php-ml/docs/machine-learning/feature-extraction/token-count-vectorizer.md

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2016-05-07 21:53:42 +00:00
# Token Count Vectorizer
Transform a collection of text samples to a vector of token counts.
### Constructor Parameters
* $tokenizer (Tokenizer) - tokenizer object (see below)
* $minDF (float) - ignore tokens that have a samples frequency strictly lower than the given threshold. This value is also called cut-off in the literature. (default 0)
```
use Phpml\FeatureExtraction\TokenCountVectorizer;
use Phpml\Tokenization\WhitespaceTokenizer;
$vectorizer = new TokenCountVectorizer(new WhitespaceTokenizer());
```
### Transformation
To transform a collection of text samples use `transform` method. Example:
```
$samples = [
'Lorem ipsum dolor sit amet dolor',
'Mauris placerat ipsum dolor',
'Mauris diam eros fringilla diam',
];
$vectorizer = new TokenCountVectorizer(new WhitespaceTokenizer());
$vectorizer->transform($samples)
// return $vector = [
// [0 => 1, 1 => 1, 2 => 2, 3 => 1, 4 => 1],
// [5 => 1, 6 => 1, 1 => 1, 2 => 1],
// [5 => 1, 7 => 2, 8 => 1, 9 => 1],
//];
```
### Vocabulary
You can extract vocabulary using `getVocabulary()` method. Example:
```
$vectorizer->getVocabulary();
// return $vocabulary = ['Lorem', 'ipsum', 'dolor', 'sit', 'amet', 'Mauris', 'placerat', 'diam', 'eros', 'fringilla'];
```
### Tokenizers
* WhitespaceTokenizer - select tokens by whitespace.
* WordTokenizer - select tokens of 2 or more alphanumeric characters (punctuation is completely ignored and always treated as a token separator).