wolffd@0: function varargout = miremotion(orig,varargin)
wolffd@0: % Predicts emotion along three dimensions and five basic concepts.
wolffd@0: % Optional parameters:
wolffd@0: %   miremotion(...,'Dimensions',0) excludes all three dimensions.
wolffd@0: %   miremotion(...,'Dimensions',3) includes all three dimensions (default).
wolffd@0: %   miremotion(...,'Activity') includes the 'Activity' dimension. 
wolffd@0: %   miremotion(...,'Valence') includes the 'Valence' dimension. 
wolffd@0: %   miremotion(...,'Tension') includes the 'Tension' dimension. 
wolffd@0: %   miremotion(...,'Dimensions',2) includes 'Activity' and 'Valence'.
wolffd@0: %   miremotion(...,'Arousal') includes 'Activity' and 'Tension'.
wolffd@0: %   miremotion(...,'Concepts',0) excludes all five concepts.
wolffd@0: %   miremotion(...,'Concepts') includes all five concepts (default).
wolffd@0: %   miremotion(...,'Happy') includes the 'Happy' concept.
wolffd@0: %   miremotion(...,'Sad') includes the 'Sad' concept.
wolffd@0: %   miremotion(...,'Tender') includes the 'Tender' concept.
wolffd@0: %   miremotion(...,'Anger') includes the 'Anger' concept.
wolffd@0: %   miremotion(...,'Fear') includes the 'Fear' concept.
wolffd@0: %   miremotion(...,'Frame',...) predict emotion frame by frame.
wolffd@0: %
wolffd@0: % Selection of features and coefficients are taken from a study: 
wolffd@0: %         Eerola, T., Lartillot, O., and Toiviainen, P. 
wolffd@0: %            (2009). Prediction of multidimensional emotional ratings in 
wolffd@0: %            music from audio using multivariate regression models. 
wolffd@0: %            In Proceedings of 10th International Conference on Music Information Retrieval 
wolffd@0: %            (ISMIR 2009), pages 621-626.
wolffd@0: %
wolffd@0: % The implemented models are based on multiple linear regression with 5 best
wolffd@0: % predictors (MLR option in the paper). The box-cox transformations have now been 
wolffd@0: % removed until the normalization values have been established with a large sample of music.
wolffd@0: % 
wolffd@0: % TODO: Revision of coefficients to (a) force the output range between 0 - 1 and 
wolffd@0: %    (b) to be based on alternative models and materials (training sets). 
wolffd@0: %
wolffd@0: % Updated 03.05.2010 TE
wolffd@0: %
wolffd@0:         frame.key = 'Frame';
wolffd@0:         frame.type = 'Integer';
wolffd@0:         frame.number = 2;
wolffd@0:         frame.default = [0 0];
wolffd@0:         frame.keydefault = [1 1];
wolffd@0:     option.frame = frame;
wolffd@0: 
wolffd@0:         dim.key = 'Dimensions';
wolffd@0:         dim.type = 'Integer';
wolffd@0:         dim.default = NaN;
wolffd@0:         dim.keydefault = 3;
wolffd@0:     option.dim = dim;
wolffd@0: 
wolffd@0:         activity.key = 'Activity';
wolffd@0:         activity.type = 'Boolean';
wolffd@0:         activity.default = NaN;
wolffd@0:     option.activity = activity;
wolffd@0: 
wolffd@0:         valence.key = 'Valence';
wolffd@0:         valence.type = 'Boolean';
wolffd@0:         valence.default = NaN;
wolffd@0:     option.valence = valence;
wolffd@0: 
wolffd@0:         tension.key = 'Tension';
wolffd@0:         tension.type = 'Boolean';
wolffd@0:         tension.default = NaN;
wolffd@0:     option.tension = tension;
wolffd@0:     
wolffd@0:         arousal.key = 'Arousal';
wolffd@0:         arousal.type = 'Boolean';
wolffd@0:         arousal.default = NaN;
wolffd@0:     option.arousal = arousal;
wolffd@0:     
wolffd@0:         concepts.key = 'Concepts';
wolffd@0:         concepts.type = 'Boolean';
wolffd@0:         concepts.default = NaN;
wolffd@0:     option.concepts = concepts;
wolffd@0: 
wolffd@0:         happy.key = 'Happy';
wolffd@0:         happy.type = 'Boolean';
wolffd@0:         happy.default = NaN;
wolffd@0:     option.happy = happy;
wolffd@0: 
wolffd@0:         sad.key = 'Sad';
wolffd@0:         sad.type = 'Boolean';
wolffd@0:         sad.default = NaN;
wolffd@0:     option.sad = sad;
wolffd@0: 
wolffd@0:         tender.key = 'Tender';
wolffd@0:         tender.type = 'Boolean';
wolffd@0:         tender.default = NaN;
wolffd@0:     option.tender = tender;
wolffd@0: 
wolffd@0:         anger.key = 'Anger';
wolffd@0:         anger.type = 'Boolean';
wolffd@0:         anger.default = NaN;
wolffd@0:     option.anger = anger;
wolffd@0: 
wolffd@0:         fear.key = 'Fear';
wolffd@0:         fear.type = 'Boolean';
wolffd@0:         fear.default = NaN;
wolffd@0:     option.fear = fear;
wolffd@0:     
wolffd@0: specif.option = option;
wolffd@0: specif.defaultframelength = 1;
wolffd@0: %specif.defaultframehop = .5;
wolffd@0: 
wolffd@0: specif.combinechunk = {'Average',@nothing};
wolffd@0: specif.extensive = 1;
wolffd@0: 
wolffd@0: varargout = mirfunction(@miremotion,orig,varargin,nargout,specif,@init,@main);
wolffd@0: 
wolffd@0: 
wolffd@0: %%
wolffd@0: function [x type] = init(x,option)
wolffd@0: 
wolffd@0: option = process(option);
wolffd@0: 
wolffd@0: if option.frame.length.val
wolffd@0:     hop = option.frame.hop.val;
wolffd@0:     if strcmpi(option.frame.hop.unit,'Hz')
wolffd@0:         hop = 1/hop;
wolffd@0:         option.frame.hop.unit = 's';
wolffd@0:     end
wolffd@0:     if strcmpi(option.frame.hop.unit,'s')
wolffd@0:         hop = hop*get(x,'Sampling');
wolffd@0:     end
wolffd@0:     if strcmpi(option.frame.hop.unit,'%')
wolffd@0:         hop = hop/100;
wolffd@0:         option.frame.hop.unit = '/1';
wolffd@0:     end
wolffd@0:     if strcmpi(option.frame.hop.unit,'/1')
wolffd@0:         hop = hop*option.frame.length.val;
wolffd@0:     end
wolffd@0:     frames = 0:hop:1000000;
wolffd@0:     x = mirsegment(x,[frames;frames+option.frame.length.val]);
wolffd@0: elseif isa(x,'mirdesign')
wolffd@0:     x = set(x,'NoChunk',1);
wolffd@0: end
wolffd@0: rm = mirrms(x,'Frame',.046,.5);
wolffd@0: 
wolffd@0: le = 0; %mirlowenergy(rm,'ASR');
wolffd@0: 
wolffd@0: o = mironsets(x,'Filterbank',15,'Contrast',0.1);
wolffd@0: at = mirattacktime(o);
wolffd@0: as = 0; %mirattackslope(o);
wolffd@0: ed = 0; %mireventdensity(o,'Option1');
wolffd@0: 
wolffd@0: fl = mirfluctuation(x,'Summary');
wolffd@0: fp = mirpeaks(fl,'Total',1);
wolffd@0: fc = 0; %mircentroid(fl);
wolffd@0: 
wolffd@0: tp = 0; %mirtempo(x,'Frame',2,.5,'Autocor','Spectrum');
wolffd@0: pc = mirpulseclarity(x,'Frame',2,.5); %%%%%%%%%%% Why 'Frame'?? 
wolffd@0: 
wolffd@0: s = mirspectrum(x,'Frame',.046,.5);
wolffd@0: sc = mircentroid(s);
wolffd@0: ss = mirspread(s);
wolffd@0: sr = mirroughness(s);
wolffd@0: 
wolffd@0: %ps = mirpitch(x,'Frame',.046,.5,'Tolonen');
wolffd@0: 
wolffd@0: c = mirchromagram(x,'Frame','Wrap',0,'Pitch',0);    %%%%%%%%%%%%%%%%%%%% Previous frame size was too small.
wolffd@0: cp = mirpeaks(c,'Total',1);
wolffd@0: ps = 0;%cp;
wolffd@0: ks = mirkeystrength(c);
wolffd@0: [k kc] = mirkey(ks);
wolffd@0: mo = mirmode(ks);
wolffd@0: hc = mirhcdf(c);
wolffd@0: 
wolffd@0: se = mirentropy(mirspectrum(x,'Collapsed','Min',40,'Smooth',70,'Frame',1.5,.5)); %%%%%%%%% Why 'Frame'?? 
wolffd@0: 
wolffd@0: ns = mirnovelty(mirspectrum(x,'Frame',.1,.5,'Max',5000),'Normal',0);
wolffd@0: nt = mirnovelty(mirchromagram(x,'Frame',.2,.25),'Normal',0);    %%%%%%%%%%%%%%%%%%%% Previous frame size was too small.
wolffd@0: nr = mirnovelty(mirchromagram(x,'Frame',.2,.25,'Wrap',0),'Normal',0);   %%%%%%%%%%%%%%%%%%%% Previous frame size was too small.
wolffd@0: 
wolffd@0: 
wolffd@0: 
wolffd@0: x = {rm,le, at,as,ed, fp,fc, tp,pc, sc,ss,sr, ps, cp,kc,mo,hc, se, ns,nt,nr};
wolffd@0: 
wolffd@0: type = {'miremotion','mirscalar','mirscalar',...
wolffd@0:                      'mirscalar','mirscalar','mirscalar',...
wolffd@0:                      'mirspectrum','mirscalar',...
wolffd@0:                      'mirscalar','mirscalar',...
wolffd@0:                      'mirscalar','mirscalar','mirscalar',...
wolffd@0:                      'mirscalar',...
wolffd@0:                      'mirchromagram','mirscalar','mirscalar','mirscalar',...
wolffd@0:                      'mirscalar',...
wolffd@0:                      'mirscalar','mirscalar','mirscalar'};
wolffd@0:                  
wolffd@0: 
wolffd@0: %%
wolffd@0: function e = main(x,option,postoption)
wolffd@0: 
wolffd@0: warning('WARNING IN MIRENOTION: The current model of miremotion is not correctly calibrated with this version of MIRtoolbox (but with version 1.3 only).');
wolffd@0: 
wolffd@0: option = process(option);
wolffd@0: rm = get(x{1},'Data');
wolffd@0: %le = get(x{2},'Data');
wolffd@0: at = get(x{3},'Data');
wolffd@0: %as = get(x{4},'Data');
wolffd@0: %ed = get(x{5},'Data');
wolffd@0: %fpp = get(x{6},'PeakPosUnit');
wolffd@0: fpv = get(x{6},'PeakVal');
wolffd@0: %fc = get(x{7},'Data');
wolffd@0: %tp = get(x{8},'Data');
wolffd@0: pc = get(x{9},'Data');
wolffd@0: sc = get(x{10},'Data');
wolffd@0: ss = get(x{11},'Data');
wolffd@0: rg = get(x{12},'Data');
wolffd@0: %ps = get(x{13},'PeakPosUnit');
wolffd@0: cp = get(x{14},'PeakPosUnit');
wolffd@0: kc = get(x{15},'Data');
wolffd@0: mo = get(x{16},'Data');
wolffd@0: hc = get(x{17},'Data');
wolffd@0: se = get(x{18},'Data');
wolffd@0: ns = get(x{19},'Data');
wolffd@0: nt = get(x{20},'Data');
wolffd@0: nr = get(x{21},'Data');
wolffd@0: 
wolffd@0: 
wolffd@0: e.dim = {};
wolffd@0: e.dimdata = mircompute(@initialise,rm);
wolffd@0: if option.activity == 1
wolffd@0:     [e.dimdata e.activity_fact] = mircompute(@activity,e.dimdata,rm,fpv,sc,ss,se);
wolffd@0:     e.dim = [e.dim,'Activity'];
wolffd@0: else   
wolffd@0:     e.activity_fact = NaN;
wolffd@0: end
wolffd@0: if option.valence == 1
wolffd@0:     [e.dimdata e.valence_fact] = mircompute(@valence,e.dimdata,rm,fpv,kc,mo,ns);
wolffd@0:     e.dim = [e.dim,'Valence'];
wolffd@0: else
wolffd@0:     e.valence_fact = NaN;
wolffd@0: end
wolffd@0: if option.tension == 1
wolffd@0:     [e.dimdata e.tension_fact] = mircompute(@tension,e.dimdata,rm,fpv,kc,hc,nr);
wolffd@0:     e.dim = [e.dim,'Tension'];
wolffd@0: else
wolffd@0:     e.tension_fact = NaN;
wolffd@0: end
wolffd@0: 
wolffd@0: e.class = {};
wolffd@0: e.classdata = mircompute(@initialise,rm);
wolffd@0: if option.happy == 1
wolffd@0:     [e.classdata e.happy_fact] = mircompute(@happy,e.classdata,fpv,ss,cp,kc,mo);
wolffd@0:     e.class = [e.class,'Happy'];
wolffd@0: else
wolffd@0:     e.happy_fact = NaN;
wolffd@0: end
wolffd@0: if option.sad == 1
wolffd@0:     [e.classdata e.sad_fact] = mircompute(@sad,e.classdata,ss,cp,mo,hc,nt);
wolffd@0:     e.class = [e.class,'Sad'];
wolffd@0: else
wolffd@0:     e.sad_fact = NaN;
wolffd@0: end
wolffd@0: if option.tender == 1
wolffd@0:     [e.classdata e.tender_fact] = mircompute(@tender,e.classdata,sc,rg,kc,hc,ns);
wolffd@0:     e.class = [e.class,'Tender'];
wolffd@0: else
wolffd@0:     e.tender_fact = NaN;
wolffd@0: end
wolffd@0: if option.anger == 1
wolffd@0:     [e.classdata e.anger_fact] = mircompute(@anger,e.classdata,rg,kc,se,nr);
wolffd@0:     e.class = [e.class,'Anger'];
wolffd@0: else
wolffd@0:     e.anger_fact = NaN;
wolffd@0: end
wolffd@0: if option.fear == 1
wolffd@0:     [e.classdata e.fear_fact] = mircompute(@fear,e.classdata,rm,at,fpv,kc,mo);
wolffd@0:     e.class = [e.class,'Fear'];
wolffd@0: else
wolffd@0:     e.fear_fact = NaN;
wolffd@0: end
wolffd@0: 
wolffd@0: e = class(e,'miremotion',mirdata(x{1}));
wolffd@0: e = purgedata(e);
wolffd@0: fp = mircompute(@noframe,get(x{1},'FramePos'));
wolffd@0: e = set(e,'Title','Emotion','Abs','emotions','Ord','magnitude','FramePos',fp);
wolffd@0:       
wolffd@0: %%      
wolffd@0: function option = process(option)
wolffd@0: if option.arousal==1
wolffd@0:     option.activity = 1;
wolffd@0:     option.tension = 1;
wolffd@0:     if isnan(option.dim)
wolffd@0:         option.dim = 0; 
wolffd@0:     end
wolffd@0: end
wolffd@0: if option.activity==1 || option.valence==1 || option.tension==1
wolffd@0:     if isnan(option.activity)
wolffd@0:         option.activity = 0;
wolffd@0:     end
wolffd@0:     if isnan(option.valence)
wolffd@0:         option.valence = 0;
wolffd@0:     end
wolffd@0:     if isnan(option.tension)
wolffd@0:         option.tension = 0;
wolffd@0:     end
wolffd@0:     if isnan(option.concepts)
wolffd@0:         option.concepts = 0;
wolffd@0:     end
wolffd@0: end
wolffd@0: if not(isnan(option.dim)) && option.dim
wolffd@0:     if isnan(option.concepts)
wolffd@0:         option.concepts = 0;
wolffd@0:     end
wolffd@0: end
wolffd@0: if not(isnan(option.concepts)) && option.concepts
wolffd@0:     if isnan(option.dim)
wolffd@0:         option.dim = 0;
wolffd@0:     end
wolffd@0: end
wolffd@0: if not(isnan(option.dim))
wolffd@0:     switch option.dim 
wolffd@0:         case 0
wolffd@0:             if isnan(option.activity)
wolffd@0:                 option.activity = 0;
wolffd@0:             end
wolffd@0:             if isnan(option.valence)
wolffd@0:                 option.valence = 0;
wolffd@0:             end
wolffd@0:             if isnan(option.tension)
wolffd@0:                 option.tension = 0;
wolffd@0:             end
wolffd@0:         case 2
wolffd@0:             option.activity = 1;
wolffd@0:             option.valence = 1;
wolffd@0:             if isnan(option.tension)
wolffd@0:                 option.tension = 0;
wolffd@0:             end
wolffd@0:         case 3
wolffd@0:             option.activity = 1;
wolffd@0:             option.valence = 1;
wolffd@0:             option.tension = 1;
wolffd@0:     end
wolffd@0: end
wolffd@0: if isnan(option.activity)
wolffd@0:     option.activity = 1;
wolffd@0: end
wolffd@0: if isnan(option.valence)
wolffd@0:     option.valence = 1;
wolffd@0: end
wolffd@0: if isnan(option.tension)
wolffd@0:     option.tension = 1;
wolffd@0: end
wolffd@0: if isnan(option.concepts)
wolffd@0:     option.concepts = 1;
wolffd@0: end
wolffd@0: if option.concepts
wolffd@0:     option.happy = 1;
wolffd@0:     option.sad = 1;
wolffd@0:     option.tender = 1;
wolffd@0:     option.anger = 1;
wolffd@0:     option.fear = 1;
wolffd@0: end
wolffd@0: if option.happy==1 || option.sad==1 || option.tender==1 ...
wolffd@0:         || option.anger==1 || option.fear==1
wolffd@0:     if isnan(option.happy)
wolffd@0:         option.happy = 0;
wolffd@0:     end
wolffd@0:     if isnan(option.sad)
wolffd@0:         option.sad = 0;
wolffd@0:     end
wolffd@0:     if isnan(option.tender)
wolffd@0:         option.tender = 0;
wolffd@0:     end
wolffd@0:     if isnan(option.anger)
wolffd@0:         option.anger = 0;
wolffd@0:     end
wolffd@0:     if isnan(option.fear)
wolffd@0:         option.fear = 0;
wolffd@0:     end
wolffd@0: end
wolffd@0: 
wolffd@0: 
wolffd@0: %%
wolffd@0: function e = initialise(rm)
wolffd@0: e = [];
wolffd@0: 
wolffd@0:       
wolffd@0: function e = activity(e,rm,fpv,sc,ss,se) % without the box-cox transformation, revised coefficients
wolffd@0: af = zeros(5,1);
wolffd@0: af(1) = 0.6664* ((mean(rm) - 0.0559)/0.0337); % 
wolffd@0: af(2) =  0.6099 * ((mean(fpv{1}) - 13270.1836)/10790.655);
wolffd@0: af(3) = 0.4486*((mean(cell2mat(sc)) - 1677.7)./570.34);
wolffd@0: af(4) = -0.4639*((mean(cell2mat(ss)) - 250.5574)./205.3147);
wolffd@0: af(5) = 0.7056*((mean(se) - 0.954)./0.0258);
wolffd@0: af(isnan(af)) = [];
wolffd@0: e(end+1,:) = sum(af)+5.4861;
wolffd@0: e = {e af};
wolffd@0: 
wolffd@0: function e = valence(e,rm,fpv,kc,mo,ns) % without the box-cox transformation, revised coefficients
wolffd@0: vf = zeros(5,1);
wolffd@0: vf(1) = -0.3161 * ((std(rm) - 0.024254)./0.015667);
wolffd@0: vf(2) =  0.6099 * ((mean(fpv{1}) - 13270.1836)/10790.655);
wolffd@0: vf(3) = 0.8802 * ((mean(kc) - 0.5123)./0.091953);
wolffd@0: vf(4) = 0.4565 * ((mean(mo) - -0.0019958)./0.048664);
wolffd@0: ns(isnan(ns)) = [];
wolffd@0: vf(5) = 0.4015 * ((mean(ns) - 131.9503)./47.6463);
wolffd@0: vf(isnan(vf)) = [];
wolffd@0: e(end+1,:) = sum(vf)+5.2749;
wolffd@0: e = {e vf};
wolffd@0: 
wolffd@0: function e = tension(e,rm,fpv,kc,hc,nr)
wolffd@0: tf = zeros(5,1);
wolffd@0: tf(1) = 0.5382 * ((std(rm) - 0.024254)./0.015667);
wolffd@0: tf(2) =  -0.5406 * ((mean(fpv{1}) - 13270.1836)/10790.655);
wolffd@0: tf(3) = -0.6808 * ((mean(kc) - 0.5124)./0.092);
wolffd@0: tf(4) = 0.8629 * ((mean(hc) - 0.2962)./0.0459);
wolffd@0: tf(5) = -0.5958 * ((mean(nr) - 71.8426)./46.9246);
wolffd@0: tf(isnan(tf)) = [];
wolffd@0: e(end+1,:) = sum(tf)+5.4679;
wolffd@0: e = {e tf};
wolffd@0: 
wolffd@0: 
wolffd@0: % BASIC EMOTION PREDICTORS
wolffd@0: 
wolffd@0: function e = happy(e,fpv,ss,cp,kc,mo)
wolffd@0: ha_f = zeros(5,1);
wolffd@0: ha_f(1) = 0.7438*((mean(cell2mat(fpv)) - 13270.1836)./10790.655);
wolffd@0: ha_f(2) = -0.3965*((mean(cell2mat(ss)) - 250.5574)./205.3147);
wolffd@0: ha_f(3) = 0.4047*((std(cell2mat(cp)) - 8.5321)./2.5899);
wolffd@0: ha_f(4) = 0.7780*((mean(kc) - 0.5124)./0.092);
wolffd@0: ha_f(5) = 0.6220*((mean(mo) - -0.002)./0.0487);
wolffd@0: ha_f(isnan(ha_f)) = [];
wolffd@0: e(end+1,:) = sum(ha_f)+2.6166;
wolffd@0: e = {e ha_f};
wolffd@0: 
wolffd@0: function e = sad(e,ss,cp,mo,hc,nt)
wolffd@0: sa_f = zeros(5,1);
wolffd@0: sa_f(1) = 0.4324*((mean(cell2mat(ss)) - 250.5574)./205.3147);
wolffd@0: sa_f(2) = -0.3137*((std(cell2mat(cp)) - 8.5321)./2.5899);
wolffd@0: sa_f(3) = -0.5201*((mean(mo) - -0.0020)./0.0487);
wolffd@0: sa_f(4) = -0.6017*((mean(hc) - 0.2962)./0.0459);
wolffd@0: sa_f(5) = 0.4493*((mean(nt) - 42.2022)./36.7782);
wolffd@0: sa_f(isnan(sa_f)) = [];
wolffd@0: e(end+1,:) = sum(sa_f)+2.9756;
wolffd@0: e = {e sa_f};
wolffd@0: 
wolffd@0: function e = tender(e,sc,rg,kc,hc,ns)
wolffd@0: te_f = zeros(5,1);
wolffd@0: te_f(1) = -0.2709*((mean(cell2mat(sc)) - 1677.7106)./570.3432);
wolffd@0: te_f(2) = -0.4904*((std(rg) - 85.9387)./106.0767);
wolffd@0: te_f(3) = 0.5192*((mean(kc) - 0.5124)./0.0920);
wolffd@0: te_f(4) = -0.3995*((mean(hc) - 0.2962)./0.0459);
wolffd@0: te_f(5) = 0.3391*((mean(ns) - 131.9503)./47.6463);
wolffd@0: te_f(isnan(te_f)) = [];
wolffd@0: e(end+1,:) = sum(te_f)+2.9756;
wolffd@0: e = {e te_f};
wolffd@0: 
wolffd@0: function e = anger(e,rg,kc,se,nr) % 
wolffd@0: an_f = zeros(5,1);
wolffd@0: %an_f(1) = -0.2353*((mean(pc) - 0.1462)./.1113);
wolffd@0: an_f(2) = 0.5517*((mean(rg) - 85.9387)./106.0767);
wolffd@0: an_f(3) = -.5802*((mean(kc) - 0.5124)./0.092);
wolffd@0: an_f(4) = .2821*((mean(se) - 0.954)./0.0258);
wolffd@0: an_f(5) = -.2971*((mean(nr) - 71.8426)./46.9246);
wolffd@0: an_f(isnan(an_f)) = [];
wolffd@0: e(end+1,:) = sum(an_f)+1.9767;
wolffd@0: e = {e an_f};
wolffd@0: 
wolffd@0: function e = fear(e,rm,at,fpv,kc,mo)
wolffd@0: fe_f = zeros(5,1);
wolffd@0: fe_f(1) = 0.4069*((std(rm) - 0.0243)./0.0157);
wolffd@0: fe_f(2) = -0.6388*((mean(at) - 0.0707)./0.015689218536423);
wolffd@0: fe_f(3) = -0.2538*((mean(cell2mat(fpv)) - 13270.1836)./10790.655);
wolffd@0: fe_f(4) = -0.9860*((mean(kc) - 0.5124)./0.0920);
wolffd@0: fe_f(5) = -0.3144*((mean(mo) - -0.0019958)./0.048663550639094);
wolffd@0: fe_f(isnan(fe_f)) = [];
wolffd@0: e(end+1,:) = sum(fe_f)+2.7847;
wolffd@0: e = {e fe_f};
wolffd@0: 
wolffd@0: function fp = noframe(fp)
wolffd@0: fp = [fp(1);fp(end)];