krls 0.0.2

Kernel Recursive Least Square (KRLS)

KRLS: Kernel Recursive Least Squares

Project description

Author: Kaike Sa Teles Rocha Alves

KRLS: Kernel Recursive Least Squares is a package that contains machine learning models developed by coded by Kaike Alves during his PhD research.

Author: Kaike Sa Teles Rocha Alves (PhD)
Email: [e-mail] or [e-mail]

Doi to cite the code: http://dx.doi.org/10.5281/zenodo.15800969

Github repository: https://github.com/kaikerochaalves/KRLS_pypi.git

It provides:

1. Kernel Recursive Least Squares: KRLS
2. Sliding Window Kernel Recursive Least Squares: SW-KRLS
3. Extended Kernel Recursive Least Squares: EX-KRLS
4. Fixed Base Kernel Recursive Least Squares: FB-KRLS
5. Kernel Recursive Least Squares Tracker: KRLS-T
6. Quantized Kernel Recursive Least Squares: QKRLS
7. Adaptive Dynamic Adjustment Kernel Recursive Least Squares: ADA-KRLS
8. Quantized Adaptive Dynamic Adjustment Kernel Recursive Least Squares: QALD-KRLS
9. Light Kernel Recursive Least Squares: Light-KRLS

Cite: SA TELES ROCHA ALVES, K. (2025). KRLS: Kernel Recursive Least Squares. Zenodo. https://doi.org/10.5281/zenodo.15800969

Description:

KRLS: A Novel Python Library for Kernel Recursive Least Squares Model

KRLS (Kernel Recursive Least Squares) is a groundbreaking Python library available on PyPI (https://pypi.org/project/krls/) that introduces a suite of advanced fuzzy inference systems. This library is specifically designed to tackle the complexities of time series forecasting and classification problems by offering machine learning models that prioritize high accuracy.

At its core, KRLS features data-driven machine learning models.

Instructions

To install the library use the command:

pip install krls

The library provides 6 models in fuzzy systems, as follows:

KRLS

To import the KRLS, simply type the command:

from krls import KRLS

Hyperparameters:

nu : float, default=0.01
Accuracy parameter determining the level of sparsity. Must be a positive float.

N : int, default=100
Accuracy parameter determining the level of sparsity. Must be a integer greater 
than 1.

kernel_type : str
The type of kernel function to use. Must be one of: 'Linear', 'Polynomial', 'RBF', 'Gaussian',
'Sigmoid', 'Powered', 'Log', 'GeneralizedGaussian', 'Hybrid', additive_chi2, and Cosine.

validate_array : bool
If True, input arrays are validated before computation.

kernel_type : str, default='Linear'
The type of kernel function to use. Must be one of the supported kernels in `base`.

**kwargs : dict
Additional hyperparameters depending on the chosen kernel:
- 'a', 'b', 'd' : Polynomial kernel parameters
- 'gamma': RBF kernel parameter
- 'sigma' : Gaussian, and Hybrid kernel parameter
- 'r' : Sigmoid kernel parameter
- 'beta' : Powered and Log kernel parameter
- 'tau' : Hybrid kernel parameter
- 'lr' : GeneralizedGaussian kernel parameters
- 'epochs' : GeneralizedGaussian kernel parameters

Example of KRLS:

from krls import KRLS
model = KRLS(nu=0.001, N=500, kernel_type="Gaussian", sigma=0.5)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

SW-KRLS

To import the SW-KRLS, simply type the command:

from krls import SW_KRLS

Hyperparameters:

N : int, default=100
Accuracy parameter determining the level of sparsity. 
Must be an integer greater than 1.

c : float, default=1e-6
Regularization parameter. Must be a very small float.

kernel_type : str
The type of kernel function to use. Must be one of: 'Linear', 'Polynomial', 'RBF', 'Gaussian',
'Sigmoid', 'Powered', 'Log', 'GeneralizedGaussian', 'Hybrid', additive_chi2, and Cosine.

validate_array : bool
If True, input arrays are validated before computation.

kernel_type : str, default='Linear'
The type of kernel function to use. Must be one of the supported kernels in `base`.

**kwargs : dict
Additional hyperparameters depending on the chosen kernel:
- 'a', 'b', 'd' : Polynomial kernel parameters
- 'gamma': RBF kernel parameter
- 'sigma' : Gaussian, and Hybrid kernel parameter
- 'r' : Sigmoid kernel parameter
- 'beta' : Powered and Log kernel parameter
- 'tau' : Hybrid kernel parameter
- 'lr' : GeneralizedGaussian kernel parameters
- 'epochs' : GeneralizedGaussian kernel parameters

Example of SW_KRLS:

from krls import SW_KRLS
model = SW_KRLS(N=500, kernel_type="Gaussian", sigma=0.5)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

EX-KRLS

To import the EX-KRLS, simply type the command:

from krls import EX_KRLS

Hyperparameters:

alpha : float, default=0.999
State forgetting factor. Must be a float between 0 and 1.

beta : float, default=0.995
Data forgetting factor. Must be a float between 0 and 1.

c : float, default=1e-6
Regularization parameter. Must be a very small float.

q : float, default=1e-6
Trade-off between modeling variation and measurement disturbance. Must be a very small float.

N : int, default=100
Accuracy parameter determining the level of sparsity. Must be an integer greater than 1.

kernel_type : str
The type of kernel function to use. Must be one of: 'Linear', 'Polynomial', 'RBF', 'Gaussian',
'Sigmoid', 'Powered', 'Log', 'GeneralizedGaussian', 'Hybrid', additive_chi2, and Cosine.

validate_array : bool
If True, input arrays are validated before computation.

kernel_type : str, default='Linear'
The type of kernel function to use. Must be one of the supported kernels in `base`.

**kwargs : dict
Additional hyperparameters depending on the chosen kernel:
- 'a', 'b', 'd' : Polynomial kernel parameters
- 'gamma': RBF kernel parameter
- 'sigma' : Gaussian, and Hybrid kernel parameter
- 'r' : Sigmoid kernel parameter
- 'beta' : Powered and Log kernel parameter
- 'tau' : Hybrid kernel parameter
- 'lr' : GeneralizedGaussian kernel parameters
- 'epochs' : GeneralizedGaussian kernel parameters

Example of EX_KRLS:

from krls import EX_KRLS
model = EX_KRLS(kernel_type="Gaussian", sigma=0.5)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

FB-KRLS

To import the EX-KRLS, simply type the command:

from krls import FB_KRLS

Hyperparameters:

N : int, default=100
Accuracy parameter determining the level of sparsity. 
Must be an integer greater than 1.

c : float, default=1e-6
Regularization parameter. Must be a very small float.

validate_array : bool
If True, input arrays are validated before computation.

kernel_type : str, default='Linear'
The type of kernel function to use. Must be one of the supported kernels in `base`.

**kwargs : dict
Additional hyperparameters depending on the chosen kernel:
- 'a', 'b', 'd' : Polynomial kernel parameters
- 'gamma': RBF kernel parameter
- 'sigma' : Gaussian, and Hybrid kernel parameter
- 'r' : Sigmoid kernel parameter
- 'beta' : Powered and Log kernel parameter
- 'tau' : Hybrid kernel parameter
- 'lr' : GeneralizedGaussian kernel parameters
- 'epochs' : GeneralizedGaussian kernel parameters

Example of FB_KRLS:

from krls import FB_KRLS
model = FB_KRLS(N=500, kernel_type="Gaussian", sigma=0.5)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

KRLS-T

To import the KRLS-T, simply type the command:

from krls import KRLS_T

Hyperparameters:

N : int, default=100
Accuracy parameter determining the level of sparsity. 
Must be an integer greater than 1.

c : float, default=1e-6
Regularization parameter. Must be a very small float.

validate_array : bool
If True, input arrays are validated before computation.

kernel_type : str, default='Linear'
The type of kernel function to use. Must be one of the supported kernels in `base`.

**kwargs : dict
Additional hyperparameters depending on the chosen kernel:
- 'a', 'b', 'd' : Polynomial kernel parameters
- 'gamma': RBF kernel parameter
- 'sigma' : Gaussian, and Hybrid kernel parameter
- 'r' : Sigmoid kernel parameter
- 'beta' : Powered and Log kernel parameter
- 'tau' : Hybrid kernel parameter
- 'lr' : GeneralizedGaussian kernel parameters
- 'epochs' : GeneralizedGaussian kernel parameters

Example of KRLS_T:

from krls import KRLS_T
model = KRLS_T(N=500, kernel_type="Gaussian", sigma=0.5)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

QKRLS

To import the QKRLS, simply type the command:

from krls import QKRLS

Hyperparameters:

N : int, default=100
Accuracy parameter determining the level of sparsity. Must be an integer greater than 1.
    
c : float, default=1e-6
Regularization parameter. Must be a very small float.

epsilon : float, default=0.01
Quantization size. Must be a float between 0 and 1.

validate_array : bool
If True, input arrays are validated before computation.

kernel_type : str, default='Linear'
The type of kernel function to use. Must be one of the supported kernels in `base`.

**kwargs : dict
Additional hyperparameters depending on the chosen kernel:
- 'a', 'b', 'd' : Polynomial kernel parameters
- 'gamma': RBF kernel parameter
- 'sigma' : Gaussian, and Hybrid kernel parameter
- 'r' : Sigmoid kernel parameter
- 'beta' : Powered and Log kernel parameter
- 'tau' : Hybrid kernel parameter
- 'lr' : GeneralizedGaussian kernel parameters
- 'epochs' : GeneralizedGaussian kernel parameters

Example of QKRLS:

from krls import QKRLS
model = QKRLS(N=500, kernel_type="Gaussian", sigma=0.5)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

ADA-KRLS

To import the ADA-KRLS, simply type the command:

from krls import ADA_KRLS

Hyperparameters:

N : int, default=100
Accuracy parameter determining the level of sparsity. 
Must be an integer greater than 1.

nu : int, default=0.01
Accuracy parameter determining the level of sparsity. Must be a float between 0 and 1.

c : float, default=1e-6
Regularization parameter. Must be a very small float.

validate_array : bool
If True, input arrays are validated before computation.

kernel_type : str, default='Linear'
The type of kernel function to use. Must be one of the supported kernels in `base`.

**kwargs : dict
Additional hyperparameters depending on the chosen kernel:
- 'a', 'b', 'd' : Polynomial kernel parameters
- 'gamma': RBF kernel parameter
- 'sigma' : Gaussian, and Hybrid kernel parameter
- 'r' : Sigmoid kernel parameter
- 'beta' : Powered and Log kernel parameter
- 'tau' : Hybrid kernel parameter
- 'lr' : GeneralizedGaussian kernel parameters
- 'epochs' : GeneralizedGaussian kernel parameters

Example of ADA-KRLS:

from krls import ADA_KRLS
model = ADA_KRLS(N=500, kernel_type="Gaussian", sigma=0.5)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

QALD-KRLS

To import the QALD-KRLS, simply type the command:

from krls import QALD_KRLS

Hyperparameters:

N : int, default=100
Accuracy parameter determining the level of sparsity. 
Must be an integer greater than 1.

c : float, default=1e-6
Regularization parameter. Must be a very small float.

nu : int, default=0.01
Accuracy parameter determining the level of sparsity. 
Must be a float between 0 and 1.

epsilon1 : int, default=0.1
Accuracy parameter determining the level of sparsity. 
Must be a float between 0 and 1.

epsilon2 : int, default=0.1
Accuracy parameter determining the level of sparsity. 
Must be a float between 0 and 1.

kernel_type : str, default='Gaussian'
The type of kernel function to use. Must be one of the 
supported kernels in `base`.

validate_array : bool
If True, input arrays are validated before computation.

kernel_type : str, default='Linear'
The type of kernel function to use. Must be one of the supported kernels in `base`.

**kwargs : dict
Additional hyperparameters depending on the chosen kernel:
- 'a', 'b', 'd' : Polynomial kernel parameters
- 'gamma': RBF kernel parameter
- 'sigma' : Gaussian, and Hybrid kernel parameter
- 'r' : Sigmoid kernel parameter
- 'beta' : Powered and Log kernel parameter
- 'tau' : Hybrid kernel parameter
- 'lr' : GeneralizedGaussian kernel parameters
- 'epochs' : GeneralizedGaussian kernel parameters

Example of QALD-KRLS:

from krls import QALD_KRLS
model = QALD_KRLS(N=500, epsilon1 = 1e-4, kernel_type="Gaussian", sigma=0.5)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

Light-KRLS

To import the Light-KRLS, simply type the command:

from krls import Light_KRLS

Hyperparameters:

N : int, default=100
Accuracy parameter determining the level of sparsity. 
Must be an integer greater than 1.

c : float, default=1e-6
Regularization parameter. Must be a very small float.

kernel_type : str, default='Gaussian'
The type of kernel function to use. Must be one of the 
supported kernels in `base`.

validate_array : bool
If True, input arrays are validated before computation.

kernel_type : str, default='Linear'
The type of kernel function to use. Must be one of the supported kernels in `base`.

**kwargs : dict
Additional hyperparameters depending on the chosen kernel:
- 'a', 'b', 'd' : Polynomial kernel parameters
- 'gamma': RBF kernel parameter
- 'sigma' : Gaussian, and Hybrid kernel parameter
- 'r' : Sigmoid kernel parameter
- 'beta' : Powered and Log kernel parameter
- 'tau' : Hybrid kernel parameter
- 'lr' : GeneralizedGaussian kernel parameters
- 'epochs' : GeneralizedGaussian kernel parameters

Example of Light-KRLS:

from krls import Light_KRLS
model = Light_KRLS(N=500, kernel_type="Gaussian", sigma=0.5)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

Extra information

Code of Conduct:

KRLS is a library developed by Kaike Alves. Please read the Code of Conduct for guidance.

Call for Contributions:

The project welcomes your expertise and enthusiasm!

Small improvements or fixes are always appreciated. If you are considering larger contributions to the source code, please contact by email first.