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