def random_walk_blobs(n_ts_per_blob=100, sz=256, d=1, n_blobs=2, noise_level=1., random_state=None):
"""Blob-based random walk time series generator.
Generate n_ts_per_blobs * n_blobs time series of size sz and dimensionality d.
Generated time series follow the model:
$$ts[t] = ts[t - 1] + a$$
where :math:`a` is drawn from a normal distribution of mean mu and standard deviation std.
Each blob contains time series derived from a same seed time series with added white noise.
Parameters
----------
n_ts_per_blob : int (default: 100)
Number of time series in each blob
sz : int (default: 256)
Length of time series (number of time instants)
d : int (default: 1)
Dimensionality of time series
n_blobs : int (default: 2)
Number of blobs
noise_level : float (default: 1.)
Standard deviation of white noise added to time series in each blob
random_state : integer or numpy.RandomState or None (default: None)
Generator used to draw the time series. If an integer is given, it fixes the seed. Defaults to the global
numpy random number generator.
Returns
-------
numpy.ndarray
A dataset of random walk time series
Examples
--------
>>> X, y = random_walk_blobs(n_ts_per_blob=100, sz=256, d=5, n_blobs=3)
>>> X.shape
(300, 256, 5)
>>> y.shape
(300,)
"""
rs = check_random_state(random_state)
base_ts = random_walks(n_ts=n_blobs, sz=sz, d=d, std=1.0, random_state=rs)
rnd = rs.randn(n_ts_per_blob * n_blobs, sz, d) * noise_level
ts = numpy.repeat(base_ts, repeats=n_ts_per_blob, axis=0)
y = numpy.repeat(range(n_blobs), repeats=n_ts_per_blob)
return ts + rnd, y
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