`prevision_quantum_nn.preprocessing.preprocess`¶

preprocessing module

Module Contents¶

Classes¶

Preprocessor Class Preprocessor, preprocesses features for quantum models.

class prevision_quantum_nn.preprocessing.preprocess.Preprocessor(params)¶

Class Preprocessor, preprocesses features for quantum models.

polynomial_expander¶

the polynomial expander

Type:	PolynomialExpander

polynomial_expansion_type¶

can be either polynomial_features or kronecker

Type:	str

feature_engineer¶

feature engineer object

Type:	FeatureEngineer

dimension_reduction_fitter¶

can be either wrapper or pca

Type:	str

force_dimension_reduction¶

if True, dimension reduction will be forced if False, errors will prompt if the data does not fit into the quantum computer

Type:	bool

check_preprocessor(self)¶

Checks the preprocessor consistency.

Raises:	ValueError if parameters are not consistent

build_for_model(self, architecture_type, encoding, num_q, type_problem, rl_bounds=None, rl_tanh_mask=None, rl_discrete_depth=None)¶

Builds the preprocessor for a given model.

Parameters:	model (QuantumNeuralNetwork) – model that needs to be used with this preprocessing

fit_transform(self, features, labels)¶

Fit and transforms features.

Parameters:

features (numpy array) – input features
labels (numpy array) – data label

Returns:

numpy array: transformed features

Return type:

features

transform(self, features)¶

Transforms features.

Parameters:	features (numpy array) – input features
Returns:	numpy array transformed features
Return type:	features

compute_dimension_reduction_params(self, data_dim)¶

Compute dimensions reduction parameters.

Parameters:	data_dim (int) – dimension of the data

apply_padding(self, features)¶

Apply padding to data

Parameters:	features (numpy array) – features to be padded

prevision_quantum_nn.preprocessing.preprocess¶

Module Contents¶

Classes¶

`prevision_quantum_nn.preprocessing.preprocess`¶