LeastSquares::train() - Code Metrics - Inspection of "add Mean::arithmetic tests" - php-ai/php-ml - Measure and Improve Code Quality continuously with Scrutinizer

Completed

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by Arkadiusz

created 2016-04-27 21:57 UTC

LeastSquares::train() A

↳ Parent: LeastSquares

Complexity

Conditions	1
Paths	1

Size

Total Lines	8
Code Lines	5

Duplication

Lines	0
Ratio	0 %

Importance

Changes	2
Bugs	0	Features	1

Metric	Value
c	2
b	0
f	1
dl	0
loc	8
rs	9.4285
cc	1
eloc	5
nc	1
nop	2

<?php

declare (strict_types = 1);

namespace Phpml\Regression;

use Phpml\Math\Statistic\Correlation;
use Phpml\Math\Statistic\StandardDeviation;
use Phpml\Math\Statistic\Mean;

class LeastSquares implements Regression
{
    /**
     * @var array
     */
    private $features;

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

    /**
     * @var float
     */
    private $slope;

    /**
     * @var
     */
    private $intercept;

    /**
     * @param array $features
     * @param array $targets
     */
    public function train(array $features, array $targets)
    {
        $this->features = $features;
        $this->targets = $targets;

        $this->computeSlope();
        $this->computeIntercept();
    }

    /**
     * @param float $feature
     *
     * @return mixed
     */
    public function predict($feature)
    {
        return $this->intercept + ($this->slope * $feature);
    }

    private function computeSlope()
    {
        $correlation = Correlation::pearson($this->features, $this->targets);
        $sdX = StandardDeviation::population($this->features);
        $sdY = StandardDeviation::population($this->targets);

        $this->slope = $correlation * ($sdY / $sdX);
    }

    private function computeIntercept()
    {
        $meanY = Mean::arithmetic($this->targets);
        $meanX = Mean::arithmetic($this->features);

        $this->intercept = $meanY - ($this->slope * $meanX);
    }
}


1			<?php
2
3			declare (strict_types = 1);
4
5			namespace Phpml\Regression;
6
7			use Phpml\Math\Statistic\Correlation;
8			use Phpml\Math\Statistic\StandardDeviation;
9			use Phpml\Math\Statistic\Mean;
10
11			class LeastSquares implements Regression
12			{
13			/**
14			* @var array
15			*/
16			private $features;
17
18			/**
19			* @var array
20			*/
21			private $targets;
22
23			/**
24			* @var float
25			*/
26			private $slope;
27
28			/**
29			* @var
30			*/
31			private $intercept;
32
33			/**
34			* @param array $features
35			* @param array $targets
36			*/
37			public function train(array $features, array $targets)
38			{
39			$this->features = $features;
40			$this->targets = $targets;
41
42			$this->computeSlope();
43			$this->computeIntercept();
44			}
45
46			/**
47			* @param float $feature
48			*
49			* @return mixed
50			*/
51			public function predict($feature)
52			{
53			return $this->intercept + ($this->slope * $feature);
54			}
55
56			private function computeSlope()
57			{
58			$correlation = Correlation::pearson($this->features, $this->targets);
59			$sdX = StandardDeviation::population($this->features);
60			$sdY = StandardDeviation::population($this->targets);
61
62			$this->slope = $correlation * ($sdY / $sdX);
63			}
64
65			private function computeIntercept()
66			{
67			$meanY = Mean::arithmetic($this->targets);
68			$meanX = Mean::arithmetic($this->features);
69
70			$this->intercept = $meanY - ($this->slope * $meanX);
71			}
72			}
73

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LeastSquares::train() A

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