Java Code Examples for org.apache.commons.math3.geometry.euclidean.oned.Interval#getInf()

public static List<Interval> refineOnLinearScale(final Interval interval, final int maxNumberOfSubIntervals, final double minimumLengthOfIntervals) {
	double min = interval.getInf();
	double max = interval.getSup();
	double length = max - min;
	List<Interval> intervals = new ArrayList<>();

	/* if no refinement is possible, return just the interval itself */
	if (length <= minimumLengthOfIntervals) {
		intervals.add(interval);
		return intervals;
	}

	/* otherwise compute the sub-intervals */
	int numberOfIntervals = Math.min((int) Math.ceil(length / minimumLengthOfIntervals), maxNumberOfSubIntervals);
	double stepSize = length / numberOfIntervals;
	for (int i = 0; i < numberOfIntervals; i++) {
		intervals.add(new Interval(min + i * stepSize, min + ((i + 1) * stepSize)));
	}
	return intervals;
}
 

private double singleOptimaRun(final int plusMinus, final double startX, final Interval range) {
	double lower = range.getInf();
	double upper = range.getSup();
	double currentX = startX;
	double currentGrad = this.grad.apply(currentX);
	double nextX = currentX;
	double nextGrad = currentGrad;
	int runs = 0;
	while (runs < maxRuns && this.nextRunFitsANegativeUpdate(nextX, lower) && this.nextRunFitsAPositiveUpdate(nextX, upper) && !this.gradIsCloseToZero(nextGrad)) {
		currentX = nextX;
		currentGrad = nextGrad;
		int gradientSignum = currentGrad < 0 ? -1 : +1;
		nextX = currentX + (plusMinus * gradientSignum * gradientDescentStepSize);
		nextGrad = this.grad.apply(currentX);
		runs++;
	}
	// either of the conditions is met
	return this.fun.apply(currentX);
}
 

private double singleOptimaRun(final int plusMinus, final double startX, final Interval range) {
	double lower = range.getInf();
	double upper = range.getSup();
	double currentX = startX;
	double currentGrad = this.grad.apply(currentX);
	double nextX = currentX;
	double nextGrad = currentGrad;
	int runs = 0;
	// System.out.println("Random start point is " + startX + ", seraching for max:"
	// + (plusMinus == +1));
	while (runs < maxRuns && this.nextRunFitsANegativeUpdate(nextX, lower) && this.nextRunFitsAPositiveUpdate(nextX, upper) && !this.gradIsCloseToZero(nextGrad)) {
		currentX = nextX;
		currentGrad = nextGrad;
		int gradientSignum = currentGrad < 0 ? -1 : +1;
		nextX = currentX + (plusMinus * gradientSignum * gradientDescentStepSize);
		nextGrad = this.grad.apply(currentX);
		runs++;
	}
	// System.out.println("Returning with X:" + currentX + ", grad:" + currentGrad +
	// " f(X):" + fun.apply(currentX));
	// either of the conditions is met
	return this.fun.apply(currentX);
}
 

public List<Interval> refineOnLinearScale(final Interval interval, final int maxNumberOfSubIntervals, final double minimumLengthOfIntervals, final boolean wasInitiallyLogarithmic, final boolean createPointIntervalsForExtremalValues) {
	double min = interval.getInf();
	double max = interval.getSup();
	double length = max - min;
	double logLength = max / min - 1;
	double relevantLength = wasInitiallyLogarithmic ? logLength : length;
	List<Interval> intervals = new ArrayList<>();
	this.logger.debug("Refining interval [{}, {}] in a linear fashion. Was initially refined on log-scale: {}", min, max, wasInitiallyLogarithmic);

	/* if no refinement is possible, return just the interval itself */
	if (relevantLength <= minimumLengthOfIntervals) {
		intervals.add(interval);
		if (createPointIntervalsForExtremalValues) {
			intervals.add(0, new Interval(min, min));
			intervals.add(new Interval(max, max));
		}
		return intervals;
	}
	/* otherwise compute the sub-intervals */
	int numberOfIntervals = Math.min((int) Math.ceil(relevantLength / minimumLengthOfIntervals), maxNumberOfSubIntervals);
	if (createPointIntervalsForExtremalValues) {
		numberOfIntervals -= 2;
	}
	numberOfIntervals = Math.max(numberOfIntervals, 1);
	this.logger.trace("Splitting interval of length {} and log-length {} into {} sub-intervals.", length, logLength, numberOfIntervals);
	double stepSize = length / numberOfIntervals;
	for (int i = 0; i < numberOfIntervals; i++) {
		intervals.add(new Interval(min + i * stepSize, min + ((i + 1) * stepSize)));
	}
	if (createPointIntervalsForExtremalValues) {
		intervals.add(0, new Interval(min, min));
		intervals.add(new Interval(max, max));
	}
	this.logger.trace("Derived sub-intervals {}", intervals.stream().map(i -> "[" + i.getInf() + ", " + i.getSup() + "]").collect(Collectors.toList()));
	return intervals;
}
 

private double getOptima(final int plusMinus, final Interval xInterval) {
	// strategy: gradient descent pick 10 random start points got into the negative
	// direction until either a local optima has been reached(gradient close enough
	// to 0), or, an upper/lower bound has been reached.
	double[] randomStart = new double[randomStarts];
	for (int i = 0; i < randomStarts; i++) {
		randomStart[i] = Math.random() * (xInterval.getSup() - xInterval.getInf()) + xInterval.getInf();
	}
	if (plusMinus == +1) {
		return Arrays.stream(randomStart).mapToObj(x -> this.singleOptimaRun(plusMinus, x, xInterval)).max(Double::compare).orElseThrow(() -> new IllegalStateException());
	} else {
		return Arrays.stream(randomStart).mapToObj(x -> this.singleOptimaRun(plusMinus, x, xInterval)).min(Double::compare).orElseThrow(() -> new IllegalStateException());
	}

}
 

private double getOptima(final int plusMinus, final Interval xInterval) {
	// strategy: gradient descent pick 10 random start points got into the negative
	// direction until either a local optima has been reached(gradient close enough
	// to 0), or, an upper/lower bound has been reached.
	double[] randomStart = new double[randomStarts];
	for (int i = 0; i < randomStarts; i++) {
		randomStart[i] = Math.random() * (xInterval.getSup() - xInterval.getInf()) + xInterval.getInf();
	}
	if (plusMinus == +1) {
		return Arrays.stream(randomStart).mapToObj(x -> this.singleOptimaRun(plusMinus, x, xInterval)).max(Double::compare).orElseThrow(() -> new IllegalStateException());
	} else {
		return Arrays.stream(randomStart).mapToObj(x -> this.singleOptimaRun(plusMinus, x, xInterval)).min(Double::compare).orElseThrow(() -> new IllegalStateException());
	}

}
 

@Override
public Interval predictInterval(final IntervalAndHeader intervalAndHeader) {
	Interval[] queriedInterval = intervalAndHeader.getIntervals();
	// the stack of elements that still have to be processed.
	Deque<Entry<Interval[], RuleNode>> stack = new ArrayDeque<>();
	// initially, the root and the queried interval
	stack.push(RQPHelper.getEntry(queriedInterval, this.getM5RootNode()));

	// the list of all leaf values
	ArrayList<Double> list = new ArrayList<>();
	while (stack.peek() != null) {
		// pick the next node to process
		Entry<Interval[], RuleNode> toProcess = stack.pop();
		RuleNode nextTree = toProcess.getValue();
		double threshold = nextTree.splitVal();
		int attribute = nextTree.splitAtt();
		// process node
		if (nextTree.isLeaf()) {
			this.predictLeaf(list, toProcess, nextTree, intervalAndHeader.getHeaderInformation());
		} else {
			Interval intervalForAttribute = queriedInterval[attribute];
			// no leaf node...
			RuleNode leftChild = nextTree.leftNode();
			RuleNode rightChild = nextTree.rightNode();
			// traverse the tree

			if (intervalForAttribute.getInf() <= threshold) {

				if (threshold <= intervalForAttribute.getSup()) {
					// scenario: x_min <= threshold <= x_max
					// query [x_min, threshold] on the left child
					// query [threshold, x_max] on the right child
					Interval[] leftInterval = RQPHelper.substituteInterval(toProcess.getKey(), new Interval(intervalForAttribute.getInf(), threshold), attribute);
					stack.push(RQPHelper.getEntry(leftInterval, leftChild));
					Interval[] rightInterval = RQPHelper.substituteInterval(toProcess.getKey(), new Interval(threshold, intervalForAttribute.getSup()), attribute);
					stack.push(RQPHelper.getEntry(rightInterval, rightChild));
				} else {
					// scenario: x_min <= x_max < threshold
					// query [x_min, x_max] on the left child
					stack.push(RQPHelper.getEntry(toProcess.getKey(), leftChild));
				}
			} else {
				stack.push(RQPHelper.getEntry(toProcess.getKey(), rightChild));
			}
		}
	}
	return this.intervalAggregator.aggregate(list);
}
 

@Override
public Interval predictInterval(final IntervalAndHeader intervalAndHeader) {
	Interval[] queriedInterval = intervalAndHeader.getIntervals();
	// the stack of elements that still have to be processed.
	Deque<Entry<Interval[], Tree>> stack = new ArrayDeque<>();
	// initially, the root and the queried interval
	stack.push(RQPHelper.getEntry(queriedInterval, this.m_Tree));

	// the list of all leaf values
	ArrayList<Double> list = new ArrayList<>();
	while (stack.peek() != null) {
		// pick the next node to process
		Entry<Interval[], Tree> toProcess = stack.pop();
		Tree nextTree = toProcess.getValue();
		double threshold = nextTree.getM_SplitPoint();
		int attribute = nextTree.getM_Attribute();
		Tree[] children = nextTree.getM_Successors();
		double[] classDistribution = nextTree.getM_Classdistribution();
		// process node
		if (attribute == -1) {
			// node is a leaf
			// for now, assume that we have regression!
			list.add(classDistribution[0]);
		} else {
			Interval intervalForAttribute = queriedInterval[attribute];
			// no leaf node...
			Tree leftChild = children[0];
			Tree rightChild = children[1];
			// traverse the tree

			if (intervalForAttribute.getInf() <= threshold) {

				if (threshold <= intervalForAttribute.getSup()) {
					// scenario: x_min <= threshold <= x_max
					// query [x_min, threshold] on the left child
					// query [threshold, x_max] right
					Interval[] newInterval = RQPHelper.substituteInterval(toProcess.getKey(), new Interval(intervalForAttribute.getInf(), threshold), attribute);
					Interval[] newMaxInterval = RQPHelper.substituteInterval(toProcess.getKey(), new Interval(threshold, intervalForAttribute.getSup()), attribute);
					stack.push(RQPHelper.getEntry(newInterval, leftChild));
					stack.push(RQPHelper.getEntry(newMaxInterval, rightChild));
				} else {
					// scenario: threshold <= x_min <= x_max
					// query [x_min, x_max] on the left child
					stack.push(RQPHelper.getEntry(toProcess.getKey(), leftChild));
				}
			}
			// analogously...
			if (intervalForAttribute.getSup() > threshold) {
				stack.push(RQPHelper.getEntry(toProcess.getKey(), rightChild));
			}
		}
	}
	return this.intervalAggregator.aggregate(list);
}