imputegap.algorithms.mean_impute package¶

Submodules¶

Modules¶

imputegap.algorithms.mean_impute.mean_impute(incomp_data, params=None)[source]¶

Impute NaN values with the mean value of the time series.

Parameters¶

incomp_datanumpy.ndarray

The input time series with contamination (missing values represented as NaNs).

paramsdict, optional

Optional parameters for the algorithm. If None, the minimum value from the contamination is used (default is None).

Returns¶

numpy.ndarray

The imputed matrix where NaN values have been replaced with the mean value from the time series.

Notes¶

This function finds the non-NaN value in the time series and replaces all NaN values with this mean value. It is a simple imputation technique for filling missing data points in a dataset.

Example¶

>>> incomp_data = np.array([[5, 2, np.nan], [3, np.nan, 6]])
>>> recov_data = mean_impute(incomp_data)
>>> print(recov_data)
array([[5., 2., 4.],
       [3., 4., 6.]])