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Nlstd - Revision history
2024-03-29T04:36:22Z
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https://www.wiki.eigenvector.com/index.php?title=Nlstd&diff=9623&oldid=prev
imported>Benjamin at 22:48, 16 August 2017
2017-08-16T22:48:13Z
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 15:48, 16 August 2017</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Important: NLSTD is an experimental algorithm that is currently only available in PLS_Toolbox as of version 8.<del style="font-weight: bold; text-decoration: none;">3</del>.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Important: NLSTD is an experimental algorithm that is currently only available in PLS_Toolbox as of version 8.<ins style="font-weight: bold; text-decoration: none;">5</ins>.</div></td></tr>
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imported>Benjamin
https://www.wiki.eigenvector.com/index.php?title=Nlstd&diff=9622&oldid=prev
imported>Benjamin at 23:50, 15 August 2017
2017-08-15T23:50:47Z
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 16:50, 15 August 2017</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>=Examples=</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">==</ins>=Examples<ins style="font-weight: bold; text-decoration: none;">==</ins>=</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Initialize the options structure, it’s good practice to initialize the options structure for the desired algorithm, make adjustments there and then assign it to the useopts field in the NLSTD options structure:</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Initialize the options structure, it’s good practice to initialize the options structure for the desired algorithm, make adjustments there and then assign it to the useopts field in the NLSTD options structure:</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:annopt = nlstd(‘options’);</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:annopt = nlstd(‘options’);</div></td></tr>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:lwrstd_pred = nlstd(inst1(7:15,:), inst2(7:15,:),win, lwropt);</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:lwrstd_pred = nlstd(inst1(7:15,:), inst2(7:15,:),win, lwropt);</div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">===See Also===</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">[[alignpeaks]], [[alignspectra]], [[baseline]], [[caltransfer]], [[deresolv]], [[distslct]], [[mscorr]], [[reducennsamples]], [[registerspec]], [[stdfir]], [[stdize]], [[stdsslct]], [[stdgen]]</ins></div></td></tr>
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imported>Benjamin
https://www.wiki.eigenvector.com/index.php?title=Nlstd&diff=9621&oldid=prev
imported>Benjamin: Created page with "===Purpose=== Create or apply non-linear instrument transfer models. Important: NLSTD is an experimental algorithm that is currently only available in PLS_Toolbox as of versi..."
2017-03-01T22:16:37Z
<p>Created page with "===Purpose=== Create or apply non-linear instrument transfer models. Important: NLSTD is an experimental algorithm that is currently only available in PLS_Toolbox as of versi..."</p>
<p><b>New page</b></p><div>===Purpose===<br />
Create or apply non-linear instrument transfer models.<br />
<br />
Important: NLSTD is an experimental algorithm that is currently only available in PLS_Toolbox as of version 8.3.<br />
<br />
<br />
===Synopsis===<br />
:modl = nlstd(xSlave, xMaster, win);<br />
:modl = nlstd(xSlave, xMaster, win, opt);<br />
:modl = nlstd(xSlave, xMaster, win, pts, opt); % for lwr<br />
:pred = nlstd(xSlave, modl, win, opt);<br />
:valid = nlstd(xSlave, xMaster, modl, win, opt);<br />
<br />
<br />
===Description===<br />
NLSTD is an experimental method which uses one of several algorithms available in PLS_Toolbox to return a model and transformed data. The three available algorithms to use are ANN, LWR, and SVM, note however that due to the huge memory requirements of using SVM, its use is limited only to relatively small datasets (otherwise the change of encountering an "out of memory" error is highly likely).<br />
<br />
<br />
===Inputs===<br />
*xSlave: (2-way matrix of class “double” or “dataset”), data collected from the ‘slave instrument’<br />
*xMaster: (2-way matrix of class “double” or “dataset”), data collected from the ‘master instrument’<br />
*modl: A NLSTD (ANN/LWR/SVM) model.<br />
*win: window size used in the (piecewise) standardization, must be an odd number.<br />
*pts: (LWR only) Like window but with respect to the samples instead of measurements.<br />
<br />
<br />
===Outputs===<br />
*model: A model structure containing the NLSTD model.<br />
*pred: predictions, a matrix containing the transformed xSlave data.<br />
*valid: validations, like predictions matrix but it is created using both test samples from both the ‘slave’ and ‘master’ instruments.<br />
<br />
Note: The output variable will depend on the input arguments. If xSlave and xMaster datasets are used a model will be the output. If a NLSTD model is used as one of the input arguments, the output will be a matrix containing the predicted values. <br />
<br />
===Options===<br />
*name: the name of the structure (‘options’)<br />
*display: [ {‘off’} | ’on’ ]<br />
*plots: [ ‘none’ | {‘final’} ]<br />
*waitbar: [ ‘off’ | {‘auto’} | ‘on’ ]<br />
*algorithm: [ {‘ann’} | ’svm’ | ’lwr’ ] governs the algorithm to be used.<br />
*useopts: [ ] user may input a complete/partial options structure of the selected algorithm.<br />
*edges: [ {‘const’} | ’zeros’ | ’linterp’ | ’pinterp’ ]<br />
*ncomp: 1 (LWR Only) sets the number of components to be used in the model.<br />
<br />
<br />
=Examples=<br />
Initialize the options structure, it’s good practice to initialize the options structure for the desired algorithm, make adjustments there and then assign it to the useopts field in the NLSTD options structure:<br />
:annopt = nlstd(‘options’);<br />
:uopt = ann(‘options’);<br />
:uopt.nhid1 = 10;<br />
:uopt.nhid2 = 0;<br />
:uopt.display = ’off’;<br />
:annopt.useopts = uopt;<br />
:win = 7;<br />
<br />
Build a calibration model:<br />
:annstd_model = nlstd(inst1(1:6,:), inst2(1:6,:), win, annopt);<br />
<br />
Get model predictions:<br />
:annstd_pred = nlstd(inst1(7:15,:), inst2(7:15,:),win, annopt);<br />
<br />
To use the LWR algorithm, and get predictions:<br />
:lwropt = nlstd(‘options’);<br />
:uopt = lwr(‘options’);<br />
:uopt.display = ’off’;<br />
:annopt.useopts = uopt;<br />
:win = 7;<br />
:pts = 5;<br />
<br />
:lwrstd_model = nlstd(inst1(1:6,:), inst2(1:6,:), win,pts, lwropt);<br />
<br />
:lwrstd_pred = nlstd(inst1(7:15,:), inst2(7:15,:),win, lwropt);</div>
imported>Benjamin