@@ -25,6 +25,7 @@ sure that it has a shape `(seq_len, n_dims)`.
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``` python
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import numpy as np
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import roerich
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+ import roerich.algorithms
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X, label = roerich.generate_dataset(period = 2000 , N_tot = 20000 )
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T = np.arange(len (X))
@@ -34,10 +35,10 @@ To perform change point detection, you can use two algorithms: `CLF` or `RuLSIF`
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followed by calling a ` predict method ` :
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``` python
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- cpd = roerich.OnlineNNClassifier(net = ' default' , scaler = " default" , metric = " KL_sym" ,
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- periods = 1 , window_size = 10 , lag_size = 500 , step = 10 , n_epochs = 10 ,
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- lr = 0.1 , lam = 0.0001 , optimizer = " Adam"
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- )
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+ cpd = roerich.algorithms. OnlineNNClassifier(net = ' default' , scaler = " default" , metric = " KL_sym" ,
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+ periods = 1 , window_size = 10 , lag_size = 500 , step = 10 , n_epochs = 10 ,
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+ lr = 0.1 , lam = 0.0001 , optimizer = " Adam"
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+ )
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# Detect change points
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score, peaks = cpd.predict(X)
@@ -49,10 +50,6 @@ roerich.display(X, T, label, score, T, peaks)
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```
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![ ] ( images/demo.png )
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- ## Changelog
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-
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- See the [ changelog] ( https://github.com/HSE-LAMBDA/roerich/blob/master/CHANGELOG.md ) for a history of notable changes to roerich.
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-
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## Thanks to all our contributors
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<a href =" https://github.com/HSE-LAMBDA/roerich/graphs/contributors " >
@@ -87,4 +84,4 @@ SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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- ```
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+ ```
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