// Load wink-nlp package.
const winkNLP = require( 'wink-nlp' );
// Load english language model.
const model = require( 'wink-eng-lite-web-model' );
// Load word embeddings.
const vectors = require( 'wink-embeddings-sg-100d' );
// Load similarity utility.
const similarity = require( 'wink-nlp/utilities/similarity.js' );

// Use only tokenization and sentence boundary detection pipe.
const nlp = winkNLP( model, [ 'sbd' ], vectors );
// Obtain "its" helper to extract item properties.
const its = nlp.its;
// Obtain "as" reducer helper to reduce a collection.
const as = nlp.as;
// The following text contains 4-sentences, where the first
// two and the last two have high similarity.
const text = `The cat rested on the carpet. The kitten slept on the rug.
The table was in the drawing room. The desk was in the study room.`;
// This will hold the array of vectors for each sentence.
const v = [];
// Run the nlp pipe.
const doc = nlp.readDoc( text );
// Compute each sentence's embedding and fill in "v[i]".
// Only use words and ignore stop words.
doc
    .sentences()
    .each( ( s, k ) => {
      v[ k ] = s
        .tokens()
        .filter( (t) => (t.out(its.type) === 'word' && !t.out(its.stopWordFlag)))
        .out(its.value, as.vector);
    })
// Compute & print similarity for each unique pair.
for ( let i = 0; i < v.length; i += 1 ) {
    for ( let j = i; j < v.length; j += 1 ) {
        if ( i !== j )
          console.log(
            doc.sentences().itemAt( i ).out(), ' & ',
            doc.sentences().itemAt( j ).out(),
            +similarity.vector.cosine( v[ i ], v[ j ] ).toFixed( 2 )
          );
    }
}