Examples with MemoryPeakResults gdsc.smlm.results.MemoryPeakResults used on opensource projects

Example 56 with MemoryPeakResults

use of gdsc.smlm.results.MemoryPeakResults in project GDSC-SMLM by aherbert.

the class Filter method filter.

/**
	 * Filter the results
	 * 
	 * @param results
	 * @return the filtered results
	 */
public MemoryPeakResults filter(MemoryPeakResults results) {
    MemoryPeakResults newResults = new MemoryPeakResults();
    newResults.copySettings(results);
    setup(results);
    for (PeakResult peak : results.getResults()) {
        if (accept(peak))
            newResults.add(peak);
    }
    end();
    return newResults;
}

Example 57 with MemoryPeakResults

use of gdsc.smlm.results.MemoryPeakResults in project GDSC-SMLM by aherbert.

the class Filter method scoreSubset.

/**
	 * Filter the results and return the performance score. Allows benchmarking the filter by marking the results as
	 * true or false.
	 * <p>
	 * Any input PeakResult with an original value that is not zero will be treated as a true result, all other results
	 * are false. The filter is run and the results are marked as true positive, false negative and false positive.
	 * <p>
	 * The number of consecutive rejections are counted per frame. When the configured number of failures is reached all
	 * remaining results for the frame are rejected. This assumes the results are ordered by the frame.
	 * <p>
	 * Note that this method is to be used to score a subset that was generated using
	 * {@link #filterSubset(MemoryPeakResults, int)} since the number of consecutive failures before each peak are
	 * expected to be stored in the origX property.
	 * 
	 * @param resultsList
	 *            a list of results to analyse
	 * @param failures
	 *            the number of failures to allow per frame before all peaks are rejected
	 * @param tn
	 *            The initial true negatives (used when the results have been pre-filtered)
	 * @param fn
	 *            The initial false negatives (used when the results have been pre-filtered)
	 * @return the score
	 */
public ClassificationResult scoreSubset(List<MemoryPeakResults> resultsList, int failures, int tn, int fn) {
    int tp = 0, fp = 0;
    for (MemoryPeakResults peakResults : resultsList) {
        setup(peakResults);
        int frame = -1;
        int failCount = 0;
        for (PeakResult peak : peakResults.getResults()) {
            final boolean isTrue = peak.origValue != 0;
            // Reset fail count for new frames
            if (frame != peak.getFrame()) {
                frame = peak.getFrame();
                failCount = 0;
            }
            failCount += peak.origX;
            // Reject all peaks if we have exceeded the fail count
            final boolean isPositive;
            if (failCount > failures) {
                isPositive = false;
            } else {
                // Otherwise assess the peak
                isPositive = accept(peak);
            }
            if (isPositive) {
                failCount = 0;
            } else {
                failCount++;
            }
            if (isTrue) {
                if (isPositive)
                    // true positive
                    tp++;
                else
                    // false negative
                    fn++;
            } else {
                if (isPositive)
                    // false positive
                    fp++;
                else
                    // true negative
                    tn++;
            }
        }
        end();
    }
    return new ClassificationResult(tp, fp, tn, fn);
}

Example 58 with MemoryPeakResults

use of gdsc.smlm.results.MemoryPeakResults in project GDSC-SMLM by aherbert.

the class Filter method score.

/**
	 * Filter the results and return the performance score. Allows benchmarking the filter by marking the results as
	 * true or false.
	 * <p>
	 * Any input PeakResult with an original value that is not zero will be treated as a true result, all other results
	 * are false. The filter is run and the results are marked as true positive, false negative and false positive.
	 * 
	 * @param resultsList
	 *            a list of results to analyse
	 * @param tn
	 *            The initial true negatives (used when the results have been pre-filtered)
	 * @param fn
	 *            The initial false negatives (used when the results have been pre-filtered)
	 * @return
	 */
public ClassificationResult score(List<MemoryPeakResults> resultsList, int tn, int fn) {
    int tp = 0, fp = 0;
    for (MemoryPeakResults peakResults : resultsList) {
        setup(peakResults);
        for (PeakResult peak : peakResults.getResults()) {
            final boolean isTrue = peak.origValue != 0;
            boolean isPositive = accept(peak);
            if (isTrue) {
                if (isPositive)
                    // true positive
                    tp++;
                else
                    // false negative
                    fn++;
            } else {
                if (isPositive)
                    // false positive
                    fp++;
                else
                    // true negative
                    tn++;
            }
        }
        end();
    }
    return new ClassificationResult(tp, fp, tn, fn);
}

Example 59 with MemoryPeakResults

use of gdsc.smlm.results.MemoryPeakResults in project GDSC-SMLM by aherbert.

the class Filter method filterSubset.

/**
	 * Filter the results
	 * <p>
	 * Input PeakResults must be allocated a score for true positive, false positive, true negative and false negative
	 * (accessed via the object property get methods). The filter is run and results that pass accumulate scores for
	 * true positive and false positive, otherwise the scores are accumulated for true negative and false negative. The
	 * simplest scoring scheme is to mark valid results as tp=fn=1 and fp=tn=0 and invalid results the opposite.
	 * <p>
	 * The number of consecutive rejections are counted per frame. When the configured number of failures is reached all
	 * remaining results for the frame are rejected. This assumes the results are ordered by the frame.
	 * <p>
	 * The number of failures before each peak is stored in the origX property of the PeakResult.
	 * 
	 * @param results
	 * @param failures
	 *            the number of failures to allow per frame before all peaks are rejected
	 * @param score
	 *            If not null will be populated with the fraction score [ tp, fp, tn, fn, p, n ]
	 * @return the filtered results
	 */
public MemoryPeakResults filterSubset(MemoryPeakResults results, int failures, double[] score) {
    MemoryPeakResults newResults = new MemoryPeakResults();
    newResults.copySettings(results);
    setup(results);
    int frame = -1;
    int failCount = 0;
    double fp = 0, fn = 0;
    double tp = 0, tn = 0;
    for (PeakResult peak : results.getResults()) {
        if (frame != peak.getFrame()) {
            frame = peak.getFrame();
            failCount = 0;
        }
        // Reject all peaks if we have exceeded the fail count
        final boolean isPositive;
        if (failCount > failures) {
            isPositive = false;
        } else {
            // Otherwise assess the peak
            isPositive = accept(peak);
        }
        if (isPositive) {
            peak.origX = failCount;
            failCount = 0;
            newResults.add(peak);
        } else {
            failCount++;
        }
        if (isPositive) {
            tp += peak.getTruePositiveScore();
            fp += peak.getFalsePositiveScore();
        } else {
            fn += peak.getFalseNegativeScore();
            tn += peak.getTrueNegativeScore();
        }
    }
    end();
    if (score != null && score.length > 5) {
        score[0] = tp;
        score[1] = fp;
        score[2] = tn;
        score[3] = fn;
        score[4] = newResults.size();
        score[5] = results.size() - newResults.size();
    }
    return newResults;
}

Example 60 with MemoryPeakResults

use of gdsc.smlm.results.MemoryPeakResults in project GDSC-SMLM by aherbert.

the class Filter method filterSubset2.

/**
	 * Filter the results
	 * <p>
	 * Input PeakResults must be allocated a score for true positive, false positive, true negative and false negative
	 * (accessed via the object property get methods). The filter is run and results that pass accumulate scores for
	 * true positive and false positive, otherwise the scores are accumulated for true negative and false negative. The
	 * simplest scoring scheme is to mark valid results as tp=fn=1 and fp=tn=0 and invalid results the opposite.
	 * <p>
	 * The number of consecutive rejections are counted per frame. When the configured number of failures is reached all
	 * remaining results for the frame are rejected. This assumes the results are ordered by the frame.
	 * <p>
	 * Note that this method is to be used to score a set of results that may have been extracted from a larger set
	 * since the number of consecutive failures before each peak are expected to be stored in the origY property. Set
	 * this to zero and the results should be identical to {@link #filterSubset(MemoryPeakResults, int, double[])}.
	 * <p>
	 * The number of failures before each peak is stored in the origX property of the PeakResult.
	 * 
	 * @param results
	 * @param failures
	 *            the number of failures to allow per frame before all peaks are rejected
	 * @param score
	 *            If not null will be populated with the fraction score [ tp, fp, tn, fn, p, n ]
	 * @return the filtered results
	 */
public MemoryPeakResults filterSubset2(MemoryPeakResults results, int failures, double[] score) {
    MemoryPeakResults newResults = new MemoryPeakResults();
    newResults.copySettings(results);
    setup(results);
    int frame = -1;
    int failCount = 0;
    double fp = 0, fn = 0;
    double tp = 0, tn = 0;
    for (PeakResult peak : results.getResults()) {
        if (frame != peak.getFrame()) {
            frame = peak.getFrame();
            failCount = 0;
        }
        failCount += peak.origY;
        // Reject all peaks if we have exceeded the fail count
        final boolean isPositive;
        if (failCount > failures) {
            isPositive = false;
        } else {
            // Otherwise assess the peak
            isPositive = accept(peak);
        }
        if (isPositive) {
            peak.origX = failCount;
            failCount = 0;
            newResults.add(peak);
        } else {
            failCount++;
        }
        if (isPositive) {
            tp += peak.getTruePositiveScore();
            fp += peak.getFalsePositiveScore();
        } else {
            fn += peak.getFalseNegativeScore();
            tn += peak.getTrueNegativeScore();
        }
    }
    end();
    if (score != null && score.length > 5) {
        score[0] = tp;
        score[1] = fp;
        score[2] = tn;
        score[3] = fn;
        score[4] = newResults.size();
        score[5] = results.size() - newResults.size();
    }
    return newResults;
}