DecisionStump - Code Metrics - Inspection of "One-v-Rest Classification technique applied to lin..." - php-ai/php-ml - Measure and Improve Code Quality continuously with Scrutinizer

Test Failed

Branch — master (01bb82)

by Arkadiusz

created 2017-03-05 15:35 UTC

DecisionStump B

↳ Parent: Project

Complexity

Total Complexity

Size/Duplication

Total Lines	350
Duplicated Lines	4.29 %

Coupling/Cohesion

Components	1
Dependencies	4

Importance

Changes

Metric	Value
wmc	43
lcom	1
cbo	4
dl	15
loc	350
rs	8.3157
c	0
b	0
f	0

10 Methods

Rating	Name	Duplication	Size	Complexity
A	__construct()	0	4	1
B	trainBinary()	0	54	9
A	setNumericalSplitCount()	0	4	1
B	getBestNumericalSplit()	10	38	6
B	getBestNominalSplit()	5	22	5
B	evaluate()	0	14	8
C	calculateErrorRate()	0	38	8
A	predictProbability()	0	9	2
A	predictSampleBinary()	0	8	2
A	__toString()	0	6	1

How to fix Duplicated Code Complexity

Duplicated Code

Duplicate code is one of the most pungent code smells. A rule that is often used is to re-structure code once it is duplicated in three or more places.

Common duplication problems, and corresponding solutions are:

If you have the same expression in different places: Extract expression to a method
If you have the same method in different sub-classes: Extract method, and pull up field to the parent class
If you have the same code in unrelated classes: Consider extracting the code to a new class, and injecting that class

Complex Class

Tip: Before tackling complexity, make sure that you eliminate any duplication first. This often can reduce the size of classes significantly.

Complex classes like DecisionStump often do a lot of different things. To break such a class down, we need to identify a cohesive component within that class. A common approach to find such a component is to look for fields/methods that share the same prefixes, or suffixes. You can also have a look at the cohesion graph to spot any un-connected, or weakly-connected components.

Once you have determined the fields that belong together, you can apply the Extract Class refactoring. If the component makes sense as a sub-class, Extract Subclass is also a candidate, and is often faster.

While breaking up the class, it is a good idea to analyze how other classes use DecisionStump, and based on these observations, apply Extract Interface, too.

<?php

declare(strict_types=1);

namespace Phpml\Classification\Linear;

use Phpml\Helper\Predictable;
use Phpml\Helper\OneVsRest;
use Phpml\Classification\WeightedClassifier;
use Phpml\Classification\DecisionTree;

class DecisionStump extends WeightedClassifier
{
    use Predictable, OneVsRest;

    const AUTO_SELECT = -1;

    /**
     * @var int
     */
    protected $givenColumnIndex;

    /**
     * @var array
     */
    protected $binaryLabels;

    /**
     * Sample weights : If used the optimization on the decision value
     * will take these weights into account. If not given, all samples
     * will be weighed with the same value of 1
     *
     * @var array
     */
    protected $weights = null;

    /**
     * Lowest error rate obtained while training/optimizing the model
     *
     * @var float
     */
    protected $trainingErrorRate;

    /**
     * @var int
     */
    protected $column;

    /**
     * @var mixed
     */
    protected $value;

    /**
     * @var string
     */
    protected $operator;

    /**
     * @var array
     */
    protected $columnTypes;

    /**
     * @var int
     */
    protected $featureCount;

    /**
     * @var float
     */
    protected $numSplitCount = 100.0;

    /**
     * Distribution of samples in the leaves
     *
     * @var array
     */
    protected $prob;

    /**
     * A DecisionStump classifier is a one-level deep DecisionTree. It is generally
     * used with ensemble algorithms as in the weak classifier role. <br>
     *
     * If columnIndex is given, then the stump tries to produce a decision node
     * on this column, otherwise in cases given the value of -1, the stump itself
     * decides which column to take for the decision (Default DecisionTree behaviour)
     *
     * @param int $columnIndex
     */
    public function __construct(int $columnIndex = self::AUTO_SELECT)
    {
        $this->givenColumnIndex = $columnIndex;
    }

    /**
     * @param array $samples
     * @param array $targets
     */
    protected function trainBinary(array $samples, array $targets)
    {
        $this->samples = array_merge($this->samples, $samples);
        $this->targets = array_merge($this->targets, $targets);
        $this->binaryLabels = array_keys(array_count_values($this->targets));
        $this->featureCount = count($this->samples[0]);

        // If a column index is given, it should be among the existing columns
        if ($this->givenColumnIndex > count($this->samples[0]) - 1) {
            $this->givenColumnIndex = self::AUTO_SELECT;
        }

        // Check the size of the weights given.
        // If none given, then assign 1 as a weight to each sample
        if ($this->weights) {

            $numWeights = count($this->weights);
            if ($numWeights != count($this->samples)) {
                throw new \Exception("Number of sample weights does not match with number of samples");
            }
        } else {
            $this->weights = array_fill(0, count($this->samples), 1);
        }

        // Determine type of each column as either "continuous" or "nominal"
        $this->columnTypes = DecisionTree::getColumnTypes($this->samples);

        // Try to find the best split in the columns of the dataset
        // by calculating error rate for each split point in each column
        $columns = range(0, count($this->samples[0]) - 1);
        if ($this->givenColumnIndex != self::AUTO_SELECT) {
            $columns = [$this->givenColumnIndex];
        }

        $bestSplit = [
            'value' => 0, 'operator' => '',
            'prob' => [], 'column' => 0,
            'trainingErrorRate' => 1.0];
        foreach ($columns as $col) {
            if ($this->columnTypes[$col] == DecisionTree::CONTINUOS) {
                $split = $this->getBestNumericalSplit($col);
            } else {
                $split = $this->getBestNominalSplit($col);
            }

            if ($split['trainingErrorRate'] < $bestSplit['trainingErrorRate']) {
                $bestSplit = $split;
            }
        }

        // Assign determined best values to the stump
        foreach ($bestSplit as $name => $value) {
            $this->{$name} = $value;
        }
    }

    /**
     * While finding best split point for a numerical valued column,
     * DecisionStump looks for equally distanced values between minimum and maximum
     * values in the column. Given <i>$count</i> value determines how many split
     * points to be probed. The more split counts, the better performance but
     * worse processing time (Default value is 10.0)
     *
     * @param float $count
     */
    public function setNumericalSplitCount(float $count)
    {
        $this->numSplitCount = $count;
    }

    /**
     * Determines best split point for the given column
     *
     * @param int $col
     *
     * @return array
     */
    protected function getBestNumericalSplit(int $col)
    {
        $values = array_column($this->samples, $col);
        // Trying all possible points may be accomplished in two general ways:
        // 1- Try all values in the $samples array ($values)
        // 2- Artificially split the range of values into several parts and try them
        // We choose the second one because it is faster in larger datasets
        $minValue = min($values);
        $maxValue = max($values);
        $stepSize = ($maxValue - $minValue) / $this->numSplitCount;

        $split = null;

        foreach (['<=', '>'] as $operator) {
            // Before trying all possible split points, let's first try
            // the average value for the cut point
            $threshold = array_sum($values) / (float) count($values);
            list($errorRate, $prob) = $this->calculateErrorRate($threshold, $operator, $values);
            if ($split == null || $errorRate < $split['trainingErrorRate']) {

                $split = ['value' => $threshold, 'operator' => $operator,
                        'prob' => $prob, 'column' => $col,
                        'trainingErrorRate' => $errorRate];
            }

            // Try other possible points one by one
            for ($step = $minValue; $step <= $maxValue; $step+= $stepSize) {
                $threshold = (float)$step;
                list($errorRate, $prob) = $this->calculateErrorRate($threshold, $operator, $values);
                if ($errorRate < $split['trainingErrorRate']) {

                    $split = ['value' => $threshold, 'operator' => $operator,
                        'prob' => $prob, 'column' => $col,
                        'trainingErrorRate' => $errorRate];
                }
            }// for
        }

        return $split;
    }

    /**
     *
     * @param int $col
     *
     * @return array
     */
    protected function getBestNominalSplit(int $col)
    {
        $values = array_column($this->samples, $col);
        $valueCounts = array_count_values($values);
        $distinctVals= array_keys($valueCounts);

        $split = null;

        foreach (['=', '!='] as $operator) {
            foreach ($distinctVals as $val) {
                list($errorRate, $prob) = $this->calculateErrorRate($val, $operator, $values);

                if ($split == null || $split['trainingErrorRate'] < $errorRate) {

                    $split = ['value' => $val, 'operator' => $operator,
                        'prob' => $prob, 'column' => $col,
                        'trainingErrorRate' => $errorRate];
                }
            }// for
        }

        return $split;
    }


    /**
     *
     * @param type $leftValue
     * @param type $operator
     * @param type $rightValue
     *
     * @return boolean
     */
    protected function evaluate($leftValue, $operator, $rightValue)
    {
        switch ($operator) {
            case '>': return $leftValue > $rightValue;
switch ($expr) {
case "A":
    doSomething(); //right
    break;
case "B":

    doSomethingElse(); //wrong
    break;
            case '>=': return $leftValue >= $rightValue;
switch ($expr) {
case "A":
    doSomething(); //right
    break;
case "B":

    doSomethingElse(); //wrong
    break;
            case '<': return $leftValue < $rightValue;
switch ($expr) {
case "A":
    doSomething(); //right
    break;
case "B":

    doSomethingElse(); //wrong
    break;
            case '<=': return $leftValue <= $rightValue;
switch ($expr) {
case "A":
    doSomething(); //right
    break;
case "B":

    doSomethingElse(); //wrong
    break;
            case '=': return $leftValue === $rightValue;
switch ($expr) {
case "A":
    doSomething(); //right
    break;
case "B":

    doSomethingElse(); //wrong
    break;
            case '!=':
            case '<>': return $leftValue !== $rightValue;
switch ($expr) {
case "A":
    doSomething(); //right
    break;
case "B":

    doSomethingElse(); //wrong
    break;
        }

        return false;
    }

    /**
     * Calculates the ratio of wrong predictions based on the new threshold
     * value given as the parameter
     *
     * @param float $threshold
     * @param string $operator
     * @param array $values
     *
     * @return array
     */
    protected function calculateErrorRate(float $threshold, string $operator, array $values)
    {
        $wrong = 0.0;
        $prob = [];
        $leftLabel = $this->binaryLabels[0];
        $rightLabel= $this->binaryLabels[1];

        foreach ($values as $index => $value) {
            if ($this->evaluate($value, $operator, $threshold)) {
function acceptsInteger($int) { }

$x = '123'; // string "123"

// Instead of
acceptsInteger($x);

// we recommend to use
acceptsInteger((integer) $x);
                $predicted = $leftLabel;
            } else {
                $predicted = $rightLabel;
            }

            $target = $this->targets[$index];
            if (strval($predicted) != strval($this->targets[$index])) {
                $wrong += $this->weights[$index];
            }

            if (! isset($prob[$predicted][$target])) {
                $prob[$predicted][$target] = 0;
            }
            $prob[$predicted][$target]++;
        }

        // Calculate probabilities: Proportion of labels in each leaf
        $dist = array_combine($this->binaryLabels, array_fill(0, 2, 0.0));
        foreach ($prob as $leaf => $counts) {
            $leafTotal = (float)array_sum($prob[$leaf]);
            foreach ($counts as $label => $count) {
                if (strval($leaf) == strval($label)) {
                    $dist[$leaf] = $count / $leafTotal;
                }
            }
        }

        return [$wrong / (float) array_sum($this->weights), $dist];
    }

    /**
     * Returns the probability of the sample of belonging to the given label
     *
     * Probability of a sample is calculated as the proportion of the label
     * within the labels of the training samples in the decision node
     *
     * @param array $sample
     * @param mixed $label
     *
     * @return float
     */
    protected function predictProbability(array $sample, $label)
    {
        $predicted = $this->predictSampleBinary($sample);
        if (strval($predicted) == strval($label)) {
            return $this->prob[$label];
        }

        return 0.0;
    }

    /**
     * @param array $sample
     *
     * @return mixed
     */
    protected function predictSampleBinary(array $sample)
    {
        if ($this->evaluate($sample[$this->column], $this->operator, $this->value)) {
function acceptsInteger($int) { }

$x = '123'; // string "123"

// Instead of
acceptsInteger($x);

// we recommend to use
acceptsInteger((integer) $x);
            return $this->binaryLabels[0];
        }

        return $this->binaryLabels[1];
    }

    /**
     * @return string
     */
    public function __toString()
    {
        return "IF $this->column $this->operator $this->value " .
            "THEN " . $this->binaryLabels[0] . " ".
            "ELSE " . $this->binaryLabels[1];
    }
}


1		<?php
2
3		declare(strict_types=1);
4
5		namespace Phpml\Classification\Linear;
6
7		use Phpml\Helper\Predictable;
8		use Phpml\Helper\OneVsRest;
9		use Phpml\Classification\WeightedClassifier;
10		use Phpml\Classification\DecisionTree;
11
12		class DecisionStump extends WeightedClassifier
13		{
14		use Predictable, OneVsRest;
15
16		const AUTO_SELECT = -1;
17
18		/**
19		* @var int
20		*/
21		protected $givenColumnIndex;
22
23		/**
24		* @var array
25		*/
26		protected $binaryLabels;
27
28		/**
29		* Sample weights : If used the optimization on the decision value
30		* will take these weights into account. If not given, all samples
31		* will be weighed with the same value of 1
32		*
33		* @var array
34		*/
35		protected $weights = null;
36
37		/**
38		* Lowest error rate obtained while training/optimizing the model
39		*
40		* @var float
41		*/
42		protected $trainingErrorRate;
43
44		/**
45		* @var int
46		*/
47		protected $column;
48
49		/**
50		* @var mixed
51		*/
52		protected $value;
53
54		/**
55		* @var string
56		*/
57		protected $operator;
58
59		/**
60		* @var array
61		*/
62		protected $columnTypes;
63
64		/**
65		* @var int
66		*/
67		protected $featureCount;
68
69		/**
70		* @var float
71		*/
72		protected $numSplitCount = 100.0;
73
74		/**
75		* Distribution of samples in the leaves
76		*
77		* @var array
78		*/
79		protected $prob;
80
81		/**
82		* A DecisionStump classifier is a one-level deep DecisionTree. It is generally
83		* used with ensemble algorithms as in the weak classifier role. <br>
84		*
85		* If columnIndex is given, then the stump tries to produce a decision node
86		* on this column, otherwise in cases given the value of -1, the stump itself
87		* decides which column to take for the decision (Default DecisionTree behaviour)
88		*
89		* @param int $columnIndex
90		*/
91		public function __construct(int $columnIndex = self::AUTO_SELECT)
92		{
93		$this->givenColumnIndex = $columnIndex;
94		}
95
96		/**
97		* @param array $samples
98		* @param array $targets
99		*/
100		protected function trainBinary(array $samples, array $targets)
101		{
102		$this->samples = array_merge($this->samples, $samples);
103		$this->targets = array_merge($this->targets, $targets);
104		$this->binaryLabels = array_keys(array_count_values($this->targets));
105		$this->featureCount = count($this->samples[0]);
106
107		// If a column index is given, it should be among the existing columns
108		if ($this->givenColumnIndex > count($this->samples[0]) - 1) {
109		$this->givenColumnIndex = self::AUTO_SELECT;
110		}
111
112		// Check the size of the weights given.
113		// If none given, then assign 1 as a weight to each sample
114		if ($this->weights) {
		0 ignored issues – show Bug Best Practice introduced 2017-02-19 10:47 UTC by Report Bug Copy Issue Report The expression `$this->weights` of type `array` is implicitly converted to a boolean; are you sure this is intended? If so, consider using `! empty($expr)` instead to make it clear that you intend to check for an array without elements. This check marks implicit conversions of arrays to boolean values in a comparison. While in PHP an empty array is considered to be equal (but not identical) to false, this is not always apparent. Consider making the comparison explicit by using `empty(..)` or `! empty(...)` instead. Loading history...
115		$numWeights = count($this->weights);
116		if ($numWeights != count($this->samples)) {
117		throw new \Exception("Number of sample weights does not match with number of samples");
118		}
119		} else {
120		$this->weights = array_fill(0, count($this->samples), 1);
121		}
122
123		// Determine type of each column as either "continuous" or "nominal"
124		$this->columnTypes = DecisionTree::getColumnTypes($this->samples);
125
126		// Try to find the best split in the columns of the dataset
127		// by calculating error rate for each split point in each column
128		$columns = range(0, count($this->samples[0]) - 1);
129		if ($this->givenColumnIndex != self::AUTO_SELECT) {
130		$columns = [$this->givenColumnIndex];
131		}
132
133		$bestSplit = [
134		'value' => 0, 'operator' => '',
135		'prob' => [], 'column' => 0,
136		'trainingErrorRate' => 1.0];
137		foreach ($columns as $col) {
138		if ($this->columnTypes[$col] == DecisionTree::CONTINUOS) {
139		$split = $this->getBestNumericalSplit($col);
140		} else {
141		$split = $this->getBestNominalSplit($col);
142		}
143
144		if ($split['trainingErrorRate'] < $bestSplit['trainingErrorRate']) {
145		$bestSplit = $split;
146		}
147		}
148
149		// Assign determined best values to the stump
150		foreach ($bestSplit as $name => $value) {
151		$this->{$name} = $value;
152		}
153		}
154
155		/**
156		* While finding best split point for a numerical valued column,
157		* DecisionStump looks for equally distanced values between minimum and maximum
158		* values in the column. Given <i>$count</i> value determines how many split
159		* points to be probed. The more split counts, the better performance but
160		* worse processing time (Default value is 10.0)
161		*
162		* @param float $count
163		*/
164		public function setNumericalSplitCount(float $count)
165		{
166		$this->numSplitCount = $count;
167		}
168
169		/**
170		* Determines best split point for the given column
171		*
172		* @param int $col
173		*
174		* @return array
175		*/
176		protected function getBestNumericalSplit(int $col)
177		{
178		$values = array_column($this->samples, $col);
179		// Trying all possible points may be accomplished in two general ways:
180		// 1- Try all values in the $samples array ($values)
181		// 2- Artificially split the range of values into several parts and try them
182		// We choose the second one because it is faster in larger datasets
183		$minValue = min($values);
184		$maxValue = max($values);
185		$stepSize = ($maxValue - $minValue) / $this->numSplitCount;
186
187		$split = null;
188
189		foreach (['<=', '>'] as $operator) {
190		// Before trying all possible split points, let's first try
191		// the average value for the cut point
192		$threshold = array_sum($values) / (float) count($values);
193		list($errorRate, $prob) = $this->calculateErrorRate($threshold, $operator, $values);
194	View Code Duplication	if ($split == null \|\| $errorRate < $split['trainingErrorRate']) {
		0 ignored issues – show Duplication introduced 2017-02-25 14:37 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...
195		$split = ['value' => $threshold, 'operator' => $operator,
196		'prob' => $prob, 'column' => $col,
197		'trainingErrorRate' => $errorRate];
198		}
199
200		// Try other possible points one by one
201		for ($step = $minValue; $step <= $maxValue; $step+= $stepSize) {
202		$threshold = (float)$step;
203		list($errorRate, $prob) = $this->calculateErrorRate($threshold, $operator, $values);
204	View Code Duplication	if ($errorRate < $split['trainingErrorRate']) {
		0 ignored issues – show Duplication introduced 2017-02-25 14:37 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...
205		$split = ['value' => $threshold, 'operator' => $operator,
206		'prob' => $prob, 'column' => $col,
207		'trainingErrorRate' => $errorRate];
208		}
209		}// for
210		}
211
212		return $split;
213		}
214
215		/**
216		*
217		* @param int $col
218		*
219		* @return array
220		*/
221		protected function getBestNominalSplit(int $col)
222		{
223		$values = array_column($this->samples, $col);
224		$valueCounts = array_count_values($values);
225		$distinctVals= array_keys($valueCounts);
226
227		$split = null;
228
229		foreach (['=', '!='] as $operator) {
230		foreach ($distinctVals as $val) {
231		list($errorRate, $prob) = $this->calculateErrorRate($val, $operator, $values);
232
233	View Code Duplication	if ($split == null \|\| $split['trainingErrorRate'] < $errorRate) {
		0 ignored issues – show Duplication introduced 2017-02-25 14:37 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...
234		$split = ['value' => $val, 'operator' => $operator,
235		'prob' => $prob, 'column' => $col,
236		'trainingErrorRate' => $errorRate];
237		}
238		}// for
239		}
240
241		return $split;
242		}
243
244
245		/**
246		*
247		* @param type $leftValue
248		* @param type $operator
249		* @param type $rightValue
250		*
251		* @return boolean
252		*/
253		protected function evaluate($leftValue, $operator, $rightValue)
254		{
255		switch ($operator) {
256		case '>': return $leftValue > $rightValue;
		0 ignored issues – show Coding Style introduced 2017-02-27 13:38 UTC by Report Bug Copy Issue Report The case body in a switch statement must start on the line following the statement. According to the PSR-2, the body of a case statement must start on the line immediately following the case statement. switch ($expr) { case "A": doSomething(); //right break; case "B": doSomethingElse(); //wrong break; } To learn more about the PSR-2 coding standard, please refer to the PHP-Fig. Loading history... Coding Style introduced 2017-02-27 13:38 UTC by Report Bug Copy Issue Report Terminating statement must be on a line by itself As per the PSR-2 coding standard, the `break` (or other terminating) statement must be on a line of its own. switch ($expr) { case "A": doSomething(); break; //wrong case "B": doSomething(); break; //right case "C:": doSomething(); return true; //right } To learn more about the PSR-2 coding standard, please refer to the PHP-Fig. Loading history...
257		case '>=': return $leftValue >= $rightValue;
		0 ignored issues – show Coding Style introduced 2017-02-27 13:38 UTC by Report Bug Copy Issue Report The case body in a switch statement must start on the line following the statement. According to the PSR-2, the body of a case statement must start on the line immediately following the case statement. switch ($expr) { case "A": doSomething(); //right break; case "B": doSomethingElse(); //wrong break; } To learn more about the PSR-2 coding standard, please refer to the PHP-Fig. Loading history... Coding Style introduced 2017-02-27 13:38 UTC by Report Bug Copy Issue Report Terminating statement must be on a line by itself As per the PSR-2 coding standard, the `break` (or other terminating) statement must be on a line of its own. switch ($expr) { case "A": doSomething(); break; //wrong case "B": doSomething(); break; //right case "C:": doSomething(); return true; //right } To learn more about the PSR-2 coding standard, please refer to the PHP-Fig. Loading history...
258		case '<': return $leftValue < $rightValue;
		0 ignored issues – show Coding Style introduced 2017-02-27 13:38 UTC by Report Bug Copy Issue Report The case body in a switch statement must start on the line following the statement. According to the PSR-2, the body of a case statement must start on the line immediately following the case statement. switch ($expr) { case "A": doSomething(); //right break; case "B": doSomethingElse(); //wrong break; } To learn more about the PSR-2 coding standard, please refer to the PHP-Fig. Loading history... Coding Style introduced 2017-02-27 13:38 UTC by Report Bug Copy Issue Report Terminating statement must be on a line by itself As per the PSR-2 coding standard, the `break` (or other terminating) statement must be on a line of its own. switch ($expr) { case "A": doSomething(); break; //wrong case "B": doSomething(); break; //right case "C:": doSomething(); return true; //right } To learn more about the PSR-2 coding standard, please refer to the PHP-Fig. Loading history...
259		case '<=': return $leftValue <= $rightValue;
		0 ignored issues – show Coding Style introduced 2017-02-27 13:38 UTC by Report Bug Copy Issue Report The case body in a switch statement must start on the line following the statement. According to the PSR-2, the body of a case statement must start on the line immediately following the case statement. switch ($expr) { case "A": doSomething(); //right break; case "B": doSomethingElse(); //wrong break; } To learn more about the PSR-2 coding standard, please refer to the PHP-Fig. Loading history... Coding Style introduced 2017-02-27 13:38 UTC by Report Bug Copy Issue Report Terminating statement must be on a line by itself As per the PSR-2 coding standard, the `break` (or other terminating) statement must be on a line of its own. switch ($expr) { case "A": doSomething(); break; //wrong case "B": doSomething(); break; //right case "C:": doSomething(); return true; //right } To learn more about the PSR-2 coding standard, please refer to the PHP-Fig. Loading history...
260		case '=': return $leftValue === $rightValue;
		0 ignored issues – show Coding Style introduced 2017-03-03 19:08 UTC by Report Bug Copy Issue Report The case body in a switch statement must start on the line following the statement. According to the PSR-2, the body of a case statement must start on the line immediately following the case statement. switch ($expr) { case "A": doSomething(); //right break; case "B": doSomethingElse(); //wrong break; } To learn more about the PSR-2 coding standard, please refer to the PHP-Fig. Loading history... Coding Style introduced 2017-03-03 19:08 UTC by Report Bug Copy Issue Report Terminating statement must be on a line by itself As per the PSR-2 coding standard, the `break` (or other terminating) statement must be on a line of its own. switch ($expr) { case "A": doSomething(); break; //wrong case "B": doSomething(); break; //right case "C:": doSomething(); return true; //right } To learn more about the PSR-2 coding standard, please refer to the PHP-Fig. Loading history...
261		case '!=':
262		case '<>': return $leftValue !== $rightValue;
		0 ignored issues – show Coding Style introduced 2017-03-03 19:08 UTC by Report Bug Copy Issue Report The case body in a switch statement must start on the line following the statement. According to the PSR-2, the body of a case statement must start on the line immediately following the case statement. switch ($expr) { case "A": doSomething(); //right break; case "B": doSomethingElse(); //wrong break; } To learn more about the PSR-2 coding standard, please refer to the PHP-Fig. Loading history... Coding Style introduced 2017-03-03 19:08 UTC by Report Bug Copy Issue Report Terminating statement must be on a line by itself As per the PSR-2 coding standard, the `break` (or other terminating) statement must be on a line of its own. switch ($expr) { case "A": doSomething(); break; //wrong case "B": doSomething(); break; //right case "C:": doSomething(); return true; //right } To learn more about the PSR-2 coding standard, please refer to the PHP-Fig. Loading history...
263		}
264
265		return false;
266		}
267
268		/**
269		* Calculates the ratio of wrong predictions based on the new threshold
270		* value given as the parameter
271		*
272		* @param float $threshold
273		* @param string $operator
274		* @param array $values
275		*
276		* @return array
277		*/
278		protected function calculateErrorRate(float $threshold, string $operator, array $values)
279		{
280		$wrong = 0.0;
281		$prob = [];
282		$leftLabel = $this->binaryLabels[0];
283		$rightLabel= $this->binaryLabels[1];
284
285		foreach ($values as $index => $value) {
286		if ($this->evaluate($value, $operator, $threshold)) {
		0 ignored issues – show Documentation introduced 2017-03-03 19:08 UTC by Report Bug Copy Issue Report `$operator` is of type `string`, but the function expects a `object<Phpml\Classification\Linear\type>`. It seems like the type of the argument is not accepted by the function/method which you are calling. In some cases, in particular if PHP’s automatic type-juggling kicks in this might be fine. In other cases, however this might be a bug. We suggest to add an explicit type cast like in the following example: function acceptsInteger($int) { } $x = '123'; // string "123" // Instead of acceptsInteger($x); // we recommend to use acceptsInteger((integer) $x); Loading history... Documentation introduced 2017-03-03 19:08 UTC by Report Bug Copy Issue Report `$threshold` is of type `double`, but the function expects a `object<Phpml\Classification\Linear\type>`. It seems like the type of the argument is not accepted by the function/method which you are calling. In some cases, in particular if PHP’s automatic type-juggling kicks in this might be fine. In other cases, however this might be a bug. We suggest to add an explicit type cast like in the following example: function acceptsInteger($int) { } $x = '123'; // string "123" // Instead of acceptsInteger($x); // we recommend to use acceptsInteger((integer) $x); Loading history...
287		$predicted = $leftLabel;
288		} else {
289		$predicted = $rightLabel;
290		}
291
292		$target = $this->targets[$index];
293		if (strval($predicted) != strval($this->targets[$index])) {
294		$wrong += $this->weights[$index];
295		}
296
297		if (! isset($prob[$predicted][$target])) {
298		$prob[$predicted][$target] = 0;
299		}
300		$prob[$predicted][$target]++;
301		}
302
303		// Calculate probabilities: Proportion of labels in each leaf
304		$dist = array_combine($this->binaryLabels, array_fill(0, 2, 0.0));
305		foreach ($prob as $leaf => $counts) {
306		$leafTotal = (float)array_sum($prob[$leaf]);
307		foreach ($counts as $label => $count) {
308		if (strval($leaf) == strval($label)) {
309		$dist[$leaf] = $count / $leafTotal;
310		}
311		}
312		}
313
314		return [$wrong / (float) array_sum($this->weights), $dist];
315		}
316
317		/**
318		* Returns the probability of the sample of belonging to the given label
319		*
320		* Probability of a sample is calculated as the proportion of the label
321		* within the labels of the training samples in the decision node
322		*
323		* @param array $sample
324		* @param mixed $label
325		*
326		* @return float
327		*/
328		protected function predictProbability(array $sample, $label)
329		{
330		$predicted = $this->predictSampleBinary($sample);
331		if (strval($predicted) == strval($label)) {
332		return $this->prob[$label];
333		}
334
335		return 0.0;
336		}
337
338		/**
339		* @param array $sample
340		*
341		* @return mixed
342		*/
343		protected function predictSampleBinary(array $sample)
344		{
345		if ($this->evaluate($sample[$this->column], $this->operator, $this->value)) {
		0 ignored issues – show Documentation introduced 2017-03-03 19:08 UTC by Report Bug Copy Issue Report `$this->operator` is of type `string`, but the function expects a `object<Phpml\Classification\Linear\type>`. It seems like the type of the argument is not accepted by the function/method which you are calling. In some cases, in particular if PHP’s automatic type-juggling kicks in this might be fine. In other cases, however this might be a bug. We suggest to add an explicit type cast like in the following example: function acceptsInteger($int) { } $x = '123'; // string "123" // Instead of acceptsInteger($x); // we recommend to use acceptsInteger((integer) $x); Loading history...
346		return $this->binaryLabels[0];
347		}
348
349		return $this->binaryLabels[1];
350		}
351
352		/**
353		* @return string
354		*/
355		public function __toString()
356		{
357		return "IF $this->column $this->operator $this->value " .
358		"THEN " . $this->binaryLabels[0] . " ".
359		"ELSE " . $this->binaryLabels[1];
360		}
361		}
362

php-ai / php-ml