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xmm-client.min.js
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xmm-client.min.js
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!function(e){if("object"==typeof exports&&"undefined"!=typeof module)module.exports=e();else if("function"==typeof define&&define.amd)define([],e);else{var t;t="undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof self?self:this,t.xmmClient=e()}}(function(){return function e(t,o,i){function r(s,a){if(!o[s]){if(!t[s]){var l="function"==typeof require&&require;if(!a&&l)return l(s,!0);if(n)return n(s,!0);var u=new Error("Cannot find module '"+s+"'");throw u.code="MODULE_NOT_FOUND",u}var d=o[s]={exports:{}};t[s][0].call(d.exports,function(e){var o=t[s][1][e];return r(o||e)},d,d.exports,e,t,o,i)}return o[s].exports}for(var n="function"==typeof require&&require,s=0;s<i.length;s++)r(i[s]);return r}({1:[function(e,t,o){"use strict";function i(e){return e&&e.__esModule?e:{default:e}}Object.defineProperty(o,"__esModule",{value:!0});var 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r=this._modelResults.likeliest>-1?this._model.models[this._modelResults.likeliest].label:null,n=this._modelResults.smoothed_normalized_likelihoods.slice(0);i={likeliest:r,likeliestIndex:this._modelResults.likeliest,likelihoods:n,outputValues:[],outputCovariance:[]},this._model.shared_parameters.bimodal&&(i.outputValues=this._modelResults.output_values.slice(0),i.outputCovariance=this._modelResults.output_covariance.slice(0))}catch(e){o="problem occured during filtering : "+e}else o="no model loaded yet";return t&&t(o,i),i}},{key:"getLikelihoodWindow",value:function(){return this._likelihoodWindow}},{key:"setLikelihoodWindow",value:function(e){this._likelihoodWindow=e,this._updateLikelihoodWindow()}},{key:"_updateLikelihoodWindow",value:function(){if(void 0!==this._model)for(var e=this._modelResults.singleClassGmmModelResults,t=0;t<this._model.models.length;t++){e[t].likelihood_buffer=new Array(this._likelihoodWindow);for(var o=0;o<this._likelihoodWindow;o++)e[t].likelihood_buffer[o]=1/this._likelihoodWindow}}},{key:"setWeights",value:function(e){if(!Array.isArray(e))throw new Error("Weights must be an array");this._weights=e,this._updateWeights()}},{key:"_updateWeights",value:function(){if(void 0!==this._model){var e=this._model,t=e.shared_parameters,o=t.bimodal?t.dimension_input:t.dimension,i=this._weights.slice();if(i.length<o)for(var r=o-i.length,n=0;n<r;n++)i.push(1);else i.length>o&&i.splice(o-1);for(var s=0;s<i.length;s++)i[s]=Math.max(i[s],0);for(var a=0;a<e.models.length;a++)for(var l=0;l<e.models[a].components.length;l++)e.models[a].components[l].weights=i}}},{key:"getModel",value:function(){if(this._model)return JSON.parse((0,n.default)(this._model))}},{key:"setModel",value:function(e){this._setModel(e)}},{key:"_setModel",value:function(e){if(this._model=void 0,this._modelResults=void 0,e&&void 0!==e.models){this._model=e,this._updateWeights();var t=this._model,o=t.models.length;this._modelResults={instant_likelihoods:new Array(o),smoothed_log_likelihoods:new Array(o),smoothed_likelihoods:new Array(o),instant_normalized_likelihoods:new Array(o),smoothed_normalized_likelihoods:new Array(o),likeliest:-1,singleClassGmmModelResults:[]};var i=t.shared_parameters,r=i.dimension-i.dimension_input;this._modelResults.output_values=new Array(r);for(var n=0;n<r;n++)this._modelResults.output_values[n]=0;var s=void 0;s=0==t.configuration.default_parameters.covariance_mode?r*r:r,this._modelResults.output_covariance=new Array(s);for(var a=0;a<r;a++)this._modelResults.output_covariance[a]=0;for(var l=0;l<o;l++){this._modelResults.instant_likelihoods[l]=0,this._modelResults.smoothed_log_likelihoods[l]=0,this._modelResults.smoothed_likelihoods[l]=0,this._modelResults.instant_normalized_likelihoods[l]=0,this._modelResults.smoothed_normalized_likelihoods[l]=0;var u={instant_likelihood:0,log_likelihood:0};u.likelihood_buffer=new Array(this._likelihoodWindow);for(var d=0;d<this._likelihoodWindow;d++)u.likelihood_buffer[d]=1/this._likelihoodWindow;u.likelihood_buffer_index=0,u.beta=new Array(t.models[l].components.length);for(var _=0;_<u.beta.length;_++)u.beta[_]=1/u.beta.length;u.output_values=this._modelResults.output_values.slice(0),u.output_covariance=this._modelResults.output_covariance.slice(0),this._modelResults.singleClassGmmModelResults.push(u)}}}},{key:"getLikeliestLabel",value:function(){return this._modelResults&&this._modelResults.likeliest>-1?this._model.models[this._modelResults.likeliest].label:"unknown"}},{key:"getNumberOfClasses",value:function(){return this._model?this._model.models.length:0}},{key:"getRegressionVectorSize",value:function(){if(this._model){var e=this._model.shared_parameters;return e.bimodal?e.dimension-e.dimension_input:0}return 0}}]),e}();o.default=c},{"../utils/gmm-utils":6,"babel-runtime/core-js/json/stringify":9,"babel-runtime/helpers/classCallCheck":15,"babel-runtime/helpers/createClass":16}],2:[function(e,t,o){"use strict";function i(e){return e&&e.__esModule?e:{default:e}}Object.defineProperty(o,"__esModule",{value:!0});var r=e("babel-runtime/core-js/json/stringify"),n=i(r),s=e("babel-runtime/helpers/classCallCheck"),a=i(s),l=e("babel-runtime/helpers/createClass"),u=i(l),d=e("../utils/hhmm-utils"),_=function(e){if(e&&e.__esModule)return e;var t={};if(null!=e)for(var o in e)Object.prototype.hasOwnProperty.call(e,o)&&(t[o]=e[o]);return t.default=e,t}(d),c=function(){function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:1;(0,a.default)(this,e),this._likelihoodWindow=t,this._model=void 0,this._modelResults=void 0,this._weights=[]}return(0,u.default)(e,[{key:"filter",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=null,i=null;if(this._model)try{_.hhmmFilter(e,this._model,this._modelResults);var r=this._modelResults.likeliest>-1?this._model.models[this._modelResults.likeliest].label:null,n=this._modelResults.smoothed_normalized_likelihoods.slice(0);i={likeliest:r,likeliestIndex:this._modelResults.likeliest,likelihoods:n,timeProgressions:new Array(this._model.models.length),alphas:new Array(this._model.models.length),outputValues:[],outputCovariance:[]};for(var s=0;s<this._model.models.length;s++)i.timeProgressions[s]=this._modelResults.singleClassHmmModelResults[s].progress,this._model.configuration.default_parameters.hierarchical?i.alphas[s]=this._modelResults.singleClassHmmModelResults[s].alpha_h[0]:i.alphas[s]=this._modelResults.singleClassHmmModelResults[s].alpha[0];this._model.shared_parameters.bimodal&&(i.outputValues=this._modelResults.output_values.slice(0),i.outputCovariance=this._modelResults.output_covariance.slice(0))}catch(e){o="problem occured during filtering : "+e}else o="no model loaded yet";return t&&t(o,i),i}},{key:"reset",value:function(){this._model&&this._setModel(this._model)}},{key:"getLikelihoodWindow",value:function(){return this._likelihoodWindow}},{key:"setLikelihoodWindow",value:function(e){this._likelihoodWindow=e,this._updateLikelihoodWindow()}},{key:"_updateLikelihoodWindow",value:function(){if(void 0!==this._model)for(var e=this._modelResults.singleClassHmmModelResults,t=0;t<this._model.models.length;t++){e[t].likelihood_buffer=new Array(this._likelihoodWindow);for(var o=0;o<this._likelihoodWindow;o++)e[t].likelihood_buffer[o]=1/this._likelihoodWindow}}},{key:"setWeights",value:function(e){if(!Array.isArray(e))throw new Error("Weights must be an array");this._weights=e,this._updateWeights()}},{key:"_updateWeights",value:function(){if(void 0!==this._model){var e=this._model,t=e.shared_parameters,o=t.bimodal?t.dimension_input:t.dimension,i=this._weights.slice();if(i.length<o)for(var r=o-i.length,n=0;n<r;n++)i.push(1);else i.length>o&&i.splice(o-1);for(var s=0;s<i.length;s++)i[s]=Math.max(i[s],0);for(var a=0;a<e.models.length;a++)for(var l=0;l<e.models[a].states.length;l++)for(var u=0;u<e.models[a].states[l].components.length;u++)e.models[a].states[l].components[u].weights=i}}},{key:"getModel",value:function(){if(this._model)return JSON.parse((0,n.default)(this._model))}},{key:"setModel",value:function(e){this._setModel(e)}},{key:"_setModel",value:function(e){if(this._model=void 0,this._modelResults=void 0,e&&void 0!==e.models){this._model=e,this._updateWeights();var t=this._model,o=t.models.length;this._modelResults={instant_likelihoods:new Array(o),smoothed_log_likelihoods:new Array(o),smoothed_likelihoods:new Array(o),instant_normalized_likelihoods:new Array(o),smoothed_normalized_likelihoods:new Array(o),likeliest:-1,frontier_v1:new Array(o),frontier_v2:new Array(o),forward_initialized:!1,singleClassHmmModelResults:[]};var i=t.shared_parameters,r=i.dimension-i.dimension_input;this._modelResults.output_values=new Array(r);for(var n=0;n<r;n++)this._modelResults.output_values[n]=0;var s=void 0;s=0==t.configuration.default_parameters.covariance_mode?r*r:r,this._modelResults.output_covariance=new Array(s);for(var a=0;a<r;a++)this._modelResults.output_covariance[a]=0;for(var l=0;l<o;l++){this._modelResults.instant_likelihoods[l]=0,this._modelResults.smoothed_log_likelihoods[l]=0,this._modelResults.smoothed_likelihoods[l]=0,this._modelResults.instant_normalized_likelihoods[l]=0,this._modelResults.smoothed_normalized_likelihoods[l]=0;for(var u=t.models[l].parameters.states,d=new Array(3),_=0;_<3;_++){d[_]=new Array(u);for(var c=0;c<u;c++)d[_][c]=0}for(var h=new Array(u),f=0;f<u;f++)h[f]=0;for(var m=new Array(this._likelihoodWindow),p=0;p<this._likelihoodWindow;p++)m[p]=0;var v={hierarchical:t.configuration.default_parameters.hierarchical,instant_likelihood:0,log_likelihood:0,likelihood_buffer:m,likelihood_buffer_index:0,progress:0,exit_likelihood:0,exit_ratio:0,likeliest_state:-1,previous_alpha:h.slice(0),alpha:h,alpha_h:d,window_minindex:0,window_maxindex:0,window_normalization_constant:0,forward_initialized:!1,singleClassGmmModelResults:[]};v.output_values=this._modelResults.output_values.slice(0),v.output_covariance=this._modelResults.output_covariance.slice(0);for(var b=0;b<u;b++){var g={instant_likelihood:0,log_likelihood:0};g.beta=new Array(this._model.models[l].parameters.gaussians);for(var y=0;y<g.beta.length;y++)g.beta[y]=1/g.beta.length;g.output_values=v.output_values.slice(0),g.output_covariance=v.output_covariance.slice(0),v.singleClassGmmModelResults.push(g)}this._modelResults.singleClassHmmModelResults.push(v)}}}},{key:"getLikeliestLabel",value:function(){return this._modelResults&&this._modelResults.likeliest>-1?this._model.models[this._modelResults.likeliest].label:"unknown"}},{key:"getNumberOfClasses",value:function(){return this._model?this._model.models.length:0}},{key:"getRegressionVectorSize",value:function(){if(this._model){var e=this._model.shared_parameters;return e.bimodal?e.dimension-e.dimension_input:0}return 0}}]),e}();o.default=c},{"../utils/hhmm-utils":7,"babel-runtime/core-js/json/stringify":9,"babel-runtime/helpers/classCallCheck":15,"babel-runtime/helpers/createClass":16}],3:[function(e,t,o){"use strict";function i(e){return e&&e.__esModule?e:{default:e}}Object.defineProperty(o,"__esModule",{value:!0});var r=e("./gmm/gmm-decoder");Object.defineProperty(o,"GmmDecoder",{enumerable:!0,get:function(){return i(r).default}});var n=e("./hhmm/hhmm-decoder");Object.defineProperty(o,"HhmmDecoder",{enumerable:!0,get:function(){return i(n).default}});var s=e("./set/xmm-phrase");Object.defineProperty(o,"PhraseMaker",{enumerable:!0,get:function(){return i(s).default}});var a=e("./set/xmm-set");Object.defineProperty(o,"SetMaker",{enumerable:!0,get:function(){return i(a).default}})},{"./gmm/gmm-decoder":1,"./hhmm/hhmm-decoder":2,"./set/xmm-phrase":4,"./set/xmm-set":5}],4:[function(e,t,o){"use strict";function i(e){return e&&e.__esModule?e:{default:e}}Object.defineProperty(o,"__esModule",{value:!0});var r=e("babel-runtime/helpers/typeof"),n=i(r),s=e("babel-runtime/core-js/number/is-integer"),a=i(s),l=e("babel-runtime/helpers/classCallCheck"),u=i(l),d=e("babel-runtime/helpers/createClass"),_=i(d),c=function(e){return e.constructor===Float32Array||Array.isArray(e)},h=function(){function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};(0,u.default)(this,e);var o={bimodal:!1,dimension:1,dimensionInput:0,columnNames:[""],label:""};this._config=o,this._setConfig(t),this.reset()}return(0,_.default)(e,[{key:"getConfig",value:function(){return this._config}},{key:"setConfig",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};this._setConfig(e)}},{key:"_setConfig",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};for(var t in e)"bimodal"===t&&"boolean"==typeof e[t]?this._config[t]=e[t]:"dimension"===t&&(0,a.default)(e[t])?this._config[t]=e[t]:"dimensionInput"===t&&(0,a.default)(e[t])?this._config[t]=e[t]:"columnNames"===t&&Array.isArray(e[t])?this._config[t]=e[t].slice(0):"label"===t&&"string"==typeof e[t]&&(this._config[t]=e[t])}},{key:"addObservation",value:function(e){var t="Bad data type: all observation values must be numbers";if(c(e)){for(var o=0;o<e.length;o++)if("number"!=typeof e[o])throw new Error(t)}else if((0,n.default)("number"!==e))throw new Error(t);if(e.length!==this._config.dimension||"number"==typeof e&&1!==this._config.dimension)throw new Error("Bad input length: observation length must match phrase dimension");if(this._config.bimodal){for(var i=0;i<this._config.dimensionInput;i++)this._dataIn.push(e[i]);for(var r=this._config.dimensionInput;r<this._config.dimension;r++)this._dataOut.push(e[r])}else if(c(e))for(var s=0;s<e.length;s++)this._data.push(e[s]);else this._data.push(e)}},{key:"getPhrase",value:function(){return this._getPhrase()}},{key:"_getPhrase",value:function(){var e={bimodal:this._config.bimodal,column_names:this._config.columnNames,dimension:this._config.dimension,dimension_input:this._config.dimensionInput,label:this._config.label,length:this._config.bimodal?this._dataIn.length/this._config.dimensionInput:this._data.length/this._config.dimension};return this._config.bimodal?(e.data_input=this._dataIn,e.data_output=this._dataOut):e.data=this._data,e}},{key:"reset",value:function(){this._data=[],this._dataIn=[],this._dataOut=[]}}]),e}();o.default=h},{"babel-runtime/core-js/number/is-integer":11,"babel-runtime/helpers/classCallCheck":15,"babel-runtime/helpers/createClass":16,"babel-runtime/helpers/typeof":17}],5:[function(e,t,o){"use strict";function i(e){return e&&e.__esModule?e:{default:e}}Object.defineProperty(o,"__esModule",{value:!0});var r=e("babel-runtime/core-js/number/is-integer"),n=i(r),s=e("babel-runtime/core-js/get-iterator"),a=i(s),l=e("babel-runtime/core-js/json/stringify"),u=i(l),d=e("babel-runtime/helpers/classCallCheck"),_=i(d),c=e("babel-runtime/helpers/createClass"),h=i(c),f=function(){function e(){(0,_.default)(this,e),this._config={},this._phrases=[]}return(0,h.default)(e,[{key:"getSize",value:function(){return this._phrases.length}},{key:"addPhrase",value:function(e){if(0===this._phrases.length)this._setConfigFrom(e);else if(!this._checkCompatibility(e))throw new Error("Bad phrase format: added phrase must match current set configuration");this._phrases.push(JSON.parse((0,u.default)(e)))}},{key:"addTrainingSet",value:function(e){if(0===this._phrases.length)this._setConfigFrom(e);else if(!this._checkCompatibility(e))throw new Error("Bad set format: added set must match current set configuration");var t=e.phrases,o=!0,i=!1,r=void 0;try{for(var n,s=(0,a.default)(t);!(o=(n=s.next()).done);o=!0){var l=n.value;this._phrases.push(l)}}catch(e){i=!0,r=e}finally{try{!o&&s.return&&s.return()}finally{if(i)throw r}}}},{key:"getPhrase",value:function(e){return e>-1&&e<this._phrases.length?JSON.parse((0,u.default)(this._phrases[e])):null}},{key:"removePhrase",value:function(e){e>-1&&e<this._phrases.length&&this._phrases.splice(e,1)}},{key:"getPhrasesOfLabel",value:function(e){var t={};for(var o in this._config)t[o]=this._config[o];t.phrases=[];var i=0,r=!0,n=!1,s=void 0;try{for(var l,d=(0,a.default)(this._phrases);!(r=(l=d.next()).done);r=!0){var _=l.value;if(_.label===e){var c=JSON.parse((0,u.default)(_));c.index=i++,t.phrases.push(c)}}}catch(e){n=!0,s=e}finally{try{!r&&d.return&&d.return()}finally{if(n)throw s}}return t}},{key:"removePhrasesOfLabel",value:function(e){for(var t=this._phrases.length-1;t>=0;t--)this._phrases[t].label===e&&this._phrases.splice(t,1)}},{key:"getTrainingSet",value:function(){var e={};for(var t in this._config)e[t]=this._config[t];e.phrases=[];var o=0,i=!0,r=!1,n=void 0;try{for(var s,l=(0,a.default)(this._phrases);!(i=(s=l.next()).done);i=!0){var d=s.value,_=JSON.parse((0,u.default)(d));_.index=o++,e.phrases.push(_)}}catch(e){r=!0,n=e}finally{try{!i&&l.return&&l.return()}finally{if(r)throw n}}return e}},{key:"clear",value:function(){this._config={},this._phrases=[]}},{key:"_setConfigFrom",value:function(e){for(var t in e)"bimodal"===t&&"boolean"==typeof e.bimodal?this._config[t]=e[t]:"column_names"===t&&Array.isArray(e[t])?this._config[t]=e[t].slice(0):"dimension"===t&&(0,n.default)(e[t])?this._config[t]=e[t]:"dimension_input"===t&&(0,n.default)(e[t])&&(this._config[t]=e[t])}},{key:"_checkCompatibility",value:function(e){if(e.bimodal!==this._config.bimodal||e.dimension!==this._config.dimension||e.dimension_input!==this._config.dimension_input)return!1;var t=e.column_names,o=this._config.column_names;if(t&&o){if(t.length!==o.length)return!1;for(var i=0;i<t.length;i++)if(t[i]!==o[i])return!1}return!0}}]),e}();o.default=f},{"babel-runtime/core-js/get-iterator":8,"babel-runtime/core-js/json/stringify":9,"babel-runtime/core-js/number/is-integer":11,"babel-runtime/helpers/classCallCheck":15,"babel-runtime/helpers/createClass":16}],6:[function(e,t,o){"use strict";Object.defineProperty(o,"__esModule",{value:!0}),o.gmmFilter=o.gmmLikelihood=o.gmmObsProbBimodal=o.gmmObsProbInput=o.gmmObsProb=o.gmmRegression=o.gmmComponentLikelihoodBimodal=o.gmmComponentLikelihoodInput=o.gmmComponentLikelihood=o.gmmComponentRegression=void 0;var i=e("babel-runtime/core-js/number/is-finite"),r=function(e){return e&&e.__esModule?e:{default:e}}(i),n=o.gmmComponentRegression=function(e,t){var o=t.dimension,i=t.dimension_input,r=o-i,n=new Array(r);if(0===t.covariance_mode)for(var s=0;s<r;s++){n[s]=t.mean[i+s];for(var a=0;a<i;a++){for(var l=0,u=0;u<i;u++)l+=t.inverse_covariance_input[a*i+u]*(e[u]-t.mean[u]);n[s]+=t.covariance[(s+i)*o+a]*l}}else for(var d=0;d<r;d++)n[d]=t.covariance[d+i];return n},s=o.gmmComponentLikelihood=function(e,t){var o=0;if(0===t.covariance_mode)for(var i=0;i<t.dimension;i++){for(var n=0,s=0;s<t.dimension;s++)n+=t.inverse_covariance[i*t.dimension+s]*(e[s]-t.mean[s])*t.weights[s];o+=(e[i]-t.mean[i])*n*t.weights[i]}else for(var a=0;a<t.dimension;a++)o+=t.inverse_covariance[a]*(e[a]-t.mean[a])*(e[a]-t.mean[a])*t.weights[a]*t.weights[a];var l=Math.exp(-.5*o)/Math.sqrt(t.covariance_determinant*Math.pow(2*Math.PI,t.dimension));return(l<1e-180||!(0,r.default)(l))&&(l=1e-180),l},a=o.gmmComponentLikelihoodInput=function(e,t){var o=0;if(0===t.covariance_mode)for(var i=0;i<t.dimension_input;i++){for(var r=0,n=0;n<t.dimension_input;n++)r+=t.inverse_covariance_input[i*t.dimension_input+n]*(e[n]-t.mean[n])*t.weights[n];o+=(e[i]-t.mean[i])*r*t.weights[i]}else for(var s=0;s<t.dimension_input;s++)o+=t.inverse_covariance_input[s]*(e[s]-t.mean[s])*(e[s]-t.mean[s])*t.weights[s]*t.weights[s];var a=Math.exp(-.5*o)/Math.sqrt(t.covariance_determinant_input*Math.pow(2*Math.PI,t.dimension_input));return(a<1e-180||isNaN(a)||isNaN(Math.abs(a)))&&(a=1e-180),a},l=o.gmmComponentLikelihoodBimodal=function(e,t,o){var i=o.dimension,r=o.dimension_input,n=i-r,s=0;if(0===o.covariance_mode)for(var a=0;a<i;a++){for(var l=0,u=0;u<r;u++)l+=o.inverse_covariance[a*i+u]*(e[u]-o.mean[u])*o.weights[u];for(var d=0;d<n;d++)l+=o.inverse_covariance[a*i+r+d]*(t[d]-o.mean[r+d]);s+=a<r?(e[a]-o.mean[a])*l*o.weights[a]:(t[a-r]-o.mean[a])*l}else{for(var _=0;_<r;_++)s+=o.inverse_covariance[_]*(e[_]-o.mean[_])*(e[_]-o.mean[_])*o.weights[_]*o.weights[_];for(var c=r;c<i;c++){var h=(t[c-r]-o.mean[c])*(t[c-r]-o.mean[c]);s+=o.inverse_covariance[c]*h}}var f=Math.exp(-.5*s)/Math.sqrt(o.covariance_determinant*Math.pow(2*Math.PI,o.dimension));return(f<1e-180||isNaN(f)||isNaN(Math.abs(f)))&&(f=1e-180),f},u=o.gmmRegression=function(e,t,o){var i=t.components[0].dimension,r=t.components[0].dimension_input,s=i-r;o.output_values=new Array(s);for(var a=0;a<s;a++)o.output_values[a]=0;var l=void 0;l=0===t.parameters.covariance_mode?s*s:s,o.output_covariance=new Array(l);for(var u=0;u<l;u++)o.output_covariance[u]=0;for(var d=void 0,_=0;_<t.components.length;_++){d=n(e,t.components[_]);for(var c=o.beta[_]*o.beta[_],h=0;h<s;h++)if(o.output_values[h]+=o.beta[_]*d[h],0===t.parameters.covariance_mode)for(var f=0;f<s;f++){var m=h*s+f;o.output_covariance[m]+=c*t.components[_].output_covariance[m]}else o.output_covariance[h]+=c*t.components[_].output_covariance[h]}},d=o.gmmObsProb=function e(t,o){var i=arguments.length>2&&void 0!==arguments[2]?arguments[2]:-1,r=o.mixture_coeffs,n=o.components,a=0;if(i<0)for(var l=0;l<n.length;l++)a+=e(t,o,l);else a=r[i]*s(t,n[i]);return a},_=o.gmmObsProbInput=function e(t,o){var i=arguments.length>2&&void 0!==arguments[2]?arguments[2]:-1,r=o.mixture_coeffs,n=o.components,s=0;if(i<0)for(var l=0;l<n.length;l++)s+=e(t,o,l);else s=r[i]*a(t,n[i]);return s},c=o.gmmObsProbBimodal=function e(t,o,i){var r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:-1,n=i.mixture_coeffs,s=i.components,a=0;if(r<0)for(var u=0;u<s.length;u++)a+=e(t,o,i,u);else a=n[r]*l(t,o,s[r]);return a},h=o.gmmLikelihood=function(e,t,o){for(var i=arguments.length>3&&void 0!==arguments[3]?arguments[3]:[],r=t.components,n=o,s=0,a=0;a<r.length;a++)r[a].bimodal?0===i.length?n.beta[a]=_(e,t,a):n.beta[a]=c(e,i,t,a):n.beta[a]=d(e,t,a),s+=n.beta[a];for(var l=0;l<r.length;l++)n.beta[l]/=s;n.instant_likelihood=s;var u=n.likelihood_buffer.length;return n.likelihood_buffer[n.likelihood_buffer_index]=Math.log(s),n.likelihood_buffer_index=(n.likelihood_buffer_index+1)%u,n.log_likelihood=n.likelihood_buffer.reduce(function(e,t){return e+t},0),n.log_likelihood/=u,s};o.gmmFilter=function(e,t,o){for(var i=t.models,r=o,n=t.shared_parameters,s=t.configuration,a=0,l=0,d=0,_=0;_<i.length;_++){var c=r.singleClassGmmModelResults[_];r.instant_likelihoods[_]=h(e,i[_],c),n.bimodal&&u(e,i[_],c),r.smoothed_log_likelihoods[_]=c.log_likelihood,r.smoothed_likelihoods[_]=Math.exp(r.smoothed_log_likelihoods[_]),r.instant_normalized_likelihoods[_]=r.instant_likelihoods[_],r.smoothed_normalized_likelihoods[_]=r.smoothed_likelihoods[_],l+=r.instant_normalized_likelihoods[_],d+=r.smoothed_normalized_likelihoods[_],(0==_||r.smoothed_log_likelihoods[_]>a)&&(a=r.smoothed_log_likelihoods[_],r.likeliest=_)}for(var f=0;f<i.length;f++)r.instant_normalized_likelihoods[f]/=l,r.smoothed_normalized_likelihoods[f]/=d;if(n.bimodal){var m=n.dimension,p=n.dimension_input,v=m-p;if(0===s.multiClass_regression_estimator)r.output_values=r.singleClassGmmModelResults[r.likeliest].output_values,r.output_covariance=r.singleClassGmmModelResults[r.likeliest].output_covariance;else{r.output_values=new Array(v);for(var b=0;b<v;b++)r.output_values[b]=0;var g=void 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t.default=e,t}(i),n=o.hmmRegression=function(e,t,o){var i=t.states[0].components[0].dimension,n=t.states[0].components[0].dimension_input,s=i-n,a=void 0;a=0===t.states[0].components[0].covariance_mode?s*s:s,o.output_values=new Array(s);for(var l=0;l<s;l++)o.output_values[l]=0;o.output_covariance=new Array(a);for(var u=0;u<a;u++)o.output_covariance[u]=0;if(2===t.parameters.regression_estimator)return r.gmmLikelihood(e,t.states[o.likeliest_state],o.singleClassGmmModelResults[o.likeliest_state]),r.gmmRegression(e,t.states[o.likeliest_state],o.singleClassGmmModelResults[o.likeliest_state]),void(o.output_values=t.states[o.likeliest_state].output_values.slice());var d=0==t.parameters.regression_estimator?0:o.window_minindex,_=0==t.parameters.regression_estimator?t.states.length:o.window_maxindex,c=0==t.parameters.regression_estimator?1:o.window_normalization_constant;c<=0&&(c=1);for(var h=d;h<_;h++){r.gmmLikelihood(e,t.states[h],o.singleClassGmmModelResults[h]),r.gmmRegression(e,t.states[h],o.singleClassGmmModelResults[h]);for(var f=o.singleClassGmmModelResults[h].output_values.slice(0),m=0;m<s;m++)if(o.hierarchical)if(o.output_values[m]+=(o.alpha_h[0][h]+o.alpha_h[1][h])*f[m]/c,0===t.parameters.covariance_mode)for(var p=0;p<s;p++)o.output_covariance[m*s+p]+=(o.alpha_h[0][h]+o.alpha_h[1][h])*(o.alpha_h[0][h]+o.alpha_h[1][h])*o.singleClassGmmModelResults[h].output_covariance[m*s+p]/c;else o.output_covariance[m]+=(o.alpha_h[0][h]+o.alpha_h[1][h])*(o.alpha_h[0][h]+o.alpha_h[1][h])*o.singleClassGmmModelResults[h].output_covariance[m]/c;else if(o.output_values[m]+=o.alpha[h]*f[m]/c,0===t.parameters.covariance_mode)for(var v=0;v<s;v++)o.output_covariance[m*s+v]+=o.alpha[h]*o.alpha[h]*o.singleClassGmmModelResults[h].output_covariance[m*s+v]/c;else o.output_covariance[m]+=o.alpha[h]*o.alpha[h]*o.singleClassGmmModelResults.output_covariance[m]/c}},s=o.hmmForwardInit=function(e,t,o){var i=arguments.length>3&&void 0!==arguments[3]?arguments[3]:[],n=t.parameters.states,s=0;if(0===t.parameters.transition_mode)for(var a=0;a<n;a++)t.states[a].components[0].bimodal?i.length>0?o.alpha[a]=t.prior[a]*r.gmmObsProbBimodal(e,i,t.states[a]):o.alpha[a]=t.prior[a]*r.gmmObsProbInput(e,t.states[a]):o.alpha[a]=t.prior[a]*r.gmmObsProb(e,t.states[a]),s+=o.alpha[a];else{for(var l=0;l<o.alpha.length;l++)o.alpha[l]=0;t.states[0].components[0].bimodal?i.length>0?o.alpha[0]=r.gmmObsProbBimodal(e,i,t.states[0]):o.alpha[0]=r.gmmObsProbInput(e,t.states[0]):o.alpha[0]=r.gmmObsProb(e,t.states[0]),s+=o.alpha[0]}if(s>0){for(var u=0;u<n;u++)o.alpha[u]/=s;return 1/s}for(var d=0;d<n;d++)o.alpha[d]=1/n;return 1},a=o.hmmForwardUpdate=function(e,t,o){var i=arguments.length>3&&void 0!==arguments[3]?arguments[3]:[],n=t.parameters.states,s=0;o.previous_alpha=o.alpha.slice(0);for(var a=0;a<n;a++){if(o.alpha[a]=0,0===t.parameters.transition_mode)for(var l=0;l<n;l++)o.alpha[a]+=o.previous_alpha[l]*o.transition[l*n+a];else o.alpha[a]+=o.previous_alpha[a]*o.transition[2*a],a>0?o.alpha[a]+=o.previous_alpha[a-1]*o.transition[2*(a-1)+1]:o.alpha[0]+=o.previous_alpha[n-1]*o.transition[2*n-1];t.states[a].components[0].bimodal?i.length>0?o.alpha[a]*=r.gmmObsProbBimodal(e,i,t.states[a]):o.alpha[a]*=r.gmmObsProbInput(e,t.states[a]):o.alpha[a]*=r.gmmObsProb(e,t.states[a]),s+=o.alpha[a]}if(s>1e-300){for(var u=0;u<n;u++)o.alpha[u]/=s;return 1/s}return 0},l=o.hmmUpdateAlphaWindow=function(e,t){var o=e.parameters.states;t.likeliest_state=0;var i=void 0;i=e.parameters.hierarchical?t.alpha_h[0][0]+t.alpha_h[1][0]:t.alpha[0];for(var r=1;r<o;r++)e.parameters.hierarchical?t.alpha_h[0][r]+t.alpha_h[1][r]>i&&(i=t.alpha_h[0][r]+t.alpha_h[1][r],t.likeliest_state=r):t.alpha[r]>i&&(i=t.alpha[r],t.likeliest_state=r);t.window_minindex=t.likeliest_state-Math.floor(o/2),t.window_maxindex=t.likeliest_state+Math.floor(o/2),t.window_minindex=t.window_minindex>=0?t.window_minindex:0,t.window_maxindex=t.window_maxindex<=o?t.window_maxindex:o,t.window_normalization_constant=0;for(var n=t.window_minindex;n<t.window_maxindex;n++)e.parameters.hierarchical?t.window_normalization_constant+=t.alpha_h[0][n]+t.alpha_h[1][n]:t.window_normalization_constant+=t.alpha[n]},u=o.hmmUpdateResults=function(e,t){var o=t.likelihood_buffer.length;t.likelihood_buffer[t.likelihood_buffer_index]=Math.log(t.instant_likelihood),t.likelihood_buffer_index=(t.likelihood_buffer_index+1)%o,t.log_likelihood=t.likelihood_buffer.reduce(function(e,t){return e+t},0),t.log_likelihood/=o,t.progress=0;for(var i=t.window_minindex;i<t.window_maxindex;i++)e.parameters.hierarchical?t.progress+=(t.alpha_h[0][i]+t.alpha_h[1][i]+t.alpha_h[2][i])*i/t.window_normalization_constant:t.progress+=t.alpha[i]*i/t.window_normalization_constant;t.progress/=Math.max(e.parameters.states,2)-1,t.progress=Math.min(t.progress,1)},d=o.hmmFilter=function(e,t,o){var i=0;if(o.forward_initialized)i=a(e,t,o);else{for(var r=0;r<o.likelihood_buffer.length;r++)o.likelihood_buffer[r]=0;i=s(e,t,o),o.forward_initialized=!0}return o.instant_likelihood=1/i,l(t,o),u(t,o),t.states[0].components[0].bimodal&&n(e,t,o),o.instant_likelihood},_=o.hhmmLikelihoodAlpha=function(e,t,o,i){if(e<0)for(var r=0;r<o.models.length;r++){t[r]=0;for(var n=0;n<3;n++)for(var s=0;s<o.models[r].parameters.states;s++)t[r]+=i.singleClassHmmModelResults[r].alpha_h[n][s]}else for(var a=0;a<o.models.length;a++){t[a]=0;for(var l=0;l<o.models[a].parameters.states;l++)t[a]+=i.singleClassHmmModelResults[a].alpha_h[e][l]}},c=o.hhmmForwardInit=function(e,t,o){for(var i=0,n=0;n<t.models.length;n++){for(var s=t.models[n],a=s.parameters.states,l=o.singleClassHmmModelResults[n],u=0;u<3;u++){l.alpha_h[u]=new Array(a);for(var d=0;d<a;d++)l.alpha_h[u][d]=0}if(0==s.parameters.transition_mode)for(var _=0;_<a;_++)t.shared_parameters.bimodal?l.alpha_h[0][_]=s.prior[_]*r.gmmObsProbInput(e,s.states[_]):l.alpha_h[0][_]=s.prior[_]*r.gmmObsProb(e,s.states[_]),l.instant_likelihood+=l.alpha_h[0][_];else l.alpha_h[0][0]=t.prior[n],t.shared_parameters.bimodal?l.alpha_h[0][0]*=r.gmmObsProbInput(e,s.states[0]):l.alpha_h[0][0]*=r.gmmObsProb(e,s.states[0]),l.instant_likelihood=l.alpha_h[0][0];i+=l.instant_likelihood}for(var c=0;c<t.models.length;c++)for(var h=t.models[c].parameters.states,f=0;f<3;f++)for(var m=0;m<h;m++)o.singleClassHmmModelResults[c].alpha_h[f][m]/=i;o.forward_initialized=!0},h=o.hhmmForwardUpdate=function(e,t,o){var i=t.models.length,n=0,s=0,a=void 0;_(1,o.frontier_v1,t,o),_(2,o.frontier_v2,t,o);for(var l=0;l<i;l++){var u=t.models[l],d=u.parameters.states,c=o.singleClassHmmModelResults[l];a=new Array(d);for(var h=0;h<d;h++)a[h]=0;if(0===u.parameters.transition_mode)for(var f=0;f<d;f++){for(var m=0;m<d;m++)a[f]+=u.transition[m*d+f]/(1-u.exitProbabilities[m])*c.alpha_h[0][m];for(var p=0;p<i;p++)a[f]+=u.prior[f]*(o.frontier_v1[p]*t.transition[p][l]+o.frontier_v2[p]*t.prior[l])}else{a[0]=u.transition[0]*c.alpha_h[0][0];for(var v=0;v<i;v++)a[0]+=o.frontier_v1[v]*t.transition[v][l]+o.frontier_v2[v]*t.prior[l];for(var b=1;b<d;b++)a[b]+=u.transition[2*b]/(1-u.exitProbabilities[b])*c.alpha_h[0][b],a[b]+=u.transition[2*(b-1)+1]/(1-u.exitProbabilities[b-1])*c.alpha_h[0][b-1];for(var g=0;g<3;g++)for(var y=0;y<d;y++)c.alpha_h[g][y]=0}c.exit_likelihood=0,c.instant_likelihood=0;for(var k=0;k<d;k++)s=t.shared_parameters.bimodal?r.gmmObsProbInput(e,u.states[k])*a[k]:r.gmmObsProb(e,u.states[k])*a[k],
c.alpha_h[2][k]=t.exit_transition[l]*u.exitProbabilities[k]*s,c.alpha_h[1][k]=(1-t.exit_transition[l])*u.exitProbabilities[k]*s,c.alpha_h[0][k]=(1-u.exitProbabilities[k])*s,c.exit_likelihood+=c.alpha_h[1][k]+c.alpha_h[2][k],c.instant_likelihood+=c.alpha_h[0][k]+c.alpha_h[1][k]+c.alpha_h[2][k],n+=s;c.instant_likelihood=c.instant_likelihood>1e-180?c.instant_likelihood:1e-180,c.exit_ratio=c.exit_likelihood/c.instant_likelihood}for(var w=0;w<i;w++)for(var j=0;j<3;j++)for(var x=0;x<t.models[w].parameters.states;x++)o.singleClassHmmModelResults[w].alpha_h[j][x]/=n},f=o.hhmmUpdateResults=function(e,t){for(var o=0,i=0,r=0,n=0;n<e.models.length;n++){var s=t.singleClassHmmModelResults[n];t.instant_likelihoods[n]=s.instant_likelihood,t.smoothed_log_likelihoods[n]=s.log_likelihood,t.smoothed_likelihoods[n]=Math.exp(t.smoothed_log_likelihoods[n]),t.instant_normalized_likelihoods[n]=t.instant_likelihoods[n],t.smoothed_normalized_likelihoods[n]=t.smoothed_likelihoods[n],i+=t.instant_normalized_likelihoods[n],r+=t.smoothed_normalized_likelihoods[n],(0==n||t.smoothed_log_likelihoods[n]>o)&&(o=t.smoothed_log_likelihoods[n],t.likeliest=n)}for(var a=0,l=0;l<e.models.length;l++)t.instant_normalized_likelihoods[l]/=i,t.smoothed_normalized_likelihoods[l]/=r,a+=t.smoothed_normalized_likelihoods[l]};o.hhmmFilter=function(e,t,o){if(t.configuration.default_parameters.hierarchical)o.forward_initialized?h(e,t,o):c(e,t,o);else for(var i=0;i<t.models.length;i++)o.instant_likelihoods[i]=d(e,t.models[i],o.singleClassHmmModelResults[i]);for(var r=0;r<t.models.length;r++)l(t.models[r],o.singleClassHmmModelResults[r]),u(t.models[r],o.singleClassHmmModelResults[r]);if(f(t,o),t.shared_parameters.bimodal){for(var s=t.shared_parameters.dimension,a=t.shared_parameters.dimension_input,_=s-a,m=0;m<t.models.length;m++)n(e,t.models[m],o.singleClassHmmModelResults[m]);if(0===t.configuration.multiClass_regression_estimator)o.output_values=o.singleClassHmmModelResults[o.likeliest].output_values.slice(0),o.output_covariance=o.singleClassHmmModelResults[o.likeliest].output_covariance.slice(0);else{for(var p=0;p<o.output_values.length;p++)o.output_values[p]=0;for(var v=0;v<o.output_covariance.length;v++)o.output_covariance[v]=0;for(var b=0;b<t.models.length;b++)for(var g=0;g<_;g++)if(o.output_values[g]+=o.smoothed_normalized_likelihoods[b]*o.singleClassHmmModelResults[b].output_values[g],0===t.configuration.covariance_mode)for(var 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