goog.rar_google_stock_stock price

function V=errperf(T,P,M) %ERRPERF Determine various error related performance metrics. % % ERRPERF(T,P,M) uses T and P, which are target and prediction vectors % respectively, and returns the value for M, which is one of several error % related performance metrics. % % T and P can be row or column vectors of the same size. M can be one of % the following performance metrics: % % mae (mean absolute error) % mse (mean squared error) % rmse (root mean squared error) % % mare (mean absolute relative error) % msre (mean squared relative error) % rmsre (root mean squared relative error) % % mape (mean absolute percentage error) % mspe (mean squared percentage error) % rmspe (root mean squared percentage error) % % EXAMPLE: % % rand('state',0) % % T = [0:0.2:1] % P = rand(size(T)).*T % % errperf(T,P,'mae') returns 0.1574 % % To compute the relevant performance metric, the function uses recursion % to first compute one or more error vectors. The function can therefore % secondarily be used to compute these error vectors. M can therefore also % be one of the following: % % e (errors) % ae (absolute errors) % se (squared errors) % % re (relative errors) % are (absolute relative errors) % sre (squared relative errors) % % pe (percentage errors) % ape (absolute percentage errors) % spe (squared percentage errors) % % REMARKS: % % - The Neural Network Toolbox also has functions to compute mae and mse. % This function does not make use of the toolbox. % % - Percentage error equals relative error times 100. % % - The abbreviations used in the code, and the calculation tree are % documented in a comments section within the file. % % VERSION: 20070703 % MATLAB VERSION: 7.4.0.287 (R2007a) % LICENSE: As-is; public domain % % See also MAE, MSE. %{ VERSION HISTORY: 20070703: - Added MATLAB version check. 20070606: - Added support for metrics MARE, MSRE, and RMSRE. - Addressed a possible division by zero condition in calculating relative and percentage errors. 20070528: - Original version. KEYWORDS: perf, performance, metric, performance measure, machine learning %} %% Comments %{ Abbreviations: a: absolute e: error(s) M: METRIC m: mean P: PREDICTIONS p: percentage r: relative (if before e) r: square root (if before m) s: squared T: TARGETS V: VALUE(S) Calculation tree: e | |-ae-mae | |-se-mse-rmse | |-re-pe | | | |-ape-mape | | | |-spe-mspe-rmspe | |-are-mare | |-sre-msre-rmsre %} %% Check MATLAB version if datenum(version('-date'))<datenum('29-Jan-2007') error(['The MATLAB version in use is ',version('-release'),'. ',... 'This function requires at least version 2007a.']) end %% Parse and validate input Inputs=inputParser; Inputs.addRequired('T',@(x) isnumeric(x) && ndims(x)==2 && ... (size(x,1)==1 || size(x,2)==1)); Inputs.addRequired('P',@(x) isnumeric(x) && ndims(x)==2 && ... (size(x,1)==1 || size(x,2)==1)); Inputs.addRequired('M',@(x) ischar(x) && ~isempty(x)); Inputs.parse(T,P,M); clear Inputs assert(isequal(size(T),size(P)),'T and P must have the same size.') %% Transform input M=lower(M); %% Compute metric switch M % Errors case 'e' V=T-P; % Absolute errors case 'ae' Ve=errperf(T,P,'e'); V=abs(Ve); % Mean absolute error case 'mae' Vae=errperf(T,P,'ae'); V=mean(Vae); % Squared errors case 'se' Ve=errperf(T,P,'e'); V=Ve.^2; % Mean squared error case 'mse' Vse=errperf(T,P,'se'); V=mean(Vse); % Root mean squared error case 'rmse' Vmse=errperf(T,P,'mse'); V=sqrt(Vmse); % Relative errors case 're' assert(all(T),'All elements of T must be nonzero.') Ve=errperf(T,P,'e'); V=Ve./T; % Absolute relative errors case 'are' Vre=errperf(T,P,'re'); V=abs(Vre); % Mean absolute relative error case 'mare' Vare=errperf(T,P,'are'); V=mean(Vare); % Squared relative errors case 'sre' Vre=errperf(T,P,'re'); V=Vre.^2; % Mean squared relative error case 'msre' Vsre=errperf(T,P,'sre'); V=mean(Vsre); % Root mean squared relative error case 'rmsre' Vmsre=errperf(T,P,'msre'); V=sqrt(Vmsre); % Percentage errors case 'pe' Vre=errperf(T,P,'re'); V=Vre*100; % Absolute percentage errors case 'ape' Vpe=errperf(T,P,'pe'); V=abs(Vpe); % Mean absolute percentage error case 'mape' Vape=errperf(T,P,'ape'); V=mean(Vape); % Squared percentage errors case 'spe' Vpe=errperf(T,P,'pe'); V=Vpe.^2; % Mean squared percentage error case 'mspe' Vspe=errperf(T,P,'spe'); V=mean(Vspe); % Root mean squared percentage error case 'rmspe' Vmspe=errperf(T,P,'mspe'); V=sqrt(Vmspe); otherwise error('M is invalid.') end