Description Usage Arguments Value Author(s) Examples

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.

1 2 3 |

`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. |

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.

Niharika Gauraha and Ola Spjuth

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 | ```
# 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 April 25, 2018, 5:05 p.m.

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