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Mean Transform

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

\[ \mu = \frac{1}{n} \sum_{i=1}^{n} x_i \]

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

Bases: Transform

Compute the mean of the values in x.

__call__(signal_window, where=lambda : not np.isnan(x))

Compute the mean of the signal window provided.

Parameters:

Name Type Description Default
signal_window ndarray

The signal window to find the mean of.

 required
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_, int_]

A scalar value representing the mean of the signal.

Examples

import numpy as np
import autonfeat as aft

# 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)

# Create transform
tf = aft.MeanTransform()

# Get featurizer
featurizer = window.use(tf)

# Get features
features = featurizer(x)

# Print features
print(window)
print(tf)
print(features)

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