KernelPCA::fit() - Code Metrics - Inspection of "Linear algebra operations, Dimensionally reduction..." - php-ai/php-ml - Measure and Improve Code Quality continuously with Scrutinizer

Test Failed

Pull Request — master (#81)

unknown

created 2017-04-19 11:46 UTC

KernelPCA::fit() A

↳ Parent: KernelPCA

Complexity

Conditions	2
Paths	2

Size

Total Lines	19
Code Lines	11

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
dl	0
loc	19
rs	9.4285
c	0
b	0
f	0
cc	2
eloc	11
nc	2
nop	1

<?php

declare(strict_types=1);

namespace Phpml\DimensionReduction;

use Phpml\Math\Distance\Euclidean;
use Phpml\Math\Distance\Manhattan;
use Phpml\Math\Matrix;

class KernelPCA extends PCA
{
    const KERNEL_RBF = 1;
    const KERNEL_SIGMOID = 2;
    const KERNEL_LAPLACIAN = 3;

    /**
     * Selected kernel function
     *
     * @var int
     */
    protected $kernel;

    /**
     * Gamma value used by the kernel
     *
     * @var float
     */
    protected $gamma;

    /**
     * Original dataset used to fit KernelPCA
     *
     * @var array
     */
    protected $data;

    /**
     *
     * @param int $kernel
     * @param float $totalVariance
     * @param int $numFeatures
     * @param float $gamma
     *
     * @throws \Exception
     */
    public function __construct(int $kernel = self::KERNEL_RBF, $totalVariance = null, $numFeatures = null, $gamma = null)
    {
        if (! in_array($kernel, [self::KERNEL_RBF, self::KERNEL_SIGMOID, self::KERNEL_LAPLACIAN])) {
            throw new \Exception("KernelPCA can be initialized with the following kernels only: RBF, Sigmoid and Laplacian");
        }

        parent::__construct($totalVariance, $numFeatures);

        $this->kernel = $kernel;
        $this->gamma = $gamma;
    }

    /**
     * Takes a data and returns a lower dimensional version
     * of this data while preserving $totalVariance or $numFeatures. <br>
     * $data is an n-by-m matrix and returned array is
     * n-by-k matrix where k <= m
     *
     * @param array $data
     *
     * @return array
     */
    public function fit(array $data)
    {
        $numRows = count($data);
        $numCols = count($data[0]);

        if ($this->gamma === null) {
            $this->gamma = 1.0 / $numRows;
        }

        $this->data = $this->normalize($data, $numCols);
        $matrix = $this->calculateKernelMatrix($this->data, $numRows);
        $matrix = $this->centerMatrix($matrix, $numRows);

        list($this->eigValues, $this->eigVectors) = $this->eigenDecomposition($matrix, $numRows);

        $this->fit = true;

        return Matrix::transposeArray($this->eigVectors);
    }

    /**
     * Calculates similarity matrix by use of selected kernel function<br>
     * An n-by-m matrix is given and an n-by-n matrix is returned
     *
     * @param array $data
     * @param int $numRows
     *
     * @return array
     */
    protected function calculateKernelMatrix(array $data, int $numRows)
    {
        $kernelFunc = $this->getKernel();

        $matrix = [];
        for ($i=0; $i < $numRows; $i++) {
            for ($k=0; $k < $numRows; $k++) {
                if ($i <= $k) {
                    $matrix[$i][$k] = $kernelFunc($data[$i], $data[$k]);
                } else {
                    $matrix[$i][$k] = $matrix[$k][$i];
                }
            }
        }

        return $matrix;
    }

    /**
     * Kernel matrix is centered in its original space by using the following
     * conversion:
     *
     * K′ = K− N . K −  K.N + N.K.N where N is n-by-n matrix filled with 1/n
     *
     * @param array $matrix
     * @param int $n
     */
    protected function centerMatrix(array $matrix, int $n)
    {
        $N = array_fill(0, $n, array_fill(0, $n, 1.0/$n));
        $N = new Matrix($N, false);
        $K = new Matrix($matrix, false);

        // K.N (This term is repeated so we cache it once)
        $K_N = $K->multiply($N);
        // N.K
        $N_K = $N->multiply($K);
        // N.K.N
        $N_K_N = $N->multiply($K_N);

        return $K->subtract($N_K)
                 ->subtract($K_N)
                 ->add($N_K_N)
                 ->toArray();
    }

    /**
     * Returns the callable kernel function
     *
     * @return \Closure
     */
    protected function getKernel()
    {
        /**
         * Each kernel function operates in two modes:
         * 1) k(x, y) mode: x and y is given
         * 2) k(x) mode: only x is given
         */
        switch ($this->kernel) {
            case self::KERNEL_RBF:

                // k(x,y)=exp(-γ.|x-y|) where |..| is Euclidean distance
                $dist = new Euclidean();
                return function ($x, $y = null) use ($dist) {
                    if ($y === null) {
                        foreach ($x as $i => $element) {
                            $x[$i] = exp(-$this->gamma * $element);
                        }

                        return $x;
                    }

                    return exp(-$this->gamma *    $dist->distance($x, $y));
                };

            case self::KERNEL_SIGMOID:
                // k(x,y)=tanh(γ.xT.y+c0) where c0=0
                return function ($x, $y = null) {
                    if ($y === null) {
                        foreach ($x as $i => $element) {
                            $x[$i] = tanh($this->gamma * $element);
                        }

                        return $x;
                    }

                    $x = new Matrix($x);
                    $y = new Matrix($y);
                    $res = $x->multiply($y->transpose());

                    return tanh($this->gamma * $res->toScalar());
                };

            case self::KERNEL_LAPLACIAN:

                // k(x,y)=exp(-γ.|x-y|) where |..| is Manhattan distance
                $dist = new Manhattan();
                return function ($x, $y = null) use ($dist) {
                    if ($y === null) {
                        foreach ($x as $i => $element) {
                            $x[$i] = exp(-$this->gamma * $element);
                        }

                        return $x;
                    }

                    return exp(-$this->gamma * $dist->distance($x, $y));
                };
        }
    }

    /**
     * @param array $sample
     *
     * @return array
     */
    protected function getDistancePairs(array $sample)
    {
        $kernel = $this->getKernel();
        $pairs = [];
        foreach ($this->data as $row) {
            $pairs[] = $kernel($row, $sample);
        }

        return $pairs;
    }

    /**
     * @param array $pairs
     *
     * @return array
     */
    protected function projectSample(array $pairs)
    {
        // Normalize eigenvectors by eig = eigVectors / eigValues
        $func = function ($eigVal, $eigVect) {
            $m = new Matrix($eigVect, false);
            $a = $m->divideByScalar(sqrt($eigVal))->toArray();

            return $a[0];
        };
        $eig = array_map($func, $this->eigValues, $this->eigVectors);

        // return k.dot(eig)
        return Matrix::dot($pairs, $eig);
    }

    /**
     * Transforms the given sample to a lower dimensional vector by using
     * the variables obtained during the last run of <code>fit</code>.
     *
     * @param array $sample
     *
     * @return array
     */
    public function transform(array $sample)
    {
        if (!$this->fit) {
            throw new \Exception("KernelPCA has not been fitted with respect to original dataset, please run KernelPCA::fit() first");
        }

        if (is_array($sample[0])) {
            throw new \Exception("KernelPCA::transform() accepts only one-dimensional arrays");
        }

        $sample = $this->normalize([$sample], count($sample));
        $sample = $sample[0];

        $pairs = $this->getDistancePairs($sample);

        return $this->projectSample($pairs);
    }
}


1		<?php
2
3		declare(strict_types=1);
4
5		namespace Phpml\DimensionReduction;
6
7		use Phpml\Math\Distance\Euclidean;
8		use Phpml\Math\Distance\Manhattan;
9		use Phpml\Math\Matrix;
10
11		class KernelPCA extends PCA
12		{
13		const KERNEL_RBF = 1;
14		const KERNEL_SIGMOID = 2;
15		const KERNEL_LAPLACIAN = 3;
16
17		/**
18		* Selected kernel function
19		*
20		* @var int
21		*/
22		protected $kernel;
23
24		/**
25		* Gamma value used by the kernel
26		*
27		* @var float
28		*/
29		protected $gamma;
30
31		/**
32		* Original dataset used to fit KernelPCA
33		*
34		* @var array
35		*/
36		protected $data;
37
38		/**
39		*
40		* @param int $kernel
41		* @param float $totalVariance
42		* @param int $numFeatures
43		* @param float $gamma
44		*
45		* @throws \Exception
46		*/
47		public function __construct(int $kernel = self::KERNEL_RBF, $totalVariance = null, $numFeatures = null, $gamma = null)
48		{
49		if (! in_array($kernel, [self::KERNEL_RBF, self::KERNEL_SIGMOID, self::KERNEL_LAPLACIAN])) {
50		throw new \Exception("KernelPCA can be initialized with the following kernels only: RBF, Sigmoid and Laplacian");
51		}
52
53		parent::__construct($totalVariance, $numFeatures);
54
55		$this->kernel = $kernel;
56		$this->gamma = $gamma;
57		}
58
59		/**
60		* Takes a data and returns a lower dimensional version
61		* of this data while preserving $totalVariance or $numFeatures. <br>
62		* $data is an n-by-m matrix and returned array is
63		* n-by-k matrix where k <= m
64		*
65		* @param array $data
66		*
67		* @return array
68		*/
69		public function fit(array $data)
70		{
71		$numRows = count($data);
72		$numCols = count($data[0]);
73
74		if ($this->gamma === null) {
75		$this->gamma = 1.0 / $numRows;
76		}
77
78		$this->data = $this->normalize($data, $numCols);
79		$matrix = $this->calculateKernelMatrix($this->data, $numRows);
80		$matrix = $this->centerMatrix($matrix, $numRows);
81
82		list($this->eigValues, $this->eigVectors) = $this->eigenDecomposition($matrix, $numRows);
83
84		$this->fit = true;
85
86		return Matrix::transposeArray($this->eigVectors);
87		}
88
89		/**
90		* Calculates similarity matrix by use of selected kernel function<br>
91		* An n-by-m matrix is given and an n-by-n matrix is returned
92		*
93		* @param array $data
94		* @param int $numRows
95		*
96		* @return array
97		*/
98		protected function calculateKernelMatrix(array $data, int $numRows)
99		{
100		$kernelFunc = $this->getKernel();
101
102		$matrix = [];
103		for ($i=0; $i < $numRows; $i++) {
104		for ($k=0; $k < $numRows; $k++) {
105		if ($i <= $k) {
106		$matrix[$i][$k] = $kernelFunc($data[$i], $data[$k]);
107		} else {
108		$matrix[$i][$k] = $matrix[$k][$i];
109		}
110		}
111		}
112
113		return $matrix;
114		}
115
116		/**
117		* Kernel matrix is centered in its original space by using the following
118		* conversion:
119		*
120		* K′ = K− N . K − K.N + N.K.N where N is n-by-n matrix filled with 1/n
121		*
122		* @param array $matrix
123		* @param int $n
124		*/
125		protected function centerMatrix(array $matrix, int $n)
126		{
127		$N = array_fill(0, $n, array_fill(0, $n, 1.0/$n));
128		$N = new Matrix($N, false);
129		$K = new Matrix($matrix, false);
130
131		// K.N (This term is repeated so we cache it once)
132		$K_N = $K->multiply($N);
133		// N.K
134		$N_K = $N->multiply($K);
135		// N.K.N
136		$N_K_N = $N->multiply($K_N);
137
138		return $K->subtract($N_K)
139		->subtract($K_N)
140		->add($N_K_N)
141		->toArray();
142		}
143
144		/**
145		* Returns the callable kernel function
146		*
147		* @return \Closure
148		*/
149		protected function getKernel()
150		{
151		/**
152		* Each kernel function operates in two modes:
153		* 1) k(x, y) mode: x and y is given
154		* 2) k(x) mode: only x is given
155		*/
156		switch ($this->kernel) {
157	View Code Duplication	case self::KERNEL_RBF:
		0 ignored issues – show Duplication introduced 2017-04-19 11:50 UTC by Report Bug Copy Issue Report This code seems to be duplicated across your project. Duplicated code is one of the most pungent code smells. If you need to duplicate the same code in three or more different places, we strongly encourage you to look into extracting the code into a single class or operation. You can also find more detailed suggestions in the “Code” section of your repository. Loading history...
158		// k(x,y)=exp(-γ.\|x-y\|) where \|..\| is Euclidean distance
159		$dist = new Euclidean();
160		return function ($x, $y = null) use ($dist) {
161		if ($y === null) {
162		foreach ($x as $i => $element) {
163		$x[$i] = exp(-$this->gamma * $element);
164		}
165
166		return $x;
167		}
168
169		return exp(-$this->gamma * $dist->distance($x, $y));
170		};
171
172		case self::KERNEL_SIGMOID:
173		// k(x,y)=tanh(γ.xT.y+c0) where c0=0
174		return function ($x, $y = null) {
175		if ($y === null) {
176		foreach ($x as $i => $element) {
177		$x[$i] = tanh($this->gamma * $element);
178		}
179
180		return $x;
181		}
182
183		$x = new Matrix($x);
184		$y = new Matrix($y);
185		$res = $x->multiply($y->transpose());
186
187		return tanh($this->gamma * $res->toScalar());
188		};
189
190	View Code Duplication	case self::KERNEL_LAPLACIAN:
		0 ignored issues – show Duplication introduced 2017-04-19 11:50 UTC by Report Bug Copy Issue Report This code seems to be duplicated across your project. Duplicated code is one of the most pungent code smells. If you need to duplicate the same code in three or more different places, we strongly encourage you to look into extracting the code into a single class or operation. You can also find more detailed suggestions in the “Code” section of your repository. Loading history...
191		// k(x,y)=exp(-γ.\|x-y\|) where \|..\| is Manhattan distance
192		$dist = new Manhattan();
193		return function ($x, $y = null) use ($dist) {
194		if ($y === null) {
195		foreach ($x as $i => $element) {
196		$x[$i] = exp(-$this->gamma * $element);
197		}
198
199		return $x;
200		}
201
202		return exp(-$this->gamma * $dist->distance($x, $y));
203		};
204		}
205		}
206
207		/**
208		* @param array $sample
209		*
210		* @return array
211		*/
212		protected function getDistancePairs(array $sample)
213		{
214		$kernel = $this->getKernel();
215		$pairs = [];
216		foreach ($this->data as $row) {
217		$pairs[] = $kernel($row, $sample);
218		}
219
220		return $pairs;
221		}
222
223		/**
224		* @param array $pairs
225		*
226		* @return array
227		*/
228		protected function projectSample(array $pairs)
229		{
230		// Normalize eigenvectors by eig = eigVectors / eigValues
231		$func = function ($eigVal, $eigVect) {
232		$m = new Matrix($eigVect, false);
233		$a = $m->divideByScalar(sqrt($eigVal))->toArray();
234
235		return $a[0];
236		};
237		$eig = array_map($func, $this->eigValues, $this->eigVectors);
238
239		// return k.dot(eig)
240		return Matrix::dot($pairs, $eig);
241		}
242
243		/**
244		* Transforms the given sample to a lower dimensional vector by using
245		* the variables obtained during the last run of <code>fit</code>.
246		*
247		* @param array $sample
248		*
249		* @return array
250		*/
251		public function transform(array $sample)
252		{
253		if (!$this->fit) {
254		throw new \Exception("KernelPCA has not been fitted with respect to original dataset, please run KernelPCA::fit() first");
255		}
256
257		if (is_array($sample[0])) {
258		throw new \Exception("KernelPCA::transform() accepts only one-dimensional arrays");
259		}
260
261		$sample = $this->normalize([$sample], count($sample));
262		$sample = $sample[0];
263
264		$pairs = $this->getDistancePairs($sample);
265
266		return $this->projectSample($pairs);
267		}
268		}
269

php-ai / php-ml

Pull Request — master (#81)

KernelPCA::fit() A

Complexity

Size

Duplication

Importance

Duplication Side-by-Side

Filter issues like