docs: update README examples to use const and let

Author: Planeshifter

lib/node_modules/@stdlib/nlp/lda/README.md

@@ -33,26 +33,23 @@ limitations under the License.
 ## Usage
 
 ```javascript
-var lda = require( '@stdlib/nlp/lda' );
+const 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 = [
+const 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 );
+const model = lda( docs, 2 );
 // returns {}
 ```
 
@@ -77,7 +74,7 @@ Returns the `no` terms with the highest probabilities for chosen topic `k`.
 <!-- run-disable -->
 
 ```javascript
-var words = model.getTerms( 0, 3 );
+const words = model.getTerms( 0, 3 );
 /* returns
     [
         { 'word': 'both', 'prob': 0.06315008476532499 },
@@ -98,62 +95,54 @@ var words = model.getTerms( 0, 3 );
 <!-- 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++ ) {
+const sotu = require( '@stdlib/datasets/sotu' );
+const roundn = require( '@stdlib/math/base/special/roundn' );
+const stopwords = require( '@stdlib/datasets/stopwords-en' );
+const lowercase = require( '@stdlib/string/lowercase' );
+const lda = require( '@stdlib/nlp/lda' );
+
+const words = stopwords();
+for ( let i = 0; i < words.length; i++ ) {
     words[ i ] = new RegExp( '\\b'+words[ i ]+'\\b', 'gi' );
 }
 
-speeches = sotu({
+const speeches = sotu({
     'range': [ 1930, 2010 ]
 });
-for ( i = 0; i < speeches.length; i++ ) {
-    str = lowercase( speeches[ i ].text );
-    for ( j = 0; j < words.length; j++ ) {
+for ( let i = 0; i < speeches.length; i++ ) {
+    let str = lowercase( speeches[ i ].text );
+    for ( let j = 0; j < words.length; j++ ) {
         str = str.replace( words[ j ], '' );
     }
     speeches[ i ] = str;
 }
 
-model = lda( speeches, 3 );
+const model = lda( speeches, 3 );
 
 model.fit( 1000, 100, 10 );
 
-for ( i = 0; i <= 80; i++ ) {
-    str = 'Year: ' + (1930+i) + '\t';
+for ( let i = 0; i <= 80; i++ ) {
+    let 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++ ) {
+let terms = model.getTerms( 0, 20 );
+for ( let 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++ ) {
+for ( let 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++ ) {
+for ( let i = 0; i < terms.length; i++ ) {
     terms[ i ] = terms[ i ].word;
 }
 console.log( 'Words most associated with third topic:\n ' + terms.join( ', ' ) );