Machine Learning

Functions
FASTCV_API fcvStatus	fcvSVMPredict2Classf32 (fcvSVMKernelType kernelType, uint32_t degree, float32_t gamma, float32_t coef0, const float32_t *__restrict sv, uint32_t svLen, uint32_t svNum, uint32_t svStride, const float32_t *__restrict svCoef, float32_t rho, const float32_t *__restrict vec, uint32_t vecNum, uint32_t vecStride, float32_t *__restrict confidence)
	SVM prediction for two classes.

---

Detailed Description

Machine learning functions such as SVM prediction, etc.

---

Function Documentation

FASTCV_API fcvStatus fcvSVMPredict2Classf32	(	fcvSVMKernelType	kernelType,
		uint32_t	degree,
		float32_t	gamma,
		float32_t	coef0,
		const float32_t *__restrict	sv,
		uint32_t	svLen,
		uint32_t	svNum,
		uint32_t	svStride,
		const float32_t *__restrict	svCoef,
		float32_t	rho,
		const float32_t *__restrict	vec,
		uint32_t	vecNum,
		uint32_t	vecStride,
		float32_t *__restrict	confidence
	)

SVM prediction for two classes.

The function returns the confidence scores for test vectors using the equation: confidence(i) = sum_j( svCoef[j] * Kernel(vec_i, sv_j) - rho; The SVM model(svCoef, sv, rho) can be obtained using the training function of libSVM or openCV.

Parameters:

kernelType	kernelType options: 'FASTCV_SVM_LINEAR','FASTCV_SVM_POLY','FASTCV_SVM_RBF','FASTCV_SVM_SIGMOID'. FASTCV_SVM_LINEAR: Kernel(xi, xj) = xi'*xj FASTCV_SVM_POLY: Kernel(xi, xi) = (gamma * xi' * xj + coef0)^degree, (gamma>0 , degree is positive integer). FASTCV_SVM_RBF: Kernel(xi,xj) = exp(-gamma*||xi-xj||^2), (gamma>0). FASTCV_SVM_SIGMOID: Kernel(xi, xj) = tanh(gamma * xi' * xj + coef0)
degree	Parameter degree of a kernel function (FASTCV_SVM_POLY). NOTE: Degree should be positive integer.
gamma	Parameter of a kernel function (FASTCV_SVM_POLY / FASTCV_SVM_RBF / FASTCV_SVM_SIGMOID). NOTE: gamma > 0 for FASTCV_SVM_Ploy and FASTCV_SVM_RBF
coef0	Parameter coef0 of a kernel function (FASTCV_SVM_LINEAR / FASTCV_SVM_POLY / FASTCV_SVM_SIGMOID).
sv	Support vectors.
svLen	Feature length, (support vector length = feature length).
svNum	Number of support vectors.
svStride	support vector stride. Stride of support vector 2D matrix (i.e., how many bytes between column 0 of row 1 and column 0 of row 2). NOTE: if 0, svStride is set as svLen*sizeof(float32_t). WARNING: should be multiple of 8, and at least as much as svLen if not 0.
svCoef	Coefficent of support vectors, length equals to the Number of SV.
rho	SVM bias.
vec	Test vectors, it has same width as sv.
vecNum	Number of test vectors.
vecStride	Stride of test vectors. Stride of test vector 2D matrix (i.e., how many bytes between column 0 of row 1 and column 0 of row 2). NOTE: if 0, vecStride is set as svLen*sizeof(float32_t). WARNING: should be multiple of 8, and at least as much as svLen if not 0.
confidence	Output, store confidence value of each test vector. The length is vecNum.

Returns:

FASTCV_SUCCESS upon success. Other status codes upon failure.