Run the OptimizedKNN main methods with the arguments given below. Optimizing In optimizing task get the data set and divide it into two parts randomly as test and training and run the knn algorithm with different k value and weight values and get the optimal solution using genetic algorithm Predicting Get the test data file and run the knn algorithm with k value and weights values predicted in the optimizing task( or params file given) and predict the values for test dataset. Parameter values Every argument has two parts type and value. Arguments are given type followed by appropriate value Here first two arguments are mandatory others are optional Argument type Parameter value Description -datafile Path of training data file in csv or arff format -testfile Path of test data file in csv or arff format This file need to have same number of attributes as in training data set , so attribute need to predict also need given. -params Path of the parameter file which is a xml file This is needed when predicting(p) Parameter file need to have weight tag which has the weight values for each attribute and k tag to have k value Default value is given as the file created in optimizing (o) which has the name of training data set name(.prm)
-k K value Integer value which is the initial value to take as number of neighbors Default value is 1 -clsindex Attribute index to predict value between 0 to number of attribute in dataset Indicate which attribute need to predict Default value is 0 -task o or p O: indicate optimizing the function and get optimized values for k and weights Default value is o -model c or r C: classifier model classifying nominal values R: regression model predict a continuous value Default value is c -ptype n or k N : normal which use ad-hoc knn algorithm (which is good for small datasets) K : kdtree which use kdtree data structure knn algorithm implementation (this is good for large data sets) Default value is n -mutations Integer value for number of mutations This is used for genetic algorithm number of mutations Default value is 100 -population Integer value for population size This is used for genetic algorithm to optimizing population size to use Default value is 20 -evals Integer value for number of evaluations This is used for genetic algorithm to optimize , number of evaluations to use Default value is 1