pysad.transform.preprocessing.InstanceStandardScaler

class pysad.transform.preprocessing.InstanceStandardScaler

Standard deviation scaling per instance. Not that the variance and mean is calculated per instance, for which the scaling is done with. The method substracts mean and divides with the standard deviation of the features, separately for each instance.

Methods

__init__()
fit(X)	Shortcut method that iteratively applies fit_partial to all instances in order.
fit_partial(X)	Fits particular (next) timestep's features to train the scaler.
fit_transform(X)	Shortcut method that iteratively applies fit_transform_partial to all instances in order.
fit_transform_partial(X)	Shortcut method that iteratively applies fit_partial and transform_partial, respectively.
transform(X)	Shortcut method that iteratively applies transform_partial to all instances in order.
transform_partial(X)	Scales particular (next) timestep's vector.

fit(X)

Shortcut method that iteratively applies fit_partial to all instances in order.

Parameters:

X (np.float64 array of shape (num_instances, num_features)) – Input feature vectors.

Returns:

The fitted transformer

Return type:

object

fit_partial(X)

Fits particular (next) timestep’s features to train the scaler.

Parameters:

X (np.float64 array of shape (num_features,)) – Input feature vector.

Returns:

self.

Return type:

object

fit_transform(X)

Shortcut method that iteratively applies fit_transform_partial to all instances in order.

Parameters:

X (np.float64 array of shape (num_instances, num_components)) – Input feature vectors.

Returns:

Projected feature vectors.

Return type:

np.float64 array of shape (num_instances, num_components)

fit_transform_partial(X)

Shortcut method that iteratively applies fit_partial and transform_partial, respectively.

Parameters:

X (np.float64 array of shape (num_components,)) – Input feature vector.

Returns:

Projected feature vector.

Return type:

transformed_X (np.float64 array of shape (num_components,))

transform(X)

Shortcut method that iteratively applies transform_partial to all instances in order.

Parameters:

X (np.float64 array of shape (num_instances, num_features)) – Input feature vectors.

Returns:

Projected feature vectors.

Return type:

np.float64 array of shape (num_instances, num_components)

transform_partial(X)

Scales particular (next) timestep’s vector.

Parameters:

X (np.float64 array of shape (num_features,)) – Input feature vector.

Returns:

Scaled feature vector.

Return type:

scaled_X (np.float64 array of shape (features,))