lightonml.projections¶
lightonml.projections.sklearn¶

class
OPUMap
(n_components, opu=None, ndims=1, n_2d_features=None, packed=False, simulated=False, max_n_features=None, verbose_level=0)[source]¶ Bases:
sklearn.base.BaseEstimator
,sklearn.base.TransformerMixin
Adapter of the OPU to scikitlearn. Transform method is mapped to transform1d or transform2d of the OPU class, depending on ndims parameter at the construction.
@see lightonopu.opu.OPU
 Parameters
n_components (int,) – dimensionality of the target projection space.
opu (lightonopu.opu.OPU,) – optical processing unit instance
ndims (int,) – number of dimensions of an input. Can be 1 or 2. if ndims is 1, transform accepts 1d vector or batch of 1d vectors. if ndims is 2, transform accepts 2d vector or batch of 2d vectors.
packed (bool, optional) – whether the input data is in bitpacked representation if packed is True and ndims is 2, each input vector is assumed to be a 1d array, and the “real” number of features must be provided using n_2d_features parameter defaults to False
n_2d_features (list(int) or tuple(int) or np.ndarray (optional)) – number of 2d features if the input is packed
simulated (bool, default False,) – use real or simulated OPU
max_n_features (int, optional) – maximum number of binary features that the OPU will transform used only if simulated=True, in order to initiate the random matrix
verbose_level (int, optional) – 0, 1 or 2. 0 = no messages, 1 = most messages, and 2 = messages from OPU device (very verbose).

opu
¶ optical processing unit instance
 Type

ndims
¶ number of dimensions of an input. Can be 1 or 2. if ndims is 1, transform accepts 1d vector or batch of 1d vectors. if ndims is 2, transform accepts 2d vector or batch of 2d vectors.
 Type
int,

packed
¶ whether the input data is in bitpacked representation if packed is True and ndims is 2, each input vector is assumed to be a 1d array, and the “real” number of features must be provided using n_2d_features parameter defaults to False
 Type
bool, optional

n_2d_features
¶ number of 2d features if the input is packed
lightonml.projections.torch¶

class
OPUMap
(n_components, opu=None, ndims=1, n_2d_features=None, packed=False, simulated=False, max_n_features=None, verbose_level=0)[source]¶ Bases:
torch.nn.Module
Adapter of the OPU to the Pytorch interface. Forward method is mapped to transform1d, transform2d, or transform3d of the OPU class, depending on ndims parameter at the construction.
@see lightonopu.opu.OPU
 Parameters
n_components (int,) – dimensionality of the target projection space.
opu (lightonopu.opu.OPU,) – optical processing unit instance
ndims (int,) – number of dimensions of an input. Can be 1, 2 or 3. if ndims is 1, transform accepts 1d vector or batch of 1d vectors. if ndims is 2, transform accepts 2d vector or batch of 2d vectors.
packed (bool, optional) – whether the input data is in bitpacked representation if packed is True and ndims is 2, each input vector is assumed to be a 1d array, and the “real” number of features must be provided using n_2d_features parameter defaults to False
n_2d_features (list(int) or tuple(int) or np.ndarray (optional)) – number of 2d features if the input is packed
simulated (bool, default False,) – use real or simulated OPU
max_n_features (int, optional) – maximum number of binary features that the OPU will transform used only if simulated=True, in order to initiate the random matrix
verbose_level (int, optional) – 0, 1 or 2. 0 = no messages, 1 = most messages, and 2 = messages from OPU device (very verbose).

opu
¶ optical processing unit instance
 Type

ndims
¶ number of dimensions of an input. Can be 1, 2 or 3. if ndims is 1, transform accepts 1d vector or batch of 1d vectors. if ndims is 2, transform accepts 2d vector or batch of 2d vectors.
 Type
int,

packed
¶ whether the input data is in bitpacked representation if packed is True and ndims is 2, each input vector is assumed to be a 1d array, and the “real” number of features must be provided using n_2d_features parameter defaults to False
 Type
bool, optional

n_2d_features
¶ number of 2d features if the input is packed