本文整理汇总了Java中mulan.data.MultiLabelInstances.getFeatureAttributes方法的典型用法代码示例。如果您正苦于以下问题：Java MultiLabelInstances.getFeatureAttributes方法的具体用法？Java MultiLabelInstances.getFeatureAttributes怎么用？Java MultiLabelInstances.getFeatureAttributes使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类mulan.data.MultiLabelInstances的用法示例。
在下文中一共展示了MultiLabelInstances.getFeatureAttributes方法的8个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: calculate
import mulan.data.MultiLabelInstances; //导入方法依赖的package包/类
/**
* Calculate metric value
*
* @param mlData Multi-label dataset to which calculate the metric
* @return Value of the metric
*/
public double calculate(MultiLabelInstances mlData){
double mean = 0.0;
int nNumeric = 0;
Instances instances = mlData.getDataSet();
Set<Attribute> attributeSet = mlData.getFeatureAttributes();
for(Attribute att : attributeSet){
if(att.isNumeric()){
nNumeric++;
mean += instances.meanOrMode(att);
}
}
mean = mean/nNumeric;
this.value = mean;
return value;
}
示例2: calculate
import mulan.data.MultiLabelInstances; //导入方法依赖的package包/类
/**
* Calculate metric value
*
* @param mlData Multi-label dataset to which calculate the metric
* @return Value of the metric
*/
public double calculate(MultiLabelInstances mlData){
double mean = 0;
int nNumeric = 0;
Instances instances = mlData.getDataSet();
Set<Attribute> attributeSet = mlData.getFeatureAttributes();
for(Attribute att : attributeSet){
if(att.isNumeric()){
nNumeric++;
mean += Math.sqrt(instances.variance(att));
}
}
if(nNumeric > 0){
this.value = mean / nNumeric;
}
else{
this.value = Double.NaN;
}
//this.value = mean;
return value;
}
示例3: calculate
import mulan.data.MultiLabelInstances; //导入方法依赖的package包/类
/**
* Calculate metric value
*
* @param mlData Multi-label dataset to which calculate the metric
* @return Value of the metric
*/
public double calculate(MultiLabelInstances mlData){
Instances instances = mlData.getDataSet();
int nInstances = mlData.getNumInstances();
Set<Attribute> attributesSet = mlData.getFeatureAttributes();
int nNumeric = 0;
double mean = 0;
double avg;
double var;
double stdev;
for(Attribute att : attributesSet){
if(att.isNumeric()){
nNumeric++;
avg = instances.meanOrMode(att);
var = 0;
for(Instance inst : instances){
var += Math.pow(inst.value(att) - avg, 3);
}
stdev = Math.sqrt(instances.variance(att));
mean += nInstances*var / ((nInstances-1)*(nInstances-2)*Math.pow(stdev, 3));
}
}
if(nNumeric > 0){
this.value = mean / nNumeric;
}
else{
this.value = Double.NaN;
}
//this.value = mean;
return value;
}
示例4: calculate
import mulan.data.MultiLabelInstances; //导入方法依赖的package包/类
/**
* Calculate metric value
*
* @param mlData Multi-label dataset to which calculate the metric
* @return Value of the metric
*/
public double calculate(MultiLabelInstances mlData){
Set<Attribute> attributeSet = mlData.getFeatureAttributes();
int count = 0;
for(Attribute att : attributeSet){
if(att.isNumeric()){
count++;
}
}
this.value = count;
return value;
}
示例5: calculate
import mulan.data.MultiLabelInstances; //导入方法依赖的package包/类
/**
* Calculate metric value
*
* @param mlData Multi-label dataset to which calculate the metric
* @return Value of the metric
*/
public double calculate(MultiLabelInstances mlData){
Set<Attribute> attributeSet = mlData.getFeatureAttributes();
int count = 0;
for(Attribute att : attributeSet){
if(att.isNominal()){
count++;
}
}
this.value = count;
return value;
}
示例6: calculate
import mulan.data.MultiLabelInstances; //导入方法依赖的package包/类
/**
* Calculate metric value
*
* @param mlData Multi-label dataset to which calculate the metric
* @return Value of the metric
*/
public double calculate(MultiLabelInstances mlData){
Set<Attribute> attributeSet = mlData.getFeatureAttributes();
int count = 0;
for(Attribute att : attributeSet){
if(att.numValues() == 2){
count++;
}
}
this.value = count;
return value;
}
示例7: calculate
import mulan.data.MultiLabelInstances; //导入方法依赖的package包/类
/**
* Calculate metric value
*
* @param mlData Multi-label dataset to which calculate the metric
* @return Value of the metric
*/
public double calculate(MultiLabelInstances mlData){
Instances instances = mlData.getDataSet();
int nInstances = mlData.getNumInstances();
double avg;
double var2;
double var4;
double val;
int nNumeric = 0;
double mean = 0;
Set<Attribute> attributesSet = mlData.getFeatureAttributes();
for(Attribute att : attributesSet){
if(att.isNumeric()){
nNumeric++;
avg = instances.meanOrMode(att);
var2 = 0;
var4 = 0;
for(Instance inst : instances){
val = inst.value(att);
var2 += Math.pow(val-avg, 2);
var4 += Math.pow(val-avg, 4);
}
double kurtosis = (nInstances*var4/Math.pow(var2,2))-3;
double sampleKurtosis = (kurtosis*(nInstances+1) + 6) * (nInstances-1)/((nInstances-2)*(nInstances-3));
mean += sampleKurtosis;
}
}
if(nNumeric > 0){
mean = mean/nNumeric;
}
else{
mean = Double.NaN;
}
this.value = mean;
return value;
}
示例8: calculate
import mulan.data.MultiLabelInstances; //导入方法依赖的package包/类
/**
* Calculate metric value
*
* @param mlData Multi-label dataset to which calculate the metric
* @return Value of the metric
*/
public double calculate(MultiLabelInstances mlData){
Instances instances = mlData.getDataSet();
int nInstances = mlData.getNumInstances();
double alpha = 0.05;
int numToTrimAtSide = (int)(nInstances*alpha / 2);
int nNumeric = 0;
int nOutliers = 0;
Set<Attribute> attributeSet = mlData.getFeatureAttributes();
double variance, varianceTrimmed;
double [] values;
double [] trimmed = new double[nInstances - (numToTrimAtSide * 2)];
double ratio;
for(Attribute att : attributeSet){
if(att.isNumeric()){
nNumeric++;
variance = instances.variance(att);
values = instances.attributeToDoubleArray(att.index());
Arrays.sort(values);
for(int i=0; i<trimmed.length; i++){
trimmed[i] = values[i + numToTrimAtSide];
}
varianceTrimmed = Utils.variance(trimmed);
ratio = varianceTrimmed / variance;
if(ratio < 0.7){
nOutliers++;
}
}
}
if(nNumeric > 0){
this.value = ((double) nOutliers) / nNumeric;
}
else{
this.value = Double.NaN;
}
return value;
}