SVMP: Creates and returns an instance of the class specified in the...

In svmplus: Implementation of Support Vector Machines Plus (SVM+)

Description

Creates and returns an instance of the class specified in the svm_type. In future, the current solver used for quadratic programming (quadprog) will be replaced by the equivaent quadprog solver defined in CVXR package. Also, LIBSVM and LIBLINEAR based faster implementaions are planned to be supported.

Usage

SVMP(cost = 1, gamma = 1, kernel_x = "rbf", degree_x = 3,
gamma_x = 0.001, kernel_xstar = "rbf", degree_xstar = 3,
gamma_xstar = 0.001, tol = 1e-05, svm_type = "QP")

Arguments

cost	cost of constraints violation
gamma	parameter needed for priviledged information
kernel_x	the kernel used for standard training data
degree_x	parameter needed for polynomial kernel for training data
gamma_x	parameter needed for rbf kernel for training data
kernel_xstar	the kernel used for priviledged information (PI)
degree_xstar	parameter needed for polynomial kernel for PI
gamma_xstar	parameter needed for rbf kernel for PI
tol	tolerance of dual variables
svm_type	optimization techiniques used: QP, LibSVM, LibLinear etc. Currently it supports only QP.

Value

an instance of the class specified in the svm_type. Currently it suports only "QP", hence returns instance of the class QPSvmPlus. The return instance can be used to call fit, project and predict methods of the QPSvmPlus.

Author(s)

Niharika Gauraha and Ola Spjuth

Examples

# This example is similar to the example given in the section 3.3 of the article:
# https://doi.org/10.1007/s10472-017-9541-2

#Generate train data
  mean1 = rep(0, 2)
  mean2 = rep(1, 2)
  cov2  = cov1 = .5 * diag(2)
  n = 20
  X1 = mvrnorm(n, mean1, Sigma = cov1)
  X2 = mvrnorm(n, mean2, Sigma = cov2)
  X_train = rbind(X1, X2)
  y_train = matrix(c(rep(1, n), rep(-1, n)), 2*n, 1)
# geberate privileged information data
  X1Star = matrix(0, n, 2)
  X2Star = matrix(0, n, 2)
  for(i in 1:n)
  {
    X1Star[i, 1] = norm(X1[i,] - mean1, type = "2")
    X1Star[i, 2] = norm(X2[i,] - mean2, type = "2")
  }
  for(i in 1:n)
  {
    X2Star[i, 1] = norm(X1[i, ] - mean2, type = "2")
    X2Star[i, 2] = norm(X2[i, ] - mean1, type = "2")
  }
  XStar = rbind(X1Star, X2Star)
# generate test data
  n_test = 10
  X1 = mvrnorm(n_test, mean1, Sigma = cov1)
  X2 = mvrnorm(n_test, mean2, Sigma = cov2)
  X_test = rbind(X1, X2)
  y_test = matrix(c(rep(1, n_test), rep(-1, n_test)), 2*n_test, 1)
# create instance of the class type QP, using RBF kernel
  qp = SVMP(cost = 100, gamma = .01,
            kernel_x = "rbf", gamma_x = .001,
            kernel_xstar = "rbf", gamma_xstar = .001,
            tol = .00001, svm_type = "QP")
# call the fit function
  qp$fit(X_train, XStar, y_train)
# call the predict function
  y_predict = qp$predict(X_test)
  print(length(y_predict[y_predict == y_test]))
  print("correct classification out of 20")



  #using polynomial kernel
  qp = SVMP(cost = 100, gamma = .01,
            kernel_x = "poly", degree_x = 3,
            kernel_xstar = "poly", gamma_xstar = 3,
            tol = .00001)

  qp$fit(X_train, XStar, y_train)

  y_predict = qp$predict(X_test)

  print(length(y_predict[y_predict == y_test]))
  print("correct classification out of 20")

  #using linear kernel
  qp = SVMP(cost = 10, gamma = .1,
            kernel_x = "linear",
            kernel_xstar = "linear",
            tol = .00001)

  qp$fit(X_train, XStar, y_train)

  y_predict = qp$predict(X_test)

  print(length(y_predict[y_predict == y_test]))
  print("correct classification out of 20")

svmplus documentation built on May 2, 2019, 3:40 p.m.