff --git a/src/Phpml/Math/LinearAlgebra/EigenvalueDecomposition.php b/src/Phpml/Math/LinearAlgebra/EigenvalueDecomposition.php
index 27557bb..5cbc121 100644
--- a/src/Phpml/Math/LinearAlgebra/EigenvalueDecomposition.php
+++ b/src/Phpml/Math/LinearAlgebra/EigenvalueDecomposition.php
@@ -130,10 +130,10 @@ class EigenvalueDecomposition
$this->e[$j] = $g;
}
$f = 0.0;
+ if ($h === 0 || $h < 1e-32) {
+ $h = 1e-32;
+ }
for ($j = 0; $j < $i; ++$j) {
- if ($h === 0) {
- $h = 1e-20;
- }
$this->e[$j] /= $h;
$f += $this->e[$j] * $this->d[$j];
}
diff --git a/src/Phpml/Math/LinearAlgebra/LUDecomposition.php b/src/Phpml/Math/LinearAlgebra/LUDecomposition.php
new file mode 100644
index 0000000..1aeb239
--- /dev/null
+++ b/src/Phpml/Math/LinearAlgebra/LUDecomposition.php
@@ -0,0 +1,297 @@
+= n, the LU decomposition is an m-by-n
+ * unit lower triangular matrix L, an n-by-n upper triangular matrix U,
+ * and a permutation vector piv of length m so that A(piv,:) = L*U.
+ * If m < n, then L is m-by-m and U is m-by-n.
+ *
+ * The LU decompostion with pivoting always exists, even if the matrix is
+ * singular, so the constructor will never fail. The primary use of the
+ * LU decomposition is in the solution of square systems of simultaneous
+ * linear equations. This will fail if isNonsingular() returns false.
+ *
+ * @author Paul Meagher
+ * @author Bartosz Matosiuk
+ * @author Michael Bommarito
+ * @version 1.1
+ * @license PHP v3.0
+ *
+ * Slightly changed to adapt the original code to PHP-ML library
+ * @date 2017/04/24
+ * @author Mustafa Karabulut
+ */
+
+namespace Phpml\Math\LinearAlgebra;
+
+use Phpml\Math\Matrix;
+use Phpml\Exception\MatrixException;
+
+class LUDecomposition
+{
+ /**
+ * Decomposition storage
+ * @var array
+ */
+ private $LU = [];
+
+ /**
+ * Row dimension.
+ * @var int
+ */
+ private $m;
+
+ /**
+ * Column dimension.
+ * @var int
+ */
+ private $n;
+
+ /**
+ * Pivot sign.
+ * @var int
+ */
+ private $pivsign;
+
+ /**
+ * Internal storage of pivot vector.
+ * @var array
+ */
+ private $piv = [];
+
+
+ /**
+ * LU Decomposition constructor.
+ *
+ * @param $A Rectangular matrix
+ * @return Structure to access L, U and piv.
+ */
+ public function __construct(Matrix $A)
+ {
+ if ($A->getRows() != $A->getColumns()) {
+ throw MatrixException::notSquareMatrix();
+ }
+
+ // Use a "left-looking", dot-product, Crout/Doolittle algorithm.
+ $this->LU = $A->toArray();
+ $this->m = $A->getRows();
+ $this->n = $A->getColumns();
+ for ($i = 0; $i < $this->m; ++$i) {
+ $this->piv[$i] = $i;
+ }
+ $this->pivsign = 1;
+ $LUrowi = $LUcolj = [];
+
+ // Outer loop.
+ for ($j = 0; $j < $this->n; ++$j) {
+ // Make a copy of the j-th column to localize references.
+ for ($i = 0; $i < $this->m; ++$i) {
+ $LUcolj[$i] = &$this->LU[$i][$j];
+ }
+ // Apply previous transformations.
+ for ($i = 0; $i < $this->m; ++$i) {
+ $LUrowi = $this->LU[$i];
+ // Most of the time is spent in the following dot product.
+ $kmax = min($i, $j);
+ $s = 0.0;
+ for ($k = 0; $k < $kmax; ++$k) {
+ $s += $LUrowi[$k] * $LUcolj[$k];
+ }
+ $LUrowi[$j] = $LUcolj[$i] -= $s;
+ }
+ // Find pivot and exchange if necessary.
+ $p = $j;
+ for ($i = $j+1; $i < $this->m; ++$i) {
+ if (abs($LUcolj[$i]) > abs($LUcolj[$p])) {
+ $p = $i;
+ }
+ }
+ if ($p != $j) {
+ for ($k = 0; $k < $this->n; ++$k) {
+ $t = $this->LU[$p][$k];
+ $this->LU[$p][$k] = $this->LU[$j][$k];
+ $this->LU[$j][$k] = $t;
+ }
+ $k = $this->piv[$p];
+ $this->piv[$p] = $this->piv[$j];
+ $this->piv[$j] = $k;
+ $this->pivsign = $this->pivsign * -1;
+ }
+ // Compute multipliers.
+ if (($j < $this->m) && ($this->LU[$j][$j] != 0.0)) {
+ for ($i = $j+1; $i < $this->m; ++$i) {
+ $this->LU[$i][$j] /= $this->LU[$j][$j];
+ }
+ }
+ }
+ } // function __construct()
+
+
+ /**
+ * Get lower triangular factor.
+ *
+ * @return array Lower triangular factor
+ */
+ public function getL()
+ {
+ $L = [];
+ for ($i = 0; $i < $this->m; ++$i) {
+ for ($j = 0; $j < $this->n; ++$j) {
+ if ($i > $j) {
+ $L[$i][$j] = $this->LU[$i][$j];
+ } elseif ($i == $j) {
+ $L[$i][$j] = 1.0;
+ } else {
+ $L[$i][$j] = 0.0;
+ }
+ }
+ }
+ return new Matrix($L);
+ } // function getL()
+
+
+ /**
+ * Get upper triangular factor.
+ *
+ * @return array Upper triangular factor
+ */
+ public function getU()
+ {
+ $U = [];
+ for ($i = 0; $i < $this->n; ++$i) {
+ for ($j = 0; $j < $this->n; ++$j) {
+ if ($i <= $j) {
+ $U[$i][$j] = $this->LU[$i][$j];
+ } else {
+ $U[$i][$j] = 0.0;
+ }
+ }
+ }
+ return new Matrix($U);
+ } // function getU()
+
+
+ /**
+ * Return pivot permutation vector.
+ *
+ * @return array Pivot vector
+ */
+ public function getPivot()
+ {
+ return $this->piv;
+ } // function getPivot()
+
+
+ /**
+ * Alias for getPivot
+ *
+ * @see getPivot
+ */
+ public function getDoublePivot()
+ {
+ return $this->getPivot();
+ } // function getDoublePivot()
+
+
+ /**
+ * Is the matrix nonsingular?
+ *
+ * @return true if U, and hence A, is nonsingular.
+ */
+ public function isNonsingular()
+ {
+ for ($j = 0; $j < $this->n; ++$j) {
+ if ($this->LU[$j][$j] == 0) {
+ return false;
+ }
+ }
+ return true;
+ } // function isNonsingular()
+
+
+ /**
+ * Count determinants
+ *
+ * @return array d matrix deterninat
+ */
+ public function det()
+ {
+ if ($this->m == $this->n) {
+ $d = $this->pivsign;
+ for ($j = 0; $j < $this->n; ++$j) {
+ $d *= $this->LU[$j][$j];
+ }
+ return $d;
+ } else {
+ throw MatrixException::notSquareMatrix();
+ }
+ } // function det()
+
+
+ /**
+ * Solve A*X = B
+ *
+ * @param Matrix $B A Matrix with as many rows as A and any number of columns.
+ *
+ * @return array X so that L*U*X = B(piv,:)
+ *
+ * @throws MatrixException
+ */
+ public function solve(Matrix $B)
+ {
+ if ($B->getRows() != $this->m) {
+ throw MatrixException::notSquareMatrix();
+ }
+
+ if (! $this->isNonsingular()) {
+ throw MatrixException::singularMatrix();
+ }
+
+ // Copy right hand side with pivoting
+ $nx = $B->getColumns();
+ $X = $this->getSubMatrix($B->toArray(), $this->piv, 0, $nx-1);
+ // Solve L*Y = B(piv,:)
+ for ($k = 0; $k < $this->n; ++$k) {
+ for ($i = $k+1; $i < $this->n; ++$i) {
+ for ($j = 0; $j < $nx; ++$j) {
+ $X[$i][$j] -= $X[$k][$j] * $this->LU[$i][$k];
+ }
+ }
+ }
+ // Solve U*X = Y;
+ for ($k = $this->n-1; $k >= 0; --$k) {
+ for ($j = 0; $j < $nx; ++$j) {
+ $X[$k][$j] /= $this->LU[$k][$k];
+ }
+ for ($i = 0; $i < $k; ++$i) {
+ for ($j = 0; $j < $nx; ++$j) {
+ $X[$i][$j] -= $X[$k][$j] * $this->LU[$i][$k];
+ }
+ }
+ }
+ return $X;
+ } // function solve()
+
+ /**
+ * @param Matrix $matrix
+ * @param int $j0
+ * @param int $jF
+ *
+ * @return array
+ */
+ protected function getSubMatrix(array $matrix, array $RL, int $j0, int $jF)
+ {
+ $m = count($RL);
+ $n = $jF - $j0;
+ $R = array_fill(0, $m, array_fill(0, $n+1, 0.0));
+
+ for ($i = 0; $i < $m; ++$i) {
+ for ($j = $j0; $j <= $jF; ++$j) {
+ $R[$i][$j - $j0]= $matrix[ $RL[$i] ][$j];
+ }
+ }
+
+ return $R;
+ }
+} // class LUDecomposition
diff --git a/src/Phpml/Math/Matrix.php b/src/Phpml/Math/Matrix.php
index 25101f3..c996e7f 100644
--- a/src/Phpml/Math/Matrix.php
+++ b/src/Phpml/Math/Matrix.php
@@ -4,6 +4,7 @@ declare(strict_types=1);
namespace Phpml\Math;
+use Phpml\Math\LinearAlgebra\LUDecomposition;
use Phpml\Exception\InvalidArgumentException;
use Phpml\Exception\MatrixException;
@@ -137,32 +138,8 @@ class Matrix
throw MatrixException::notSquareMatrix();
}
- return $this->determinant = $this->calculateDeterminant();
- }
-
- /**
- * @return float|int
- *
- * @throws MatrixException
- */
- private function calculateDeterminant()
- {
- $determinant = 0;
- if ($this->rows == 1 && $this->columns == 1) {
- $determinant = $this->matrix[0][0];
- } elseif ($this->rows == 2 && $this->columns == 2) {
- $determinant =
- $this->matrix[0][0] * $this->matrix[1][1] -
- $this->matrix[0][1] * $this->matrix[1][0];
- } else {
- for ($j = 0; $j < $this->columns; ++$j) {
- $subMatrix = $this->crossOut(0, $j);
- $minor = $this->matrix[0][$j] * $subMatrix->getDeterminant();
- $determinant += fmod((float) $j, 2.0) == 0 ? $minor : -$minor;
- }
- }
-
- return $determinant;
+ $lu = new LUDecomposition($this);
+ return $this->determinant = $lu->det();
}
/**
@@ -303,21 +280,26 @@ class Matrix
throw MatrixException::notSquareMatrix();
}
- if ($this->isSingular()) {
- throw MatrixException::singularMatrix();
+ $LU = new LUDecomposition($this);
+ $identity = $this->getIdentity();
+ $inverse = $LU->solve($identity);
+
+ return new self($inverse, false);
+ }
+
+ /**
+ * Returns diagonal identity matrix of the same size of this matrix
+ *
+ * @return Matrix
+ */
+ protected function getIdentity()
+ {
+ $array = array_fill(0, $this->rows, array_fill(0, $this->columns, 0));
+ for ($i=0; $i < $this->rows; $i++) {
+ $array[$i][$i] = 1;
}
- $newMatrix = [];
- for ($i = 0; $i < $this->rows; ++$i) {
- for ($j = 0; $j < $this->columns; ++$j) {
- $minor = $this->crossOut($i, $j)->getDeterminant();
- $newMatrix[$i][$j] = fmod((float) ($i + $j), 2.0) == 0 ? $minor : -$minor;
- }
- }
-
- $cofactorMatrix = new self($newMatrix, false);
-
- return $cofactorMatrix->transpose()->divideByScalar($this->getDeterminant());
+ return new self($array, false);
}
/**
diff --git a/tests/Phpml/DimensionReduction/LDATest.php b/tests/Phpml/DimensionReduction/LDATest.php
new file mode 100644
index 0000000..5ebe018
--- /dev/null
+++ b/tests/Phpml/DimensionReduction/LDATest.php
@@ -0,0 +1,65 @@
+fit($dataset->getSamples(), $dataset->getTargets());
+
+ // Some samples of the Iris data will be checked manually
+ // First 3 and last 3 rows from the original dataset
+ $data = [
+ [5.1, 3.5, 1.4, 0.2],
+ [4.9, 3.0, 1.4, 0.2],
+ [4.7, 3.2, 1.3, 0.2],
+ [6.5, 3.0, 5.2, 2.0],
+ [6.2, 3.4, 5.4, 2.3],
+ [5.9, 3.0, 5.1, 1.8]
+ ];
+ $transformed2 = [
+ [-1.4922092756753, 1.9047102045574],
+ [-1.2576556684358, 1.608414450935],
+ [-1.3487505965419, 1.749846351699],
+ [1.7759343101456, 2.0371552314006],
+ [2.0059819019159, 2.4493123003226],
+ [1.701474913008, 1.9037880473772]
+ ];
+
+ $control = [];
+ $control = array_merge($control, array_slice($transformed, 0, 3));
+ $control = array_merge($control, array_slice($transformed, -3));
+
+ $check = function ($row1, $row2) use ($epsilon) {
+ // Due to the fact that the sign of values can be flipped
+ // during the calculation of eigenValues, we have to compare
+ // absolute value of the values
+ $row1 = array_map('abs', $row1);
+ $row2 = array_map('abs', $row2);
+ $this->assertEquals($row1, $row2, '', $epsilon);
+ };
+ array_map($check, $control, $transformed2);
+
+ // Fitted LDA object should be able to return same values again
+ // for each projected row
+ foreach ($data as $i => $row) {
+ $newRow = [$transformed2[$i]];
+ $newRow2= $lda->transform($row);
+
+ array_map($check, $newRow, $newRow2);
+ }
+ }
+}