ml-kernel

A factory for kernel functions.

Installation

$ npm i ml-kernel

Usage

new Kernel(type, options)

This function can be called with a matrix of input vectors. and optional landmarks. If no landmark is provided, the input vectors will be used.

Available kernels:

• linear - Linear kernel
• gaussian or rbf - Gaussian (radial basis function) kernel
• polynomial or poly - Polynomial kernel
• exponential - Exponential kernel
• laplacian - Laplacian kernel
• anova - ANOVA kernel
• rational - Rational Quadratic kernel
• multiquadratic - Multiquadratic kernel
• cauchy - Cauchy kernel
• histogram or min - Histogram Intersection kernel
• sigmoid or `mlp' - Sigmoid (hyperbolic tangent) kernel

kernel.compute(inputs, landmarks)

This function can be called with a matrix of input vectors and optional landmarks. If no landmark is provided, the input vectors will be used.
The function returns a kernel matrix of feature space vectors.

License

MIT

3.0.0 (2019-06-29)

2.3.4 (2017-07-21)

2.3.3 (2017-04-25)

2.3.2 (2016-08-16)

2.3.1 (2016-08-03)

Bug Fixes

• require correct euclidean distance (4882644)

2.3.0 (2016-08-03)

Features

• add sigmoid kernel (dcdc0e0)

2.2.0 (2016-07-19)

2.1.0 / 2016-06-21

• add exponential kernel
• add laplacian kernel
• add anova kernel
• add rational quadratic kernel
• add multiquadratic kernel
• add cauchy kernel
• add histogram intersection kernel

2.0.0 / 2015-11-18

• refactor to a class

1.1.0 / 2015-11-18

• add support for polynomial kernel

1.0.0 / 2015-11-18

• first release