package edu.ntnu.idi.goldfish;

import java.io.File;
import java.io.IOException;
import java.util.ArrayList;
import java.util.Collections;
import java.util.Comparator;

import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.example.grouplens.GroupLensDataModel;
import org.apache.mahout.cf.taste.model.DataModel;

import edu.ntnu.idi.goldfish.MemoryBased.Similarity;


public class Main {
	
	// disable Mahout logging output
	static { System.setProperty("org.apache.commons.logging.Log", "org.apache.commons.logging.impl.NoOpLog"); }
	
	/**
	 * @param args
	 * @throws IOException 
	 * @throws TasteException 
	 */
	public static void main(String[] args) throws IOException, TasteException {
		
		//DataModel model = new GroupLensDataModel(new File("data/movielens-1m/ratings.dat.gz"));
		DataModel dataModel = new GroupLensDataModel(new File("data/sample100/ratings.dat"));
		
		Evaluator evaluator = new Evaluator(dataModel);
		
		
		
		/*
		 * MEMORY-based evaluation 
		 * 
		 * Evaluate KNN and Threshold based neighborhood models.
		 * Try all different similarity metrics found in ModelBased.Similarity.
		 */
		for(Similarity similarity : Similarity.values()) {
			
			// KNN: try different neighborhood sizes
			for(int K = 7; K >= 1; K -= 1) {
				evaluator.add(new KNN(similarity, K));			
			}
			
			// THRESHOLD: try different thresholds
			for(double T = 0.70; T <= 1.00; T += 0.05) {
				evaluator.add(new Threshold(similarity, T));
			}
		}
		
		evaluator.add(new SVD());

		ArrayList<EvaluationResult> results = evaluator.evaluateAll();
		
		// sort on RMSE (lower is better)
		Collections.sort(results, new Comparator<EvaluationResult>() {  
			public int compare(EvaluationResult a, EvaluationResult b) {
				return (a.RMSE > b.RMSE) ? -1 : (a.RMSE < b.RMSE) ? 1 : 0;
			}
		});
		
		for(EvaluationResult res : results) {
			System.out.println(res);
		}

		/**
		 * MODEL-based evaluation
		 */
		
		// clustering models (KMeans ... EM?)
		
		// latent semantic models (Matrix Factorizations, etc..)
		
		
		
	}
}

package edu.ntnu.idi.goldfish;

import java.io.File;
import java.io.IOException;
import java.util.ArrayList;
import java.util.Collections;
import java.util.Comparator;

import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.example.grouplens.GroupLensDataModel;
import org.apache.mahout.cf.taste.model.DataModel;

import edu.ntnu.idi.goldfish.MemoryBased.Similarity;


public class Main {
	
	// disable Mahout logging output
	static { System.setProperty("org.apache.commons.logging.Log", "org.apache.commons.logging.impl.NoOpLog"); }
	
	/**
	 * @param args
	 * @throws IOException 
	 * @throws TasteException 
	 */
	public static void main(String[] args) throws IOException, TasteException {
		
		//DataModel model = new GroupLensDataModel(new File("data/movielens-1m/ratings.dat.gz"));
		DataModel dataModel = new GroupLensDataModel(new File("datasets/sample100/ratings.dat.gz"));
		
		Evaluator evaluator = new Evaluator(dataModel);
		
		
		
		/*
		 * MEMORY-based evaluation 
		 * 
		 * Evaluate KNN and Threshold based neighborhood models.
		 * Try all different similarity metrics found in ModelBased.Similarity.
		 */
		for(Similarity similarity : Similarity.values()) {
			
			// KNN: try different neighborhood sizes
			for(int K = 7; K >= 1; K -= 1) {
				evaluator.add(new KNN(similarity, K));			
			}
			
			// THRESHOLD: try different thresholds
			for(double T = 0.70; T <= 1.00; T += 0.05) {
				evaluator.add(new Threshold(similarity, T));
			}
		}
		
		evaluator.add(new SVD());

		ArrayList<EvaluationResult> results = evaluator.evaluateAll();
		
		// sort on RMSE (lower is better)
		Collections.sort(results, new Comparator<EvaluationResult>() {  
			public int compare(EvaluationResult a, EvaluationResult b) {
				return (a.RMSE > b.RMSE) ? -1 : (a.RMSE < b.RMSE) ? 1 : 0;
			}
		});
		
		for(EvaluationResult res : results) {
			System.out.println(res);
		}

		/**
		 * MODEL-based evaluation
		 */
		
		// clustering models (KMeans ... EM?)
		
		// latent semantic models (Matrix Factorizations, etc..)
		
		
		
	}
}

Fix path to dataset.