List of usage examples for java.lang Math abs
@HotSpotIntrinsicCandidate public static double abs(double a)
From source file:edu.cmu.lti.oaqa.knn4qa.apps.CollectionSplitter.java
public static void main(String[] args) { Options options = new Options(); options.addOption("i", null, true, "Input file"); options.addOption("o", null, true, "Output file prefix"); options.addOption("p", null, true, "Comma separated probabilities e.g., 0.1,0.2,0.7."); options.addOption("n", null, true, "Comma separated part names, e.g., dev,test,train"); CommandLineParser parser = new org.apache.commons.cli.GnuParser(); try {/*from w ww . j ava 2 s . c om*/ CommandLine cmd = parser.parse(options, args); InputStream input = null; if (cmd.hasOption("i")) { input = CompressUtils.createInputStream(cmd.getOptionValue("i")); } else { Usage("Specify Input file"); } ArrayList<Double> probs = new ArrayList<Double>(); String[] partNames = null; if (cmd.hasOption("p")) { String parts[] = cmd.getOptionValue("p").split(","); try { double sum = 0; for (String s : parts) { double p = Double.parseDouble(s); if (p <= 0 || p > 1) Usage("All probabilities must be in the range (0,1)"); sum += p; probs.add(p); } if (Math.abs(sum - 1.0) > Float.MIN_NORMAL) { Usage("The sum of probabilities should be equal to 1, but it's: " + sum); } } catch (NumberFormatException e) { Usage("Can't convert some of the probabilities to a floating-point number."); } } else { Usage("Specify part probabilities."); } if (cmd.hasOption("n")) { partNames = cmd.getOptionValue("n").split(","); if (partNames.length != probs.size()) Usage("The number of probabilities is not equal to the number of parts!"); } else { Usage("Specify part names"); } BufferedWriter[] outFiles = new BufferedWriter[partNames.length]; if (cmd.hasOption("o")) { String outPrefix = cmd.getOptionValue("o"); for (int partId = 0; partId < partNames.length; ++partId) { outFiles[partId] = new BufferedWriter(new OutputStreamWriter( CompressUtils.createOutputStream(outPrefix + "_" + partNames[partId] + ".gz"))); } } else { Usage("Specify Output file prefix"); } System.out.println("Using probabilities:"); for (int partId = 0; partId < partNames.length; ++partId) System.out.println(partNames[partId] + " : " + probs.get(partId)); System.out.println("================================================="); XmlIterator inpIter = new XmlIterator(input, YahooAnswersReader.DOCUMENT_TAG); String oneRec = inpIter.readNext(); int docNum = 1; for (; !oneRec.isEmpty(); ++docNum, oneRec = inpIter.readNext()) { double p = Math.random(); if (docNum % 1000 == 0) { System.out.println(String.format("Processed %d documents", docNum)); } BufferedWriter out = null; for (int partId = 0; partId < partNames.length; ++partId) { double pp = probs.get(partId); if (p <= pp || partId + 1 == partNames.length) { out = outFiles[partId]; break; } p -= pp; } oneRec = oneRec.trim() + System.getProperty("line.separator"); out.write(oneRec); } System.out.println(String.format("Processed %d documents", docNum - 1)); // It's important to close all the streams here! for (BufferedWriter f : outFiles) f.close(); } catch (ParseException e) { Usage("Cannot parse arguments"); } catch (Exception e) { System.err.println("Terminating due to an exception: " + e); System.exit(1); } }
From source file:com.cloud.test.longrun.PerformanceWithAPI.java
public static void main(String[] args) { List<String> argsList = Arrays.asList(args); Iterator<String> iter = argsList.iterator(); String host = "http://localhost"; int numThreads = 1; while (iter.hasNext()) { String arg = iter.next(); if (arg.equals("-h")) { host = "http://" + iter.next(); }/* ww w .j ava 2 s . c o m*/ if (arg.equals("-t")) { numThreads = Integer.parseInt(iter.next()); } if (arg.equals("-n")) { numVM = Integer.parseInt(iter.next()); } } final String server = host + ":" + _apiPort + "/"; final String developerServer = host + ":" + _developerPort + _apiUrl; s_logger.info("Starting test in " + numThreads + " thread(s). Each thread is launching " + numVM + " VMs"); for (int i = 0; i < numThreads; i++) { new Thread(new Runnable() { public void run() { try { String username = null; String singlePrivateIp = null; String singlePublicIp = null; Random ran = new Random(); username = Math.abs(ran.nextInt()) + "-user"; //Create User User myUser = new User(username, username, server, developerServer); try { myUser.launchUser(); myUser.registerUser(); } catch (Exception e) { s_logger.warn("Error code: ", e); } if (myUser.getUserId() != null) { s_logger.info("User " + myUser.getUserName() + " was created successfully, starting VM creation"); //create VMs for the user for (int i = 0; i < numVM; i++) { //Create a new VM, add it to the list of user's VMs VirtualMachine myVM = new VirtualMachine(myUser.getUserId()); myVM.deployVM(_zoneId, _serviceOfferingId, _templateId, myUser.getDeveloperServer(), myUser.getApiKey(), myUser.getSecretKey()); myUser.getVirtualMachines().add(myVM); singlePrivateIp = myVM.getPrivateIp(); if (singlePrivateIp != null) { s_logger.info( "VM with private Ip " + singlePrivateIp + " was successfully created"); } else { s_logger.info("Problems with VM creation for a user" + myUser.getUserName()); break; } //get public IP address for the User myUser.retrievePublicIp(_zoneId); singlePublicIp = myUser.getPublicIp().get(myUser.getPublicIp().size() - 1); if (singlePublicIp != null) { s_logger.info("Successfully got public Ip " + singlePublicIp + " for user " + myUser.getUserName()); } else { s_logger.info("Problems with getting public Ip address for user" + myUser.getUserName()); break; } //create ForwardProxy rules for user's VMs int responseCode = CreateForwardingRule(myUser, singlePrivateIp, singlePublicIp, "22", "22"); if (responseCode == 500) break; } s_logger.info("Deployment successful..." + numVM + " VMs were created. Waiting for 5 min before performance test"); Thread.sleep(300000L); // Wait //Start performance test for the user s_logger.info("Starting performance test for Guest network that has " + myUser.getPublicIp().size() + " public IP addresses"); for (int j = 0; j < myUser.getPublicIp().size(); j++) { s_logger.info("Starting test for user which has " + myUser.getVirtualMachines().size() + " vms. Public IP for the user is " + myUser.getPublicIp().get(j) + " , number of retries is " + _retry + " , private IP address of the machine is" + myUser.getVirtualMachines().get(j).getPrivateIp()); guestNetwork myNetwork = new guestNetwork(myUser.getPublicIp().get(j), _retry); myNetwork.setVirtualMachines(myUser.getVirtualMachines()); new Thread(myNetwork).start(); } } } catch (Exception e) { s_logger.error(e); } } }).start(); } }
From source file:com.cloud.test.utils.TestClient.java
public static void main(String[] args) { String host = "http://localhost"; String port = "8080"; String testUrl = "/client/test"; int numThreads = 1; try {//from w w w.ja v a2 s. c om // Parameters List<String> argsList = Arrays.asList(args); Iterator<String> iter = argsList.iterator(); while (iter.hasNext()) { String arg = iter.next(); // host if (arg.equals("-h")) { host = "http://" + iter.next(); } if (arg.equals("-p")) { port = iter.next(); } if (arg.equals("-t")) { numThreads = Integer.parseInt(iter.next()); } if (arg.equals("-s")) { sleepTime = Long.parseLong(iter.next()); } if (arg.equals("-c")) { cleanUp = Boolean.parseBoolean(iter.next()); if (!cleanUp) sleepTime = 0L; // no need to wait if we don't ever cleanup } if (arg.equals("-r")) { repeat = Boolean.parseBoolean(iter.next()); } if (arg.equals("-u")) { numOfUsers = Integer.parseInt(iter.next()); } if (arg.equals("-i")) { internet = Boolean.parseBoolean(iter.next()); } } final String server = host + ":" + port + testUrl; s_logger.info("Starting test against server: " + server + " with " + numThreads + " thread(s)"); if (cleanUp) s_logger.info("Clean up is enabled, each test will wait " + sleepTime + " ms before cleaning up"); if (numOfUsers > 0) { s_logger.info("Pre-generating users for test of size : " + numOfUsers); users = new String[numOfUsers]; Random ran = new Random(); for (int i = 0; i < numOfUsers; i++) { users[i] = Math.abs(ran.nextInt()) + "-user"; } } for (int i = 0; i < numThreads; i++) { new Thread(new Runnable() { public void run() { do { String username = null; try { long now = System.currentTimeMillis(); Random ran = new Random(); if (users != null) { username = users[Math.abs(ran.nextInt()) % numOfUsers]; } else { username = Math.abs(ran.nextInt()) + "-user"; } NDC.push(username); String url = server + "?email=" + username + "&password=" + username + "&command=deploy"; s_logger.info("Launching test for user: " + username + " with url: " + url); HttpClient client = new HttpClient(); HttpMethod method = new GetMethod(url); int responseCode = client.executeMethod(method); boolean success = false; String reason = null; if (responseCode == 200) { if (internet) { s_logger.info("Deploy successful...waiting 5 minute before SSH tests"); Thread.sleep(300000L); // Wait 60 seconds so the linux VM can boot up. s_logger.info("Begin Linux SSH test"); reason = sshTest(method.getResponseHeader("linuxIP").getValue()); if (reason == null) { s_logger.info("Linux SSH test successful"); s_logger.info("Begin Windows SSH test"); reason = sshWinTest(method.getResponseHeader("windowsIP").getValue()); } } if (reason == null) { if (internet) { s_logger.info("Windows SSH test successful"); } else { s_logger.info("deploy test successful....now cleaning up"); if (cleanUp) { s_logger.info( "Waiting " + sleepTime + " ms before cleaning up vms"); Thread.sleep(sleepTime); } else { success = true; } } if (users == null) { s_logger.info("Sending cleanup command"); url = server + "?email=" + username + "&password=" + username + "&command=cleanup"; } else { s_logger.info("Sending stop DomR / destroy VM command"); url = server + "?email=" + username + "&password=" + username + "&command=stopDomR"; } method = new GetMethod(url); responseCode = client.executeMethod(method); if (responseCode == 200) { success = true; } else { reason = method.getStatusText(); } } else { // Just stop but don't destroy the VMs/Routers s_logger.info("SSH test failed with reason '" + reason + "', stopping VMs"); url = server + "?email=" + username + "&password=" + username + "&command=stop"; responseCode = client.executeMethod(new GetMethod(url)); } } else { // Just stop but don't destroy the VMs/Routers reason = method.getStatusText(); s_logger.info("Deploy test failed with reason '" + reason + "', stopping VMs"); url = server + "?email=" + username + "&password=" + username + "&command=stop"; client.executeMethod(new GetMethod(url)); } if (success) { s_logger.info("***** Completed test for user : " + username + " in " + ((System.currentTimeMillis() - now) / 1000L) + " seconds"); } else { s_logger.info("##### FAILED test for user : " + username + " in " + ((System.currentTimeMillis() - now) / 1000L) + " seconds with reason : " + reason); } } catch (Exception e) { s_logger.warn("Error in thread", e); try { HttpClient client = new HttpClient(); String url = server + "?email=" + username + "&password=" + username + "&command=stop"; client.executeMethod(new GetMethod(url)); } catch (Exception e1) { } } finally { NDC.clear(); } } while (repeat); } }).start(); } } catch (Exception e) { s_logger.error(e); } }
From source file:mlbench.pagerank.PagerankMerge.java
@SuppressWarnings({ "rawtypes", "unchecked" })
public static void main(String[] args) throws IOException, InterruptedException {
try {/* w ww. j a v a 2 s . c o m*/
parseArgs(args);
HashMap<String, String> conf = new HashMap<String, String>();
initConf(conf);
MPI_D.Init(args, MPI_D.Mode.Common, conf);
JobConf jobConf = new JobConf(confPath);
if (MPI_D.COMM_BIPARTITE_O != null) {
// O communicator
int rank = MPI_D.Comm_rank(MPI_D.COMM_BIPARTITE_O);
int size = MPI_D.Comm_size(MPI_D.COMM_BIPARTITE_O);
if (rank == 0) {
LOG.info(PagerankMerge.class.getSimpleName() + " O start.");
}
FileSplit[] inputs = DataMPIUtil.HDFSDataLocalLocator.getTaskInputs(MPI_D.COMM_BIPARTITE_O, jobConf,
inDir, rank);
for (int i = 0; i < inputs.length; i++) {
FileSplit fsplit = inputs[i];
LineRecordReader kvrr = new LineRecordReader(jobConf, fsplit);
LongWritable key = kvrr.createKey();
Text value = kvrr.createValue();
{
while (kvrr.next(key, value)) {
String line_text = value.toString();
final String[] line = line_text.split("\t");
if (line.length >= 2) {
MPI_D.Send(new IntWritable(Integer.parseInt(line[0])), new Text(line[1]));
}
}
}
}
} else if (MPI_D.COMM_BIPARTITE_A != null) {
// A communicator
int rank = MPI_D.Comm_rank(MPI_D.COMM_BIPARTITE_A);
if (rank == 0) {
LOG.info(PagerankMerge.class.getSimpleName() + " A start.");
}
HadoopWriter<IntWritable, Text> outrw = HadoopIOUtil.getNewWriter(jobConf, outDir,
IntWritable.class, Text.class, TextOutputFormat.class, null, rank, MPI_D.COMM_BIPARTITE_A);
IntWritable oldKey = null;
double next_rank = 0;
double previous_rank = 0;
double diff = 0;
int local_diffs = 0;
random_coeff = (1 - mixing_c) / (double) number_nodes;
converge_threshold = ((double) 1.0 / (double) number_nodes) / 10;
Object[] keyValue = MPI_D.Recv();
while (keyValue != null) {
IntWritable key = (IntWritable) keyValue[0];
Text value = (Text) keyValue[1];
if (oldKey == null) {
oldKey = key;
}
if (!key.equals(oldKey)) {
next_rank = next_rank * mixing_c + random_coeff;
outrw.write(oldKey, new Text("v" + next_rank));
diff = Math.abs(previous_rank - next_rank);
if (diff > converge_threshold) {
local_diffs += 1;
}
oldKey = key;
next_rank = 0;
previous_rank = 0;
}
String cur_value_str = value.toString();
if (cur_value_str.charAt(0) == 's') {
previous_rank = Double.parseDouble(cur_value_str.substring(1));
} else {
next_rank += Double.parseDouble(cur_value_str.substring(1));
}
keyValue = MPI_D.Recv();
}
if (previous_rank != 0) {
next_rank = next_rank * mixing_c + random_coeff;
outrw.write(oldKey, new Text("v" + next_rank));
diff = Math.abs(previous_rank - next_rank);
if (diff > converge_threshold)
local_diffs += 1;
}
outrw.close();
reduceDiffs(local_diffs, rank);
}
MPI_D.Finalize();
} catch (MPI_D_Exception e) {
e.printStackTrace();
}
}
From source file:com.yobidrive.diskmap.buckets.BucketFNVHash.java
public static void main(String[] args) { // need to pass Ethernet address; can either use real one (shown here) EthernetAddress nic = EthernetAddress.fromInterface(); // or bogus which would be gotten with: EthernetAddress.constructMulticastAddress() TimeBasedGenerator uuidGenerator = Generators.timeBasedGenerator(nic); // also: we don't specify synchronizer, getting an intra-JVM syncer; there is // also external file-locking-based synchronizer if multiple JVMs run JUG int numIterations = 4096 * 16 * 24; int numBuckets = 4096 * 16 * 2; ;// www . jav a 2 s .c o m int[] buckets = new int[numBuckets]; BucketFNVHash hash = new BucketFNVHash(numBuckets); long smallest = Integer.MAX_VALUE; long biggest = Integer.MIN_VALUE; int collisionsI = 0; int collisions6 = 0; int collisions2 = 0; int collisions3 = 0; int collisions4 = 0; int collisions5 = 0; long smallestI = Integer.MAX_VALUE; long biggestI = Integer.MIN_VALUE; int counter = 0; int maxCols = 0; for (int i = 0; i < numIterations; i++) { String key = uuidGenerator.generate().toString(); long valVoldemort = Math.abs(hash.hashVoldemort(key.getBytes())); if (valVoldemort < smallest) smallest = valVoldemort; if (valVoldemort > biggest) biggest = valVoldemort; long partition = valVoldemort % 16; // 16 partitions if (partition == 7) { counter++; // 1 more in partition int val = (int) hash.hash(key.getBytes()); if (val < smallestI) smallestI = val; if (val > biggestI) biggestI = val; buckets[val]++; if (buckets[val] > maxCols) maxCols = buckets[val]; if (buckets[val] > 1) { collisionsI++; } if (buckets[val] == 2) { collisions2++; } if (buckets[val] == 3) { collisions3++; } if (buckets[val] == 4) { collisions4++; } if (buckets[val] == 5) { collisions5++; } if (buckets[val] > 5) { collisions6++; } } } System.out.println("Smallest=" + smallest + ", Biggest=" + biggest + ", SmallestI=" + smallestI + ", BiggestI=" + biggestI + "/" + numBuckets + ", Partition rate=" + ((float) counter / numIterations * 100) + "% (target 6,25%), Collision rate=" + ((float) collisionsI * 100 / counter) + "%, Fill rate" + ((float) counter / numBuckets * 100) + ", Max cols=" + (maxCols - 1)); System.out.println("Chains 2:" + ((float) collisions2 * 100 / collisionsI) + ", 3:" + ((float) collisions3 * 100 / collisionsI) + ", 4:" + ((float) collisions4 * 100 / collisionsI) + ", 5:" + ((float) collisions5 * 100 / collisionsI) + ", 6:" + ((float) collisions6 * 100 / collisionsI)); }
From source file:cc.twittertools.search.api.RunQueriesBaselineThrift.java
@SuppressWarnings("static-access") public static void main(String[] args) throws Exception { Options options = new Options(); options.addOption(OptionBuilder.withArgName("string").hasArg().withDescription("host").create(HOST_OPTION)); options.addOption(OptionBuilder.withArgName("port").hasArg().withDescription("port").create(PORT_OPTION)); options.addOption(OptionBuilder.withArgName("file").hasArg() .withDescription("file containing topics in TREC format").create(QUERIES_OPTION)); options.addOption(OptionBuilder.withArgName("num").hasArg().withDescription("number of results to return") .create(NUM_RESULTS_OPTION)); options.addOption(/*from w ww. j a v a 2s .c o m*/ OptionBuilder.withArgName("string").hasArg().withDescription("group id").create(GROUP_OPTION)); options.addOption( OptionBuilder.withArgName("string").hasArg().withDescription("access token").create(TOKEN_OPTION)); options.addOption( OptionBuilder.withArgName("string").hasArg().withDescription("runtag").create(RUNTAG_OPTION)); options.addOption(new Option(VERBOSE_OPTION, "print out complete document")); CommandLine cmdline = null; CommandLineParser parser = new GnuParser(); try { cmdline = parser.parse(options, args); } catch (ParseException exp) { System.err.println("Error parsing command line: " + exp.getMessage()); System.exit(-1); } if (!cmdline.hasOption(HOST_OPTION) || !cmdline.hasOption(PORT_OPTION) || !cmdline.hasOption(QUERIES_OPTION)) { HelpFormatter formatter = new HelpFormatter(); formatter.printHelp(RunQueriesThrift.class.getName(), options); System.exit(-1); } String queryFile = cmdline.getOptionValue(QUERIES_OPTION); if (!new File(queryFile).exists()) { System.err.println("Error: " + queryFile + " doesn't exist!"); System.exit(-1); } String runtag = cmdline.hasOption(RUNTAG_OPTION) ? cmdline.getOptionValue(RUNTAG_OPTION) : DEFAULT_RUNTAG; TrecTopicSet topicsFile = TrecTopicSet.fromFile(new File(queryFile)); int numResults = 1000; try { if (cmdline.hasOption(NUM_RESULTS_OPTION)) { numResults = Integer.parseInt(cmdline.getOptionValue(NUM_RESULTS_OPTION)); } } catch (NumberFormatException e) { System.err.println("Invalid " + NUM_RESULTS_OPTION + ": " + cmdline.getOptionValue(NUM_RESULTS_OPTION)); System.exit(-1); } String group = cmdline.hasOption(GROUP_OPTION) ? cmdline.getOptionValue(GROUP_OPTION) : null; String token = cmdline.hasOption(TOKEN_OPTION) ? cmdline.getOptionValue(TOKEN_OPTION) : null; boolean verbose = cmdline.hasOption(VERBOSE_OPTION); PrintStream out = new PrintStream(System.out, true, "UTF-8"); TrecSearchThriftClient client = new TrecSearchThriftClient(cmdline.getOptionValue(HOST_OPTION), Integer.parseInt(cmdline.getOptionValue(PORT_OPTION)), group, token); for (cc.twittertools.search.TrecTopic query : topicsFile) { List<TResult> results = client.search(query.getQuery(), query.getQueryTweetTime(), numResults); SortedSet<TResultComparable> sortedResults = new TreeSet<TResultComparable>(); for (TResult result : results) { // Throw away retweets. if (result.getRetweeted_status_id() == 0) { sortedResults.add(new TResultComparable(result)); } } int i = 1; int dupliCount = 0; double rsvPrev = 0; for (TResultComparable sortedResult : sortedResults) { TResult result = sortedResult.getTResult(); double rsvCurr = result.rsv; if (Math.abs(rsvCurr - rsvPrev) > 0.0000001) { dupliCount = 0; } else { dupliCount++; rsvCurr = rsvCurr - 0.000001 / numResults * dupliCount; } out.println(String.format("%s Q0 %d %d %." + (int) (6 + Math.ceil(Math.log10(numResults))) + "f %s", query.getId(), result.id, i, rsvCurr, runtag)); if (verbose) { out.println("# " + result.toString().replaceAll("[\\n\\r]+", " ")); } i++; rsvPrev = result.rsv; } } out.close(); }
From source file:eu.crisis_economics.abm.model.configuration.UniformDistributionModelParameterConfiguration.java
/** * A lightweight test for this {@link ConfigurationComponent}. This snippet * creates a {@link Parameter}{@code <Double>} using an instance of * {@link UniformDistributionModelParameterConfiguration} and then tests * whether {@link Double} values drawn from this {@link Parameter} are * strictly positive and have the expected mean. * //from w ww .j ava 2 s .c o m */ public static void main(String[] args) { final UniformDistributionModelParameterConfiguration configuration = new UniformDistributionModelParameterConfiguration(); configuration.setMinimum(.5); configuration.setMaximum(1.5); final ModelParameter<Double> distribution = configuration.createInjector() .getInstance(Key.get(new TypeLiteral<ModelParameter<Double>>() { })); double mean = 0.; final int numSamples = 10000000; for (int i = 0; i < numSamples; ++i) { final double value = distribution.get(); mean += value; Assert.assertTrue(value >= .5 && value <= 1.5); } mean /= numSamples; Assert.assertTrue(Math.abs(mean - 1.) < 1.e-3); System.out.println(mean); }
From source file:ch.epfl.lsir.xin.test.GlobalMeanTest.java
/** * @param args//ww w. j a v a 2s . co m */ public static void main(String[] args) throws Exception { // TODO Auto-generated method stub PrintWriter logger = new PrintWriter(".//results//GlobalMean"); PropertiesConfiguration config = new PropertiesConfiguration(); config.setFile(new File("conf//GlobalMean.properties")); try { config.load(); } catch (ConfigurationException e) { // TODO Auto-generated catch block e.printStackTrace(); } logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " Read rating data..."); DataLoaderFile loader = new DataLoaderFile(".//data//MoveLens100k.txt"); loader.readSimple(); DataSetNumeric dataset = loader.getDataset(); System.out.println("Number of ratings: " + dataset.getRatings().size() + " Number of users: " + dataset.getUserIDs().size() + " Number of items: " + dataset.getItemIDs().size()); logger.println("Number of ratings: " + dataset.getRatings().size() + ", Number of users: " + dataset.getUserIDs().size() + ", Number of items: " + dataset.getItemIDs().size()); double totalMAE = 0; double totalRMSE = 0; int F = 5; logger.println(F + "- folder cross validation."); logger.flush(); ArrayList<ArrayList<NumericRating>> folders = new ArrayList<ArrayList<NumericRating>>(); for (int i = 0; i < F; i++) { folders.add(new ArrayList<NumericRating>()); } while (dataset.getRatings().size() > 0) { int index = new Random().nextInt(dataset.getRatings().size()); int r = new Random().nextInt(F); folders.get(r).add(dataset.getRatings().get(index)); dataset.getRatings().remove(index); } for (int folder = 1; folder <= F; folder++) { System.out.println("Folder: " + folder); logger.println("Folder: " + folder); ArrayList<NumericRating> trainRatings = new ArrayList<NumericRating>(); ArrayList<NumericRating> testRatings = new ArrayList<NumericRating>(); for (int i = 0; i < folders.size(); i++) { if (i == folder - 1)//test data { testRatings.addAll(folders.get(i)); } else {//training data trainRatings.addAll(folders.get(i)); } } //create rating matrix HashMap<String, Integer> userIDIndexMapping = new HashMap<String, Integer>(); HashMap<String, Integer> itemIDIndexMapping = new HashMap<String, Integer>(); for (int i = 0; i < dataset.getUserIDs().size(); i++) { userIDIndexMapping.put(dataset.getUserIDs().get(i), i); } for (int i = 0; i < dataset.getItemIDs().size(); i++) { itemIDIndexMapping.put(dataset.getItemIDs().get(i), i); } RatingMatrix trainRatingMatrix = new RatingMatrix(dataset.getUserIDs().size(), dataset.getItemIDs().size()); for (int i = 0; i < trainRatings.size(); i++) { trainRatingMatrix.set(userIDIndexMapping.get(trainRatings.get(i).getUserID()), itemIDIndexMapping.get(trainRatings.get(i).getItemID()), trainRatings.get(i).getValue()); } RatingMatrix testRatingMatrix = new RatingMatrix(dataset.getUserIDs().size(), dataset.getItemIDs().size()); for (int i = 0; i < testRatings.size(); i++) { testRatingMatrix.set(userIDIndexMapping.get(testRatings.get(i).getUserID()), itemIDIndexMapping.get(testRatings.get(i).getItemID()), testRatings.get(i).getValue()); } System.out.println("Training: " + trainRatingMatrix.getTotalRatingNumber() + " vs Test: " + testRatingMatrix.getTotalRatingNumber()); logger.println("Initialize a recommendation model based on global average method."); GlobalAverage algo = new GlobalAverage(trainRatingMatrix); algo.setLogger(logger); algo.build(); algo.saveModel(".//localModels//" + config.getString("NAME")); logger.println("Save the model."); logger.flush(); System.out.println(trainRatings.size() + " vs. " + testRatings.size()); double RMSE = 0; double MAE = 0; int count = 0; for (int i = 0; i < testRatings.size(); i++) { NumericRating rating = testRatings.get(i); double prediction = algo.predict(rating.getUserID(), rating.getItemID()); if (Double.isNaN(prediction)) { System.out.println("no prediction"); continue; } MAE = MAE + Math.abs(rating.getValue() - prediction); RMSE = RMSE + Math.pow((rating.getValue() - prediction), 2); count++; } MAE = MAE / count; RMSE = Math.sqrt(RMSE / count); // System.out.println("MAE: " + MAE + " RMSE: " + RMSE); logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " MAE: " + MAE + " RMSE: " + RMSE); logger.flush(); totalMAE = totalMAE + MAE; totalRMSE = totalRMSE + RMSE; } System.out.println("MAE: " + totalMAE / F + " RMSE: " + totalRMSE / F); logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " Final results: MAE: " + totalMAE / F + " RMSE: " + totalRMSE / F); logger.flush(); logger.close(); //MAE: 0.9338607074893257 RMSE: 1.1170971131112037 (MovieLens1M) //MAE: 0.9446876509332618 RMSE: 1.1256517870920375 (MovieLens100K) }
From source file:ch.epfl.lsir.xin.test.UserAverageTest.java
/** * @param args//w w w .j a v a 2 s . c o m */ public static void main(String[] args) throws Exception { // TODO Auto-generated method stub PrintWriter logger = new PrintWriter(".//results//UserAverage"); PropertiesConfiguration config = new PropertiesConfiguration(); config.setFile(new File(".//conf//UserAverage.properties")); try { config.load(); } catch (ConfigurationException e) { // TODO Auto-generated catch block e.printStackTrace(); } logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " Read rating data..."); DataLoaderFile loader = new DataLoaderFile(".//data//MoveLens100k.txt"); loader.readSimple(); DataSetNumeric dataset = loader.getDataset(); System.out.println("Number of ratings: " + dataset.getRatings().size() + " Number of users: " + dataset.getUserIDs().size() + " Number of items: " + dataset.getItemIDs().size()); logger.println("Number of ratings: " + dataset.getRatings().size() + " Number of users: " + dataset.getUserIDs().size() + " Number of items: " + dataset.getItemIDs().size()); logger.flush(); double totalMAE = 0; double totalRMSE = 0; int F = 5; logger.println(F + "- folder cross validation."); ArrayList<ArrayList<NumericRating>> folders = new ArrayList<ArrayList<NumericRating>>(); for (int i = 0; i < F; i++) { folders.add(new ArrayList<NumericRating>()); } while (dataset.getRatings().size() > 0) { int index = new Random().nextInt(dataset.getRatings().size()); int r = new Random().nextInt(F); folders.get(r).add(dataset.getRatings().get(index)); dataset.getRatings().remove(index); } for (int folder = 1; folder <= F; folder++) { logger.println("Folder: " + folder); System.out.println("Folder: " + folder); ArrayList<NumericRating> trainRatings = new ArrayList<NumericRating>(); ArrayList<NumericRating> testRatings = new ArrayList<NumericRating>(); for (int i = 0; i < folders.size(); i++) { if (i == folder - 1)//test data { testRatings.addAll(folders.get(i)); } else {//training data trainRatings.addAll(folders.get(i)); } } //create rating matrix HashMap<String, Integer> userIDIndexMapping = new HashMap<String, Integer>(); HashMap<String, Integer> itemIDIndexMapping = new HashMap<String, Integer>(); for (int i = 0; i < dataset.getUserIDs().size(); i++) { userIDIndexMapping.put(dataset.getUserIDs().get(i), i); } for (int i = 0; i < dataset.getItemIDs().size(); i++) { itemIDIndexMapping.put(dataset.getItemIDs().get(i), i); } RatingMatrix trainRatingMatrix = new RatingMatrix(dataset.getUserIDs().size(), dataset.getItemIDs().size()); for (int i = 0; i < trainRatings.size(); i++) { trainRatingMatrix.set(userIDIndexMapping.get(trainRatings.get(i).getUserID()), itemIDIndexMapping.get(trainRatings.get(i).getItemID()), trainRatings.get(i).getValue()); } trainRatingMatrix.calculateGlobalAverage(); RatingMatrix testRatingMatrix = new RatingMatrix(dataset.getUserIDs().size(), dataset.getItemIDs().size()); for (int i = 0; i < testRatings.size(); i++) { testRatingMatrix.set(userIDIndexMapping.get(testRatings.get(i).getUserID()), itemIDIndexMapping.get(testRatings.get(i).getItemID()), testRatings.get(i).getValue()); } System.out.println("Training: " + trainRatingMatrix.getTotalRatingNumber() + " vs Test: " + testRatingMatrix.getTotalRatingNumber()); logger.println("Initialize a recommendation model based on user average method."); UserAverage algo = new UserAverage(trainRatingMatrix); algo.setLogger(logger); algo.build(); algo.saveModel(".//localModels//" + config.getString("NAME")); logger.println("Save the model."); System.out.println(trainRatings.size() + " vs. " + testRatings.size()); double RMSE = 0; double MAE = 0; int count = 0; for (int i = 0; i < testRatings.size(); i++) { NumericRating rating = testRatings.get(i); double prediction = algo.predict(userIDIndexMapping.get(rating.getUserID()), itemIDIndexMapping.get(rating.getItemID())); if (Double.isNaN(prediction)) { System.out.println("no prediction"); continue; } MAE = MAE + Math.abs(rating.getValue() - prediction); RMSE = RMSE + Math.pow((rating.getValue() - prediction), 2); count++; } MAE = MAE / count; RMSE = Math.sqrt(RMSE / count); logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " MAE: " + MAE + " RMSE: " + RMSE); logger.flush(); totalMAE = totalMAE + MAE; totalRMSE = totalRMSE + RMSE; } System.out.println("MAE: " + totalMAE / F + " RMSE: " + totalRMSE / F); logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " Final results: MAE: " + totalMAE / F + " RMSE: " + totalRMSE / F); logger.flush(); logger.close(); //MAE: 0.8353035962363073 RMSE: 1.0422971886952053 (MovieLens 100k) }
From source file:ch.epfl.lsir.xin.test.ItemAverageTest.java
/** * @param args//from w w w.j a v a 2s . co m */ public static void main(String[] args) throws Exception { // TODO Auto-generated method stub PrintWriter logger = new PrintWriter(".//results//ItemAverage"); PropertiesConfiguration config = new PropertiesConfiguration(); config.setFile(new File(".//conf//ItemAverage.properties")); try { config.load(); } catch (ConfigurationException e) { // TODO Auto-generated catch block e.printStackTrace(); } logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " Read rating data..."); DataLoaderFile loader = new DataLoaderFile(".//data//MoveLens100k.txt"); loader.readSimple(); DataSetNumeric dataset = loader.getDataset(); System.out.println("Number of ratings: " + dataset.getRatings().size() + " Number of users: " + dataset.getUserIDs().size() + " Number of items: " + dataset.getItemIDs().size()); logger.println("Number of ratings: " + dataset.getRatings().size() + ", Number of users: " + dataset.getUserIDs().size() + ", Number of items: " + dataset.getItemIDs().size()); logger.flush(); double totalMAE = 0; double totalRMSE = 0; int F = 5; logger.println(F + "- folder cross validation."); ArrayList<ArrayList<NumericRating>> folders = new ArrayList<ArrayList<NumericRating>>(); for (int i = 0; i < F; i++) { folders.add(new ArrayList<NumericRating>()); } while (dataset.getRatings().size() > 0) { int index = new Random().nextInt(dataset.getRatings().size()); int r = new Random().nextInt(F); folders.get(r).add(dataset.getRatings().get(index)); dataset.getRatings().remove(index); } for (int folder = 1; folder <= F; folder++) { logger.println("Folder: " + folder); logger.flush(); System.out.println("Folder: " + folder); ArrayList<NumericRating> trainRatings = new ArrayList<NumericRating>(); ArrayList<NumericRating> testRatings = new ArrayList<NumericRating>(); for (int i = 0; i < folders.size(); i++) { if (i == folder - 1)//test data { testRatings.addAll(folders.get(i)); } else {//training data trainRatings.addAll(folders.get(i)); } } //create rating matrix HashMap<String, Integer> userIDIndexMapping = new HashMap<String, Integer>(); HashMap<String, Integer> itemIDIndexMapping = new HashMap<String, Integer>(); for (int i = 0; i < dataset.getUserIDs().size(); i++) { userIDIndexMapping.put(dataset.getUserIDs().get(i), i); } for (int i = 0; i < dataset.getItemIDs().size(); i++) { itemIDIndexMapping.put(dataset.getItemIDs().get(i), i); } RatingMatrix trainRatingMatrix = new RatingMatrix(dataset.getUserIDs().size(), dataset.getItemIDs().size()); for (int i = 0; i < trainRatings.size(); i++) { trainRatingMatrix.set(userIDIndexMapping.get(trainRatings.get(i).getUserID()), itemIDIndexMapping.get(trainRatings.get(i).getItemID()), trainRatings.get(i).getValue()); } trainRatingMatrix.calculateGlobalAverage(); RatingMatrix testRatingMatrix = new RatingMatrix(dataset.getUserIDs().size(), dataset.getItemIDs().size()); for (int i = 0; i < testRatings.size(); i++) { testRatingMatrix.set(userIDIndexMapping.get(testRatings.get(i).getUserID()), itemIDIndexMapping.get(testRatings.get(i).getItemID()), testRatings.get(i).getValue()); } System.out.println("Training: " + trainRatingMatrix.getTotalRatingNumber() + " vs Test: " + testRatingMatrix.getTotalRatingNumber()); logger.println("Initialize a recommendation model based on item average method."); ItemAverage algo = new ItemAverage(trainRatingMatrix); algo.setLogger(logger); algo.build(); algo.saveModel(".//localModels//" + config.getString("NAME")); logger.println("Save the model."); logger.flush(); System.out.println(trainRatings.size() + " vs. " + testRatings.size()); double RMSE = 0; double MAE = 0; int count = 0; for (int i = 0; i < testRatings.size(); i++) { NumericRating rating = testRatings.get(i); double prediction = algo.predict(userIDIndexMapping.get(rating.getUserID()), itemIDIndexMapping.get(rating.getItemID())); if (Double.isNaN(prediction)) { System.out.println("no prediction"); continue; } MAE = MAE + Math.abs(rating.getValue() - prediction); RMSE = RMSE + Math.pow((rating.getValue() - prediction), 2); count++; } MAE = MAE / count; RMSE = Math.sqrt(RMSE / count); logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " MAE: " + MAE + " RMSE: " + RMSE); logger.flush(); // System.out.println("MAE: " + MAE + " RMSE: " + RMSE); totalMAE = totalMAE + MAE; totalRMSE = totalRMSE + RMSE; } System.out.println("MAE: " + totalMAE / F + " RMSE: " + totalRMSE / F); logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " Final results: MAE: " + totalMAE / F + " RMSE: " + totalRMSE / F); logger.flush(); //MAE: 0.8173633324758338 RMSE: 1.0251973503888645 (MovieLens 100K) }