fix: Minor fix for future compatibility of using CrossValidate with…

… other neural networks

Bump version number, and as well bump bower version number

README.md

@@ -279,9 +279,9 @@ With multiple networks you can train in parallel like this:
 ### Cross Validation
 [Cross Validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics)) can provide a less fragile way of training on larger data sets.  The brain.js api provides Cross Validation in this example:
 ```js
-const crossValidate = new CrossValidate(brain.NeuralNetwork);
-const stats = crossValidate.train(data, networkOptions, trainingOptions, k); //note k (or KFolds) is optional
-const net = crossValidate.toNetwork();
+const crossValidate = new CrossValidate(brain.NeuralNetwork, networkOptions);
+const stats = crossValidate.train(data, trainingOptions, k); //note k (or KFolds) is optional
+const net = crossValidate.toNeuralNetwork();
 
 
 // optionally later

bower.json

@@ -31,5 +31,5 @@
     "node_modules",
     "test"
   ],
-  "version": "1.1.1"
+  "version": "1.3.1"
 }

browser.js

@@ -6,7 +6,7 @@
  *   license: MIT (http://opensource.org/licenses/MIT)
  *   author: Heather Arthur <[email protected]>
  *   homepage: https://github.com/brainjs/brain.js#readme
- *   version: 1.3.0
+ *   version: 1.3.1
  *
  * acorn:
  *   license: MIT (http://opensource.org/licenses/MIT)
@@ -127,17 +127,18 @@ var CrossValidate = function () {
   /**
    *
    * @param {NeuralNetwork|constructor} Classifier
+   * @param {object} [options]
    */
-  function CrossValidate(Classifier) {
+  function CrossValidate(Classifier, options) {
     _classCallCheck(this, CrossValidate);
 
     this.Classifier = Classifier;
+    this.options = options;
     this.json = null;
   }
 
   /**
    *
-   * @param {object} options
    * @param {object} trainOpts
    * @param {object} trainSet
    * @param {object} testSet
@@ -147,8 +148,8 @@ var CrossValidate = function () {
 
   _createClass(CrossValidate, [{
     key: "testPartition",
-    value: function testPartition(options, trainOpts, trainSet, testSet) {
-      var classifier = new this.Classifier(options);
+    value: function testPartition(trainOpts, trainSet, testSet) {
+      var classifier = new this.Classifier(this.options);
       var beginTrain = Date.now();
       var trainingStats = classifier.train(trainSet, trainOpts);
       var beginTest = Date.now();
@@ -188,7 +189,6 @@ var CrossValidate = function () {
     /**
      *
      * @param {object} data
-     * @param {object} options
      * @param {object} trainOpts
      * @param {number} [k]
      * @returns {
@@ -214,7 +214,7 @@ var CrossValidate = function () {
 
   }, {
     key: "train",
-    value: function train(data, options, trainOpts, k) {
+    value: function train(data, trainOpts, k) {
       k = k || 4;
       var size = data.length / k;
 
@@ -291,8 +291,8 @@ var CrossValidate = function () {
       };
     }
   }, {
-    key: "toNetwork",
-    value: function toNetwork() {
+    key: "toNeuralNetwork",
+    value: function toNeuralNetwork() {
       return this.fromJSON(this.json);
     }
   }, {

examples-typescript/cross-validate.ts

@@ -2,7 +2,7 @@ import * as assert from 'assert';
 import * as brain from '../index';
 
 const trainingData = [
-  // xor
+  // xor data, repeating to simulate that we have a lot of data
   { input: [0, 1], output: [1] },
   { input: [0, 0], output: [0] },
   { input: [1, 1], output: [0] },
@@ -29,17 +29,17 @@ const trainingData = [
 
 const netOptions = {
   hiddenSizes: [3]
-} as brain.INeuralNetworkDefaultOptions;
+} as brain.INeuralNetworkOptions;
 
 const trainingOptions = {
   iterations: 20000,
   log: details => console.log(details)
 } as brain.INeuralNetworkTrainingOptions;
 
-const crossValidate = new brain.CrossValidate(brain.NeuralNetwork);
-const stats = crossValidate.train(trainingData, netOptions, trainingOptions);
+const crossValidate = new brain.CrossValidate(brain.NeuralNetwork, netOptions);
+const stats = crossValidate.train(trainingData, trainingOptions);
 console.log(stats);
-const net = crossValidate.toNetwork();
+const net = crossValidate.toNeuralNetwork();
 const result01 = net.run([0, 1]);
 const result00 = net.run([0, 0]);
 const result11 = net.run([1, 1]);

examples/cross-validate.js

@@ -2,7 +2,7 @@ const assert = require('assert');
 const brain = require('../dist/index').default;
 
 const trainingData = [
-  // xor
+  // xor data, repeating to simulate that we have a lot of data
   { input: [0, 1], output: [1] },
   { input: [0, 0], output: [0] },
   { input: [1, 1], output: [0] },
@@ -36,10 +36,10 @@ const trainingOptions = {
   log: details => console.log(details)
 };
 
-const crossValidate = new brain.CrossValidate(brain.NeuralNetwork);
-const stats = crossValidate.train(trainingData, netOptions, trainingOptions);
+const crossValidate = new brain.CrossValidate(brain.NeuralNetwork, netOptions);
+const stats = crossValidate.train(trainingData, trainingOptions);
 console.log(stats);
-const net = crossValidate.toNetwork();
+const net = crossValidate.toNeuralNetwork();
 const result01 = net.run([0, 1]);
 const result00 = net.run([0, 0]);
 const result11 = net.run([1, 1]);

index.d.ts

@@ -1,5 +1,5 @@
 /* NeuralNetwork section */
-export interface INeuralNetworkDefaultOptions {
+export interface INeuralNetworkOptions {
   binaryThresh?: number;
   hiddenLayers?: number[];
   activation?: NeuralNetworkActivation;
@@ -45,7 +45,7 @@ export interface INeuralNetworkTrainingData {
 export type NeuralNetworkTrainingValue = number[];
 
 export class NeuralNetwork {
-  public constructor(options?: INeuralNetworkDefaultOptions);
+  public constructor(options?: INeuralNetworkOptions);
   public train(data: INeuralNetworkTrainingData[], options?: INeuralNetworkTrainingOptions): INeuralNetworkState;
   public train<T>(data: T, options?: INeuralNetworkTrainingOptions): INeuralNetworkState;
   public trainAsync(data: INeuralNetworkTrainingData, options?: INeuralNetworkTrainingOptions): Promise<INeuralNetworkState>;
@@ -83,22 +83,20 @@ export interface ICrossValidationTestPartitionResults {
 }
 
 export class CrossValidate {
-  public constructor(Classifier: typeof NeuralNetwork);
+  public constructor(Classifier: typeof NeuralNetwork, options?: INeuralNetworkOptions);
   public fromJSON(json: ICrossValidateJSON): NeuralNetwork;
   public toJSON(): ICrossValidateJSON;
   public train(
     data: INeuralNetworkTrainingData[],
-    networkOptions: INeuralNetworkDefaultOptions,
     trainingOptions: INeuralNetworkTrainingOptions,
     k?: number): ICrossValidateStats;
   public train<T>(
     data: T,
-    networkOptions: INeuralNetworkDefaultOptions,
     trainingOptions: INeuralNetworkTrainingOptions,
     k?: number): ICrossValidateStats;
   public testPartition(): ICrossValidationTestPartitionResults;
-  public toNetwork(): NeuralNetwork;
-  public toNetwork<T>(): T;
+  public toNeuralNetwork(): NeuralNetwork;
+  public toNeuralNetwork<T>(): T;
 }
 
 /* TrainStream section */
@@ -118,7 +116,7 @@ export class TrainStream {
 /* recurrent section */
 export type RNNTrainingValue = string;
 export type RNNTimeStepTrainingValue = NeuralNetworkTrainingValue | number | number[] | number[][];
-export interface IRNNDefaultOptions extends INeuralNetworkDefaultOptions {
+export interface IRNNDefaultOptions extends INeuralNetworkOptions {
   inputSize?: number;
   outputSize?: number;
 }

package.json

@@ -1,7 +1,7 @@
 {
   "name": "brain.js",
   "description": "Neural network library",
-  "version": "1.3.0",
+  "version": "1.3.1",
   "author": "Heather Arthur <[email protected]>",
   "repository": {
     "type": "git",

src/cross-validate.js

@@ -3,22 +3,23 @@ export default class CrossValidate {
   /**
    *
    * @param {NeuralNetwork|constructor} Classifier
+   * @param {object} [options]
    */
-  constructor(Classifier) {
+  constructor(Classifier, options) {
     this.Classifier = Classifier;
+    this.options = options;
     this.json = null;
   }
 
   /**
    *
-   * @param {object} options
    * @param {object} trainOpts
    * @param {object} trainSet
    * @param {object} testSet
    * @returns {void|*}
    */
-  testPartition(options, trainOpts, trainSet, testSet) {
-    let classifier = new this.Classifier(options);
+  testPartition(trainOpts, trainSet, testSet) {
+    let classifier = new this.Classifier(this.options);
     let beginTrain = Date.now();
     let trainingStats = classifier.train(trainSet, trainOpts);
     let beginTest = Date.now();
@@ -55,7 +56,6 @@ export default class CrossValidate {
   /**
    *
    * @param {object} data
-   * @param {object} options
    * @param {object} trainOpts
    * @param {number} [k]
    * @returns {
@@ -78,7 +78,7 @@ export default class CrossValidate {
    *  }
    * }
    */
-  train(data, options, trainOpts, k) {
+  train(data, trainOpts, k) {
     k = k || 4;
     let size = data.length / k;
 
@@ -156,7 +156,7 @@ export default class CrossValidate {
     };
   }
 
-  toNetwork() {
+  toNeuralNetwork() {
     return this.fromJSON(this.json);
   }
 

test/base/json.js

@@ -257,8 +257,6 @@ describe('default net json', () => {
     });
 
     it('training options timeout', () => {
-      console.log(originalNet.trainOpts.timeout);
-      console.log(serializedNet.trainOpts.timeout);
       assert.equal(originalNet.trainOpts.timeout, serializedNet.trainOpts.timeout, `originalNet.trainOpts are: ${originalNet.trainOpts.timeout} serializedNet should be the same but are: ${serializedNet.trainOpts.timeout}`);
     });
   });