Index
All Classes and Interfaces|All Packages|Constant Field Values|Serialized Form
C
- centroids - Variable in class org.tribuo.clustering.kmeans.KMeansOptions
-
Number of centroids in K-Means.
- centroids - Variable in class org.tribuo.clustering.kmeans.TrainTest.KMeansOptions
-
Number of clusters to infer.
- copy(String, ModelProvenance) - Method in class org.tribuo.clustering.kmeans.KMeansModel
- COSINE - Enum constant in enum org.tribuo.clustering.kmeans.KMeansTrainer.Distance
-
Deprecated.Cosine similarity as a distance measure.
- CURRENT_VERSION - Static variable in class org.tribuo.clustering.kmeans.KMeansModel
-
Protobuf serialization version.
D
- deserializeFromProto(int, String, Any) - Static method in class org.tribuo.clustering.kmeans.KMeansModel
-
Deserialization factory.
- distType - Variable in class org.tribuo.clustering.kmeans.KMeansOptions
-
Distance function in K-Means.
- distType - Variable in class org.tribuo.clustering.kmeans.TrainTest.KMeansOptions
-
Distance function to use in the e step.
E
- EUCLIDEAN - Enum constant in enum org.tribuo.clustering.kmeans.KMeansTrainer.Distance
-
Deprecated.Euclidean (or l2) distance.
G
- general - Variable in class org.tribuo.clustering.kmeans.TrainTest.KMeansOptions
-
The data loading options.
- getCentroids() - Method in class org.tribuo.clustering.kmeans.KMeansModel
-
Returns a list of features, one per centroid.
- getCentroidVectors() - Method in class org.tribuo.clustering.kmeans.KMeansModel
-
Returns a copy of the centroids.
- getDistanceType() - Method in enum org.tribuo.clustering.kmeans.KMeansTrainer.Distance
-
Deprecated.Returns the
DistanceTypemapping for the enumeration's value. - getExcuse(Example<ClusterID>) - Method in class org.tribuo.clustering.kmeans.KMeansModel
- getInvocationCount() - Method in class org.tribuo.clustering.kmeans.KMeansTrainer
- getOptionsDescription() - Method in class org.tribuo.clustering.kmeans.TrainTest.KMeansOptions
- getProvenance() - Method in class org.tribuo.clustering.kmeans.KMeansTrainer
- getTopFeatures(int) - Method in class org.tribuo.clustering.kmeans.KMeansModel
- getTrainer() - Method in class org.tribuo.clustering.kmeans.KMeansOptions
-
Gets the configured KMeansTrainer using the options in this object.
I
- initialisation - Variable in class org.tribuo.clustering.kmeans.KMeansOptions
-
Initialisation function in K-Means.
- initialisation - Variable in class org.tribuo.clustering.kmeans.TrainTest.KMeansOptions
-
Type of initialisation to use for centroids.
- iterations - Variable in class org.tribuo.clustering.kmeans.KMeansOptions
-
Iterations of the k-means algorithm.
- iterations - Variable in class org.tribuo.clustering.kmeans.TrainTest.KMeansOptions
-
Maximum number of iterations.
K
- KMeansModel - Class in org.tribuo.clustering.kmeans
-
A K-Means model with a selectable distance function.
- KMeansOptions - Class in org.tribuo.clustering.kmeans
-
OLCUT
Optionsfor the K-Means implementation. - KMeansOptions() - Constructor for class org.tribuo.clustering.kmeans.KMeansOptions
- KMeansOptions() - Constructor for class org.tribuo.clustering.kmeans.TrainTest.KMeansOptions
- KMeansTrainer - Class in org.tribuo.clustering.kmeans
-
A K-Means trainer, which generates a K-means clustering of the supplied data.
- KMeansTrainer(int, int, KMeansTrainer.Distance, int, long) - Constructor for class org.tribuo.clustering.kmeans.KMeansTrainer
-
Deprecated.This Constructor is deprecated in version 4.3.
- KMeansTrainer(int, int, KMeansTrainer.Distance, KMeansTrainer.Initialisation, int, long) - Constructor for class org.tribuo.clustering.kmeans.KMeansTrainer
-
Deprecated.This Constructor is deprecated in version 4.3.
- KMeansTrainer(int, int, Distance, int, long) - Constructor for class org.tribuo.clustering.kmeans.KMeansTrainer
-
Constructs a K-Means trainer using the supplied parameters and the default random initialisation.
- KMeansTrainer(int, int, Distance, KMeansTrainer.Initialisation, int, long) - Constructor for class org.tribuo.clustering.kmeans.KMeansTrainer
-
Constructs a K-Means trainer using the supplied parameters.
- KMeansTrainer.Distance - Enum in org.tribuo.clustering.kmeans
-
Deprecated.This Enum is deprecated in version 4.3, replaced by
DistanceType - KMeansTrainer.Initialisation - Enum in org.tribuo.clustering.kmeans
-
Possible initialization functions.
L
- L1 - Enum constant in enum org.tribuo.clustering.kmeans.KMeansTrainer.Distance
-
Deprecated.L1 (or Manhattan) distance.
M
- main(String[]) - Static method in class org.tribuo.clustering.kmeans.TrainTest
-
Runs a TrainTest CLI.
- mStep(ForkJoinPool, DenseVector[], Map<Integer, List<Integer>>, SGDVector[], double[]) - Method in class org.tribuo.clustering.kmeans.KMeansTrainer
-
Runs the mStep, writing to the
centroidVectorsarray.
N
- numThreads - Variable in class org.tribuo.clustering.kmeans.KMeansOptions
-
Number of computation threads in K-Means.
- numThreads - Variable in class org.tribuo.clustering.kmeans.TrainTest.KMeansOptions
-
Number of threads to use (range (1, num hw threads)).
O
- org.tribuo.clustering.kmeans - package org.tribuo.clustering.kmeans
-
Provides a multithreaded implementation of K-Means, with a configurable distance function.
P
- PLUSPLUS - Enum constant in enum org.tribuo.clustering.kmeans.KMeansTrainer.Initialisation
-
KMeans++ initialisation.
- postConfig() - Method in class org.tribuo.clustering.kmeans.KMeansTrainer
-
Used by the OLCUT configuration system, and should not be called by external code.
- predict(Example<ClusterID>) - Method in class org.tribuo.clustering.kmeans.KMeansModel
R
- RANDOM - Enum constant in enum org.tribuo.clustering.kmeans.KMeansTrainer.Initialisation
-
Initialize centroids by choosing uniformly at random from the data points.
S
- serialize() - Method in class org.tribuo.clustering.kmeans.KMeansModel
- setInvocationCount(int) - Method in class org.tribuo.clustering.kmeans.KMeansTrainer
T
- toString() - Method in class org.tribuo.clustering.kmeans.KMeansTrainer
- train(Dataset<ClusterID>) - Method in class org.tribuo.clustering.kmeans.KMeansTrainer
- train(Dataset<ClusterID>, Map<String, Provenance>) - Method in class org.tribuo.clustering.kmeans.KMeansTrainer
- train(Dataset<ClusterID>, Map<String, Provenance>, int) - Method in class org.tribuo.clustering.kmeans.KMeansTrainer
- TrainTest - Class in org.tribuo.clustering.kmeans
-
Build and run a k-means clustering model for a standard dataset.
- TrainTest() - Constructor for class org.tribuo.clustering.kmeans.TrainTest
- TrainTest.KMeansOptions - Class in org.tribuo.clustering.kmeans
-
Options for the K-Means CLI.
V
- valueOf(String) - Static method in enum org.tribuo.clustering.kmeans.KMeansTrainer.Distance
-
Deprecated.Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum org.tribuo.clustering.kmeans.KMeansTrainer.Initialisation
-
Returns the enum constant of this type with the specified name.
- values() - Static method in enum org.tribuo.clustering.kmeans.KMeansTrainer.Distance
-
Deprecated.Returns an array containing the constants of this enum type, in the order they are declared.
- values() - Static method in enum org.tribuo.clustering.kmeans.KMeansTrainer.Initialisation
-
Returns an array containing the constants of this enum type, in the order they are declared.
All Classes and Interfaces|All Packages|Constant Field Values|Serialized Form