Examples of org.apache.commons.math3.ml.clustering.FuzzyKMeansClusterer

Package org.apache.commons.math3.ml.clustering

Examples of org.apache.commons.math3.ml.clustering.FuzzyKMeansClusterer

org.apache.commons.math3.exception.NotStrictlyPositiveException
Fuzzy K-Means clustering algorithm.
The Fuzzy K-Means algorithm is a variation of the classical K-Means algorithm, with the major difference that a single data point is not uniquely assigned to a single cluster. Instead, each point i has a set of weights u_ij which indicate the degree of membership to the cluster j.
The algorithm then tries to minimize the objective function:
```
 J = ∑_i=1..C∑_k=1..N u_ik^md_ik² 
```
with d_ik being the distance between data point i and the cluster center k.
The algorithm requires two parameters:
- k: the number of clusters
- fuzziness: determines the level of cluster fuzziness, larger values lead to fuzzier clusters
Additional, optional parameters:
- maxIterations: the maximum number of iterations
- epsilon: the convergence criteria, default is 1e-3
The fuzzy variant of the K-Means algorithm is more robust with regard to the selection of the initial cluster centers. @param < T> type of the points to cluster @since 3.3

     * #optimize(int,MultivariateVectorFunction,double[],double[],double[]) optimize} has not been
     * called.
     */
    public PointVectorValuePair[] getOptima() {
        if (optima == null) {
            throw new MathIllegalStateException(LocalizedFormats.NO_OPTIMUM_COMPUTED_YET);
        }
        return optima.clone();
    }

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     * @throws MathIllegalStateException if {@link #optimize(OptimizationData[])
     * optimize} has not been called.
     */
    public UnivariatePointValuePair[] getOptima() {
        if (optima == null) {
            throw new MathIllegalStateException(LocalizedFormats.NO_OPTIMUM_COMPUTED_YET);
        }
        return optima.clone();
    }

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                searchIntervalIndex = i;
                continue;
            }
        }
        if (maxEvalIndex == -1) {
            throw new MathIllegalStateException();
        }
        if (searchIntervalIndex == -1) {
            throw new MathIllegalStateException();
        }


        RuntimeException lastException = null;
        optima = new UnivariatePointValuePair[starts];
        totalEvaluations = 0;

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     * Throws IllegalStateException if n > 0.
     * @throws MathIllegalStateException if data has been added
     */
    private void checkEmpty() throws MathIllegalStateException {
        if (n > 0) {
            throw new MathIllegalStateException(
                LocalizedFormats.VALUES_ADDED_BEFORE_CONFIGURING_STATISTIC, n);
        }
    }

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     * #optimize(int,MultivariateFunction,GoalType,double[]) optimize}
     * has not been called.
     */
    public PointValuePair[] getOptima() {
        if (optima == null) {
            throw new MathIllegalStateException(LocalizedFormats.NO_OPTIMUM_COMPUTED_YET);
        }
        return optima.clone();
    }

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     * @throws MathUnsupportedOperationException in all cases
     */
    @Override
    public int getN()
        throws MathUnsupportedOperationException {
        throw new MathUnsupportedOperationException();
    }

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     * {@link #optimize(OptimizationData[]) optimize} method.
     */
    private void checkParameters() {
        if (getLowerBound() != null ||
            getUpperBound() != null) {
            throw new MathUnsupportedOperationException(LocalizedFormats.CONSTRAINT);
        }
    }

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        if (optimizer == null ||
            generator == null) {
            throw new NullArgumentException();
        }
        if (starts < 1) {
            throw new NotStrictlyPositiveException(starts);
        }


        this.optimizer = optimizer;
        this.starts = starts;
        this.generator = generator;

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                                   double inverseCumAccuracy)
        throws NotStrictlyPositiveException {
        super(rng);


        if (mean <= 0) {
            throw new NotStrictlyPositiveException(LocalizedFormats.MEAN, mean);
        }
        this.mean = mean;
        logMean = FastMath.log(mean);
        solverAbsoluteAccuracy = inverseCumAccuracy;
    }

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                                         final int starts,
                                         final RandomGenerator generator) {
        super(optimizer.getConvergenceChecker());


        if (starts < 1) {
            throw new NotStrictlyPositiveException(starts);
        }


        this.optimizer = optimizer;
        this.starts = starts;
        this.generator = generator;

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Related Classes of org.apache.commons.math3.ml.clustering.FuzzyKMeansClusterer

org.apache.commons.math3.analysis.differentiation.DerivativeStructure

org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction

org.apache.commons.math3.analysis.function.HarmonicOscillator

org.apache.commons.math3.analysis.function.Sin

org.apache.commons.math3.analysis.function.Sinc

org.apache.commons.math3.analysis.MultivariateFunction

org.apache.commons.math3.analysis.QuinticFunction

org.apache.commons.math3.analysis.solvers.BrentSolver

org.apache.commons.math3.analysis.UnivariateFunction

org.apache.commons.math3.complex.Complex

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