Method train

src/recurrent/rnn.js:384–425

* * @param {Object[]|String[]} data an array of objects: `{input: 'string', output: 'string'}` or an array of strings * @param {Object} [options] * @returns {{error: number, iterations: number}}

(data, options = {})

   * @returns {{error: number, iterations: number}}
   */
  train(data, options = {}) {
    options = Object.assign({}, this.constructor.trainDefaults, options);
    let iterations = options.iterations;
    let errorThresh = options.errorThresh;
    let log = options.log === true ? console.log : options.log;
    let logPeriod = options.logPeriod;
    let learningRate = options.learningRate || this.learningRate;
    let callback = options.callback;
    let callbackPeriod = options.callbackPeriod;
    let error = Infinity;
    let i;

    if (this.hasOwnProperty('setupData')) {
      data = this.setupData(data);
    }

    if (!this.model) {
      this.initialize();
    }

    for (i = 0; i < iterations && error > errorThresh; i++) {
      let sum = 0;
      for (let j = 0; j < data.length; j++) {
        let err = this.trainPattern(data[j], learningRate);
        sum += err;
      }
      error = sum / data.length;

      if (isNaN(error)) throw new Error('network error rate is unexpected NaN, check network configurations and try again');
      if (log && (i % logPeriod === 0)) {
        log(`iterations: ${ i }, training error: ${ error }`);
      }
      if (callback && (i % callbackPeriod === 0)) {
        callback({ error: error, iterations: i });
      }
    }

    return {
      error: error,
      iterations: i
    };
  }

  /**
   *