make_sinusoidal_process#
- skfda.datasets.make_sinusoidal_process(n_samples=15, n_features=100, *, start=0, stop=1, period=1, phase_mean=0, phase_std=0.6, amplitude_mean=1, amplitude_std=0.05, error_std=0.2, random_state=None)[source]#
Generate sinusoidal proccess.
Each sample \(x_i(t)\) is generated as:
\[x_i(t) = \alpha_i \sin(\omega t + \phi_i) + \epsilon_i(t)\]where \(\omega=\frac{2 \pi}{\text{period}}\). Amplitudes \(\alpha_i\) and phases \(\phi_i\) are normally distributed. \(\epsilon_i(t)\) is a gaussian white noise process.
- Parameters:
n_samples (int) – Total number of samples.
n_features (int) – Points per sample.
start (float) – Starting point of the samples.
stop (float) – Ending point of the samples.
period (float) – Period of the sine function.
phase_mean (float) – Mean of the phase.
phase_std (float) – Standard deviation of the phase.
amplitude_mean (float) – Mean of the amplitude.
amplitude_std (float) – Standard deviation of the amplitude.
error_std (float) – Standard deviation of the gaussian Noise.
random_state (int | RandomState | Generator | None) – Random state.
- Returns:
FDataGrid
object comprising all the samples.- Return type:
Examples using skfda.datasets.make_sinusoidal_process
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Radius neighbors classification