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spectrochempy.SNVTransformer

class SNVTransformer

Standard Normal Variate (SNV) transformer.

Equivalent to AutoscaleTransformer with dim='x'. Each observation (spectrum) is mean-centered and scaled to unit variance individually.

This is a thin wrapper that hard-codes dim='x' and provides a more descriptive name for the common NIR preprocessing step.

Examples

>>> scaler = scp.SNVTransformer()
>>> scaler.fit(train)
>>> test_snv = scaler.transform(test)

See also

snv

Procedural SNV function.

AutoscaleTransformer

General autoscaling transformer.

Methods Summary

fit(dataset)	Learn parameters from dataset.
fit_transform(dataset)	Fit to dataset, then transform it.
inverse_transform(dataset)	Reverse the learned transformation on dataset.
transform(dataset)	Apply the learned transformation to dataset.

Methods Documentation

fit(dataset)

Learn parameters from dataset.

Parameters:

dataset (NDDataset) – Training data.

Returns:

self – The fitted instance.

fit_transform(dataset)

Fit to dataset, then transform it.

Equivalent to self.fit(dataset).transform(dataset) but avoids an intermediate copy when possible.

Parameters:

dataset (NDDataset) – Training data.

Returns:

NDDataset – Transformed dataset.

inverse_transform(dataset)

Reverse the learned transformation on dataset.

Parameters:

dataset (NDDataset) – Data to invert.

Returns:

NDDataset – Dataset in the original space.

Raises:

SpectroChemPyError – If fit() has not been called first.

transform(dataset)

Apply the learned transformation to dataset.

Parameters:

dataset (NDDataset) – Data to transform.

Returns:

NDDataset – Transformed dataset.

Raises:

SpectroChemPyError – If fit() has not been called first.

Examples using spectrochempy.SNVTransformer