//------------------------------------------------------------------------------// name: word2vec-basic.ck// desc: word2vec basic usage//// version: need chuck version 1.5.0.0 or higher// sorting: part of ChAI (ChucK for AI)//// NOTE: download pre-trained word vector model from://       https://chuck.stanford.edu/chai/data/glove///         | (can use any of these)//       glove-wiki-gigaword-50.txt (400000 words x 50 dimensions)//       glove-wiki-gigaword-50-pca-3.txt (400000 words x 3 dimensions)//       glove-wiki-gigaword-50-tsne-2.txt (400000 words x 2 dimensions)//// author: Yikai Li and Ge Wang// date: Spring 2023//------------------------------------------------------------------------------ // instantiateWord2Vec model; // pre-trained model to load"glove-wiki-gigaword-50.txt" => string filepath; <<< "loading model:", filepath >>>;<<< "(this could take a few seconds)...", "" >>>;// NOTE: while loading can take a long time initially; this is internally cached;// subsequent loads on the same filepath will use cached dictionary, as long as// ChucK VM keeps running// HINT: while caching should "just work" in miniAudicle, in command-line chuck// one could keep one chuck running (using --loop) and chuk code to it from a // second terminal (using chuck + code). for example://------------------------------------------------------------------------------//     terminal-1> chuck --loop  *****  terminal-2> chuck + word2vec-basic//------------------------------------------------------------------------------// load a pre-trained model (see URLs above for download)if( !model.load( me.dir() + filepath ) ){    <<< "cannot load model file...", "" >>>;    me.exit();} // print info<<< "dictionary size:", model.size() >>>;<<< "embedding dimensions:", model.dim() >>>;<<< "using KDTree for search:", model.useKDTree() ? "YES" : "NO" >>>;float mins[0], maxs[0];// get bounds for each dimensionmodel.minMax( mins, maxs );// print the min/max for each dimensionfor( int i; i < model.dim(); i++ ){ cherr <= "dimension " <= i <= ": [" <= mins[i]        <= " <-range-> " <= maxs[i] <= "]" <= IO.newline(); } string words[10];<<< "=========================================================", "" >>>; // get vector<<< "vector associated with 'cat' is:", "" >>>;float vector[model.dim()];model.getVector("cat", vector);for( int i; i < vector.size(); i++ ) { chout <= vector[i] <= " "; }chout <= IO.newline(); <<< "=========================================================", "" >>>; // get most similar by word<<< words.size(), "words most similar to 'stanford' are:", "" >>>;model.getSimilar("stanford", words.size(), words);for( int i; i < words.size(); i++ ){ <<< words[i], "" >>>; } <<< "=========================================================", "" >>>; // get similar words via vector<<< words.size(), "words closest to the vector of 'cat' are:", "" >>>;model.getSimilar( vector, words.size(), words );for( int i; i < words.size(); i++ ){ <<< words[i], "" >>>; } <<< "=========================================================", "" >>>; // eval an expression<<< "similar words to expression 'rome - italy + china':", "" >>>;W2V.eval( model, "rome - italy + china", 10 ) @=> words;for( 0 => int i; i < words.size(); i++ ){ <<< words[i], "" >>>; } <<< "=========================================================", "" >>>; "dog" => string A; "puppy" => string B; "cat" => string C;// logical analogy<<< A, "is to", B, "as", C, "is to..." >>>;// e.g., dog:puppy::cat:[?] (this returns words for [?])W2V.analogy( model, A, B, C, 10 ) @=> words;for( 0 => int i; i < words.size(); i++ ){ <<< words[i], "" >>>; } <<< "=========================================================", "" >>>;    //------------------------------------------------------------------------------// word2vec helper//------------------------------------------------------------------------------class W2V{    fun static void copy( float to[], float from[] )    {        to.size(from.size());        // add into element        for( int i; i < to.size(); i++ ) from[i] => to[i];    }        fun static float[] dup( float from[] )    {        float to[from.size()];        // add into element        for( int i; i < to.size(); i++ ) from[i] => to[i];        // return        return to;    }        // result stored in x    fun static void add( float x[], float y[] )    {        // smaller of the two        x.size() < y.size() ? x.size() : y.size() => int size;        // add into element        for( int i; i < size; i++ ) y[i] +=> x[i];    }        // result stored in x    fun static void minus( float x[], float y[] )    {        // smaller of the two        x.size() < y.size() ? x.size() : y.size() => int size;        // add into element        for( int i; i < size; i++ ) y[i] -=> x[i];    }        fun static void scale( float x[], float scalar )    {        // scale        for( int i; i < x.size(); i++ ) scalar *=> x[i];    }        fun static void scale( float result[], float x[], float scalar )    {        // scale        for( int i; i < x.size(); i++ ) scalar * x[i] => result[i];    }        fun static string[] eval( Word2Vec @ w, string expr, int k )    {        // init         int pos;        1.0 => float multiplier;        string word;        float wordVector[w.dim()];        float exprVector[w.dim()];                // compute        while( true )        {            expr.find(" ") => pos;            if( pos == -1 )            {                w.getVector(expr, wordVector);                for( 0 => int i; i < w.dim(); i++ )                {                    wordVector[i] * multiplier +=> exprVector[i];                }                break;            }            else             {                expr.substring(0, pos) => word;                w.getVector(word, wordVector);                for( 0 => int i; i < w.dim(); i++ )                {                    wordVector[i] * multiplier +=> exprVector[i];                }                expr.substring(pos + 1) => expr;                if( expr.charAt(0) == '+' )                {                    1.0 => multiplier;                    expr.substring(2) => expr;                }                else                {                    -1.0 => multiplier;                    expr.substring(2) => expr;                }            }        }                // return        string results[k];        w.getSimilar( exprVector, k, results );                return results;    }        // logical analogy: A to B is as C is to [what this function returns]    fun static string[] analogy( Word2Vec @ w, string A, string B, string C, int k )    {        // the vector sum        B + " - " + A + " + " + C => string v;        return eval( w, v, k );    }}