Density

class probpy.density.nonparametric_convolution.UCKD(variance: float = 2.0, **_)

Un-normalised Convolution Kernel Density

__init__(variance: float = 2.0, **_)

Parameters:

• variance – variance of kernel
• _ –

fit(particles: numpy.ndarray)

Parameters:particles – particles to use for estimate Returns:

p(particles: numpy.ndarray)

Parameters:particles – densities to estimtae Returns:densities

class probpy.density.nonparametric_convolution.RCKD(variance: float = 2.0, sampling_sz: int = 100, error: float = 0.1, verbose: bool = False)

Renormalized Convolution Kernel Density

__init__(variance: float = 2.0, sampling_sz: int = 100, error: float = 0.1, verbose: bool = False)

Parameters:

• variance – variance of kernel
• sampling_sz – sampling size in normalization integral
• error – error metric for normalization constant
• verbose – print progress of estimating normalization constant

fit(particles: numpy.ndarray)

Parameters:particles – particles to use for estimate Returns:

p(particles: numpy.ndarray)

Parameters:particles – estimate density of particles Returns:densities

class probpy.density.radial_basis.URBK(variance: float = 1.0, **_)

__init__(variance: float = 1.0, **_)

Parameters:

• variance – variance in rbf kernel
• _ –

epsilon = 0.01

Un-normalised Radial-Basis Kernel

fit(particles: numpy.ndarray, densities: numpy.ndarray)

Parameters:

• particles – particles to estimate density
• densities – unnormalized density of particles

Returns:

p(particles: numpy.ndarray)

Parameters:particles – particles to estimate Returns:densities