Added getDecisionFunction to svm

Author: plarsson

cc/modules/machinelearning/SVM.cc

@@ -26,6 +26,7 @@ NAN_MODULE_INIT(SVM::Init) {
   Nan::SetPrototypeMethod(ctor, "predict", Predict);
   Nan::SetPrototypeMethod(ctor, "getSupportVectors", GetSupportVectors);
   Nan::SetPrototypeMethod(ctor, "getUncompressedSupportVectors", GetUncompressedSupportVectors);
+  Nan::SetPrototypeMethod(ctor, "getDecisionFunction", GetDecisionFunction);
   Nan::SetPrototypeMethod(ctor, "calcError", CalcError);
   Nan::SetPrototypeMethod(ctor, "save", Save);
   Nan::SetPrototypeMethod(ctor, "load", Load);
@@ -139,6 +140,25 @@ NAN_METHOD(SVM::GetUncompressedSupportVectors) {
 #endif
 }
 
+NAN_METHOD(SVM::GetDecisionFunction) {
+  FF_METHOD_CONTEXT("SVM::GetDecisionFunction");
+
+  if (!info[0]->IsNumber()) {
+    FF_THROW("expected arg 0 to be a Int");
+  }
+
+  FF_OBJ alpha = FF_NEW_INSTANCE(Mat::constructor);
+  FF_OBJ svidx = FF_NEW_INSTANCE(Mat::constructor);
+  FF_ARG_INT(0, int i);
+  float rho = FF_UNWRAP(info.This(), SVM)->svm->getDecisionFunction(i, FF_UNWRAP_MAT_AND_GET(alpha), FF_UNWRAP_MAT_AND_GET(svidx));
+
+  FF_OBJ ret = FF_NEW_OBJ();
+  Nan::Set(ret, FF_NEW_STRING("rho"), Nan::New((double)rho));
+  Nan::Set(ret, FF_NEW_STRING("alpha"), alpha);
+  Nan::Set(ret, FF_NEW_STRING("svidx"), svidx);
+  FF_RETURN(ret);
+}
+
 NAN_METHOD(SVM::CalcError) {
   FF_METHOD_CONTEXT("SVM::CalcError");
   FF_ARG_INSTANCE(0, cv::Ptr<cv::ml::TrainData> trainData, TrainData::constructor, FF_UNWRAP_TRAINDATA_AND_GET);

cc/modules/machinelearning/SVM.h

@@ -44,6 +44,7 @@ class SVM: public Nan::ObjectWrap {
 	static NAN_METHOD(Predict);
 	static NAN_METHOD(GetSupportVectors);
 	static NAN_METHOD(GetUncompressedSupportVectors);
+	static NAN_METHOD(GetDecisionFunction);
 	static NAN_METHOD(CalcError);
 	static NAN_METHOD(Save);
 	static NAN_METHOD(Load);

lib/typings/SVM.d.ts

@@ -17,6 +17,7 @@ export class SVM {
   constructor(params: { c?: number, coef0?: number, degree?: number, gamma?: number, nu?: number, p?: number, kernelType?: number, classWeights?: Mat });
   calcError(trainData: TrainData, test: boolean): { error: number, responses: Mat };
   getSupportVectors(): Mat;
+  getDecisionFunction(): { rho: number, alpha: Mat, svidx: Mat };
   load(file: string): void;
   predict(sample: number[], flags?: number): number;
   predict(samples: Mat, flags?: number): number[];

test/tests/modules/machinelearning/SVMTests.js

@@ -190,6 +190,15 @@ module.exports = () => {
         });
       });
 
+      describe('getDecisionFunction', () => {
+        it('should return decision function', () => {
+          const ret = svm.getDecisionFunction(0);
+          expect(ret).to.have.property('rho').to.be.a('number');
+          expect(ret).to.have.property('alpha').to.be.instanceOf(cv.Mat);
+          expect(ret).to.have.property('svidx').to.be.instanceOf(cv.Mat);
+        });
+      });
+
       describe('calcError', () => {
         it.skip('calcError', () => {});
       });