Waveform¶

Waveform stream generator.

Generates samples with 21 numeric features and 3 classes, based on a random differentiation of some base waveforms. Supports noise addition, in this case the samples will have 40 features.

Parameters¶

seed

Type → int | None

Default → None

Random seed for reproducibility.
has_noise

Type → bool

Default → False

Adds 19 unrelated features to the stream.

Attributes¶

desc

Return the description from the docstring.

Examples¶

from river.datasets import synth

dataset = synth.Waveform(seed=42, has_noise=True)

for x, y in dataset:
    break

x

{0: -0.0397, 1: -0.7484, 2: 0.2974, 3: 0.3574, 4: -0.0735, 5: -0.3647, 6: 1.5631,     7: 2.5291, 8: 4.1599, 9: 4.9587, 10: 4.52587, 11: 4.0097, 12: 3.6705, 13: 1.7033,     14: 1.4898, 15: 1.9743, 16: 0.0898, 17: 2.319, 18: 0.2552, 19: -0.4775, 20: -0.71339,     21: 0.3770, 22: 0.3671, 23: 1.6579, 24: 0.7828, 25: 0.5855, 26: -0.5807, 27: 0.7112,     28: -0.0271, 29: 0.2968, 30: -0.4997, 31: 0.1302, 32: 0.3578, 33: -0.1900, 34: -0.3771,     35: 1.3560, 36: 0.7124, 37: -0.6245, 38: 0.1346, 39: 0.3550}

Methods¶

take

Iterate over the k samples.

Parameters

k — 'int'

Notes¶

An instance is generated based on the parameters passed. The generator will randomly choose one of the hard coded waveforms, as well as random multipliers. For each feature, the actual value generated will be a a combination of the hard coded functions, with the multipliers and a random value.

If noise is added then the features 21 to 40 will be replaced with a random normal value.