| Modifier and Type | Method and Description | 
|---|---|
| abstract Example | ClusterExample. getExample() | 
| Modifier and Type | Method and Description | 
|---|---|
| Example | SimpleDataset. getExample(int exampleIndex)Return the example stored in the  exampleIndexposition | 
| Example | SimpleDataset. getNextExample() | 
| Example | Dataset. getNextExample()Returns the next  n Examples stored in the Dataset or a fewer number 
 ifnexamples are not available. | 
| Example | SimpleDataset. getRandExample() | 
| Example | Dataset. getRandExample() | 
| Example | DatasetReader. readNextExample()Returns the next example | 
| Modifier and Type | Method and Description | 
|---|---|
| List<Example> | SimpleDataset. getExamples() | 
| List<Example> | Dataset. getExamples()Returns an array containing all the stored examples | 
| List<Example> | SimpleDataset. getNextExamples(int n) | 
| List<Example> | Dataset. getNextExamples(int n)Returns the next  Examplestored in the Dataset | 
| List<Example> | SimpleDataset. getRandExamples(int k) | 
| List<Example> | Dataset. getRandExamples(int k) | 
| Modifier and Type | Method and Description | 
|---|---|
| void | SimpleDataset. addExample(Example example)Add an example to the dataset | 
| void | Dataset. addExample(Example e) | 
| Modifier and Type | Class and Description | 
|---|---|
| class  | ExamplePairIt is the instance of an example pair, i.e. | 
| class  | SimpleExampleAn  Examplecomposed by a set ofRepresentations. | 
| Modifier and Type | Method and Description | 
|---|---|
| Example | Example. clone() | 
| Example | ExamplePair. getLeftExample()Returns the left example in the pair | 
| Example | ExamplePair. getRightExample()Returns the right example in the pair | 
| static Example | ExampleFactory. parseExample(String exampleDescription) | 
| Constructor and Description | 
|---|
| ExamplePair(Example left,
           Example right) | 
| Modifier and Type | Method and Description | 
|---|---|
| Example | EnrichedTreeNode. getExample() | 
| Modifier and Type | Method and Description | 
|---|---|
| void | EnrichedTreeNode. setExample(Example example,
          float weight) | 
| Modifier and Type | Method and Description | 
|---|---|
| float | Kernel. innerProduct(Example exA,
            Example exB)Returns the kernel similarity between the given examples. | 
| protected abstract float | Kernel. kernelComputation(Example exA,
                 Example exB)Returns the kernel similarity between the given examples. | 
| protected float | DirectKernel. kernelComputation(Example exA,
                 Example exB) | 
| float | Kernel. squaredNorm(Example example)Returns the squared norm of the given example in the RKHS defined by this kernel | 
| float | Kernel. squaredNormOfTheDifference(Example exA,
                          Example exB)Returns the squared norm of the difference between the given examples in the RKHS. | 
| Modifier and Type | Method and Description | 
|---|---|
| Float | KernelCache. getKernelValue(Example exA,
              Example exB)Retrieves in the cache the kernel operation between two examples | 
| Float | SquaredNormCache. getSquaredNorm(Example example)Returns a previously stored norm of a given example | 
| Float | FixIndexSquaredNormCache. getSquaredNorm(Example example) | 
| protected abstract Float | KernelCache. getStoredKernelValue(Example exA,
                    Example exB)Retrieves in the cache the kernel operation between two examples | 
| protected Float | FixIndexKernelCache. getStoredKernelValue(Example exA,
                    Example exB) | 
| abstract void | KernelCache. setKernelValue(Example exA,
              Example exB,
              float value)Stores a kernel computation in cache | 
| void | FixIndexKernelCache. setKernelValue(Example exA,
              Example exB,
              float value) | 
| void | SquaredNormCache. setSquaredNormVaue(Example example,
                  float squaredNorm)Stores a squared norm in the cache | 
| void | FixIndexSquaredNormCache. setSquaredNormVaue(Example example,
                  float squaredNorm) | 
| Modifier and Type | Method and Description | 
|---|---|
| protected float | PreferenceKernel. kernelComputation(Example exA,
                 Example exB) | 
| Modifier and Type | Method and Description | 
|---|---|
| protected float | RbfKernel. kernelComputation(Example exA,
                 Example exB) | 
| protected float | PolynomialKernel. kernelComputation(Example exA,
                 Example exB) | 
| protected float | NormalizationKernel. kernelComputation(Example exA,
                 Example exB) | 
| protected float | LinearKernelCombination. kernelComputation(Example exA,
                 Example exB) | 
| float | NormalizationKernel. squaredNorm(Example example) | 
| Modifier and Type | Method and Description | 
|---|---|
| protected float | PassiveAggressive. computeWeight(Example example,
             float lossValue,
             float exampleSquaredNorm,
             float aggressiveness) | 
| Prediction | OnlineLearningAlgorithm. learn(Example example)Applies the learning process on a single example, updating its current model | 
| Modifier and Type | Method and Description | 
|---|---|
| Prediction | BudgetedLearningAlgorithm. learn(Example example) | 
| Prediction | Stoptron. predictAndLearnWithFullBudget(Example example) | 
| Prediction | RandomizedBudgetPerceptron. predictAndLearnWithFullBudget(Example example) | 
| abstract Prediction | BudgetedLearningAlgorithm. predictAndLearnWithFullBudget(Example example)Learns from a single example applying a specific policy that must be adopted when the budget is reached | 
| Modifier and Type | Field and Description | 
|---|---|
| protected Example[] | LibSvmSolver. examplesThe input examples | 
| Modifier and Type | Method and Description | 
|---|---|
| Example[] | SvmSolution. getSupporVectors() | 
| Modifier and Type | Method and Description | 
|---|---|
| protected float | LibSvmSolver. kernel(Example exA,
      Example exB)This function embeds the call to the kernel function | 
| protected void | SvmSolution. setSupporVectors(Example[] supporVectors) | 
| protected void | LibSvmSolver. swap(Example[] array,
    int i,
    int j) | 
| Modifier and Type | Method and Description | 
|---|---|
| BinaryMarginClassifierOutput | PassiveAggressiveClassification. learn(Example example) | 
| Modifier and Type | Method and Description | 
|---|---|
| BinaryMarginClassifierOutput | Perceptron. learn(Example example) | 
| Modifier and Type | Method and Description | 
|---|---|
| Example | KernelBasedKMeansExample. getExample() | 
| Modifier and Type | Method and Description | 
|---|---|
| float | KernelBasedKMeansEngine. calculateDistance(Example example,
                 Cluster cluster)Estimate the distance of an example from the centroid | 
| float | KernelBasedKMeansEngine. evaluateKernel(Example e1,
              Example e2) | 
| void | KernelBasedKMeansExample. setExample(Example example) | 
| Constructor and Description | 
|---|
| KernelBasedKMeansExample(Example e,
                        float dist) | 
| Modifier and Type | Method and Description | 
|---|---|
| UnivariateRegressionOutput | PassiveAggressiveRegression. learn(Example example) | 
| Modifier and Type | Method and Description | 
|---|---|
| Prediction | PredictionFunction. predict(Example example) | 
| Modifier and Type | Method and Description | 
|---|---|
| ClassificationOutput | Classifier. predict(Example example) | 
| BinaryMarginClassifierOutput | BinaryLinearClassifier. predict(Example example) | 
| BinaryMarginClassifierOutput | BinaryKernelMachineClassifier. predict(Example example)Classifies an example applying the following formula:
 y(x) = \sum_{i \in SV}\alpha_i k(x_i, x) + b | 
| abstract BinaryMarginClassifierOutput | BinaryClassifier. predict(Example example) | 
| Modifier and Type | Method and Description | 
|---|---|
| OneVsOneClassificationOutput | OneVsOneClassifier. predict(Example example) | 
| OneVsAllClassificationOutput | OneVsAllClassifier. predict(Example example) | 
| Modifier and Type | Method and Description | 
|---|---|
| Example | SupportVector. getInstance() | 
| Modifier and Type | Method and Description | 
|---|---|
| abstract void | BinaryModel. addExample(float weight,
          Example example)Adds an example to the model with a given weight. | 
| void | BinaryLinearModel. addExample(float weight,
          Example example) | 
| void | BinaryKernelMachineModel. addExample(float weight,
          Example example) | 
| abstract float | BinaryModel. getSquaredNorm(Example example)Computes the squared norm of a given example according to the space in which the model
 is operating | 
| float | BinaryLinearModel. getSquaredNorm(Example example) | 
| float | BinaryKernelMachineModel. getSquaredNorm(Example example) | 
| SupportVector | BinaryKernelMachineModel. getSupportVector(Example instance)Returns the support vector associated to a given instance, null the instance
 is not a support vector in this model | 
| boolean | KernelMachineModel. isSupportVector(Example instance)Returns whether  instanceis a support vector in this model | 
| boolean | BinaryKernelMachineModel. isSupportVector(Example instance) | 
| void | SupportVector. setInstance(Example instance) | 
| Constructor and Description | 
|---|
| SupportVector(float weight,
             Example instance) | 
| Modifier and Type | Method and Description | 
|---|---|
| abstract UnivariateRegressionOutput | UnivariateRegressionFunction. predict(Example example) | 
| UnivariateRegressionOutput | UnivariateLinearRegressionFunction. predict(Example example) | 
| UnivariateRegressionOutput | UnivariateKernelMachineRegressionFunction. predict(Example example) | 
| RegressionOutput | RegressionFunction. predict(Example example) | 
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