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Trainer
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class Trainer
|
package logo
object
Trainer
extends
Serializable
Linear Supertypes
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,
Serializable
,
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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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protected[
java.lang
]
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(
...
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final
def
eq
(
arg0:
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)
:
Boolean
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def
equals
(
arg0:
Any
)
:
Boolean
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def
finalize
()
:
Unit
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protected[
java.lang
]
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@throws
(
classOf[java.lang.Throwable]
)
final
def
getClass
()
:
Class
[_]
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def
hashCode
()
:
Int
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final
def
isInstanceOf
[
T0
]
:
Boolean
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final
def
ne
(
arg0:
AnyRef
)
:
Boolean
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def
newL1LogLossMIRATrainer
[
T
,
W
,
OracleS
,
MaxerS
]
(
oracleInferencer:
OracleInferencer
[
T
,
W
,
OracleS
]
,
summer:
ExpectationInferencer
[
T
,
W
,
MaxerS
]
,
iterationCallback:
IterationCallback
[
T
,
W
,
OracleS
,
MaxerS
]
,
C:
Double
=
1.0
,
maxNumIters:
Int
=
100
,
opts:
LogoOpts
=
new LogoOpts()
,
average:
Boolean
=
true
,
addInitialConstraint:
Option
[
W
] =
None
)
(
implicit
space:
MutableInnerProductModule
[
W
,
Double
]
)
:
Trainer
[
T
,
W
,
OracleS
,
MaxerS
]
def
newL1LogLossTrainer
[
T
,
Y
,
W
,
OracleS
,
MaxerS
]
(
oracleInferencer:
OracleInferencer
[
T
,
W
,
OracleS
]
,
summer:
ExpectationInferencer
[
T
,
W
,
MaxerS
]
,
iterationCallback:
IterationCallback
[
T
,
W
,
OracleS
,
MaxerS
]
,
C:
Double
=
1.0
,
maxNumIters:
Int
=
100
,
opts:
LogoOpts
=
new LogoOpts()
,
addInitialConstraint:
Option
[
W
] =
None
)
(
implicit
space:
MutableInnerProductModule
[
W
,
Double
]
)
:
Trainer
[
T
,
W
,
OracleS
,
MaxerS
]
def
newL1MIRATrainer
[
T
,
Y
,
W
,
OracleS
,
MaxerS
]
(
oracleInferencer:
OracleInferencer
[
T
,
W
,
OracleS
]
,
argmaxer:
LossAugmentedArgmaxInferencer
[
T
,
W
,
MaxerS
]
,
iterationCallback:
IterationCallback
[
T
,
W
,
OracleS
,
MaxerS
]
,
C:
Double
=
1.0
,
maxNumIters:
Int
=
100
,
opts:
LogoOpts
=
new LogoOpts()
,
average:
Boolean
=
true
,
addInitialConstraint:
Option
[
W
] =
None
)
(
implicit
space:
MutableInnerProductModule
[
W
,
Double
]
)
:
Trainer
[
T
,
W
,
OracleS
,
MaxerS
]
def
newL1MarginRankTrainer
[
T
,
W
,
MaxerS
]
(
argmaxer:
LossAugmentedArgmaxInferencer
[
T
,
W
,
MaxerS
]
,
iterationCallback:
IterationCallback
[
T
,
W
,
MaxerS
,
MaxerS
]
,
C:
Double
=
1.0
,
gamma:
Double
=
0.0
,
maxNumIters:
Int
=
100
,
opts:
LogoOpts
=
new LogoOpts()
,
addInitialConstraint:
Option
[
W
] =
None
)
(
implicit
space:
MutableInnerProductModule
[
W
,
Double
]
)
:
Trainer
[
T
,
W
,
MaxerS
,
MaxerS
]
def
newL1MaxMarginTrainer
[
T
,
W
,
OracleS
,
MaxerS
]
(
oracleInferencer:
OracleInferencer
[
T
,
W
,
OracleS
]
,
argmaxer:
LossAugmentedArgmaxInferencer
[
T
,
W
,
MaxerS
]
,
iterationCallback:
IterationCallback
[
T
,
W
,
OracleS
,
MaxerS
] =
NullIterationCallback()
,
C:
Double
=
1.0
,
maxNumIters:
Int
=
100
,
opts:
LogoOpts
=
new LogoOpts()
,
addInitialConstraint:
Option
[
W
] =
None
)
(
implicit
space:
MutableInnerProductModule
[
W
,
Double
]
)
:
Trainer
[
T
,
W
,
OracleS
,
MaxerS
]
def
newL2MaxMarginTrainer
[
T
,
Y
,
W
,
OracleS
,
MaxerS
]
(
oracleInferencer:
OracleInferencer
[
T
,
W
,
OracleS
]
,
argmaxer:
LossAugmentedArgmaxInferencer
[
T
,
W
,
MaxerS
]
,
iterationCallback:
IterationCallback
[
T
,
W
,
OracleS
,
MaxerS
]
,
C:
Double
=
1.0
,
maxNumIters:
Int
=
100
,
opts:
LogoOpts
=
new LogoOpts()
,
addInitialConstraint:
Option
[
W
] =
None
)
(
implicit
space:
MutableInnerProductModule
[
W
,
Double
]
)
:
Trainer
[
T
,
W
,
OracleS
,
MaxerS
]
def
newPerceptronTrainer
[
T
,
Y
,
W
,
OracleS
,
MaxerS
]
(
oracleInferencer:
OracleInferencer
[
T
,
W
,
OracleS
]
,
argmaxer:
ArgmaxInferencer
[
T
,
W
,
MaxerS
]
,
iterationCallback:
IterationCallback
[
T
,
W
,
OracleS
,
MaxerS
]
,
learningRate:
Double
=
1.0
,
maxNumIters:
Int
=
100
,
opts:
LogoOpts
=
new LogoOpts()
,
average:
Boolean
=
true
)
(
implicit
space:
MutableInnerProductModule
[
W
,
Double
]
)
:
Trainer
[
T
,
W
,
OracleS
,
MaxerS
]
def
newStochasticGradientDescentTrainer
[
T
,
W
,
OracleS
,
MaxerS
]
(
oracleInferencer:
OracleInferencer
[
T
,
W
,
OracleS
]
,
summer:
ExpectationInferencer
[
T
,
W
,
MaxerS
]
,
iterationCallback:
IterationCallback
[
T
,
W
,
OracleS
,
MaxerS
]
,
C:
Double
=
1.0
,
learningRate:
Double
=
1.0
,
maxNumIters:
Int
=
100
,
opts:
LogoOpts
=
new LogoOpts()
,
average:
Boolean
=
true
)
(
implicit
space:
MutableInnerProductModule
[
W
,
Double
]
)
:
Trainer
[
T
,
W
,
OracleS
,
MaxerS
]
final
def
notify
()
:
Unit
Definition Classes
AnyRef
final
def
notifyAll
()
:
Unit
Definition Classes
AnyRef
final
def
synchronized
[
T0
]
(
arg0: ⇒
T0
)
:
T0
Definition Classes
AnyRef
def
toString
()
:
String
Definition Classes
AnyRef → Any
def
trainL1MaxMarginMulticlassClassifier
[
L
,
F
,
W
]
(
labels:
IndexedSeq
[
L
]
,
data:
Seq
[
LabeledDatum
[
L
,
F
]]
,
labelConjoiner: (
L
,
F
) ⇒
W
,
initialConstraint:
W
,
iterationCallback:
IterationCallback
[
LabeledDatum
[
L
,
F
],
W
,
Unit
,
Unit
] =
...
,
oneSlackFormulation:
Boolean
=
true
,
C:
Double
=
1.0
,
maxNumIters:
Int
=
100
,
opts:
LogoOpts
=
new LogoOpts()
)
(
implicit
space:
MutableInnerProductModule
[
W
,
Double
]
)
:
MulticlassClassifier
[
L
,
F
,
W
]
final
def
wait
()
:
Unit
Definition Classes
AnyRef
Annotations
@throws
(
...
)
final
def
wait
(
arg0:
Long
,
arg1:
Int
)
:
Unit
Definition Classes
AnyRef
Annotations
@throws
(
...
)
final
def
wait
(
arg0:
Long
)
:
Unit
Definition Classes
AnyRef
Annotations
@throws
(
...
)
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