Comments (3)
Hi @drandynisbet ,
Thanks for you interest in ruptures
!
Why not checking for instance Cost Normal that do detect changes in the mean and covariance matrix of a sequence of multivariate Gaussian random variables ?
For instance :
import ruptures as rpt
import numpy as np
from numpy.random import RandomState
import matplotlib.pyplot as plt # for display purposes
rd = RandomState(1234)
segment_length = 100
signal = np.r_[rd.normal(0, 0.1, segment_length),
rd.normal(0, 0.01, segment_length),
rd.normal(1, 0.05, segment_length)]
algo = rpt.Pelt(model="normal", min_size=1, jump=1).fit(signal)
my_bkps = algo.predict(pen=100)
print(my_bkps)
fig, ax = rpt.display(signal, [100, 200, 300], my_bkps)
plt.show()
from ruptures.
As mentionned by @oboulant, this cost function will simultaneously detect changes in the mean and variance/covariance matrix for multidimensional signals. This should emulate the behaviour of cpt_meanvar
.
from ruptures.
I am closing the issue. Feel free re-open one or a discussion thread if needed !
from ruptures.
Related Issues (20)
- Does Ruptures have requirements of the length of the dataset? HOT 2
- Links to docs at https://centre-borelli.github.io/ruptures-docs/ are circular HOT 1
- AttributeError: 'kernel_hyper' object has no attribute 'signal' HOT 6
- Docs: Penalty term explainer HOT 2
- Docs: Cost function illustrations HOT 1
- multidimensional data HOT 2
- More metrics ? e.g. adjusted randindex HOT 2
- [BUG] Two badges not rendering correctly in readme HOT 2
- How to `set_xticklabels` HOT 1
- Significance Value for Change Point HOT 2
- Piecewise linear HOT 2
- Provide Python 3.11 wheels HOT 1
- Algorithm and threshold advice for piecewise linear
- Rand index over 1 HOT 1
- Pruning rule in Pelt HOT 1
- Normalization and Cost HOT 3
- Clarification on Breakpoint Indexing with Dataframe Input HOT 2
- Python 3.12 wheels HOT 4
- Unable to install in Poetry 1.7.1 HOT 3
- Breakpoints found in DataFrame but not in NumpyArray with same data HOT 3
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