/** * Creates a new instance of AbstractLogisticRegression * @param regularization * L2 ridge regularization term, must be nonnegative, a value of zero is * equivalent to unregularized regression. * @param tolerance * Tolerance change in weights before stopping * @param maxIterations * Maximum number of iterations before stopping */ public AbstractLogisticRegression( double regularization, double tolerance, int maxIterations ) { super( maxIterations ); this.setRegularization(regularization); this.setTolerance( tolerance ); }
@Override public AbstractLogisticRegression<InputType,OutputType,FunctionType> clone() { @SuppressWarnings("unchecked") AbstractLogisticRegression<InputType,OutputType,FunctionType> clone = (AbstractLogisticRegression<InputType,OutputType,FunctionType>) super.clone(); clone.setObjectToOptimize( ObjectUtil.cloneSmart( this.getObjectToOptimize() ) ); clone.result = ObjectUtil.cloneSmart( this.getResult() ); return clone; }
@Override public AbstractLogisticRegression<InputType,OutputType,FunctionType> clone() { @SuppressWarnings("unchecked") AbstractLogisticRegression<InputType,OutputType,FunctionType> clone = (AbstractLogisticRegression<InputType,OutputType,FunctionType>) super.clone(); clone.setObjectToOptimize( ObjectUtil.cloneSmart( this.getObjectToOptimize() ) ); clone.result = ObjectUtil.cloneSmart( this.getResult() ); return clone; }
@Override public AbstractLogisticRegression<InputType,OutputType,FunctionType> clone() { @SuppressWarnings("unchecked") AbstractLogisticRegression<InputType,OutputType,FunctionType> clone = (AbstractLogisticRegression<InputType,OutputType,FunctionType>) super.clone(); clone.setObjectToOptimize( ObjectUtil.cloneSmart( this.getObjectToOptimize() ) ); clone.result = ObjectUtil.cloneSmart( this.getResult() ); return clone; }
/** * Creates a new instance of AbstractLogisticRegression * @param regularization * L2 ridge regularization term, must be nonnegative, a value of zero is * equivalent to unregularized regression. * @param tolerance * Tolerance change in weights before stopping * @param maxIterations * Maximum number of iterations before stopping */ public AbstractLogisticRegression( double regularization, double tolerance, int maxIterations ) { super( maxIterations ); this.setRegularization(regularization); this.setTolerance( tolerance ); }
/** * Creates a new instance of AbstractLogisticRegression * @param regularization * L2 ridge regularization term, must be nonnegative, a value of zero is * equivalent to unregularized regression. * @param tolerance * Tolerance change in weights before stopping * @param maxIterations * Maximum number of iterations before stopping */ public AbstractLogisticRegression( double regularization, double tolerance, int maxIterations ) { super( maxIterations ); this.setRegularization(regularization); this.setTolerance( tolerance ); }