forked from protonphoton/LJ
72 lines
1.8 KiB
JavaScript
72 lines
1.8 KiB
JavaScript
"use strict";
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var emotionClassifier = function() {
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var previousParameters = [];
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var classifier = {};
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var emotions = [];
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var coefficient_length;
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this.getEmotions = function() {
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return emotions;
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}
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this.init = function(model) {
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// load it
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for (var m in model) {
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emotions.push(m);
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classifier[m] = {};
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classifier[m]['bias'] = model[m]['bias'];
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classifier[m]['coefficients'] = model[m]['coefficients'];
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}
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coefficient_length = classifier[emotions[0]]['coefficients'].length;
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}
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this.getBlank = function() {
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var prediction = [];
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for (var j = 0;j < emotions.length;j++) {
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prediction[j] = {"emotion" : emotions[j], "value" : 0.0};
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}
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return prediction;
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}
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this.predict = function(parameters) {
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var prediction = [];
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for (var j = 0;j < emotions.length;j++) {
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var e = emotions[j];
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var score = classifier[e].bias;
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for (var i = 0;i < coefficient_length;i++) {
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score += classifier[e].coefficients[i]*parameters[i+6];
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}
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prediction[j] = {"emotion" : e, "value" : 0.0};
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prediction[j]['value'] = 1.0/(1.0 + Math.exp(-score));
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}
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return prediction;
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}
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this.meanPredict = function (parameters) {
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// store to array of 10 previous parameters
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previousParameters.splice(0, previousParameters.length == 10 ? 1 : 0);
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previousParameters.push(parameters.slice(0));
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if (previousParameters.length > 9) {
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// calculate mean of parameters?
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var meanParameters = [];
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for (var i = 0;i < parameters.length;i++) {
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meanParameters[i] = 0;
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}
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for (var i = 0;i < previousParameters.length;i++) {
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for (var j = 0;j < parameters.length;j++) {
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meanParameters[j] += previousParameters[i][j];
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}
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}
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for (var i = 0;i < parameters.length;i++) {
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meanParameters[i] /= 10;
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}
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// calculate logistic regression
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return this.predict(meanParameters);
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} else {
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return false;
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}
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}
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} |