List of runAlgorithm() Examples

Examples of runAlgorithm()

image.processing.algorithms.clustering.kmeans.AlgoHierarchicalClustering.runAlgorithm()
Run the algorithm. @param inputFile an input file containing vectors of doubles @param maxDistance the maximum distance allowed for merging two clusters @return a list of Clusters @throws IOException exception if error while reading the file
image.processing.algorithms.clustering.kmeans.AlgoKMeansCol.runAlgorithm()
Run the K-Means algorithm @param inputFile an input file path containing a list of vectors of double values @param k the parameter k @return a list of clusters (some of them may be empty) @throws IOException exception if an error while writing the file occurs
image.processing.algorithms.clustering.kmeans.AlgoKMeansFeat.runAlgorithm()
Run the K-Means algorithm @param inputFile an input file path containing a list of vectors of double values @param k the parameter k @return a list of clusters (some of them may be empty) @throws IOException exception if an error while writing the file occurs

Examples of image.processing.algorithms.clustering.kmeans.AlgoHierarchicalClustering.runAlgorithm()

    String output = ".//output_colour.txt";
    int maxdistance = _k;
    
    // Apply the algorithm
    AlgoHierarchicalClustering algo = new AlgoHierarchicalClustering();  // we request 3 clusters
    algo.runAlgorithm(input, maxdistance);
    algo.printStatistics();
    algo.saveToColFile(output);


  }

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Examples of image.processing.algorithms.clustering.kmeans.AlgoHierarchicalClustering.runAlgorithm()

    String output = ".//output_feature.txt";
    int maxdistance = _k;
    
    // Apply the algorithm
    AlgoHierarchicalClustering algo = new AlgoHierarchicalClustering();  // we request 3 clusters
    algo.runAlgorithm(input, maxdistance);
    algo.printStatistics();
    algo.saveToFeatFile(output);


  }

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Examples of image.processing.algorithms.clustering.kmeans.AlgoHierarchicalClustering.runAlgorithm()

    String output = ".//output_combined.txt";
    double maxdistance = _k;
    
    // Apply the algorithm
    AlgoHierarchicalClustering algo = new AlgoHierarchicalClustering();  // we request 3 clusters
    algo.runAlgorithm(input, maxdistance);
    algo.printStatistics();
    algo.saveToCombFile(output);


  }

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Examples of image.processing.algorithms.clustering.kmeans.AlgoKMeansCol.runAlgorithm()

    String output = ".//output_colour.txt";
    int k = _k;
                
    // Apply the algorithm for colour
    AlgoKMeansCol algoKMeans = new AlgoKMeansCol();
    algoKMeans.runAlgorithm(input, k);
    algoKMeans.printStatistics();
    algoKMeans.saveToFile(output);
                
                //Reenable button
                Interface.clusterButton.setEnabled(true);

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Examples of image.processing.algorithms.clustering.kmeans.AlgoKMeansFeat.runAlgorithm()

    String output = ".//output_feature.txt";
    int k = _k;
                
    // Apply the algorithm for colour
    AlgoKMeansFeat algoKMeans = new AlgoKMeansFeat();
    algoKMeans.runAlgorithm(input, 3);
    algoKMeans.printStatistics();
    algoKMeans.saveToFile(output);
                
                //Reenable button
                Interface.clusterButton.setEnabled(true);

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