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RubixML/RubixML | src/Transformers/GaussianRandomProjector.php | GaussianRandomProjector.transform | public function transform(array &$samples) : void
{
if (!$this->r) {
throw new RuntimeException('Transformer has not been fitted.');
}
$samples = Matrix::quick($samples)
->matmul($this->r)
->asArray();
} | php | public function transform(array &$samples) : void
{
if (!$this->r) {
throw new RuntimeException('Transformer has not been fitted.');
}
$samples = Matrix::quick($samples)
->matmul($this->r)
->asArray();
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RubixML/RubixML | src/Other/Tokenizers/SkipGram.php | SkipGram.tokenize | public function tokenize(string $string) : array
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$sentences = preg_split(self::SENTENCE_REGEX, $string) ?: [];
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}
}
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$skipGrams[] = implode(self::SEPARATOR, $last);
}
return $skipGrams;
} | php | public function tokenize(string $string) : array
{
$sentences = preg_split(self::SENTENCE_REGEX, $string) ?: [];
$skipGrams = [];
foreach ($sentences as $sentence) {
$words = [];
preg_match_all(self::WORD_REGEX, $sentence, $words);
$words = $words[0];
$length = count($words) - ($this->n + $this->skip);
for ($i = 0; $i <= $length; $i++) {
$first = $words[$i];
for ($j = 0; $j <= $this->skip; $j++) {
$skipGram = $first;
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}
$skipGrams[] = $skipGram;
}
}
$last = array_slice($words, -$this->n);
$skipGrams[] = implode(self::SEPARATOR, $last);
}
return $skipGrams;
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RubixML/RubixML | src/Datasets/Generators/Blob.php | Blob.generate | public function generate(int $n) : Dataset
{
$samples = Matrix::gaussian($n, $this->dimensions())
->multiply($this->stddev)
->add($this->center)
->asArray();
return Unlabeled::quick($samples);
} | php | public function generate(int $n) : Dataset
{
$samples = Matrix::gaussian($n, $this->dimensions())
->multiply($this->stddev)
->add($this->center)
->asArray();
return Unlabeled::quick($samples);
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RubixML/RubixML | src/NeuralNet/Initializers/Xavier2.php | Xavier2.initialize | public function initialize(int $fanIn, int $fanOut) : Matrix
{
$scale = (6. / ($fanIn + $fanOut)) ** 0.25;
return Matrix::uniform($fanOut, $fanIn)->multiply($scale);
} | php | public function initialize(int $fanIn, int $fanOut) : Matrix
{
$scale = (6. / ($fanIn + $fanOut)) ** 0.25;
return Matrix::uniform($fanOut, $fanIn)->multiply($scale);
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{
$fanOut = $fanIn;
$this->width = $fanOut;
return $fanOut;
} | php | public function initialize(int $fanIn) : int
{
$fanOut = $fanIn;
$this->width = $fanOut;
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RubixML/RubixML | src/NeuralNet/Layers/Activation.php | Activation.forward | public function forward(Matrix $input) : Matrix
{
$this->input = $input;
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return $this->computed;
} | php | public function forward(Matrix $input) : Matrix
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$this->input = $input;
$this->computed = $this->activationFunction->compute($input);
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RubixML/RubixML | src/Datasets/Unlabeled.php | Unlabeled.fromIterator | public static function fromIterator(iterable $samples) : self
{
$samples = is_array($samples)
? $samples
: iterator_to_array($samples, false);
return self::build($samples);
} | php | public static function fromIterator(iterable $samples) : self
{
$samples = is_array($samples)
? $samples
: iterator_to_array($samples, false);
return self::build($samples);
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{
return self::quick(array_slice($this->samples, 0, $n));
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RubixML/RubixML | src/Datasets/Unlabeled.php | Unlabeled.prepend | public function prepend(Dataset $dataset) : Dataset
{
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} | php | public function prepend(Dataset $dataset) : Dataset
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RubixML/RubixML | src/Datasets/Unlabeled.php | Unlabeled.append | public function append(Dataset $dataset) : Dataset
{
return self::quick(array_merge($this->samples, $dataset->samples()));
} | php | public function append(Dataset $dataset) : Dataset
{
return self::quick(array_merge($this->samples, $dataset->samples()));
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RubixML/RubixML | src/Datasets/Unlabeled.php | Unlabeled.splice | public function splice(int $offset, int $n) : self
{
return self::quick(array_splice($this->samples, $offset, $n));
} | php | public function splice(int $offset, int $n) : self
{
return self::quick(array_splice($this->samples, $offset, $n));
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@param int $n
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RubixML/RubixML | src/Datasets/Unlabeled.php | Unlabeled.split | public function split(float $ratio = 0.5) : array
{
if ($ratio <= 0. or $ratio >= 1.) {
throw new InvalidArgumentException('Split ratio must be strictly'
. " between 0 and 1, $ratio given.");
}
$p = (int) ($ratio * $this->numRows());
$left = self::quick(array_slice($this->samples, 0, $p));
$right = self::quick(array_slice($this->samples, $p));
return [$left, $right];
} | php | public function split(float $ratio = 0.5) : array
{
if ($ratio <= 0. or $ratio >= 1.) {
throw new InvalidArgumentException('Split ratio must be strictly'
. " between 0 and 1, $ratio given.");
}
$p = (int) ($ratio * $this->numRows());
$left = self::quick(array_slice($this->samples, 0, $p));
$right = self::quick(array_slice($this->samples, $p));
return [$left, $right];
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RubixML/RubixML | src/Datasets/Unlabeled.php | Unlabeled.fold | public function fold(int $k = 3) : array
{
if ($k < 2) {
throw new InvalidArgumentException('Cannot create less than 2'
. " folds, $k given.");
}
$samples = $this->samples;
$n = (int) floor(count($samples) / $k);
$folds = [];
for ($i = 0; $i < $k; $i++) {
$folds[] = self::quick(array_splice($samples, 0, $n));
}
return $folds;
} | php | public function fold(int $k = 3) : array
{
if ($k < 2) {
throw new InvalidArgumentException('Cannot create less than 2'
. " folds, $k given.");
}
$samples = $this->samples;
$n = (int) floor(count($samples) / $k);
$folds = [];
for ($i = 0; $i < $k; $i++) {
$folds[] = self::quick(array_splice($samples, 0, $n));
}
return $folds;
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RubixML/RubixML | src/Datasets/Unlabeled.php | Unlabeled.batch | public function batch(int $n = 50) : array
{
$batches = [];
$samples = $this->samples;
foreach (array_chunk($this->samples, $n) as $batch) {
$batches[] = self::quick($batch);
}
return $batches;
} | php | public function batch(int $n = 50) : array
{
$batches = [];
$samples = $this->samples;
foreach (array_chunk($this->samples, $n) as $batch) {
$batches[] = self::quick($batch);
}
return $batches;
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RubixML/RubixML | src/Datasets/Unlabeled.php | Unlabeled.partition | public function partition(int $column, $value) : array
{
if (!is_string($value) and !is_numeric($value)) {
throw new InvalidArgumentException('Value must be a string or'
. ' numeric type, ' . gettype($value) . ' given.');
}
$left = $right = [];
if ($this->columnType($column) === DataType::CATEGORICAL) {
foreach ($this->samples as $i => $sample) {
if ($sample[$column] === $value) {
$left[] = $sample;
} else {
$right[] = $sample;
}
}
} else {
foreach ($this->samples as $i => $sample) {
if ($sample[$column] < $value) {
$left[] = $sample;
} else {
$right[] = $sample;
}
}
}
return [
self::quick($left),
self::quick($right),
];
} | php | public function partition(int $column, $value) : array
{
if (!is_string($value) and !is_numeric($value)) {
throw new InvalidArgumentException('Value must be a string or'
. ' numeric type, ' . gettype($value) . ' given.');
}
$left = $right = [];
if ($this->columnType($column) === DataType::CATEGORICAL) {
foreach ($this->samples as $i => $sample) {
if ($sample[$column] === $value) {
$left[] = $sample;
} else {
$right[] = $sample;
}
}
} else {
foreach ($this->samples as $i => $sample) {
if ($sample[$column] < $value) {
$left[] = $sample;
} else {
$right[] = $sample;
}
}
}
return [
self::quick($left),
self::quick($right),
];
} | [
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@param mixed $value
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RubixML/RubixML | src/Datasets/Unlabeled.php | Unlabeled.spatialPartition | public function spatialPartition(array $leftCentroid, array $rightCentroid, Distance $kernel)
{
if (count($leftCentroid) !== count($rightCentroid)) {
throw new InvalidArgumentException('Dimensionality mismatch between'
. ' left and right centroids.');
}
$left = $right = [];
foreach ($this->samples as $i => $sample) {
$lHat = $kernel->compute($sample, $leftCentroid);
$rHat = $kernel->compute($sample, $rightCentroid);
if ($lHat < $rHat) {
$left[] = $sample;
} else {
$right[] = $sample;
}
}
return [
self::quick($left),
self::quick($right),
];
} | php | public function spatialPartition(array $leftCentroid, array $rightCentroid, Distance $kernel)
{
if (count($leftCentroid) !== count($rightCentroid)) {
throw new InvalidArgumentException('Dimensionality mismatch between'
. ' left and right centroids.');
}
$left = $right = [];
foreach ($this->samples as $i => $sample) {
$lHat = $kernel->compute($sample, $leftCentroid);
$rHat = $kernel->compute($sample, $rightCentroid);
if ($lHat < $rHat) {
$left[] = $sample;
} else {
$right[] = $sample;
}
}
return [
self::quick($left),
self::quick($right),
];
} | [
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RubixML/RubixML | src/Datasets/Unlabeled.php | Unlabeled.randomSubsetWithReplacement | public function randomSubsetWithReplacement(int $n) : self
{
if ($n < 1) {
throw new InvalidArgumentException('Cannot generate a subset of'
. " less than 1 sample, $n given.");
}
$max = $this->numRows() - 1;
$subset = [];
for ($i = 0; $i < $n; $i++) {
$subset[] = $this->samples[rand(0, $max)];
}
return self::quick($subset);
} | php | public function randomSubsetWithReplacement(int $n) : self
{
if ($n < 1) {
throw new InvalidArgumentException('Cannot generate a subset of'
. " less than 1 sample, $n given.");
}
$max = $this->numRows() - 1;
$subset = [];
for ($i = 0; $i < $n; $i++) {
$subset[] = $this->samples[rand(0, $max)];
}
return self::quick($subset);
} | [
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RubixML/RubixML | src/Regressors/ExtraTreeRegressor.php | ExtraTreeRegressor.split | protected function split(Labeled $dataset) : Decision
{
$bestImpurity = INF;
$bestColumn = $bestValue = null;
$bestGroups = [];
$max = $dataset->numRows() - 1;
shuffle($this->columns);
foreach (array_slice($this->columns, 0, $this->maxFeatures) as $column) {
$sample = $dataset->row(rand(0, $max));
$value = $sample[$column];
$groups = $dataset->partition($column, $value);
$impurity = $this->splitImpurity($groups);
if ($impurity < $bestImpurity) {
$bestColumn = $column;
$bestValue = $value;
$bestGroups = $groups;
$bestImpurity = $impurity;
}
if ($impurity < $this->tolerance) {
break 1;
}
}
return new Decision($bestColumn, $bestValue, $bestGroups, $bestImpurity);
} | php | protected function split(Labeled $dataset) : Decision
{
$bestImpurity = INF;
$bestColumn = $bestValue = null;
$bestGroups = [];
$max = $dataset->numRows() - 1;
shuffle($this->columns);
foreach (array_slice($this->columns, 0, $this->maxFeatures) as $column) {
$sample = $dataset->row(rand(0, $max));
$value = $sample[$column];
$groups = $dataset->partition($column, $value);
$impurity = $this->splitImpurity($groups);
if ($impurity < $bestImpurity) {
$bestColumn = $column;
$bestValue = $value;
$bestGroups = $groups;
$bestImpurity = $impurity;
}
if ($impurity < $this->tolerance) {
break 1;
}
}
return new Decision($bestColumn, $bestValue, $bestGroups, $bestImpurity);
} | [
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] | Randomized algorithm that chooses the split point with the lowest
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@param \Rubix\ML\Datasets\Labeled $dataset
@return \Rubix\ML\Graph\Nodes\Decision | [
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RubixML/RubixML | src/GridSearch.php | GridSearch.combineGrid | public static function combineGrid(array $grid) : array
{
$combinations = [[]];
foreach ($grid as $i => $params) {
$append = [];
foreach ($combinations as $product) {
foreach ($params as $param) {
$product[$i] = $param;
$append[] = $product;
}
}
$combinations = $append;
}
return $combinations;
} | php | public static function combineGrid(array $grid) : array
{
$combinations = [[]];
foreach ($grid as $i => $params) {
$append = [];
foreach ($combinations as $product) {
foreach ($params as $param) {
$product[$i] = $param;
$append[] = $product;
}
}
$combinations = $append;
}
return $combinations;
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@param array $grid
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RubixML/RubixML | src/GridSearch.php | GridSearch.train | public function train(Dataset $dataset) : void
{
if (!$dataset instanceof Labeled) {
throw new InvalidArgumentException('This Estimator requires a'
. ' Labeled training set.');
}
DatasetIsCompatibleWithEstimator::check($dataset, $this);
if ($this->logger) {
$this->logger->info('Learner init ' . Params::stringify([
'base' => $this->base,
'metric' => $this->metric,
'validator' => $this->validator,
'workers' => $this->workers,
]));
}
$results = [];
Loop::run(function () use (&$results, $dataset) {
$pool = new DefaultPool($this->workers);
$coroutines = [];
foreach ($this->combinations as $params) {
$estimator = new $this->base(...$params);
$task = new CallableTask(
[$this, 'score'],
[$estimator, $dataset]
);
$coroutines[] = call(function () use ($pool, $task, $params) {
$score = yield $pool->enqueue($task);
if ($this->logger) {
$constructor = array_combine($this->args, $params) ?: [];
$this->logger->info('Test complete: ' . Params::stringify([
Params::shortName($this->metric) => $score,
]) . ' Params: ' . Params::stringify($constructor));
}
return [$score, $params];
});
}
$results = yield all($coroutines);
return yield $pool->shutdown();
});
$bestScore = -INF;
$bestParams = [];
foreach ($results as [$score, $params]) {
if ($score > $bestScore) {
$bestScore = $score;
$bestParams = $params;
}
}
$this->best = [
'score' => $bestScore,
'params' => $bestParams,
];
if ($this->logger) {
$constructor = array_combine($this->args, $bestParams) ?: [];
$this->logger->info('Best params: ' . Params::stringify($constructor));
}
$estimator = new $this->base(...$bestParams);
$estimator->train($dataset);
$this->estimator = $estimator;
if ($this->logger) {
$this->logger->info('Training complete');
}
} | php | public function train(Dataset $dataset) : void
{
if (!$dataset instanceof Labeled) {
throw new InvalidArgumentException('This Estimator requires a'
. ' Labeled training set.');
}
DatasetIsCompatibleWithEstimator::check($dataset, $this);
if ($this->logger) {
$this->logger->info('Learner init ' . Params::stringify([
'base' => $this->base,
'metric' => $this->metric,
'validator' => $this->validator,
'workers' => $this->workers,
]));
}
$results = [];
Loop::run(function () use (&$results, $dataset) {
$pool = new DefaultPool($this->workers);
$coroutines = [];
foreach ($this->combinations as $params) {
$estimator = new $this->base(...$params);
$task = new CallableTask(
[$this, 'score'],
[$estimator, $dataset]
);
$coroutines[] = call(function () use ($pool, $task, $params) {
$score = yield $pool->enqueue($task);
if ($this->logger) {
$constructor = array_combine($this->args, $params) ?: [];
$this->logger->info('Test complete: ' . Params::stringify([
Params::shortName($this->metric) => $score,
]) . ' Params: ' . Params::stringify($constructor));
}
return [$score, $params];
});
}
$results = yield all($coroutines);
return yield $pool->shutdown();
});
$bestScore = -INF;
$bestParams = [];
foreach ($results as [$score, $params]) {
if ($score > $bestScore) {
$bestScore = $score;
$bestParams = $params;
}
}
$this->best = [
'score' => $bestScore,
'params' => $bestParams,
];
if ($this->logger) {
$constructor = array_combine($this->args, $bestParams) ?: [];
$this->logger->info('Best params: ' . Params::stringify($constructor));
}
$estimator = new $this->base(...$bestParams);
$estimator->train($dataset);
$this->estimator = $estimator;
if ($this->logger) {
$this->logger->info('Training complete');
}
} | [
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] | Train one estimator per combination of parameters given by the grid and
assign the best one as the base estimator of this instance.
@param \Rubix\ML\Datasets\Dataset $dataset
@throws \InvalidArgumentException | [
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RubixML/RubixML | src/GridSearch.php | GridSearch.score | public function score(Learner $estimator, Labeled $dataset) : float
{
return $this->validator->test($estimator, $dataset, $this->metric);
} | php | public function score(Learner $estimator, Labeled $dataset) : float
{
return $this->validator->test($estimator, $dataset, $this->metric);
} | [
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RubixML/RubixML | src/Transformers/ImageResizer.php | ImageResizer.transform | public function transform(array &$samples) : void
{
foreach ($samples as &$sample) {
$vectors = [];
foreach ($sample as $column => &$value) {
if (is_resource($value) ? get_resource_type($value) === 'gd' : false) {
$image = $this->intervention->make($value);
$resize = $image->getWidth() !== $this->width
and $image->getHeight() !== $this->height;
if ($resize) {
$image = $image->fit($this->width, $this->height);
}
$value = $image->getCore();
}
}
}
} | php | public function transform(array &$samples) : void
{
foreach ($samples as &$sample) {
$vectors = [];
foreach ($sample as $column => &$value) {
if (is_resource($value) ? get_resource_type($value) === 'gd' : false) {
$image = $this->intervention->make($value);
$resize = $image->getWidth() !== $this->width
and $image->getHeight() !== $this->height;
if ($resize) {
$image = $image->fit($this->width, $this->height);
}
$value = $image->getCore();
}
}
}
} | [
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RubixML/RubixML | src/Regressors/KNNRegressor.php | KNNRegressor.predict | public function predict(Dataset $dataset) : array
{
if (empty($this->samples) or empty($this->labels)) {
throw new RuntimeException('Estimator has not been trained.');
}
DatasetIsCompatibleWithEstimator::check($dataset, $this);
$predictions = [];
foreach ($dataset as $sample) {
[$labels, $distances] = $this->nearest($sample);
if ($this->weighted) {
$weights = [];
foreach ($distances as $i => $distance) {
$weights[] = 1. / (1. + $distance);
}
$outcome = Stats::weightedMean($labels, $weights);
} else {
$outcome = Stats::mean($labels);
}
$predictions[] = $outcome;
}
return $predictions;
} | php | public function predict(Dataset $dataset) : array
{
if (empty($this->samples) or empty($this->labels)) {
throw new RuntimeException('Estimator has not been trained.');
}
DatasetIsCompatibleWithEstimator::check($dataset, $this);
$predictions = [];
foreach ($dataset as $sample) {
[$labels, $distances] = $this->nearest($sample);
if ($this->weighted) {
$weights = [];
foreach ($distances as $i => $distance) {
$weights[] = 1. / (1. + $distance);
}
$outcome = Stats::weightedMean($labels, $weights);
} else {
$outcome = Stats::mean($labels);
}
$predictions[] = $outcome;
}
return $predictions;
} | [
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RubixML/RubixML | src/CrossValidation/Reports/ResidualAnalysis.php | ResidualAnalysis.generate | public function generate(array $predictions, array $labels) : array
{
if (count($predictions) !== count($labels)) {
throw new InvalidArgumentException('The number of labels'
. ' must equal the number of predictions.');
}
$muHat = Stats::mean($labels);
$errors = $l1 = $l2 = [];
$sse = $sst = 0.;
foreach ($predictions as $i => $prediction) {
$label = $labels[$i];
$errors[] = $error = $label - $prediction;
$l1[] = abs($error);
$l2[] = $t = $error ** 2;
$sse += $t;
$sst += ($label - $muHat) ** 2;
}
[$mean, $variance] = Stats::meanVar($errors);
$mse = Stats::mean($l2);
$r2 = 1. - ($sse / ($sst ?: EPSILON));
return [
'mean_absolute_error' => Stats::mean($l1),
'median_absolute_error' => Stats::median($l1),
'mean_squared_error' => $mse,
'rms_error' => sqrt($mse),
'mean_squared_log_error' => log($mse),
'r_squared' => $r2,
'error_mean' => $mean,
'error_variance' => $variance,
'error_skewness' => Stats::skewness($errors, $mean),
'error_kurtosis' => Stats::kurtosis($errors, $mean),
'error_min' => min($errors),
'error_max' => max($errors),
'cardinality' => count($predictions),
];
} | php | public function generate(array $predictions, array $labels) : array
{
if (count($predictions) !== count($labels)) {
throw new InvalidArgumentException('The number of labels'
. ' must equal the number of predictions.');
}
$muHat = Stats::mean($labels);
$errors = $l1 = $l2 = [];
$sse = $sst = 0.;
foreach ($predictions as $i => $prediction) {
$label = $labels[$i];
$errors[] = $error = $label - $prediction;
$l1[] = abs($error);
$l2[] = $t = $error ** 2;
$sse += $t;
$sst += ($label - $muHat) ** 2;
}
[$mean, $variance] = Stats::meanVar($errors);
$mse = Stats::mean($l2);
$r2 = 1. - ($sse / ($sst ?: EPSILON));
return [
'mean_absolute_error' => Stats::mean($l1),
'median_absolute_error' => Stats::median($l1),
'mean_squared_error' => $mse,
'rms_error' => sqrt($mse),
'mean_squared_log_error' => log($mse),
'r_squared' => $r2,
'error_mean' => $mean,
'error_variance' => $variance,
'error_skewness' => Stats::skewness($errors, $mean),
'error_kurtosis' => Stats::kurtosis($errors, $mean),
'error_min' => min($errors),
'error_max' => max($errors),
'cardinality' => count($predictions),
];
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@throws \InvalidArgumentException
@return array | [
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RubixML/RubixML | src/Datasets/Labeled.php | Labeled.fromIterator | public static function fromIterator(iterable $samples, iterable $labels) : self
{
$samples = is_array($samples)
? $samples
: iterator_to_array($samples, false);
$labels = is_array($labels)
? $labels
: iterator_to_array($labels, false);
return self::build($samples, $labels);
} | php | public static function fromIterator(iterable $samples, iterable $labels) : self
{
$samples = is_array($samples)
? $samples
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$labels = is_array($labels)
? $labels
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return self::build($samples, $labels);
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RubixML/RubixML | src/Datasets/Labeled.php | Labeled.stack | public static function stack(array $datasets) : self
{
$samples = $labels = [];
foreach ($datasets as $dataset) {
if (!$dataset instanceof self) {
throw new InvalidArgumentException('Dataset must be'
. ' an instance of Labeled, ' . get_class($dataset)
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}
$samples = array_merge($samples, $dataset->samples());
$labels = array_merge($labels, $dataset->labels());
}
return self::quick($samples, $labels);
} | php | public static function stack(array $datasets) : self
{
$samples = $labels = [];
foreach ($datasets as $dataset) {
if (!$dataset instanceof self) {
throw new InvalidArgumentException('Dataset must be'
. ' an instance of Labeled, ' . get_class($dataset)
. ' given.');
}
$samples = array_merge($samples, $dataset->samples());
$labels = array_merge($labels, $dataset->labels());
}
return self::quick($samples, $labels);
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RubixML/RubixML | src/Datasets/Labeled.php | Labeled.zip | public function zip() : array
{
$rows = $this->samples;
foreach ($rows as $i => &$row) {
$row[] = $this->labels[$i];
}
return $rows;
} | php | public function zip() : array
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$rows = $this->samples;
foreach ($rows as $i => &$row) {
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RubixML/RubixML | src/Datasets/Labeled.php | Labeled.label | public function label(int $index)
{
if (!isset($this->labels[$index])) {
throw new InvalidArgumentException("Row at offset $index"
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}
return $this->labels[$index];
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if (!isset($this->labels[0])) {
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} | php | public function transformLabels(callable $fn) : void
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RubixML/RubixML | src/Datasets/Labeled.php | Labeled.head | public function head(int $n = 10) : self
{
$samples = array_slice($this->samples, 0, $n);
$labels = array_slice($this->labels, 0, $n);
return self::quick($samples, $labels);
} | php | public function head(int $n = 10) : self
{
$samples = array_slice($this->samples, 0, $n);
$labels = array_slice($this->labels, 0, $n);
return self::quick($samples, $labels);
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RubixML/RubixML | src/Datasets/Labeled.php | Labeled.leave | public function leave(int $n = 1) : self
{
if ($n < 0) {
throw new InvalidArgumentException('Cannot leave less than 0 samples.');
}
return $this->splice($n, $this->numRows());
} | php | public function leave(int $n = 1) : self
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RubixML/RubixML | src/Datasets/Labeled.php | Labeled.prepend | public function prepend(Dataset $dataset) : self
{
if (!$dataset instanceof Labeled) {
throw new InvalidArgumentException('Can only merge with a labeled'
. 'dataset.');
}
$samples = array_merge($dataset->samples(), $this->samples);
$labels = array_merge($dataset->labels(), $this->labels);
return self::quick($samples, $labels);
} | php | public function prepend(Dataset $dataset) : self
{
if (!$dataset instanceof Labeled) {
throw new InvalidArgumentException('Can only merge with a labeled'
. 'dataset.');
}
$samples = array_merge($dataset->samples(), $this->samples);
$labels = array_merge($dataset->labels(), $this->labels);
return self::quick($samples, $labels);
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RubixML/RubixML | src/Datasets/Labeled.php | Labeled.splice | public function splice(int $offset, int $n) : self
{
$samples = array_splice($this->samples, $offset, $n);
$labels = array_splice($this->labels, $offset, $n);
return self::quick($samples, $labels);
} | php | public function splice(int $offset, int $n) : self
{
$samples = array_splice($this->samples, $offset, $n);
$labels = array_splice($this->labels, $offset, $n);
return self::quick($samples, $labels);
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RubixML/RubixML | src/Datasets/Labeled.php | Labeled.randomize | public function randomize() : self
{
$order = range(0, $this->numRows() - 1);
shuffle($order);
array_multisort($order, $this->samples, $this->labels);
return $this;
} | php | public function randomize() : self
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$order = range(0, $this->numRows() - 1);
shuffle($order);
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RubixML/RubixML | src/Datasets/Labeled.php | Labeled.filterByColumn | public function filterByColumn(int $index, callable $fn) : self
{
$samples = $labels = [];
foreach ($this->samples as $i => $sample) {
if ($fn($sample[$index])) {
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return self::quick($samples, $labels);
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RubixML/RubixML | src/Datasets/Labeled.php | Labeled.filterByLabel | public function filterByLabel(callable $fn) : self
{
$samples = $labels = [];
foreach ($this->labels as $i => $label) {
if ($fn($label)) {
$samples[] = $this->samples[$i];
$labels[] = $label;
}
}
return self::quick($samples, $labels);
} | php | public function filterByLabel(callable $fn) : self
{
$samples = $labels = [];
foreach ($this->labels as $i => $label) {
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$labels[] = $label;
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RubixML/RubixML | src/Datasets/Labeled.php | Labeled.sortByColumn | public function sortByColumn(int $index, bool $descending = false)
{
$order = $this->column($index);
array_multisort(
$order,
$this->samples,
$this->labels,
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return $this;
} | php | public function sortByColumn(int $index, bool $descending = false)
{
$order = $this->column($index);
array_multisort(
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$this->labels,
$descending ? SORT_DESC : SORT_ASC
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return $this;
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{
array_multisort(
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$descending ? SORT_DESC : SORT_ASC
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return $this;
} | php | public function sortByLabel(bool $descending = false) : Dataset
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RubixML/RubixML | src/Datasets/Labeled.php | Labeled.stratify | public function stratify() : array
{
$strata = [];
foreach ($this->_stratify() as $label => $stratum) {
$labels = array_fill(0, count($stratum), $label);
$strata[$label] = self::quick($stratum, $labels);
}
return $strata;
} | php | public function stratify() : array
{
$strata = [];
foreach ($this->_stratify() as $label => $stratum) {
$labels = array_fill(0, count($stratum), $label);
$strata[$label] = self::quick($stratum, $labels);
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return $strata;
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RubixML/RubixML | src/Datasets/Labeled.php | Labeled.split | public function split(float $ratio = 0.5) : array
{
if ($ratio <= 0 or $ratio >= 1) {
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}
$n = (int) ($ratio * $this->numRows());
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{
if ($ratio <= 0 or $ratio >= 1) {
throw new InvalidArgumentException('Split ratio must be strictly'
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}
$n = (int) ($ratio * $this->numRows());
$leftSamples = array_slice($this->samples, 0, $n);
$leftLabels = array_slice($this->labels, 0, $n);
$rightSamples = array_slice($this->samples, $n);
$rightLabels = array_slice($this->labels, $n);
return [
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RubixML/RubixML | src/Datasets/Labeled.php | Labeled.stratifiedSplit | public function stratifiedSplit(float $ratio = 0.5) : array
{
if ($ratio <= 0. or $ratio >= 1.) {
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}
$leftSamples = $leftLabels = $rightSamples = $rightLabels = [];
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}
return [
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];
} | php | public function stratifiedSplit(float $ratio = 0.5) : array
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}
$leftSamples = $leftLabels = $rightSamples = $rightLabels = [];
foreach ($this->_stratify() as $label => $stratum) {
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$leftSamples = array_merge($leftSamples, array_splice($stratum, 0, $n));
$leftLabels = array_merge($leftLabels, array_fill(0, $n, $label));
$rightSamples = array_merge($rightSamples, $stratum);
$rightLabels = array_merge($rightLabels, array_fill(0, count($stratum), $label));
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@return self[] | [
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RubixML/RubixML | src/Datasets/Labeled.php | Labeled.fold | public function fold(int $k = 10) : array
{
if ($k < 2) {
throw new InvalidArgumentException('Cannot create less than'
. " 2 folds, $k given.");
}
$n = (int) floor($this->numRows() / $k);
$folds = [];
for ($i = 0; $i < $k; $i++) {
$offset = $i * $n;
$samples = array_slice($this->samples, $offset, $n);
$labels = array_slice($this->labels, $offset, $n);
$folds[] = self::quick($samples, $labels);
}
return $folds;
} | php | public function fold(int $k = 10) : array
{
if ($k < 2) {
throw new InvalidArgumentException('Cannot create less than'
. " 2 folds, $k given.");
}
$n = (int) floor($this->numRows() / $k);
$folds = [];
for ($i = 0; $i < $k; $i++) {
$offset = $i * $n;
$samples = array_slice($this->samples, $offset, $n);
$labels = array_slice($this->labels, $offset, $n);
$folds[] = self::quick($samples, $labels);
}
return $folds;
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RubixML/RubixML | src/Datasets/Labeled.php | Labeled.stratifiedFold | public function stratifiedFold(int $k = 10) : array
{
if ($k < 2) {
throw new InvalidArgumentException('Cannot create less than'
. " 2 folds, $k given.");
}
$folds = [];
for ($i = 0; $i < $k; $i++) {
$samples = $labels = [];
foreach ($this->_stratify() as $label => $stratum) {
$n = (int) floor(count($stratum) / $k);
$samples = array_merge($samples, array_slice($stratum, $i * $n, $n));
$labels = array_merge($labels, array_fill(0, $n, $label));
}
$folds[] = self::quick($samples, $labels);
}
return $folds;
} | php | public function stratifiedFold(int $k = 10) : array
{
if ($k < 2) {
throw new InvalidArgumentException('Cannot create less than'
. " 2 folds, $k given.");
}
$folds = [];
for ($i = 0; $i < $k; $i++) {
$samples = $labels = [];
foreach ($this->_stratify() as $label => $stratum) {
$n = (int) floor(count($stratum) / $k);
$samples = array_merge($samples, array_slice($stratum, $i * $n, $n));
$labels = array_merge($labels, array_fill(0, $n, $label));
}
$folds[] = self::quick($samples, $labels);
}
return $folds;
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RubixML/RubixML | src/Datasets/Labeled.php | Labeled._stratify | protected function _stratify() : array
{
$strata = [];
foreach ($this->labels as $index => $label) {
$strata[$label][] = $this->samples[$index];
}
return $strata;
} | php | protected function _stratify() : array
{
$strata = [];
foreach ($this->labels as $index => $label) {
$strata[$label][] = $this->samples[$index];
}
return $strata;
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RubixML/RubixML | src/Datasets/Labeled.php | Labeled.batch | public function batch(int $n = 50) : array
{
$sChunks = array_chunk($this->samples, $n);
$lChunks = array_chunk($this->labels, $n);
$batches = [];
foreach ($sChunks as $i => $samples) {
$batches[] = self::quick($samples, $lChunks[$i]);
}
return $batches;
} | php | public function batch(int $n = 50) : array
{
$sChunks = array_chunk($this->samples, $n);
$lChunks = array_chunk($this->labels, $n);
$batches = [];
foreach ($sChunks as $i => $samples) {
$batches[] = self::quick($samples, $lChunks[$i]);
}
return $batches;
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RubixML/RubixML | src/Datasets/Labeled.php | Labeled.partition | public function partition(int $column, $value) : array
{
if (!is_string($value) and !is_numeric($value)) {
throw new InvalidArgumentException('Value must be a string or'
. ' numeric type, ' . gettype($value) . ' given.');
}
$leftSamples = $leftLabels = $rightSamples = $rightLabels = [];
if ($this->columnType($column) === DataType::CATEGORICAL) {
foreach ($this->samples as $i => $sample) {
if ($sample[$column] === $value) {
$leftSamples[] = $sample;
$leftLabels[] = $this->labels[$i];
} else {
$rightSamples[] = $sample;
$rightLabels[] = $this->labels[$i];
}
}
} else {
foreach ($this->samples as $i => $sample) {
if ($sample[$column] < $value) {
$leftSamples[] = $sample;
$leftLabels[] = $this->labels[$i];
} else {
$rightSamples[] = $sample;
$rightLabels[] = $this->labels[$i];
}
}
}
return [
self::quick($leftSamples, $leftLabels),
self::quick($rightSamples, $rightLabels),
];
} | php | public function partition(int $column, $value) : array
{
if (!is_string($value) and !is_numeric($value)) {
throw new InvalidArgumentException('Value must be a string or'
. ' numeric type, ' . gettype($value) . ' given.');
}
$leftSamples = $leftLabels = $rightSamples = $rightLabels = [];
if ($this->columnType($column) === DataType::CATEGORICAL) {
foreach ($this->samples as $i => $sample) {
if ($sample[$column] === $value) {
$leftSamples[] = $sample;
$leftLabels[] = $this->labels[$i];
} else {
$rightSamples[] = $sample;
$rightLabels[] = $this->labels[$i];
}
}
} else {
foreach ($this->samples as $i => $sample) {
if ($sample[$column] < $value) {
$leftSamples[] = $sample;
$leftLabels[] = $this->labels[$i];
} else {
$rightSamples[] = $sample;
$rightLabels[] = $this->labels[$i];
}
}
}
return [
self::quick($leftSamples, $leftLabels),
self::quick($rightSamples, $rightLabels),
];
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RubixML/RubixML | src/Datasets/Labeled.php | Labeled.spatialPartition | public function spatialPartition(array $leftCentroid, array $rightCentroid, Distance $kernel)
{
if (count($leftCentroid) !== count($rightCentroid)) {
throw new InvalidArgumentException('Dimensionality mismatch between'
. ' left and right centroids.');
}
$leftSamples = $leftLabels = $rightSamples = $rightLabels = [];
foreach ($this->samples as $i => $sample) {
$lHat = $kernel->compute($sample, $leftCentroid);
$rHat = $kernel->compute($sample, $rightCentroid);
if ($lHat < $rHat) {
$leftSamples[] = $sample;
$leftLabels[] = $this->labels[$i];
} else {
$rightSamples[] = $sample;
$rightLabels[] = $this->labels[$i];
}
}
return [
self::quick($leftSamples, $leftLabels),
self::quick($rightSamples, $rightLabels),
];
} | php | public function spatialPartition(array $leftCentroid, array $rightCentroid, Distance $kernel)
{
if (count($leftCentroid) !== count($rightCentroid)) {
throw new InvalidArgumentException('Dimensionality mismatch between'
. ' left and right centroids.');
}
$leftSamples = $leftLabels = $rightSamples = $rightLabels = [];
foreach ($this->samples as $i => $sample) {
$lHat = $kernel->compute($sample, $leftCentroid);
$rHat = $kernel->compute($sample, $rightCentroid);
if ($lHat < $rHat) {
$leftSamples[] = $sample;
$leftLabels[] = $this->labels[$i];
} else {
$rightSamples[] = $sample;
$rightLabels[] = $this->labels[$i];
}
}
return [
self::quick($leftSamples, $leftLabels),
self::quick($rightSamples, $rightLabels),
];
} | [
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RubixML/RubixML | src/Datasets/Labeled.php | Labeled.randomSubsetWithReplacement | public function randomSubsetWithReplacement(int $n) : self
{
if ($n < 1) {
throw new InvalidArgumentException('Cannot generate a'
. " subset of less than 1 sample, $n given.");
}
$max = $this->numRows() - 1;
$samples = $labels = [];
for ($i = 0; $i < $n; $i++) {
$index = rand(0, $max);
$samples[] = $this->samples[$index];
$labels[] = $this->labels[$index];
}
return self::quick($samples, $labels);
} | php | public function randomSubsetWithReplacement(int $n) : self
{
if ($n < 1) {
throw new InvalidArgumentException('Cannot generate a'
. " subset of less than 1 sample, $n given.");
}
$max = $this->numRows() - 1;
$samples = $labels = [];
for ($i = 0; $i < $n; $i++) {
$index = rand(0, $max);
$samples[] = $this->samples[$index];
$labels[] = $this->labels[$index];
}
return self::quick($samples, $labels);
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RubixML/RubixML | src/Datasets/Labeled.php | Labeled.randomWeightedSubsetWithReplacement | public function randomWeightedSubsetWithReplacement(int $n, array $weights) : self
{
if ($n < 1) {
throw new InvalidArgumentException('Cannot generate a'
. " subset of less than 1 sample, $n given.");
}
if (count($weights) !== count($this->samples)) {
throw new InvalidArgumentException('The number of weights'
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. ' dataset, ' . count($this->samples) . ' needed'
. ' but ' . count($weights) . ' given.');
}
$total = array_sum($weights);
$max = (int) round($total * self::PHI);
$samples = $labels = [];
for ($i = 0; $i < $n; $i++) {
$delta = rand(0, $max) / self::PHI;
foreach ($weights as $index => $weight) {
$delta -= $weight;
if ($delta <= 0.) {
$samples[] = $this->samples[$index];
$labels[] = $this->labels[$index];
break 1;
}
}
}
return self::quick($samples, $labels);
} | php | public function randomWeightedSubsetWithReplacement(int $n, array $weights) : self
{
if ($n < 1) {
throw new InvalidArgumentException('Cannot generate a'
. " subset of less than 1 sample, $n given.");
}
if (count($weights) !== count($this->samples)) {
throw new InvalidArgumentException('The number of weights'
. ' must be equal to the number of samples in the'
. ' dataset, ' . count($this->samples) . ' needed'
. ' but ' . count($weights) . ' given.');
}
$total = array_sum($weights);
$max = (int) round($total * self::PHI);
$samples = $labels = [];
for ($i = 0; $i < $n; $i++) {
$delta = rand(0, $max) / self::PHI;
foreach ($weights as $index => $weight) {
$delta -= $weight;
if ($delta <= 0.) {
$samples[] = $this->samples[$index];
$labels[] = $this->labels[$index];
break 1;
}
}
}
return self::quick($samples, $labels);
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RubixML/RubixML | src/Classifiers/RadiusNeighbors.php | RadiusNeighbors.predict | public function predict(Dataset $dataset) : array
{
if ($this->tree->bare()) {
throw new RuntimeException('The learner has not'
. ' been trained.');
}
DatasetIsCompatibleWithEstimator::check($dataset, $this);
$predictions = [];
foreach ($dataset as $sample) {
[$samples, $labels, $distances] = $this->tree->range($sample, $this->radius);
if (empty($labels)) {
$predictions[] = self::OUTLIER;
continue 1;
}
if ($this->weighted) {
$weights = array_fill_keys($labels, 0.);
foreach ($labels as $i => $label) {
$weights[$label] += 1. / (1. + $distances[$i]);
}
} else {
$weights = array_count_values($labels);
}
$predictions[] = argmax($weights);
}
return $predictions;
} | php | public function predict(Dataset $dataset) : array
{
if ($this->tree->bare()) {
throw new RuntimeException('The learner has not'
. ' been trained.');
}
DatasetIsCompatibleWithEstimator::check($dataset, $this);
$predictions = [];
foreach ($dataset as $sample) {
[$samples, $labels, $distances] = $this->tree->range($sample, $this->radius);
if (empty($labels)) {
$predictions[] = self::OUTLIER;
continue 1;
}
if ($this->weighted) {
$weights = array_fill_keys($labels, 0.);
foreach ($labels as $i => $label) {
$weights[$label] += 1. / (1. + $distances[$i]);
}
} else {
$weights = array_count_values($labels);
}
$predictions[] = argmax($weights);
}
return $predictions;
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@param \Rubix\ML\Datasets\Dataset $dataset
@throws \RuntimeException
@throws \InvalidArgumentException
@return array | [
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] | train | https://github.com/RubixML/RubixML/blob/837385e2eb9f05c76c9522fc1673bdb87d11742d/src/Classifiers/RadiusNeighbors.php#L164-L198 |
RubixML/RubixML | src/NeuralNet/Optimizers/RMSProp.php | RMSProp.step | public function step(Parameter $param, Tensor $gradient) : void
{
$g2 = $this->cache[$param->id()];
$g2 = $g2->multiply($this->decay)
->add($gradient->square()->multiply(1. - $this->decay));
$this->cache[$param->id()] = $g2;
$step = $gradient->multiply($this->rate)
->divide($g2->sqrt()->clipLower(EPSILON));
$param->update($step);
} | php | public function step(Parameter $param, Tensor $gradient) : void
{
$g2 = $this->cache[$param->id()];
$g2 = $g2->multiply($this->decay)
->add($gradient->square()->multiply(1. - $this->decay));
$this->cache[$param->id()] = $g2;
$step = $gradient->multiply($this->rate)
->divide($g2->sqrt()->clipLower(EPSILON));
$param->update($step);
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RubixML/RubixML | src/Clusterers/FuzzyCMeans.php | FuzzyCMeans.train | public function train(Dataset $dataset) : void
{
DatasetIsCompatibleWithEstimator::check($dataset, $this);
if ($this->logger) {
$this->logger->info('Learner init ' . Params::stringify([
'c' => $this->c,
'fuzz' => $this->fuzz,
'kernel' => $this->kernel,
'epochs' => $this->epochs,
'min_change' => $this->minChange,
'seeder' => $this->seeder,
]));
}
$this->centroids = $this->seeder->seed($dataset, $this->c);
$this->steps = $memberships = [];
$samples = $dataset->samples();
$rotated = $dataset->columns();
$previous = INF;
for ($epoch = 1; $epoch <= $this->epochs; $epoch++) {
$memberships = array_map([self::class, 'membership'], $samples);
foreach ($this->centroids as $cluster => &$centroid) {
foreach ($rotated as $column => $values) {
$sigma = $total = 0.;
foreach ($memberships as $i => $probabilities) {
$weight = $probabilities[$cluster] ** $this->fuzz;
$sigma += $weight * $values[$i];
$total += $weight;
}
$centroid[$column] = $sigma / ($total ?: EPSILON);
}
}
$inertia = $this->inertia($dataset, $memberships);
$this->steps[] = $inertia;
if ($this->logger) {
$this->logger->info("Epoch $epoch complete, inertia=$inertia");
}
if (is_nan($inertia)) {
break 1;
}
if (abs($previous - $inertia) < $this->minChange) {
break 1;
}
$previous = $inertia;
}
if ($this->logger) {
$this->logger->info('Training complete');
}
} | php | public function train(Dataset $dataset) : void
{
DatasetIsCompatibleWithEstimator::check($dataset, $this);
if ($this->logger) {
$this->logger->info('Learner init ' . Params::stringify([
'c' => $this->c,
'fuzz' => $this->fuzz,
'kernel' => $this->kernel,
'epochs' => $this->epochs,
'min_change' => $this->minChange,
'seeder' => $this->seeder,
]));
}
$this->centroids = $this->seeder->seed($dataset, $this->c);
$this->steps = $memberships = [];
$samples = $dataset->samples();
$rotated = $dataset->columns();
$previous = INF;
for ($epoch = 1; $epoch <= $this->epochs; $epoch++) {
$memberships = array_map([self::class, 'membership'], $samples);
foreach ($this->centroids as $cluster => &$centroid) {
foreach ($rotated as $column => $values) {
$sigma = $total = 0.;
foreach ($memberships as $i => $probabilities) {
$weight = $probabilities[$cluster] ** $this->fuzz;
$sigma += $weight * $values[$i];
$total += $weight;
}
$centroid[$column] = $sigma / ($total ?: EPSILON);
}
}
$inertia = $this->inertia($dataset, $memberships);
$this->steps[] = $inertia;
if ($this->logger) {
$this->logger->info("Epoch $epoch complete, inertia=$inertia");
}
if (is_nan($inertia)) {
break 1;
}
if (abs($previous - $inertia) < $this->minChange) {
break 1;
}
$previous = $inertia;
}
if ($this->logger) {
$this->logger->info('Training complete');
}
} | [
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@throws \InvalidArgumentException | [
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] | train | https://github.com/RubixML/RubixML/blob/837385e2eb9f05c76c9522fc1673bdb87d11742d/src/Clusterers/FuzzyCMeans.php#L212-L276 |
RubixML/RubixML | src/Clusterers/FuzzyCMeans.php | FuzzyCMeans.membership | protected function membership(array $sample) : array
{
$membership = $deltas = [];
foreach ($this->centroids as $centroid) {
$deltas[] = $this->kernel->compute($sample, $centroid);
}
foreach ($this->centroids as $cluster => $centroid) {
$alpha = $this->kernel->compute($sample, $centroid);
$sigma = 0.;
foreach ($deltas as $delta) {
$sigma += ($alpha / ($delta ?: EPSILON)) ** $this->lambda;
}
$membership[$cluster] = 1. / ($sigma ?: EPSILON);
}
return $membership;
} | php | protected function membership(array $sample) : array
{
$membership = $deltas = [];
foreach ($this->centroids as $centroid) {
$deltas[] = $this->kernel->compute($sample, $centroid);
}
foreach ($this->centroids as $cluster => $centroid) {
$alpha = $this->kernel->compute($sample, $centroid);
$sigma = 0.;
foreach ($deltas as $delta) {
$sigma += ($alpha / ($delta ?: EPSILON)) ** $this->lambda;
}
$membership[$cluster] = 1. / ($sigma ?: EPSILON);
}
return $membership;
} | [
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RubixML/RubixML | src/Clusterers/FuzzyCMeans.php | FuzzyCMeans.inertia | protected function inertia(Dataset $dataset, array $memberships) : float
{
$inertia = 0.;
foreach ($dataset as $i => $sample) {
$membership = $memberships[$i];
foreach ($this->centroids as $cluster => $centroid) {
$inertia += $membership[$cluster]
* $this->kernel->compute($sample, $centroid);
}
}
return $inertia;
} | php | protected function inertia(Dataset $dataset, array $memberships) : float
{
$inertia = 0.;
foreach ($dataset as $i => $sample) {
$membership = $memberships[$i];
foreach ($this->centroids as $cluster => $centroid) {
$inertia += $membership[$cluster]
* $this->kernel->compute($sample, $centroid);
}
}
return $inertia;
} | [
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RubixML/RubixML | src/CrossValidation/Reports/MulticlassBreakdown.php | MulticlassBreakdown.generate | public function generate(array $predictions, array $labels) : array
{
$n = count($predictions);
if ($n !== count($labels)) {
throw new InvalidArgumentException('The number of labels'
. ' must equal the number of predictions.');
}
$classes = array_unique(array_merge($predictions, $labels));
$k = count($classes);
$truePos = $trueNeg = $falsePos = $falseNeg = array_fill_keys($classes, 0);
foreach ($predictions as $i => $prediction) {
$label = $labels[$i];
if ($prediction === $label) {
$truePos[$prediction]++;
foreach ($classes as $class) {
if ($class !== $prediction) {
$trueNeg[$class]++;
}
}
} else {
$falsePos[$prediction]++;
$falseNeg[$label]++;
}
}
$averages = array_fill_keys([
'accuracy', 'precision', 'recall', 'specificity', 'negative_predictive_value',
'false_discovery_rate', 'miss_rate', 'fall_out', 'false_omission_rate',
'f1_score', 'mcc', 'informedness', 'markedness',
], 0.);
$counts = array_fill_keys([
'true_positives', 'true_negatives', 'false_positives', 'false_negatives',
], 0);
$overall = array_replace($averages, $counts);
$table = [];
foreach ($truePos as $label => $tp) {
$tn = $trueNeg[$label];
$fp = $falsePos[$label];
$fn = $falseNeg[$label];
$accuracy = ($tp + $tn) / ($tp + $tn + $fp + $fn);
$precision = $tp / (($tp + $fp) ?: EPSILON);
$recall = $tp / (($tp + $fn) ?: EPSILON);
$specificity = $tn / (($tn + $fp) ?: EPSILON);
$npv = $tn / (($tn + $fn) ?: EPSILON);
$cardinality = $tp + $fn;
$f1score = 2. * (($precision * $recall))
/ (($precision + $recall) ?: EPSILON);
$mcc = ($tp * $tn - $fp * $fn)
/ sqrt((($tp + $fp) * ($tp + $fn)
* ($tn + $fp) * ($tn + $fn)) ?: EPSILON);
$metrics = [];
$metrics['accuracy'] = $accuracy;
$metrics['precision'] = $precision;
$metrics['recall'] = $recall;
$metrics['specificity'] = $specificity;
$metrics['negative_predictive_value'] = $npv;
$metrics['false_discovery_rate'] = 1. - $precision;
$metrics['miss_rate'] = 1. - $recall;
$metrics['fall_out'] = 1. - $specificity;
$metrics['false_omission_rate'] = 1. - $npv;
$metrics['f1_score'] = $f1score;
$metrics['mcc'] = $mcc;
$metrics['informedness'] = $recall + $specificity - 1.;
$metrics['markedness'] = $precision + $npv - 1.;
$metrics['true_positives'] = $tp;
$metrics['true_negatives'] = $tn;
$metrics['false_positives'] = $fp;
$metrics['false_negatives'] = $fn;
$metrics['cardinality'] = $cardinality;
$metrics['density'] = $cardinality / $n;
$table[$label] = $metrics;
$overall['accuracy'] += $accuracy;
$overall['precision'] += $precision;
$overall['recall'] += $recall;
$overall['specificity'] += $specificity;
$overall['negative_predictive_value'] += $npv;
$overall['false_discovery_rate'] += 1. - $precision;
$overall['miss_rate'] += 1. - $recall;
$overall['fall_out'] += 1. - $specificity;
$overall['false_omission_rate'] += 1. - $npv;
$overall['f1_score'] += $f1score;
$overall['mcc'] += $mcc;
$overall['informedness'] += $recall + $specificity - 1.;
$overall['markedness'] += $precision + $npv - 1.;
$overall['true_positives'] += $tp;
$overall['true_negatives'] += $tn;
$overall['false_positives'] += $fp;
$overall['false_negatives'] += $fn;
}
foreach (array_keys($averages) as $metric) {
$overall[$metric] /= $k;
}
$overall['cardinality'] = $n;
return [
'overall' => $overall,
'label' => $table,
];
} | php | public function generate(array $predictions, array $labels) : array
{
$n = count($predictions);
if ($n !== count($labels)) {
throw new InvalidArgumentException('The number of labels'
. ' must equal the number of predictions.');
}
$classes = array_unique(array_merge($predictions, $labels));
$k = count($classes);
$truePos = $trueNeg = $falsePos = $falseNeg = array_fill_keys($classes, 0);
foreach ($predictions as $i => $prediction) {
$label = $labels[$i];
if ($prediction === $label) {
$truePos[$prediction]++;
foreach ($classes as $class) {
if ($class !== $prediction) {
$trueNeg[$class]++;
}
}
} else {
$falsePos[$prediction]++;
$falseNeg[$label]++;
}
}
$averages = array_fill_keys([
'accuracy', 'precision', 'recall', 'specificity', 'negative_predictive_value',
'false_discovery_rate', 'miss_rate', 'fall_out', 'false_omission_rate',
'f1_score', 'mcc', 'informedness', 'markedness',
], 0.);
$counts = array_fill_keys([
'true_positives', 'true_negatives', 'false_positives', 'false_negatives',
], 0);
$overall = array_replace($averages, $counts);
$table = [];
foreach ($truePos as $label => $tp) {
$tn = $trueNeg[$label];
$fp = $falsePos[$label];
$fn = $falseNeg[$label];
$accuracy = ($tp + $tn) / ($tp + $tn + $fp + $fn);
$precision = $tp / (($tp + $fp) ?: EPSILON);
$recall = $tp / (($tp + $fn) ?: EPSILON);
$specificity = $tn / (($tn + $fp) ?: EPSILON);
$npv = $tn / (($tn + $fn) ?: EPSILON);
$cardinality = $tp + $fn;
$f1score = 2. * (($precision * $recall))
/ (($precision + $recall) ?: EPSILON);
$mcc = ($tp * $tn - $fp * $fn)
/ sqrt((($tp + $fp) * ($tp + $fn)
* ($tn + $fp) * ($tn + $fn)) ?: EPSILON);
$metrics = [];
$metrics['accuracy'] = $accuracy;
$metrics['precision'] = $precision;
$metrics['recall'] = $recall;
$metrics['specificity'] = $specificity;
$metrics['negative_predictive_value'] = $npv;
$metrics['false_discovery_rate'] = 1. - $precision;
$metrics['miss_rate'] = 1. - $recall;
$metrics['fall_out'] = 1. - $specificity;
$metrics['false_omission_rate'] = 1. - $npv;
$metrics['f1_score'] = $f1score;
$metrics['mcc'] = $mcc;
$metrics['informedness'] = $recall + $specificity - 1.;
$metrics['markedness'] = $precision + $npv - 1.;
$metrics['true_positives'] = $tp;
$metrics['true_negatives'] = $tn;
$metrics['false_positives'] = $fp;
$metrics['false_negatives'] = $fn;
$metrics['cardinality'] = $cardinality;
$metrics['density'] = $cardinality / $n;
$table[$label] = $metrics;
$overall['accuracy'] += $accuracy;
$overall['precision'] += $precision;
$overall['recall'] += $recall;
$overall['specificity'] += $specificity;
$overall['negative_predictive_value'] += $npv;
$overall['false_discovery_rate'] += 1. - $precision;
$overall['miss_rate'] += 1. - $recall;
$overall['fall_out'] += 1. - $specificity;
$overall['false_omission_rate'] += 1. - $npv;
$overall['f1_score'] += $f1score;
$overall['mcc'] += $mcc;
$overall['informedness'] += $recall + $specificity - 1.;
$overall['markedness'] += $precision + $npv - 1.;
$overall['true_positives'] += $tp;
$overall['true_negatives'] += $tn;
$overall['false_positives'] += $fp;
$overall['false_negatives'] += $fn;
}
foreach (array_keys($averages) as $metric) {
$overall[$metric] /= $k;
}
$overall['cardinality'] = $n;
return [
'overall' => $overall,
'label' => $table,
];
} | [
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RubixML/RubixML | src/Graph/Nodes/Hypersphere.php | Hypersphere.split | public static function split(Dataset $dataset, Distance $kernel) : self
{
$samples = $dataset->samples();
$center = Matrix::quick($samples)
->transpose()
->mean()
->asArray();
$distances = [];
foreach ($samples as $sample) {
$distances[] = $kernel->compute($sample, $center);
}
$radius = max($distances);
$leftCentroid = $dataset->row(argmax($distances));
$distances = [];
foreach ($samples as $sample) {
$distances[] = $kernel->compute($sample, $leftCentroid);
}
$rightCentroid = $dataset->row(argmax($distances));
$subsets = $dataset->spatialPartition($leftCentroid, $rightCentroid, $kernel);
return new self($center, $radius, $subsets);
} | php | public static function split(Dataset $dataset, Distance $kernel) : self
{
$samples = $dataset->samples();
$center = Matrix::quick($samples)
->transpose()
->mean()
->asArray();
$distances = [];
foreach ($samples as $sample) {
$distances[] = $kernel->compute($sample, $center);
}
$radius = max($distances);
$leftCentroid = $dataset->row(argmax($distances));
$distances = [];
foreach ($samples as $sample) {
$distances[] = $kernel->compute($sample, $leftCentroid);
}
$rightCentroid = $dataset->row(argmax($distances));
$subsets = $dataset->spatialPartition($leftCentroid, $rightCentroid, $kernel);
return new self($center, $radius, $subsets);
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RubixML/RubixML | src/Classifiers/DummyClassifier.php | DummyClassifier.train | public function train(Dataset $dataset) : void
{
if (!$dataset instanceof Labeled) {
throw new InvalidArgumentException('This estimator requires a'
. ' labeled training set.');
}
$this->strategy->fit($dataset->labels());
$this->trained = true;
} | php | public function train(Dataset $dataset) : void
{
if (!$dataset instanceof Labeled) {
throw new InvalidArgumentException('This estimator requires a'
. ' labeled training set.');
}
$this->strategy->fit($dataset->labels());
$this->trained = true;
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RubixML/RubixML | src/Classifiers/DummyClassifier.php | DummyClassifier.predict | public function predict(Dataset $dataset) : array
{
if (!$this->trained) {
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}
$n = $dataset->numRows();
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return $predictions;
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if (!$this->trained) {
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}
$n = $dataset->numRows();
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RubixML/RubixML | src/Pipeline.php | Pipeline.trained | public function trained() : bool
{
return ($this->estimator instanceof Learner
and $this->estimator->trained() and $this->fitted)
or $this->fitted;
} | php | public function trained() : bool
{
return ($this->estimator instanceof Learner
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RubixML/RubixML | src/Pipeline.php | Pipeline.train | public function train(Dataset $dataset) : void
{
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if ($this->estimator instanceof Learner) {
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}
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$this->fitted = true;
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RubixML/RubixML | src/Pipeline.php | Pipeline.partial | public function partial(Dataset $dataset) : void
{
if ($this->elastic) {
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}
if ($this->estimator instanceof Online) {
$this->estimator->partial($dataset);
}
} | php | public function partial(Dataset $dataset) : void
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RubixML/RubixML | src/Pipeline.php | Pipeline.predict | public function predict(Dataset $dataset) : array
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RubixML/RubixML | src/Pipeline.php | Pipeline.proba | public function proba(Dataset $dataset) : array
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return $base->proba($dataset);
}
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RubixML/RubixML | src/Pipeline.php | Pipeline.fit | protected function fit(Dataset $dataset) : void
{
foreach ($this->transformers as $transformer) {
if ($transformer instanceof Stateful) {
$transformer->fit($dataset);
if ($this->logger) {
$this->logger->info('Fitted '
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}
}
$dataset->apply($transformer);
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}
}
} | php | protected function fit(Dataset $dataset) : void
{
foreach ($this->transformers as $transformer) {
if ($transformer instanceof Stateful) {
$transformer->fit($dataset);
if ($this->logger) {
$this->logger->info('Fitted '
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$dataset->apply($transformer);
if ($this->logger) {
$this->logger->info('Applied '
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RubixML/RubixML | src/Pipeline.php | Pipeline.update | protected function update(Dataset $dataset) : void
{
foreach ($this->transformers as $transformer) {
if ($transformer instanceof Elastic) {
$transformer->update($dataset);
if ($this->logger) {
$this->logger->info('Updated '
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}
}
$dataset->apply($transformer);
if ($this->logger) {
$this->logger->info('Applied '
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}
}
} | php | protected function update(Dataset $dataset) : void
{
foreach ($this->transformers as $transformer) {
if ($transformer instanceof Elastic) {
$transformer->update($dataset);
if ($this->logger) {
$this->logger->info('Updated '
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}
}
$dataset->apply($transformer);
if ($this->logger) {
$this->logger->info('Applied '
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}
}
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RubixML/RubixML | src/Pipeline.php | Pipeline.preprocess | protected function preprocess(Dataset $dataset) : void
{
foreach ($this->transformers as $transformer) {
$dataset->apply($transformer);
}
} | php | protected function preprocess(Dataset $dataset) : void
{
foreach ($this->transformers as $transformer) {
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RubixML/RubixML | src/Graph/Nodes/BinaryNode.php | BinaryNode.children | public function children() : Generator
{
if ($this->left) {
yield $this->left;
}
if ($this->right) {
yield $this->right;
}
} | php | public function children() : Generator
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yield $this->left;
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yield $this->right;
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RubixML/RubixML | src/Graph/Nodes/BinaryNode.php | BinaryNode.height | public function height() : int
{
return 1 + max(
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);
} | php | public function height() : int
{
return 1 + max(
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RubixML/RubixML | src/Graph/Nodes/BinaryNode.php | BinaryNode.balance | public function balance() : int
{
return ($this->right ? $this->right->height() : 0)
- ($this->left ? $this->left->height() : 0);
} | php | public function balance() : int
{
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RubixML/RubixML | src/Graph/Nodes/BinaryNode.php | BinaryNode.attachLeft | public function attachLeft(BinaryNode $node) : void
{
$node->setParent($this);
$this->left = $node;
} | php | public function attachLeft(BinaryNode $node) : void
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RubixML/RubixML | src/Graph/Nodes/BinaryNode.php | BinaryNode.attachRight | public function attachRight(BinaryNode $node) : void
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RubixML/RubixML | src/Graph/Nodes/BinaryNode.php | BinaryNode.detachLeft | public function detachLeft() : void
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} | php | public function detachLeft() : void
{
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RubixML/RubixML | src/Graph/Nodes/BinaryNode.php | BinaryNode.detachRight | public function detachRight() : void
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$this->right = null;
}
} | php | public function detachRight() : void
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RubixML/RubixML | src/Clusterers/KMeans.php | KMeans.train | public function train(Dataset $dataset) : void
{
DatasetIsCompatibleWithEstimator::check($dataset, $this);
$this->centroids = $this->seeder->seed($dataset, $this->k);
$sizes = array_fill(1, $this->k, 0);
$sizes[0] = $dataset->numRows();
$this->sizes = $sizes;
$this->steps = [];
$this->partial($dataset);
} | php | public function train(Dataset $dataset) : void
{
DatasetIsCompatibleWithEstimator::check($dataset, $this);
$this->centroids = $this->seeder->seed($dataset, $this->k);
$sizes = array_fill(1, $this->k, 0);
$sizes[0] = $dataset->numRows();
$this->sizes = $sizes;
$this->steps = [];
$this->partial($dataset);
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RubixML/RubixML | src/Clusterers/KMeans.php | KMeans.partial | public function partial(Dataset $dataset) : void
{
if (empty($this->centroids) or empty($this->sizes)) {
$this->train($dataset);
return;
}
DatasetIsCompatibleWithEstimator::check($dataset, $this);
if ($this->logger) {
$this->logger->info('Learner init ' . Params::stringify([
'k' => $this->k,
'batch_size' => $this->batchSize,
'kernel' => $this->kernel,
'epochs' => $this->epochs,
'min_change' => $this->minChange,
'seeder' => $this->seeder,
]));
}
$n = $dataset->numRows();
$samples = $dataset->samples();
$labels = array_fill(0, $n, 0);
$order = range(0, $n - 1);
$randomize = $n > $this->batchSize ? true : false;
$previous = INF;
for ($epoch = 1; $epoch <= $this->epochs; $epoch++) {
if ($randomize) {
shuffle($order);
array_multisort($order, $samples, $labels);
}
$sBatches = array_chunk($samples, $this->batchSize, true);
$lBatches = array_chunk($labels, $this->batchSize, true);
foreach ($sBatches as $i => $batch) {
$assignments = array_map([self::class, 'assign'], $batch);
$lHat = $lBatches[$i];
foreach ($assignments as $j => $cluster) {
$expected = $lHat[$j];
if ($cluster !== $expected) {
$labels[$j] = $cluster;
$this->sizes[$expected]--;
$this->sizes[$cluster]++;
}
}
$strata = Labeled::quick($batch, $lHat)->stratify();
foreach ($strata as $cluster => $stratum) {
$centroid = $this->centroids[$cluster];
$size = $this->sizes[$cluster];
$step = Matrix::quick($stratum->samples())
->transpose()
->mean()
->asArray();
$weight = 1. / ($size ?: EPSILON);
foreach ($centroid as $i => &$mean) {
$mean = (1. - $weight) * $mean + $weight * $step[$i];
}
$this->centroids[$cluster] = $centroid;
}
}
$inertia = $this->inertia($samples);
$this->steps[] = $inertia;
if ($this->logger) {
$this->logger->info("Epoch $epoch complete, inertia=$inertia");
}
if (is_nan($inertia)) {
break 1;
}
if (abs($previous - $inertia) < $this->minChange) {
break 1;
}
$previous = $inertia;
}
if ($this->logger) {
$this->logger->info('Training complete');
}
} | php | public function partial(Dataset $dataset) : void
{
if (empty($this->centroids) or empty($this->sizes)) {
$this->train($dataset);
return;
}
DatasetIsCompatibleWithEstimator::check($dataset, $this);
if ($this->logger) {
$this->logger->info('Learner init ' . Params::stringify([
'k' => $this->k,
'batch_size' => $this->batchSize,
'kernel' => $this->kernel,
'epochs' => $this->epochs,
'min_change' => $this->minChange,
'seeder' => $this->seeder,
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}
$n = $dataset->numRows();
$samples = $dataset->samples();
$labels = array_fill(0, $n, 0);
$order = range(0, $n - 1);
$randomize = $n > $this->batchSize ? true : false;
$previous = INF;
for ($epoch = 1; $epoch <= $this->epochs; $epoch++) {
if ($randomize) {
shuffle($order);
array_multisort($order, $samples, $labels);
}
$sBatches = array_chunk($samples, $this->batchSize, true);
$lBatches = array_chunk($labels, $this->batchSize, true);
foreach ($sBatches as $i => $batch) {
$assignments = array_map([self::class, 'assign'], $batch);
$lHat = $lBatches[$i];
foreach ($assignments as $j => $cluster) {
$expected = $lHat[$j];
if ($cluster !== $expected) {
$labels[$j] = $cluster;
$this->sizes[$expected]--;
$this->sizes[$cluster]++;
}
}
$strata = Labeled::quick($batch, $lHat)->stratify();
foreach ($strata as $cluster => $stratum) {
$centroid = $this->centroids[$cluster];
$size = $this->sizes[$cluster];
$step = Matrix::quick($stratum->samples())
->transpose()
->mean()
->asArray();
$weight = 1. / ($size ?: EPSILON);
foreach ($centroid as $i => &$mean) {
$mean = (1. - $weight) * $mean + $weight * $step[$i];
}
$this->centroids[$cluster] = $centroid;
}
}
$inertia = $this->inertia($samples);
$this->steps[] = $inertia;
if ($this->logger) {
$this->logger->info("Epoch $epoch complete, inertia=$inertia");
}
if (is_nan($inertia)) {
break 1;
}
if (abs($previous - $inertia) < $this->minChange) {
break 1;
}
$previous = $inertia;
}
if ($this->logger) {
$this->logger->info('Training complete');
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RubixML/RubixML | src/Clusterers/KMeans.php | KMeans.predict | public function predict(Dataset $dataset) : array
{
if (!$this->centroids) {
throw new RuntimeException('Estimator has not been trained.');
}
DatasetIsCompatibleWithEstimator::check($dataset, $this);
return array_map([self::class, 'assign'], $dataset->samples());
} | php | public function predict(Dataset $dataset) : array
{
if (!$this->centroids) {
throw new RuntimeException('Estimator has not been trained.');
}
DatasetIsCompatibleWithEstimator::check($dataset, $this);
return array_map([self::class, 'assign'], $dataset->samples());
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RubixML/RubixML | src/Clusterers/KMeans.php | KMeans.inertia | protected function inertia(array $samples) : float
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foreach ($samples as $sample) {
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$inertia += $this->kernel->compute($sample, $centroid);
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}
return $inertia;
} | php | protected function inertia(array $samples) : float
{
$inertia = 0.;
foreach ($samples as $sample) {
foreach ($this->centroids as $centroid) {
$inertia += $this->kernel->compute($sample, $centroid);
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RubixML/RubixML | src/Other/Loggers/Screen.php | Screen.log | public function log($level, $message, array $context = []) : void
{
$prefix = '';
if ($this->timestamps) {
$prefix .= '[' . date(self::TIMESTAMP_FORMAT) . '] ';
}
$prefix .= $this->channel . '.' . strtoupper((string) $level) . ': ';
echo $prefix . $message . PHP_EOL;
} | php | public function log($level, $message, array $context = []) : void
{
$prefix = '';
if ($this->timestamps) {
$prefix .= '[' . date(self::TIMESTAMP_FORMAT) . '] ';
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RubixML/RubixML | src/NeuralNet/CostFunctions/LeastSquares.php | LeastSquares.compute | public function compute(Tensor $expected, Tensor $output) : float
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} | php | public function compute(Tensor $expected, Tensor $output) : float
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return $output->subtract($expected)->square()->sum()->mean();
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RubixML/RubixML | src/AnomalyDetectors/KDLOF.php | KDLOF.train | public function train(Dataset $dataset) : void
{
DatasetIsCompatibleWithEstimator::check($dataset, $this);
$samples = $dataset->samples();
$dataset = Labeled::quick($samples, array_keys($samples));
$this->tree->grow($dataset);
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$distances[] = $dHat;
$indices[] = $iHat;
$this->kdistances[] = end($dHat);
}
foreach ($distances as $i => $row) {
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$lofs = [];
foreach ($dataset as $sample) {
$lofs[] = $this->localOutlierFactor($sample);
}
$shift = Stats::percentile($lofs, 100. * $this->contamination);
$this->offset = self::THRESHOLD + $shift;
} | php | public function train(Dataset $dataset) : void
{
DatasetIsCompatibleWithEstimator::check($dataset, $this);
$samples = $dataset->samples();
$dataset = Labeled::quick($samples, array_keys($samples));
$this->tree->grow($dataset);
$this->kdistances = $this->lrds = [];
$indices = $distances = [];
foreach ($dataset as $sample) {
[$iHat, $dHat] = $this->tree->nearest($sample, $this->k);
$distances[] = $dHat;
$indices[] = $iHat;
$this->kdistances[] = end($dHat);
}
foreach ($distances as $i => $row) {
$this->lrds[] = $this->localReachabilityDensity($indices[$i], $row);
}
$lofs = [];
foreach ($dataset as $sample) {
$lofs[] = $this->localOutlierFactor($sample);
}
$shift = Stats::percentile($lofs, 100. * $this->contamination);
$this->offset = self::THRESHOLD + $shift;
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RubixML/RubixML | src/AnomalyDetectors/KDLOF.php | KDLOF.rank | public function rank(Dataset $dataset) : array
{
if ($this->tree->bare()) {
throw new RuntimeException('The learner has not'
. ' been trained.');
}
DatasetIsCompatibleWithEstimator::check($dataset, $this);
return array_map([self::class, 'localOutlierFactor'], $dataset->samples());
} | php | public function rank(Dataset $dataset) : array
{
if ($this->tree->bare()) {
throw new RuntimeException('The learner has not'
. ' been trained.');
}
DatasetIsCompatibleWithEstimator::check($dataset, $this);
return array_map([self::class, 'localOutlierFactor'], $dataset->samples());
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RubixML/RubixML | src/AnomalyDetectors/KDLOF.php | KDLOF.localOutlierFactor | protected function localOutlierFactor(array $sample) : float
{
if (empty($this->lrds)) {
throw new RuntimeException('Local reachability distances have'
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}
[$indices, $distances] = $this->tree->nearest($sample, $this->k);
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foreach ($indices as $index) {
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}
$ratios = [];
foreach ($lrds as $lHat) {
$ratios[] = $lHat / $lrd;
}
return Stats::mean($ratios);
} | php | protected function localOutlierFactor(array $sample) : float
{
if (empty($this->lrds)) {
throw new RuntimeException('Local reachability distances have'
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}
[$indices, $distances] = $this->tree->nearest($sample, $this->k);
$lrd = $this->localReachabilityDensity($indices, $distances);
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RubixML/RubixML | src/AnomalyDetectors/KDLOF.php | KDLOF.localReachabilityDensity | protected function localReachabilityDensity(array $indices, array $distances) : float
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return 1. / ($mean ?: EPSILON);
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RubixML/RubixML | src/Kernels/SVM/Polynomial.php | Polynomial.options | public function options() : array
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RubixML/RubixML | src/Graph/Nodes/Neighborhood.php | Neighborhood.terminate | public static function terminate(Labeled $dataset) : self
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RubixML/RubixML | src/PersistentModel.php | PersistentModel.load | public static function load(Persister $persister) : self
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if (!$learner instanceof Learner) {
throw new InvalidArgumentException('Peristable must be a'
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}
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$learner = $persister->load();
if (!$learner instanceof Learner) {
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}
}
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RubixML/RubixML | src/CrossValidation/Reports/ConfusionMatrix.php | ConfusionMatrix.generate | public function generate(array $predictions, array $labels) : array
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foreach ($predictions as $i => $prediction) {
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}
return $matrix;
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RubixML/RubixML | src/NeuralNet/Layers/Dense.php | Dense.initialize | public function initialize(int $fanIn) : int
{
$fanOut = $this->neurons;
$w = $this->weightInitializer->initialize($fanIn, $fanOut);
$b = $this->biasInitializer->initialize(1, $fanOut)->columnAsVector(0);
$this->weights = new MatrixParam($w);
$this->biases = new VectorParam($b);
return $fanOut;
} | php | public function initialize(int $fanIn) : int
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$w = $this->weightInitializer->initialize($fanIn, $fanOut);
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RubixML/RubixML | src/NeuralNet/Layers/Dense.php | Dense.back | public function back(Deferred $prevGradient, Optimizer $optimizer) : Deferred
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RubixML/RubixML | src/Persisters/RedisDB.php | RedisDB.save | public function save(Persistable $persistable) : void
{
$data = $this->serializer->serialize($persistable);
$success = $this->connector->set($this->key, $data);
if (!$success) {
throw new RuntimeException('Failed to save persistable'
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}
} | php | public function save(Persistable $persistable) : void
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$data = $this->serializer->serialize($persistable);
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RubixML/RubixML | src/Persisters/RedisDB.php | RedisDB.load | public function load() : Persistable
{
$data = $this->connector->get($this->key) ?: '';
$persistable = $this->serializer->unserialize($data);
if (!$persistable instanceof Persistable) {
throw new RuntimeException('Persistable could not be'
. ' reconstituted.');
}
return $persistable;
} | php | public function load() : Persistable
{
$data = $this->connector->get($this->key) ?: '';
$persistable = $this->serializer->unserialize($data);
if (!$persistable instanceof Persistable) {
throw new RuntimeException('Persistable could not be'
. ' reconstituted.');
}
return $persistable;
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RubixML/RubixML | src/Other/Strategies/WildGuess.php | WildGuess.fit | public function fit(array $values) : void
{
if (empty($values)) {
throw new InvalidArgumentException('Strategy must be fit with'
. ' at least 1 value.');
}
$this->min = min($values);
$this->max = max($values);
} | php | public function fit(array $values) : void
{
if (empty($values)) {
throw new InvalidArgumentException('Strategy must be fit with'
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}
$this->min = min($values);
$this->max = max($values);
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RubixML/RubixML | src/Other/Strategies/WildGuess.php | WildGuess.guess | public function guess() : float
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throw new RuntimeException('Strategy has not been fitted.');
}
$min = (int) round($this->min * $this->precision);
$max = (int) round($this->max * $this->precision);
return rand($min, $max) / $this->precision;
} | php | public function guess() : float
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if ($this->min === null or $this->max === null) {
throw new RuntimeException('Strategy has not been fitted.');
}
$min = (int) round($this->min * $this->precision);
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RubixML/RubixML | src/CrossValidation/Metrics/MCC.php | MCC.mcc | public static function mcc(int $tp, int $tn, int $fp, int $fn) : float
{
return ($tp * $tn - $fp * $fn)
/ (sqrt(($tp + $fp) * ($tp + $fn)
* ($tn + $fp) * ($tn + $fn)) ?: EPSILON);
} | php | public static function mcc(int $tp, int $tn, int $fp, int $fn) : float
{
return ($tp * $tn - $fp * $fn)
/ (sqrt(($tp + $fp) * ($tp + $fn)
* ($tn + $fp) * ($tn + $fn)) ?: EPSILON);
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RubixML/RubixML | src/CrossValidation/Metrics/MCC.php | MCC.score | public function score(array $predictions, array $labels) : float
{
if (empty($predictions)) {
return 0.;
}
if (count($predictions) !== count($labels)) {
throw new InvalidArgumentException('The number of labels'
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}
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$truePos = $trueNeg = $falsePos = $falseNeg = array_fill_keys($classes, 0);
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$falseNeg[$label]++;
}
}
return Stats::mean(
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} | php | public function score(array $predictions, array $labels) : float
{
if (empty($predictions)) {
return 0.;
}
if (count($predictions) !== count($labels)) {
throw new InvalidArgumentException('The number of labels'
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}
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if ($prediction === $label) {
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foreach ($classes as $class) {
if ($class !== $prediction) {
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}
}
} else {
$falsePos[$prediction]++;
$falseNeg[$label]++;
}
}
return Stats::mean(
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RubixML/RubixML | src/Regressors/Ridge.php | Ridge.train | public function train(Dataset $dataset) : void
{
if (!$dataset instanceof Labeled) {
throw new InvalidArgumentException('This estimator requires a'
. ' labeled training set.');
}
DatasetIsCompatibleWithEstimator::check($dataset, $this);
$samples = $dataset->samples();
$labels = $dataset->labels();
$biases = Matrix::ones($dataset->numRows(), 1);
$x = Matrix::build($samples)->augmentLeft($biases);
$y = Vector::build($labels);
$alphas = array_fill(0, $x->n() - 1, $this->alpha);
$penalty = Matrix::diagonal(array_merge([0.], $alphas));
$xT = $x->transpose();
$coefficients = $xT->matmul($x)
->add($penalty)
->inverse()
->dot($xT->dot($y))
->asArray();
$this->bias = (float) array_shift($coefficients);
$this->weights = Vector::quick($coefficients);
} | php | public function train(Dataset $dataset) : void
{
if (!$dataset instanceof Labeled) {
throw new InvalidArgumentException('This estimator requires a'
. ' labeled training set.');
}
DatasetIsCompatibleWithEstimator::check($dataset, $this);
$samples = $dataset->samples();
$labels = $dataset->labels();
$biases = Matrix::ones($dataset->numRows(), 1);
$x = Matrix::build($samples)->augmentLeft($biases);
$y = Vector::build($labels);
$alphas = array_fill(0, $x->n() - 1, $this->alpha);
$penalty = Matrix::diagonal(array_merge([0.], $alphas));
$xT = $x->transpose();
$coefficients = $xT->matmul($x)
->add($penalty)
->inverse()
->dot($xT->dot($y))
->asArray();
$this->bias = (float) array_shift($coefficients);
$this->weights = Vector::quick($coefficients);
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