spiegel-keyman/common/predictive-text/worker/models/dummy-model.ts

/*
 * Copyright (c) 2018 National Research Council Canada (author: Eddie A. Santos)
 * Copyright (c) 2018 SIL International
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy of
 * this software and associated documentation files (the "Software"), to deal in
 * the Software without restriction, including without limitation the rights to
 * use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
 * the Software, and to permit persons to whom the Software is furnished to do so,
 * subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in all
 * copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
 * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
 * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
 * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
 * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 */

namespace models {
  /**
   * @file dummy-model.ts
   * 
   * Defines the Dummy model, which is used for testing the
   * prediction API exclusively.
   */

  /**
   * The Dummy Model that returns nonsensical, but predictable results. 
   */
  export class DummyModel implements LexicalModel {
    configuration: Configuration;
    punctuation?: LexicalModelPunctuation;
    private _futureSuggestions: Suggestion[][];

    constructor(options?: any) {
      options = options || {};
      // Create a shallow copy of the suggestions;
      // this class mutates the array.
      this._futureSuggestions = options.futureSuggestions
        ? options.futureSuggestions.slice() : [];

      if (options.punctuation) {
        this.punctuation = options.punctuation;
      }
    }

    configure(capabilities: Capabilities): Configuration {
      this.configuration = {
        leftContextCodePoints: capabilities.maxLeftContextCodePoints,
        rightContextCodePoints: capabilities.maxRightContextCodePoints
      };
      
      return this.configuration;
    }

    predict(transform: Transform, context: Context, injectedSuggestions?: Suggestion[]): Distribution<Suggestion> {
      let makeUniformDistribution = function(suggestions: Suggestion[]): Distribution<Suggestion> {
        let distribution: Distribution<Suggestion> = [];
        let n = suggestions.length;

        for(let s of suggestions) {
          distribution.push({sample: s, p: 1});  // For a dummy model, this is sufficient.  The uniformness is all that matters.
        }

        return distribution;
      }

      if (injectedSuggestions) {
        return makeUniformDistribution(injectedSuggestions);
      }

      let currentSet = this._futureSuggestions.shift();
      
      if(!currentSet) {
        return [];
      } else {
        return makeUniformDistribution(currentSet);
      }
    }
  };
}