EVRI Test Wiki 02192024 - User contributions [en]

Faq java lang OutOfMemoryError

2019-03-04T17:54:38Z

Lyle:

===Issue:===

What can I do if I get a java.lang.OutOfMemoryError error?

===Possible Solutions:===

Matlab or Solo may return a Java error like this:

> lasterror.message = 'Error using svmengine: Error using svmengine:
> Java exception occurred: java.lang.OutOfMemoryError: Java heap space
> at libsvm.evri.SvmTrain2.load_problem(SvmTrain2.java:222)
...

Matlab and Solo's engine use their own Java virtual machine (JVM) and they both allocate a certain amount of memory (or heap space) to the JVM. This error occurs because the code being executed requires more memory than the JVM has allocated to it. You may avoid this problem by configuring Matlab or Solo to grant more memory to Java.

'''Matlab and PLS_Toolbox'''

In Matlab 2009b, and earlier versions, you increase the allocated Java heap space by creating a file named "java.opts" in your
\Program Files\MATLAB\R2009b\bin\win32\
directory (or \win64 if you are using 64-bit Windows) which contains the single line specifying how much memory to allocate to Java:
-Xmx256m
...if you want to set 256 Mb for the Java heap. This takes effect only after Matlab is restarted. Take care to match the upper and lower case characters in that line and have no spaces between any characters.

If the error still occurs, try increasing the memory allocation and repeat. If you have specified a larger amount of memory than is available on your system then Matlab will not restart; reduce the memory setting and continue. Be aware that your system may not be able to supply enough memory for the Java task.

This java.opts approach applies to other operating systems as well, with the path of the java.opts file being
$MATLABROOT/bin/$ARCH

directory. <code>$MATLABROOT</code> is the MATLAB root directory and <code>$ARCH</code> is your system architecture. See [https://www.mathworks.com/matlabcentral/answers/92813-how-do-i-increase-the-heap-space-for-the-java-vm-in-matlab-6-0-r12-and-later-versions this Mathworks page for more details of this approach.])

Matlab 2010a and later versions make it easier to specify the Java memory allocation by providing a graphical user interface. This GUI is accessed from Preferences, go to: Home → Preferences → General → Java Heap Memory. (You can also access the Preferences window by typing:

>> preferences

in the Matlab Command Window.)

'''Solo'''

For Solo and its various related products, you increase the allocated Java heap space by creating a file named "java.opts" in your
\Program Files\EVRI\Solo\application\bin\win32\
directory (or \win64 if you are using 64-bit version of the application). The java.opts file should contain the single line specifying how much memory to allocate to Java:
-Xmx256m
...if you want to set 256 Mb for the Java heap. This takes effect only after Solo is restarted. Take care to match the upper and lower case characters in that line and have no spaces between any characters.

If the error still occurs, try increasing the memory allocation and repeat. If you have specified a larger amount of memory than is available on your system then Solo will not restart; reduce the memory setting and continue. Be aware that your system may not be able to supply enough memory for the Java task.

'''Still having problems? Please contact our helpdesk at [mailto:helpdesk@eigenvector.com helpdesk@eigenvector.com]'''

[[Category:FAQ]]

Installation

2019-02-26T15:02:18Z

Lyle:

__TOC__
==Installing PLS_Toolbox==
PLS_Toolbox is delivered as a Windows Installer (.exe for Windows
only) or as a compressed ZIP file (all platforms). Installation is a two-part process of
copying and decompressing files into a local folder and then adding those files to your
MATLAB path. You will need your '''License Code''' (obtained at the time of purchase).
===System Requirements===
* Compatible with any Matlab released within 5 years of released date of this product.
* 100 MB of disk space.
* 2 GB of RAM required, 4+ GB recommended

===Matlab Compatibility===
Although we try to maintain inter-compatibility with Matlab add-on Toolboxes to the greatest extent possible some incompatibilities do exist. Most of these cases originate from situations where PLS_Toolbox has an established function and The MathWorks (TMW) introduces a new function with the same name and is unwilling to rename it. Because we don't have access to the entire TMW product family this list may not include all incompatible functions:

* Incompatible Functions
** cluster.m (Stats Toolbox)
** crossval.m (Stats Toolbox)
** regression.m (Neural Net Toolbox)
** ridge.m (Stats Toolbox)

* Intentional overloads:
** boxplot.m (Stats Toolbox because of DataSet Object [http://www.eigenvector.com/evriblog/?p=10 conflict])

* Compatible Functions
** dendrogram.m (Stats Toolbox)
** range.m (Stats Toolbox)
** kstest.m (Stats Toolbox)

To manage which function is being called, relocate PLS_Toolbox on your Matlab path. Putting PLS_Toolbox at the bottom of the path will allow the use of Matlab functions but will often make PLS_Toolbox inoperable until it's moved back to the top of the path. Within Browse you can select Help>Move Path Folders and indicate the location (Top or Bottom). The [[evrimovepath]] function can be used from the command line.

===Using the Windows EXE Installer===
It is recommended that Windows users use the PLS_Toolbox Windows Installer.
#Copy the .exe file to your Desktop (or other suitable local location).
#Double click the icon and follow the instructions. Be sure to verify the location of the installation folder; by default it will be the "toolbox" folder of your current MATLAB installation. '''Windows users:''' may need to choose a folder within your personal Documents folder in order to avoid permissions problems caused by Windows security.
#When the Installer has completed, it will prompt you to start MATLAB and run an install script.
#If MATLAB doesn't start see Step 2 below.

===Manually Installing from ZIP File (all platforms)===
# '''File Extraction''' Decompress the PLS_Toolbox ZIP file and move it to the MATLAB "toolbox" folder (e.g. C:\Program Files\MATLAB\R2016a\toolbox).
#:'''Note''': If you already have a version of PLS_Toolbox installed, be sure the folders do not have the same name (by default they should not). Rename your existing copy of PLS_Toolbox if necessary.
#:'''Note''': Windows users may find that they do not have sufficient system permissions to install in a folder under the ''C:\Program Files\'' folder or that, after installation, they get warnings due to the inability to set program configuration. In these cases, simply install PLS_Toolbox into a folder such as your personal Documents folder.
# '''Run <code>evriinstall</code>''' For MATLAB to find PLS_Toolbox, the directories that contain the toolbox files must be added to the MATLAB search path.
#:'''Please Note''': If you are familiar with installing toolboxes purchased from The MathWorks, this process is different. TMW installers typically are able to set the path automatically. PLS_Toolbox has its own installation method.
#:Start MATLAB and follow these steps:
#:* Start MATLAB and use the Current Directory toolbar to navigate to the PLS_Toolbox directory (created in step 1).
#:* Type evriinstall at the command line (in the COMMAND WINDOW): <pre>>> evriinstall</pre>A dialog box will appear (see below.) Change the settings as needed then click the '''Install''' button. EVRIINSTALL will add PLS_Toolbox to the MATLAB path, check for any installation problems, and output results to the command window.
#:* See the [[PLS_Toolbox_Topics | Reference Topics]] page for information about PLS_Toolbox functions. Continue reading the [[Software User Guide]] for information using Solo and PLS_Toolbox.
#::[[Image:Installation_Guide_Window_PLS_TB_8.7.PNG]]

===Configuring Java===
In some older versions of Matlab, including version 7.0.4 (R14SP2), it is necessary to manually add the jar files included with PLS_Toolbox to the Matlab java class path.
# Open Matlab.
# Navigate to the "..\PLS_Toolbox_65\extensions\javatools" folder (where "PLS_Toolbox_65" is the folder containing the version of PLS_Toolbox you are installing. The "65" will change for each version)
# Edit the Java class path file:<pre>>> edit classpath.txt</pre>
# At the bottom of the classpath.txt file add lines for each .jar file in the extensions folder using the correct path. In the following example, PLS_Toolbox version 6.5 (named "PLS_Toolbox_65") is installed in the "c:\Toolbox\" folder so the corresponding entries look like:
<pre>c:\Toolbox\PLS_Toolbox_65\extensions\javatools\evriJavaClasses.jar
c:\Toolbox\PLS_Toolbox_65\extensions\javatools\rsyntaxtextarea.jar
c:\Toolbox\PLS_Toolbox_65\extensions\javatools\mysql.jar
c:\Toolbox\PLS_Toolbox_65\extensions\javatools\libsvm_jre15.jar
c:\Toolbox\PLS_Toolbox_65\extensions\javatools\derby.jar
c:\Toolbox\PLS_Toolbox_65\extensions\javatools\commons-dbutils-1.3.jar
c:\Toolbox\PLS_Toolbox_65\extensions\javatools\jsoup.jar</pre>
<ol start="5">
<li>Save the file and restart Matlab.</li>
<li>If MIA_Toolbox is installed, any .jar files in the "..\MIA_Toolbox\utilities" folder should be added using the same procedure as above.</li>
</ol>

==Installing Solo==

===System Requirements===
====Windows====
* Windows XP or newer.
* 600 MB of disk space.
* 4GB RAM (more RAM may be necessary for large DataSets).

====MAC====
* OS X 10.9 or newer.
* Intel Mac (64 bit).
* 1.5 GB of disk space.
* 4GB RAM (more RAM may be necessary for large DataSets).

===Installation===
==== Windows ====
#Copy the .exe file to your Desktop (or other suitable local location).
#If installing with a license file (evrilicense.lic) or license server file (evrilm.xml), move these files into the same folder as the .exe installer.
#Double click the icon and follow the instructions. Be sure to verify the location of the installation folder; by default it will be in the "EVRI" folder of your program files folder. (To customize the installation process, see [[Solo Installer Flags]])
#Identify if you want desktop and Start menu shortcuts created as well as if you want to associate .mat files with Solo.
#Identify if you want to add Solo to the system path. Doing so will assure 3rd-party programs will be able to start up Solo (where the 3rd-party program offers this feature.)
#When the Installer has completed, you will be asked if you want to start Solo now. The first time you run Solo, it will need to unpack several libraries and make take a minute or so to complete installation. Subsequent startups will take less time. Note that when installed in the standard Program Files folder, this step needs to be done as a user with administrative privileges so it is best to do from the installer.

'''NOTE:''' If installing Solo with a license server, see the [[Evri_license_server]] documentation.

'''Troubleshooting:''' If having problems downloading, installing or running Solo, see the [[Solo Troubleshooting]] page.

==== Mac ====

#'''Image Mounting''' Open the Solo.dmg (or Solo+MIA.dmg) disk image file by double-clicking on it. The disk image will mount on your computer.
#'''Copy Solo APP''' Copy the Solo.app (or Solo+MIA.app) bundle to your Applications folder.
#'''Launching The Applicatioin''' Right-click Solo.app and select '''Open''' to open the application.
#'''Errors on Startup''' If you receive a message saying "the Disk has been damaged" (OS X 10.7+) you may need to disable Mac Gatekeeper as described [http://support.apple.com/kb/ht5290 here] and for Mac OS Sierra see [https://www.tekrevue.com/tip/gatekeeper-macos-sierra/ here].

'''NOTE''': The first time you run Solo, it will need to unpack several libraries and may take several minutes (or longer depending on system resources) to complete installation. Subsequent startups will take less time.

'''Troubleshooting:''' If having problems downloading, installing or running Solo, see the [[Solo Troubleshooting]] page.

==== Linux ====

#'''Unzip/UnTar''' Copy the tar.gz file into a target folder (create one using the <tt>mkdir</tt> command) in any convenient location (e.g. the user's home directory or any accessible location) and use the following command to unzip/untar the application:<pre> tar -xzf solo.tar.gz</pre>(note: archive name will be depend on the exact application you are installing)
#'''Launching The Applicatioin''' In a console window, change the working directory to the target folder and use the command:<pre> ./solo</pre> (application name will depend on the exact application you purchased). If starting from a different working directory, use the full path to the solo application. You can also create a shortcut which performs this operation.
::'''First Run Notes''': The first time you run Solo, it will need to unpack several libraries and make take several minutes (or longer depending on system resources) to complete installation. Subsequent startups will take less time. If the program has been installed in a general system folder (such as /usr/local/bin ) then the initial run of Solo may need to be done using SUDO so the program has sufficient privileges to unpack the libraries.

'''Troubleshooting:''' If having problems downloading, installing or running Solo, see the [[Solo Troubleshooting]] page.

=== Troubleshooting Solo ===

If having problems downloading, installing or running Solo, see the [[Solo Troubleshooting]] page.

==Starting Solo==

On all platforms, Solo can be started using the installed shortcuts (double-clicking the shortcut), or by calling the application directly from either a shortcut or DOS/Linux command window. The application can also take [[Solo_Startup_Flags|various command-line flags]] that modify startup behavior.

==Updating==
The most up-to-date version of PLS_Toolbox and Solo will be made available to users on our web
site, www.eigenvector.com. Registered users can download new copies of the toolbox
that incorporate the latest bug fixes. A username and password is required for access to
the upgrades. Usernames and passwords are supplied when licenses are
purchased (older version users should have received this information by registering their
software). If you have forgotten your username and/or password, please write to
helpdesk@eigenvector.com or use the lost username/password page accessible from the download page [http://download.eigenvector.com/ on the Eigenvector Research website].

To check for updates select '''Check for Updates''' from the '''Help''' menu of the Workspace Browser or Analysis interface, or, for PLS_Toolbox users, use the command:
>> evriupdate
at the Matlab command line. This will bring up a small dialog box indicating the status of your installed products.

==Checking Your Installation==
You can check that your software is installed and working correctly by:
*PLS_Toolbox: Run the "evridebug" Matlab command. Running this should report that "No PLS_Toolbox installation problems were identified".
*Solo: Solo should open with the Browser window presented. You can confirm Solo is installed correctly by running any analysis method, for example PCA. See the short video on using PCA at: [http://www.eigenvector.com/eigenguide.php?m=PCA_2014 PCA].

Solo or PLS_Toolbox users can get product information from the Help > About menu in the Workspace Browser or Analysis window.

PLS_Toolbox users can use the following commands to verify a new or existing installation. We request the output from these commands when you submit a request to [mailto:helpdesk@eigenvector.com helpdesk@eigenvector.com].
* ver - Matlab command show a list of installed products.
* path - Matlab command showing the current folders on the Matlab path. '''ALL''' PLS_Toolbox folders (and sub-folders) should be on the Matlab path.
* [[evridebug]] - PLS_Toolbox command that checks for common installation problems.

Also check [http://www.eigenvector.com/faq our FAQ page], there are several more detailed issues including [http://www.eigenvector.com/faq/index.php?id=141 trouble with the Matlab path] discussed there.

==Getting Help==
Start by checking our [http://www.eigenvector.com/faq FAQ Pages].
For Solo: also check the [[Solo_Troubleshooting|Solo Troubleshooting Pages]]

If you cannot find the information you need there, send problem reports to [mailto://helpdesk@eigenvector.com helpdesk@eigenvector.com]

Please send your message with the following information:
*Product License/Registration Number
*Computer type, model and operating system version
*Exact errors you are receiving (if any)
*Hardware and memory configuration (if reporting a memory issue)
*Your contact information including either your Eigenvector Username or the following information
::First Name
::Last Name
::Company Name
::Phone Country Code
::Phone Area Code
::Phone Main
::Phone Extension

File:Installation Guide Window PLS TB 8.7.PNG

2019-02-26T15:01:38Z

Lyle:

Opusreadr

2019-02-18T20:41:09Z

Lyle:

===Purpose===

Reads Bruker OPUS files into a DataSet object.

===Synopsis===

:data = opusreadr(filename,''options'')
:data = opusreadr({'filename' 'filename2'},''options'')

===Description===

OPUSREADR reads in Burker OPUS files (which use a numeric extensions, .#, .####, etc).

====Inputs====

Input is the filename of a Bruker OPUS file to read. If omitted, the user is prompted for a file. The reader reads files of type:

:interferogram,
:phase,
:absorbance,
:transmittance,
:kubelka-munck spectrum,
:raman spectrum,
:emission,
:reflectance,
:trace,
:gc file, interferograms,
:gc file, spectra,
:power,
:- log reflectance,
:photoacoustic spectrum.

Multiple input files must contain the same axisscale for the wavelength (variables) mode. The imported files are returned in a Dataset Object.

====Optional Inputs====
*'''filename''' = specifies the file(s) to read using:
::'''(a)''' a string specifying a single file to read
::'''(b)''' a cell array of strings specifying multiple files to read
::'''(c)''' the output of the MATLAB "dir" command specifying multiple files to read
:If fname is an empty string or matrix or is not supplied, the user is prompted to identify file(s) to load.
*'''options''' = an optional options structure. See Options below.

====Outputs====
*'''data''' = containing the spectrum or spectra from the file(s) takes one of two forms:

# If input is a single file, or multiple files containing data that can be combined (same number of data points, same x-axis range, same type of data), the output is a dataset object,
# If the input consists of multiple files containing data that cannot simply be combined (different number of data points, differing x-axis ranges, etc), the output is either:
::: a) a cell array with a dataset object for each input file if the 'nonmatching' option has value 'cell', or
::: b) a dataset object containing the input data combined using the MATCHVARS function if the 'nonmatching' option has value 'matchvars'.

===Options===
Input 'options' is a structure containing the following fields
*'''spectrumtype''': <tt>[{<nowiki>''</nowiki>} | othertypes ]</tt> Specify which data type to read. If empty, the most commonly used type is selected (based on the "typepriority" option below) Spectrum types include: 'absorbance', 'transmittance', 'kubelka-munck spectrum', 'trace', 'raman spectrum', 'emission', 'reflectance' (among others). The entire list of types can be obtained by calling opusreader with the keyword 'types'. The single channel spectrum can be imported by setting '''spectrumtype''' to 'undefined Y-units'. For Solo and Solo+MIA use the [[Expert_Preferences_GUI | Expert Preferences GUI]] to change this setting.
*'''typepriority''': Specifies a list of spectrum types (in numerical format) in the order that the given block type should be selected if present. This list is used when the spectrumtype is empty, indicating that the most common block should be used.
*'''multiblock''': [{1}] Defines which block to read when more than one block in a file has the "spectrumtype" type. The value can be any integer between 0 (zero) and infinity but the following are typical settings:
::0 = Throws an error "multiple blocks are present"
::1 = Always choose the first block matching the type. The first block is usually the original, un-preprocessed version of the data.
::inf = Always choose the last block mathcing the type. The last block is usually a modified (processed) version of the data.
::2-n = Chooses the specified block matching the type Mostly useful when an exact sequence of steps have been used in creating the OPUS file and the user knows a specific step in that sequence is the version of the data needed.
*'''multiselect''': [ 'off' | {'on'} ] governs whether file selection dialog should allow multiple files to be selected and imported. Setting to 'off' will restrict user to importing only one file at a time.
*'''nonmatching''' : [ 'error' |{'matchvars'} 'cell'] Governs behavior when multiple files are being read which cannot be combined due to mismatched types, sizes, etc.
::: 'matchvars' returns a dataset object,
::: 'cell' returns cell (see Outputs),
::: 'error' gives an error.
*'''waitbar''': <tt>[ 'off' |{'on'}]</tt> Governs display of a progress bar when doing multiple file reading.
*'''sorttype''' <tt> [{'none'} |'time'|'name'] </tt> Determines whether the output dataset will be sorted.

===See Also===

[[Data Importing Formats]], [[brukerxrpdreadr]], [[jcampreadr]], [[pereadr]], [[spareadr]], [[spcreadr]], [[writespc]], [[xclreadr]]

Installation

2019-02-15T15:41:56Z

Lyle: /* Using the Windows EXE Installer */

__TOC__
==Installing PLS_Toolbox==
PLS_Toolbox is delivered as a Windows Installer (.exe for Windows
only) or as a compressed ZIP file (all platforms). Installation is a two-part process of
copying and decompressing files into a local folder and then adding those files to your
MATLAB path. You will need your '''License Code''' (obtained at the time of purchase).
===System Requirements===
* Compatible with any Matlab released within 5 years of released date of this product.
* 100 MB of disk space.
* 2 GB of RAM required, 4+ GB recommended

===Matlab Compatibility===
Although we try to maintain inter-compatibility with Matlab add-on Toolboxes to the greatest extent possible some incompatibilities do exist. Most of these cases originate from situations where PLS_Toolbox has an established function and The MathWorks (TMW) introduces a new function with the same name and is unwilling to rename it. Because we don't have access to the entire TMW product family this list may not include all incompatible functions:

* Incompatible Functions
** cluster.m (Stats Toolbox)
** crossval.m (Stats Toolbox)
** regression.m (Neural Net Toolbox)
** ridge.m (Stats Toolbox)

* Intentional overloads:
** boxplot.m (Stats Toolbox because of DataSet Object [http://www.eigenvector.com/evriblog/?p=10 conflict])

* Compatible Functions
** dendrogram.m (Stats Toolbox)
** range.m (Stats Toolbox)
** kstest.m (Stats Toolbox)

To manage which function is being called, relocate PLS_Toolbox on your Matlab path. Putting PLS_Toolbox at the bottom of the path will allow the use of Matlab functions but will often make PLS_Toolbox inoperable until it's moved back to the top of the path. Within Browse you can select Help>Move Path Folders and indicate the location (Top or Bottom). The [[evrimovepath]] function can be used from the command line.

===Using the Windows EXE Installer===
It is recommended that Windows users use the PLS_Toolbox Windows Installer.
#Copy the .exe file to your Desktop (or other suitable local location).
#Double click the icon and follow the instructions. Be sure to verify the location of the installation folder; by default it will be the "toolbox" folder of your current MATLAB installation. '''Windows users:''' may need to choose a folder within your personal Documents folder in order to avoid permissions problems caused by Windows security.
#When the Installer has completed, it will prompt you to start MATLAB and run an install script.
#If MATLAB doesn't start see Step 2 below.

===Manually Installing from ZIP File (all platforms)===
# '''File Extraction''' Decompress the PLS_Toolbox ZIP file and move it to the MATLAB "toolbox" folder (e.g. C:\Program Files\MATLAB\R2016a\toolbox).
#:'''Note''': If you already have a version of PLS_Toolbox installed, be sure the folders do not have the same name (by default they should not). Rename your existing copy of PLS_Toolbox if necessary.
#:'''Note''': Windows users may find that they do not have sufficient system permissions to install in a folder under the ''C:\Program Files\'' folder or that, after installation, they get warnings due to the inability to set program configuration. In these cases, simply install PLS_Toolbox into a folder such as your personal Documents folder.
# '''Run <code>evriinstall</code>''' For MATLAB to find PLS_Toolbox, the directories that contain the toolbox files must be added to the MATLAB search path.
#:'''Please Note''': If you are familiar with installing toolboxes purchased from The MathWorks, this process is different. TMW installers typically are able to set the path automatically. PLS_Toolbox has its own installation method.
#:Start MATLAB and follow these steps:
#:* Start MATLAB and use the Current Directory toolbar to navigate to the PLS_Toolbox directory (created in step 1).
#:* Type evriinstall at the command line (in the COMMAND WINDOW): <pre>>> evriinstall</pre>A dialog box will appear (see below.) Change the settings as needed then click the '''Install''' button. EVRIINSTALL will add PLS_Toolbox to the MATLAB path, check for any installation problems, and output results to the command window.
#:* See the [[PLS_Toolbox_Topics | Reference Topics]] page for information about PLS_Toolbox functions. Continue reading the [[Software User Guide]] for information using Solo and PLS_Toolbox.
#::[[Image:Install.png]]

===Configuring Java===
In some older versions of Matlab, including version 7.0.4 (R14SP2), it is necessary to manually add the jar files included with PLS_Toolbox to the Matlab java class path.
# Open Matlab.
# Navigate to the "..\PLS_Toolbox_65\extensions\javatools" folder (where "PLS_Toolbox_65" is the folder containing the version of PLS_Toolbox you are installing. The "65" will change for each version)
# Edit the Java class path file:<pre>>> edit classpath.txt</pre>
# At the bottom of the classpath.txt file add lines for each .jar file in the extensions folder using the correct path. In the following example, PLS_Toolbox version 6.5 (named "PLS_Toolbox_65") is installed in the "c:\Toolbox\" folder so the corresponding entries look like:
<pre>c:\Toolbox\PLS_Toolbox_65\extensions\javatools\evriJavaClasses.jar
c:\Toolbox\PLS_Toolbox_65\extensions\javatools\rsyntaxtextarea.jar
c:\Toolbox\PLS_Toolbox_65\extensions\javatools\mysql.jar
c:\Toolbox\PLS_Toolbox_65\extensions\javatools\libsvm_jre15.jar
c:\Toolbox\PLS_Toolbox_65\extensions\javatools\derby.jar
c:\Toolbox\PLS_Toolbox_65\extensions\javatools\commons-dbutils-1.3.jar
c:\Toolbox\PLS_Toolbox_65\extensions\javatools\jsoup.jar</pre>
<ol start="5">
<li>Save the file and restart Matlab.</li>
<li>If MIA_Toolbox is installed, any .jar files in the "..\MIA_Toolbox\utilities" folder should be added using the same procedure as above.</li>
</ol>

==Installing Solo==

===System Requirements===
====Windows====
* Windows XP or newer.
* 600 MB of disk space.
* 4GB RAM (more RAM may be necessary for large DataSets).

====MAC====
* OS X 10.9 or newer.
* Intel Mac (64 bit).
* 1.5 GB of disk space.
* 4GB RAM (more RAM may be necessary for large DataSets).

===Installation===
==== Windows ====
#Copy the .exe file to your Desktop (or other suitable local location).
#If installing with a license file (evrilicense.lic) or license server file (evrilm.xml), move these files into the same folder as the .exe installer.
#Double click the icon and follow the instructions. Be sure to verify the location of the installation folder; by default it will be in the "EVRI" folder of your program files folder. (To customize the installation process, see [[Solo Installer Flags]])
#Identify if you want desktop and Start menu shortcuts created as well as if you want to associate .mat files with Solo.
#Identify if you want to add Solo to the system path. Doing so will assure 3rd-party programs will be able to start up Solo (where the 3rd-party program offers this feature.)
#When the Installer has completed, you will be asked if you want to start Solo now. The first time you run Solo, it will need to unpack several libraries and make take a minute or so to complete installation. Subsequent startups will take less time. Note that when installed in the standard Program Files folder, this step needs to be done as a user with administrative privileges so it is best to do from the installer.

'''NOTE:''' If installing Solo with a license server, see the [[Evri_license_server]] documentation.

'''Troubleshooting:''' If having problems downloading, installing or running Solo, see the [[Solo Troubleshooting]] page.

==== Mac ====

#'''Image Mounting''' Open the Solo.dmg (or Solo+MIA.dmg) disk image file by double-clicking on it. The disk image will mount on your computer.
#'''Copy Solo APP''' Copy the Solo.app (or Solo+MIA.app) bundle to your Applications folder.
#'''Launching The Applicatioin''' Right-click Solo.app and select '''Open''' to open the application.
#'''Errors on Startup''' If you receive a message saying "the Disk has been damaged" (OS X 10.7+) you may need to disable Mac Gatekeeper as described [http://support.apple.com/kb/ht5290 here] and for Mac OS Sierra see [https://www.tekrevue.com/tip/gatekeeper-macos-sierra/ here].

'''NOTE''': The first time you run Solo, it will need to unpack several libraries and may take several minutes (or longer depending on system resources) to complete installation. Subsequent startups will take less time.

'''Troubleshooting:''' If having problems downloading, installing or running Solo, see the [[Solo Troubleshooting]] page.

==== Linux ====

#'''Unzip/UnTar''' Copy the tar.gz file into a target folder (create one using the <tt>mkdir</tt> command) in any convenient location (e.g. the user's home directory or any accessible location) and use the following command to unzip/untar the application:<pre> tar -xzf solo.tar.gz</pre>(note: archive name will be depend on the exact application you are installing)
#'''Launching The Applicatioin''' In a console window, change the working directory to the target folder and use the command:<pre> ./solo</pre> (application name will depend on the exact application you purchased). If starting from a different working directory, use the full path to the solo application. You can also create a shortcut which performs this operation.
::'''First Run Notes''': The first time you run Solo, it will need to unpack several libraries and make take several minutes (or longer depending on system resources) to complete installation. Subsequent startups will take less time. If the program has been installed in a general system folder (such as /usr/local/bin ) then the initial run of Solo may need to be done using SUDO so the program has sufficient privileges to unpack the libraries.

'''Troubleshooting:''' If having problems downloading, installing or running Solo, see the [[Solo Troubleshooting]] page.

=== Troubleshooting Solo ===

If having problems downloading, installing or running Solo, see the [[Solo Troubleshooting]] page.

==Starting Solo==

On all platforms, Solo can be started using the installed shortcuts (double-clicking the shortcut), or by calling the application directly from either a shortcut or DOS/Linux command window. The application can also take [[Solo_Startup_Flags|various command-line flags]] that modify startup behavior.

==Updating==
The most up-to-date version of PLS_Toolbox and Solo will be made available to users on our web
site, www.eigenvector.com. Registered users can download new copies of the toolbox
that incorporate the latest bug fixes. A username and password is required for access to
the upgrades. Usernames and passwords are supplied when licenses are
purchased (older version users should have received this information by registering their
software). If you have forgotten your username and/or password, please write to
helpdesk@eigenvector.com or use the lost username/password page accessible from the download page [http://download.eigenvector.com/ on the Eigenvector Research website].

To check for updates select '''Check for Updates''' from the '''Help''' menu of the Workspace Browser or Analysis interface, or, for PLS_Toolbox users, use the command:
>> evriupdate
at the Matlab command line. This will bring up a small dialog box indicating the status of your installed products.

==Checking Your Installation==
You can check that your software is installed and working correctly by:
*PLS_Toolbox: Run the "evridebug" Matlab command. Running this should report that "No PLS_Toolbox installation problems were identified".
*Solo: Solo should open with the Browser window presented. You can confirm Solo is installed correctly by running any analysis method, for example PCA. See the short video on using PCA at: [http://www.eigenvector.com/eigenguide.php?m=PCA_2014 PCA].

Solo or PLS_Toolbox users can get product information from the Help > About menu in the Workspace Browser or Analysis window.

PLS_Toolbox users can use the following commands to verify a new or existing installation. We request the output from these commands when you submit a request to [mailto:helpdesk@eigenvector.com helpdesk@eigenvector.com].
* ver - Matlab command show a list of installed products.
* path - Matlab command showing the current folders on the Matlab path. '''ALL''' PLS_Toolbox folders (and sub-folders) should be on the Matlab path.
* [[evridebug]] - PLS_Toolbox command that checks for common installation problems.

Also check [http://www.eigenvector.com/faq our FAQ page], there are several more detailed issues including [http://www.eigenvector.com/faq/index.php?id=141 trouble with the Matlab path] discussed there.

==Getting Help==
Start by checking our [http://www.eigenvector.com/faq FAQ Pages].
For Solo: also check the [[Solo_Troubleshooting|Solo Troubleshooting Pages]]

If you cannot find the information you need there, send problem reports to [mailto://helpdesk@eigenvector.com helpdesk@eigenvector.com]

Please send your message with the following information:
*Product License/Registration Number
*Computer type, model and operating system version
*Exact errors you are receiving (if any)
*Hardware and memory configuration (if reporting a memory issue)
*Your contact information including either your Eigenvector Username or the following information
::First Name
::Last Name
::Company Name
::Phone Country Code
::Phone Area Code
::Phone Main
::Phone Extension

Installation

2019-02-15T15:33:27Z

Lyle: /* Manually Installing from ZIP File (all platforms) */

__TOC__
==Installing PLS_Toolbox==
PLS_Toolbox is delivered as a Windows Installer (.exe for Windows
only) or as a compressed ZIP file (all platforms). Installation is a two-part process of
copying and decompressing files into a local folder and then adding those files to your
MATLAB path. You will need your '''License Code''' (obtained at the time of purchase).
===System Requirements===
* Compatible with any Matlab released within 5 years of released date of this product.
* 100 MB of disk space.
* 2 GB of RAM required, 4+ GB recommended

===Matlab Compatibility===
Although we try to maintain inter-compatibility with Matlab add-on Toolboxes to the greatest extent possible some incompatibilities do exist. Most of these cases originate from situations where PLS_Toolbox has an established function and The MathWorks (TMW) introduces a new function with the same name and is unwilling to rename it. Because we don't have access to the entire TMW product family this list may not include all incompatible functions:

* Incompatible Functions
** cluster.m (Stats Toolbox)
** crossval.m (Stats Toolbox)
** regression.m (Neural Net Toolbox)
** ridge.m (Stats Toolbox)

* Intentional overloads:
** boxplot.m (Stats Toolbox because of DataSet Object [http://www.eigenvector.com/evriblog/?p=10 conflict])

* Compatible Functions
** dendrogram.m (Stats Toolbox)
** range.m (Stats Toolbox)
** kstest.m (Stats Toolbox)

To manage which function is being called, relocate PLS_Toolbox on your Matlab path. Putting PLS_Toolbox at the bottom of the path will allow the use of Matlab functions but will often make PLS_Toolbox inoperable until it's moved back to the top of the path. Within Browse you can select Help>Move Path Folders and indicate the location (Top or Bottom). The [[evrimovepath]] function can be used from the command line.

===Using the Windows EXE Installer===
It is recommended that Windows users use the PLS_Toolbox Windows Installer.
#Copy the .exe file to your Desktop (or other suitable local location).
#Double click the icon and follow the instructions. Be sure to verify the location of the installation folder; by default it will be the "toolbox" folder of your current MATLAB installation. '''Windows users:''' may need to choose a folder within your personal Documents folder in order to avoid permissions problems caused by the Windows security.
#When the Installer has completed, it will prompt you to start MATLAB and run an install script.
#If MATLAB doesn't start see Step 2 below.

===Manually Installing from ZIP File (all platforms)===
# '''File Extraction''' Decompress the PLS_Toolbox ZIP file and move it to the MATLAB "toolbox" folder (e.g. C:\Program Files\MATLAB\R2016a\toolbox).
#:'''Note''': If you already have a version of PLS_Toolbox installed, be sure the folders do not have the same name (by default they should not). Rename your existing copy of PLS_Toolbox if necessary.
#:'''Note''': Windows users may find that they do not have sufficient system permissions to install in a folder under the ''C:\Program Files\'' folder or that, after installation, they get warnings due to the inability to set program configuration. In these cases, simply install PLS_Toolbox into a folder such as your personal Documents folder.
# '''Run <code>evriinstall</code>''' For MATLAB to find PLS_Toolbox, the directories that contain the toolbox files must be added to the MATLAB search path.
#:'''Please Note''': If you are familiar with installing toolboxes purchased from The MathWorks, this process is different. TMW installers typically are able to set the path automatically. PLS_Toolbox has its own installation method.
#:Start MATLAB and follow these steps:
#:* Start MATLAB and use the Current Directory toolbar to navigate to the PLS_Toolbox directory (created in step 1).
#:* Type evriinstall at the command line (in the COMMAND WINDOW): <pre>>> evriinstall</pre>A dialog box will appear (see below.) Change the settings as needed then click the '''Install''' button. EVRIINSTALL will add PLS_Toolbox to the MATLAB path, check for any installation problems, and output results to the command window.
#:* See the [[PLS_Toolbox_Topics | Reference Topics]] page for information about PLS_Toolbox functions. Continue reading the [[Software User Guide]] for information using Solo and PLS_Toolbox.
#::[[Image:Install.png]]

===Configuring Java===
In some older versions of Matlab, including version 7.0.4 (R14SP2), it is necessary to manually add the jar files included with PLS_Toolbox to the Matlab java class path.
# Open Matlab.
# Navigate to the "..\PLS_Toolbox_65\extensions\javatools" folder (where "PLS_Toolbox_65" is the folder containing the version of PLS_Toolbox you are installing. The "65" will change for each version)
# Edit the Java class path file:<pre>>> edit classpath.txt</pre>
# At the bottom of the classpath.txt file add lines for each .jar file in the extensions folder using the correct path. In the following example, PLS_Toolbox version 6.5 (named "PLS_Toolbox_65") is installed in the "c:\Toolbox\" folder so the corresponding entries look like:
<pre>c:\Toolbox\PLS_Toolbox_65\extensions\javatools\evriJavaClasses.jar
c:\Toolbox\PLS_Toolbox_65\extensions\javatools\rsyntaxtextarea.jar
c:\Toolbox\PLS_Toolbox_65\extensions\javatools\mysql.jar
c:\Toolbox\PLS_Toolbox_65\extensions\javatools\libsvm_jre15.jar
c:\Toolbox\PLS_Toolbox_65\extensions\javatools\derby.jar
c:\Toolbox\PLS_Toolbox_65\extensions\javatools\commons-dbutils-1.3.jar
c:\Toolbox\PLS_Toolbox_65\extensions\javatools\jsoup.jar</pre>
<ol start="5">
<li>Save the file and restart Matlab.</li>
<li>If MIA_Toolbox is installed, any .jar files in the "..\MIA_Toolbox\utilities" folder should be added using the same procedure as above.</li>
</ol>

==Installing Solo==

===System Requirements===
====Windows====
* Windows XP or newer.
* 600 MB of disk space.
* 4GB RAM (more RAM may be necessary for large DataSets).

====MAC====
* OS X 10.9 or newer.
* Intel Mac (64 bit).
* 1.5 GB of disk space.
* 4GB RAM (more RAM may be necessary for large DataSets).

===Installation===
==== Windows ====
#Copy the .exe file to your Desktop (or other suitable local location).
#If installing with a license file (evrilicense.lic) or license server file (evrilm.xml), move these files into the same folder as the .exe installer.
#Double click the icon and follow the instructions. Be sure to verify the location of the installation folder; by default it will be in the "EVRI" folder of your program files folder. (To customize the installation process, see [[Solo Installer Flags]])
#Identify if you want desktop and Start menu shortcuts created as well as if you want to associate .mat files with Solo.
#Identify if you want to add Solo to the system path. Doing so will assure 3rd-party programs will be able to start up Solo (where the 3rd-party program offers this feature.)
#When the Installer has completed, you will be asked if you want to start Solo now. The first time you run Solo, it will need to unpack several libraries and make take a minute or so to complete installation. Subsequent startups will take less time. Note that when installed in the standard Program Files folder, this step needs to be done as a user with administrative privileges so it is best to do from the installer.

'''NOTE:''' If installing Solo with a license server, see the [[Evri_license_server]] documentation.

'''Troubleshooting:''' If having problems downloading, installing or running Solo, see the [[Solo Troubleshooting]] page.

==== Mac ====

#'''Image Mounting''' Open the Solo.dmg (or Solo+MIA.dmg) disk image file by double-clicking on it. The disk image will mount on your computer.
#'''Copy Solo APP''' Copy the Solo.app (or Solo+MIA.app) bundle to your Applications folder.
#'''Launching The Applicatioin''' Right-click Solo.app and select '''Open''' to open the application.
#'''Errors on Startup''' If you receive a message saying "the Disk has been damaged" (OS X 10.7+) you may need to disable Mac Gatekeeper as described [http://support.apple.com/kb/ht5290 here] and for Mac OS Sierra see [https://www.tekrevue.com/tip/gatekeeper-macos-sierra/ here].

'''NOTE''': The first time you run Solo, it will need to unpack several libraries and may take several minutes (or longer depending on system resources) to complete installation. Subsequent startups will take less time.

'''Troubleshooting:''' If having problems downloading, installing or running Solo, see the [[Solo Troubleshooting]] page.

==== Linux ====

#'''Unzip/UnTar''' Copy the tar.gz file into a target folder (create one using the <tt>mkdir</tt> command) in any convenient location (e.g. the user's home directory or any accessible location) and use the following command to unzip/untar the application:<pre> tar -xzf solo.tar.gz</pre>(note: archive name will be depend on the exact application you are installing)
#'''Launching The Applicatioin''' In a console window, change the working directory to the target folder and use the command:<pre> ./solo</pre> (application name will depend on the exact application you purchased). If starting from a different working directory, use the full path to the solo application. You can also create a shortcut which performs this operation.
::'''First Run Notes''': The first time you run Solo, it will need to unpack several libraries and make take several minutes (or longer depending on system resources) to complete installation. Subsequent startups will take less time. If the program has been installed in a general system folder (such as /usr/local/bin ) then the initial run of Solo may need to be done using SUDO so the program has sufficient privileges to unpack the libraries.

'''Troubleshooting:''' If having problems downloading, installing or running Solo, see the [[Solo Troubleshooting]] page.

=== Troubleshooting Solo ===

If having problems downloading, installing or running Solo, see the [[Solo Troubleshooting]] page.

==Starting Solo==

On all platforms, Solo can be started using the installed shortcuts (double-clicking the shortcut), or by calling the application directly from either a shortcut or DOS/Linux command window. The application can also take [[Solo_Startup_Flags|various command-line flags]] that modify startup behavior.

==Updating==
The most up-to-date version of PLS_Toolbox and Solo will be made available to users on our web
site, www.eigenvector.com. Registered users can download new copies of the toolbox
that incorporate the latest bug fixes. A username and password is required for access to
the upgrades. Usernames and passwords are supplied when licenses are
purchased (older version users should have received this information by registering their
software). If you have forgotten your username and/or password, please write to
helpdesk@eigenvector.com or use the lost username/password page accessible from the download page [http://download.eigenvector.com/ on the Eigenvector Research website].

To check for updates select '''Check for Updates''' from the '''Help''' menu of the Workspace Browser or Analysis interface, or, for PLS_Toolbox users, use the command:
>> evriupdate
at the Matlab command line. This will bring up a small dialog box indicating the status of your installed products.

==Checking Your Installation==
You can check that your software is installed and working correctly by:
*PLS_Toolbox: Run the "evridebug" Matlab command. Running this should report that "No PLS_Toolbox installation problems were identified".
*Solo: Solo should open with the Browser window presented. You can confirm Solo is installed correctly by running any analysis method, for example PCA. See the short video on using PCA at: [http://www.eigenvector.com/eigenguide.php?m=PCA_2014 PCA].

Solo or PLS_Toolbox users can get product information from the Help > About menu in the Workspace Browser or Analysis window.

PLS_Toolbox users can use the following commands to verify a new or existing installation. We request the output from these commands when you submit a request to [mailto:helpdesk@eigenvector.com helpdesk@eigenvector.com].
* ver - Matlab command show a list of installed products.
* path - Matlab command showing the current folders on the Matlab path. '''ALL''' PLS_Toolbox folders (and sub-folders) should be on the Matlab path.
* [[evridebug]] - PLS_Toolbox command that checks for common installation problems.

Also check [http://www.eigenvector.com/faq our FAQ page], there are several more detailed issues including [http://www.eigenvector.com/faq/index.php?id=141 trouble with the Matlab path] discussed there.

==Getting Help==
Start by checking our [http://www.eigenvector.com/faq FAQ Pages].
For Solo: also check the [[Solo_Troubleshooting|Solo Troubleshooting Pages]]

If you cannot find the information you need there, send problem reports to [mailto://helpdesk@eigenvector.com helpdesk@eigenvector.com]

Please send your message with the following information:
*Product License/Registration Number
*Computer type, model and operating system version
*Exact errors you are receiving (if any)
*Hardware and memory configuration (if reporting a memory issue)
*Your contact information including either your Eigenvector Username or the following information
::First Name
::Last Name
::Company Name
::Phone Country Code
::Phone Area Code
::Phone Main
::Phone Extension

Installation

2019-02-15T15:32:40Z

Lyle: /* Using the Windows EXE Installer */

__TOC__
==Installing PLS_Toolbox==
PLS_Toolbox is delivered as a Windows Installer (.exe for Windows
only) or as a compressed ZIP file (all platforms). Installation is a two-part process of
copying and decompressing files into a local folder and then adding those files to your
MATLAB path. You will need your '''License Code''' (obtained at the time of purchase).
===System Requirements===
* Compatible with any Matlab released within 5 years of released date of this product.
* 100 MB of disk space.
* 2 GB of RAM required, 4+ GB recommended

===Matlab Compatibility===
Although we try to maintain inter-compatibility with Matlab add-on Toolboxes to the greatest extent possible some incompatibilities do exist. Most of these cases originate from situations where PLS_Toolbox has an established function and The MathWorks (TMW) introduces a new function with the same name and is unwilling to rename it. Because we don't have access to the entire TMW product family this list may not include all incompatible functions:

* Incompatible Functions
** cluster.m (Stats Toolbox)
** crossval.m (Stats Toolbox)
** regression.m (Neural Net Toolbox)
** ridge.m (Stats Toolbox)

* Intentional overloads:
** boxplot.m (Stats Toolbox because of DataSet Object [http://www.eigenvector.com/evriblog/?p=10 conflict])

* Compatible Functions
** dendrogram.m (Stats Toolbox)
** range.m (Stats Toolbox)
** kstest.m (Stats Toolbox)

To manage which function is being called, relocate PLS_Toolbox on your Matlab path. Putting PLS_Toolbox at the bottom of the path will allow the use of Matlab functions but will often make PLS_Toolbox inoperable until it's moved back to the top of the path. Within Browse you can select Help>Move Path Folders and indicate the location (Top or Bottom). The [[evrimovepath]] function can be used from the command line.

===Using the Windows EXE Installer===
It is recommended that Windows users use the PLS_Toolbox Windows Installer.
#Copy the .exe file to your Desktop (or other suitable local location).
#Double click the icon and follow the instructions. Be sure to verify the location of the installation folder; by default it will be the "toolbox" folder of your current MATLAB installation. '''Windows users:''' may need to choose a folder within your personal Documents folder in order to avoid permissions problems caused by the Windows security.
#When the Installer has completed, it will prompt you to start MATLAB and run an install script.
#If MATLAB doesn't start see Step 2 below.

===Manually Installing from ZIP File (all platforms)===
# '''File Extraction''' Decompress the PLS_Toolbox ZIP file and move it to the MATLAB "toolbox" folder (e.g. C:\Program Files\MATLAB\R2016a\toolbox).
#:'''Note''': If you already have a version of PLS_Toolbox installed, be sure the folders do not have the same name (by default they should not). Rename your existing copy of PLS_Toolbox if necessary.
#:'''Note''': Windows 7 users may find that they do not have sufficient system permissions to install in a folder under the ''C:\Program Files\'' folder or that, after installation, they get warnings due to the inability to set program configuration. In these cases, simply install PLS_Toolbox into a folder such as your personal Documents folder.
# '''Run <code>evriinstall</code>''' For MATLAB to find PLS_Toolbox, the directories that contain the toolbox files must be added to the MATLAB search path.
#:'''Please Note''': If you are familiar with installing toolboxes purchased from The MathWorks, this process is different. TMW installers typically are able to set the path automatically. PLS_Toolbox has its own installation method.
#:Start MATLAB and follow these steps:
#:* Start MATLAB and use the Current Directory toolbar to navigate to the PLS_Toolbox directory (created in step 1).
#:* Type evriinstall at the command line (in the COMMAND WINDOW): <pre>>> evriinstall</pre>A dialog box will appear (see below.) Change the settings as needed then click the '''Install''' button. EVRIINSTALL will add PLS_Toolbox to the MATLAB path, check for any installation problems, and output results to the command window.
#:* See the [[PLS_Toolbox_Topics | Reference Topics]] page for information about PLS_Toolbox functions. Continue reading the [[Software User Guide]] for information using Solo and PLS_Toolbox.
#::[[Image:Install.png]]

===Configuring Java===
In some older versions of Matlab, including version 7.0.4 (R14SP2), it is necessary to manually add the jar files included with PLS_Toolbox to the Matlab java class path.
# Open Matlab.
# Navigate to the "..\PLS_Toolbox_65\extensions\javatools" folder (where "PLS_Toolbox_65" is the folder containing the version of PLS_Toolbox you are installing. The "65" will change for each version)
# Edit the Java class path file:<pre>>> edit classpath.txt</pre>
# At the bottom of the classpath.txt file add lines for each .jar file in the extensions folder using the correct path. In the following example, PLS_Toolbox version 6.5 (named "PLS_Toolbox_65") is installed in the "c:\Toolbox\" folder so the corresponding entries look like:
<pre>c:\Toolbox\PLS_Toolbox_65\extensions\javatools\evriJavaClasses.jar
c:\Toolbox\PLS_Toolbox_65\extensions\javatools\rsyntaxtextarea.jar
c:\Toolbox\PLS_Toolbox_65\extensions\javatools\mysql.jar
c:\Toolbox\PLS_Toolbox_65\extensions\javatools\libsvm_jre15.jar
c:\Toolbox\PLS_Toolbox_65\extensions\javatools\derby.jar
c:\Toolbox\PLS_Toolbox_65\extensions\javatools\commons-dbutils-1.3.jar
c:\Toolbox\PLS_Toolbox_65\extensions\javatools\jsoup.jar</pre>
<ol start="5">
<li>Save the file and restart Matlab.</li>
<li>If MIA_Toolbox is installed, any .jar files in the "..\MIA_Toolbox\utilities" folder should be added using the same procedure as above.</li>
</ol>

==Installing Solo==

===System Requirements===
====Windows====
* Windows XP or newer.
* 600 MB of disk space.
* 4GB RAM (more RAM may be necessary for large DataSets).

====MAC====
* OS X 10.9 or newer.
* Intel Mac (64 bit).
* 1.5 GB of disk space.
* 4GB RAM (more RAM may be necessary for large DataSets).

===Installation===
==== Windows ====
#Copy the .exe file to your Desktop (or other suitable local location).
#If installing with a license file (evrilicense.lic) or license server file (evrilm.xml), move these files into the same folder as the .exe installer.
#Double click the icon and follow the instructions. Be sure to verify the location of the installation folder; by default it will be in the "EVRI" folder of your program files folder. (To customize the installation process, see [[Solo Installer Flags]])
#Identify if you want desktop and Start menu shortcuts created as well as if you want to associate .mat files with Solo.
#Identify if you want to add Solo to the system path. Doing so will assure 3rd-party programs will be able to start up Solo (where the 3rd-party program offers this feature.)
#When the Installer has completed, you will be asked if you want to start Solo now. The first time you run Solo, it will need to unpack several libraries and make take a minute or so to complete installation. Subsequent startups will take less time. Note that when installed in the standard Program Files folder, this step needs to be done as a user with administrative privileges so it is best to do from the installer.

'''NOTE:''' If installing Solo with a license server, see the [[Evri_license_server]] documentation.

'''Troubleshooting:''' If having problems downloading, installing or running Solo, see the [[Solo Troubleshooting]] page.

==== Mac ====

#'''Image Mounting''' Open the Solo.dmg (or Solo+MIA.dmg) disk image file by double-clicking on it. The disk image will mount on your computer.
#'''Copy Solo APP''' Copy the Solo.app (or Solo+MIA.app) bundle to your Applications folder.
#'''Launching The Applicatioin''' Right-click Solo.app and select '''Open''' to open the application.
#'''Errors on Startup''' If you receive a message saying "the Disk has been damaged" (OS X 10.7+) you may need to disable Mac Gatekeeper as described [http://support.apple.com/kb/ht5290 here] and for Mac OS Sierra see [https://www.tekrevue.com/tip/gatekeeper-macos-sierra/ here].

'''NOTE''': The first time you run Solo, it will need to unpack several libraries and may take several minutes (or longer depending on system resources) to complete installation. Subsequent startups will take less time.

'''Troubleshooting:''' If having problems downloading, installing or running Solo, see the [[Solo Troubleshooting]] page.

==== Linux ====

#'''Unzip/UnTar''' Copy the tar.gz file into a target folder (create one using the <tt>mkdir</tt> command) in any convenient location (e.g. the user's home directory or any accessible location) and use the following command to unzip/untar the application:<pre> tar -xzf solo.tar.gz</pre>(note: archive name will be depend on the exact application you are installing)
#'''Launching The Applicatioin''' In a console window, change the working directory to the target folder and use the command:<pre> ./solo</pre> (application name will depend on the exact application you purchased). If starting from a different working directory, use the full path to the solo application. You can also create a shortcut which performs this operation.
::'''First Run Notes''': The first time you run Solo, it will need to unpack several libraries and make take several minutes (or longer depending on system resources) to complete installation. Subsequent startups will take less time. If the program has been installed in a general system folder (such as /usr/local/bin ) then the initial run of Solo may need to be done using SUDO so the program has sufficient privileges to unpack the libraries.

'''Troubleshooting:''' If having problems downloading, installing or running Solo, see the [[Solo Troubleshooting]] page.

=== Troubleshooting Solo ===

If having problems downloading, installing or running Solo, see the [[Solo Troubleshooting]] page.

==Starting Solo==

On all platforms, Solo can be started using the installed shortcuts (double-clicking the shortcut), or by calling the application directly from either a shortcut or DOS/Linux command window. The application can also take [[Solo_Startup_Flags|various command-line flags]] that modify startup behavior.

==Updating==
The most up-to-date version of PLS_Toolbox and Solo will be made available to users on our web
site, www.eigenvector.com. Registered users can download new copies of the toolbox
that incorporate the latest bug fixes. A username and password is required for access to
the upgrades. Usernames and passwords are supplied when licenses are
purchased (older version users should have received this information by registering their
software). If you have forgotten your username and/or password, please write to
helpdesk@eigenvector.com or use the lost username/password page accessible from the download page [http://download.eigenvector.com/ on the Eigenvector Research website].

To check for updates select '''Check for Updates''' from the '''Help''' menu of the Workspace Browser or Analysis interface, or, for PLS_Toolbox users, use the command:
>> evriupdate
at the Matlab command line. This will bring up a small dialog box indicating the status of your installed products.

==Checking Your Installation==
You can check that your software is installed and working correctly by:
*PLS_Toolbox: Run the "evridebug" Matlab command. Running this should report that "No PLS_Toolbox installation problems were identified".
*Solo: Solo should open with the Browser window presented. You can confirm Solo is installed correctly by running any analysis method, for example PCA. See the short video on using PCA at: [http://www.eigenvector.com/eigenguide.php?m=PCA_2014 PCA].

Solo or PLS_Toolbox users can get product information from the Help > About menu in the Workspace Browser or Analysis window.

PLS_Toolbox users can use the following commands to verify a new or existing installation. We request the output from these commands when you submit a request to [mailto:helpdesk@eigenvector.com helpdesk@eigenvector.com].
* ver - Matlab command show a list of installed products.
* path - Matlab command showing the current folders on the Matlab path. '''ALL''' PLS_Toolbox folders (and sub-folders) should be on the Matlab path.
* [[evridebug]] - PLS_Toolbox command that checks for common installation problems.

Also check [http://www.eigenvector.com/faq our FAQ page], there are several more detailed issues including [http://www.eigenvector.com/faq/index.php?id=141 trouble with the Matlab path] discussed there.

==Getting Help==
Start by checking our [http://www.eigenvector.com/faq FAQ Pages].
For Solo: also check the [[Solo_Troubleshooting|Solo Troubleshooting Pages]]

If you cannot find the information you need there, send problem reports to [mailto://helpdesk@eigenvector.com helpdesk@eigenvector.com]

Please send your message with the following information:
*Product License/Registration Number
*Computer type, model and operating system version
*Exact errors you are receiving (if any)
*Hardware and memory configuration (if reporting a memory issue)
*Your contact information including either your Eigenvector Username or the following information
::First Name
::Last Name
::Company Name
::Phone Country Code
::Phone Area Code
::Phone Main
::Phone Extension

Analysis Window: Model Cache Pane

2019-02-14T18:04:46Z

Lyle: /* Moving/Changing a Cache */

__TOC__

[[TableOfContents|Table of Contents]] | [[AnalysisWindow_AnalysisFlowChartPane|Previous]] | [[ModelBuilding_PreProcessingMethods|Next]]

===Analysis window Model Cache pane===

Data that is used to create models and predictions during data analysis as well as all of the models and predictions created from this data is automatically [[modelcache|cached]]. After a model is created, the model and the data used to create it are added to the cache. If new data is loaded and a prediction is made, this new data and the prediction are added to the cache. Every time you edit data and create a new model or prediction from the data, each version of the modified data and the corresponding model or prediction are added to the cache. The Model Cache pane displays this complete history of all your cached items-all of the data that was used to create models and predictions during data analysis as well as all of the models and predictions that were created from this data-in a hierarchical view.

:''Analysis window Model Cache pane''

::[[Image:Model_cache_pane.png|247x288px]]

To reload a previously created model:
# Set the Model Cache view to the mode which best organizes the list to help you find the item desired (see the [[AnalysisWindow_ModelCachepane#Model Cache pane view|Model Cache pane view]] descriptions for suggestions.)
# Locate the model to load in the list (Note that after other sorting criteria are met, models on a specific branch are sorted by date in all views.)
# Double-click on the old model to load it into the Analysis window. The calibration data corresponding to the model will also be automatically loaded (if it is available in the cache.) Right-clicking the model will give other options - see [[AnalysisWindow_ModelCachepane#Manipulating cached items|Manipulating cached items]].
For more usage hints, see the [[AnalysisWindow_ModelCachepane#Model Cache pane view|Model Cache pane view]] descriptions below.

'''Note:''' By default, the Model Cache pane is turned on when Solo first opens. You can close the Model Cache pane by clicking the Close ("X") button in the upper right hand corner of the pane. To turn the Model Cache on again, on the Tools menu on the Analysis window, click View Cache, and then select a viewing option. Models and data are cached even if the Model Cache pane is not open.

Multiple options are available to you for changing the Model Cache view, managing your cache, and manipulating cache items. See:

* [[AnalysisWindow_ModelCachepane#Model Cache pane view|Model Cache pane view]].
* [[AnalysisWindow_ModelCachepane#Cache management|Cache management]].
* [[AnalysisWindow_ModelCachepane#Manipulating cached items|Manipulating cached items]].

===Model Cache pane view===

The options to change the Model Cache pane view are found in two locations:

* In the Model Cache pane itself under Cache Settings and View
* On the Tools menu (the View Cache option) for the Analysis windows

:''Options to change the Model Cache pane view''

::[[Image:Model_cache_settings_options.png|171x56px]]

You can select from three different organizational views:

* View Cache by Lineage-In the View Cache by Lineage view, the cache items are sorted by the parent data item. Under the parent data item, the child items (modified data, model, and predictions) are sorted by the timestamps at each modification point.
: This is a convenient view to use in identifying a set of models which were all created from a specific data set or ''version'' of a data set (such as a specific data set with different sets of included variables and/or samples.)

:''View Cache by Lineage view''

::[[Image:Model_cache_view_lineage.png|247x288px]]

* View Cache by Date-In the View Cache by Date view, all cached items are simply sorted by creation date in chronological order.
: This is a convenient view to use when attempting to locate a model or data which was used on a specific date. For example, to reload a model which you knew was built yesterday, switch to the Date view, locate yesterday's date, and expand the tree. All data and models used on that day will be listed. Reload any model by double-clicking it.

:''View Cache by Date view''

::[[Image:Model_cache_view_date.png|247x288px]]

* View Cache by Type-In the View Cache by Type view, all cached items are simply sorted by object type-data, model, or prediction.
: This is a convenient view to use when attempting to locate a specific type of model when the data used and model's created date are not known. All data, models, and predictions are listed under their own branch, and then sorted by name (for data), type (for models and predictions), and date. For example, to locate an MCR model you know you created on some previous date, switch to the Type view, expand the Models branch, and locate the MCR models in that branch. You can then reload any model by double-clicking it.

:''View Cache by Type view''

::[[Image:Model_cache_view_type.png|247x288px]]

===Cache management===

Several options are available to you for managing your cache. You can modify all of these options in the Options dialog box.

'''Note:''' For a detailed discussion about the Options dialog box, see [[Solo_CommonApplicationFeatures|Options dialog box]].

{|

|- valign="top"

|1.

|Do one of the following:

|}

{| style="margin-left:18pt"

|- valign="top"

|
* In the Model Cache pane, expand the Cache Settings and View option, and under this option, click Edit Model Cache Settings.

|}

:''Cache Settings and View options''

::[[Image:Edit_Model_Cache_settings_paneoptions.png|232x122px]]
::

::
::

{| style="margin-left:18pt"

|- valign="top"

|
* On the Analysis window menu, click Edit > Options > Model Cache Settings.

|}

::The Options dialog box for the Model Cache pane opens.

:''Options dialog box''

::[[Image:Options_Model_Cache_dialogbox.png|650px]]
::

::
::

{|

|- valign="top"

|2.

|Modify the value for any option, and then click OK.

|}

:The Options dialog box closes and you return to the Model Cache pane. Any changes that you made are effective immediately.

::
::
<table style="border-collapse: collapse; margin-bottom: 12.0pt; margin-left: 0pt; margin-right: 0pt; margin-top: 3.0pt; padding-bottom: 1pt; padding-left: 6pt; padding-right: 6pt; padding-top: 6pt; text-align: left; width: 465.75144pt;" cellspacing="0" summary="">

<tr>
<td style="background-color: #E8E8E8; border-bottom-color: #000000; border-bottom-style: solid; border-bottom-width: 1px; border-left-color: #000000; border-left-style: solid; border-left-width: 1px; border-right-color: #000000; border-right-style: solid; border-right-width: 1px; border-top-color: #000000; border-top-style: solid; border-top-width: 1px; padding-bottom: 1pt; padding-left: 6pt; padding-right: 6pt; padding-top: 6pt; vertical-align: top; width: 97.50024pt;">

'''Option'''
</td>
<td style="background-color: #E8E8E8; border-bottom-color: #000000; border-bottom-style: solid; border-bottom-width: 1px; border-left-color: #000000; border-left-style: solid; border-left-width: 1px; border-right-color: #000000; border-right-style: solid; border-right-width: 1px; border-top-color: #000000; border-top-style: solid; border-top-width: 1px; padding-bottom: 1pt; padding-left: 6pt; padding-right: 6pt; padding-top: 6pt; vertical-align: top; width: 368.2512pt;">

'''Description'''
</td>
</tr>
<tr>
<td style="border-bottom-color: #000000; border-bottom-style: solid; border-bottom-width: 1px; border-left-color: #000000; border-left-style: solid; border-left-width: 1px; border-right-color: #000000; border-right-style: solid; border-right-width: 1px; border-top-color: #000000; border-top-style: solid; border-top-width: 1px; padding-bottom: 1pt; padding-left: 6pt; padding-right: 6pt; padding-top: 6pt; vertical-align: top;">
cache
</td>
<td style="border-bottom-color: #000000; border-bottom-style: solid; border-bottom-width: 1px; border-left-color: #000000; border-left-style: solid; border-left-width: 1px; border-right-color: #000000; border-right-style: solid; border-right-width: 1px; border-top-color: #000000; border-top-style: solid; border-top-width: 1px; padding-bottom: 1pt; padding-left: 6pt; padding-right: 6pt; padding-top: 6pt; vertical-align: top;">
Controls the operation of the cache. "On" records all data, models, and predictions. "readonly" locks down the cache and prevents recording to the cache and clearing of the cache.
</td>
</tr>
<tr>
<td style="border-bottom-color: #000000; border-bottom-style: solid; border-bottom-width: 1px; border-left-color: #000000; border-left-style: solid; border-left-width: 1px; border-right-color: #000000; border-right-style: solid; border-right-width: 1px; border-top-color: #000000; border-top-style: solid; border-top-width: 1px; padding-bottom: 1pt; padding-left: 6pt; padding-right: 6pt; padding-top: 6pt; vertical-align: top;">
project
</td>
<td style="border-bottom-color: #000000; border-bottom-style: solid; border-bottom-width: 1px; border-left-color: #000000; border-left-style: solid; border-left-width: 1px; border-right-color: #000000; border-right-style: solid; border-right-width: 1px; border-top-color: #000000; border-top-style: solid; border-top-width: 1px; padding-bottom: 1pt; padding-left: 6pt; padding-right: 6pt; padding-top: 6pt; vertical-align: top;">
Specifies an optional subfolder within the cachefolder for holding the cached items. The default name for project is "general." You can use the project setting to separate cached items among different work projects.
'''Note: '''No option is available in Solo to delete a project folder after you create it. You can delete old project folders only by manually locating the project folder on your computer and deleting the folder.
'''Note:''' You can also change value for the project setting using the Change Project option under Cache Settings and View in the Model Cache pane.
</td>
</tr>
<tr>
<td style="border-bottom-color: #000000; border-bottom-style: solid; border-bottom-width: 1px; border-left-color: #000000; border-left-style: solid; border-left-width: 1px; border-right-color: #000000; border-right-style: solid; border-right-width: 1px; border-top-color: #000000; border-top-style: solid; border-top-width: 1px; padding-bottom: 1pt; padding-left: 6pt; padding-right: 6pt; padding-top: 6pt; vertical-align: top;">
maxage
</td>
<td style="border-bottom-color: #000000; border-bottom-style: solid; border-bottom-width: 1px; border-left-color: #000000; border-left-style: solid; border-left-width: 1px; border-right-color: #000000; border-right-style: solid; border-right-width: 1px; border-top-color: #000000; border-top-style: solid; border-top-width: 1px; padding-bottom: 1pt; padding-left: 6pt; padding-right: 6pt; padding-top: 6pt; vertical-align: top;">
The maximum number of days an item is saved in the cache. An item that is older than this value is permanently deleted from the cache.
'''Note:''' See [[AnalysisWindow_ModelCachepane#Manipulating cached items|"Manipulating cached items"]] below for information about saving cached items to a location other than the cachefolder.
</td>
</tr>
<tr>
<td style="border-bottom-color: #000000; border-bottom-style: solid; border-bottom-width: 1px; border-left-color: #000000; border-left-style: solid; border-left-width: 1px; border-right-color: #000000; border-right-style: solid; border-right-width: 1px; border-top-color: #000000; border-top-style: solid; border-top-width: 1px; padding-bottom: 1pt; padding-left: 6pt; padding-right: 6pt; padding-top: 6pt; vertical-align: top;">
maxdatasize
</td>
<td style="border-bottom-color: #000000; border-bottom-style: solid; border-bottom-width: 1px; border-left-color: #000000; border-left-style: solid; border-left-width: 1px; border-right-color: #000000; border-right-style: solid; border-right-width: 1px; border-top-color: #000000; border-top-style: solid; border-top-width: 1px; padding-bottom: 1pt; padding-left: 6pt; padding-right: 6pt; padding-top: 6pt; vertical-align: top;">
Specifies the maximum size of a data item (in total number of table elements) that can be stored in the cache.
</td>
</tr>
</table>

===Manipulating cached items===

You can right-click on an entry for a cached item Model Cache pane to open a context menu that contains options for manipulating the item.

::
::
<table style="border-collapse: collapse; margin-bottom: 12.0pt; margin-left: 0pt; margin-right: 0pt; margin-top: 3.0pt; padding-bottom: 1pt; padding-left: 6pt; padding-right: 6pt; padding-top: 6pt; text-align: left; width: 405.75096pt;" cellspacing="0" summary="">

<tr>
<td style="background-color: #E8E8E8; border-bottom-color: #000000; border-bottom-style: solid; border-bottom-width: 1px; border-left-color: #000000; border-left-style: solid; border-left-width: 1px; border-right-color: #000000; border-right-style: solid; border-right-width: 1px; border-top-color: #000000; border-top-style: solid; border-top-width: 1px; padding-bottom: 1pt; padding-left: 6pt; padding-right: 6pt; padding-top: 6pt; vertical-align: top; width: 108.75024pt;">

'''Menu Option'''
</td>
<td style="background-color: #E8E8E8; border-bottom-color: #000000; border-bottom-style: solid; border-bottom-width: 1px; border-left-color: #000000; border-left-style: solid; border-left-width: 1px; border-right-color: #000000; border-right-style: solid; border-right-width: 1px; border-top-color: #000000; border-top-style: solid; border-top-width: 1px; padding-bottom: 1pt; padding-left: 6pt; padding-right: 6pt; padding-top: 6pt; vertical-align: top; width: 297.00072pt;">

'''Description'''
</td>
</tr>

<tr>
<td style="border-bottom-color: #000000; border-bottom-style: solid; border-bottom-width: 1px; border-left-color: #000000; border-left-style: solid; border-left-width: 1px; border-right-color: #000000; border-right-style: solid; border-right-width: 1px; border-top-color: #000000; border-top-style: solid; border-top-width: 1px; padding-bottom: 1pt; padding-left: 6pt; padding-right: 6pt; padding-top: 6pt; vertical-align: top;">
View Details
</td>
<td style="border-bottom-color: #000000; border-bottom-style: solid; border-bottom-width: 1px; border-left-color: #000000; border-left-style: solid; border-left-width: 1px; border-right-color: #000000; border-right-style: solid; border-right-width: 1px; border-top-color: #000000; border-top-style: solid; border-top-width: 1px; padding-bottom: 1pt; padding-left: 6pt; padding-right: 6pt; padding-top: 6pt; vertical-align: top;">
Opens and displays the selected item in a separate window-either the DataSet Editor window or the Model Reader window.
</td>
</tr>

<tr>
<td style="border-bottom-color: #000000; border-bottom-style: solid; border-bottom-width: 1px; border-left-color: #000000; border-left-style: solid; border-left-width: 1px; border-right-color: #000000; border-right-style: solid; border-right-width: 1px; border-top-color: #000000; border-top-style: solid; border-top-width: 1px; padding-bottom: 1pt; padding-left: 6pt; padding-right: 6pt; padding-top: 6pt; vertical-align: top;">
Load
</td>
<td style="border-bottom-color: #000000; border-bottom-style: solid; border-bottom-width: 1px; border-left-color: #000000; border-left-style: solid; border-left-width: 1px; border-right-color: #000000; border-right-style: solid; border-right-width: 1px; border-top-color: #000000; border-top-style: solid; border-top-width: 1px; padding-bottom: 1pt; padding-left: 6pt; padding-right: 6pt; padding-top: 6pt; vertical-align: top;">
Loads the selected cached item into the Analysis window. By default, if no data is loaded into the Analysis window, then the cached item is loaded into the X calibration component; otherwise, you are prompted to override the loaded data or load the data to another location.
'''Note:''' You can also double-click a cached item to load it into the Analysis window.
</td>
</tr>

<tr>
<td style="border-bottom-color: #000000; border-bottom-style: solid; border-bottom-width: 1px; border-left-color: #000000; border-left-style: solid; border-left-width: 1px; border-right-color: #000000; border-right-style: solid; border-right-width: 1px; border-top-color: #000000; border-top-style: solid; border-top-width: 1px; padding-bottom: 1pt; padding-left: 6pt; padding-right: 6pt; padding-top: 6pt; vertical-align: top;">
Load in New Window
</td>
<td style="border-bottom-color: #000000; border-bottom-style: solid; border-bottom-width: 1px; border-left-color: #000000; border-left-style: solid; border-left-width: 1px; border-right-color: #000000; border-right-style: solid; border-right-width: 1px; border-top-color: #000000; border-top-style: solid; border-top-width: 1px; padding-bottom: 1pt; padding-left: 6pt; padding-right: 6pt; padding-top: 6pt; vertical-align: top;">
Opens a new Analysis window and loads item same as Load above.
</td>
</tr>

<tr>
<td style="border-bottom-color: #000000; border-bottom-style: solid; border-bottom-width: 1px; border-left-color: #000000; border-left-style: solid; border-left-width: 1px; border-right-color: #000000; border-right-style: solid; border-right-width: 1px; border-top-color: #000000; border-top-style: solid; border-top-width: 1px; padding-bottom: 1pt; padding-left: 6pt; padding-right: 6pt; padding-top: 6pt; vertical-align: top;">
Export Cache Model
</td>
<td style="border-bottom-color: #000000; border-bottom-style: solid; border-bottom-width: 1px; border-left-color: #000000; border-left-style: solid; border-left-width: 1px; border-right-color: #000000; border-right-style: solid; border-right-width: 1px; border-top-color: #000000; border-top-style: solid; border-top-width: 1px; padding-bottom: 1pt; padding-left: 6pt; padding-right: 6pt; padding-top: 6pt; vertical-align: top;">
Save calibration data and model to a MAT file. These files can then be dragged and dropped onto an Analysis window to load.
</td>
</tr>

<tr>
<td style="border-bottom-color: #000000; border-bottom-style: solid; border-bottom-width: 1px; border-left-color: #000000; border-left-style: solid; border-left-width: 1px; border-right-color: #000000; border-right-style: solid; border-right-width: 1px; border-top-color: #000000; border-top-style: solid; border-top-width: 1px; padding-bottom: 1pt; padding-left: 6pt; padding-right: 6pt; padding-top: 6pt; vertical-align: top;">
Compare
</td>
<td style="border-bottom-color: #000000; border-bottom-style: solid; border-bottom-width: 1px; border-left-color: #000000; border-left-style: solid; border-left-width: 1px; border-right-color: #000000; border-right-style: solid; border-right-width: 1px; border-top-color: #000000; border-top-style: solid; border-top-width: 1px; padding-bottom: 1pt; padding-left: 6pt; padding-right: 6pt; padding-top: 6pt; vertical-align: top;">
Send a model to the [[Modeloptimizergui | Model Optimizer]].
</td>
</tr>

<tr>
<td style="border-bottom-color: #000000; border-bottom-style: solid; border-bottom-width: 1px; border-left-color: #000000; border-left-style: solid; border-left-width: 1px; border-right-color: #000000; border-right-style: solid; border-right-width: 1px; border-top-color: #000000; border-top-style: solid; border-top-width: 1px; padding-bottom: 1pt; padding-left: 6pt; padding-right: 6pt; padding-top: 6pt; vertical-align: top;">
Save As
</td>
<td style="border-bottom-color: #000000; border-bottom-style: solid; border-bottom-width: 1px; border-left-color: #000000; border-left-style: solid; border-left-width: 1px; border-right-color: #000000; border-right-style: solid; border-right-width: 1px; border-top-color: #000000; border-top-style: solid; border-top-width: 1px; padding-bottom: 1pt; padding-left: 6pt; padding-right: 6pt; padding-top: 6pt; vertical-align: top;">
Save the cached item to the workspace or to a file.
</td>
</tr>

<tr>
<td style="border-bottom-color: #000000; border-bottom-style: solid; border-bottom-width: 1px; border-left-color: #000000; border-left-style: solid; border-left-width: 1px; border-right-color: #000000; border-right-style: solid; border-right-width: 1px; border-top-color: #000000; border-top-style: solid; border-top-width: 1px; padding-bottom: 1pt; padding-left: 6pt; padding-right: 6pt; padding-top: 6pt; vertical-align: top;">
Rename
</td>
<td style="border-bottom-color: #000000; border-bottom-style: solid; border-bottom-width: 1px; border-left-color: #000000; border-left-style: solid; border-left-width: 1px; border-right-color: #000000; border-right-style: solid; border-right-width: 1px; border-top-color: #000000; border-top-style: solid; border-top-width: 1px; padding-bottom: 1pt; padding-left: 6pt; padding-right: 6pt; padding-top: 6pt; vertical-align: top;">
Rename the cached item.
</td>
</tr>

<tr>
<td style="border-bottom-color: #000000; border-bottom-style: solid; border-bottom-width: 1px; border-left-color: #000000; border-left-style: solid; border-left-width: 1px; border-right-color: #000000; border-right-style: solid; border-right-width: 1px; border-top-color: #000000; border-top-style: solid; border-top-width: 1px; padding-bottom: 1pt; padding-left: 6pt; padding-right: 6pt; padding-top: 6pt; vertical-align: top;">
Remove From Cache
</td>
<td style="border-bottom-color: #000000; border-bottom-style: solid; border-bottom-width: 1px; border-left-color: #000000; border-left-style: solid; border-left-width: 1px; border-right-color: #000000; border-right-style: solid; border-right-width: 1px; border-top-color: #000000; border-top-style: solid; border-top-width: 1px; padding-bottom: 1pt; padding-left: 6pt; padding-right: 6pt; padding-top: 6pt; vertical-align: top;">
Remove selected item from the cache.
</td>
</tr>

</table>

===Moving/Changing a Cache===

You can copy and move your Model Cache folder to a new location without any special consideration. When moving to a new computer, copy the cache folder to the new EVRI [[evridir | home folder]] and restart Matlab. In Solo, the location of the home folder can be found by selecting Help → About Solo. Once the folder is in the new location select '''File/Change Cache''' from the Workspace Browser and select the new folder. The cache will refresh automatically.

From the Workspace Browser '''File>Change Cache''' menu item. From the Matlab command line the cache folder location can be change with:

modelcache('changecachedir','path/to/new/location')

If you move your modelcache and experience problems getting the Model Cache to recognize the new location you may need to clear your preferences (matlabprefs.mat) file. Contact helpdesk@eigenvector.com for more details on how to do this.

The modelcache folder consists of the '''database''' folder and '''project''' folders. This folder and its contents are required to move/recover the model cache.

[[Image:cachefolder.jpg|700px| ]]

===Exporting and Importing Models===

Models can be exported from the cache via the right-click menu '''Export For Cache''' item. This will export the model and the data used to create it (if available) to a MAT file. The file can then be Imported into a different cache. To import a cache model, expand the Cache Settings and View leaf and click the '''Import Model''' leaf. The model and data can also be loaded into an Analysis window by dragging and dropping the MAT file onto the window.

[[Image:ImportExportCacheModel.jpg| 350px]]

Model Exporter Reference Manual

2019-02-14T17:40:26Z

Lyle: /* Exporting a Model */

__TOC__
==Introduction==

[[Model_Exporter]] converts models created within the [[Software_User_Guide|PLS_Toolbox or Solo]] chemometrics modeling environments into an interpretable format for use outside of these products. Models exported to Matlab or Python produce a stand-alone "predictor" script which includes a simple-to-implement formula to perform a model prediction.
Models exported to XML can be used with the included C# or Java interpreters or with a user-supplied interpreter to make predictions on new data.
Model_Exporter takes as input a standard model structure created in PLS_Toolbox or Solo and outputs the model into one of three formats: an XML file (executable by a user-supplied external parser or the Java or C# [[Model_Exporter_Interpreter|Model Exporter Interpreter]] class provided with Model_Exporter), an m-file (executable in MATLAB – separately distributed by Mathworks, Inc – without any additional toolboxes, or LabView with their MathScript addon package) or a Python file (executable in a Python 3.6 or higher environment).

The exported models require very few resources to be executed. Specifically, they requires floating-point numerical calculations, a small amount of memory, and the overhead resources required by the specific interpreter or language environment.

This documentation describes the use of the Model_Exporter, the use of exported M-file and Python file formats as well as to help in the design of external XML parsing engines. Model_Exporter includes a freely-distributable interpreter class with versions in C# and Java as described on the [[Model_Exporter_Interpreter|Model Exporter Interpreter]] page. In addition, an example interpreter engine is supplied for the PHP language (often used for web-page scripting predictions; see http://www.php.net for more information on PHP). Additional engines may be available - [mailto:helpdesk@eigenvector.com Contact Eigenvector Research, Inc.] for more information.
Latest version release notes can be found at http://wiki.eigenvector.com/index.php?title=Model_Exporter_Release_Notes

==System Requirements==

Model_Exporter can be executed from either the MATLAB computational environment ([http://mathworks.com Mathworks, Inc., Natick, MA]), or [[Software User Guide|Solo]] (Eigenvector Research, Inc., Wenatchee, WA). Model_Exporter converts models created by PLS_Toolbox 3.5 or higher or Solo 4.0 or higher.

===Matlab-Based Exporter Requirements===

For execution of Model_Exporter within the MATLAB environment, the following is required:

:Compatible with any version of Matlab released within five years of this product's release. For example, Model exporter 3.3.0 released in 2016, is guaranteed to be compatible with any version of Matlab released within 5 years of the 2016 release data, so in this case Matlab 2011 would be the oldest version of Matlab that we fully guarantee compatibility.
:256 MB RAM (recommended – less may be required)

===Solo-Based Exporter Requirements===

For execution of the Model_Exporter, the following is recommended

:Solo+Model_Exporter 4.1 or higher
:Operating system requirements as listed for the specified Solo version
:200 MB Disk Space (for installation; some models may require additional space)
:256 MB RAM (recommended – less may be required)

===Requirements for Using Exported Models===

The requirements to execute an exported model vary depending on the interpreter used, the number of variables in the modeled data, and the complexity of the model (i.e. the number of factors/components included in the model and the types of preprocessing used).

Memory requirements depend on the precision required for the application, the number of variables in the data and the total number of factors in the model. For example, a model working on 10,000 variables and 5 factors would require around 1MB for double-precision calculations and 500KB single-precision calculations.

The software which executes the specific file formats may have additional requirements. See the file format description sections later in this manual for where to locate model execution details.

==Installation==

Model_Exporter is installed by adding the Model_Exporter folder to your Matlab path. Once installed, PLS_Toolbox will recognize the installation and enable the proper menu items. Solo+Model_Exporter has the folder pre-installed.

For Matlab:
# Make the Model_Exporter folder your Current Folder in Matlab.
# Run the command 'setpath' from the command line.

==Supported Methods==

Model_Exporter supports the following model types:

:PCA – Principal Components Analysis model
:PLS – Partial Least Squares regression model
:PLSDA – Partial Least Squares discriminant analysis model
:PCR – Principal Components Regression model
:CLS – Classical Least Squares Regression model
:SVM – Support Vector Machine Regression model
:SVMDA – Support Vector Machine Classification model
:ANN – Artificial Neural Network Regression model using BPN algorithm
:MLR - Multivariate Linear Regression model

and preprocessing methods:

:Absolute value
:Arithmetic
:Autoscale
:Baseline (specified)
:Derivative (SavGol)
:Detrend
:ELS
:EPO
:GLS weighting
:Log Decay Scaling
:Log10
:MSC
:Mean center
:Median center
:Normalize
:OSC
:Pareto Scaling
:Poisson Scaling
:SNV
:Smooth (SavGol)
:Sqrt Mean Scale
:Transmission to Absorbance
:Variance Scaling

Normalization and Baseline support windowing. Normalization supports type 1 (area) and type 2 (length) normalization, but does not support 'Inf' type normalization.

Model_Exporter does not support replacement of missing values (values must be measured for all variables).

==Validity of Exported Model Predictions==
Exported models have been validated against "native" PLS_Toolbox/Solo model predictions for all supported analysis and preprocessing methods, with one exception. The exception is the case where the model was built using calibration X-block which has excluded variables and Savgol (smoothing or derivative) preprocessing is used. The one situation where exact agreement may NOT occur requires the following three conditions for the exported model:

:1. The model is built from X-block data which has excluded variables
:2. Savgol preprocessing with option "useexcluded"= true is the second or later preprocessing step
:3. The Savgol preprocessing step must be preceded by one of the following preprocessing methods: 'arithmetic', 'Autoscale', 'simple baseline', 'derivative', 'EpO', 'GLS Weighting', 'logdecay','Normalize', 'sqmnsc', 'smooth', or 'detrend'

The cause of this exceptional case is related to exported models using only included variables, as this affects the ability of smoothing or derivative (Savgol) preprocessing steps to correctly process variables near excluded variables.

If a model with the conditions above is exported then a warning dialog will alert the user of the potential problem. The user can decide to cancel the exporting or not. In the latter case the user should test the exported model using typical samples to see if the prediction behavior is acceptable.

If Savgol preprocessing is used then it is best, if possible, to include it as the first preprocessing step, since this usage will be valid.

==Exporting a Model==

===Exporting from PLS_Toolbox and MATLAB===

Model_Exporter is easily called from the MATLAB environment. After adding the Model_Exporter folder to the MATLAB path, a model can be exported by simply calling the exportmodel function, passing the model structure itself, and an optional input specifying the file name and type to which the exported model should be written. When filename is omitted, Model_Exporter will prompt for a filename, file type, and location.

exportmodel(model,filename)

exportmodel(model,filename,options)

The third parameter, options, allows specification of how excluded variables are handled, how numerical values are stored (text or binary), and whether the exported m-file is a script or function. See below for further details of options.

Model_Exporter is also accessible from the PLS_Toolbox through the Analysis GUI. With the model to export loaded into the Analysis GUI, go to the '''File > Export Model > To Predictor…''' menu and select the file type to export from the flyout menu.

===Exporting from Solo===

When installed with the stand-alone Solo software, a model is exported from the Analyis GUI. With the model to export loaded into the Analysis GUI, Go to the '''File > Export Model > To Predictor…''' menu and select the file type to export from the flyout menu.

===Handling Excluded Variables===

When excluded variables are detected within a model, the user will be given two options for how to handle these variables.

# Compress Model – Model_Exporter will attempt to remove all references to excluded variables. The created predictor will expect values for only the included variables.
# Use Placeholders – Model_Exporter will create a predictor which expects values for all variables, excluded or included, although excluded values will be ignored.

The choice between these two methods depends on the environment in which the exported model is going to be used. If it is easier to always provide all variables to the predictor, then the “Use Placeholders” option is probably preferred. If, instead, only the included variables will be available (e.g. the excluded variables are not going to be measured), compressing the model is the correct approach.

In general, the two methods give identical numerical results with the sole exception of models which make use of smoothing and derivative preprocessing. These methods may give slightly different “edge effects” after compressing a model and validation of such models is encouraged.

In either case, the header information in the exported model will always reflect the number of variables expected and any labels or axisscale information for those variables.

===Storing Numerical Values as Binary===

With large numbers of variables, and with certain types of preprocessing (e.g. derivatives and smoothing), the numerical matrices needed to apply the model can become quite large, particularly when stored in the standard text format of an exported model. When the '''m-file format''' is selected as the output target, you have the choice to store the numerical values in one of three formats:

* Text in the script (Default)
* Binary data file in DOUBLE (64-bit) precision
* Binary data file in SINGLE (32-bit) precision

Text in the script is the default format to store numerical values and allows all the model information to be included in a single file (the text script.) The second two options instead store these values in a separate binary file as a simple stream of numerical values of the indicated precision. When the binary formats are selected, the script is written to automatically open the binary file and read in the values from there instead of parsing them out of the script.

:'''Notes:'''
:# This file format is currently only available for scripts exported in the m-file format. [mailto:helpdesk@eigenvector.com|Contact Eigenvector Research] if you have interest in using a similar format for other export formats.
:# Single Precision Binary will reduce the accuracy of the predictions due to rounding error. The extent of error will depend greatly on the noise level of the data and the precision required by the model. Models exported with this precision should be validated with known samples to determine the effect of rounding on the predictions for the given model.
:# It is assumed that the binary file is in the "current working directory" (unless the script is edited to change the file location.)

==M-file Format==

The m-files output by [[Model Exporter]] are stand-alone. That is, they can be run by the MATLAB computational environment (available from Mathworks, Inc., http://www.mathworks.com) without any additional toolboxes or the LabVIEW environment (available from National Instruments, Inc., http://www.ni.com) with any MathScript-enabled package.

For maximum flexibility, an exported model is written as a script which expects only to find a variable named x in its workspace. This variable provides the input data to which the model should be applied. It is important to note that the variable x will be modified by the script and, thus, the caller should not expect the variable to remain unchanged. See "Creating Functions from Exported Models", below, for more information on how to isolate the script and call it as a function. (Those unfamiliar with MATLAB scripts and functions should read the MATLAB documentation describing these concepts and the associated "variable scope" documentation.)

The input variable x should be a vector, representing a single sample, and the output will be a prediction for this one sample.

===Options===
When exporting from the Matlab environment to a .m file using the ''options'' parameter it is possible to specify preferred behavior for these choices. The available options are:
* '''handleexcludes''': [ {'ask'} | 'ignore' | 'placeholders'} Governs how excluded variables should be handled.
:'ignore' = attempt to remove all references to excluded variables. Only included values will be expected.
:'placeholders' = expect values for all variables, although excluded values will not be used by model.
:'ask' = prompt user for desired behavior.

* '''datastorageformat''': [ {'ask'} | 'text' | 'binarydouble' | 'binarysingle' ] Governs output format of numerical values.
:'text' = store numerical values as text in the script (the normal output mode).
:'binarydouble' = store as binary data file in DOUBLE precision.
:'binarysingle' = store as binary data file in SINGLE precision.
:'ask' = prompt user for desired format.
Note: Single Precision Binary will reduce the accuracy of the predictions due to rounding error. Validate results against known samples if single precision is used. Note: Binary output formats provide for smaller memory footprint but require parsers that can execute binary file read instructions.

* '''creatematlabfunction''': [ {'no'} | 'yes' ] Governs m-file format specifying whether to create a Matlab script or function.
:'yes' outputs m-files with appropriate code to allow calls to the model application in a functional form.
:'no' outputs m-file in script form.

===Input Data===

The expected length (number of elements) and contents of the input x vector are defined in the comments and initial sections of the exported model script. The script, as exported, does not use this information to perform any validity testing on the input variable. This information is only provided to indicate to the user what type of data is expected.

The example below shows the part of an exported model which indicates the expected data size and associated context information. This particular model expects input data of ten variables as a row vector (as described by inputdata.size).

% .size = [ 75 10 ];
% .include_size = [ 75 10 ];

The user can make use of this information to assure the data being passed to the model is correct. Again, as written, the script provides no testing. Incorrect data sizes will be indicated by a runtime error when executing the script.

===Returned Results===

The results available from a model prediction will be present as variables in the script's workspace. The user is responsible for making use of these variables as needed. The following list specifies the supported results which may be of interest to the user.

:'''scores''' - Scores for each component as a row vector.
:'''T2''' - The Hotelling's T^2 as a scalar value.
:'''Q''' - The sum squared x residuals (Q value) as a scalar value.
:'''Tcon''' - Variable contributions to T2 as a row vector.
:'''Qcon''' - Q residuals contributions (x residuals) as a row vector.
:'''x''' - The preprocessed version of the input data.
:'''Xhat''' - Model estimate of the data as a row vector (in preprocessed units - comparable to the preprocessed '''x''').

All regression and PLSDA models return the following additional value:

:'''yhat''' - Model prediction for y (predicted y value) as a scalar value or vector.

PLSDA discriminant analysis models also return an additional value

:'''probs''' - Model predicted probability of the input sample belonging to each class, where the classes are ordered as unique(y), as a vector. (y refers to the classes variable originally used in building the model).

SVM regression analysis models return values:
:'''yhat''' - Model prediction for y (predicted y value) as a scalar.
:'''nsvs''' - Number of support vectors used by the model, as a scalar.

SVMDA discriminant analysis (classification) models return values:
:'''probs''' - Model predicted "probability" of the input sample belonging to each class, where the classes are ordered as shown in classorder (below). Note that the probability reported here is '''not''' the same as the probability reported by the SVM algorithm (based on a maximum likelihood calculation). Instead, this is based on the classvotes reported below. The class votes are normalized to give fraction of votes for each class. This fraction is raised to the power of 10, then normalized to unit area again. This gives a ROUGH estimate of probability where the class with the highest votes also gets the highest probability and the remaining classes are ranked in decreasing order. The log of the probability is roughly proportional to the number of class votes that would have to change to cause the assignment to change.
::NOTE: For historical reasons, the output '''prob''' will also contain the identical probabilities as '''probs'''.
:'''classvotes''' - Votes cast in favor of each class, as a vector. The class with most votes is the predicted class of the input sample.
:'''classorder''' - Is a vector of class numbers identifying which class each classvotes value is associated with. For example, if the second entry in classvotes (or probs) has the largest value then the second value in classorder give the winning class number. See the model's model.classification.classnums and model.classification.classids to translate between class numbers with class names. Ties between two or more classes are resolved by choosing the first.
:'''nsvs''' - Number of support vectors used by the model, as a scalar.
:'''df''' - Vector of decision function values for pairwise classifiers, as a vector. If there are N classes then there are N*(N-1)/2 pairwise classifiers used. The decision functions are in order: class 1-2, 1-3,...1-N, 2-3, 2-4, ...,2-N,..., (N-1)-N. The classvotes are based on the decision function values.

:Note these exported model results should be the same as results from SVMDA when using option probabilityestimates = 0 (even if the exported model was built using option probabilityestimates = 1). Thus the exported model's predictions should only be validated against SVMDA models built using probabilityestimates = 0.

ANN regression analysis models return values:
:'''yhat''' - Model prediction for y (predicted y value) as a scalar.
:'''anntype''' - ANN algorithm 1 = 'BPN', 2 = 'Encog'.

===Creating Functions from Exported Models===

Although the exported model is written as a script which would normally operate in the base workspace of MATLAB, the user can also wrap the script into a function by simply adding a standard function definition to the script file. A function wrapper keeps the input variable x from being modified outside the function. This approach tends to be safer than a script, but is not implemented by default in order to provide the widest flexibility to the user.

An example function line is provided in the exported model file (commented out) along with instructions for customization. In addition, there is an example block of code (also commented out by default) which will return “expected information” about x if the function is called without any inputs.

In general, the function definition requires only one input, x, and can output any of the variables which are present after the script's execution. An example would be:

function [scores,Q,T2,Qcon,Tcon] = mymodel(x)

This function definition returns the vectors: scores, Qcon, and Tcon, as well as the scalar values: Q and T2 to the caller.

Note, as discussed above, the user can have this conversion of the exported m-file from a script to a function applied automatically by specifying the exportmodel option '''creatematlabfunction''' = 'yes'.

==Python File Format==

The Python file output by [[Model Exporter]] can be run in a Python version 3.6 or higher environment. It requires the [http://www.numpy.org/ NumPy] package be available.

For maximum flexibility, an exported model is written as a Python module (.py file) which expects only to find a NumPy array named x in its variable space. This variable provides the input data to which the model should be applied. It is important to note that the variable x will be modified by the script and, thus, the caller should not expect the variable to remain unchanged.

The input variable x should be a 1xn or mxn array, representing a single sample or multiple samples, and the output will be a prediction(s).

===Input Data===

The expected length (number of elements) and contents of the input x vector are defined in the comments and initial sections of the exported model script. The script, as exported, does not use this information to perform any validity testing on the input variable. This information is only provided to indicate to the user what type of data is expected.

The example below shows the part of an exported model which indicates the expected data size and associated context information. This particular model expects input data of ten variables as a row vector.

# .size = [ 75 10 ];
# .include_size = [ 75 10 ];

The user can make use of this information assure the data being passed to the model is correct. Again, such testing is not provided by the script as written. Incorrect data sizes will be indicated by a runtime error when executing the script.

===Using Exported Models===

The exported model is written as a Python module with the user's specified name, for example 'pcaexample.py'. This module can be imported and used in Python code.

All exported model Python modules contain an "apply" function which requires only one input, x, and returns a dictionary containing results. The input, x, should be a NumPy array of test samples with shape 1xn or mxn for single or multiple sample data. An example would be:

# PCA model was exported to Python file named 'pcaexample.py'
import pcaexample

# Use absolute or relative path
filename = '../arch_5row.csv'

x = np.loadtxt(open(filename, "rb"), delimiter=",", skiprows=0)

# Apply exported model
res = pcaexample.apply(x)

# This PCA example function returns a dictionary containing the scores, loads, Qcon, and Tcon,
# as well as the scalar values Q and T2 to the caller. # Report scores
print('PCA: [\'scores\']: \n', res['scores'])

===Returned Results===

The particular results returned from applying the exported model depend on the model type and are the same as those listed under the [[#Returned_Results|M-file format description]]. These will be contained in a returned dictionary 'res' and can be accessed as in this example:
print('PCA: [\'scores\']: \n', res['scores'])

The full list of returned quantities is available from the dictionary keys:
# Query the resulting dictionary for keys
print('\nPCA: res keys: ', res.keys())

==XML File Format==

The XML files output by Model Exporter are not directly usable. They can be used in any programming language for which an interpreter is available to translate the model's XML representation into a form which can be used by that programming language. The Model Exporter software package includes two such interpreters, one for .NET languages and one for Java. For more details see [http://wiki.eigenvector.com/index.php?title=Model_Exporter_Interpreter Model_Interpreter]. The input variable x should be a vector, representing a single sample, and the output will be a prediction for this one sample.

===Numerical Matrix Definitions===

The XML format utilizes custom tags to define various parts of the model. For some tags, the content is a vector or matrix of values. In these cases, a comma character delineates different column elements and semicolon indicates the end of a matrix row and the beginning of the next. All white space is ignored. If a given matrix contains only one row, it is described as a "row vector". A matrix with a single column is described as a "column vector". Orientation of such vectors is critical to the mathematical operations and must be parsed appropriately.

===XML Structure===

The XML file will consist of a top level <model> tag which will contain an <information> tag, an <inputdata> tag, and one or more step segments, each wrapped in a separate <step> tag.

'''<model>'''
:'''<information>''' General information on the encoded model.
::'''<source>''' Text description of file source (EVRI Model_Exporter).
::'''<modeltype>''' Standard model method acronym (PCA, PLS, etc).
::'''<description>''' Text description of model including preprocessing, data size(s), and number of components. Each row of this multi-row string is delineated by <sr> (string row) tags.
::'''<datasource>''' Information block of modeled calibration data. <datasource> is a multi-cell table format. There will be one column of information for each block of data required by the given modeltype (e.g. PCA requires 1 block, PLS requires 2). Each <td> tag will contain a number of sub-fields describing the data used for the given block. Informational only, sub-fields may change.
::'''<outputs>''' Table-formatted (TR and TD wrapped) array of the names associated with the columns of the yhat output (if any). These can be used by the caller to assign string descriptions to each output. They are not used by the interpreter itself, however.
:'''</information>'''
:'''<inputdata>''' Specific requirements for input data including the following information:
::'''<size>''' Numeric class row vector describing the size expected for the input data (x). The first element of the vector gives the expected number of rows, the second is the expected number of columns.
::'''<axisscale>''' Numeric class row vector providing the expected axisscale of the input values. The actual values stored in the axisscale vector are completely dependent on the application and the analytical method used and may be empty.
::'''<label>''' Strings (delimited by <sr> sub-tags) defining the names of the variables expected in the input data (x). The names are dependent on the application and the analytical method used and may be empty.
:'''</inputdata>'''
:'''<step>''' Repeated tag for each step required for making a prediction using this model. Will contain the following sub-fields:
::'''<sequence>''' Numeric class single value indicating the order in which this step should be performed. The steps are generally included in the XML file in sequence-order (sequence 1 will be the first step in the file), but this field can be used to assure in-order processing of steps.
::'''<description>''' String class description of the step (informational only)
::'''<constants>''' Contains information on constants required by this step. Each constant is defined as a sub-tag herein. The name of the constant is the sub-tag name and will contain a matrix (or vector) of values to use for the given constant. See below for more information.
::'''<script>''' One or more rows of strings describing the mathematical operations to perform this step. When more than one mathematical operation is to be performed, each will be given in a separate string row <sr> tag. However, these can be ignored. Each mathematical operation will be terminated with a semicolon.
:'''</step>'''
''(Additional <step> tags located here…)''

'''</model>'''

See the provided files "pcaexample.xml" and "plsexample.xml" for full examples of the XML structure.

===Using XML-Exported Models===

TODO: describe briefly the .NET and Java ModelInterpreters and refer to the examples

====Using XML-Exported Models in .Net====
TODO:

====Using XML-Exported Models in Java====
TODO:

==Requirements for XML Interpreters==

To execute each of the <step> segments contained in the XML file, an interpreter must be able to parse the constants defined into matrices and be able to execute the script commands. The following give the specifications for an interpreter.

For examples of interpreters, see the [[Model_Exporter Interpreter]] objects in folder: interpreters/MEInterpreter, or the PHP interpreter in interpreters/predict.php. These are all distributed with Model_Exporter and are ''freely-distributable without additional licensing''.

===Managing of Constants and Variables===
* The interpreter must maintain a "workspace" of stored constants and variables in which the matrices can be accessed by a variable name (specified by the tag in which the given constant was read, for example:<pre><s class="numeric" size="[1,1]">4</s></pre>
:would define a constant "s" which was equal to the scalar value 4).
* Constants are NOT case sensitive and any interpreter must be written to consider the upper or lower case variables as the same.
* "Constants" are just pre-defined variables. Although every effort will be made to avoid changing these values, it is NOT a rule that these "constants" cannot be changed – scripts may modify and overwrite these values. They are called "constants" because they are initially defined by the model.
* The enclosing tag for the constant will define the class of the constant (in this application, constants will always be "numeric") and will also define the size of the constant using the attribute 'size'. For example, <pre><s class="numeric" size="[1,5]"></pre> defines that the enclosed constant will be a row vector (1 row) of 5 elements (5 columns).
* Prior to the execution of the script(s), the XML interpreter must place a variable named "x" (lower-case) in their workspace. This variable must contain the data to which the model should be applied. The value of "x" will be modified by the script so, following initial assignment, no alteration of this variable should be done outside what is specified by the script.
* All constants/variables must be retained for the entirety of a given step. In many cases, the variables remaining in the workspace will contain results of interest to the caller and, therefore, all workspace values should be retained. The variable "x" must always be present.

===Script Execution===

The following lists define the script commands which must be supported by the interpreter (scripts may contain only these commands). When applicable, the Matlab operator corresponding to the given function is given. Interpreters do not need to interpret these operators. They will never be used in any script and are provided here only for reference.

====Single Input Functions====

C = function(A);
abs Absolute Value Removal of sign of elements ( abs(A) )
log10 log (base 10) Base 10 logarithm of elements ( log10(A) )
transpose transpose array Exchange rows for columns ( A' )

====Double Input Functions====

C = function(A,B);
plus Plus Addition of paired elements ( A+B )
minus Minus Subtraction of paired elements ( A-B )
mtimes Matrix multiply (dot product) Dot product of matrices ( A*B )
times Array multiply Multiplication of paired elements ( A.*B )
power Array power Exponent using paired elements ( A.^B )
rdivide Right array divide Division of paired elements ( A./B )
cols Index into columns of matrix Select or replicate columns ( A(:,B) )
rows Index into rows of matrix Select or replicate rows ( A(B,:) )

===Mathematical Operation Requirements===

* All mathematical operations are expected to be performed using signed, single precision numbers.
* With the exception of mtimes (dot product), all operations are "element-by-element". That is, the two matrices passed will be equal in size (see scalar exception below) and the mathematical operation is performed between each element of matrix A and its corresponding element in matrix B. The output matrix C is always the same size as A and B.
* Scalar Exception (except mtimes): A or B may be a scalar even if the other isn't. In this situation, the scalar input must be interpreted as an appropriately-sized matrix containing all the same value.
* mtimes (dot product) is performed using the standard linear-algebraic dot-product operation. In generic terms, the input matrix A will contain m rows and k columns, the input matrix B will contain k rows and n columns and the output matrix C will contain m rows and n columns. The following equation is used to calculate each element of the C matrix (loop for i = 1 to m and for j = 1 to n):
::<math>C_{i,j}=A_{i,1}B_{1,j} + A_{i,2}B_{2,j} + A_{i,3}B_{3,j} + ... + A_{i,k}B_{k,j}</math>
:Subscripts indicate the row and column indexing (respectively) into the given array. When either A or B is a scalar, the mtimes operation should be handled as a "times" operation. That is, the operation becomes an element-by-element multiplication where each element of the matrix input is multiplied by the scalar value and C is the same size as the input matrix.
* cols and rows indexing operations should expect a row vector for B that may have repeated elements (which allows replication of a given row or column). For example, given a row vector for B of
::B = [1 1 1 2 2 2]
:passed into the cols operation, this would replicate column 1 three times then replicate column 2 three times giving a total of 6 columns in the output.

===Script Execution Requirements===
* The format for a single script command is: <pre> C = function(A,B);</pre> where function is one of the above functions, A and B are the pre-defined constants / variables to use as input to function, and C is the output. Input B will be omitted for functions which require only one input. Each command of the script will end in a semi-colon ";". All commands must be performed in the order in which they appear in the script.
* The expected size, axisscale, and labels associated with x will be stored in the <sourcedata> tab (if any exist). These values can be used by an XML interpreter to verify the data being analyzed.
* Constants are NOT case sensitive and any interpreter must be written to consider the upper or lower case variables as the same.

===Returned Results===

The results returned by a model prediction will be present as variables in the interpreter's workspace upon completion of the XML parsing. The returned results are the same as those listed for the [[#Returned_Results|M-file format]].

==Requirements for XML Writers==

The following rules are to be followed by the script creation algorithm of Model_Exporter. These rules may be of interest to script interpreters, but should not have any critical impact on interpreter design.

* Nesting of functions is not allowed. Functions can only take variables or pre-defined constants as input.
* NO iterative processes are supported. All scripts must be straight-through executing (no control structures such as "ifs", "while", etc are supported.)
* Missing data replacement is not supported.
* As of version 1.0 of this product, only variables or pre-defined constants may be used in a function. No "in-line" constants may be used. For example:

C = minus(A,1);

is invalid because the constant "1" has to be pre-defined. This command should instead be written where the "1" is pre-defined as a constant and the name of that constant is used.

* Variables are NOT case sensitive and any interpreter must be written to consider the upper or lower case variables as the same. Note, however, that the Matlab output of this function will be case-sensitive code so the scripts should try to be consistent in case, even if other interpreters won't care.

Release Notes Version 8 5

2019-02-11T18:42:16Z

Lyle:

==Version 8.5==
Version 8.5 of PLS_Toolbox and Solo was released in September, 2017.

For general product information, see [http://www.eigenvector.com/software/pls_toolbox.htm PLS_Toolbox Product Page]. For information on Solo, see [http://www.eigenvector.com/software/solo.htm Solo Product Page].

(back to [[Release Notes PLS Toolbox and Solo]])

==Overview==

* The primary focus of version 8.5 has been in Calibration Transfer and additional importers.

* PLS_Toolbox/Solo 8.5 has been tested extensively with Matlab 2017b prerelease and should be compatible with the coming release.

* Simplified interface design changes. Various controls have been moved to simplify interfaces. Seldom used settings have been moved to options in several cases (e.g., simplified [[Svm|SVM]] panel).

* The Analysis window's confusion matrix and table toolbar icon now returns classification results for both [http://wiki.eigenvector.com/index.php?title=Sample_Classification_Predictions#Class_Pred_Most_Probable 'most probable'] and [http://wiki.eigenvector.com/index.php?title=Sample_Classification_Predictions#Class_Pred_Strict 'strict'] classifications. In the 'strict' case it also lists the 'strictthreshold' value used.

==Calibration Transfer==
* [[MCCTTool | New Model-centric Calibration Transfer]] - Interface to develop instrument specific models with calibration transfer.

* [[nlstd| NLSTD]] - Create or apply non-linear instrument transfer models (PLS_Toolbox only).

* [[sstcal| SST]] - Spectral Subspace Transformation calibration transfer.

* [[Demonstration_Datasets|Corn DSO]] - New calibration transfer demonstration data set added. 80 samples of corn measured on 3 different NIR spectrometers with moisture, oil, protein and starch values for each of the samples is also included.

==Importers==

* [[shimadzueemreadr]] - Imports Shimadzu EEM formatted text files.
* [[visionairxmlreadr]] - Imports Vision Air formatted XML files (X- & Y-Blocks).
* [[pltreadr]] - Imports Vision Air model files (.plt).

* [[aqualogreadr]] - Improved capability of loading multiple files containing samples of varying sizes (PLS_Toolbox only).

* [[jascoeemreadr]] - Improved capability of loading multiple files containing samples of varying sizes (PLS_Toolbox only).

* [[hitachieemreadr]] - Improved capability of loading multiple files containing samples of varying sizes (PLS_Toolbox only).

* [[rawread|RAWREAD]] - Added support for new version format (3 & 4) (See MIA_Toolbox).

* [[spgreadr|SPGREADR]] Added new feature, options.spectrumindex now can be an integer, an array of integers (indices, & order doesn't matter), or 'all'. When loading multiple files, options.spectrumindex must be either a single value or 'all'.

==Exporters==

*[[writeplt]] - Converts an EVRI Model to a Vision Air PLT model, as a .PLT file.

==Other Features and Improvements==
* [[Cooksd| Cook's Distance]] - Calculates Cooks Distance for samples in a regression model.

* [[rpls| RPLS]] - A recursive PLS and PCR variable selection algorithm.

* [[manhattandist| MANHATTANDIST]] - Calculate Manhattan Distance between rows of a matrix.

* [[Confusionmatrix]] and [[Confusiontable]] - Classification results formatting options added. Can now specify use of mostprobable or strict classification rule.
* [[calcdifference]] - Calculate difference between two datasets.
* Added GLS Weighting to model optimizer.