Variance Function¶

The variance function computes the variance of a window. When combined with the SlidingWindow abstraction, the variance function can be used to compute the var feature of a time series. The variance is defined as:

\[ \sigma^2 = \frac{1}{n} \sum_{i=1}^{n} (x_i - \mu)^2 \]

where \(x_i\) is the \(i\)-th element of the window, \(n\) is the number of elements in the window, and \(\mu\) is the mean of the window.

Compute the variance of the values in x.

Parameters:

Name	Type	Description	Default
`x`	`ndarray`	The array to compute the variance of.	required
`ddof`	`Union[int, int_]`	The delta degrees of freedom. Default is `0`. See `numpy.var` for more information.	`0`
`where`	`Callable[[Union[int, float, int_, float_]], Union[bool, bool_]]`	A function that takes a value and returns `True` or `False`. Default is `lambda x: not np.isnan(x)` i.e. a measurement is valid if it is not a `NaN` value.	`lambda : not numpy.isnan(x)`

Returns:

Type	Description
`Union[float, float_]`	The variance of the values in `x`.

Examples¶

import numpy as np
import autonfeat as aft
import autonfeat.functional as F

# Random data
n_samples = 100
x = np.random.rand(n_samples)

# Create sliding window
ws = 10
ss = 10
window = aft.SlidingWindow(window_size=ws, step_size=ss)

# Get featurizer
featurizer = window.use(F.var_tf)

# Get features
features = featurizer(x)

# Print features
print(features)

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