Package labelrank. November 22, 2015
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1 Tpe Package Title Predictig Rakigs of Labels Versio 0.1 Date Depeds R (>= 2.10) Imports pdist Package labelrak November 22, 2015 A implemetatio of distace-based rakig algorithms to predict rakigs of labels. Two commo algorithms are icluded: the aive Baes ad the earest eighbor algorithms. Suggests kitr VigetteBuilder kitr RoxgeNote LazData true Licese GPL-3 NeedsCompilatio o Author Artur Aiguzhiov [cre], Carlos Soares [aut] Maitaier Artur Aiguzhiov <aiguzhiov@gmail.com> Repositor CRAN Date/Publicatio :48:10 R topics documeted: knn lr.om lr.um model_br b_rak _rak time_weights
2 2 lr.om Idex 7 knn Nearest eighbor A auxiliar fuctio to fid the earest eighbors from the distace matrix knn(model, k) model k earest eighbor rakig model umber of the earest eighbors to cosider This fuctio is applied to fid the earest eighbors i the distace matrix. a vector of legth of model lr.om Sthetic data for label rakig experimets. Sthetic data for label rakig experimets. lr.om Format A matrix of 20 discrete values ad 2 colums
3 lr.um 3 lr.um Sthetic data for label rakig experimets. Sthetic data for label rakig experimets. lr.um Format A matrix of 20 umeric values ad 2 colums model_br A aive Baes label rakig model This is a auxiliar fuctio to build a ecessar iputs to predict rakigs. model_br(x,, = 1) x is x p matrix of observatios ad p traiig attributes ad ca have cotiuous or omial values. is x j matrix of traiig rakigs (permutatios). is a parameter of memor ; that is, how fast past gets forgotte. (see details of time_weights). a list of size two: prior ad coditioal label rakig probabilities.
4 4 b_rak b_rak Predictig label rakigs based o the aive Baes rakig model This fuctio predicts the rakigs give prior ad coditioal probabilities obtaied from model_br b_rak(x,, ew.x, = 1) x ew.x is x p matrix of observatios ad p traiig attributes ad ca have cotiuous or omial values. is x j matrix of label rakigs is a vector of ew attributes is a parameter of memor ; that is, how fast past gets forgotte. (see details of time_weights). This fuctio predicts a rakig for test.x attributes. It iitiall builds a model for aive Baes algorithm that calculates priors ad coditioal label rakig probabilities ad the use them to predict rakigs. The attributes ca be omial or cotiuous data. a umeric vector of rakig Examples trai.x <- lr.om[1:16,] test.x <- lr.om[17,] predrak <- b_rak(trai.x,,test.x,=1)
5 _rak 5 _rak Predictig rakigs usig the earest eighbor algorithm This fuctio makes predictio of rakigs based o the earest eighbor _rak(trai.x,, test.x, = 1, k = 3) trai.x test.x k is matrix of umeric attributes i traiig sample is matrix of traiig rakigs is a vector of ew umeric attributes for which to predict rakigs is a parameter of memor of how fast the past rakigs gets forgotte. (see details of time_weights). B default, =1 which meas that a label rakig problem does ot have timig effect. is the umber of the earest eighbors to cosider (default k=3) A fuctio predicts the rakigs based o the euclidea distace betwee trai ad test attributes. a vector of predicted rakig for attribute test.x Examples trai.x <- lr.um[1:16,] test.x <- lr.um[17,] rakig <- _rak(trai.x,, test.x, =1,k=3)
6 6 time_weights Weights for timig This fuctio calculates the dimiishig weights for label rakig probabilities i case of timig ature of rakigs. time_weights(x, ) x a scalar of timig periods. is a parameter of memor of how fast the past gets forgotte. Sometimes rakigs have a timig compoet (for example, weekl sport teams stadig) ad a recet evet ca be more importat tha the past. The model ca take advatage of this differece i importace b weightig the rakig probabilities. The weights are calculated usig a expoetial fuctio. I case of =1, weights are a uitar vector of legth ; thus, o time ature i rakigs. a vector of values. Permutatios to be used as rakigs Permutatios to be used as rakigs Format A matrix of 20 rows ad 3 colums
7 Idex Topic datasets lr.om, 2 lr.um, 3, 6 knn, 2 lr.om, 2 lr.um, 3 model_br, 3 b_rak, 4 _rak, 5 time_weights, 3 5, 6, 6 7
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