CODEWITHSUNDEEP
c#machine-learningaccord.net
Pablo Gonzalez
Pablo Gonzalez

Reputation: 693

Accord.Net Get equation of the SVM model

I have been testing the sample Kernel Support Vector Machines for regression problems and I would like to know how do you get the equation of the model.

For example, if the machine is created using a polynomial kernel (degree = 1), how do you get the line equation (mx + b) of this model. Is there any method in the SupportVectorMachine Class to get the model equation? or is there any way to calculate the parameters of the equation from the variables obtained after the machine is created.

Thanks in advance.

Upvotes: 1

Views: 471

Answers (3)

Avi Shmidman
Avi Shmidman

Reputation: 940

As @zrolfs noted, if you are using Accord.NET with Sequential Minimal Optimization, the ToWeights() function does not currently return relevant coefficients for the decision function. Nevertheless, you can calculate these coefficients directly. In order to do so, multiply the SVM weights vector by the matrix of support vectors, like so:

double[] DecisionFunctionCoefficients = new double[dwTotalFeatures];
for (int iFeature = 0; iFeature < dwTotalFeatures; iFeature++) {
     for (int iVector = 0; iVector < SVM.SupportVectors.Length; iVector++) {
         DecisionFunctionCoefficients[iFeature] += (SVM.SupportVectors[iVector][iFeature] * SVM.Weights[iVector]);
     }
}

Upvotes: 0

zrolfs
zrolfs

Reputation: 21

I got weird coefficients from ToWeights() when using SequentialMinimalOptimization() from which I couldn't derive the hyperplane equation. Using LinearCoordinateDescent() yielded usable coefficients for the model, however, in the form of [a,b,c...] which could be plugged in as 0 = a + bx + cy + ... Hope that helps!

Upvotes: 2

greeness
greeness

Reputation: 16124

Looks like you can use this method below:

ToWeights(), which

Converts a Linear-kernel machine into an array of linear coefficients. The first position in the array is the Threshold value.

So in your language, the first position in the array is the bias b and the rest are your linear coefficients m.

Upvotes: 2

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