ml Namespace Reference

Namespace containing traditional machine learning algorithms. More...

Classes
class	KMeans
class	PCA
class	SVM

Typedefs
using	KMeansF = KMeans< float >
	Type aliases for common use cases.
using	KMeansD = KMeans< double >
using	PCAF = PCA< float >
using	PCAD = PCA< double >
using	SVMF = SVM< float >
using	SVMD = SVM< double >

Enumerations
enum class	KernelType { LINEAR , POLYNOMIAL , RBF , SIGMOID }
	Kernel function types for SVM. More...

Detailed Description

Namespace containing traditional machine learning algorithms.

This namespace provides implementations of classical machine learning algorithms that are commonly used for data preprocessing, dimensionality reduction, clustering, and classification tasks.

The algorithms are designed to work seamlessly with the matrix utilities provided in the utils namespace.

Typedef Documentation

KMeansD

using ml::KMeansD = typedef KMeans<double>

Examples

/home/runner/work/deep-learning-algo-impls/deep-learning-algo-impls/include/ml/kmeans.hpp.

Definition at line 142 of file kmeans.hpp.

KMeansF

using ml::KMeansF = typedef KMeans<float>

Type aliases for common use cases.

Examples

/home/runner/work/deep-learning-algo-impls/deep-learning-algo-impls/include/ml/kmeans.hpp.

Definition at line 141 of file kmeans.hpp.

PCAD

using ml::PCAD = typedef PCA<double>

Examples

/home/runner/work/deep-learning-algo-impls/deep-learning-algo-impls/include/ml/pca.hpp.

Definition at line 110 of file pca.hpp.

PCAF

using ml::PCAF = typedef PCA<float>

Examples

/home/runner/work/deep-learning-algo-impls/deep-learning-algo-impls/include/ml/pca.hpp.

Definition at line 109 of file pca.hpp.

SVMD

using ml::SVMD = typedef SVM<double>

Examples

/home/runner/work/deep-learning-algo-impls/deep-learning-algo-impls/include/ml/svm.hpp.

Definition at line 188 of file svm.hpp.

SVMF

using ml::SVMF = typedef SVM<float>

Examples

/home/runner/work/deep-learning-algo-impls/deep-learning-algo-impls/include/ml/svm.hpp.

Definition at line 187 of file svm.hpp.

Enumeration Type Documentation

KernelType

enum class ml::KernelType

strong

Kernel function types for SVM.

Enumerator
LINEAR	Linear kernel: K(x, y) = x^T * y.
POLYNOMIAL	Polynomial kernel: K(x, y) = (gamma * x^T * y + coef0)^degree.
RBF	Radial Basis Function kernel: K(x, y) = exp(-gamma * ||x - y||^2)
SIGMOID	Sigmoid kernel: K(x, y) = tanh(gamma * x^T * y + coef0)

Examples

/home/runner/work/deep-learning-algo-impls/deep-learning-algo-impls/include/ml/svm.hpp.

Definition at line 21 of file svm.hpp.