pysad.transform.projection.StreamhashProjector

class pysad.transform.projection.StreamhashProjector(num_components, density=0.3333333333333333)

Streamhash projection method from Manzoor et. al.that is similar (or equivalent) to SparseRandomProjection. [BMLA18] The implementation is taken from the cmuxstream-core repository.

Parameters:

• num_components (int) – The number of dimensions that the target will be projected into.

• density (float) – Density parameter of the streamhash projection.

Methods

__init__(num_components[, density])
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 projector.
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)	Projects particular (next) timestep's vector to (possibly) lower dimensional space.

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 projector.

Parameters:

X (np.float64 array of shape (n_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)

transform_partial(X)

Projects particular (next) timestep’s vector to (possibly) lower dimensional space.

Parameters:

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

Returns:

Projected feature vector.

Return type:

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