Mixed utility functions to compute accuracy, norms, labels from scores and to perform stratified cross-validation.
compute.acc(pred, labels)
compute.F(pred, labels)
norm1(x)
Unit.sphere.norm(K)
do.stratified.cv.data(examples, positives, k = 5, seed = NULL)
do.cv.data(examples, positives, k = 5, seed = NULL)
labelsfromscores(scores, thresh)
Multiple.labels.from.scores(S, thresh.vect)
selection.test(pos.scores, av.scores, ind.positives, alpha = 0.05, thresh.pos = 0)compute.acc returns the accuracy
compute.F returns the F-score
norm1 returns the L1-norm value
Unit.sphere.norm returns the kernel normalized according to the unit sphere
do.stratified.cv.data returns a list with 2 two components:
a list with k components. Each component is a vector with the indices of the non positive elements of the fold
a list with k components. Each component is a vector with the indices of the positive elements of the fold
Indices refer to row numbers of the data matrix
do.cv.data returns a list with 2 two components:
a list with k components. Each component is a vector with the indices of the non positive elements of the fold
a list with k components. Each component is a vector with the indices of the positive elements of the fold
Indices refer to row numbers of the data matrix
labelsfromscores returns a numeric vector res with 0 or 1 values. The label res[i]=1 if scores[i]>thresh, otherwise res[i]=0
Multiple.labels.from.scores returns a binary matrix with the labels of the predictions. Rows represent examples, columns classes. Element L[i,j] is the label of example i w.r.t. class j. L[i,j]=1 if i belongs to j, 0 otherwise.
selection.test returns a list with 5 components:
a named vector with the components of av.scores selected by the test
a named vector with the labeled components of av.scores selected by the test
a named vector with the unlabeled components of av.scores selected by the test
the score threshold selected by the test
significance level (the same value of the input)
vector of the predicted labels
vector of the true labels. Note that 0 stands for negative and 1 for positive. In general the first level is negative and the second positive
numeric vector
a kernel matrix
indices of the examples (a vector of integer)
vector of integer. Indices of the positive examples. The indices refer to the indices of examples
number of folds (def = 5)
seed of the random generator (def=NULL). If is set to NULL no initiazitation is performed
numeric. Vector of scores: each element correspond to the score of an example
real value. Threshold for the classification
numeric matrix. Matrix of scores: rows represent examples, columns classes
numeric vector. Vector of the thresholds for multiple classes (one threshold for each class)
vector with scores of positive examples. It is returned from multiple.ker.score.cv.
a vector with the average scores computed by multiple.ker.score.cv. It may be a named vector. If not, the names attributes corresponding to the indices of the vector are added.
indices of the positive examples. They are the indices of av.scores corresponding to positive examples.
quantile level (def. 0.05)
only values larger than thresh.pos are retained in pos.scores (def.: 0)
compute.acc computes the accuracy for a single class
compute.F computes the F-score for a single class
norm1 computes the L1-norm of a numeric vector
Unit.sphere.norm normalize a kernel according to the unit sphere
do.stratified.cv.data generates data for the stratified cross-validation. In particular subdivides the indices that refer to the rows of the data matrix in different folds (separated for positive and negative examples)
do.cv.data generates data for the vanilla not stratified cross-validation.
labelsfromscores computes the labels of a single class from the corresponding scores
Multiple.labels.from.scores computes the labels of multiple classes from the corresponding scores
selection.test is a non parametric test to select the most significant unlabeled examples
# L1-norm of a vector
norm1(rnorm(10));
# generation of 5 stratified folds;
do.stratified.cv.data(1:100, 1:10, k = 5, seed = NULL);
# generation of labels form scores.
labelsfromscores(runif(20), thresh=0.3);
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