Package FLR. R topics documented: February 19, Title Fuzzy Logic Rule Classifier Version 1.0 Date
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1 Title Fuzzy Logic Rule Classifier Version 1.0 Date Package FLR February 19, 2015 Author Constantinos Mavridis and Ioannis N. Athanasiadis Maintainer Constantinos Mavridis FLR algorithm for classification License GPL (>= 2) Repository CRAN Date/Publication :19:42 Depends combinat NeedsCompilation no R topics documented: accis dataset denormdatal fuzzylatticec indexcalc mat normdata prepdata sepflag set_bounds spatdt testd testnow trainnow Index 11 1
2 2 dataset001 accis Accuracy of FLR Accuracy of the flr classification. accis(testdata,testdatab) testdata testdatab an input data.frame of the test after classification. an input data.frame of the original test data. return the accuracy of the classification dataset001 dataset001 Dataset with 296 instances if 25 attributes. data(dataset001) Format A data frame with 296 instances on the following 25 variables. state 9 US states. county County. site.id Site id. latitude Latitude. longitude Longtitude. X dv dv. X dv dv. X design.value..ppm design value (ppm)2,3 X design.value..ppm...estimated design value (ppm) [estimated].
3 denormdatal 3 X design.value.status design value status4. percent.complete.in percent complete in percent.complete.in percent complete in percent.complete.in percent complete in X average.percent.complete average percent complete. X..of.days.above.the.naaqs.in.2009 # of days above the naaqs in X..of.days.above.the.naaqs.in.2010 # of days above the naaqs in X..of.days.above.the.naaqs.in.2011 # of days above the naaqs in X..of.days.above.the.naaqs.in.2012 # of days above the naaqs in X4th.highest.daily.max.value.in th highest daily max value in X4th.highest.daily.max.value.in th highest daily max value in X4th.highest.daily.max.value.in th highest daily max value in X4th.highest.daily.max.value.in th highest daily max value in column_27 Column_27. column_29 Column_29. class Class category. Source geocommons.com References geocommons.com denormdatal Denormalize Fuzzy Lattices. Denormalize fuzzy lattices. denormdatal(fuzlat,bounds) fuzlat bounds a fuzzy lattice containing mix and max value for each instance of the data set at the first columns,from left to right, followed by classname and categ. a 2 comumn matrix containing min and max value for each instance of the dataset. return denormalized fuzzy lattice.
4 4 indexcalc fuzzylatticec Constructs A Fuzzy Lattice Constructs a Fuzzy Lattice from an instance of the dataset. fuzzylatticec(df,dr,bounds) df dr bounds an empty list containing just the names for each fuzzy lattice column. an instance of the dataset a 2 comumn matrix containing min and max value for each instance of the dataset. return a fuzzy lattice (min and max value for each attribute, classname,categ). indexcalc Index Calculator Returns a vector that contains the number of rules created for each class. indexcalc(learnedcode) learnedcode a data.frame of fuzzy lattices. Each lattice is a rule created with the trainnow function. return a vector that contains the number of rules created for each class.
5 mat 5 mat Graph distance matrix A matrix containing the distances of the nodes in a graph. data(mat) Format A data frame of 9 rows and 9 columns. Illinois number Indiana number Kentucky number Michigan number North.Carolina number Ohio number Pennsylvania number Tennessee number Virginia number normdata Normalize Data and Denormalize data. Normalize Data to be in range of 0~1. normdata(data1) denormdata(data1,bounds) data1 bounds an input data.frame where last instance must be the class instance and be named class. a 2 comumn matrix containing min and max value for each instance of the dataset. return normalized or denormalized data.frame.
6 6 sepflag prepdata Prepare Dataset Alters the dataset in a form that can be used for training and classification. prepdata(data) data an input data.frame where last instance must be the class instance and be named class. return the data.frame without missing class instances and converts nominal attributes into numeric. sepflag Flags Instances Randomly flags instances in order to be used as training(0) or testing(1) data with the ratio depending on variable gg. sepflag(gg,data1) gg data1 percentage of instances to be used as training data for the classification. an input data.frame where last instance must be the class instance and be named class. return original data with a flag column added at the end.
7 set_bounds 7 set_bounds Creates A Boundaries File. Creates a boundaries of min and max columns for each attribute of a dataset. set_bounds(data1) data1 an input data.frame where last instance must be the class instance and be named class. return a data.frame of 2 columns (min,max) for each instance of the data(not class). spatdt Spatial Data Handling Creates a linear connection between spatial data in order to be used for classification. spatdt(data,idx,mat,pre_order=0,snd=0) get.cost(zzz,mat) get.cost2(pre_order,mat) get.pos(instz) winner.route(cost) data idx mat pre_order snd zzz instz cost an input data.frame indicates the position of the spatial data attribute. a matrix indicating distances predefined order indicates which node will be used as the starting one. The default value 0 means that the best route will be chosen, without taking into consideration which the starting node will be. a route instance cost of routes
8 8 testd return a list of 3 objects: a) The modified dataset, b) winner route, c) the total distance of the route. Examples #Import data data(dataset001) data<-dataset001 data(mat) idx<-1 rhoa<-0.6 param<-"sigmoid" pre_order<-c(1,2,3,4,5,6,7,8,9) #Data preprocess data<-spatdt(data,idx,mat,pre_order) testd Creates Testing And Training Samples Creates testing and training samples from the original data. testd(data2) traind(data2) data2 a data.frame flaged with the sepflag function. return the training and testing samples that will be used for the classification.
9 testnow 9 testnow Testing Phase Of FLR Implements classification using FLR on a data.frame. testnow(testdata,learnedcode) testdata learnedcode an input data.frame. a data.frame of fuzzy lattices. Each lattice is a rule created with the trainnow function. return the testdata data.frame after classification. trainnow Training Phase Of FLR Creates rules for classification using FLR. trainnow(traindata,param,rhoa=0.5,l=6,x0=0.5,epsilon=10^(-6)) join(inpbuf,num) theta(x,x0,param) ufun(x,x0,l,param) valuation(fuzlat,x0,l,param) createnframe(traindata) createnlist(traindata)
10 10 trainnow traindata param an input data.frame. parameter indicating linear positive valuation for 0 and sigmoid positive valuation for 1. The default value is set to 0. rhoa vigilance parameter in range [0,1]. The default value is set to 0.6. l parameter of u and theta functions of FLR. The default value is set to 6. x0 parameter of u and theta functions of FLR. The default value is set to 0.4. EPSILON inpbuf num x fuzlat parameter EPSILON.The default value is set to 10^(-6). input buffer. num fuzzy lattice fuzzy lattice return a data.frame of the learned code.
11 Index Topic datasets dataset001, 2 mat, 5 accis, 2 createnframe (trainnow), 9 createnlist (trainnow), 9 dataset001, 2 denormdata (normdata), 5 denormdatal, 3 fuzzylatticec, 4 get.cost (spatdt), 7 get.cost2 (spatdt), 7 get.pos (spatdt), 7 indexcalc, 4 join (trainnow), 9 mat, 5 normdata, 5 prepdata, 6 sepflag, 6 set_bounds, 7 spatdt, 7 testd, 8 testnow, 9 theta (trainnow), 9 traind (testd), 8 trainnow, 9 ufun (trainnow), 9 valuation (trainnow), 9 winner.route (spatdt), 7 11
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