deepreplay.datasets package¶
Submodules¶
deepreplay.datasets.ball module¶
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deepreplay.datasets.ball.
load_data
(n_dims=10, n_points=1000, classif_radius_fraction=0.5, only_sphere=False, shuffle=True, seed=13)[source]¶ Parameters: - n_dims (int, optional) – Number of dimensions of the n-ball. Default is 10.
- n_points (int, optional) – Number of points in each parabola. Default is 1,000.
- classif_radius_fraction (float, optional) – Points farther away from the center than classification_radius_fraction * ball radius are considered to be positive cases. The remaining points are the negative cases.
- only_sphere (boolean) – If True, generates a n-sphere, that is, a hollow n-ball. Default is False.
- shuffle (boolean, optional) – If True, the points are shuffled. Default is True.
- seed (int, optional) – Random seed. Default is 13.
Returns: X, y – X is an array of shape (n_points, n_dims) containing the points in the n-ball. y is an array of shape (n_points, 1) containing the classes of the samples.
Return type: tuple of ndarray
deepreplay.datasets.hypercube module¶
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deepreplay.datasets.hypercube.
load_data
(n_dims=10, vertices=(-1.0, 1.0), shuffle=True, seed=13)[source]¶ Parameters: Returns: X, y – X is an array of shape (2 ** n_dims, n_dims) containing the vertices coordinates of the hypercube. y is an array of shape (2 ** n_dims, 1) containing the classes of the samples.
Return type: tuple of ndarray
deepreplay.datasets.parabola module¶
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deepreplay.datasets.parabola.
load_data
(xlim=(-1, 1), n_points=1000, shuffle=True, seed=13)[source]¶ Generates a dataset composed of two parabolas, with n_points each. The upper parabola represents the negative cases (class 0) and the lower parabola, shifted 0.5 from the first, represents the positive cases (class 1).
Parameters: Returns: X, y – X is an array of shape (2 * n_points, 2) containing the samples for the two parabolas. y is an array of shape (2 * n_points, 1) containing the classes of the samples.
Return type: tuple of ndarray