\[\DeclareMathOperator{\erf}{erf} \DeclareMathOperator{\argmin}{argmin} \newcommand{\R}{\mathbb{R}} \newcommand{\n}{\boldsymbol{n}}\]

Module pyqt_fit.nonparam_regression

Author:Pierre Barbier de Reuille <pierre.barbierdereuille@gmail.com>

Module implementing non-parametric regressions using kernel methods.

class pyqt_fit.nonparam_regression.NonParamRegression(xdata, ydata, **kwords)[source]

Class performing kernel-based non-parametric regression.

The calculation is split in three parts:

N[source]

Number of points in the dataset (set by the fitting)

bandwidth[source]

Bandwidth of the kernel.

This is defined as the square root of the covariance matrix

covariance[source]

Covariance matrix of the kernel.

It must be of the right dimension!

dim[source]

Dimension of the domain (set by the fitting)

fit()[source]

Method to call to fit the parameters of the fitting

fitted[source]

Check if the fitting needs to be performed.

fitted_method[source]

Method actually used after fitting.

The main method may choose to provide a more tuned method during fitting.

kernel[source]

Kernel object. Should provide the following methods:

kernel.pdf(xs)
Density of the kernel, denoted \(K(x)\)
kernel_type[source]

Type of the kernel. The kernel type is a class or function accepting the dimension of the domain as argument and returning a valid kernel object.

lower[source]

Lower bound of the domain for each dimension

method[source]

Regression method itself. It should be an instance of the class following the template pyqt_fit.npr_methods.RegressionKernelMethod.

need_fit()[source]

Calling this function will mark the object as needing fitting.

set_actual_bandwidth(bandwidth, covariance)[source]

Method computing the bandwidth if needed (i.e. if it was defined by functions)

upper[source]

Lower bound of the domain for each dimension

xdata[source]

2D array (D,N) with D the dimension of the domain and N the number of points.

ydata[source]

1D array (N,) of values for each point in xdata

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