/*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
/*
* EstimatorUtils.java
* Copyright (C) 2004-2012 University of Waikato, Hamilton, New Zealand
*
*/
package weka.estimators;
import java.io.FileOutputStream;
import java.io.PrintWriter;
import java.util.Enumeration;
import java.util.Vector;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.RevisionHandler;
import weka.core.RevisionUtils;
/**
* Contains static utility functions for Estimators.<p>
*
* @author Gabi Schmidberger (gabi@cs.waikato.ac.nz)
* @version $Revision: 8034 $
*/
public class EstimatorUtils
implements RevisionHandler {
/**
* Find the minimum distance between values
* @param inst sorted instances, sorted
* @param attrIndex index of the attribute, they are sorted after
* @return the minimal distance
*/
public static double findMinDistance(Instances inst, int attrIndex) {
double min = Double.MAX_VALUE;
int numInst = inst.numInstances();
double diff;
if (numInst < 2) return min;
int begin = -1;
Instance instance = null;
do {
begin++;
if (begin < numInst)
{ instance = inst.instance(begin); }
} while (begin < numInst && instance.isMissing(attrIndex));
double secondValue = inst.instance(begin).value(attrIndex);
for (int i = begin; i < numInst && !inst.instance(i).isMissing(attrIndex); i++) {
double firstValue = secondValue;
secondValue = inst.instance(i).value(attrIndex);
if (secondValue != firstValue) {
diff = secondValue - firstValue;
if (diff < min && diff > 0.0) {
min = diff;
}
}
}
return min;
}
/**
* Find the minimum and the maximum of the attribute and return it in
* the last parameter..
* @param inst instances used to build the estimator
* @param attrIndex index of the attribute
* @param minMax the array to return minimum and maximum in
* @return number of not missing values
* @exception Exception if parameter minMax wasn't initialized properly
*/
public static int getMinMax(Instances inst, int attrIndex, double [] minMax)
throws Exception {
double min = Double.NaN;
double max = Double.NaN;
Instance instance = null;
int numNotMissing = 0;
if ((minMax == null) || (minMax.length < 2)) {
throw new Exception("Error in Program, privat method getMinMax");
}
Enumeration enumInst = inst.enumerateInstances();
if (enumInst.hasMoreElements()) {
do {
instance = (Instance) enumInst.nextElement();
} while (instance.isMissing(attrIndex) && (enumInst.hasMoreElements()));
// add values if not missing
if (!instance.isMissing(attrIndex)) {
numNotMissing++;
min = instance.value(attrIndex);
max = instance.value(attrIndex);
}
while (enumInst.hasMoreElements()) {
instance = (Instance) enumInst.nextElement();
if (!instance.isMissing(attrIndex)) {
numNotMissing++;
if (instance.value(attrIndex) < min) {
min = (instance.value(attrIndex));
} else {
if (instance.value(attrIndex) > max) {
max = (instance.value(attrIndex));
}
}
}
}
}
minMax[0] = min;
minMax[1] = max;
return numNotMissing;
}
/**
* Returns a dataset that contains all instances of a certain class value.
*
* @param data dataset to select the instances from
* @param attrIndex index of the relevant attribute
* @param classIndex index of the class attribute
* @param classValue the relevant class value
* @return a dataset with only
*/
public static Vector getInstancesFromClass(Instances data, int attrIndex,
int classIndex,
double classValue, Instances workData) {
//Oops.pln("getInstancesFromClass classValue"+classValue+" workData"+data.numInstances());
Vector dataPlusInfo = new Vector(0);
int num = 0;
int numClassValue = 0;
//workData = new Instances(data, 0);
for (int i = 0; i < data.numInstances(); i++) {
if (!data.instance(i).isMissing(attrIndex)) {
num++;
if (data.instance(i).value(classIndex) == classValue) {
workData.add(data.instance(i));
numClassValue++;
}
}
}
Double alphaFactor = new Double((double)numClassValue/(double)num);
dataPlusInfo.add(workData);
dataPlusInfo.add(alphaFactor);
return dataPlusInfo;
}
/**
* Returns a dataset that contains of all instances of a certain class value.
* @param data dataset to select the instances from
* @param classIndex index of the class attribute
* @param classValue the class value
* @return a dataset with only instances of one class value
*/
public static Instances getInstancesFromClass(Instances data, int classIndex,
double classValue) {
Instances workData = new Instances(data, 0);
for (int i = 0; i < data.numInstances(); i++) {
if (data.instance(i).value(classIndex) == classValue) {
workData.add(data.instance(i));
}
}
return workData;
}
/**
* Output of an n points of a density curve.
* Filename is parameter f + ".curv".
*
* @param f string to build filename
* @param est
* @param min
* @param max
* @param numPoints
* @throws Exception if something goes wrong
*/
public static void writeCurve(String f, Estimator est,
double min, double max,
int numPoints) throws Exception {
PrintWriter output = null;
StringBuffer text = new StringBuffer("");
if (f.length() != 0) {
// add attribute indexnumber to filename and extension .hist
String name = f + ".curv";
output = new PrintWriter(new FileOutputStream(name));
} else {
return;
}
double diff = (max - min) / ((double)numPoints - 1.0);
try {
text.append("" + min + " " + est.getProbability(min) + " \n");
for (double value = min + diff; value < max; value += diff) {
text.append("" + value + " " + est.getProbability(value) + " \n");
}
text.append("" + max + " " + est.getProbability(max) + " \n");
} catch (Exception ex) {
ex.printStackTrace();
System.out.println(ex.getMessage());
}
output.println(text.toString());
// close output
if (output != null) {
output.close();
}
}
/**
* Output of an n points of a density curve.
* Filename is parameter f + ".curv".
*
* @param f string to build filename
* @param est
* @param classEst
* @param classIndex
* @param min
* @param max
* @param numPoints
* @throws Exception if something goes wrong
*/
public static void writeCurve(String f, Estimator est,
Estimator classEst,
double classIndex,
double min, double max,
int numPoints) throws Exception {
PrintWriter output = null;
StringBuffer text = new StringBuffer("");
if (f.length() != 0) {
// add attribute indexnumber to filename and extension .hist
String name = f + ".curv";
output = new PrintWriter(new FileOutputStream(name));
} else {
return;
}
double diff = (max - min) / ((double)numPoints - 1.0);
try {
text.append("" + min + " " +
est.getProbability(min) * classEst.getProbability(classIndex)
+ " \n");
for (double value = min + diff; value < max; value += diff) {
text.append("" + value + " " +
est.getProbability(value) * classEst.getProbability(classIndex)
+ " \n");
}
text.append("" + max + " " +
est.getProbability(max) * classEst.getProbability(classIndex)
+ " \n");
} catch (Exception ex) {
ex.printStackTrace();
System.out.println(ex.getMessage());
}
output.println(text.toString());
// close output
if (output != null) {
output.close();
}
}
/**
* Returns a dataset that contains of all instances of a certain value
* for the given attribute.
* @param data dataset to select the instances from
* @param index the index of the attribute
* @param v the value
* @return a subdataset with only instances of one value for the attribute
*/
public static Instances getInstancesFromValue(Instances data, int index,
double v) {
Instances workData = new Instances(data, 0);
for (int i = 0; i < data.numInstances(); i++) {
if (data.instance(i).value(index) == v) {
workData.add(data.instance(i));
}
}
return workData;
}
/**
* Returns a string representing the cutpoints
*/
public static String cutpointsToString(double [] cutPoints, boolean [] cutAndLeft) {
StringBuffer text = new StringBuffer("");
if (cutPoints == null) {
text.append("\n# no cutpoints found - attribute \n");
} else {
text.append("\n#* "+cutPoints.length+" cutpoint(s) -\n");
for (int i = 0; i < cutPoints.length; i++) {
text.append("# "+cutPoints[i]+" ");
text.append(""+cutAndLeft[i]+"\n");
}
text.append("# end\n");
}
return text.toString();
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 8034 $");
}
}