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In a quadratic programming problem, we consider a quadratic objective function: $$ Q(x) = \frac)$ (more risk) compared to the lower risk solution to the problem with the 1% restriction. Before we dive into some examples with quadprog, we’ll give a brief overview of the terminology and mechanics of quadratic programs. Here, we’ll work with the quadprog package. There are several packages available to solve quadratic programs in R. Quadratic programs appear in many practical applications, including portfolio optimization and in solving support vector machine (SVM) classification problems. Public toString() Overrides: toString in class this post, we’ll explore a special type of nonlinear constrained optimization problems called quadratic programs. Public Viewer toGUI() Description copied from interface: Visible Create a view of this object Public featureIterator() Iterate over all features with non-zero weight. Public double featureScore( Feature feature) Weight for a feature in the hyperplane. Public void increment( Hyperplane b) Add hyperplane b to this hyperplane. Public boolean hasFeature( Feature feat) Checks the presence of a feature in hyperplaneĭouble delta) Add hyperplane b*delta to this hyperplane. Public void multiply(double factor) Multiply all weights by a factorĭouble delta) Multiply one feature from the hyperplane by delta

Public void increment( Instance instance,ĭouble delta) Add the value of the features in the instance to this hyperplane. Public void setBias(double delta) Set the bias term for the hyperplane to delta

Public void incrementBias(double delta) Increment the bias term for the hyperplane by delta Specified by: getExplanation in interface Classifierĭouble delta) Increment one feature from the hyperplane by delta Public Explanation getExplanation( Instance instance) Description copied from interface: Classifier Return an Explanation for the classification Specified by: explain in interface Classifier

Public explain( Instance instance) Justify inner product of hyperplane and instance weights. Specified by: score in class Binar圜lassifier Public double score( Instance instance) Inner product of hyperplane and instance weights. Increment(instance,delta) or increment(hyperplane,delta), feature This class represents an hyperplane as the zero set of the implicit equation where is a unit normal vector of the plane (linear part. Notice that the dimension of the hyperplane is AmbientDim-1. the dimension of the ambient space, can be a compile time value or Dynamic. Public void startIgnoringWeights() After this call is made, the hyperplane will assume that allįeature weights are one in instances. the scalar type, i.e., the type of the coefficients. Protected transient hyperplaneWeights Constructor Detail X.score(f)*h.featureScore(f) + h.featureScore(BIAS_TERM). Score of a hyperplane h on an instance x is sum_ Public static final Feature BIAS_TERM Weight for an invisible 'bias feature' which is considered to be Methods inherited from class Ĭlone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait Binar圜lassifierĬlassification, getClassifierLearner, setClassifierLearner Set the bias term for the hyperplane to deltaĪfter this call is made, the hyperplane will assume that all Inner product of hyperplane and instance weights. Multiply one feature from the hyperplane by delta Increment the bias term for the hyperplane by delta Increment one feature from the hyperplane by deltaĪdd hyperplane b*delta to this hyperplane.Īdd the value of the features in the instance to this hyperplane. Return an Explanation for the classificationĬhecks the presence of a feature in hyperplane Iterate over all features with non-zero weight. Justify inner product of hyperplane and instance weights. Weight for an invisible 'bias feature' which is considered to beįields inherited from class. Public class Hyperplane extends Binar圜lassifier implements Visible, java.io.SerializableĪuthor: William Cohen See Also: Serialized Form SUMMARY: NESTED | FIELD | CONSTR | METHODĮdu.linearĮdu.圜lassifier. All Implemented Interfaces: Classifier, Visible, java.io.Serializable