Shift in linear model

Description

This function simulates a piecewise linear model (see Linear model change). The covariates standard Gaussian random variables. The response variable is a (piecewise) linear combination of the covariates.

Usage

Start with the usual imports and create a signal.

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

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

Return piecewise linear signal and the associated changepoints.

Parameters

  • 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

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

Return type

tuple