org
.
clustering4ever
.
clustering
.
kcenters
.
scala
KModes
Related Docs:
class KModes
|
package scala
object
KModes
extends
Serializable
Linear Supertypes
Serializable
,
Serializable
,
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KModes
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final
def
!=
(
arg0:
Any
)
:
Boolean
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final
def
##
()
:
Int
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final
def
==
(
arg0:
Any
)
:
Boolean
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final
def
asInstanceOf
[
T0
]
:
T0
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def
clone
()
:
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Attributes
protected[
java.lang
]
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@throws
(
...
)
final
def
eq
(
arg0:
AnyRef
)
:
Boolean
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def
equals
(
arg0:
Any
)
:
Boolean
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def
finalize
()
:
Unit
Attributes
protected[
java.lang
]
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@throws
(
classOf[java.lang.Throwable]
)
final
def
generateAnyAlgorithmArgumentsCombination
[
V <:
Seq
[
Int
]
,
D
[
X <:
Seq
[
Int
]
]
<:
BinaryDistance
[
X
]
]
(
kValues:
Seq
[
Int
] =
Seq(4, 6, 8)
,
metricValues:
Seq
[
D
[
V
]] =
Seq(Hamming[V] _)
,
epsilonValues:
Seq
[
Double
] =
Seq(0.0001)
,
maxIterationsValues:
Seq
[
Int
] =
Seq(100)
,
initializedCentersValues:
Seq
[
HashMap
[
Int
,
BinaryVector
[
V
]]] =
...
)
:
Seq
[
KModes
[
V
,
D
]]
final
def
getClass
()
:
Class
[_]
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def
hashCode
()
:
Int
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final
def
isInstanceOf
[
T0
]
:
Boolean
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Any
final
def
ne
(
arg0:
AnyRef
)
:
Boolean
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final
def
notify
()
:
Unit
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final
def
notifyAll
()
:
Unit
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final
def
run
[
V <:
Seq
[
Int
]
,
D
[
X <:
Seq
[
Int
]
]
<:
BinaryDistance
[
X
]
,
GS
[
Y
]
<:
GenSeq
[
Y
]
]
(
data:
GS
[
V
]
,
k:
Int
,
metric:
D
[
V
]
,
maxIterations:
Int
,
epsilon:
Double
)
:
KModesModel
[
V
,
D
]
Run the K-Modes with any binary distance
final
def
synchronized
[
T0
]
(
arg0: ⇒
T0
)
:
T0
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def
toString
()
:
String
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final
def
wait
()
:
Unit
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Annotations
@throws
(
...
)
final
def
wait
(
arg0:
Long
,
arg1:
Int
)
:
Unit
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Annotations
@throws
(
...
)
final
def
wait
(
arg0:
Long
)
:
Unit
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@throws
(
...
)
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