Package | Description |
---|---|
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans |
K-means clustering and variations
|
de.lmu.ifi.dbs.elki.algorithm.itemsetmining |
Algorithms for frequent itemset mining such as APRIORI.
|
de.lmu.ifi.dbs.elki.data |
Basic classes for different data types, database object types and label types
|
de.lmu.ifi.dbs.elki.data.projection.random |
Random projection families
|
de.lmu.ifi.dbs.elki.data.type |
Data type information, also used for type restrictions
|
de.lmu.ifi.dbs.elki.datasource.filter.normalization.columnwise |
Normalizations operating on columns / variates; where each column is treated independently.
|
de.lmu.ifi.dbs.elki.datasource.filter.typeconversions |
Filters to perform data type conversions.
|
de.lmu.ifi.dbs.elki.datasource.parser |
Parsers for different file formats and data types
The general use-case for any parser is to create objects out of an
InputStream (e.g. by reading a data file). |
de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski |
Minkowski space Lp norms such as the popular Euclidean and
Manhattan distances.
|
de.lmu.ifi.dbs.elki.index.invertedlist |
Indexes using inverted lists.
|
Modifier and Type | Method and Description |
---|---|
private static double[][] |
AbstractKMeans.sparseMeans(java.util.List<? extends DBIDs> clusters,
double[][] means,
Relation<? extends SparseNumberVector> relation)
Returns the mean vectors of the given clusters in the given database.
|
Modifier and Type | Method and Description |
---|---|
boolean |
Itemset.containedIn(SparseNumberVector bv)
Test whether the itemset is contained in a bit vector.
|
boolean |
OneItemset.containedIn(SparseNumberVector bv) |
boolean |
DenseItemset.containedIn(SparseNumberVector bv) |
boolean |
SmallDenseItemset.containedIn(SparseNumberVector bv) |
Modifier and Type | Interface and Description |
---|---|
static interface |
SparseNumberVector.Factory<V extends SparseNumberVector>
Factory for sparse number vectors: make from a dim-value map.
|
Modifier and Type | Class and Description |
---|---|
class |
BitVector
Vector using a dense bit set encoding, based on
long[] storage. |
class |
SparseByteVector
Sparse vector type, using
byte[] for storing the values, and
int[] for storing the indexes, approximately 5 bytes per non-zero
value (limited to -128..+127). |
class |
SparseDoubleVector
Sparse vector type, using
double[] for storing the values, and
int[] for storing the indexes, approximately 12 bytes per non-zero
value. |
class |
SparseFloatVector
Sparse vector type, using
float[] for storing the values, and
int[] for storing the indexes, approximately 8 bytes per non-zero
value. |
class |
SparseIntegerVector
Sparse vector type, using
int[] for storing the values, and
int[] for storing the indexes, approximately 8 bytes per non-zero
integer value. |
class |
SparseShortVector
Sparse vector type, using
short[] for storing the values, and
int[] for storing the indexes, approximately 6 bytes per non-zero
value. |
Modifier and Type | Field and Description |
---|---|
static VectorFieldTypeInformation<SparseNumberVector> |
SparseNumberVector.FIELD
Input data type: Sparse vector field.
|
static VectorTypeInformation<SparseNumberVector> |
SparseNumberVector.VARIABLE_LENGTH
Input data type: Sparse vectors with variable length.
|
Modifier and Type | Method and Description |
---|---|
static double |
VectorUtil.angleSparse(SparseNumberVector v1,
SparseNumberVector v2)
Compute the angle for sparse vectors.
|
static double |
VectorUtil.angleSparseDense(SparseNumberVector v1,
NumberVector v2)
Compute the angle for a sparse and a dense vector.
|
static double |
VectorUtil.dotSparse(SparseNumberVector v1,
SparseNumberVector v2)
Compute the dot product for two sparse vectors.
|
static double |
VectorUtil.dotSparseDense(SparseNumberVector v1,
NumberVector v2)
Compute the dot product for a sparse and a dense vector.
|
Modifier and Type | Method and Description |
---|---|
private double[] |
AbstractRandomProjectionFamily.MatrixProjection.projectSparse(SparseNumberVector in,
double[] ret)
Project, exploiting sparsity; but the transposed matrix layout would have
been better.
|
Modifier and Type | Field and Description |
---|---|
static VectorFieldTypeInformation<SparseNumberVector> |
TypeUtil.SPARSE_VECTOR_FIELD
Sparse vector field.
|
static VectorTypeInformation<SparseNumberVector> |
TypeUtil.SPARSE_VECTOR_VARIABLE_LENGTH
Sparse float vector field.
|
Modifier and Type | Class and Description |
---|---|
class |
InverseDocumentFrequencyNormalization<V extends SparseNumberVector>
Normalization for text frequency (TF) vectors, using the inverse document
frequency (IDF).
|
Modifier and Type | Class and Description |
---|---|
class |
SparseVectorFieldFilter<V extends SparseNumberVector>
Class that turns sparse float vectors into a proper vector field, by setting
the maximum dimensionality for each vector.
|
Modifier and Type | Class and Description |
---|---|
class |
LibSVMFormatParser<V extends SparseNumberVector>
Parser to read libSVM format files.
|
static class |
LibSVMFormatParser.Parameterizer<V extends SparseNumberVector>
Parameterization class.
|
class |
SparseNumberVectorLabelParser<V extends SparseNumberVector>
Parser for parsing one point per line, attributes separated by whitespace.
|
static class |
SparseNumberVectorLabelParser.Parameterizer<V extends SparseNumberVector>
Parameterization class.
|
class |
TermFrequencyParser<V extends SparseNumberVector>
A parser to load term frequency data, which essentially are sparse vectors
with text keys.
|
static class |
TermFrequencyParser.Parameterizer<V extends SparseNumberVector>
Parameterization class.
|
Modifier and Type | Method and Description |
---|---|
SimpleTypeInformation<? super SparseNumberVector> |
SparseSquaredEuclideanDistanceFunction.getInputTypeRestriction() |
SimpleTypeInformation<? super SparseNumberVector> |
SparseLPNormDistanceFunction.getInputTypeRestriction() |
Modifier and Type | Method and Description |
---|---|
double |
SparseSquaredEuclideanDistanceFunction.distance(SparseNumberVector v1,
SparseNumberVector v2) |
double |
SparseManhattanDistanceFunction.distance(SparseNumberVector v1,
SparseNumberVector v2) |
double |
SparseMaximumDistanceFunction.distance(SparseNumberVector v1,
SparseNumberVector v2) |
double |
SparseEuclideanDistanceFunction.distance(SparseNumberVector v1,
SparseNumberVector v2) |
double |
SparseLPNormDistanceFunction.distance(SparseNumberVector v1,
SparseNumberVector v2) |
double |
SparseSquaredEuclideanDistanceFunction.norm(SparseNumberVector v1) |
double |
SparseManhattanDistanceFunction.norm(SparseNumberVector v1) |
double |
SparseMaximumDistanceFunction.norm(SparseNumberVector v1) |
double |
SparseEuclideanDistanceFunction.norm(SparseNumberVector v1) |
double |
SparseLPNormDistanceFunction.norm(SparseNumberVector v1) |
Modifier and Type | Method and Description |
---|---|
private void |
InMemoryInvertedIndex.indexSparse(DBIDRef ref,
SparseNumberVector obj)
Index a single (sparse) instance.
|
private double |
InMemoryInvertedIndex.naiveQuerySparse(SparseNumberVector obj,
WritableDoubleDataStore scores,
HashSetModifiableDBIDs cands)
Query the most similar objects, sparse version.
|
Copyright © 2019 ELKI Development Team. License information.