r"""
.. _sec-pw-linear:
Shift in linear model
====================================================================================================
Description
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This function simulates a piecewise linear model (see :ref:`sec-linear`).
The covariates standard Gaussian random variables.
The response variable is a (piecewise) linear combination of the covariates.
Usage
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Start with the usual imports and create a signal.
.. code-block:: python
import numpy as np
import matplotlib.pylab as plt
import ruptures as rpt
# creation of data
n, dim = 500, 3 # number of samples, dimension of the covariates
n_bkps, sigma = 3, 5 # number of change points, noise standart deviation
signal, bkps = rpt.pw_linear(n, dim, n_bkps, noise_std=sigma)
rpt.display(signal, bkps)
Code explanation
----------------------------------------------------------------------------------------------------
.. autofunction:: ruptures.datasets.pw_linear.pw_linear
"""
import numpy as np
from numpy.random import normal
from . import pw_constant
from ruptures.utils import draw_bkps
[docs]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