@mrizki/natural/lib/natural/classifiers/maxent/Feature.js

General natural language (tokenizing, stemming (English, Russian, Spanish), part-of-speech tagging, sentiment analysis, classification, inflection, phonetics, tfidf, WordNet, jaro-winkler, Levenshtein distance, Dice's Coefficient) facilities for node.

/*
    Feature class for features that fire (or don't) on combinations of context
    and class
    Copyright (C) 2017 Hugo W.L. ter Doest

    This program is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with this program.  If not, see <http://www.gnu.org/licenses/>.
*/


function Feature(f, name, parameters) {
  this.evaluate = f;
  this.name = name;
  this.parameters = parameters;

  var tmp = "";
  parameters.forEach(function(par) {
    tmp += par + "|";
  });
  this.parametersKey = tmp.substr(0, tmp.length - 1);
}

Feature.prototype.apply = function(x) {
  return this.evaluate(x);
};

Feature.prototype.expectationApprox = function(p, sample) {
  var totalSum = 0;
  var that = this;
  var sum = 0;
  var seen = {};
  var A = sample.getClasses();
  sample.elements.forEach(function(sampleElement) {
    var b_i = sampleElement.b;

    if (!seen[b_i.toString()]) {
      seen[b_i.toString()] = true;
      var Element = require('./Element');

      A.forEach(function(a) {
        var x = new Element(a, b_i);
        sum += sample.observedProbabilityOfContext(b_i) * p.calculateAPosteriori(x) * that.apply(x);
      });
    }
  });
  return sum;
};

// Diect calculation of expected value of this feature according to distribution p
// In real-life applications with a lot of features this is not tractable
Feature.prototype.expectation = function(p, A, B) {
  var sum = 0;
  var that = this;
  A.forEach(function(a) {
    B.forEach(function(b) {
        var x = new Element(a, b);
        sum += (p.calculateAPriori(x) * that.apply(x));
    });
  });
  return sum;
};

// Observed expectation of this feature in the sample
Feature.prototype.observedExpectation = function(sample) {
  if (this.observedExpect) {
    return this.observedExpect;
  }
  var N = sample.size();
  var sum = 0;
  var that = this;
  sample.elements.forEach(function(x) {
    sum += that.apply(x);
  });
  this.observedExpect = sum / N;
  return this.observedExpect;
};

module.exports = Feature;