Max Function¶

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

\[ \text{max}(x) = \max_{i=1}^n x_i \]

where \(x\) is a vector of length \(n\).

Compute the max of the values in x where where is True.

Parameters:

Name	Type	Description	Default
`x`	`ndarray`	The array to compute the max 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)`
`initial`	`Union[int, float, int_, float_]`	The initial value to use when computing the max. Default is `-np.inf`.	`-inf`

Returns:

Type	Description
`Union[float, float_]`	The max of the values in `x` where `where` is `True`.

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.max_tf)

# Get features
features = featurizer(x)

# Print features
print(features)

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