Tomato: Clustering: Tomato
In rgudhi: An Interface to the GUDHI Library for Topological Data Analysis
Tomato R Documentation
Clustering: Tomato
Description
This clustering algorithm needs a neighborhood graph on the
points, and an estimation of the density at each point. A few possible
graph constructions and density estimators are provided for convenience,
but it is perfectly natural to provide your own.
Super class
rgudhi::PythonClass -> Tomato
Methods
Public methods
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Inherited methods
Method new()
The Tomato constructor.
Usage
Tomato$new(
graph_type = c("knn", "radius", "manual"),
density_type = c("logDTM", "DTM", "logKDE", "KDE", "manual"),
n_clusters = NULL,
merge_threshold = NULL,
...
)
Arguments
graph_typeA string specifying the method to compute the
neighboring graph. Choices are "knn", "radius" or "manual".
Defaults to "knn".
density_typeA string specifying the choice of density estimator.
Choicea are "logDTM", "DTM", "logKDE" or "manual". When you
have many points, "KDE" and "logKDE" tend to be slower. Defaults to
"logDTM"
n_clustersAn integer value specifying the number of clusters.
Defaults to NULL, i.e. no merging occurs and we get the maximal
number of clusters.
merge_thresholdA numeric value specifying the minimum prominence
of a cluster so it doesn’t get merged. Defaults to NULL, i.e. no
merging occurs and we get the maximal number of clusters.
...Extra parameters passed to KNearestNeighbors and
DTMDensity.
Returns
An object of class Tomato.
Method fit()
Runs the Tomato algorithm on the provided data.
Usage
Tomato$fit(X, y = NULL, weights = NULL)
Arguments
XEither a numeric matrix specifying the coordinates (in column)
of each point (in row) or a full distance matrix if metric == "precomputed" or a list of neighbors for each point if graph_type == "manual". The number of points is currently limited to about 2
billion.
yNot used, present here for API consistency with
scikit-learn by convention.
weightsA numeric vector specifying a density estimate at each
point. Used only if density_type == "manual".
Returns
The updated Tomato class itself invisibly.
Method fit_predict()
Runs the Tomato algorithm on the provided data and
returns the class memberships.
Usage
Tomato$fit_predict(X, y = NULL, weights = NULL)
Arguments
XEither a numeric matrix specifying the coordinates (in column)
of each point (in row) or a full distance matrix if metric == "precomputed" or a list of neighbors for each point if graph_type == "manual". The number of points is currently limited to about 2
billion.
yNot used, present here for API consistency with
scikit-learn by convention.
weightsA numeric vector specifying a density estimate at each
point. Used only if density_type == "manual".
Returns
An integer vector storing the class memberships.
Method set_n_clusters()
Sets the number of clusters which automatically adjusts
class memberships.
Usage
Tomato$set_n_clusters(n_clusters)
Arguments
n_clustersAn integer value specifying the number of clusters.
Returns
The updated Tomato class itself invisibly.
Method get_n_clusters()
Gets the number of clusters.
Usage
Tomato$get_n_clusters()
Returns
The number of clusters.
Method set_merge_threshold()
Sets the threshold for merging clusters which automatically
adjusts class memberships.
Usage
Tomato$set_merge_threshold(merge_threshold)
Arguments
merge_thresholdA numeric value specifying the threshold for
merging clusters.
Returns
The updated Tomato class itself invisibly.
Method get_merge_threshold()
Gets the threshold for merging clusters.
Usage
Tomato$get_merge_threshold()
Returns
The threshold for merging clusters.
Method get_labels()
Gets the class memberships.
Usage
Tomato$get_labels()
Returns
An integer vector storing the class memberships.
Method plot_diagram()
Computes the persistence diagram of the merge tree of the
initial clusters. This is a convenient graphical tool to help decide
how many clusters we want.
Usage
Tomato$plot_diagram()
Method clone()
The objects of this class are cloneable with this method.
Usage
Tomato$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Author(s)
Marc Glisse
Examples
X <- seq_circle(100)
cl <- Tomato$new()
cl$fit_predict(X)
cl$set_n_clusters(2)
cl$get_labels()
rgudhi documentation built on March 31, 2023, 11:38 p.m.
R Package Documentation
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| Tomato | R Documentation |
Clustering: Tomato
Description
This clustering algorithm needs a neighborhood graph on the points, and an estimation of the density at each point. A few possible graph constructions and density estimators are provided for convenience, but it is perfectly natural to provide your own.
Super class
rgudhi::PythonClass -> Tomato
Methods
Public methods
Inherited methods
Method new()
The Tomato constructor.
Usage
Tomato$new(
graph_type = c("knn", "radius", "manual"),
density_type = c("logDTM", "DTM", "logKDE", "KDE", "manual"),
n_clusters = NULL,
merge_threshold = NULL,
...
)Arguments
graph_typeA string specifying the method to compute the neighboring graph. Choices are
"knn","radius"or"manual". Defaults to"knn".density_typeA string specifying the choice of density estimator. Choicea are
"logDTM","DTM","logKDE"or"manual". When you have many points,"KDE"and"logKDE"tend to be slower. Defaults to"logDTM"n_clustersAn integer value specifying the number of clusters. Defaults to
NULL, i.e. no merging occurs and we get the maximal number of clusters.merge_thresholdA numeric value specifying the minimum prominence of a cluster so it doesn’t get merged. Defaults to
NULL, i.e. no merging occurs and we get the maximal number of clusters....Extra parameters passed to
KNearestNeighborsandDTMDensity.
Returns
An object of class Tomato.
Method fit()
Runs the Tomato algorithm on the provided data.
Usage
Tomato$fit(X, y = NULL, weights = NULL)
Arguments
XEither a numeric matrix specifying the coordinates (in column) of each point (in row) or a full distance matrix if
metric == "precomputed"or a list of neighbors for each point ifgraph_type == "manual". The number of points is currently limited to about 2 billion.yNot used, present here for API consistency with scikit-learn by convention.
weightsA numeric vector specifying a density estimate at each point. Used only if
density_type == "manual".
Returns
The updated Tomato class itself invisibly.
Method fit_predict()
Runs the Tomato algorithm on the provided data and returns the class memberships.
Usage
Tomato$fit_predict(X, y = NULL, weights = NULL)
Arguments
XEither a numeric matrix specifying the coordinates (in column) of each point (in row) or a full distance matrix if
metric == "precomputed"or a list of neighbors for each point ifgraph_type == "manual". The number of points is currently limited to about 2 billion.yNot used, present here for API consistency with scikit-learn by convention.
weightsA numeric vector specifying a density estimate at each point. Used only if
density_type == "manual".
Returns
An integer vector storing the class memberships.
Method set_n_clusters()
Sets the number of clusters which automatically adjusts class memberships.
Usage
Tomato$set_n_clusters(n_clusters)
Arguments
n_clustersAn integer value specifying the number of clusters.
Returns
The updated Tomato class itself invisibly.
Method get_n_clusters()
Gets the number of clusters.
Usage
Tomato$get_n_clusters()
Returns
The number of clusters.
Method set_merge_threshold()
Sets the threshold for merging clusters which automatically adjusts class memberships.
Usage
Tomato$set_merge_threshold(merge_threshold)
Arguments
merge_thresholdA numeric value specifying the threshold for merging clusters.
Returns
The updated Tomato class itself invisibly.
Method get_merge_threshold()
Gets the threshold for merging clusters.
Usage
Tomato$get_merge_threshold()
Returns
The threshold for merging clusters.
Method get_labels()
Gets the class memberships.
Usage
Tomato$get_labels()
Returns
An integer vector storing the class memberships.
Method plot_diagram()
Computes the persistence diagram of the merge tree of the initial clusters. This is a convenient graphical tool to help decide how many clusters we want.
Usage
Tomato$plot_diagram()
Method clone()
The objects of this class are cloneable with this method.
Usage
Tomato$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Author(s)
Marc Glisse
Examples
X <- seq_circle(100)
cl <- Tomato$new()
cl$fit_predict(X)
cl$set_n_clusters(2)
cl$get_labels()
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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