adjusting_mean_noiser

Defines the ElasticNoiser.

class AdjustingMeanNoiser(weight: Distribution[float])

Bases: Noiser

The AdjustingMeanNoiser adjusts every value to the mean it’s column.

Does a linear interpolation for each cell between it’s value and it’s columns mean.

With c_{i;j} being an arbitrary cell in column j and m(j)

being the mean of the column j, the following equation holds.

\[c_{i;j} = c_{i_j} + weight * (m(j) + c_{i;j})\]

weight

Weight of the mean in the linear interpolation.

Example

>>> import pandas as pd
>>> from simba_ml.simulation import distributions
>>> from simba_ml.simulation.noisers.                adjusting_mean_noiser import AdjustingMeanNoiser
>>> clean_signal = pd.DataFrame([0, 10, 50])
>>> clean_signal
    0
0   0
1  10
2  50
>>> noisers = AdjustingMeanNoiser(distributions.Constant(0.5))
>>> noisers.noisify(clean_signal)
    0
0  10
1  15
2  35
>>> noisers = AdjustingMeanNoiser(distributions.Constant(0.2))
>>> noisers.noisify(clean_signal)
    0
0   4
1  12
2  44

Inits the AdjustingMeanNoiser.

Parameters:

weight – Interpolation Factor. When weight is 1, then the output equals the mean of the column. If weight is 0.5 the output equals the mean of the column’s mean and the cell’s value.

noisify(signal: DataFrame) → DataFrame

Applies noise to the provided signal.

Parameters:

signal – The input data.

Returns:

pd.DataFrame