wildboar.datasets.preprocess

Module Contents

Functions

maxabs_scale(x)	Scale each time series by its maximum absolute value.
minmax_scale(x[, min, max])	Scale x along the time dimension so that each value is between min and max
named_preprocess(name)
standardize(x)	Scale x along the time dimension to have zero mean and unit standard deviation
truncate(x[, n_shortest])	Truncate x to the shortest sequence.

wildboar.datasets.preprocess.maxabs_scale(x)

Scale each time series by its maximum absolute value.

Parameters:

x (ndarray of shape (n_samples, n_timestep) or (n_samples, n_dims, n_timestep)) – The dataset

Returns:

x – The transformed dataset

Return type:

ndarray of shape (n_samples, n_timestep) or (n_samples, n_dims, n_timestep)

wildboar.datasets.preprocess.minmax_scale(x, min=0, max=1)

Scale x along the time dimension so that each value is between min and max

Parameters:

• x (ndarray of shape (n_samples, n_timestep) or (n_samples, n_dims, n_timestep)) – The dataset

• min (float, optional) – The minimum value

• max (float, optional) – The maximum value

Returns:

x – The transformed dataset

Return type:

ndarray of shape (n_samples, n_timestep) or (n_samples, n_dims, n_timestep)

wildboar.datasets.preprocess.named_preprocess(name)

wildboar.datasets.preprocess.standardize(x)

Scale x along the time dimension to have zero mean and unit standard deviation

Parameters:

x (ndarray of shape (n_samples, n_timestep) or (n_samples, n_dims, n_timestep)) – The dataset

Returns:

x – The standardized dataset

Return type:

ndarray of shape (n_samples, n_timestep) or (n_samples, n_dims, n_timestep)

wildboar.datasets.preprocess.truncate(x, n_shortest=None)

Truncate x to the shortest sequence.

Parameters:

• x (ndarray of shape (n_samples, n_timestep) or (n_samples, n_dims, n_timestep)) – The dataset

• n_shortest (int, optional) – The maximum size

Returns:

x – The truncated dataset

Return type:

ndarray of shape (n_samples, n_shortest) or (n_samples, n_dims, n_shortest)