jpt.distributions.univariate.gaussian

Classes

Gaussian

Extension of dnutils.stats.Gaussian

Module Contents

class jpt.distributions.univariate.gaussian.Gaussian(mean=None, cov=None, data=None, weights=None)

Bases: dnutils.stats.Gaussian

Extension of dnutils.stats.Gaussian

Creates a new Gaussian distribution.

Parameters:

mean (float if multivariate else [float] if multivariate) – the mean of the Gaussian
cov (float if multivariate else [[float]] if multivariate) – the covariance of the Gaussian
data ([[float]]) – if mean and cov are not provided, data may be a data set (matrix) from which the parameters of the distribution are estimated.
weights ([float]) – [optional] weights for the data points. The weight do not need to be normalized.

PRECISION = 1e-15

_cl = 'jpt.distributions.univariate.gaussian.Gaussian'

_sum_w = 0

_sum_w_sq = 0

_mean

_cov

data = []

mean()

cov()

var()

property std

deviation(x)

Computes the deviation of x in multiples of the standard deviation.

Parameters:: x –
Returns:

__add__(alpha)

__radd__(other)

__iadd__(other)

__mul__(alpha)

__rmul__(other)

__imul__(other)

static wasserstein_distance(d1: Gaussian, d2: Gaussian) → float

dim()

sample(n)

Return n samples from this Gaussian distribution.

Parameters:: n – number of samples
Returns:: array of shape (n,) for 1-D or (n, d) for d-dimensional Gaussians

property pdf

cdf(*x)

eval(lower, upper)

copy()

__eq__(other)

linreg()

Compute a 4-tuple <m, b, rss, noise> of a linear regression represented by this Gaussian.

Returns:: m - the slope of the line b - the intercept of the line rss - the residual sum-of-squares error noise - the square of the sample correlation coefficient r^2

References:

update_all(data, weights=None): Update the distribution with new data points given in data.

estimate(data, weights=None): Estimate the distribution parameters with subject to the given data points.

update(x, w=1): update the Gaussian distribution with a new data point x and weight w.

retract(x, w=1)

Retract the data point x with weight w from the Gaussian distribution.

In case the data points are being kept in the distribution, it must actually exist and have the right weight associated. Otherwise, a ValueError will be raised.

sym()

plot(engine=None, **kwargs) → Any

Plots the distribution using the given engine.

Parameters:

engine – Can be either one of ["plotly", "matplotlib"], or an instance of a rendering engine subclassing DistributionRendering.
kwargs – The keyword arguments to pass to the engine as defined in the .plot_gaussian() function of DistributionRendering or its respective subclass defined by engine.

Returns:

the figure object of the plotting engine