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[DOC] fix incorrect references of classes in getting started page (#2762)
* fix: incorrect references of classes in getting started page * fix: typo in BaseSimilaritySearch --------- Co-authored-by: Matthew Middlehurst <pfm15hbu@gmail.com>
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docs/getting_started.md

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@@ -317,14 +317,15 @@ classification or clustering.
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Similarly to the transformation module, similarity search estimators are either defined
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for single series or for collection of series. The estimators are inheriting from the
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[BaseSimiliaritySearch](similarity_search._base.BaseSimiliaritySearch) class, which
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both [BaseSeriesSimiliaritySearch](similarity_search.series._base.BaseSeriesSimiliaritySearch)
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and [BaseCollectionSimiliaritySearch](similarity_search.collection._base.BaseCollectionSimiliaritySearch)
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[BaseSimilaritySearch](similarity_search._base.BaseSimilaritySearch) class, which
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both [BaseSeriesSimilaritySearch](similarity_search.series._base.BaseSeriesSimilaritySearch)
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and [BaseCollectionSimilaritySearch](similarity_search.collection._base.BaseCollectionSimilaritySearch)
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inherit from.
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All estimators use a `fit` `predict` interface, where `predict` outputs both the
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indexes of the neighbors or motifs and a distance or similarity measure linked to them.
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For example, using `StompMotif` to compute the matrix profile between two series :
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```{code-block} python
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>>> import numpy as np
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>>> from aeon.similarity_search.series import StompMotif
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>>> top_k = StompMotif(4).fit(X1) # 4 is length of the motif to search
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>>> distances, indexes = top_k.predict(X2, k=1)
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```
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Some things to note on this example :
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- We defined `1D` series of shape `(n_timepoints)`, but internally, series estimator
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### Transformers for Single Time Series
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Transformers inheriting from the [BaseSeriesTransformer](transformations.base.BaseSeriesTransformer)
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Transformers inheriting from the [BaseSeriesTransformer](transformations.series.base.BaseSeriesTransformer)
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in the `aeon.transformations.series` package transform a single (possibly multivariate)
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time series into a different time series or a feature vector. More info to follow.
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transformation into an alternative feature space. For example, we might extract some
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summary time series features from each series, and fit a traditional classifier or
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regressor on these features. For example, we could use
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[Catch22](transformations.collection.feauture_based), which calculates 22 summary
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[Catch22](transformations.collection.feature_based.Catch22), which calculates 22 summary
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statistics for each series.
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```{code-block} python
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```
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There are also series-to-series transformations, such as the
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[Padder](transformations.collection) to lengthen
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[Padder](transformations.collection.Padder) to lengthen
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series and process unequal length collections.
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```{code-block} python
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`scikit-learn` `make_pipeline` functionality can be used if the transformer outputs
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a valid input type.
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The following example uses the [Catch22](transformations.collection.catch22.Catch22)
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The following example uses the [Catch22](transformations.collection.feature_based.Catch22)
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feature extraction transformer and a random forest classifier to classify.
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```{code-block} python

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