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Piecewise linear (pw_linear)#

ruptures.datasets.pw_linear.pw_linear(n_samples=200, n_features=1, n_bkps=3, noise_std=None) #

Return piecewise linear signal and the associated changepoints.

Parameters:

Name Type Description Default
n_samples int

signal length

200
n_features int

number of covariates

1
n_bkps int

number of change points

3
noise_std float

noise std. If None, no noise is added

None

Returns:

Type Description
tuple

signal of shape (n_samples, n_features+1), list of breakpoints

Source code in ruptures/datasets/pw_linear.py
def pw_linear(n_samples=200, n_features=1, n_bkps=3, noise_std=None):
    """Return piecewise linear signal and the associated changepoints.

    Args:
        n_samples (int, optional): signal length
        n_features (int, optional): number of covariates
        n_bkps (int, optional): number of change points
        noise_std (float, optional): noise std. If None, no noise is added
    Returns:
        tuple: signal of shape (n_samples, n_features+1), list of breakpoints
    """

    covar = normal(size=(n_samples, n_features))
    linear_coeff, bkps = pw_constant(
        n_samples=n_samples, n_bkps=n_bkps, n_features=n_features, noise_std=None
    )
    var = np.sum(linear_coeff * covar, axis=1)
    if noise_std is not None:
        var += normal(scale=noise_std, size=var.shape)
    signal = np.c_[var, covar]
    return signal, bkps