本文整理汇总了Java中net.sf.javaml.core.DenseInstance类的典型用法代码示例。如果您正苦于以下问题:Java DenseInstance类的具体用法?Java DenseInstance怎么用?Java DenseInstance使用的例子?那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
DenseInstance类属于net.sf.javaml.core包,在下文中一共展示了DenseInstance类的18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,您的评价将有助于我们的系统推荐出更棒的Java代码示例。
示例1: getDTWScore
import net.sf.javaml.core.DenseInstance; //导入依赖的package包/类
private double getDTWScore(double[] array1, double[] array2) {
double[] constantArray1 = new double[array1.length];
Arrays.fill(constantArray1, 0);
double[] constantArray2 = new double[array2.length];
Arrays.fill(constantArray2, 0);
double dtwAB = DTW.getWarpDistBetween(
new TimeSeries(new DenseInstance(array1)),
new TimeSeries(new DenseInstance(array2)));
double dtwA0 = DTW.getWarpDistBetween(
new TimeSeries(new DenseInstance(array1)),
new TimeSeries(new DenseInstance(constantArray2)));
double dtwB0 = DTW.getWarpDistBetween(
new TimeSeries(new DenseInstance(array2)),
new TimeSeries(new DenseInstance(constantArray1)));
if ((dtwA0 + dtwB0) == 0)
return 0;
return (1 - (dtwAB/(dtwA0 + dtwB0)));
}
开发者ID:ViDA-NYU,项目名称:data-polygamy,代码行数:22,代码来源:CorrelationTechniquesReducer.java
示例2: clustering
import net.sf.javaml.core.DenseInstance; //导入依赖的package包/类
@SuppressWarnings("rawtypes")
@Override
public List[] clustering(List<Objective> objectives) {
Dataset ds = new DefaultDataset();
for (Objective obj : objectives) {
ds.add(new DenseInstance(obj.getArray(),obj));
}
long time = System.currentTimeMillis();
//SpearmanRankCorrelation sc = new SpearmanRankCorrelation();
//System.out.print("Correlation " + sc.measure(ds.get(1), ds.get(2)) + "\n");
//SpearmanRankCorrelation
CustomKMean ckm = new CustomKMean(2, 1000, new SpearmanDistance());
Dataset[] clusters = ckm.cluster(ds);
System.out.print("Time taken on clustering: " + ( System.currentTimeMillis() - time) + "\n");
return clusters;
}
开发者ID:taochen,项目名称:ssascaling,代码行数:18,代码来源:JavaMLNeighborhood.java
示例3: filter
import net.sf.javaml.core.DenseInstance; //导入依赖的package包/类
@Override
public void filter(Instance instance) {
if (currentRange == null || currentMiddle == null)
throw new TrainingRequiredException();
if (instance instanceof DenseInstance) {
Instance tmp = instance.minus(currentMiddle).divide(currentRange).multiply(normalRange).add(normalMiddle);
instance.clear();
instance.putAll(tmp);
}
if (instance instanceof SparseInstance) {
for (int index : instance.keySet()) {
instance.put(index, ((instance.value(index) - currentMiddle.value(index)) / currentRange.value(index))
* normalRange + normalMiddle);
}
}
new ReplaceValueFilter(Double.NEGATIVE_INFINITY, normalMiddle).filter(instance);
new ReplaceValueFilter(Double.POSITIVE_INFINITY, normalMiddle).filter(instance);
new ReplaceValueFilter(Double.NaN, normalMiddle).filter(instance);
}
开发者ID:eracle,项目名称:gap,代码行数:22,代码来源:NormalizeMidrange.java
示例4: normalize
import net.sf.javaml.core.DenseInstance; //导入依赖的package包/类
/**
* Normalizes the data to mean 0 and standard deviation 1. This method
* discards all instances that cannot be normalized, i.e. they have the same
* value for all attributes.
*
* @param data
* @return
*/
private Vector<TaggedInstance> normalize(Dataset data) {
Vector<TaggedInstance> out = new Vector<TaggedInstance>();
for (int i = 0; i < data.size(); i++) {
Double[] old = data.instance(i).values().toArray(new Double[0]);
double[] conv = new double[old.length];
for (int j = 0; j < old.length; j++) {
conv[j] = old[j];
}
Mean m = new Mean();
double MU = m.evaluate(conv);
// System.out.println("MU = "+MU);
StandardDeviation std = new StandardDeviation();
double SIGM = std.evaluate(conv, MU);
// System.out.println("SIGM = "+SIGM);
if (!MathUtils.eq(SIGM, 0)) {
double[] val = new double[old.length];
for (int j = 0; j < old.length; j++) {
val[j] = (float) ((old[j] - MU) / SIGM);
}
// System.out.println("VAL "+i+" = "+Arrays.toString(val));
out.add(new TaggedInstance(new DenseInstance(val, data.instance(i).classValue()), i));
}
}
// System.out.println("FIRST = "+out.get(0));
return out;
}
开发者ID:eracle,项目名称:gap,代码行数:40,代码来源:AQBC.java
示例5: retrieveInstances
import net.sf.javaml.core.DenseInstance; //导入依赖的package包/类
private Vector<TaggedInstance> retrieveInstances(Vector<TaggedInstance> sp, double[] me2, double radnw2) {
Instance tmp = new DenseInstance(me2);
Vector<TaggedInstance> out = new Vector<TaggedInstance>();
for (TaggedInstance inst : sp) {
if (dm.measure(inst.inst, tmp) < radnw2)
out.add(inst);
}
return out;
}
开发者ID:eracle,项目名称:gap,代码行数:10,代码来源:AQBC.java
示例6: average
import net.sf.javaml.core.DenseInstance; //导入依赖的package包/类
/**
* Creates an instance that contains the average values for the attributes.
*
* @param data
* data set to calculate average attribute values for
* @return Instance representing the average attribute values
*/
public static Instance average(Dataset data) {
double[] tmpOut = new double[data.noAttributes()];
for (int i = 0; i < data.noAttributes(); i++) {
double sum=0;
for (int j = 0; j < data.size(); j++) {
sum+= data.get(j).value(i);
}
tmpOut[i] = sum/data.size();
}
return new DenseInstance(tmpOut);
}
开发者ID:eracle,项目名称:gap,代码行数:21,代码来源:DatasetTools.java
示例7: percentile
import net.sf.javaml.core.DenseInstance; //导入依赖的package包/类
/**
* Calculates the percentile hinge for a given percentile.
*
* @param data
* data set to calculate percentile for
* @param perc
* percentile to calculate, Q1=25, Q2=median=50,Q3=75
* @return
*/
public static Instance percentile(Dataset data, double perc) {
double[] tmpOut = new double[data.noAttributes()];
for (int i = 0; i < data.noAttributes(); i++) {
double[] vals = new double[data.size()];
for (int j = 0; j < data.size(); j++) {
vals[j] = data.get(j).value(i);
}
tmpOut[i] = StatUtils.percentile(vals, perc);
}
return new DenseInstance(tmpOut);
}
开发者ID:eracle,项目名称:gap,代码行数:22,代码来源:DatasetTools.java
示例8: load
import net.sf.javaml.core.DenseInstance; //导入依赖的package包/类
public static Dataset load(Reader in, int classIndex, String separator) {
LineIterator it = new LineIterator(in);
it.setSkipBlanks(true);
it.setSkipComments(true);
Dataset out = new DefaultDataset();
for (String line : it) {
String[] arr = line.split(separator);
double[] values;
if (classIndex == -1)
values = new double[arr.length];
else
values = new double[arr.length - 1];
String classValue = null;
for (int i = 0; i < arr.length; i++) {
if (i == classIndex) {
classValue = arr[i];
} else {
double val;
try {
val = Double.parseDouble(arr[i]);
} catch (NumberFormatException e) {
val = Double.NaN;
}
if (classIndex != -1 && i > classIndex)
values[i - 1] = val;
else
values[i] = val;
}
}
out.add(new DenseInstance(values, classValue));
}
return out;
}
开发者ID:eracle,项目名称:gap,代码行数:36,代码来源:StreamHandler.java
示例9: normalizeMidrange
import net.sf.javaml.core.DenseInstance; //导入依赖的package包/类
private Instance normalizeMidrange(double normalMiddle, double normalRange, Instance min, Instance max,
Instance instance) {
double[] out = new double[instance.noAttributes()];
for (int i = 0; i < out.length; i++) {
double range = Math.abs(max.value(i) - min.value(i));
double middle = Math.abs(max.value(i) + min.value(i)) / 2;
out[i] = ((instance.value(i) - middle) / range) * normalRange + normalMiddle;
}
return new DenseInstance(out, instance);
}
开发者ID:eracle,项目名称:gap,代码行数:11,代码来源:NormalizedEuclideanDistance.java
示例10: predictFalsePositive
import net.sf.javaml.core.DenseInstance; //导入依赖的package包/类
public int predictFalsePositive(Boolean IsSQLI) throws IOException{
int fp;
int sum_att = this.getSumOfAttributes();
if (IsSQLI == true){
if (sum_att == 0)
return 0; // is a real vv
}
else{
if (sum_att == 8)
return 0; // is a real vv
}
/*
for (int i = 0; i<this.getAttributes().length; i++){
System.out.print(this.getValueOfAttribute(i) + " ");
}
System.out.println();
*/
/* predict fp with machine learning
* 1. Logistic Regression (LR)
* 2. If LR returns FP, then aplly Random Tree (RT)
* 3. If RT returns FP, then apply Support Vector Machine (SVM)
* 4. If SVM returns FP, then we have an FP
*/
/* Create the instance to classify */
Instance instance = new DenseInstance(this.attributes);
int result_fp = 0;
fp = this.applyClassifier("lr", instance);
if (fp == 1){
result_fp++;
//GlobalDataApp.cf = GlobalDataApp.loadClassifier("rt");
fp = this.applyClassifier("rt", instance);
if (fp == 1){
result_fp++;
//GlobalDataApp.cf = GlobalDataApp.loadClassifier("svm");
fp = this.applyClassifier("svm", instance);
if (fp == 1){
result_fp++;
}
}
}
// volta a colocar cf em LR para a classificaçao da proxima vv
//GlobalDataApp.cf = GlobalDataApp.loadClassifier("lr");
if (result_fp == 3){
//GlobalDataApp.cf = GlobalDataApp.loadClassifier("jrip");
fp = this.applyClassifier("jrip", instance);
//FastVector fv = GlobalDataApp.jrip.getRuleset();
//System.out.println("Rules: " + fv.size());
//double[] distributionForInstance = GlobalDataApp.jrip.distributionForInstance((weka.core.Instance) instance);
return 1;
}
else
return 0;
}
开发者ID:asrulhadi,项目名称:wap,代码行数:62,代码来源:FP_Attributes.java
示例11: loadARFF
import net.sf.javaml.core.DenseInstance; //导入依赖的package包/类
/**
* Load a data set from an ARFF formatted file. Due to limitations in the
* Java-ML design only numeric attributes can be read.
*
* @param file
* the file to read the data from
* @param classIndex
* the index of the class label
* @return the data set represented in the provided file
* @throws FileNotFoundException
* if the file can not be found.
*/
public static Dataset loadARFF(File file, int classIndex) throws FileNotFoundException {
LineIterator it = new LineIterator(file);
it.setSkipBlanks(true);
it.setCommentIdentifier("%");
it.setSkipComments(true);
Dataset out = new DefaultDataset();
/* Indicates whether we are reading data */
boolean dataMode = false;
for (String line : it) {
/* When we passed the @data tag, we are reading data */
if (dataMode) {
String[] arr = line.split(",");
double[] values;
if (classIndex == -1)
values = new double[arr.length];
else
values = new double[arr.length - 1];
String classValue = null;
for (int i = 0; i < arr.length; i++) {
if (i == classIndex) {
classValue = arr[i];
} else {
double val;
try {
val = Double.parseDouble(arr[i]);
} catch (NumberFormatException e) {
val = Double.NaN;
}
if (classIndex!=-1 && i > classIndex)
values[i - 1] = val;
else
values[i] = val;
}
}
out.add(new DenseInstance(values, classValue));
}
/* Ignore everything in the header, i.e. everything up to @data */
if (line.equalsIgnoreCase("@data"))
dataMode = true;
}
return out;
}
开发者ID:eracle,项目名称:gap,代码行数:57,代码来源:ARFFHandler.java
示例12: standardDeviation
import net.sf.javaml.core.DenseInstance; //导入依赖的package包/类
/**
* Creates an instance that contains the standard deviation of the values
* for each attribute.
*
* @param data
* data set to calculate attribute value standard deviations for
* @param avg
* the average instance for the data set
* @return Instance representing the standard deviation of the values for
* each attribute
*/
public static Instance standardDeviation(Dataset data, Instance avg) {
Instance sum = new DenseInstance(new double[avg.noAttributes()]);
for (Instance i : data) {
Instance diff = i.minus(avg);
sum = sum.add(diff.multiply(diff));
}
sum = sum.divide(data.size());
return sum.sqrt();
}
开发者ID:eracle,项目名称:gap,代码行数:22,代码来源:DatasetTools.java
示例13: createInstanceFromClass
import net.sf.javaml.core.DenseInstance; //导入依赖的package包/类
/**
* Creates an Instance from the class labels over all Instances in a data
* set.
*
* The indices of the class labels are used because the class labels can be
* any Object.
*
* @param data
* data set to create class label instance for
* @return instance with class label indices as values.
*/
public static Instance createInstanceFromClass(Dataset data) {
Instance out = new DenseInstance(data.size());
int index = 0;
for (Instance inst : data)
out.put(index++, (double) data.classIndex(inst.classValue()));
return out;
}
开发者ID:eracle,项目名称:gap,代码行数:19,代码来源:DatasetTools.java
示例14: createInstanceFromAttribute
import net.sf.javaml.core.DenseInstance; //导入依赖的package包/类
/**
* Creates an Instance from the values of one particular attribute over all
* Instances in a data set.
*
* @param data
* @param i
* @return
*/
public static Instance createInstanceFromAttribute(Dataset data, int i) {
Instance out = new DenseInstance(data.size());
int index = 0;
for (Instance inst : data)
out.put(index++, inst.value(i));
return out;
}
开发者ID:eracle,项目名称:gap,代码行数:16,代码来源:DatasetTools.java
示例15: randomInstance
import net.sf.javaml.core.DenseInstance; //导入依赖的package包/类
/**
* Creates a random instance with the given number of attributes. The values
* of all attributes are between 0 and 1.
*
* @param length
* the number of attributes in the instance.
* @return a random instance
*/
public static Instance randomInstance(int length) {
double[] values = new double[length];
for (int i = 0; i < values.length; i++) {
values[i] = rg.nextDouble();
}
return new DenseInstance(values);
}
开发者ID:eracle,项目名称:gap,代码行数:16,代码来源:InstanceTools.java
示例16: randomGaussianInstance
import net.sf.javaml.core.DenseInstance; //导入依赖的package包/类
/**
* Creates a random instance with the given number of attributes. The values
* of the attributes follow a normal distribution with mean 0 and std 1.
*
* @param length
* the number of attributes in the instance.
* @return a random instance
*/
public static Instance randomGaussianInstance(int length) {
double[] values = new double[length];
for (int i = 0; i < values.length; i++) {
values[i] = rg.nextGaussian();
}
return new DenseInstance(values);
}
开发者ID:eracle,项目名称:gap,代码行数:16,代码来源:InstanceTools.java
示例17: getDistance
import net.sf.javaml.core.DenseInstance; //导入依赖的package包/类
/**
* Calculates the distance between two vectors using the distance function
* that was supplied during creation of the SOM. If no distance measure was
* specified, the Euclidean Distance will be used by default.
*
* @param double[] x - 1st vector.
* @param double[] y - 2nd vector.
* @return double - returns the distance between two vectors, x and y
*/
private double getDistance(double[] x, double[] y) {
return dm.measure(new DenseInstance(x), new DenseInstance(y));
}
开发者ID:seqcode,项目名称:seqcode-core,代码行数:13,代码来源:SOM.java
示例18: main
import net.sf.javaml.core.DenseInstance; //导入依赖的package包/类
public static void main(String[] args) throws Exception {
String line = " ";
File f = new File("List_RES_line_sinSW.txt");
try{
Scanner scanner = new Scanner( f );
while (scanner.hasNextLine()) {
line = scanner.nextLine();
frecuencia( line );
}
scanner.close();
} catch (Exception e) {
e.printStackTrace();
}
/* values of the attributes. */
double[] values = new double[] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };
Instance instance = new DenseInstance(values);
System.out.println();
System.out.println();
System.out.println();
System.out.println("Instance with only values set: ");
System.out.println(instance);
System.out.println();
Instance instanceWithClassValue = new DenseInstance(values, 1);
Instance in = new SparseInstance(10);
in.put(1, 1.0);
}
开发者ID:jaimeguzman,项目名称:data_mining,代码行数:46,代码来源:Main.java
注:本文中的net.sf.javaml.core.DenseInstance类示例整理自Github/MSDocs等源码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。 |
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