Operators

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
def +(other: String): String

Implicit information
This member is added by an implicit conversion from Operators to any2stringadd[Operators] performed by method any2stringadd in scala.Predef.
Definition Classes
any2stringadd
def ->[B](y: B): (Operators, B)

Implicit information
This member is added by an implicit conversion from Operators to ArrowAssoc[Operators] performed by method ArrowAssoc in scala.Predef.
Definition Classes
ArrowAssoc
Annotations
@inline()
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
def bootstrap[T <: AnyRef](x: Array[T], y: Array[Double], k: Int, measures: RegressionMeasure*)(trainer: ⇒ (Array[T], Array[Double]) ⇒ Regression[T]): Array[Double]

Bootstrap validation on a generic regression model.
Bootstrap validation on a generic regression model.
x
data samples.
y
response variable.
k
k-round bootstrap estimation.
measures
validation measures such as MSE, AbsoluteDeviation, etc.
trainer
a code block to return a regression model trained on the given data.
returns
measure results.
def bootstrap[T <: AnyRef](x: Array[T], y: Array[Int], k: Int, measures: ClassificationMeasure*)(trainer: ⇒ (Array[T], Array[Int]) ⇒ Classifier[T]): Array[Double]

Bootstrap validation on a generic classifier.
Bootstrap validation on a generic classifier. The bootstrap is a general tool for assessing statistical accuracy. The basic idea is to randomly draw datasets with replacement from the training data, each sample the same size as the original training set. This is done many times (say k = 100), producing k bootstrap datasets. Then we refit the model to each of the bootstrap datasets and examine the behavior of the fits over the k replications.
x
data samples.
y
sample labels.
k
k-round bootstrap estimation.
measures
validation measures such as accuracy, specificity, etc.
trainer
a code block to return a classifier trained on the given data.
returns
measure results.
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def cv[T <: AnyRef](x: Array[T], y: Array[Double], k: Int, measures: RegressionMeasure*)(trainer: ⇒ (Array[T], Array[Double]) ⇒ Regression[T]): Array[Double]

Cross validation on a generic regression model.
Cross validation on a generic regression model.
x
data samples.
y
response variable.
k
k-fold cross validation.
measures
validation measures such as MSE, AbsoluteDeviation, etc.
trainer
a code block to return a regression model trained on the given data.
returns
measure results.
def cv[T <: AnyRef](x: Array[T], y: Array[Int], k: Int, measures: ClassificationMeasure*)(trainer: ⇒ (Array[T], Array[Int]) ⇒ Classifier[T]): Array[Double]

Cross validation on a generic classifier.
Cross validation on a generic classifier. Cross-validation is a technique for assessing how the results of a statistical analysis will generalize to an independent data set. It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice. One round of cross-validation involves partitioning a sample of data into complementary subsets, performing the analysis on one subset (called the training set), and validating the analysis on the other subset (called the validation set or testing set). To reduce variability, multiple rounds of cross-validation are performed using different partitions, and the validation results are averaged over the rounds.
x
data samples.
y
sample labels.
k
k-fold cross validation.
measures
validation measures such as accuracy, specificity, etc.
trainer
a code block to return a classifier trained on the given data.
returns
measure results.
def ensuring(cond: (Operators) ⇒ Boolean, msg: ⇒ Any): Operators

Implicit information
This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.
Definition Classes
Ensuring
def ensuring(cond: (Operators) ⇒ Boolean): Operators

Implicit information
This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.
Definition Classes
Ensuring
def ensuring(cond: Boolean, msg: ⇒ Any): Operators

Implicit information
This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.
Definition Classes
Ensuring
def ensuring(cond: Boolean): Operators

Implicit information
This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.
Definition Classes
Ensuring
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def formatted(fmtstr: String): String

Implicit information
This member is added by an implicit conversion from Operators to StringFormat[Operators] performed by method StringFormat in scala.Predef.
Definition Classes
StringFormat
Annotations
@inline()
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def hashCode(): Int

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def loocv[T <: AnyRef](x: Array[T], y: Array[Double], measures: RegressionMeasure*)(trainer: ⇒ (Array[T], Array[Double]) ⇒ Regression[T]): Array[Double]

Leave-one-out cross validation on a generic regression model.
Leave-one-out cross validation on a generic regression model.
x
data samples.
y
response variable.
measures
validation measures such as MSE, AbsoluteDeviation, etc.
trainer
a code block to return a regression model trained on the given data.
returns
measure results.
def loocv[T <: AnyRef](x: Array[T], y: Array[Int], measures: ClassificationMeasure*)(trainer: ⇒ (Array[T], Array[Int]) ⇒ Classifier[T]): Array[Double]

Leave-one-out cross validation on a generic classifier.
Leave-one-out cross validation on a generic classifier. LOOCV uses a single observation from the original sample as the validation data, and the remaining observations as the training data. This is repeated such that each observation in the sample is used once as the validation data. This is the same as a K-fold cross-validation with K being equal to the number of observations in the original sample. Leave-one-out cross-validation is usually very expensive from a computational point of view because of the large number of times the training process is repeated.
x
data samples.
y
sample labels.
measures
validation measures such as accuracy, specificity, etc.
trainer
a code block to return a classifier trained on the given data.
returns
measure results.
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
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 test[T, C <: Classifier[T]](x: Array[T], y: Array[Int], testx: Array[T], testy: Array[Int], parTest: Boolean = true)(trainer: ⇒ (Array[T], Array[Int]) ⇒ C): C

Test a generic classifier.
Test a generic classifier. The accuracy will be measured and printed out on standard output.
T
the type of training and test data.
x
training data.
y
training labels.
testx
test data.
testy
test data labels.
parTest
Parallel test if true.
trainer
a code block to return a classifier trained on the given data.
returns
the trained classifier.
def test2[T, C <: Classifier[T]](x: Array[T], y: Array[Int], testx: Array[T], testy: Array[Int], parTest: Boolean = true)(trainer: ⇒ (Array[T], Array[Int]) ⇒ C): C

Test a binary classifier.
Test a binary classifier. The accuracy, sensitivity, specificity, precision, F-1 score, F-2 score, and F-0.5 score will be measured and printed out on standard output.
T
the type of training and test data.
x
training data.
y
training labels.
testx
test data.
testy
test data labels.
parTest
Parallel test if true.
trainer
a code block to return a binary classifier trained on the given data.
returns
the trained classifier.
def test2soft[T, C <: SoftClassifier[T]](x: Array[T], y: Array[Int], testx: Array[T], testy: Array[Int], parTest: Boolean = true)(trainer: ⇒ (Array[T], Array[Int]) ⇒ C): C

Test a binary soft classifier.
Test a binary soft classifier. The accuracy, sensitivity, specificity, precision, F-1 score, F-2 score, F-0.5 score, and AUC will be measured and printed out on standard output.
T
the type of training and test data.
x
training data.
y
training labels.
testx
test data.
testy
test data labels.
parTest
Parallel test if true.
trainer
a code block to return a binary classifier trained on the given data.
returns
the trained classifier.
def toString(): String

Definition Classes
AnyRef → Any
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( ... )
def →[B](y: B): (Operators, B)

Implicit information
This member is added by an implicit conversion from Operators to ArrowAssoc[Operators] performed by method ArrowAssoc in scala.Predef.
Definition Classes
ArrowAssoc

Inherited from AnyRef

Inherited from Any

Related Doc: package validation

trait Operators extends AnyRef

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

def +(other: String): String

def ->[B](y: B): (Operators, B)

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def bootstrap[T <: AnyRef](x: Array[T], y: Array[Double], k: Int, measures: RegressionMeasure*)(trainer: ⇒ (Array[T], Array[Double]) ⇒ Regression[T]): Array[Double]

def bootstrap[T <: AnyRef](x: Array[T], y: Array[Int], k: Int, measures: ClassificationMeasure*)(trainer: ⇒ (Array[T], Array[Int]) ⇒ Classifier[T]): Array[Double]

def clone(): AnyRef

def cv[T <: AnyRef](x: Array[T], y: Array[Double], k: Int, measures: RegressionMeasure*)(trainer: ⇒ (Array[T], Array[Double]) ⇒ Regression[T]): Array[Double]

def cv[T <: AnyRef](x: Array[T], y: Array[Int], k: Int, measures: ClassificationMeasure*)(trainer: ⇒ (Array[T], Array[Int]) ⇒ Classifier[T]): Array[Double]

def ensuring(cond: (Operators) ⇒ Boolean, msg: ⇒ Any): Operators

def ensuring(cond: (Operators) ⇒ Boolean): Operators

def ensuring(cond: Boolean, msg: ⇒ Any): Operators

def ensuring(cond: Boolean): Operators

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

def formatted(fmtstr: String): String

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def loocv[T <: AnyRef](x: Array[T], y: Array[Double], measures: RegressionMeasure*)(trainer: ⇒ (Array[T], Array[Double]) ⇒ Regression[T]): Array[Double]

def loocv[T <: AnyRef](x: Array[T], y: Array[Int], measures: ClassificationMeasure*)(trainer: ⇒ (Array[T], Array[Int]) ⇒ Classifier[T]): Array[Double]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

final def synchronized[T0](arg0: ⇒ T0): T0

def test[T, C <: Classifier[T]](x: Array[T], y: Array[Int], testx: Array[T], testy: Array[Int], parTest: Boolean = true)(trainer: ⇒ (Array[T], Array[Int]) ⇒ C): C

def test2[T, C <: Classifier[T]](x: Array[T], y: Array[Int], testx: Array[T], testy: Array[Int], parTest: Boolean = true)(trainer: ⇒ (Array[T], Array[Int]) ⇒ C): C

def test2soft[T, C <: SoftClassifier[T]](x: Array[T], y: Array[Int], testx: Array[T], testy: Array[Int], parTest: Boolean = true)(trainer: ⇒ (Array[T], Array[Int]) ⇒ C): C

def toString(): String

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

def →[B](y: B): (Operators, B)

Inherited from AnyRef

Inherited from Any

Inherited by implicit conversion any2stringadd from Operators to any2stringadd[Operators]

Inherited by implicit conversion StringFormat from Operators to StringFormat[Operators]

Inherited by implicit conversion Ensuring from Operators to Ensuring[Operators]

Inherited by implicit conversion ArrowAssoc from Operators to ArrowAssoc[Operators]

Ungrouped