echotorch.datasets package¶
Submodules¶
echotorch.datasets.MackeyGlassDataset module¶
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class
echotorch.datasets.MackeyGlassDataset.
MackeyGlassDataset
(sample_len, n_samples, tau=17, seed=None)¶ Bases:
torch.utils.data.dataset.Dataset
Mackey Glass dataset
echotorch.datasets.MemTestDataset module¶
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class
echotorch.datasets.MemTestDataset.
MemTestDataset
(sample_len, n_samples, n_delays=10, seed=None)¶ Bases:
torch.utils.data.dataset.Dataset
Generates a series of input timeseries and delayed versions as outputs. Delay is given in number of timesteps. Can be used to empirically measure the memory capacity of a system.
echotorch.datasets.NARMADataset module¶
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class
echotorch.datasets.NARMADataset.
NARMADataset
(sample_len, n_samples, system_order=10, seed=None)¶ Bases:
torch.utils.data.dataset.Dataset
xth order NARMA task WARNING: this is an unstable dataset. There is a small chance the system becomes unstable, leading to an unusable dataset. It is better to use NARMA30 which where this problem happens less often.
Module contents¶
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class
echotorch.datasets.
DatasetComposer
(datasets, *args, **kwargs)¶ Bases:
torch.utils.data.dataset.Dataset
Compose dataset
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class
echotorch.datasets.
HenonAttractor
(sample_len, n_samples, xy, a, b, washout=0, normalize=False, seed=None)¶ Bases:
torch.utils.data.dataset.Dataset
The Rössler attractor is the attractor for the Rössler system, a system of three non-linear ordinary differential equations originally studied by Otto Rössler. These differential equations define a continuous-time dynamical system that exhibits chaotic dynamics associated with the fractal properties of the attractor.
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regenerate
()¶ Regenerate :return:
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class
echotorch.datasets.
LambdaDataset
(sample_len, n_samples, func, start=0, dtype=torch.float32)¶ Bases:
torch.utils.data.dataset.Dataset
Create simple periodic signal timeseries
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class
echotorch.datasets.
LogisticMapDataset
(sample_len, n_samples, alpha=5, beta=11, gamma=13, c=3.6, b=0.13, seed=None)¶ Bases:
torch.utils.data.dataset.Dataset
Logistic Map dataset
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class
echotorch.datasets.
LorenzAttractor
(sample_len, n_samples, xyz, sigma, b, r, dt=0.01, washout=0, normalize=False, seed=None)¶ Bases:
torch.utils.data.dataset.Dataset
The Rössler attractor is the attractor for the Rössler system, a system of three non-linear ordinary differential equations originally studied by Otto Rössler. These differential equations define a continuous-time dynamical system that exhibits chaotic dynamics associated with the fractal properties of the attractor.
-
regenerate
()¶ Regenerate :return:
-
-
class
echotorch.datasets.
MackeyGlassDataset
(sample_len, n_samples, tau=17, seed=None)¶ Bases:
torch.utils.data.dataset.Dataset
Mackey Glass dataset
-
class
echotorch.datasets.
MemTestDataset
(sample_len, n_samples, n_delays=10, seed=None)¶ Bases:
torch.utils.data.dataset.Dataset
Generates a series of input timeseries and delayed versions as outputs. Delay is given in number of timesteps. Can be used to empirically measure the memory capacity of a system.
-
class
echotorch.datasets.
NARMADataset
(sample_len, n_samples, system_order=10, seed=None)¶ Bases:
torch.utils.data.dataset.Dataset
xth order NARMA task WARNING: this is an unstable dataset. There is a small chance the system becomes unstable, leading to an unusable dataset. It is better to use NARMA30 which where this problem happens less often.
-
class
echotorch.datasets.
RosslerAttractor
(sample_len, n_samples, xyz, a, b, c, dt=0.01, washout=0, normalize=False, seed=None)¶ Bases:
torch.utils.data.dataset.Dataset
The Rössler attractor is the attractor for the Rössler system, a system of three non-linear ordinary differential equations originally studied by Otto Rössler. These differential equations define a continuous-time dynamical system that exhibits chaotic dynamics associated with the fractal properties of the attractor.
-
regenerate
()¶ Regenerate :return:
-
-
class
echotorch.datasets.
SinusoidalTimeseries
(sample_len, n_samples, period, a=1.0, m=0.0, start=1, dtype=torch.float64)¶ Bases:
torch.utils.data.dataset.Dataset
Sinusoidal timeseries
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random_initial_points
()¶ Random initial points :return:
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regenerate
()¶ Regenerate :return:
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class
echotorch.datasets.
PeriodicSignalDataset
(sample_len, n_samples, period, start=1, dtype=torch.float64)¶ Bases:
torch.utils.data.dataset.Dataset
Create simple periodic signal timeseries