maskit.datasets package

Submodules

maskit.datasets.circles module

maskit.datasets.circles.circles(train_size=100, test_size=50, shuffle=True) → maskit.datasets.utils.DataSet

maskit.datasets.iris module

maskit.datasets.iris.iris(train_size=100, test_size=50, shuffle=True) → maskit.datasets.utils.DataSet

maskit.datasets.mnist module

maskit.datasets.mnist.apply_PCA(wires: int, x_train: numpy.ndarray)

maskit.datasets.mnist.convert_label(y: int, classes: List[int]) → List[float]

maskit.datasets.mnist.downscale(x_data, y_data, size) → Tuple[numpy.ndarray, numpy.ndarray]

This function does several things including the reduction of image size, conversion to binary as well as removal of duplicates.

Parameters

• x_data – Input data

• y_data – Target data

• size – Maximum number of data to prepare

maskit.datasets.mnist.mnist(wires: int = 4, classes=(6, 9), train_size: int = 100, validation_size: int = 0, test_size: int = 50, shuffle: Union[bool, int] = 1337) → maskit.datasets.utils.DataSet

maskit.datasets.mnist.prepare_data(x_data: numpy.ndarray, y_data: numpy.ndarray, size, classes) → Tuple[numpy.ndarray, numpy.ndarray]

maskit.datasets.mnist.reduce_image(x: numpy.ndarray) → numpy.ndarray

maskit.datasets.utils module

class maskit.datasets.utils.DataSet(train_data, train_target, validation_data, validation_target, test_data, test_target)

Bases: tuple

property test_data

Alias for field number 4

property test_target

Alias for field number 5

property train_data

Alias for field number 0

property train_target

Alias for field number 1

property validation_data

Alias for field number 2

property validation_target

Alias for field number 3

maskit.datasets.utils.one_hot(a: numpy.ndarray, num_classes: int) → numpy.ndarray

maskit.datasets.utils.pad_data(data: numpy.ndarray, axis: int, padding: int) → numpy.ndarray

Function pads 0 data to the end of the given axis.

Parameters

• data – The data to pad 0 data to

• axis – Axis to pad on

• padding – How many elements to pad

Module contents

maskit.datasets.load_data(dataset: str, train_size: int = 100, test_size: int = 50, validation_size: int = 0, shuffle: Union[bool, int] = 1337, classes: Tuple[int, ...] = (6, 9), wires: int = 4, target_length: Optional[int] = None) → maskit.datasets.utils.DataSet

Returns the data for the requested dataset.

Parameters

• dataset – Name of the chosen dataset. Available datasets are: iris, mnist and circles.

• train_size – Size of the training dataset

• test_size – Size of the testing dataset

• shuffle – if the dataset should be shuffled, used also as a seed

• classes – which numbers of the mnist dataset should be included

• wires – number of wires in the circuit

• target_length – Normalised length of target arrays

Raises

ValueError – Raised if a not supported dataset is requested