HingeLoss (Flink ML : Lib 2.2.0 API)

java.lang.Object
- org.apache.flink.ml.common.lossfunc.HingeLoss

所有已实现的接口:

Serializable, LossFunc
```
@Internal
public class HingeLoss
extends Object
implements LossFunc
```
The loss function for hinge loss. See LinearSVC for example.
See https://en.wikipedia.org/wiki/Hinge_loss.

另请参阅:

序列化表格

字段概要

字段
修饰符和类型字段说明

static HingeLoss INSTANCE

方法概要

所有方法实例方法具体方法
修饰符和类型	方法	说明
`void`	`computeGradient(org.apache.flink.ml.common.feature.LabeledPointWithWeight dataPoint, org.apache.flink.ml.linalg.DenseVector coefficient, org.apache.flink.ml.linalg.DenseVector cumGradient)`	Computes the gradient on the given data point and adds the computed gradient to cumGradient.
`double`	`computeLoss(org.apache.flink.ml.common.feature.LabeledPointWithWeight dataPoint, org.apache.flink.ml.linalg.DenseVector coefficient)`	Computes the loss on the given data point.

从类继承的方法 java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- 字段详细资料
  - INSTANCE
```
public static final HingeLoss INSTANCE
```
- 方法详细资料
  - computeLoss
```
public double computeLoss(org.apache.flink.ml.common.feature.LabeledPointWithWeight dataPoint,
                          org.apache.flink.ml.linalg.DenseVector coefficient)
```
    从接口复制的说明: LossFunc
    
    Computes the loss on the given data point.
    
    指定者:
    
    computeLoss 在接口中 LossFunc
    
    参数:
    
    dataPoint - A training data point.
    
    coefficient - The model parameters.
    
    返回:
    
    The loss of the input data.
  - computeGradient
```
public void computeGradient(org.apache.flink.ml.common.feature.LabeledPointWithWeight dataPoint,
                            org.apache.flink.ml.linalg.DenseVector coefficient,
                            org.apache.flink.ml.linalg.DenseVector cumGradient)
```
    从接口复制的说明: LossFunc
    
    Computes the gradient on the given data point and adds the computed gradient to cumGradient.
    
    指定者:
    
    computeGradient 在接口中 LossFunc
    
    参数:
    
    dataPoint - A training data point.
    
    coefficient - The model parameters.
    
    cumGradient - The accumulated gradient.