public function partial(Dataset $dataset) : void
    {
        if (!$dataset instanceof Labeled) {
            throw new InvalidArgumentException('This estimator requires a'
                . ' labeled training set.');
        }

        DatasetIsCompatibleWithEstimator::check($dataset, $this);

        $this->classes = array_unique(array_merge($this->classes, $dataset->possibleOutcomes()));
        $this->samples = array_merge($this->samples, $dataset->samples());
        $this->labels = array_merge($this->labels, $dataset->labels());
    }
public function partial(Dataset $dataset) : void
    {
        if (!$dataset instanceof Labeled) {
            throw new InvalidArgumentException('This estimator requires a'
                . ' labeled training set.');
        }

        DatasetIsCompatibleWithEstimator::check($dataset, $this);

        $this->classes = array_unique(array_merge($this->classes, $dataset->possibleOutcomes()));
        $this->samples = array_merge($this->samples, $dataset->samples());
        $this->labels = array_merge($this->labels, $dataset->labels());
    }
public function partial ( Dataset $ dataset ) : void { if ( ! $ dataset instanceof Labeled ) { throw new InvalidArgumentException ( 'This estimator requires a' . ' labeled training set.' ) ; } DatasetIsCompatibleWithEstimator :: check ( $ dataset , $ this ) ; $ this -> classes = array_unique ( array_merge ( $ this -> classes , $ dataset -> possibleOutcomes ( ) ) ) ; $ this -> samples = array_merge ( $ this -> samples , $ dataset -> samples ( ) ) ; $ this -> labels = array_merge ( $ this -> labels , $ dataset -> labels ( ) ) ; }
Store the sample and outcome arrays. No other work to be done as this is
a lazy learning algorithm.

@param \Rubix\ML\Datasets\Dataset $dataset
@throws \InvalidArgumentException
Store the sample and outcome arrays . No other work to be done as this is a lazy learning algorithm .