diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..a116156 --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +/node_modules +/.project diff --git a/associative.js b/associative.js new file mode 100644 index 0000000..99b76f3 --- /dev/null +++ b/associative.js @@ -0,0 +1,126 @@ +/** + * Utilities for associative arrays (= hashes = Javascript objects). + * @author Erel Segal-Halevi + */ + +var api = {}; + +/** + * add one associative array to another. + * @param target [input and output] + * @param source [input]: will be added to target. + */ +api.add = function(target, source) { + for (var feature in source) { + if (!(feature in target)) + target[feature]=0; + target[feature] += source[feature]; + } +} + +/** + * multiply one associative array by another. + * @param target [input and output] + * @param source [input]: target will be multiplied by it. + */ +api.multiply = function(target, source) { + for (var feature in source) { + if (!(feature in target)) + target[feature]=1; + target[feature] *= source[feature]; + } +} + +/** + * multiply an associative array by a scalar. + * @param target [input and output] + * @param source [input]: target will be multiplied by it. + */ +api.multiply_scalar = function(target, source) { + for (var feature in target) { + target[feature] *= source; + } +} + +/** + * calculate the scalar product of the given two arrays. + * @param features [input] + * @param weights [input] + * @note Usually, there are much less features than weights. + */ +api.inner_product = function(features, weights) { + var result = 0; + for (var feature in features) { + if (feature in weights) { + result += features[feature] * weights[feature] + } else { + /* the sample contains a feature that was never seen in training - ignore it for now */ + } + } + return result; +} + +api.sum_of_values = function(weights) { + var result = 0; + for (var feature in weights) + result += weights[feature]; + return result; +} + +api.sum_of_absolute_values = function(weights) { + var result = 0; + for (var feature in weights) + result += Math.abs(weights[feature]); + return result; +} + +api.sum_of_square_values = function(weights) { + var result = 0; + for (var feature in weights) + result += Math.pow(weights[feature],2); + return result; +} + +/** + * Normalize the given associative array, such that the sum of values is 1. + * Unless, of course, the current sum is 0, in which case, nothing is done. + */ +api.normalize_sum_of_values_to_1 = function(features) { + var sum = api.sum_of_absolute_values(features); + if (sum!=0) + api.multiply_scalar(features, 1/sum); +} + +/** + * Normalize the given associative array, such that the sum of squares of the values is 1. + * Unless, of course, the current sum is 0, in which case, nothing is done. + */ +api.normalize_sum_of_squares_to_1 = function(features) { + var sum = api.sum_of_square_values(features); + if (sum!=0) + api.multiply_scalar(features, 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b/demos/rtedemo.js @@ -0,0 +1,62 @@ +/** + * A perceptron for recognizing textual entailment, based on BIUTEE's proofs. + */ + +var perceptron = require('../perceptron_associative') + , associative = require('../associative') + , fs = require('fs'); + +var opts = { + feature_extractor: function(pair, features) { + if (pair.task) + features[pair.task]=1; + if (pair.hypothesis_length) + features.InverseHypothesisLength = 1 / pair.hypothesis_length; + for (var iStep=0; iStep + *
  • debug + *
  • weights: initial weights (if not given, initialized to default_weight). + *
  • default_weight: default weight for a newly discovered feature (default = 0). + *
  • learningrate: defaults to 0.1. + *
  • feature_extractor: function that converts input samples to feature arrays. If not given, the input sample itself is treated as an associative array of features. + * + */ +function PerceptronAssociative(opts) { + if (this === global) return new PerceptronAssociative(opts); + if (!opts) opts = {} + + + if (!('debug' in opts)) + opts.debug = false; + if (!('default_weight' in opts)) + opts.default_weight = 0; + if (!('do_averaging' in opts)) + opts.do_averaging = false; + if (!('do_normalization' in opts)) + opts.do_normalization = false; + if (!('learningrate' in opts)) + opts.learningrate = 0.1; + + var feature_extractor = 'feature_extractor' in opts + ? opts.feature_extractor + : 0; + + var weights = 'weights' in opts + ? opts.weights /* should be an associative array */ + : {} + var weights_sum = {}; // for averaging; see http://ciml.info/dl/v0_8/ciml-v0_8-ch03.pdf . But count only weight vectors with successful predictions (Carvalho and Cohen, 2006). + //if (opts.do_averaging) associative.add(weights_sum, weights); + + var fs = require('fs'), mkpath = require('mkpath'); + + /** + * Keep track of ALL training samples, their perceived values and their correct (target) values. + */ + var data = [] + + var api = { + weights: weights, + + save: function(folder) { + mkpath.sync(folder); + fs.writeFileSync(folder+"/opts.json", "{\n"+ + '\t"opts": '+JSON.stringify(opts, null, "\t")+"\n,"+ + '\t"weights": '+associative.stringify_sorted(weights, "\n\t\t")+",\n"+ + '\t"weights_sum": '+associative.stringify_sorted(weights_sum, "\n\t\t")+"\n"+ + "}\n" + ); + fs.writeFileSync(folder+"/data.json", JSON.stringify(data,null,"\t")); + }, + + load: function(folder) { + data = JSON.parse(fs.readFileSync(folder+"/data.json")); + opts = JSON.parse(fs.readFileSync(folder+"/opts.json")); + + api.weights = weights = opts.weights; + weights_sum = opts.weights_sum; + opts = opts.opts; + }, + + normalized_features: function (inputs) { + if (feature_extractor) { + var features = {}; + feature_extractor(inputs, features); + } else { + var features = inputs; + } + features['bias'] = 1; + if (opts.do_normalization) associative.normalize_sum_of_values_to_1(features); + return features; + }, + + /** + * Run through all past training samples, and use them for training once more. + * @return true if ALL samples got their correct classification (i.e. no change made). + */ + retrain: function() { + var length = data.length + var success = true + for(var i=0; i value). + * @param expected the classification value for that sample (0 or 1) + * @return true if the input sample got its correct classification (i.e. no change made). + */ + train_features: function(features, expected) { + for (feature in features) { + if (!(feature in weights)) { + weights[feature] = opts.default_weight; + } + } + + var result = api.perceive_features(features, /*net=*/false, weights); // always use the running 'weights' vector for training, and NOT the weights_sum! + + data.push({features: features, classification: expected/*, prev: result*/}) + + if (opts.debug) console.log('> training ',features,', expecting: ',expected, ' got: ', result); + + if (result != expected) { + // Current model is incorrect - adjustment needed! + if (opts.debug) console.log('> adjusting weights...', weights, features); + for (var feature in features) + api.adjust(result, expected, features[feature], feature); + if (opts.debug) console.log(' -> weights:', weights) + } else { + if (opts.do_averaging) associative.add(weights_sum, weights); + } + + return (result == expected); + }, + + /** + * @param inputs a SINGLE training sample; converted to a feature array with feature_extractor (if available). + * @param expected the classification value for that sample (0 or 1) + * @return true if the input sample got its correct classification (i.e. no change made). + */ + train: function(inputs, expected) { + return api.train_features(api.normalized_features(inputs), expected); + }, + + + adjust: function(result, expected, input, feature) { + var delta = api.delta(result, expected, input, opts.learningrate); + if (isNaN(delta)) throw new Error('delta is NaN!! result='+result+" expected="+expected+" input="+input+" feature="+feature); + weights[feature] += delta; + if (isNaN(weights[feature])) throw new Error('weights['+feature+'] went to NaN!! delta='+d); + }, + + delta: function(actual, expected, input, learnrate) { + return (expected - actual) * learnrate * input; + }, + + + /** + * @param inputs a SINGLE sample; an associative array (feature => value). + * @param weights_for_classification the weights vector to use (either the running 'weights' or 'weights_sum'). + * @param net if true, return the net classification value. If false [default], return 0 or 1. + * @return the classification of the sample. + */ + perceive_features: function(features, net, weights_for_classification) { + var result = associative.inner_product(features, weights_for_classification); + if (opts.debug) console.log("> perceive_features ",features," = ",result); + return net + ? result + : result > 0 ? 1 : 0 + }, + + /** + * @param inputs a SINGLE sample; converted to a feature array with feature_extractor (if available). + * @param net if true, return the net classification value. If false [default], return 0 or 1. + * @return the classification of the sample. + */ + perceive: function(inputs, net) { + return api.perceive_features(api.normalized_features(inputs), net, + (opts.do_averaging? weights_sum: weights) ); + }, + + test_start: function() { + test_stats = { + count: 0, + TP: 0, + TN: 0, + FP: 0, + FN: 0 + }; + }, + + test_features: function(features,expected) { + var actual = api.perceive_features(features, /*net=*/false, + (opts.do_averaging? weights_sum: weights) ); + test_stats.count++; + if (expected && actual) test_stats.TP++; + if (!expected && actual) test_stats.FP++; + if (expected && !actual) test_stats.FN++; + if (!expected && !actual) test_stats.TN++; + }, + + /** + * Run through all the given samples, and use them for testing. + * @return statistics about the test. + */ + test: function(inputs, expected) { + api.test_features(api.normalized_features(inputs), expected); + }, + + test_results: function() { + test_stats.Accuracy = (test_stats.TP+test_stats.TN)/(test_stats.count); + test_stats.Precision = test_stats.TP/(test_stats.TP+test_stats.FP); + test_stats.Recall = test_stats.TP/(test_stats.TP+test_stats.FN); + test_stats.F1 = 2/(1/test_stats.Recall+1/test_stats.Precision); + return test_stats; + }, + + + /** + * Run through all past training samples, and use them for testing. + * @return statistics about the test. + */ + test_on_train: function() { + api.test_start(); + for (var i=0; i 0); + test.ok(news.perceive("This story is too old to be interesting", true) < 0); +}) + +specify.run() diff --git a/tests/test-save_load.js b/tests/test-save_load.js new file mode 100644 index 0000000..24066f1 --- /dev/null +++ b/tests/test-save_load.js @@ -0,0 +1,39 @@ +var perceptron = require('../perceptron_associative') + , should = require('should') + +{ /* start test: */ + var opts = { + weights: {'a': 0, 'b': 0, 'c': 0, 'threshold': 0}, + learningrate: 1, + debug:0 + } + var p = perceptron(opts); + + p.train({'a': 1, 'b': 0, 'c': 1}, 1) + p.train({'a': 0, 'b': 1, 'c': 1}, 0) + console.dir(p.weights); + + // Test results before save: + p.perceive({'a': 1, 'b': 0, 'c': 1}).should.equal(1); + p.perceive({'a': 0, 'b': 1, 'c': 1}).should.equal(0); + p.perceive({'a': 0, 'b': 1, 'c': 0}).should.equal(0); + p.perceive({'a': 1, 'b': 0, 'c': 0}).should.equal(1); + + p.save("abc"); + + // Test results after load: + q = perceptron(); + q.load("abc"); + console.dir(q.weights); + q.perceive({'a': 1, 'b': 0, 'c': 1}).should.equal(1); + q.perceive({'a': 0, 'b': 1, 'c': 1}).should.equal(0); + q.perceive({'a': 0, 'b': 1, 'c': 0}).should.equal(0); + q.perceive({'a': 1, 'b': 0, 'c': 0}).should.equal(1); + + // Test train after load: + q.train({'a': 1, 'b': 0, 'c': 0}, 0); + console.dir(q.weights); + q.perceive({'a': 1, 'b': 0, 'c': 0}).should.equal(0); + + console.log("OK!"); +}