@Override public LogisticRegression clone() { LogisticRegression clone = (LogisticRegression) super.clone(); clone.setObjectToOptimize( ObjectUtil.cloneSafe( this.getObjectToOptimize() ) ); clone.setResult( ObjectUtil.cloneSafe( this.getResult() ) ); return clone; }
/** * Creates a new instance of LogisticRegression * @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 LogisticRegression( double regularization, double tolerance, int maxIterations ) { super( maxIterations ); this.setRegularization(regularization); this.setTolerance( tolerance ); }
int N = this.data.size(); if( this.getObjectToOptimize() == null ) this.setObjectToOptimize( new Function( M ) ); this.setResult( this.getObjectToOptimize().clone() );
LogisticRegression.Function f = this.getResult(); for( InputOutputPair<? extends Vectorizable,Double> sample : this.data ) return delta > this.getTolerance();
int N = this.data.size(); if( this.getObjectToOptimize() == null ) this.setObjectToOptimize( new Function( M ) ); this.setResult( this.getObjectToOptimize().clone() );
LogisticRegression.Function f = this.getResult(); for( InputOutputPair<? extends Vectorizable,Double> sample : this.data ) return delta > this.getTolerance();
@Override public LogisticRegression clone() { LogisticRegression clone = (LogisticRegression) super.clone(); clone.setObjectToOptimize( ObjectUtil.cloneSafe( this.getObjectToOptimize() ) ); clone.setResult( ObjectUtil.cloneSafe( this.getResult() ) ); return clone; }
int N = this.data.size(); if( this.getObjectToOptimize() == null ) this.setObjectToOptimize( new Function( M ) ); this.setResult( this.getObjectToOptimize().clone() );
/** * Creates a new instance of LogisticRegression * @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 LogisticRegression( double regularization, double tolerance, int maxIterations ) { super( maxIterations ); this.setRegularization(regularization); this.setTolerance( tolerance ); }
LogisticRegression.Function f = this.getResult(); for( InputOutputPair<? extends Vectorizable,Double> sample : this.data ) return delta > this.getTolerance();
@Override public LogisticRegression clone() { LogisticRegression clone = (LogisticRegression) super.clone(); clone.setObjectToOptimize( ObjectUtil.cloneSafe( this.getObjectToOptimize() ) ); clone.setResult( ObjectUtil.cloneSafe( this.getResult() ) ); return clone; }
/** * Creates a new instance of LogisticRegression * @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 LogisticRegression( double regularization, double tolerance, int maxIterations ) { super( maxIterations ); this.setRegularization(regularization); this.setTolerance( tolerance ); }