KNearestNeighbors::predictSample() - Code Metrics - Inspection of "integration tests for knn classifier" - php-ai/php-ml - Measure and Improve Code Quality continuously with Scrutinizer

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by Arkadiusz

created 2016-04-08 20:49 UTC

KNearestNeighbors::predictSample() A

↳ Parent: KNearestNeighbors

Complexity

Conditions	2
Paths	2

Size

Total Lines	15
Code Lines	8

Duplication

Lines	0
Ratio	0 %

Metric	Value
dl	0
loc	15
rs	9.4285
cc	2
eloc	8
nc	2
nop	1

<?php

declare (strict_types = 1);

namespace Phpml\Classifier;

use Phpml\Metric\Distance;

class KNearestNeighbors implements Classifier
{
    /**
     * @var int
     */
    private $k;

    /**
     * @var array
     */
    private $samples;

    /**
     * @var array
     */
    private $labels;

    /**
     * @param int $k
     */
    public function __construct(int $k = 3)
    {
        $this->k = $k;
        $this->samples = [];
        $this->labels = [];
    }

    /**
     * @param array $samples
     * @param array $labels
     */
    public function train(array $samples, array $labels)
    {
        $this->samples = $samples;
        $this->labels = $labels;
    }

    /**
     * @param array $samples
     *
     * @return mixed
     */
    public function predict(array $samples)
    {
        if (!is_array($samples[0])) {
            $predicted = $this->predictSample($samples);
        } else {
            $predicted = [];
            foreach ($samples as $index => $sample) {
                $predicted[$index] = $this->predictSample($sample);
            }
        }

        return $predicted;
    }

    /**
     * @param array $sample
     *
     * @return mixed
     */
    private function predictSample(array $sample)
    {
        $distances = $this->kNeighborsDistances($sample);

        $predictions = array_combine(array_values($this->labels), array_fill(0, count($this->labels), 0));

        foreach ($distances as $index => $distance) {
            ++$predictions[$this->labels[$index]];
        }

        arsort($predictions);
        reset($predictions);

        return key($predictions);
    }

    /**
     * @param array $sample
     *
     * @return array
     *
     * @throws \Phpml\Exception\InvalidArgumentException
     */
    private function kNeighborsDistances(array $sample): array
    {
        $distances = [];

        foreach ($this->samples as $index => $neighbor) {
            $distances[$index] = Distance::euclidean($sample, $neighbor);
        }

        asort($distances);

        return array_slice($distances, 0, $this->k, true);
    }
}


1			<?php
2
3			declare (strict_types = 1);
4
5			namespace Phpml\Classifier;
6
7			use Phpml\Metric\Distance;
8
9			class KNearestNeighbors implements Classifier
10			{
11			/**
12			* @var int
13			*/
14			private $k;
15
16			/**
17			* @var array
18			*/
19			private $samples;
20
21			/**
22			* @var array
23			*/
24			private $labels;
25
26			/**
27			* @param int $k
28			*/
29			public function __construct(int $k = 3)
30			{
31			$this->k = $k;
32			$this->samples = [];
33			$this->labels = [];
34			}
35
36			/**
37			* @param array $samples
38			* @param array $labels
39			*/
40			public function train(array $samples, array $labels)
41			{
42			$this->samples = $samples;
43			$this->labels = $labels;
44			}
45
46			/**
47			* @param array $samples
48			*
49			* @return mixed
50			*/
51			public function predict(array $samples)
52			{
53			if (!is_array($samples[0])) {
54			$predicted = $this->predictSample($samples);
55			} else {
56			$predicted = [];
57			foreach ($samples as $index => $sample) {
58			$predicted[$index] = $this->predictSample($sample);
59			}
60			}
61
62			return $predicted;
63			}
64
65			/**
66			* @param array $sample
67			*
68			* @return mixed
69			*/
70			private function predictSample(array $sample)
71			{
72			$distances = $this->kNeighborsDistances($sample);
73
74			$predictions = array_combine(array_values($this->labels), array_fill(0, count($this->labels), 0));
75
76			foreach ($distances as $index => $distance) {
77			++$predictions[$this->labels[$index]];
78			}
79
80			arsort($predictions);
81			reset($predictions);
82
83			return key($predictions);
84			}
85
86			/**
87			* @param array $sample
88			*
89			* @return array
90			*
91			* @throws \Phpml\Exception\InvalidArgumentException
92			*/
93			private function kNeighborsDistances(array $sample): array
94			{
95			$distances = [];
96
97			foreach ($this->samples as $index => $neighbor) {
98			$distances[$index] = Distance::euclidean($sample, $neighbor);
99			}
100
101			asort($distances);
102
103			return array_slice($distances, 0, $this->k, true);
104			}
105			}
106

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KNearestNeighbors::predictSample() A

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