time-to-botec/squiggle/node_modules/@stdlib/nlp/lda/README.md

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# LDA

> [Latent Dirichlet Allocation][lda] via collapsed Gibbs sampling.

<section class="intro">

</section>

<!-- /.intro -->

<section class="usage">

## Usage

```javascript
var lda = require( '@stdlib/nlp/lda' );
```

#### lda( docs, K\[, options] )

[Latent Dirichlet Allocation][lda] via collapsed Gibbs sampling. To create a model, call the `lda` function by passing it an `array` of `strings` and the number of topics `K` that should be identified.

```javascript
var model;
var docs;

docs = [
    'I loved you first',
    'For one is both and both are one in love',
    'You never see my pain',
    'My love is such that rivers cannot quench',
    'See a lot of pain, a lot of tears'
];

model = lda( docs, 2 );
// returns {}
```

After initialization, model parameters are estimated by calling the `.fit()` method, which performs collapsed Gibbs sampling.

The model object contains the following methods:

#### model.fit( iter, burnin, thin )

<!-- run-disable -->

```javascript
model.fit( 1000, 100, 10 );
```

The `iter` parameter denotes the number of sampling iterations. While a common choice, one thousand iterations might not always be appropriate. Empirical diagnostics can be used to assess whether the constructed Markov Chain has converged. `burnin` denotes the number of estimates that are thrown away at the beginning, whereas `thin` controls the number of estimates discarded in-between iterations.

#### model.getTerms( k\[, no = 10] )

Returns the `no` terms with the highest probabilities for chosen topic `k`.

<!-- run-disable -->

```javascript
var words = model.getTerms( 0, 3 );
/* returns
    [
        { 'word': 'both', 'prob': 0.06315008476532499 },
        { 'word': 'pain', 'prob': 0.05515729517235543 },
        { 'word': 'one', 'prob': 0.05486669737616135 }
    ]
*/
```

</section>

<!-- /.usage -->

<section class="examples">

## Examples

<!-- eslint no-undef: "error" -->

```javascript
var sotu = require( '@stdlib/datasets/sotu' );
var roundn = require( '@stdlib/math/base/special/roundn' );
var stopwords = require( '@stdlib/datasets/stopwords-en' );
var lowercase = require( '@stdlib/string/lowercase' );
var lda = require( '@stdlib/nlp/lda' );

var speeches;
var words;
var terms;
var model;
var str;
var i;
var j;

words = stopwords();
for ( i = 0; i < words.length; i++ ) {
    words[ i ] = new RegExp( '\\b'+words[ i ]+'\\b', 'gi' );
}

speeches = sotu({
    'range': [ 1930, 2010 ]
});
for ( i = 0; i < speeches.length; i++ ) {
    str = lowercase( speeches[ i ].text );
    for ( j = 0; j < words.length; j++ ) {
        str = str.replace( words[ j ], '' );
    }
    speeches[ i ] = str;
}

model = lda( speeches, 3 );

model.fit( 1000, 100, 10 );

for ( i = 0; i <= 80; i++ ) {
    str = 'Year: ' + (1930+i) + '\t';
    str += 'Topic 1: ' + roundn( model.avgTheta.get( i, 0 ), -3 ) + '\t';
    str += 'Topic 2: ' + roundn( model.avgTheta.get( i, 1 ), -3 ) + '\t';
    str += 'Topic 3: ' + roundn( model.avgTheta.get( i, 2 ), -3 );
    console.log( str );
}

terms = model.getTerms( 0, 20 );
for ( i = 0; i < terms.length; i++ ) {
    terms[ i ] = terms[ i ].word;
}
console.log( 'Words most associated with first topic:\n ' + terms.join( ', ' ) );

terms = model.getTerms( 1, 20 );
for ( i = 0; i < terms.length; i++ ) {
    terms[ i ] = terms[ i ].word;
}
console.log( 'Words most associated with second topic:\n ' + terms.join( ', ' ) );

terms = model.getTerms( 2, 20 );
for ( i = 0; i < terms.length; i++ ) {
    terms[ i ] = terms[ i ].word;
}
console.log( 'Words most associated with third topic:\n ' + terms.join( ', ' ) );
```

</section>

<!-- /.examples -->

<section class="links">

[lda]: https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation

</section>

<!-- /.links -->