pysad.core.BaseTransformer

class pysad.core.BaseTransformer(output_dims)

Base class for transforming methods.

Methods

__init__(output_dims)
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 transformer.
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)	Transforms 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

abstract fit_partial(X)

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

Parameters:

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

abstract transform_partial(X)

Transforms particular (next) timestep’s vector.

Parameters:

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

Returns:

Projected feature vector.

Return type:

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