Package gov.nih.mipav.model.algorithms

Class SVM

java.lang.Object
java.lang.Thread
gov.nih.mipav.model.algorithms.AlgorithmBase
gov.nih.mipav.model.algorithms.SVM
All Implemented Interfaces:
ActionListener, WindowListener, Runnable, EventListener
public class SVM extends AlgorithmBase

  • Field Details

  • Constructor Details

    • SVM

      public SVM()

  • Method Details

    • learn_linear_SVM_on_double_data

      public void learn_linear_SVM_on_double_data()

    • vl_svmdataset_new

      public SVM.VlSvmDataset vl_svmdataset_new(double[] data, int dimension, int numData)
      Parameters:
      data - pointer to the data.
      dimension - the dimension of a data vector.
      numData - number of wrapped data vectors.
      dataType - of data (@c float and @c double supported).
      Returns:
      new object. The function allocates and returns a new SVM dataset object wrapping the data pointed by @a data. Note that no copy is made of data, so the caller should keep the data allocated as the object exists.

    • vl_svmdataset_new

      public SVM.VlSvmDataset vl_svmdataset_new(double[] data, int dimension, int numData, SVM.VlHomogeneousKernelMap hom)

    • vl_svm_new_with_dataset

      public SVM.VlSvm vl_svm_new_with_dataset(SVM.VlSvmSolverType solver, SVM.VlSvmDataset dataset, double[] labels, double lambda)
      Parameters:
      solver - type of SMV solver.
      dataset - SVM dataset object
      labels - training samples labels.
      lambda - regularizer parameter.
      Returns:
      the new object.

    • vl_homogeneouskernelmap_get_dimension

      public int vl_homogeneouskernelmap_get_dimension(SVM.VlHomogeneousKernelMap self)
      Parameters:
      self - object.
      Returns:
      the map dimension (2 @c order +1).

    • vl_svmdataset_get_dimension

      public int vl_svmdataset_get_dimension(SVM.VlSvmDataset self)
      Parameters:
      self - object.
      Returns:
      dimension of the wrapped data.

    • vl_svmdataset_get_num_data

      public int vl_svmdataset_get_num_data(SVM.VlSvmDataset self)
      Parameters:
      self - object.
      Returns:
      number of wrapped data elements.

    • vl_svmdataset_get_inner_product_function

      public int vl_svmdataset_get_inner_product_function(SVM.VlSvmDataset self)
      Parameters:
      self - object.
      Returns:
      a pointer to the inner product function to use with this data.

    • vl_svmdataset_get_accumulate_function

      public int vl_svmdataset_get_accumulate_function(SVM.VlSvmDataset self)
      Parameters:
      self - object.
      Returns:
      a pointer to the accumulate function to use with this data.

    • vl_svm_new_with_abstract_data

      public SVM.VlSvm vl_svm_new_with_abstract_data(SVM.VlSvmSolverType solver, SVM.VlSvmDataset data, int dimension, int numData, double[] labels, double lambda)
      Parameters:
      solver - type of SMV solver.
      data - pointer to the data.
      dimension - dimension of the SVM model.
      numData - num training samples.
      labels - training samples labels.
      lambda - regularizer parameter.
      Returns:
      the new object. After calling this function, ::vl_svm_set_data_functions *must* be used to setup suitable callbacks for the inner product and accumulation operations (@see svm-data-abstraction).

    • vl_svm_set_data_functions

      public void vl_svm_set_data_functions(SVM.VlSvm self, int inner, int acc)
      Parameters:
      self - object.
      inner - inner product function.
      acc - accumulate function. See @ref svm-data-abstraction.

    • vl_svm_train

      public void vl_svm_train(SVM.VlSvm self)
      Parameters:
      self - object. The data on which the SVM operates is passed upon the cration of the ::VlSvm object. This function runs a solver to learn a corresponding model. See @ref svm-starting.

    • _vl_svm_sdca_train

      public void _vl_svm_sdca_train(SVM.VlSvm self)

    • vl_rand_permute_indexes

      public void vl_rand_permute_indexes(Random self, int[] array, int size)
      Parameters:
      self - random number generator.
      array - array of indexes.
      size - number of element in the array. The function uses *Algorithm P*, also known as *Knuth shuffle*.

    • accumulateFunction

      public void accumulateFunction(int acc, SVM.VlSvmDataset self, int element, double[] model, double multiplier)

    • vl_svmdataset_accumulate

      public void vl_svmdataset_accumulate(SVM.VlSvmDataset self, int element, double[] model, double multiplier)

    • vl_svmdataset_accumulate_hom

      public void vl_svmdataset_accumulate_hom(SVM.VlSvmDataset self, int element, double[] model, double multiplier)

    • vl_homogeneouskernelmap_evaluate

      public void vl_homogeneouskernelmap_evaluate(SVM.VlHomogeneousKernelMap self, double[] destination, int stride, double x)

    • frexp

      public double frexp(double x, int[] exponent)

    • inner_productFunction

      public double inner_productFunction(int inp, SVM.VlSvmDataset self, int element, double[] model)

    • vl_svmdataset_inner_product

      public double vl_svmdataset_inner_product(SVM.VlSvmDataset self, int element, double[] model)

    • vl_svmdataset_inner_product_hom

      public double vl_svmdataset_inner_product_hom(SVM.VlSvmDataset self, int element, double[] model)

    • dca_updateFunction

      public double dca_updateFunction(int update, double alpha, double inner, double norm2, double label)

    • vl_svm_hinge_dca_update

      public double vl_svm_hinge_dca_update(double alpha, double inner, double norm2, double label)

    • vl_svm_hinge2_dca_update

      public double vl_svm_hinge2_dca_update(double alpha, double inner, double norm2, double label)

    • vl_svm_l1_dca_update

      public double vl_svm_l1_dca_update(double alpha, double inner, double norm2, double label)

    • vl_svm_l2_dca_update

      public double vl_svm_l2_dca_update(double alpha, double inner, double norm2, double label)

    • vl_svm_logistic_dca_update

      public double vl_svm_logistic_dca_update(double alpha, double inner, double norm2, double label)

    • _vl_svm_update_statistics

      public void _vl_svm_update_statistics(SVM.VlSvm self)
      Parameters:
      self - object.

    • lossFunction

      public double lossFunction(int loss, double inner, double label)

    • vl_svm_hinge_loss

      public double vl_svm_hinge_loss(double inner, double label)

    • vl_svm_hinge2_loss

      public double vl_svm_hinge2_loss(double inner, double label)

    • vl_svm_l1_loss

      public double vl_svm_l1_loss(double inner, double label)

    • vl_svm_l2_loss

      public double vl_svm_l2_loss(double inner, double label)

    • vl_svm_logistic_loss

      public double vl_svm_logistic_loss(double inner, double label)

    • conjugate_lossFunction

      public double conjugate_lossFunction(int loss, double u, double label)

    • vl_svm_hinge_conjugate_loss

      public double vl_svm_hinge_conjugate_loss(double u, double label)
      Parameters:
      u - dual variable.
      label - label value.
      Returns:
      conjugate loss.

    • vl_svm_hinge2_conjugate_loss

      public double vl_svm_hinge2_conjugate_loss(double u, double label)

    • vl_svm_l1_conjugate_loss

      public double vl_svm_l1_conjugate_loss(double u, double label)

    • vl_svm_l2_conjugate_loss

      public double vl_svm_l2_conjugate_loss(double u, double label)

    • xlogx

      public double xlogx(double x)

    • vl_svm_logistic_conjugate_loss

      public double vl_svm_logistic_conjugate_loss(double u, double label)

    • _vl_svm_sgd_train

      void _vl_svm_sgd_train(SVM.VlSvm self)

    • loss_derivativeFunction

      public double loss_derivativeFunction(int loss, double inner, double label)

    • vl_svm_hinge_loss_derivative

      public double vl_svm_hinge_loss_derivative(double inner, double label)

    • vl_svm_hinge2_loss_derivative

      public double vl_svm_hinge2_loss_derivative(double inner, double label)

    • vl_svm_l1_loss_derivative

      public double vl_svm_l1_loss_derivative(double inner, double label)

    • vl_svm_l2_loss_derivative

      public double vl_svm_l2_loss_derivative(double inner, double label)

    • vl_svm_logistic_loss_derivative

      public double vl_svm_logistic_loss_derivative(double inner, double label)

    • _vl_svm_evaluate

      public void _vl_svm_evaluate(SVM.VlSvm self)

    • vl_svm_get_model

      public double[] vl_svm_get_model(SVM.VlSvm self)
      Parameters:
      self - object.
      Returns:
      model. This is the weight vector $\bw$.

    • vl_svm_get_bias

      public double vl_svm_get_bias(SVM.VlSvm self)
      Parameters:
      self - object.
      Returns:
      bias $b$. The value of the bias returned already include the effect of bias mutliplier.

    • vl_svm_delete

      public void vl_svm_delete(SVM.VlSvm self)
      Parameters:
      self - object.

    • vl_svmdataset_delete

      void vl_svmdataset_delete(SVM.VlSvmDataset self)
      Parameters:
      self - object to delete. The function frees the resources allocated by ::vl_svmdataset_new(). Notice that the wrapped data will *not* be freed as it is not owned by the object.

    • Dan_Ventura_sgd_linear_example

      public void Dan_Ventura_sgd_linear_example()

    • Dan_Ventura_sgd_nonlinear_example

      public void Dan_Ventura_sgd_nonlinear_example()

    • Dan_Ventura_sdca_linear_example

      public void Dan_Ventura_sdca_linear_example()

    • Dan_Ventura_sdca_nonlinear_example

      public void Dan_Ventura_sdca_nonlinear_example()

    • setup

      public SVM.svmtest setup()

    • test_sgd_basic

      public void test_sgd_basic()

    • test_sdca_basic

      public void test_sdca_basic()

    • test_weights

      public void test_weights()

    • test_homkermap

      public void test_homkermap()

    • vl_svmtrain

      public void vl_svmtrain(double[] model, double[] bias, double[] scores, double[][] data, SVM.VlSvmDataset dataset, double[] labels, double lambda, SVM.VlSvmSolverType solver, double biasMultiplier, double biasLearningRate, int maxNumIterations, double epsilon, SVM.VlSvmLossType loss, boolean verbose, double[] initialModel, double initialBias, double[] weights, int startingIteration, int diagnosticFrequency)

    • vl_homogeneouskernelmap_delete

      public void vl_homogeneouskernelmap_delete(SVM.VlHomogeneousKernelMap self)
      Parameters:
      self - object. The function deletes the specified map object.

    • vl_svmdataset_set_homogeneous_kernel_map

      public void vl_svmdataset_set_homogeneous_kernel_map(SVM.VlSvmDataset self, SVM.VlHomogeneousKernelMap hom)
      Parameters:
      self - object.
      hom - homogeneous kernel map object to use. After changing the kernel map, the inner product and accumulator function should be queried again (::vl_svmdataset_get_inner_product_function adn ::vl_svmdataset_get_accumulate_function). Set this to @c NULL to avoid using a kernel map. Note that this does *not* transfer the ownership of the object to the function. Furthermore, ::VlSvmDataset holds to the object until it is destroyed or the object is replaced or removed by calling this function again.

    • vl_svmdataset_get_homogeneous_kernel_map

      public SVM.VlHomogeneousKernelMap vl_svmdataset_get_homogeneous_kernel_map(SVM.VlSvmDataset self)
      Parameters:
      self - object.
      Returns:
      homogeneous kernel map object (or @c NULL if any).

    • vl_svm_get_scores

      public double[] vl_svm_get_scores(SVM.VlSvm self)
      Parameters:
      self - object.
      Returns:
      vector of scores. After training or during the diagnostic callback, this function can be used to retrieve the scores of the points, i.e. $\langle \bx_i, \bw \rangle + b$.

    • vl_svm_get_bias_learning_rate

      public double vl_svm_get_bias_learning_rate(SVM.VlSvm self)
      Parameters:
      self - object
      Returns:
      bias learning rate.

    • vl_svm_get_diagnostic_frequency

      public int vl_svm_get_diagnostic_frequency(SVM.VlSvm self)
      Parameters:
      self - object.
      Returns:
      diagnostic frequency.

    • vl_svm_get_epsilon

      public double vl_svm_get_epsilon(SVM.VlSvm self)
      Parameters:
      self - object
      Returns:
      epsilon threshold.

    • vl_svm_get_max_num_iterations

      public int vl_svm_get_max_num_iterations(SVM.VlSvm self)
      Parameters:
      self - object.
      Returns:
      maximum number of iterations.

    • vl_svm_get_num_data

      public int vl_svm_get_num_data(SVM.VlSvm self)
      Parameters:
      self - object.
      Returns:
      model number of data samples This is the dimensionality of the weight vector $\bw$.

    • vl_svm_get_dimension

      public int vl_svm_get_dimension(SVM.VlSvm self)
      Parameters:
      self - object.
      Returns:
      model dimension. This is the dimensionality of the weight vector $\bw$.

    • vl_svm_set_model

      void vl_svm_set_model(SVM.VlSvm self, double[] model)
      Parameters:
      self - object.
      model - model. The function *copies* the content of the vector @a model to the internal model buffer. This operation can be used for warm start with the SGD algorithm, but has undefined effect with the SDCA algorithm.

    • vl_svm_set_bias

      public void vl_svm_set_bias(SVM.VlSvm self, double b)
      Parameters:
      self - object.
      b - bias. The function set the internal representation of the SVM bias to be equal to @a b (the bias multiplier is applied). The same remark that applies to ::vl_svm_set_model applies here too.

    • vl_svm_set_epsilon

      public void vl_svm_set_epsilon(SVM.VlSvm self, double epsilon)
      Parameters:
      self - object
      epsilon - threshold (non-negative).

    • vl_svm_set_max_num_iterations

      public void vl_svm_set_max_num_iterations(SVM.VlSvm self, int n)
      Parameters:
      self - object.
      n - maximum number of iterations.

    • vl_svm_set_bias_multiplier

      public void vl_svm_set_bias_multiplier(SVM.VlSvm self, double b)
      Parameters:
      self - object
      b - bias multiplier. The *bias multiplier* is the value of the constant feature appended to the data vectors to implement the bias (@ref svm-bias).

    • vl_svm_set_bias_learning_rate

      public void vl_svm_set_bias_learning_rate(SVM.VlSvm self, double rate)
      Parameters:
      self - object
      rate - bias learning rate (positive). This parameter applies only to the SGD solver.

    • vl_svm_set_diagnostic_frequency

      public void vl_svm_set_diagnostic_frequency(SVM.VlSvm self, int f)
      Parameters:
      self - object.
      f - diagnostic frequency (@c >= 1). A diagnostic round (to test for convergence and to printout information) is performed every @a f iterations.

    • vl_svm_set_iteration_number

      public void vl_svm_set_iteration_number(SVM.VlSvm self, int n)
      Parameters:
      self - object.
      n - iteration number. If called before training, this can be used with SGD for a warm start, as the net effect is to slow down the learning rate.

    • vl_svm_set_weights

      public void vl_svm_set_weights(SVM.VlSvm self, double[] weights)
      Parameters:
      self - object.
      weights - data weights.

    • vl_svm_set_loss

      public void vl_svm_set_loss(SVM.VlSvm self, SVM.VlSvmLossType loss)

    • vl_svm_set_loss_function

      public void vl_svm_set_loss_function(SVM.VlSvm self, int f)
      Parameters:
      self - object.
      f - loss function callback. Note that setting up a loss requires specifying more than just one callback. See @ref svm-loss-functions for details.

    • vl_svm_set_loss_derivative_function

      public void vl_svm_set_loss_derivative_function(SVM.VlSvm self, int f)

    • vl_svm_set_conjugate_loss_function

      public void vl_svm_set_conjugate_loss_function(SVM.VlSvm self, int f)

    • vl_svm_set_dca_update_function

      public void vl_svm_set_dca_update_function(SVM.VlSvm self, int f)

    • diagnostic1

      public void diagnostic1(SVM.VlSvm svm)

    • makeInfoStruct

      public void makeInfoStruct(SVM.VlSvm svm)

    • vl_svm_get_lambda

      public double vl_svm_get_lambda(SVM.VlSvm self)
      Parameters:
      self - object.
      Returns:
      diagnostic frequency.

    • vl_svm_get_bias_multiplier

      public double vl_svm_get_bias_multiplier(SVM.VlSvm self)
      Parameters:
      self - object.
      Returns:
      bias multiplier.

    • vl_svm_get_solver

      public SVM.VlSvmSolverType vl_svm_get_solver(SVM.VlSvm self)
      Parameters:
      self - object.
      Returns:
      SVM solver type.

    • vl_svm_get_statistics

      SVM.VlSvmStatistics vl_svm_get_statistics(SVM.VlSvm self)
      Parameters:
      self - object.
      Returns:
      statistics.

    • vl_homogeneouskernelmap_new

      public SVM.VlHomogeneousKernelMap vl_homogeneouskernelmap_new(SVM.VlHomogeneousKernelType kernelType, double gamma, int order, double period, SVM.VlHomogeneousKernelMapWindowType windowType)
      Parameters:
      kernelType - type of homogeneous kernel.
      gamma - kernel homogeneity degree.
      order - approximation order.
      period - kernel period.
      windowType - type of window used to truncate the kernel.
      Returns:
      the new homogeneous kernel map. The function intializes a new homogeneous kernel map for the specified kernel type, homogeneity degree, approximation order, period, and truncation window. See @ref homkermap-fundamentals for details. The homogeneity degree @c gamma must be positive (the standard kernels are obtained by setting @c gamma to 1). When unsure, set

    • sinc

      public double sinc(double x)

    • vl_homogeneouskernelmap_get_smooth_spectrum

      public double vl_homogeneouskernelmap_get_smooth_spectrum(SVM.VlHomogeneousKernelMap self, double omega)
      Parameters:
      self - homogeneous kernel map.
      omega - sampling frequency.
      Returns:
      the spectrum sampled at @a omega after smoothing.

    • vl_homogeneouskernelmap_get_spectrum

      public double vl_homogeneouskernelmap_get_spectrum(SVM.VlHomogeneousKernelMap self, double omega)
      Parameters:
      self - homogeneous kernel map.
      omega - sampling frequency.
      Returns:
      the spectrum sampled at @a omega.

    • runAlgorithm

      public void runAlgorithm()
      Description copied from class: AlgorithmBase
      Actually runs the algorithm. Implemented by inheriting algorithms.
      Specified by:
      runAlgorithm in class AlgorithmBase