Standard Deviation Function¶

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

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

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 standard deviation of the values in x.

Parameters:

Name	Type	Description	Default
`x`	`ndarray`	The array to compute the median of.	required
`ddof`	`Union[int, int_]`	The delta degrees of freedom. Default is `0`. See `numpy.std` 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 standard deviation 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.std_tf)

# Get features
features = featurizer(x)

# Print features
print(features)

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