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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This version represents the developer version, the
"bleeding edge" of development, you could say. New functionality gets added
to this version.
/*
* 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 m_TM = new TreeMap();
/** The interval boundaries. */
protected double[] m_Boundaries = null;
/** The weight of each interval. */
protected double[] m_Weights = null;
/** The weighted sum of values */
protected double m_WeightedSum = 0;
/** The weighted sum of squared values */
protected double m_WeightedSumSquared = 0;
/** The total sum of weights. */
protected double m_SumOfWeights = 0;
/** The number of bins to use. */
protected int m_NumBins = 10;
/** The current bandwidth (only computed when needed) */
protected double m_Width = Double.MAX_VALUE;
/** The exponent to use in computation of bandwidth (default: -0.25) */
protected double m_Exponent = -0.25;
/** The minimum allowed value of the kernel width (default: 1.0E-6) */
protected double m_MinWidth = 1.0E-6;
/** Constant for Gaussian density. */
public static final double CONST = -0.5 * Math.log(2 * Math.PI);
/** The number of intervals used to approximate prediction interval. */
protected int m_NumIntervals = 1000;
/** Whether boundaries are updated or only weights. */
protected boolean m_UpdateWeightsOnly = false;
/**
* Returns a string describing the estimator.
*/
public String globalInfo() {
return "Provides a univariate histogram estimator based on equal-frequency bins.";
}
/**
* Gets the number of bins
*
* @return the number of bins.
*/
public int getNumBins() {
return m_NumBins;
}
/**
* Sets the number of bins
*
* @param numBins the number of bins
*/
public void setNumBins(int numBins) {
m_NumBins = numBins;
}
/**
* Triggers construction of estimator based on current data and then
* initializes the statistics.
*/
public void initializeStatistics() {
updateBoundariesAndOrWeights();
m_TM = new TreeMap();
m_WeightedSum = 0;
m_WeightedSumSquared = 0;
m_SumOfWeights = 0;
m_Weights = null;
}
/**
* Sets whether only weights should be udpated.
*/
public void setUpdateWeightsOnly(boolean flag) {
m_UpdateWeightsOnly = flag;
}
/**
* Gets whether only weights should be udpated.*
*/
public boolean getUpdateWeightsOnly() {
return m_UpdateWeightsOnly;
}
/**
* Adds a value to the density estimator.
*
* @param value the value to add
* @param weight the weight of the value
*/
@Override
public void addValue(double value, double weight) {
// Add data point to collection
m_WeightedSum += value * weight;
m_WeightedSumSquared += value * value * weight;
m_SumOfWeights += weight;
if (m_TM.get(value) == null) {
m_TM.put(value, weight);
} else {
m_TM.put(value, m_TM.get(value) + weight);
}
// Make sure estimator is updated
if (!getUpdateWeightsOnly()) {
m_Boundaries = null;
}
m_Weights = null;
}
/**
* Updates the boundaries if necessary.
*/
protected void updateBoundariesAndOrWeights() {
// Do we need to update?
if (m_Weights != null) {
return;
}
// Update widths for cases that are out of bounds,
// using same code as in kernel estimator
// First, compute variance for scaling
double mean = m_WeightedSum / m_SumOfWeights;
double variance = m_WeightedSumSquared / m_SumOfWeights - mean * mean;
if (variance < 0) {
variance = 0;
}
// Compute kernel bandwidth
m_Width = Math.sqrt(variance) * Math.pow(m_SumOfWeights, m_Exponent);
if (m_Width <= m_MinWidth) {
m_Width = m_MinWidth;
}
// Do we need to update weights only
if (getUpdateWeightsOnly()) {
updateWeightsOnly();
} else {
updateBoundariesAndWeights();
}
}
/**
* Updates the weights only.
*/
protected void updateWeightsOnly() throws IllegalArgumentException {
// Get values and keys from tree map
Iterator> itr = m_TM.entrySet().iterator();
int j = 1;
m_Weights = new double[m_Boundaries.length - 1];
while (itr.hasNext()) {
Map.Entry entry = itr.next();
double value = entry.getKey();
double weight = entry.getValue();
if ((value < m_Boundaries[0])
|| (value > m_Boundaries[m_Boundaries.length - 1])) {
throw new IllegalArgumentException(
"Out-of-range value during weight update");
}
while (value > m_Boundaries[j]) {
j++;
}
m_Weights[j - 1] += weight;
}
}
/**
* Updates the boundaries and weights.
*/
protected void updateBoundariesAndWeights() {
// Get values and keys from tree map
double[] values = new double[m_TM.size()];
double[] weights = new double[m_TM.size()];
Iterator> itr = m_TM.entrySet().iterator();
int j = 0;
while (itr.hasNext()) {
Map.Entry entry = itr.next();
values[j] = entry.getKey();
weights[j] = entry.getValue();
j++;
}
double freq = m_SumOfWeights / m_NumBins;
double[] cutPoints = new double[m_NumBins - 1];
double[] binWeights = new double[m_NumBins];
double sumOfWeights = m_SumOfWeights;
// Compute break points
double weightSumSoFar = 0, lastWeightSum = 0;
int cpindex = 0, lastIndex = -1;
for (int i = 0; i < values.length - 1; i++) {
// Update weight statistics
weightSumSoFar += weights[i];
sumOfWeights -= weights[i];
// Have we passed the ideal size?
if (weightSumSoFar >= freq) {
// Is this break point worse than the last one?
if (((freq - lastWeightSum) < (weightSumSoFar - freq))
&& (lastIndex != -1)) {
cutPoints[cpindex] = (values[lastIndex] + values[lastIndex + 1]) / 2;
weightSumSoFar -= lastWeightSum;
binWeights[cpindex] = lastWeightSum;
lastWeightSum = weightSumSoFar;
lastIndex = i;
} else {
cutPoints[cpindex] = (values[i] + values[i + 1]) / 2;
binWeights[cpindex] = weightSumSoFar;
weightSumSoFar = 0;
lastWeightSum = 0;
lastIndex = -1;
}
cpindex++;
freq = (sumOfWeights + weightSumSoFar)
/ ((cutPoints.length + 1) - cpindex);
} else {
lastIndex = i;
lastWeightSum = weightSumSoFar;
}
}
// Check whether there was another possibility for a cut point
if ((cpindex < cutPoints.length) && (lastIndex != -1)) {
cutPoints[cpindex] = (values[lastIndex] + values[lastIndex + 1]) / 2;
binWeights[cpindex] = lastWeightSum;
cpindex++;
binWeights[cpindex] = weightSumSoFar - lastWeightSum;
} else {
binWeights[cpindex] = weightSumSoFar;
}
// Did we find any cutpoints?
if (cpindex == 0) {
m_Boundaries = null;
m_Weights = null;
} else {
// Need to add weight of last data point to right-most bin
binWeights[cpindex] += weights[values.length - 1];
// Copy over boundaries and weights
m_Boundaries = new double[cpindex + 2];
m_Boundaries[0] = m_TM.firstKey();
m_Boundaries[cpindex + 1] = m_TM.lastKey();
System.arraycopy(cutPoints, 0, m_Boundaries, 1, cpindex);
m_Weights = new double[cpindex + 1];
System.arraycopy(binWeights, 0, m_Weights, 0, cpindex + 1);
}
}
/**
* Returns the interval for the given confidence value.
*
* @param conf the confidence value in the interval [0, 1]
* @return the interval
*/
@Override
public double[][] predictIntervals(double conf) {
// Update the bandwidth
updateBoundariesAndOrWeights();
// Compute minimum and maximum value, and delta
double val = Statistics.normalInverse(1.0 - (1.0 - conf) / 2);
double min = m_TM.firstKey() - val * m_Width;
double max = m_TM.lastKey() + val * m_Width;
double delta = (max - min) / m_NumIntervals;
// Create array with estimated probabilities
double[] probabilities = new double[m_NumIntervals];
double leftVal = Math.exp(logDensity(min));
for (int i = 0; i < m_NumIntervals; i++) {
double rightVal = Math.exp(logDensity(min + (i + 1) * delta));
probabilities[i] = 0.5 * (leftVal + rightVal) * delta;
leftVal = rightVal;
}
// Sort array based on area of bin estimates
int[] sortedIndices = Utils.sort(probabilities);
// Mark the intervals to use
double sum = 0;
boolean[] toUse = new boolean[probabilities.length];
int k = 0;
while ((sum < conf) && (k < toUse.length)) {
toUse[sortedIndices[toUse.length - (k + 1)]] = true;
sum += probabilities[sortedIndices[toUse.length - (k + 1)]];
k++;
}
// Don't need probabilities anymore
probabilities = null;
// Create final list of intervals
ArrayList intervals = new ArrayList();
// The current interval
double[] interval = null;
// Iterate through kernels
boolean haveStartedInterval = false;
for (int i = 0; i < m_NumIntervals; i++) {
// Should the current bin be used?
if (toUse[i]) {
// Do we need to create a new interval?
if (haveStartedInterval == false) {
haveStartedInterval = true;
interval = new double[2];
interval[0] = min + i * delta;
}
// Regardless, we should update the upper boundary
interval[1] = min + (i + 1) * delta;
} else {
// We need to finalize and store the last interval
// if necessary.
if (haveStartedInterval) {
haveStartedInterval = false;
intervals.add(interval);
}
}
}
// Add last interval if there is one
if (haveStartedInterval) {
intervals.add(interval);
}
return intervals.toArray(new double[0][0]);
}
/**
* Returns the quantile for the given percentage.
*
* @param percentage the percentage
* @return the quantile
*/
@Override
public double predictQuantile(double percentage) {
// Update the bandwidth
updateBoundariesAndOrWeights();
// Compute minimum and maximum value, and delta
double val = Statistics.normalInverse(1.0 - (1.0 - 0.95) / 2);
double min = m_TM.firstKey() - val * m_Width;
double max = m_TM.lastKey() + val * m_Width;
double delta = (max - min) / m_NumIntervals;
double sum = 0;
double leftVal = Math.exp(logDensity(min));
for (int i = 0; i < m_NumIntervals; i++) {
if (sum >= percentage) {
return min + i * delta;
}
double rightVal = Math.exp(logDensity(min + (i + 1) * delta));
sum += 0.5 * (leftVal + rightVal) * delta;
leftVal = rightVal;
}
return max;
}
/**
* Returns the natural logarithm of the density estimate at the given point.
*
* @param value the value at which to evaluate
* @return the natural logarithm of the density estimate at the given value
*/
@Override
public double logDensity(double value) {
// Update boundaries if necessary
updateBoundariesAndOrWeights();
if (m_Boundaries == null) {
return Math.log(Double.MIN_VALUE);
}
// Find the bin
int index = Arrays.binarySearch(m_Boundaries, value);
// Is the value outside?
if ((index == -1) || (index == -m_Boundaries.length - 1)) {
// Use normal density outside
double val = 0;
if (index == -1) { // Smaller than minimum
val = m_TM.firstKey() - value;
} else {
val = value - m_TM.lastKey();
}
return (CONST - Math.log(m_Width) - 0.5 * (val * val / (m_Width * m_Width)))
- Math.log(m_SumOfWeights + 2);
}
// Is value exactly equal to right-most boundary?
if (index == m_Boundaries.length - 1) {
index--;
} else {
// Need to reverse index if necessary
if (index < 0) {
index = -index - 2;
}
}
// Figure out of width
double width = m_Boundaries[index + 1] - m_Boundaries[index];
// Density compontent from smeared-out data point
double densSmearedOut = 1.0 / ((m_SumOfWeights + 2) * (m_Boundaries[m_Boundaries.length - 1] - m_Boundaries[0]));
// Return log of density
if (m_Weights[index] <= 0) {
/*
* System.out.println(value); System.out.println(this); System.exit(1);
*/
// Just use one smeared-out data point
return Math.log(densSmearedOut);
} else {
return Math.log(densSmearedOut + m_Weights[index]
/ ((m_SumOfWeights + 2) * width));
}
}
/**
* Returns the revision string.
*
* @return the revision
*/
@Override
public String getRevision() {
return RevisionUtils.extract("$Revision: 11318 $");
}
/**
* Returns textual description of this estimator.
*/
@Override
public String toString() {
StringBuffer text = new StringBuffer();
text.append("EqualFrequencyHistogram estimator\n\n"
+ "Bandwidth for out of range cases " + m_Width + ", total weight "
+ m_SumOfWeights);
if (m_Boundaries != null) {
text.append("\nLeft boundary\tRight boundary\tWeight\n");
for (int i = 0; i < m_Boundaries.length - 1; i++) {
text.append(m_Boundaries[i] + "\t" + m_Boundaries[i + 1] + "\t"
+ m_Weights[i] + "\t"
+ Math.exp(logDensity((m_Boundaries[i + 1] + m_Boundaries[i]) / 2))
+ "\n");
}
}
return text.toString();
}
/**
* Main method, used for testing this class.
*/
public static void main(String[] args) {
// Get random number generator initialized by system
Random r = new Random();
// Create density estimator
UnivariateEqualFrequencyHistogramEstimator e = new UnivariateEqualFrequencyHistogramEstimator();
// Output the density estimator
System.out.println(e);
// Monte Carlo integration
double sum = 0;
for (int i = 0; i < 1000; i++) {
sum += Math.exp(e.logDensity(r.nextDouble() * 10.0 - 5.0));
}
System.out.println("Approximate integral: " + 10.0 * sum / 1000);
// Add Gaussian values into it
for (int i = 0; i < 1000; i++) {
e.addValue(0.1 * r.nextGaussian() - 3, 1);
e.addValue(r.nextGaussian() * 0.25, 3);
}
// Monte Carlo integration
sum = 0;
int points = 10000000;
for (int i = 0; i < points; i++) {
double value = r.nextDouble() * 20.0 - 10.0;
sum += Math.exp(e.logDensity(value));
}
// Output the density estimator
System.out.println(e);
System.out.println("Approximate integral: " + 20.0 * sum / points);
// Check interval estimates
double[][] Intervals = e.predictIntervals(0.9);
System.out.println("Printing histogram intervals ---------------------");
for (double[] interval : Intervals) {
System.out.println("Left: " + interval[0] + "\t Right: " + interval[1]);
}
System.out
.println("Finished histogram printing intervals ---------------------");
double Covered = 0;
for (int i = 0; i < 1000; i++) {
double val = -1;
if (r.nextDouble() < 0.25) {
val = 0.1 * r.nextGaussian() - 3.0;
} else {
val = r.nextGaussian() * 0.25;
}
for (double[] interval : Intervals) {
if (val >= interval[0] && val <= interval[1]) {
Covered++;
break;
}
}
}
System.out.println("Coverage at 0.9 level for histogram intervals: "
+ Covered / 1000);
for (int j = 1; j < 5; j++) {
double numTrain = Math.pow(10, j);
System.out.println("Number of training cases: " + numTrain);
// Compare performance to normal estimator on normally distributed data
UnivariateEqualFrequencyHistogramEstimator eHistogram = new UnivariateEqualFrequencyHistogramEstimator();
UnivariateNormalEstimator eNormal = new UnivariateNormalEstimator();
// Add training cases
for (int i = 0; i < numTrain; i++) {
double val = r.nextGaussian() * 1.5 + 0.5;
/*
* if (j == 4) { System.err.println(val); }
*/
eHistogram.addValue(val, 1);
eNormal.addValue(val, 1);
}
// Monte Carlo integration
sum = 0;
points = 10000000;
for (int i = 0; i < points; i++) {
double value = r.nextDouble() * 20.0 - 10.0;
sum += Math.exp(eHistogram.logDensity(value));
}
System.out.println(eHistogram);
System.out.println("Approximate integral for histogram estimator: "
+ 20.0 * sum / points);
// Evaluate estimators
double loglikelihoodHistogram = 0, loglikelihoodNormal = 0;
for (int i = 0; i < 1000; i++) {
double val = r.nextGaussian() * 1.5 + 0.5;
loglikelihoodHistogram += eHistogram.logDensity(val);
loglikelihoodNormal += eNormal.logDensity(val);
}
System.out.println("Loglikelihood for histogram estimator: "
+ loglikelihoodHistogram / 1000);
System.out.println("Loglikelihood for normal estimator: "
+ loglikelihoodNormal / 1000);
// Check interval estimates
double[][] histogramIntervals = eHistogram.predictIntervals(0.95);
double[][] normalIntervals = eNormal.predictIntervals(0.95);
System.out.println("Printing histogram intervals ---------------------");
for (double[] histogramInterval : histogramIntervals) {
System.out.println("Left: " + histogramInterval[0] + "\t Right: "
+ histogramInterval[1]);
}
System.out
.println("Finished histogram printing intervals ---------------------");
System.out.println("Printing normal intervals ---------------------");
for (double[] normalInterval : normalIntervals) {
System.out.println("Left: " + normalInterval[0] + "\t Right: "
+ normalInterval[1]);
}
System.out
.println("Finished normal printing intervals ---------------------");
double histogramCovered = 0;
double normalCovered = 0;
for (int i = 0; i < 1000; i++) {
double val = r.nextGaussian() * 1.5 + 0.5;
for (double[] histogramInterval : histogramIntervals) {
if (val >= histogramInterval[0] && val <= histogramInterval[1]) {
histogramCovered++;
break;
}
}
for (double[] normalInterval : normalIntervals) {
if (val >= normalInterval[0] && val <= normalInterval[1]) {
normalCovered++;
break;
}
}
}
System.out.println("Coverage at 0.95 level for histogram intervals: "
+ histogramCovered / 1000);
System.out.println("Coverage at 0.95 level for normal intervals: "
+ normalCovered / 1000);
histogramIntervals = eHistogram.predictIntervals(0.8);
normalIntervals = eNormal.predictIntervals(0.8);
histogramCovered = 0;
normalCovered = 0;
for (int i = 0; i < 1000; i++) {
double val = r.nextGaussian() * 1.5 + 0.5;
for (double[] histogramInterval : histogramIntervals) {
if (val >= histogramInterval[0] && val <= histogramInterval[1]) {
histogramCovered++;
break;
}
}
for (double[] normalInterval : normalIntervals) {
if (val >= normalInterval[0] && val <= normalInterval[1]) {
normalCovered++;
break;
}
}
}
System.out.println("Coverage at 0.8 level for histogram intervals: "
+ histogramCovered / 1000);
System.out.println("Coverage at 0.8 level for normal intervals: "
+ normalCovered / 1000);
}
}
}
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