Package | Description |
---|---|
org.simmetrics | |
org.simmetrics.metrics |
Modifier and Type | Interface and Description |
---|---|
interface |
ListMetric<T>
Measures the similarity between two arbitrary lists containing elements of
the same type.
|
interface |
SetMetric<T>
Measures the similarity between two arbitrary sets containing elements of the
same type.
|
interface |
StringMetric
Measures the similarity between two strings.
|
Modifier and Type | Method and Description |
---|---|
static StringMetric |
StringMetrics.create(Metric<String> metric)
Either constructs a new string metric or returns the original metric.
|
static StringMetric |
StringMetrics.create(Metric<String> metric,
Simplifier simplifier)
Constructs a new composite string metric.
|
static StringMetric |
StringMetrics.createForListMetric(Metric<List<String>> metric,
Simplifier simplifier,
Tokenizer tokenizer)
Creates a new composite string metric.The tokenizer is used to tokenize
the simplified strings.
|
static StringMetric |
StringMetrics.createForListMetric(Metric<List<String>> metric,
Tokenizer tokenizer)
Creates a new composite string metric.
|
static StringMetric |
StringMetrics.createForSetMetric(Metric<Set<String>> metric,
Simplifier simplifier,
Tokenizer tokenizer)
Creates a new composite string metric.The tokenizer is used to tokenize
the simplified strings.
|
static StringMetric |
StringMetrics.createForSetMetric(Metric<Set<String>> metric,
Tokenizer tokenizer)
Creates a new composite string metric.
|
Modifier and Type | Class and Description |
---|---|
class |
BlockDistance<T>
Block distance algorithm whereby vector space block distance between tokens
is used to determine a similarity.
|
class |
CosineSimilarity<T>
Cosine Similarity algorithm providing a similarity measure between two set
from the angular divergence within token based vector space.
|
class |
DamerauLevenshtein
Damerau-Levenshtein algorithm providing a similarity measure between two
strings.
|
class |
DiceSimilarity<T>
Dice similarity algorithm providing a similarity measure between two sets
using the vector space of presented tokens.
|
class |
EuclideanDistance<T>
Euclidean Distance algorithm providing a similarity measure between two lists
using the vector space of combined terms as the dimensions.
|
class |
Identity<T>
Identity metric that returns 1.0 when the inputs are equals, and 0.0 when
they're not.
|
class |
JaccardSimilarity<T>
Jaccard similarity algorithm providing a similarity measure between two sets
using the vector space of presented tokens.
|
class |
Jaro
Jaro algorithm providing a similarity measure between two strings.
|
class |
JaroWinkler
Jaro-Winkler algorithm providing a similarity measure between two strings.
|
class |
Levenshtein
Levenshtein algorithm providing a similarity measure between two strings.
|
class |
MatchingCoefficient<T>
Matching coefficient algorithm providing a similarity measure between two
lists.
|
class |
MongeElkan
Monge Elkan algorithm providing an matching style similarity measure between
two strings.
|
class |
NeedlemanWunch
Needleman-Wunsch algorithm providing a similarity measure between two
strings.
|
class |
OverlapCoefficient<T>
Overlap Coefficient algorithm providing a similarity measure between two sets
where it is determined to what degree a set is a subset of another.
|
class |
SimonWhite<T>
Simon White algorithm providing a similarity measure between two lists.
|
class |
SmithWaterman
Smith-Waterman algorithm providing a similarity measure between two strings.
|
class |
SmithWatermanGotoh
Smith-Waterman algorithm providing a similarity measure between two strings.
|
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