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}

y

2

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.