Cheat-sheet perClass Matlab Toolbox Interface
Table of contents
- 1. Examples and help
- 2. sddata class: Handling data sets
- 2.1. Data subsets
- 2.2. Interactive data visualization
- 2.3. Handling data set labels
- 2.4. Handling data set feature labels
- 2.5. Handling data set properties
- 2.6. Operations on data sets
- 3. sdlab class: Handling labels, decisions and indexed properties
- 4. sdlist class: List of categories
- 5. Feature extraction
- 6. sdppl class: Pipelines and classifiers
- 6.1. Constructing and using pipelines
- 6.2. Dimensionality reduction and data representation
- 6.3. Classifiers (models)
- 6.4. High-level classifier tools
- 7. sdroc class: ROC analysis and evaluation
- 7.1. Performing ROC analysis; accessing operating points and performance estimates
- 7.2. Selecting a specific operating point
- 7.3. Evaluation and performance testing
- 8. sdalg class: Custom algorithms
- 9. sddb class: SQLite database object
- 10. sdsql class: SQLite database query
1. Examples and help ↩
sddemo- Demo examplessdfeedback- Send feedback or bug reports to PR Sys Designsdversion- Display version and license information
2. sddata class: Handling data sets ↩
sddata- Data set objectsdimport- Import sddata object from text filesdexport- Export sddata object into text filegetdata- Get data matrixsetdata- Set data matrix (useful for creating a data set with newly computed features preserving all other meta-data)double- Convert to doublegetname- Get data set name stringsetname- Set data set name stringsize- Get data set size (number of samples, features and classes)sdnominal- View and handle nominal features
2.1. Data subsets ↩
subset- Subset of samples given label/property valuesfind- Get indices of samples by label/property valuesrandsubset- Random subset sampling classes or any property
2.2. Interactive data visualization ↩
sdscatter- Interactive scatter plot; hand-painting class labels; filtering samplessdimage- Image plot with hand-painting of image labels, cropping, connected componentssdfeatplot- Interactive plot of per-feature class distributions
2.3. Handling data set labels ↩
getlab- Get current labelssetlab- Set a current labels by name or find out what is current label setgetsizes- Get vector of class sizes for current labelsisclass- Test if class or classes are presentsdrelab- Rename classes, define meta-classes, extract information from labels with regular expressions
2.4. Handling data set feature labels ↩
getfeatlab- Get current feature labelssetfeatlab- Set a property as feature labels or find what property is used as labels
2.5. Handling data set properties ↩
getprop- Get propertysetprop- Set (create) propertyrmprop- Remove property from data setgetproplist- Get list of properties available in data setisprop- Test if property is present
2.6. Operations on data sets ↩
abs- Absolute valuecov- Compute covariance matrix per classmean- Compute class mean vectorslog- Natural logarithm of data matrixlog2- Base 2 logarithm of data matrixlog10- Base 10 logarithm of data matrix
3. sdlab class: Handling labels, decisions and indexed properties ↩
sdlab- Create sdlab objectlength- Get number of entriesgetlistorlab.list- Get the label list object describing categoriesgetnames- Get string name for each label entrygetindices- Get index to label list for each label entrygetsizesorlab.sizes- Get number of entries per category (i.e. samples per class)lab.fractions- Get the relative fraction for each categorysdrelab- Rename classes or define meta-classes
4. sdlist class: List of categories ↩
sdlist- Create list objectgetnames- Get category nameslength- Get number of categoriesind2name- Convert category index to namename2ind- Convert name to category indexisname- Test if a category is present
5. Feature extraction ↩
sdprep- Preprocessing and spectral indicessdextract- Extract features- from local image regions
- from each object defined by object labels
- from bands in spectral data
- trasform color space
sdsegment- Define connected components in an image
6. sdppl class: Pipelines and classifiers ↩
6.1. Constructing and using pipelines ↩
getlaborp.lab- Get pipeline labels (feature labels of the output data set)getlistorp.list- Get pipeline decision listp.inlab- Get pipeline input labels (names of features it expects on its input)getoutput- Return pipeline output type (decision, similarity, distance, ...)sdexe- Execute pipeline on data using perClass Runtime (in Matlab)sdexport- Export pipeline for execution out of Matlab using perClass Runtime
6.2. Dimensionality reduction and data representation ↩
sdmissing- Missing data handling and imputationsdpca- Principal Component Analysis (PCA)sdlda- Linear Discriminant Analysis (Fisher projection)sdprox- Construction of proxmity representation (distances or similarities to prototypes)sdscale- Data scalingsdnorm- Normalization of soft-outputssdfeatsel- Feature selectionsdexpand- Polynomial feature space expansion
6.3. Classifiers (models) ↩
sdbox- Bounding box classifiersdnmean- Nearest mean classifiersdlinear- Linear discriminant assuming normal densitiessdquadratic- Quadratic discriminant assuming normal densitiessdgauss- Gaussian modelsdmixture- Gaussian mixture automatically estimated number of componentssdfisher- Fisher linear discriminant (LDA + sdlinear)sdknn- k-th nearest neighbor classifiersdkmeans- k-means classifiersdkcentres- k-centres classifiersdlms- Least-mean squares classifiersdlogistic- Logistic regression classifiersdlut- Lookup table classifiersdparzen- Parzen classifiersdrandforest- Random forest classifiersdneural- Feed-forward and RBF neural networksdnbayes- Naive Bayes classifiersdtree- Decision tree classifiersdsvc- Support vector classifier
6.4. High-level classifier tools ↩
sddetect- Detector for any model based on ROC analysis or one-class approachsdcascade- Classifier cascade or hierarchy of classifierssdcombine- Combine multiple classifiers (soft or crisp combining)sdreject- Adding reject option to a trained pipelinesdstackgen- Stacked generalization for building trained combiners (produces unbiased model output on the training set by cross-validation)sdrelab- Arbitrarily rename decisions of a classifier (also joining decisions)
7. sdroc class: ROC analysis and evaluation ↩
7.1. Performing ROC analysis; accessing operating points and performance estimates ↩
sdroc- Performing ROC analysis on classifier outputsgetdata- Get data (performance estimates) stored in the sdroc objectsddrawroc- Interactive ROC plot
7.2. Selecting a specific operating point ↩
getcurop- Get index of the current operating pointsetcurop- Set the current operating point by index or performancesubset- Select a subset of operating pointsconstrain- Select a subset of operating point by applying performance constraintssddecide- Add a default operating point to a trained classifier model
7.3. Evaluation and performance testing ↩
sdtest- Estimating error/performance of a classifiersdconfmat- Estimate confusion matrices from decisions or classifer outputsdconfmatind- Get indices of samples falling into a specific confusion matrix cellsdloss- Compute loss based on confusion matricessdcrossval- Cross-validation over samples or a user-defined property
8. sdalg class: Custom algorithms ↩
sdalg- Construct algorithm objectistrained- Test if the algorithm is trainedsda_pca_clf- Algorithm example: PCA dim.reduction followed by a classifiersda_pca_clf_roc- Algorithm example: PCA dim.reduction + classifier + set operating point by ROC analysis
9. sddb class: SQLite database object ↩
sddb- Data base object (open data base)tables- Return tables in a data basefields- List table fieldsinsert- Insert data into table
10. sdsql class: SQLite database query ↩
sdsql- Data base query objectclose- Close the querydouble- Convert query results into double matrixcell- Convert query results into cell array