WhiteNoise#
- class skfda.misc.covariances.WhiteNoise(*, variance=1)[source]#
Gaussian covariance function.
The covariance function is
\[\begin{split}K(x, x')= \begin{cases} \sigma^2, \quad x = x' \\ 0, \quad x \neq x'\\ \end{cases}\end{split}\]where \(\sigma^2\) is the variance.
Heatmap plot of the covariance function:
from skfda.misc.covariances import WhiteNoise import matplotlib.pyplot as plt WhiteNoise().heatmap(limits=(0, 1)) plt.show()
Example of Gaussian process trajectories using this covariance:
from skfda.misc.covariances import WhiteNoise from skfda.datasets import make_gaussian_process import matplotlib.pyplot as plt gp = make_gaussian_process( n_samples=10, cov=WhiteNoise(), random_state=0) gp.plot() plt.show()
Default representation in a Jupyter notebook:
from skfda.misc.covariances import WhiteNoise WhiteNoise()
\[K(x, x')= \begin{cases} \sigma^2, \quad x = x' \\0, \quad x \neq x'\\ \end{cases} \\\text{where:}\begin{aligned}\qquad\sigma^2 &= 1 \\\end{aligned}\]Methods
heatmap
([limits])Return a heatmap plot of the covariance function.
Convert it to a sklearn kernel, if there is one.
- Parameters:
variance (float) –
Examples using skfda.misc.covariances.WhiteNoise
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One-way functional ANOVA with synthetic data
One-way functional ANOVA with synthetic data