osl_dynamics.simulation.sm
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Classes for simulating a soft mixture of modes.
Module Contents#
Classes#
Simulates sinusoidal oscilations in mode time courses. |
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Simulates sinusoidal alphas with a multivariable normal observation |
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Simulates sinusoidal alphas with a multivariable normal observation model |
- class osl_dynamics.simulation.sm.MixedSine(n_modes, relative_activation, amplitudes, frequencies, sampling_frequency)[source]#
Simulates sinusoidal oscilations in mode time courses.
- Parameters:
n_modes (int) – Number of modes.
relative_activation (np.ndarray or list) – Average value for each sine wave. Note, this might not be the mean value for each mode time course because there is a softmax operation. This argument can use use to change the relative values of each mode time course.
amplitudes (np.ndarray or list) – Amplitude of each sinusoid.
frequencies (np.ndarray or list) – Frequency of each sinusoid.
sampling_frequency (float) – Sampling frequency.
- class osl_dynamics.simulation.sm.MixedSine_MVN(n_samples, relative_activation, amplitudes, frequencies, sampling_frequency, means, covariances, n_covariances_act=1, n_modes=None, n_channels=None, observation_error=0.0)[source]#
Bases:
osl_dynamics.simulation.base.Simulation
Simulates sinusoidal alphas with a multivariable normal observation model.
- Parameters:
n_samples (int) – Number of samples to draw from the model.
relative_activation (np.ndarray or list) – Average value for each sine wave. Note, this might not be the mean value for each mode time course because there is a softmax operation. This argument can use use to change the relative values of each mode time course. Shape must be (n_modes,).
amplitudes (np.ndarray or list) – Amplitude of each sinusoid. Shape must be (n_modes,).
frequencies (np.ndarray or list) – Frequency of each sinusoid. Shape must be (n_modes,).
sampling_frequency (float) – Sampling frequency.
means (np.ndarray or str) – Mean vector for each mode, shape should be (n_modes, n_channels). Either a numpy array or
'zero'
or'random'
.covariances (np.ndarray or str) – Covariance matrix for each mode, shape should be (n_modes, n_channels, n_channels). Either a numpy array or
'random'
.n_covariances_act (int, optional) – Number of iterations to add activations to covariance matrices.
n_modes (int, optional) – Number of modes.
n_channels (int, optional) – Number of channels.
observation_error (float, optional) – Standard deviation of the error added to the generated data.
- class osl_dynamics.simulation.sm.MSess_MixedSine_MVN(n_samples, relative_activation, amplitudes, frequencies, sampling_frequency, session_means, session_covariances, n_covariances_act=1, n_modes=None, n_channels=None, n_sessions=None, embeddings_dim=None, spatial_embeddings_dim=None, embeddings_scale=None, n_groups=None, between_group_scale=None, observation_error=0.0)[source]#
Bases:
osl_dynamics.simulation.base.Simulation
Simulates sinusoidal alphas with a multivariable normal observation model for each session.
- Parameters:
n_samples (int) – Number of samples per session to draw from the model.
relative_activation (np.ndarray or list) – Average value for each sine wave. Note, this might not be the mean value for each mode time course because there is a softmax operation. This argument can use use to change the relative values of each mode time course. Shape must be (n_modes,).
amplitudes (np.ndarray or list) – Amplitude of each sinusoid. Shape must be (n_modes,).
frequencies (np.ndarray or list) – Frequency of each sinusoid. Shape must be (n_modes,).
sampling_frequency (float) – Sampling frequency.
session_means (np.ndarray or str) – Session mean vector for each mode, shape should be (n_sessions, n_modes, n_channels). Either a numpy array or
'zero'
or'random'
.session_covariances (np.ndarray or str) – Session covariance matrix for each mode, shape should be (n_sessions, n_modes, n_channels, n_channels). Either a numpy array or
'random'
.n_covariances_act (int, optional) – Number of iterations to add activations to covariance matrices.
n_modes (int, optional) – Number of modes.
n_channels (int, optional) – Number of channels.
n_sessions (int, optional) – Number of sessions.
embeddings_dim (int, optional) – Number of dimensions for embedding vectors.
spatial_embeddings_dim (int, optional) – Number of dimensions for spatial embedding vectors.
embeddings_scale (float, optional) – Scale of variability between session observation parameters.
n_groups (int, optional) – Number of groups when session means or covariances are
'random'
.between_group_scale (float, optional) – Scale of variability between groups.
observation_error (float, optional) – Standard deviation of the error added to the generated data.