/** * 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 tolerance * @param tolerance * Tolerance change in weights before stopping, must be nonnegative. */ public void setTolerance( double tolerance ) { ArgumentChecker.assertIsNonNegative("tolerance", tolerance); this.tolerance = tolerance; }
/** * 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 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; }
@Override final public boolean isSymmetric( final double effectiveZero) { ArgumentChecker.assertIsNonNegative("effectiveZero", effectiveZero); return true; }
/** * 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; }
/** * Setter for tolerance * @param tolerance * Tolerance before stopping, must be greater than or equal to 0 */ public void setTolerance( double tolerance) { ArgumentChecker.assertIsNonNegative("tolerance", tolerance); this.tolerance = tolerance; }
/** * 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 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; }
@Override final public boolean isSymmetric( final double effectiveZero) { ArgumentChecker.assertIsNonNegative("effectiveZero", effectiveZero); return true; }
/** * 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 tolerance * @param tolerance * Tolerance before stopping, must be greater than or equal to 0 */ public void setTolerance( double tolerance) { ArgumentChecker.assertIsNonNegative("tolerance", tolerance); this.tolerance = tolerance; }
/** * Setter for tolerance * @param tolerance * Tolerance before stopping, must be greater than or equal to 0 */ public void setTolerance( double tolerance) { ArgumentChecker.assertIsNonNegative("tolerance", tolerance); this.tolerance = tolerance; }
/** * Sets the value for the parameter controlling the factor matrix * regularization. * * @param factorRegularization * The regularization term for the factors. Cannot be negative. */ public void setFactorRegularization( final double factorRegularization) { ArgumentChecker.assertIsNonNegative("factorRegularization", factorRegularization); this.factorRegularization = factorRegularization; }
/** * 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; }
/** * 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; }
@Override public void add( final MemberType member, final double weight) { ArgumentChecker.assertIsNonNegative("weight", weight); super.add(member, weight); }
@Override public void add( final MemberType member, final double weight) { ArgumentChecker.assertIsNonNegative("weight", weight); super.add(member, weight); }
@Override public double getFScore( final double beta) { ArgumentChecker.assertIsNonNegative("beta", beta); final double betaSquared = beta * beta; final double precision = this.getPrecision(); final double recall = this.getRecall(); return (1.0 + betaSquared) * (precision * recall) / (betaSquared * precision + recall); }
@Override public double getFScore( final double beta) { ArgumentChecker.assertIsNonNegative("beta", beta); final double betaSquared = beta * beta; final double precision = this.getPrecision(); final double recall = this.getRecall(); return (1.0 + betaSquared) * (precision * recall) / (betaSquared * precision + recall); }