pysad.transform.preprocessing.InstanceUnitNormScaler

class pysad.transform.preprocessing.InstanceUnitNormScaler(pow=2)

A scaler that makes the instance feature vector’s norm equal to 1, i.e., the unit vector.

Parameters:

pow (float) – The power, for which the norm is calculated. pow=2 is equivalent to the euclidean distance.

Methods

__init__([pow])
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,))