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perClass Documentation version 5.4 (7-Dec-2018)

User's Guide Release notes Toolbox Ref. Runtime Ref. Knowledge Base Glossary Cheat-sheet

• 1.1. This manual
• 1.2. Introduction to perClass
• 1.2.1. Versions
• 1.2.2. System requirements
• 1.2.3. Useful general commands
• 1.2.3.1. Displaying perClass version and license information
• 1.2.3.2. Demo examples
• 1.2.3.3. Provide direct feedback to PR Sys Design
• 1.2.3.4. Control messages displayed by perClass
• 1.3. Release notes

1.1. This manual ↩

This manual assumes basic knowledge of pattern recognition and Matlab environment. In order to embed trained classifiers into custom applications, basic familiarity with C language is also assumed.

The manual is structured in four parts:

• User's guide - explains software functionality
• Reference manuals for the perClass Toolbox and for perClass Runtime library describe the programing interface
• Knowledge base - collects number of step-by-step usage examples and "howtos"
• Glossary - explains basic pattern recognition terminology

1.2. Introduction to perClass ↩

perClass is a software package that provides quick development of custom machine learning solutions. With perClass, R&D specialists in many innovative companies, have developed algorithms to detect cancer, sort luggage at the airports, classify defects of machine parts or spot traffic accidents.

perClass is composed of two parts, namely perClass Toolbox for quick design of classifiers in Matlab and perClass Runtime for classifier deployment in production.

perClass provides tools for:

• Construction of data sets
• Handling of multiple sets of labels and arbitrary meta-data
• Interactive visualization of data and meta-data
• Feature extraction for images and spectra
• Training statistical detectors and classifiers
• Quick evaluation of classifiers
• Optimizing classifier decisions according to performance requirements using two-class and multi-class ROC analysis
• Building hierarchies of classifiers and classifier fusion
• Deploying trained classifiers in custom applications out of Matlab in real-time applications

1.2.1. Versions ↩

perClass comes in the following versions for commercial use:

• perClass Toolbox for development of machine learning algorithms. The permanent license is bound to a hardware dongle.

• perClass SDK for embedding trained classifiers into custom applications. Annual license is bound to the hardware dongle of the toolbox.

• perClass Pro: bundle of Toolbox and SDK. Pro is the complete solution for design of algorithms and their embedding in custom applications.

• perClass Enterprise: the complete solution for design of algorithms with perClass Toolbox and embedding them in custom applications with perClass SDK. All functionality is permanent.

These versions are available for academic research and teaching:

• Lite: Free limited version for non-commercial use intended for people who are learning about pattern recognition. It contains only perClass Toolbox and is limited to data sets with maximum 300 samples and three classes.

• perClass Toolbox Academic: perClass Matlab Toolbox discounted for use by university students and researchers for non-commercial projects. The license is permanent and bound a hardware dongle which allows the researchers to move between different machines.

• perClass Pro Academic: perClass Pro discounted for use by university students and researchers for non-commercial projects only. The license is permanent, bound a hardware dongle and includes both the perClass Toolbox and the perClass Runtime library for execution of trained classifiers out of Matlab in research demos.

For Academic and Commercial versions, also group licensing is available using floating licenses provided by a license server.

1.2.2. System requirements ↩

perClass is supported on the following platforms:

• MS Windows 32-bit
• MS Windows 64-bit
• Linux 32-bit (x86)
• Linux 64-bit (x86)
• Apple Mac OS X 32-bit (x86)
• Apple Mac OS X 64-bit (x86)

perClass requires Matlab 7.5 or later

1.2.3. Useful general commands ↩

1.2.3.1. Displaying perClass version and license information ↩

perClass version information may be displayed using sdversion. It consists of a numerical part (e.g. 4.0) and a build date (6-May-2013).

sdversion also provides several license-related details such as license type (Commercial, Academic or Lite), licensee name and the license expiration date.

>> sdversion
perClass 4.0 (06-May-2013), Copyright (C) 2007-2013, PR Sys Design, All rights reserved
 Customer: PR Sys Design (PRSD)  Issued: 27-mar-2013
 Toolbox with DB,imaging: The license expires on 1-jul-2013.
 SDK: The license expires on 1-jul-2013.
 Installation directory: '/Users/pavel/matlab/toolboxes/perclass'

1.2.3.2. Demo examples ↩

sddemo lists several basic examples to get started

>> sddemo
run perclass_exampleX.m where X is the index of the desired example

 1 : Working with data sets
 2 : Training a classifier and visualizing decisions
 3 : Tuning a classifier using ROC analysis
 4 : Multi-class ROC analysis
 5 : Building detectors
 6 : Building a detector-classifier cascade

1.2.3.3. Provide direct feedback to PR Sys Design ↩

sdfeedback command allows users to submit feedback such as error messages to PR Sys Design directly from within Matlab. Running sdfeedback without arguments opens an edit dialog where the user may paste or type the desired message. An alternative is to provide the message to sdfeedback as a string.

1.2.3.4. Control messages displayed by perClass ↩

sddisplay command provides global verbosity control in perClass. Running sddisplay without arguments prints the current display state (on/off). To switch off messages printed by perClass, use:

>> sddisplay off

Default sddisplay state is on. When perclass_mex library is re-loaded into memory, this default state is re-introduced.

Alternatively, you may use the 'nodisplay' option in the functions that support it: sdrelab, sdroc, sddetect and sdcrossval.

1.3. Release notes ↩

Version 5.4 (7-Dec-2018)

• adding single precision deployment for models exported from perClass Mira GUI
• sdsvc adding support for support vector regression with 'target' option
• fix of error in sdscatter with decision backdrop when multiple figure windows were open
• fix in sdlist allowing use of logical indices
• fixing the problem with multiple license checkouts on floating license servers

Version 5.3 (7-Jul-2018)

• internal release adding perClass Runtime deployment in Imec integration

Version 5.2 (9-Jan-2018)

• sdconfmat enables interactive tuning of operating points directly from a confusion matrix (try: load fruit; p=sdfisher(a); r=sdroc(a,p); sdconfmat(r))
  • define multiple contraints by clicking on the confusion matrix fields (directly constrain errors, performance and per-decision precision)
  • specific errors in a confusion matrix can be interactively lowered by mouse scroll-wheel or cursor keys (this is really cool new way to leverage application-specific knowledge)
  • can be opened from any ROC figure (sddrawroc) via toolboar or 'c' key
  • making constraints in a confusion matrix disables corresponding operating points in the attached ROC plot. This allows one to quickly explore viable subsets of solutions
  • this update makes multi-class classifier tuning easy and practical
• sdsvc support for class weighting (of the C parameter) with 'w' option. This enables a practical way to find good default solution in skewed problems (some classes much bigger than others)
  • RBF, polynomial and linear SVM supported
  • one-against-one multi-class SVM supported
• sdneural RBF networks significantly faster in training
• sdbox command gets new options to remove outliers:
  • 'bounds' option allows setting min/max values applicable to all features
  • 'min' and 'max' options allow specification of min/max values per feature
• sdimage visualization allows setting color scaling limits by percentile with 'o' key (outliers). This is useful to avoid influence of outliers on visualization.
• new data preprocessing option for spectra (dark-current and white-background normalization) in sdprep
• quickly define a random set of categories using randsubset on sdlist objects. For example, in a medical data set where each sample has a patient label, we may quickly define a set of training/test patients with [Ltr,Lts]=randsubset(data.patient.list,0.5) and then get respective data sets with [tr,ts]=subset(data,'patient',Ltr).
• sdexport for Cubert plugins supports class colors and multiple ROCs (e.g. cascaded detector + classifier with separate ROCs)

Version 5.1 (29-May-2017)

• adding support for Matlab 2017a
• multi-channel input support in deep learning (color or multi-band images)
  • showing figure number for each network to easily match training progress to specific model
• sdextract adds support for raw multi-band extractor (useful e.g. for deep learning on multi-channel inputs)
• support for GPU training (requires Parallel Computing Toolbox)
  • sddeepnet supports CUDA and optionally (cnDNN) if installed on Linux 64-bit
  • sdneural for both multi-layer perceptron (MLP) and radial basis function (RBF) networks
  • sdpca and sdparzen
• Support Vector Machine improvements:
  • probabilistic output for two-class SVM classifiers with 'prob' option
  • linear SVM can be approximated by affine projection
• sdimage support for moving to specific band when opening figure ('band' option or 'b' keystroke). Also possible to change band in already opened figures
• GUI fixes in sdconfmat and sddrawroc for class names with underscores
• specify multiple constrains for multi-class ROC given a matrix (same size as confusion matrix, NaN fields ignored)
• sdscatter adds a command to change class markers randomly (':' keystroke). This is useful for situations with many classes or cases where a data set contains indentical marker for each class.
• sdroc stored confusion matrices by default (earlier requires 'confmat' option). This allows us to directly view confusion matrices or use it for selection of operating points. There is a new 'no confmat' option to disable this behaviour.
• when expanding results computed on image grid with expand method, labels or decisions can be provided as second argument. This is useful to quickly expand decisions back to the original high-resolution image
• Stacked generalization sdstackgen adds support for user-defined algorithms (that can be converted into a pipeline and provide soft outputs)
• new sdprep command for data preprocessing:
  • divide by the sum (divsum) or mean (divmean) of each feature vector
  • divide by value of a specific band (if a sample represents a 1D spectrum)
  • smoothing and (smoothed) derivative of spectra
  • user-defined kernel for 1D convolution of spectra
  • compute band ratios (such as NDVI index)
  • support for "adding" new computed features after all input features
  • sdprep returns a pipeline that can be added to a classifier and exported for direct deployment using perClass Runtime
  • multiple spectral indices can be computed by a single pipeline using stacked pipelines, see example.
• sdrun_mex supports decision output for stacked decision pipelines ("crisp" combined classifiers)
• licensing:
  • sdactivate command included to allow license activation
  • sd_Activate API for activate of perClass Runtime
  • sd_GetLicenseInfo API to retrieve details of perClass Runtime license
  • sdrun command-line utility gets '-l' option to show license details
• fixes:
  • fix for weighting-based decisions in situations where soft outputs are negative (before, all-negative outputs would all get assigned to the first category)
  • pipeline buffer in sd_LoadPipelineFromBuffer is constant

Version 5.0 (21-Sep-2016)

• improved support for Matlab R2016a. perClass keeps the support for R2007b and higher.
• support for deep learning
  • easy to train deep networks via a single sddeepnet command
  • simple way to define network architecture and get feedback with most common errors
  • support for convolution, spatial pooling, relu, dropout and batch normalization
  • architecture may be defined in a separate cell array ('arch' option)
  • support for filter groups (multiple filters trained on identical input bands)
  • network training is repeatable if random seed is fixed (via rand('state',X))
  • special GUI showing training progress and best solution found
  • possibility to train further, fine-tune learning rate, batch size or update training/val sets
  • stop when a specific error is reached with 'stop at' option. By default stops at zero error.
  • trained networks are standard perClass pipelines and can be mixed and matched with any other tools such as ROC or cascading
  • training is based on Matconvnet toolbox (bundled in perClass distribution), training support for 64-bit platforms
  • on the deployment side, perClass Runtime remains single self-contained DLL (there is no change to Runtime API, it's sufficient to drop-in 5.0 dll in the project without recompile to load and execute deep networks)
  • execution speedup via joining 'conv' and 'bnorm' pipeline stages with + operator
• imaging improvements:
  • new 'resize' object extractor in sdextract resizing any object to a regular grid. This is useful to define data set for deep learning.
  • new easy way to pass decisions on local image regions, computed on a grid, back to the original full resolution image with expand method. This greatly simplifies execution of computationally-intensive algorithms such as deep learning over entire high-res images. The expand method supports also passing of labels defined on a grid back to original image.
  • sdextract can now compute local regions for specific pixels given by linear pixel indices with 'pixels' option
  • region "raw" extractor in sdextract now automatically sets the imsize of the output data set
• other improvements
  • sdscatter adds a context-menu command to select a subset based on a specific label of a current sample (e.g. 'Select only the patient as this sample represents.')
  • all commands returning figure handles (sdscatter, sdimage, sddrawroc etc.) return only figure number in R2014b and later. This is to avoid excessive display of Matlab figure object details. It is possible to use the returned number to focus the figure using figure(f)
  • sdsvc uses updated libsvm binaries version 3.21 on all platforms
    • solution for problems, where libsvm optimizer does not converge or takes extremely long time with new 'verbose' and 'no shrink' options
    • new option 'verbose' shows detailed libsvm output
    • it is now possible to disable "shrinking" heuristics of libsvm (using 'no shrink' option). This may significantly speed up optimization in some cases (when 'verbose' output indicates "Warning: using -h 0 may be faster")
    • in order to repeat exactly past experiments based on earlier perClass versions, the older libsvm 2.91 binaries from older perClass distributions are still supported by sdsvc command
  • affine projections (PCA/LDA) now offer 'weights2' output returning relative weights of input features for each output dimension (see output of info(p) or p' for PCA/LDA pipelines)
• fixes
  • sdlms fix: When building regression model, it is not necessary to have more than one class defined (there is no difference in model output)
  • sdimport now judges type of entire column as nominal or real based on all values, not only first row
  • sdlab insert fix for singleton labels (one entry per category) where the order of categories and entries could become inconsistent
  • fixing problem with horizontal concat of labels in R2016a
  • fixing issues related to subsref on sddata and sdppl related to changed subsref behaviour in R2015b (e.g. p(1:2).output)
  • sddata fix for assignments given a logical subset (data( dec=='target' ).lab='target')
  • fix for a problem when reading comma-separated values in sdrun command
  • fix for sddrawroc legend warning in R2016a

Version 4.8 (19-Apr-2016)

• show all keyboard shortcuts in any sdscatter or sdimage figure with ? key or toolbar button
• sdimage improvements
  • support for zoom and pan with standard Matlab tools
  • zoom is also quickly accessible with +/- keystrokes that follow the cursor and do not require mode switching
  • legend support. This also enables one to use standard print command to save content of sdimage figure
  • New 'go-to-band' functionality with 'g' keystroke
  • cropped images may be related back to the original. Both, the reference and assignment operations are supported (Example: Pass the labels from a cropped image to its original image: origim( croppedim ).lab=croppedim.lab)
• sdextract improvements
  • new orientation histogram feature extractor (orihist). Supported both for image neighborhoods ('region' domain) and for objects (defined e.g. by segmentation or clustering)
  • new grey-level morphology extractor (erosion, dilation, opening, closing, edge extraction)
  • sdextract now requires that input is an image data set (sddata object created with sdimage command), not an arbitrary image matrix
• sdscale adds support for non-linear scaling (different variants of exp and log scaling)
• fix for the sdsegment crash on images with more than 5000 regions (new maxsize option allows to specify max region count)

Version 4.7 (15-Dec-2015)

• improvements of interactive ROC visualization (sddrawroc) showing confusion matrices:
  • it is now possible to visualize a difference confusion matrix showing improvements to a selected operating point. This significantly simplifies selection of a good trade-off in multi-class situations.
  • new toolbar buttons to show confusion matrix (or normalized confmat) in ROC figures
• sdsvc adds support for one-against-one multi-class strategy.
• sdkmeans training may stop earlier if stability is reached. The 'iters' option now specifies the upper bound on the number of iterations.
• sdexport improvements
  • it is possible to specify numerical format used when exporting data to text files ('numformat' option). This allows us to tweak the precision needed.
  • fixes for WEKA Arff format export to support class names with spaces
• the least-mean-square classifier sdlms adds support for per-sample weights (useful to create base learners for boosting algorithms)
• sddetect adds 'confmat' option to store confusion matrices in the detector. This allows us to visualize them later or to set operating points by cost-sensitive optimization.
• sdextract adds support for custom convolution filter banks ('fbank' extractor)
• details on data sets, labels and pipelines may be displayed using info method (or the transpose operator shortcut ')
• fixes addressing incopatibilities due to new Matlab engine in R2015b
• fix for sdparzen NaNs appearing in EM algorithm optimization
• fix for sdcascade with sdrelab actions
• added multiple dongle support for all platforms and all products including perClass Runtime DLLs (see discussion at http://perclass.com/index.php/forums/viewthread/440/).
• visual fix for sdscatter making sure there is precise alignment between scatter plot and decision backdrop on Windows and Linux

Version 4.6 (29-Jun-2015)

• improved setting of a current ROC operating point
  • setcurop method now in one command combines a performance constrain with the minimization/maximization of an error criteria, example: r=setcurop(r,'constrain','TPr(apple)',0.9,'min','FPr(apple)')
  • @sdalg/@sdalg algorithms can re-run setting of an operating point with setcurop method.
• runtime improvements
  • support for classifier execution directly on uint8/uint16 input buffers (e.g. direct in-place processing of RGB images).
    • No need to loop over your data and cast into double precision on your application side.
    • Simply add an sdconvert step to any pipeline performing the conversion from uint8 or uint16 to double in inside the runtime.
    • Feature selection is supported with the 'select' option. This brings significant speed-ups by processing only desired image chanels.
  • new SDK utility/example ex_diff.c executes a classifier pipeline on a saved binary data and compares results to a stored binary outputs. See ex_diff.m example.
• new deployment platform
  • support of royalty-free deployment to LabView real-time OS (PharLap-ETS platform). For details contact us at info@perclass.com.
• local image feature extractors
  • Leung-Malik multi-orientation/multi-scale filter bank 'fbank:LM'
  • Schmid rotationally-invariant filter bank 'fbank:S'
  • Maximum Response filters combining multiple orientations 'fbank:MR8' and combining both orientations and scales 'fbank:MR4'
  • 'raw' extractor unroling pixel neighborhood into a feature vector
• other improvements
  • sdimage and sdextract now report an error if an image data set contains multiple copies of the same pixel (this can happen by manually joining image sets multiple times)
  • fix in sdextract for 'class fractions' object-level extractor that now correctly supports situations where a subsets of classes was detected over an object
  • sdimage figure now shows the size of a class under cursor
  • setmarkers and setcolors without extra arguments set default values into a data set

Version 4.5 (17-Mar-2015)

• new RBF neural network available via sdneural with sdneural(data,'rbf'). It is implemented using a highly scalable and fast direct (not iterative) formulation.
• improvements of sdmissing
  • new 'class mean' and 'class median' imputation methods that are providing more realistic imputation values based on prior knowledge of class labels
  • new 'from',DATA option allowing imputation based on externally-supplied data set. This is useful when performing missing value imputation in new/test data only based on training data statistics
  • maximizing the amount of data used to estimate imputation statistics with per-feature imputation by default
• sdimage improvements
  • it is now possible to retrieve a data set from a figure via command-line using sdimage(fign,'getdata')
  • improved painting mode - context menu shows the list of all available classes + new one and allows to freely switch between them (via corresponding digits)
  • single pixel brush option available
• sdsvc pipelines now expose multiple parameters:
  • sigma for RBF or degree for polynomial kernel
  • C parameter used in training
  • svcount - the number of support vectors
  • svind - indices of support vectors in the training set
• support for joining multiple feature selection pipelines with + operator
• new sddata median method
• sdalg improves descriptive display for embedded algorithms (alg in an alg)
• setprop now requires correct shape of sample properties to avoid ambiguity
• fix of isclass issue with sometimes incorrectly functioning 'only' option
• sdimport fix improving detection of nominal data from numerical NaN values
• when converting a custom algorithm into pipeline with sdconvert, check is made whether the algorithm function is on Matlab path

Version 4.4 (3-Dec-2014)

• significantly faster multi-class ROC estimation
• compatibility with Matlab R2014b new graphics subsystem (sdscatter and sdimage)
• sdsvc added support for one-class classification based on RBF kernel
• sddetect support for user-specified performance measures for internally-estimated ROC sddetect(data,'target',model,'measures',{'TPr','target','precision','target'})
• sdextract improvements:
  • object-level shape features (Hu moments and shape eigenvalues). Shape representation can be computed both from a binary object mask or from object image content (on a single band/feature)
  • object-level extractor computing class fractions inside each object from per-pixel decisions. This is useful for defect detection/material classification inside pre-segmented objects.
  • fix of single pixel shift in extracted data
• sdrelab accepts relabeling rules defined by two sdlab objects of the same size (category in the first gets relabeled into the corresponding category in the second sdlab object)
  • this provides very easy passing of object labels/decisions back to all object pixels in an original image
• sdlda now supports user-specified class priors with 'prior' option
• speed up of pipeline execution by user-defined joining of pipeline steps with plus (+) operator (applicable to scaling, affine projections and neural nets)
• added support for long SQL statements (up to 32kB)
• added sddb istable method to test if a table exists in an SQLite data base
• sdfeatsel improvements
  • adding support for nominal variables (feature selection steps can report nominal feature values via sdnominal)
  • fix of sdexe error when applying single feature selection step to nominal data set
• sdversion adds a platform string to simplify platform-specific support

Version 4.3 (24-Jun-2014)

• fix for sdsvc problem with soft-output offset which impacted performance of multi-class classifiers. Two-class SVMs, followed with ROC, were not affected.
• more informative warning given for sdrandforest when the data set size requires larger number of nodes with 'maxnodes' parameter

Version 4.3 (26-May-2014)

• Complete perClass documentation is now available from within Matlab via doc command. In Matlab 2012b or later, you need to click on "Supplemental Software" to access documentation for 3rd-party toolboxes.
• new sdlogistic classifier with non-linear decision boundary due to polynomial feature space expansion
  • direct multi-class formulation, optimization by gradient descent
  • polynomial expansion accessible also separately via sdexpand
• sdextract feature extraction improvements
  • simplified syntax: sdextract(data,domain,feature,options)
    • domain string describes where features are computed ('region','object','color','bands')
    • feature name is always given as third argument
  • 'region' extractors (local image features in a sliding window)
  • 'object' extractors (one feature vector per object defined by object labels)
• sdsegment improvements (connected components in images)
  • significant speedup for large images
  • fixed problem in connected component labeling for complex shapes
• sdimage improvements
  • create new label set from current (copy labeling, 'N' key shortcut). Use-case: Quick labeling of image regions by hand. First, find connected components, copy labeling into a new set and use "Rename class" to assign meaningful class labels.
  • create new image from data of a specific class (or subset of classes by name/regular-expression with '/' key). Use-case: Quickly extract a subset of an image.
  • support for bounding boxes with 'bbox' option in GUI, sddata sets and matrix output
  • improved class rename command (support for class merging, does not change class colors), now bound to 'r' keystroke.
  • improved color handling (added commands for random color change for all classes ('R' key) or specific class ('.' key))
  • fixed error when drawing classifier decision with interactive ROC on a subset of an image (sdimage(sub,p,'roc'))
  • fixed a problem where it was not possible to switch back to the label layer with 'space' key
• sdscatter improvements
  • Improved handling of axes limits:
    • There is an "auto" mode and "manual" mode of axes limits
    • The default "auto" mode sets the limits of the entire data set (or to visible subset with 'v' keystroke)
    • The "manual" mode is toggled by standard Matlab zooming, panning or by setting the x- and y- limits via toolbar buttons or 'x'/'y' key-strokes
    • New toolbar button shows/switches the "auto"/"manual" modes (also via 'a' key-stroke)
  • fix in the label painting: New markers are chosen to be different than the existing ones, markers/colors do not change
  • fixed error in "new from current labels" command when label set already present
  • fix in sdconfmat opened from the scatter plot ('c' keystroke). It can now be closed with a standard keyboard shortcut.
• other improvements
  • easy access to image subsets with a( b ).lab='something' syntax when a and b are images of the same size. a(b) then means "return a subset of a with pixels available also in b".
  • fixed problem with sdrelab renaming large label sets (e.g. when loading large sets of file names into sdlab object (example: sdlab(dir('path/*jpeg'))) and extracting sub-strings using regular expressions)
  • sdcrossval for leave-one-out throws an error if 'seed' option is used (does not make sense in leave-one-out)
  • sdrelab makes sure that makers for new categories are different from already present ones (default markers are user-adjustable in default_markers.m function)
  • fix for multi-class sdsvc: colors are now set properly
• C/C++ API improvements
  • fixed performance timer problem on MS Windows platforms.

Version 4.2 (5-Mar-2014)

• sdconfmat visualization in a figure with 'figure' option provides easy visual representation of confusion matrix
• sdscatter improvements
  • show confusion matrix with visible subset of data ('c' key)
  • filter improvements
    • visual indication of all filters above the plot
    • invert filter on current labels ('i' key)
    • remove filter on current labels ('r' key)
  • tagging individual samples with double-click or 't' key.
  • create new set of labels from current labels ('N' key)
  • label visible samples as ... ('L' key)
  • switch label painting on and off with 'p' key
• sddrawroc visualizes confusion matrix in a separate figure with 'c' key
• sdscatter fixes
  • fixed feature plot with scatter misalignment
  • fixed issue with feature plot update with auto limits ('a' key)
  • when painting already existing class, use its marker
  • fixed 'previous filter' glitch when applying 'hide this class' command multiple times
• SDK
  • fix for compiler warning 'null character(s) preserved in literal' when embedding pipelines into C header file with sdexport 'header' option.

Version 4.1 (3-Dec-2013)

• classifier improvements and changes
  • new sdmixture implementation scalable to very large data sets
    • new fast automatic estimation of number of components
    • Gaussian models provide access to component labels p.complab and component subsets with p(1,ind)
  • crisp combination of classifier decisions
    • sdstack and horizontal concatenation [] support for crisp combiners (e.g. p1=sdlinear(a); p2=mixture(a); pc=[p1 p2])
    • sdcombine provides new combining rules ('all agree' and at least N agree with 'at least' option)
  • new least mean-square classifier sdlms
  • sdkmeans and sdknn set default threshold to accept all training data (not only prototypes)
  • sdscale allows manual construction of scaling pipelines
  • sdneural fix when training further from initial model
  • fix in lookup-table classifier sdlut which now rejects examples precisely at the border
• ROC improvements
  • multi-class ROC optimizers return richer sets of operating points (also for cost-sensitive optimization)
  • if sdroc is provided cost specification matrix, it is used to set the operating point by default
• feature extraction and selection improvements
  • convolution based local image features in sdextract (Gaussian and Gaussian derivatives, Sobel)
  • new extractor for spectral data sdbands supporting both mean and linear discriminant per band. Bands may be defined manually, sequentially or by clustering.
  • fix in sdfeatsel for backward selection using a classifier error as criterion
  • fix in sdextract for error when providing a mask
  • sdprox can be used to create data sets from a custom-computed proximity matrix. It adds all meta-data of prototypes as new feature properties of the output set.
• GUI improvements
  • sdimage uses colors defined in class lists
  • sdimage change color via context menu
  • sdscatter allows manual setting of x and y axis limits via toolbar buttons or 'x' and 'y' keystrokes
  • fix in sdscatter where changing marker failed when on filter subset
  • fix in sdscatter feature distribution plots (small classes do not disappear)
• core functionality
  • speedup of data set, label and list operations when many categories (thousands) are present
  • sdlab adds 'size'/'sizes' and 'fraction' fields
  • sdlab now supports construction with category + number of consecutive entries also when categories are repeating: sdlab('apple',4,'banana',3,'apple',2)
  • fix in sdimport when loading mixed real-value and nominal data set
  • sddata returns empty if feature does not exist
  • all objects now supports isempty method (discussed in http://perclass.com/index.php/forums/viewthread/411/ )
• SDK
  • new API function sd_GetClassifierCount returns the number of classifiers in a pipeline (a pipeline describing a cascaded system contains multiple classifiers)
  • sdrun utility now displays the number of classifiers in a pipeline together with number of op.points and current op.point for each classifier
  • the sd_LoadPipeline function now returns error -226 if a pipeline file cannot be opened instead of reporting an "unknown pipeline format (-224)"

Version 4.0 (26-Jul-2013)

How to upgrade to 4.0

• support for nominal features and embedded SQLite database
  • create sddata sets from cell arrays with categorical data
  • train classifiers on categorical data
  • easy conversion between categories as strings/sdlab labels and numerical representation
  • new command sdnominal to check if nom.rep. is identical in data sets and classifiers
  • work with large local databases from within Matlab using SQL language
  • multiple active connections, multiple live queries per connection (live updates in underlying database available in open connections)
• licensing changes
  • introducing separate DB and imaging licensing options for perClass Toolbox
  • single license file per customer (mutliple host definitions in one license file)
  • Enterprise version now has all functionality permanent (toolbox and SDK)
  • in addition to deployment dongles, deployment host-based licenses are available (no need for extra hardware)
  • new deployment option for servers
• new products
  • perClassNET - a .Net interface allowing calling perClass classifiers from C#, VisualBasic.Net, Excel etc.
  • perClass Connector - executing classifiers within the MS SQL Server environment via stored procedures
  • perClass OEM product for high volume deployment (contact sales@perclass.com)
• new pipeline functionality (making pipelines simpler to use and smarter)
  • all classifiers return decisions by default
  • pipelines store input feature labels (p.inlab) and descriptive output (p.output)
  • pipeline details via transpose operator (p')
  • unary minus removes decision step if present p=sdfisher(data); soft_out=data*-p;
• data and label improvements
  • new syntax for sdlab creation with 'sizes' option: lab=sdlab(class_names, 'sizes', vector_of_sizes)
  • feature labels data.featlab can be assigned a string cell array
  • sdfeatsel allows selection of features by regular expression pf=sdfeatsel(data,'/Moment') selects all features containing 'Moment'.
• classifier improvements
  • new fast implementation of sdneural scalable to thousands of samples
  • sddetector renamed into sddetect. By default, one-class detectors do not loose any training target.
  • simplified classifier combining with sdcombine, sdstack and [ ] concatenation
  • sdreject by default makes one-class detector accepting all training examples
  • arbitrary relabeling of classifier decisions with sdrelab
  • improved handling of sdsvc optimizer failures (adding NaNs in grid search results)
• ROC improvements
  • sdroc accepts test set and a trained classifier (after p=sdfisher(a) the r=sdroc(b,p) is equivalent to r=sdroc(b*-p))
  • AUC shown in the sddrawroc plot. Can be computed via auc(r).
  • no additional setcurop is needed for multi-class ROC with cost specification matrix M (example: r=sdroc(ts,p,'cost',M))
• GUI improvements
  • toolbar buttons for most common tasks
  • sdscatter zoom and pan
  • significantly faster opening of sdscatter and sdimage
  • better guidelines in sdimage crop
  • interactive color selector to change backdrop decition colors in sdscatter (press 'c' key)
  • marker colors (Matlab markeredgecolor) may be set in a data set using setmarkers
  • sdfeatplot unique-value mode ('u' key) uses stem plot by default
• feature extraction improvements
  • sdfeatsel speedup by supporting a user-defined number of steps and subset initialization
  • band extraction for spectral data
  • RGB2HSV color conversion
• Runtime API improvements
  • added sd_GetOpCount and sd_GetOp functions to retrieve details on operating points

Version 3.4 (9-Oct-2012)

• local image feature extraction with custom callbacks in sdextract (out=sdextract(data,'block',16,'feat',@my_extractor))
  • user-defined feature extractors may use additional parameters out=sdextract(data,'block',16,'feat',@my_extractor,{'levels',8','range',[0 256]} ), read more
• sdimage improvements
  • creating RGB label image impainting labels/decisions to image regions LI=sdimage(im,'labim'), read more
  • label images may be blended with another RGB image with LI=sdimage(im,'labim','blend',origim)
  • shrink image data set to grid by a command in Image menu or using sdimage(im,'grid'). This allows us to easily inspect feature images. read more
• new sdsegment command to define connected components based on labels of an image data set
• support for regularization in sdgauss, sdlinear and sdquadratic. This is an alternative to dimensionality reduction. Regularization allows for training good models in problems with limited amount of training data. read more
• sdcrossval support for precomputed proximity matrices. With 'prox' option, care is taken that both training and test sets are represented only by training prototypes.
• sdpca can optimize dimensionality based on error of a given classifier. Example: p=sdpca(data,sdlinear) returns PCA minimizing the sdlinear error, read more
• sdexport got a 'no header' option and support for export of raw data matrices
• added support for direct assignments into feature and data properties: a.featlab(3)='moment'
• added sdlab support for concatenation with a string [lab; 'aaa']
• fixed handling of features with constant values in sdscatter plots
• fixed a problem under Windows where scatter plot became unresponsive when changing dimennsions
• fix of sdconfmat display output for normalized matrices

Version 3.3.1 (7-Aug-2012)

• sdlab object supports == and ~= operators for quick comparisons (example: get a number of errors with sum(a.lab~=dec))
• sdscatter toolbar legend button shows current data set legend
• fix for possible crash when creating nested stacked combiners
• fix for sdrandforest repeatability problem (example: rand('state',1); p=sdrandforest(data); gives identical results)
• fix for sdrelab display with multiple rounds of renaming

Version 3.3 (21-May-2012)

• sdversion now displays installation directory
• improvements of sdsvc
  • reporting a clear error message when libsvm optimizer does not find any solution
  • improving grid-search performance
  • removing output normalization for multi-class
• sdscatter improvements:
  • sample inspector is positioned next to the scatter figure
  • user may pass a parameter to a calback functions which is executed when the user clicks on data sample
• sdtree and sdrandforest show number of thresholds and base classifiers in the pipeline display string
• fix in sdlda improving performance for badly-conditioned data sets e.g. with binary values
• sdrelab now shows informative error message if relabeling map is not composed of input/output pairs.
• fixes of sddata class subset allowing logical or cell array sub=a(:,:,{'banana'}) or sub=a(:,:,a.lab.list~='stone')
• sddecide fix to properly handle sddecide(p*r) call
• sdfeatplot adds 'absolute' option to visualize absolute frequencies instead of default relative ones
• sdrun MEX library can now return decision names with L=sdrun(pind,'list') call.

Version 3.2 (14-Mar-2012)

• improvements of interactive image view sdimage
  • interactive crop function
  • definition of connected objects. Small object are by default tagged for easy removal.
  • custom level of label trasparency may be set using 'alpha' option (sdimage(im,'alpha',0.4))
• hand-drawn polygon classifier improvements in sdscatter (see the video)
  • polygon classifier returns directly decisions
  • inside/outside decisions may be changed from sdscatter
  • confusion matrix may be shown for the current subset of data
• feature handling improvements
  • sdfeatsel can find/remove features with zero variance
    • example: sdfeatsel(data,'var>0') returns features with non-zero variance
  • features may be selected in sddata and sdfeatsel using a cell string of names. Groups of features may be easily selected by substring with support for regular expressions. For example:
    • data(:,'/Moment') selects all features containing substring 'Moment'
    • sdfeatsel(data,{'Skew','~/Energy'}) selects 'Skew' and all features that do not contain 'Energy'
  • features can be removed from sddata given logical or cell array with names (data(:,data.featlab=='/Moment')=[])

Version 3.1.2 (22-Dec-2011)

• sdexport now supports export to C45 data format
• sdscatter visualization of sdtree decisions now allows to interactively change the number of thresholds with a slider
• fixing the problem with sdrun MEX which was returning int not a double pipeline index (issue pC-1267)
• fixing the issue with setting the number of decision tree nodes

Version 3.1.1 (24-Nov-2011)

• sdscatter now shows a value of the soft output under the mouse cursor in the figure title
• polygon drawn in sdscatter figure may be saved also by pressing the 's' key
• fix of sdscatter problem when showing soft outputs
• sddecide now gives informative error message when given an empty ROC object (result of constraining when no operating point is available)
• fix of erroneous sdp_combine product combiner output

Version 3.1 (14-Nov-2011)

• fast and highly scalable decision tree (sdtree) and random forest (sdrandforest) classifiers
• polygon classifier may be drawn interactively in sdscatter figures
• classifier acceleration with 2D lookup table (sdlut) classifier
• sdfeatsel now supports feature selection based on trained decision tree
• sdimport supports reading of sdlab labels separately
• randsubset method supports bootstrap sampling
• sdexport displays minimum version of perClass runtime required to execute exported pipeline. This means that deployed runtimes may be updated only when needed.
• sdfeatplot adds interactive selection of feature threshold.
• sdcascade now supports classifier cascades as inputs
• Improvements of sdscatter displaying classifier decisions:
  • Decision under cursor is now shown in the Figure title
  • The color of the decision under cursor may be changed using 'c' key
  • Pipeline drawing decisions may be saved back into Matlab workspace with the 's' key. Pipeline contains changed decision colors.
• sdimage improvements:
  • show decisions of arbitrary pipeline in Matlab workspace
  • execute k-means clustering on image data using 'Cluster with k-means' menu command ('c' keystroke)
  • switching to a different class by pressing a digit
• fix for the sdscatter with backdrop problem with flipped fonts (bug in Windows ATI drivers). added Shift-A keystroke to switch off the alpha level (see http://perclass.com/index.php/forums/viewthread/260)
• fix for crash due to memory leak occuring when computing local histogram features for specific data ranges
• fix for internal use of tic/toc functions inside sdexe and classifier execution.
• fix for possible crash when using rejection-based operating point
• fix for the error raised when switching between multiple scatter/ROC plots

Version 3.0.0 (6-Jun-2011)

• PRSD Studio is renamed into perClass (How to transition to 3.0)

• new functionality for handling image data

  • support for storing image data in data sets through sdimage command. Arbitrarily-shaped regions from multiple images may be stored in a sddata object. Single or multi-band images are supported.
  • visualization of arbitrarily-shaped pixel subsets. See example
  • texture and appearance features may be computed in local image regions using new sdextract function
    • support for user-defined grid (region size and step)
    • local histograms, features of local histograms, co-occurrence matrices
    • high-speed feature extraction (extracting 86000 co-occurrence matrices from 1024x1300 image takes 230 ms on a laptop)

• new functionality for execution runtime

  • significantly faster execution runtime
  • labels and decisions are represented by integers, not doubles
  • C API for precision timers (sd_Tic and sd_Toc) available to custom applications out of Matlab on all platforms
  • new deployment tool sdrun for easy execution of trained classifiers using Matlab compiler. sdrun is implemented as a single statically-linked mex. To bring perClass classifier execution into custom Matlab application you only need to copy one mex binary and include the pipeline and license files.
  • new ASCI-based pipeline file format allows embedding of classifiers directly in a source code (see ex_buffer.c SDK example)

• new toolbox functionality

  • new sdcluster function for direct clustering of a data set with user-defined model. sdcluster returns data set with cluster labels. Clustering is performed per class and original labels are preserved. See the new chapter on clustering
  • .* operator. Applying a pipeline returning decisions to a data set with .* operator returns a data set with decisions set as new labels. This is useful to get clustering results or image labels in one step. Example: b=a.*p is equivalent to dec=a*p; b=a; b.lab=dec;
  • sdscatter improvements
    • 'show all' menu command for each property
    • save filter to workspace. Filter is stored as a structure which may be easily edited by hand and loaded back into sdscatter
  • sdfeatplot improvements (read more)
    • left/right cursor keys move to first/last feature, respectively
    • 's' keystroke switches to stem-plot highlighting individual histogram bins
    • 'u' keystroke uses only unique values, instead of default histogram bining
    • 'a' keystroke sets automatic binning
    • 'x' keystroke allows to specify name of variable defining x-axis bins (e.g. logarithmic)
    • 'lab' option specifies the label set used (default: 'lab', example: sdfeatplot(data,'lab','tissue'))
    • 'bins' option allows bin specification from command-line
  • sdrelab includes new 'all' option that sets all samples to a specific class. This works both for labels and data sets.
  • sdrelab may rename pipeline decisions by providing a new list. Example: pd=sddetector(a,'target',sdgauss); pd2=sdrelab(pd,sdlist('accept','reject')); read more
  • generate more data from a Gaussian model using sdgenerate. Example: p=sdmixture(data); b=sdgenerate(p,1000);
  • sdconfmat header lines may be suppressed with 'no header' option. This is usefull when concatenating multiple confusion matrices, e.g. for each patient into one larger table.
  • sdknn accepts k also directly after data set as the second argument. Example p=~sdknn~(a,10) instead of p=~sdknn~(a,'k',10)
  • sdsvc accepts the type (linear, RBF or polynomial) as a direct parameter. Example: p=sdsvc(a,'linear')
  • operating point marker and color in sddrawroc may be changed by the second parameter. See example

• new core-level functionality

  • length of sddata returns number of samples (see discussion at: http://prsdstudio.com/index.php/forums/viewthread/301)
  • subset and sdrelab preserve the user-specified order of classes when processing a single set of labels.
  • sorting label list using sdlist sort method or sdlab sortlist method. Only order of classes is changed, not the sample labeling.
  • sddata find supports regular expressions to return sample indices. Example: ind=find(a,'/substring') returns indices of all samples with classname containing substring.
  • direct assignments into sddata property. Example: a(1:10).lab='orange' or a.lab(1:10)='orange'
  • label assignment supports also class indices. Example: lab(1:10)=2 assigns first ten samples to second class in the lab.list.

• fixes:

  • sdp_affine fix for scaling, labels optional
  • sdlab does not include extra space in sdlab('Feature',1:10) constructor
  • sdpca allows 1D output
  • sdscatter window will not jump out of the screen when switching on the distribution plots
  • sddata/randsusbet and sddata/subset return subset indices in column order

Version 2.4.0 (7-Feb-2011)

• new execution utilities (commercial and academic full versions only)
  • execution of classifiers from Microsoft Excel worksheets (Windows only)
  • command-line utility sdrun for direct execution of classifiers outside Matlab (all platforms)
  • GUI execution demo (Windows only)
  • LabView interface example
• toolbox improvements
  • improvements in horizontal label concat (omitting internal spaces + scalability to very large data sets (one million samples uner half a second))
  • fixing sdmixture problem where training set priors were not used by default
  • sdtree classifier adds 'levels' option that may significantly speedup training
  • PRTools AdaBoost classifiers with decision tree or stump base learners may be converted into pipelines using the sdconvert command.

Version 2.3.0 (13-Dec-2010)

• sdcrossval now provides per-fold measurements of execution speed
• fixing a bug in sdkmeans that could cause crash for multi-class data sets with high overlap
• fixing the problem in sdscatter where regular expression could not be applied to a subset of samples
• fix for the call subset(data,'lab',{}) which was not throwing error
• randsubset now works for PRTools datasets

Version 2.2.5 (24-Nov-2010)

• regular expressions allow simple definition of data subsets and sdrelab. Strings starting with slash / character are interpreted as regular expressions. For example: subset(data,'/good') returns all classes containing the word 'good'.
• sdscatter enhancements:
  • undo the last label painting operation (u key or Undo painting command in scatter right-click menu)
  • cycle through all classes showing one at a time (< and > keys)
  • select class subset by regular expression (/ key)
  • class to top (t key)
• new dissimilarity measures in sdprox (Spectral Angle Mapper, Kolmogorov distance, Match distance)
• sdmindist classifier directly applicable to dissimilarity representations
• sdfeatplot enhancements:
  • allows selection of a label set used for plotting the per-group distributions. sdfeatplot(data,'lab','patient') will show per-patient histogram for each feature.
  • change of default behaviour: sdfeatplot now uses all data to construct histograms, use 'maxsamples' option to limit sample count used for large data sets.
  • fixing the problem in sdfeatplot related to constant-value features; sdfeatplot now also shows the constant feature value if present.
• sddrawroc supports interactive zooming
• fix in sdscatter sample inspector showing correct labels when focusing on a sample subset

Version 2.2.4 (5-Oct-2010)

• support for Mac OS X 64-bit platform
• new sdimport command for loading sddata objects from text files. User may specify what columns correspond to data matrix, labels and additional sample properties. (read more)
• sdexport command can store sddata in a comma-separated file (read more)
• sdsvc support for linear and polynomial kernels including automatic grid search (read more)
• support for incremental Support Vector Data Description (incsvdd) from DD_Tools.
• adding support for creating sdlab labels using a vector and class names
• sdfeatplot allows user definition of line styles used for plotting class-feature distributions (see example)
• sdp_affine turns empty offsets into zero vectors (forum discussion)
• sddetector now supports test sets also in one-class mode ('reject' and 'test' options used together)
• fix of a bug related to scaling proximity data with sdscale
• fix of a bug in sdprox where prototypes were unnecessarily sorted

Version 2.2.3 (29-July-2010)

• sdsvc allows to identify training samples that became support vectors (using original property of support vectors set p{1}.proto)
• sddetector support for externally defined test set using test option
• sdfeatsel floating search provides history of feature subsets selected by individual steps.
• sdfeatsel adds a test option which may be used to supply external data set used for evaluation of 1-NN error criterion
• sddecide allows construction of an operating point manually. Support for both weighting-based discriminants and thresholding-based detectors.
• sdsvc support for setting external data set used for error estimation in parameter grid search with `test option
• sddata supports cell array properties
• sdscatter user callbacks are now accessible using 'callback' option
• untrained classifier pipelines now return names using getname

Version 2.2.2 (22-June-2010)

• fast feature selection sdfeatsel scalable to large data sets (forward search with 1000 samples, 50 features under five seconds). Individual, random selection, forward, backward and floating searches are supported using 1-NN error on a validation set as a criterion. Feature subset size is selected automatically.
• sdscatter called when clicking on an data sample. This allows to custom visualization such as loading an image corresponding to a sample form disk and showing it in a separate figure.
• support for untrained high-level operations on data (subset, randsubset, sdrelab, sdroc). This allows one to easily express complex sequences of training operations.
• extended sdscale supporting also robust domain scaling (robust in presence of outliers)
• cascades may be now trimmed to return output after specific stage using sdconvert, e.g. pc2=sdconvert(pc,'until',2). This helps us to understand how the later stages of hierarchical classifiers improve performance.
• experimental support for Mac OS X 64-bit platform
• sdscatter fix for decision colormap when showing classifier decisions

Version 2.2.1 (3-May-2010)

• interactive visualization of feature distributions in sdscatter for both axes (use 'Show feature distribution' in 'Scatter' menu or press 'd'). This greatly simplifies understandingo of overlap in very large data sets where scatter plot is not too informative. (example)
• sdkmeans classifier and clustering scalable to very large data sets (1 million samples, 10 clusters in 3.3 sec). sdkmeans provides fast prototype selection method for k-NN classifiers. Classification performance is further improved by prototype pruning (similar effect to editing the training set).
• sdkcentres classifier and clustering
• randsubset allows to limit the maximum number of samples using 'atmax' option. This is useful to limit samples size but tolerate that some classes have less samples.
• find and subset now allow that some of the class names do not exist and return what is present (and not empty [] as before)

Version 2.1.0 (21-Apr-2010)

• fixing a bug in sddecide related to adding an operating point in an ROC object
• fixing an error message in sdlab constructor
• adding RBF support vector machine training using sdsvc command. sdsvc is based on libSVM and offers automatic grid search for sigma and C parameters and one-against-all multi-class support. (examples)
• adding a reject option to a trained discriminant using the sdreject function (also for multi-class classifiers; both outlier rejection and rejection close to the decision boundary) (examples)
• sdcrossval support for estimating ROC with variances using operating point averaging (cross-validate pipline returning soft outputs and provide fixed operating points using the 'ops' option), (example)
• adding sdcrossval support for custom sdalg algorithms that are not convertible into a pipeline (algorithm needs to return the list of all possible decisions)
• sddrawroc now saves complete sdroc objects back in the workspace, not only operating points (by pressing 's' key)
• sddecide support for default op.point based on thresholding (e.g. for sdsvc on two-class problems)
• support for clustering using sdmixture with 'cluster' option
• sdscatter adding the "show only this class" command (press 'o' key)
• default mean-error performance measure in sdcrossval is not anymore included if user requests a specific set of measures
• sdneural may switch off the default use of validation for teaching purposes (to illustrate overfitting of the network). Use 'valfrac',[] to suppres the use of validation set.
• fixing the problem with sdroc using 'confmat' and 'reject' options together
• fixing the bug in sdlab constructor for single label per class
• improving compatibility with PRTools (sdimage,sddetector,sdreject,sdcrossval, sdstackgen, sdscatter visualizing images using sample inspector)

Version 2.0.9 (8-Mar-2010)

• adding support for subset by logical array for sddata and sdlab objects (example: a( a.lab=='banana' ))
• sdtest raises a warning if some of the true classes are not matched to classifier decisions (all samples from these classes are considered misclassified)
• fixed sdscatter problem with the order of classes in "class on top" and "change markers"
• usability improvements in sdfeatplot (click to change figure title; legend properly displaying special characters)
• 'mean-error' performance measure may specify optional class priors used for weighting the class errors
• global display verbosity may be handled using prsd_display command (use prsd_display off to switch off display output of PRSD Studio functions).
• 'nodisplay' options added in sdmixture, sdparzen, sdcrossval
• randsubset supports random selection of objects from some classes only (example: [tr,ts]=randsubset(a,[0.5 0]) returns 50% of the first class for the training)
• sdcrossval outputs string with the result summary, result struct and the evaluation object.

Version 2.0.8 (19-Feb-2010)

• fix in sdimage for multi-dimensional images (image cubes)
• pipelines now provide operating points via p.ops field
• API interface simplification and cleanup
• low-level output of pipelines on matrices and using C API returns indices to decision list as decisions, not the internal codes
• sdlist and sdlab internal numerical representation is not exposed to the user anymore
• feature selection pipelie sdp_fsel now may get the feature labels directly from the data set pf=sdp_fsel(data,[3 4])
• sddetector handles output polarity automatically (k-NN output is distance, mixture output is similarity)
• adding easy display of sdlab object details (class sizes, fractions) using the transpose operator (lab')

Version 2.0.5 (22-Dec-2009)

• classifier output visualization using sdscatter can now switch between different soft outputs interactively using cursor keys
• added constrain method for easy application of ROC performance constraints
• enhanced setcurop method to choose operating point minimizing or maximizing specific performance measure or setting op.point based on costs
• new performance measure nconfmat - the entry in normalized confusion matrix
• 'target' and 'non-target' options in sddetector and sdroc setting the desired target/non-target names
• setstate method in sdalg algorithm allows to call algorithm function directly (instead of using the multiplication operator)

Version 2.0.4 (14-Dec-2009)

• sdrelab now allows to add string prefix to all classes in all labels present using 'add to all' option. This makes it easy to compare two data sets with multiple labelings (classes, patients, tissues).
• adding sdscale command for data scaling

Version 2.0.3 (9-Dec-2009)

• isclass method for quick check if certain classes are present (useful for custom algorithms)
• sdnorm function adding normalization step to a trained pipeline (this construct a general discriminant)
• sdlab fix for incorrect class size when initialized with a list and indices
• adding initial version of auto-conversion for older-format @sdppl/sdppl and @sdops/sdops objects
• fix for the inMathOverflow warning/error in sdtree training

Version 2.0.2 (4-Dec-2009)

• new sdlab object simplifies handling of labels, decisions and indexed meta-data
• new sddata object brings easy handling of sample meta-data
• multiple sets of labels or meta-data in a dataset, unified access to sample properties
• simple queries using multiple criteria (give me all samples labeled as "Cancer" from patient 1,2 and 5 using subset(a,'class','Cancer','patient',[1 2 5]))
• access to classes is greatly simplified
• sdroc handles classifier output polarity automatically (sdexe stores the output type in output_type data property)
• user may change class markers. Data set remembers class markers. Scatter markers are stored in the 'marker' property inside the class list.
• dissimilarity representation contains as feature properties all prototype sample properties
• labels and decisions may be easily concatenated. This allows us to add new labels with brake-down of errors (confusion-matrix entries) in one command.
• writing custom sdalg algorithms is significantly simplified

1.x Compatibility changes

• sdppl objects use new internal format.
• sderror replaced by sdtest

Version 1.3 (30-Nov-2009)

• fix in sdnmean classifier: now computing pooled diagonal covariance using class priors
• adding missing parse_measures.p file
• fixing p-code copatibility problem with Matlab 7.4

Version 1.2.5 (12-Oct-2009)

• fixes in findprop for numerical properties
• adding 'all' and 'nodisplay' options to sdrelab

Version 1.2.4 (13-Aug-2009)

• sdtree implements training of decision tree classifier scalable to large number of samples (example)
• fix in prsd_feedback correcting the problem with PRTools not on Matlab path

Version 1.2.3 (15-Jul-2009)

• visualization: sdscatter provides more detailed information in sample inspector including all sample meta-data
• sdrelab: adding prefix or suffix to all class names. (example)
• sdrelab: renaming a single class by relative index
• simpler installation: PRSD Studio Lite installation does not anymore require software activation
• sdroc: support for reject option on classifiers with distance soft output (sdknn)
• selprop,findprop support for set of property values defined by cell array

Version 1.2.2 (16-Jun-2009)

• libPRSD: support for AdaBoost execution using decision tree as base classifiers
• visualization: sdscatter allows interactive change of classifier parameters using slider (k in k-NN, smoothing in Parzen, number of base classifiers in AdaBoost)
• visualization: sdimage may be connected to ROC plot and visualize decisions at different operating points in real-time
• sdneural provides target option that allows one to approximate trained classifiers (example)
• sdroc: fraction of all objects may be rejected by specifying fraction after reject option

Version 1.2.1 (27-May-2009)

• sdnbayes implementing Naive Bayes classifier with automatic selection of number of histogram bins
• sdroc now supports cost-based selection of operating point for two-class scenario (in addition to the existing multi-class cost-based optimization)
• sddecide may be used in pipelines to define default operating point
• sdp_affine can construct simple feature scaling pipelines

Version 1.2 (19-May-2009)

• sdmixture supports automatic estimation of number of components
• sdneural implementing feed-forward neural network training
• sdcrossval now supports untrained pipelines

Version 1.1.6 (9-May-2009)

• sdparzen Parzen classifier implementing scalar and vector smoothing
• sdknn k-th nearest neighbor classifier with for prototype selection and support for both detection and multi-class classification

Version 1.1.5 (1-May-2009)

• libPRSD now supports loading pipelines also from a buffer using prsd_LoadPipelineFromBuffer (pipelines may be now stored in application resources or sent over network).
• sdroc supports rejection both far away and close to the decision boundary using the reject option.
• sdscatter: the figure title may be selected interactively by clicking on the title area
• simplified selection us performance measures in sdroc

Version 1.1.4 (19-Mar-2009)

• adding support for group licenses via license server
• support for construction of arbitrary hierarchical classifiers using decision-level fusion and their execution through libPRSD
• sddetector brings one-command construction of detectors based on arbitrary model (both in one-class setting specifying a threshold using fraction of rejected samples and in two-class setup using ROC analysis to fix the threshold minimizing mean error).
• sddrawroc allows to save the current operating point into any relevant object (sdroc,sdops, pipelines, sddecide mappings, custom sdalg algorithms)
• introducing sdmixture for training Gaussian mixture models (one- or multi-class, variable number of components per class, different stopping criteria (iterations or likelihood delta))
• sdrelab allows to define classes by ~ (tilda) negation operator (e.g. turn all what is not not apple into "non-apple")
• sdscatter allows the user to flip through order of classes (z-order) by + and - key-strokes
• number of usability improvements in construction of pipelines and interaction with PRTools (sdroc and sdops objects can be now directly concatenated into pipelines; sdmap wraps pipelines for use in PRTools)
• many improvements in confusion matrix estimation: sdconfmat
• setprop now allows to quickly set property to a constant value. This makes it very easy to quickly tag a group of samples with a specific label.
• sdconfmat can now add new labels with all confusion matrix combinations as a property. This can be used to quickly visualize different types of error directly in the feature-space
• sdconfmat cosmetic fix: string confusion matrix scales nicely with long class names
• new function selprop returning a subset of a dataset with given property values
• significant improvements in scalability of sdroc to large datasets in speed and memory usage. Practical even for datasets with 100 000 samples and tens of thousands of operating points.
• improved ROC optimizer brings better quality sets of operating points
• sdconfmat can now estimate confusion matrices for sets of operating points from the soft outputs
• sdexe can return numerical decision codes ('code' option). This is useful for low-level work with classifier outputs.
• pipelines can return numerical decision codes using .* operator (e.g. dec=data.*p)
• sdeaclust clustering can be now executed on new data. Scalable to very large datasets (images).

Version 1.1.3 (26-Jan-2009)

• fix in sdscatter allowing to paint labels with legend switched on
• fix in sdscatter retaining the type of numerical properties in a dataset saved back to workspace
• sdscatter can now switch visibility of classes or groups on/off. That's helpful when inspecting large datasets with many overlapping sample groups (patients). See context menu in sdscatter Figure windows. Painting now applies only to visible samples.
• initial support for hierarchical systems composed of multiple classifiers returning decisions (sdp_cascade)
  • support for meta-classes and different features at each classifier node.
  • ROC analysis for hierarchical systems
• sdconfmat added
  • the order of labels and decisions (lablists) can be fixed by the user
  • sdconfmat can correctly handle situations where only some classes/decisions are present in the test set (given the full lablists)
  • sdconfmat can return the string with a table
  • support for normalization of confusion matrices
  • lablists may be supplied as cell arrays of strings or string arrays
• support for weight-based operating points with reject option (rejection both close to the boundary and distance-based)
• sdroc automatically shows rejected fraction and all per-class TPrs
• support for similarity-based nad distance-based classifier outputs
• adding reject fraction estimate to sdroc
• support for leave-one-out over a property (object, person, patient...)
• fix for the bug where sdscatter made error when mouse pointer was moved too quickly over the new window

Version 1.1.2 (18-Nov-2008)

• adding fast approximated k-NN see example in our blog
• adding a k-centres classifier capable of both one-class classification and multi-class discrimination
• feature selection algorithm sda_featsel now supports also backward feature selection

Version 1.1.1 (09-Nov-2008)

• adding leave-one-out evaluation to sdcrossval
• adding sdfeatsel: robust feature selection using internal cross-validation loop. It supports custom-made feature selection algorithms
• two example algorithms added illustrating the use of feature selection during training (sda_featsel_example1) and in inner cross-validation loop based on sdfeatsel (sda_featsel_example2)

Version 1.1 (04-Nov-2008)

• fixing critical bug in 1.1 26-Oct-2008 related to problem with dongles
• fixing the issues with one-sample test sets in ROC

Version 1.1 (26-Oct-2008)

• sdscatter gets full support for GUI menus and class renaming
• new sdimage command visualizing image stored in a dataset. Support for label paiting, class renaming, multiple sample groupings, connection to sdscatter
• sdscatter support for interactive sample inspector (datasets with 1D data using bar plot or 2D images)
• sddrawroc can now show confusion matrices at the cursor and at the selected operating point (if present i.e. if 'confmat' flag was specified in sdroc command)
• sdexe now automatically converts sdalg algorithms into pipelines
• sdstackgen now returns also a robust base classifier (mean fusion of per-fold trained base classifiers) as a second output
• improved support for prtools classifiers with output conversion
• fix: sdroc now stores confusion matrices in multi-class situations using 'confmat' flag
• fix to scaling using affine projection. scalem is now supported for all affine scaling types

Version 1.0 (15-Sep-2008)

• added randomization cross-validation scheme sdcrossval(nmc,data,'method','random')
• ROC object may be queried using short names of measurements r(:,'err(Cancer)')
• activation support for commercial demos

Version 1.0 (02-Sep-2008)

• fix: included missing sda_prtools wrapper
• new feature: sdscatter now allows for user-defined titles (sample details moved to the figure title bar)