/** * Setter for regularization * @param regularization * L2 ridge regularization term, must be nonnegative, a value of zero is * equivalent to unregularized regression. */ public void setRegularization( double regularization) { ArgumentChecker.assertIsNonNegative("regularization", regularization); this.regularization = regularization; }
/** * Setter for variance * @param variance * Second central moment (square of standard deviation) of the distribution */ public void setVariance( final double variance ) { ArgumentChecker.assertIsPositive("variance", variance); this.variance = variance; }
/** * Setter for treatmentCount * @param treatmentCount * Number of comparisons made */ public void setTreatmentCount( final int treatmentCount) { ArgumentChecker.assertIsInRangeInclusive( "treatmentCount", treatmentCount, 2.0, Double.POSITIVE_INFINITY ); this.treatmentCount = treatmentCount; }
/** * Sets the cluster creator. * * @param creator The creator for clusters. */ public void setCreator( IncrementalClusterCreator<ClusterType, DataType> creator) { ArgumentChecker.assertIsNotNull("creator", creator); this.creator = creator; }
/** * Sets the cluster creator. * * @param creator The creator for clusters. */ public void setCreator( IncrementalClusterCreator<ClusterType, DataType> creator) { ArgumentChecker.assertIsNotNull("creator", creator); this.creator = creator; }
/** * Sets the power used for the distance. * * @param power * The power used for the distance. Must be positive. */ public void setPower( final double power) { ArgumentChecker.assertIsPositive("power", power); this.power = power; }
/** * Setter for tolerance * @param tolerance * Tolerance change in weights before stopping, must be nonnegative. */ public void setTolerance( double tolerance ) { ArgumentChecker.assertIsNonNegative("tolerance", tolerance); this.tolerance = tolerance; }
@Override public void setRandom(Random random) { ArgumentChecker.assertIsNotNull("random", random); this.random = random; }
/** * Setter for treatmentCount * @param treatmentCount * Number of comparisons made */ public void setTreatmentCount( final int treatmentCount) { ArgumentChecker.assertIsInRangeInclusive( "treatmentCount", treatmentCount, 2.0, Double.POSITIVE_INFINITY ); this.treatmentCount = treatmentCount; }
/** * Setter for degreesOfFreedom * @param degreesOfFreedom * Number of subjects in each treatment minus one. */ public void setDegreesOfFreedom( final double degreesOfFreedom) { ArgumentChecker.assertIsPositive("degreesOfFreedom", degreesOfFreedom); this.degreesOfFreedom = degreesOfFreedom; }
/** * Setter for regularization * @param regularization * L2 ridge regularization term, must be nonnegative, a value of zero is * equivalent to unregularized regression. */ public void setRegularization( double regularization) { ArgumentChecker.assertIsNonNegative("regularization", regularization); this.regularization = regularization; }
/** * Sets the cluster creator used to create the initial clusters. * * @param creator * The new cluster creator. */ public void setCreator( ClusterCreator<ClusterType, DataType> creator) { ArgumentChecker.assertIsNotNull("creator", creator); this.creator = creator; }
/** * Sets the leakage, which is the multiplier for the value when it is * less than zero. It is usually a small value. * * @param leakage * The leakage amount. Must be between 0 and 1. */ public void setLeakage( final double leakage) { ArgumentChecker.assertIsInRangeInclusive("leakage", leakage, 0.0, 1.0); this.leakage = leakage; }
/** * Sets the radius parameter. * * @param radius * The radius parameter. Must be positive. */ public void setRadius( final double radius) { ArgumentChecker.assertIsPositive("radius", radius); this.radius = radius; }
/** * Sets the value for the parameter controlling the bias regularization. * * @param biasRegularization * The regularization term for the bias. Cannot be negative. */ public void setBiasRegularization( final double biasRegularization) { ArgumentChecker.assertIsNonNegative("biasRegularization", biasRegularization); this.biasRegularization = biasRegularization; }
/** * Sets the cluster creator used to create the initial clusters. * * @param creator * The new cluster creator. */ public void setCreator( ClusterCreator<ClusterType, DataType> creator) { ArgumentChecker.assertIsNotNull("creator", creator); this.creator = creator; }
/** * Setter for treatmentCount * @param treatmentCount * Number of comparisons made */ public void setTreatmentCount( final int treatmentCount) { ArgumentChecker.assertIsInRangeInclusive( "treatmentCount", treatmentCount, 2.0, Double.POSITIVE_INFINITY ); this.treatmentCount = treatmentCount; }
/** * Sets the power used for the distance. * * @param power * The power used for the distance. Must be positive. */ public void setPower( final double power) { ArgumentChecker.assertIsPositive("power", power); this.power = power; }
@Override final public boolean isSymmetric( final double effectiveZero) { ArgumentChecker.assertIsNonNegative("effectiveZero", effectiveZero); return true; }
@Override public void setRandom(Random random) { ArgumentChecker.assertIsNotNull("random", random); this.random = random; }