pkoptsvm(1)

💖 pkoptsvm — like, the manual page 💖

okay bestie, gather round, because this is pkoptsvm(1) and she is that girl. she's the little utility that finds the ✨perfect✨ parameters for your SVM classification, and honestly? she ate. let's get into it.

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🌸 NAME 🌸

pkoptsvm — a whole program that optimizes parameters for SVM classification 💅

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🎀 SYNOPSIS 🎀

pkoptsvm -t training [options] [advanced options]

that's it. that's the whole entrance. she's lowkey minimal.

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🦋 DESCRIPTION 🦋

so here's the tea, bestie: the support vector machine depends on several parameters. and like, ideally, you'd optimize these parameters for each classification problem, because every dataset is a different vibe and deserves its own glow-up. 🌈

in case of a radial basis kernel function, two important parameters are {cost} and {gamma}. and that's literally the whole point of pkoptsvm — she can optimize these two parameters based on an accuracy assessment (the Kappa value). 👑

if you provide an input test set (-i), that's what gets used for the accuracy assessment. and if you don't? no worries, she just bases it on a cross validation (-cv) of the training sample instead. either way, we're getting a number. periodt.

the optimization routine uses a grid search. the initial and final values of the parameters can be set with -cc startvalue -cc endvalue and -g startvalue -g endvalue for cost and gamma respectively. the search uses a multiplicative step for iterating the parameters (set with the options -stepcc and -stepg).

now here's the iconic strategy that people use all the time: define a relatively large multiplicative step first (e.g 10) to get an initial estimate for both parameters. then — once you have that rough little estimate — you optimize it by defining a smaller step (>1) with constrained start and end values for the parameters cost and gamma. it's giving coarse-to-fine, it's giving strategy, it's giving main character energy. 🧚

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✨ OPTIONS ✨

• -t filename, --training filename — training vector file. a single vector file contains all training features (must be set as: b0, b1, b2,...) for all classes (class numbers identified by label option).
• -i filename, --input filename — input test vector file
• -cc startvalue -cc endvalue, --ccost startvalue --ccost endvalue — min and max boundaries the parameter C of C-SVC, epsilon-SVR, and nu-SVR (optional: initial value)
• -g startvalue -g endvalue, --gamma startvalue --gamma endvalue — min max boundaries for gamma in kernel function (optional: initial value)
• -step stepsize, --step stepsize — multiplicative step for ccost and gamma in GRID search
• -v level, --verbose level — use 1 to output intermediate results for plotting 👀

💜 Advanced options 💜

• -tln layer, --tln layer — training layer name(s)
• -label attribute, --label attribute — identifier for class label in training vector file. (default: label)
• -bal size, --balance size — balance the input data to this number of samples for each class (default: 0)
• -random, --random — in case of balance, randomize input data
• -min number, --min number — if number of training pixels is less then min, do not take this class into account
• -b band, --band band — band index (starting from 0, either use band option or use start to end)
• -sband band, --startband band — start band sequence number
• -eband band, --endband band — end band sequence number
• -offset value, --offset value — offset value for each spectral band input features: refl[band]=(DN[band]-offset[band])/scale[band]
• -scale value, --scale value — scale value for each spectral band input features: refl=(DN[band]-offset[band])/scale[band] (use 0 if scale min and max in each band to -1.0 and 1.0)
• -svmt type, --svmtype type — type of SVM (C_SVC, nu_SVC, one_class, epsilon_SVR, nu_SVR)
• -kt type, --kerneltype type — type of kernel function (linear, polynomial, radial, sigmoid)
• -kd value, --kd value — degree in kernel function
• -c0 value, --coef0 value — coef0 in kernel function
• -nu value, --nu value — the parameter nu of nu-SVC, one-class SVM, and nu-SVR
• -eloss value, --eloss value — the epsilon in loss function of epsilon-SVR
• -cache number, --cache number — cache memory size in MB (default: 100)
• -etol value, --etol value — the tolerance of termination criterion (default: 0.001)
• -shrink, --shrink — whether to use the shrinking heuristics
• -cv value, --cv value — n-fold cross validation mode (default: 0)
• -cf, --cf — use Overall Accuracy instead of kappa
• -maxit number, --maxit number — maximum number of iterations
• -tol value, --tolerance value — relative tolerance for stopping criterion (default: 0.0001)
• -a value, --algorithm value — GRID, or any optimization algorithm from http://ab-initio.mit.edu/wiki/index.php/NLopt_Algorithms
• -c name, --class name — list of class names.
• -r value, --reclass value — list of class values (use same order as in --class option).

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no cap bestie, that's every single flag. don't skip the advanced ones, they're lowkey the whole personality 🫶

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and that's the page!! mwah 💋 this one was dated 01 January 2025, and yeah she lives in section 1 of the manual — pkoptsvm(1), forever iconic, no notes. go optimize something gorgeous, bestie 💖✨