List of usage examples for java.io ObjectOutputStream writeObject
public final void writeObject(Object obj) throws IOException
From source file:com.linkedin.sample.Main.java
public static void main(String[] args) { /*/*from w w w. java 2 s . com*/ we need a OAuthService to handle authentication and the subsequent calls. Since we are going to use the REST APIs we need to generate a request token as the first step in the call. Once we get an access toke we can continue to use that until the API key changes or auth is revoked. Therefore, to make this sample easier to re-use we serialize the AuthHandler (which stores the access token) to disk and then reuse it. When you first run this code please insure that you fill in the API_KEY and API_SECRET above with your own credentials and if there is a service.dat file in the code please delete it. */ //The Access Token is used in all Data calls to the APIs - it basically says our application has been given access //to the approved information in LinkedIn Token accessToken = null; //Using the Scribe library we enter the information needed to begin the chain of Oauth2 calls. OAuthService service = new ServiceBuilder().provider(LinkedInApi.class).apiKey(API_KEY) .apiSecret(API_SECRET).build(); /************************************* * This first piece of code handles all the pieces needed to be granted access to make a data call */ try { File file = new File("service.dat"); if (file.exists()) { //if the file exists we assume it has the AuthHandler in it - which in turn contains the Access Token ObjectInputStream inputStream = new ObjectInputStream(new FileInputStream(file)); AuthHandler authHandler = (AuthHandler) inputStream.readObject(); accessToken = authHandler.getAccessToken(); } else { System.out.println("There is no stored Access token we need to make one"); //In the constructor the AuthHandler goes through the chain of calls to create an Access Token AuthHandler authHandler = new AuthHandler(service); ObjectOutputStream outputStream = new ObjectOutputStream(new FileOutputStream("service.dat")); outputStream.writeObject(authHandler); outputStream.close(); accessToken = authHandler.getAccessToken(); } } catch (Exception e) { System.out.println("Threw an exception when serializing: " + e.getClass() + " :: " + e.getMessage()); } /* * We are all done getting access - time to get busy getting data *************************************/ /************************** * * Querying the LinkedIn API * **************************/ System.out.println(); System.out.println("********A basic user profile call********"); //The ~ means yourself - so this should return the basic default information for your profile in XML format //https://developer.linkedin.com/documents/profile-api String url = "http://api.linkedin.com/v1/people/~"; OAuthRequest request = new OAuthRequest(Verb.GET, url); service.signRequest(accessToken, request); Response response = request.send(); System.out.println(response.getBody()); System.out.println(); System.out.println(); System.out.println("********Get the profile in JSON********"); //This basic call profile in JSON format //You can read more about JSON here http://json.org url = "http://api.linkedin.com/v1/people/~"; request = new OAuthRequest(Verb.GET, url); request.addHeader("x-li-format", "json"); service.signRequest(accessToken, request); response = request.send(); System.out.println(response.getBody()); System.out.println(); System.out.println(); System.out.println("********Get the profile in JSON using query parameter********"); //This basic call profile in JSON format. Please note the call above is the preferred method. //You can read more about JSON here http://json.org url = "http://api.linkedin.com/v1/people/~"; request = new OAuthRequest(Verb.GET, url); request.addQuerystringParameter("format", "json"); service.signRequest(accessToken, request); response = request.send(); System.out.println(response.getBody()); System.out.println(); System.out.println(); System.out.println("********Get my connections - going into a resource********"); //This basic call gets all your connections each one will be in a person tag with some profile information //https://developer.linkedin.com/documents/connections-api url = "http://api.linkedin.com/v1/people/~/connections"; request = new OAuthRequest(Verb.GET, url); service.signRequest(accessToken, request); response = request.send(); System.out.println(response.getBody()); System.out.println(); System.out.println(); System.out.println("********Get only 10 connections - using parameters********"); //This basic call gets only 10 connections - each one will be in a person tag with some profile information //https://developer.linkedin.com/documents/connections-api //more basic about query strings in a URL here http://en.wikipedia.org/wiki/Query_string url = "http://api.linkedin.com/v1/people/~/connections"; request = new OAuthRequest(Verb.GET, url); request.addQuerystringParameter("count", "10"); service.signRequest(accessToken, request); response = request.send(); System.out.println(response.getBody()); System.out.println(); System.out.println(); System.out.println("********GET network updates that are CONN and SHAR********"); //This basic call get connection updates from your connections //https://developer.linkedin.com/documents/get-network-updates-and-statistics-api //specifics on updates https://developer.linkedin.com/documents/network-update-types url = "http://api.linkedin.com/v1/people/~/network/updates"; request = new OAuthRequest(Verb.GET, url); request.addQuerystringParameter("type", "SHAR"); request.addQuerystringParameter("type", "CONN"); service.signRequest(accessToken, request); response = request.send(); System.out.println(response.getBody()); System.out.println(); System.out.println(); System.out.println("********People Search using facets and Encoding input parameters i.e. UTF8********"); //This basic call get connection updates from your connections //https://developer.linkedin.com/documents/people-search-api#Facets //Why doesn't this look like //people-search?title=developer&location=fr&industry=4 //url = "http://api.linkedin.com/v1/people-search?title=D%C3%A9veloppeur&facets=location,industry&facet=location,fr,0"; url = "http://api.linkedin.com/v1/people-search:(people:(first-name,last-name,headline),facets:(code,buckets))"; request = new OAuthRequest(Verb.GET, url); request.addQuerystringParameter("title", "Dveloppeur"); request.addQuerystringParameter("facet", "industry,4"); request.addQuerystringParameter("facets", "location,industry"); System.out.println(request.getUrl()); service.signRequest(accessToken, request); response = request.send(); System.out.println(response.getBody()); System.out.println(); System.out.println(); /////////////////field selectors System.out.println("********A basic user profile call with field selectors********"); //The ~ means yourself - so this should return the basic default information for your profile in XML format //https://developer.linkedin.com/documents/field-selectors url = "http://api.linkedin.com/v1/people/~:(first-name,last-name,positions)"; request = new OAuthRequest(Verb.GET, url); service.signRequest(accessToken, request); response = request.send(); System.out.println(response.getHeaders().toString()); System.out.println(response.getBody()); System.out.println(); System.out.println(); System.out .println("********A basic user profile call with field selectors going into a subresource********"); //The ~ means yourself - so this should return the basic default information for your profile in XML format //https://developer.linkedin.com/documents/field-selectors url = "http://api.linkedin.com/v1/people/~:(first-name,last-name,positions:(company:(name)))"; request = new OAuthRequest(Verb.GET, url); service.signRequest(accessToken, request); response = request.send(); System.out.println(response.getHeaders().toString()); System.out.println(response.getBody()); System.out.println(); System.out.println(); System.out.println("********A basic user profile call into a subresource return data in JSON********"); //The ~ means yourself - so this should return the basic default information for your profile //https://developer.linkedin.com/documents/field-selectors url = "https://api.linkedin.com/v1/people/~/connections:(first-name,last-name,headline)?format=json"; request = new OAuthRequest(Verb.GET, url); service.signRequest(accessToken, request); response = request.send(); System.out.println(response.getHeaders().toString()); System.out.println(response.getBody()); System.out.println(); System.out.println(); System.out.println("********A more complicated example using postings into groups********"); //https://developer.linkedin.com/documents/field-selectors //https://developer.linkedin.com/documents/groups-api url = "http://api.linkedin.com/v1/groups/3297124/posts:(id,category,creator:(id,first-name,last-name),title,summary,creation-timestamp,site-group-post-url,comments,likes)"; request = new OAuthRequest(Verb.GET, url); service.signRequest(accessToken, request); response = request.send(); System.out.println(response.getHeaders().toString()); System.out.println(response.getBody()); System.out.println(); System.out.println(); /************************** * * Wrting to the LinkedIn API * **************************/ /* * Commented out so we don't write into your LinkedIn/Twitter feed while you are just testing out * some code. Uncomment if you'd like to see writes in action. * * System.out.println("********Write to the share - using XML********"); //This basic shares some basic information on the users activity stream //https://developer.linkedin.com/documents/share-api url = "http://api.linkedin.com/v1/people/~/shares"; request = new OAuthRequest(Verb.POST, url); request.addHeader("Content-Type", "text/xml"); //Make an XML document Document doc = DocumentHelper.createDocument(); Element share = doc.addElement("share"); share.addElement("comment").addText("Guess who is testing the LinkedIn REST APIs"); Element content = share.addElement("content"); content.addElement("title").addText("A title for your share"); content.addElement("submitted-url").addText("http://developer.linkedin.com"); share.addElement("visibility").addElement("code").addText("anyone"); request.addPayload(doc.asXML()); service.signRequest(accessToken, request); response = request.send(); //there is no body just a header System.out.println(response.getBody()); System.out.println(response.getHeaders().toString()); System.out.println();System.out.println(); System.out.println("********Write to the share and to Twitter - using XML********"); //This basic shares some basic information on the users activity stream //https://developer.linkedin.com/documents/share-api url = "http://api.linkedin.com/v1/people/~/shares"; request = new OAuthRequest(Verb.POST, url); request.addQuerystringParameter("twitter-post","true"); request.addHeader("Content-Type", "text/xml"); //Make an XML document doc = DocumentHelper.createDocument(); share = doc.addElement("share"); share.addElement("comment").addText("Guess who is testing the LinkedIn REST APIs and sending to twitter"); content = share.addElement("content"); content.addElement("title").addText("A title for your share"); content.addElement("submitted-url").addText("http://developer.linkedin.com"); share.addElement("visibility").addElement("code").addText("anyone"); request.addPayload(doc.asXML()); service.signRequest(accessToken, request); response = request.send(); //there is no body just a header System.out.println(response.getBody()); System.out.println(response.getHeaders().toString()); System.out.println();System.out.println(); System.out.println("********Write to the share and to twitter - using JSON ********"); //This basic shares some basic information on the users activity stream //https://developer.linkedin.com/documents/share-api //NOTE - a good troubleshooting step is to validate your JSON on jsonlint.org url = "http://api.linkedin.com/v1/people/~/shares"; request = new OAuthRequest(Verb.POST, url); //set the headers to the server knows what we are sending request.addHeader("Content-Type", "application/json"); request.addHeader("x-li-format", "json"); //make the json payload using json-simple Map<String, Object> jsonMap = new HashMap<String, Object>(); jsonMap.put("comment", "Posting from the API using JSON"); JSONObject contentObject = new JSONObject(); contentObject.put("title", "This is a another test post"); contentObject.put("submitted-url","http://www.linkedin.com"); contentObject.put("submitted-image-url", "http://press.linkedin.com/sites/all/themes/presslinkedin/images/LinkedIn_WebLogo_LowResExample.jpg"); jsonMap.put("content", contentObject); JSONObject visibilityObject = new JSONObject(); visibilityObject.put("code", "anyone"); jsonMap.put("visibility", visibilityObject); request.addPayload(JSONValue.toJSONString(jsonMap)); service.signRequest(accessToken, request); response = request.send(); //again no body - just headers System.out.println(response.getBody()); System.out.println(response.getHeaders().toString()); System.out.println();System.out.println(); */ /************************** * * Understanding the response, creating logging, request and response headers * **************************/ System.out.println(); System.out.println("********A basic user profile call and response dissected********"); //This sample is mostly to help you debug and understand some of the scaffolding around the request-response cycle //https://developer.linkedin.com/documents/debugging-api-calls url = "https://api.linkedin.com/v1/people/~"; request = new OAuthRequest(Verb.GET, url); service.signRequest(accessToken, request); response = request.send(); //get all the headers System.out.println("Request headers: " + request.getHeaders().toString()); System.out.println("Response headers: " + response.getHeaders().toString()); //url requested System.out.println("Original location is: " + request.getHeaders().get("content-location")); //Date of response System.out.println("The datetime of the response is: " + response.getHeader("Date")); //the format of the response System.out.println("Format is: " + response.getHeader("x-li-format")); //Content-type of the response System.out.println("Content type is: " + response.getHeader("Content-Type") + "\n\n"); //get the HTTP response code - such as 200 or 404 int responseNumber = response.getCode(); if (responseNumber >= 199 && responseNumber < 300) { System.out.println("HOORAY IT WORKED!!"); System.out.println(response.getBody()); } else if (responseNumber >= 500 && responseNumber < 600) { //you could actually raise an exception here in your own code System.out.println("Ruh Roh application error of type 500: " + responseNumber); System.out.println(response.getBody()); } else if (responseNumber == 403) { System.out.println("A 403 was returned which usually means you have reached a throttle limit"); } else if (responseNumber == 401) { System.out.println("A 401 was returned which is a Oauth signature error"); System.out.println(response.getBody()); } else if (responseNumber == 405) { System.out.println( "A 405 response was received. Usually this means you used the wrong HTTP method (GET when you should POST, etc)."); } else { System.out.println("We got a different response that we should add to the list: " + responseNumber + " and report it in the forums"); System.out.println(response.getBody()); } System.out.println(); System.out.println(); System.out.println("********A basic error logging function********"); // Now demonstrate how to make a logging function which provides us the info we need to // properly help debug issues. Please use the logged block from here when requesting // help in the forums. url = "https://api.linkedin.com/v1/people/FOOBARBAZ"; request = new OAuthRequest(Verb.GET, url); service.signRequest(accessToken, request); response = request.send(); responseNumber = response.getCode(); if (responseNumber < 200 || responseNumber >= 300) { logDiagnostics(request, response); } else { System.out.println("You were supposed to submit a bad request"); } System.out.println("******Finished******"); }
From source file:ComplexCompany.java
public static void main(String args[]) throws Exception { Socket socket1;/*from w w w. j a v a 2 s . co m*/ int portNumber = 1777; String str = ""; socket1 = new Socket(InetAddress.getLocalHost(), portNumber); ObjectInputStream ois = new ObjectInputStream(socket1.getInputStream()); ObjectOutputStream oos = new ObjectOutputStream(socket1.getOutputStream()); ComplexCompany comp = new ComplexCompany("A"); ComplexEmployee emp0 = new ComplexEmployee("B", 1000); comp.addPresident(emp0); ComplexDepartment sales = new ComplexDepartment("C"); ComplexEmployee emp1 = new ComplexEmployee("D", 1200); sales.addManager(emp1); comp.addDepartment(sales); ComplexDepartment accounting = new ComplexDepartment("E"); ComplexEmployee emp2 = new ComplexEmployee("F", 1230); accounting.addManager(emp2); comp.addDepartment(accounting); ComplexDepartment maintenance = new ComplexDepartment("Maintenance"); ComplexEmployee emp3 = new ComplexEmployee("Greg Hladlick", 1020); maintenance.addManager(emp3); comp.addDepartment(maintenance); oos.writeObject(comp); while ((str = (String) ois.readObject()) != null) { System.out.println(str); oos.writeObject("bye"); if (str.equals("bye")) break; } ois.close(); oos.close(); socket1.close(); }
From source file:edu.usc.cssl.tacit.classify.naivebayes.services.Vectors2Classify.java
public static ArrayList<String> main(String[] args) throws bsh.EvalError, java.io.IOException { result.clear();// w w w. j a v a2 s .co m classifierTrainerStrings = new ArrayList<String>(); ReportOptions = new boolean[][] { { false, false, false, false }, { false, false, false, false }, { false, false, false, false } }; double pvalue = 0; // Process the command-line options CommandOption.setSummary(Vectors2Classify.class, "A tool for training, saving and printing diagnostics from a classifier on vectors."); CommandOption.process(Vectors2Classify.class, args); // handle default trainer here for now; default argument processing // doesn't work if (!trainerConstructor.wasInvoked()) { classifierTrainerStrings.add("new NaiveBayesTrainer()"); } if (!report.wasInvoked()) { ReportOptions = new boolean[][] { { true, false, false, false }, { true, false, true, false }, { false, false, false, false } }; //report.postParsing(null); // force postprocessing of default value } int verbosity = verbosityOption.value; Logger rootLogger = ((MalletLogger) progressLogger).getRootLogger(); if (verbosityOption.wasInvoked()) { rootLogger.setLevel(MalletLogger.LoggingLevels[verbosity]); } if (noOverwriteProgressMessagesOption.value == false) { // install special formatting for progress messages // find console handler on root logger; change formatter to one // that knows about progress messages Handler[] handlers = rootLogger.getHandlers(); for (int i = 0; i < handlers.length; i++) { if (handlers[i] instanceof ConsoleHandler) { handlers[i].setFormatter(new ProgressMessageLogFormatter()); } } } boolean separateIlists = testFile.wasInvoked() || trainingFile.wasInvoked() || validationFile.wasInvoked(); InstanceList ilist = null; InstanceList testFileIlist = null; InstanceList trainingFileIlist = null; InstanceList validationFileIlist = null; if (!separateIlists) { // normal case, --input-file specified // Read in the InstanceList, from stdin if the input filename is // "-". ilist = InstanceList.load(new File(inputFile.value)); //ilist = new InstanceList(ilist.getAlphabet(), ilist.getAlphabet()); } else { // user specified separate files for testing and training sets. trainingFileIlist = InstanceList.load(new File(trainingFile.value)); logger.info("Training vectors loaded from " + trainingFile.value); if (testFile.wasInvoked()) { testFileIlist = InstanceList.load(new File(testFile.value)); logger.info("Testing vectors loaded from " + testFile.value); if (!testFileIlist.getPipe().alphabetsMatch(trainingFileIlist.getPipe())) { throw new RuntimeException(trainingFileIlist.getPipe().getDataAlphabet() + "\n" + testFileIlist.getPipe().getDataAlphabet() + "\n" + trainingFileIlist.getPipe().getTargetAlphabet() + "\n" + testFileIlist.getPipe().getTargetAlphabet() + "\n" + "Training and testing alphabets don't match!\n"); } } if (validationFile.wasInvoked()) { validationFileIlist = InstanceList.load(new File(validationFile.value)); logger.info("validation vectors loaded from " + validationFile.value); if (!validationFileIlist.getPipe().alphabetsMatch(trainingFileIlist.getPipe())) { throw new RuntimeException(trainingFileIlist.getPipe().getDataAlphabet() + "\n" + validationFileIlist.getPipe().getDataAlphabet() + "\n" + trainingFileIlist.getPipe().getTargetAlphabet() + "\n" + validationFileIlist.getPipe().getTargetAlphabet() + "\n" + "Training and validation alphabets don't match!\n"); } } else { validationFileIlist = new InstanceList(new cc.mallet.pipe.Noop()); } } if (crossValidation.wasInvoked() && trainingProportionOption.wasInvoked()) { logger.warning( "Both --cross-validation and --training-portion were invoked. Using cross validation with " + crossValidation.value + " folds."); } if (crossValidation.wasInvoked() && validationProportionOption.wasInvoked()) { logger.warning( "Both --cross-validation and --validation-portion were invoked. Using cross validation with " + crossValidation.value + " folds."); } if (crossValidation.wasInvoked() && numTrialsOption.wasInvoked()) { logger.warning("Both --cross-validation and --num-trials were invoked. Using cross validation with " + crossValidation.value + " folds."); } int numTrials; if (crossValidation.wasInvoked()) { numTrials = crossValidation.value; } else { numTrials = numTrialsOption.value; } Random r = randomSeedOption.wasInvoked() ? new Random(randomSeedOption.value) : new Random(); int numTrainers = classifierTrainerStrings.size(); double trainAccuracy[][] = new double[numTrainers][numTrials]; double testAccuracy[][] = new double[numTrainers][numTrials]; double validationAccuracy[][] = new double[numTrainers][numTrials]; String trainConfusionMatrix[][] = new String[numTrainers][numTrials]; String testConfusionMatrix[][] = new String[numTrainers][numTrials]; String validationConfusionMatrix[][] = new String[numTrainers][numTrials]; double t = trainingProportionOption.value; double v = validationProportionOption.value; if (!separateIlists) { if (crossValidation.wasInvoked()) { logger.info("Cross-validation folds = " + crossValidation.value); } else { logger.info("Training portion = " + t); logger.info(" Unlabeled training sub-portion = " + unlabeledProportionOption.value); logger.info("Validation portion = " + v); logger.info("Testing portion = " + (1 - v - t)); } } // for (int i=0; i<3; i++){ // for (int j=0; j<4; j++){ // System.out.print(" " + ReportOptions[i][j]); // } // System.out.println(); // } CrossValidationIterator cvIter; if (crossValidation.wasInvoked()) { if (crossValidation.value < 2) { throw new RuntimeException( "At least two folds (set with --cross-validation) are required for cross validation"); } //System.out.println("Alphabets : "+ ilist.getDataAlphabet() +":"+ ilist.getTargetAlphabet()); cvIter = new CrossValidationIterator(ilist, crossValidation.value, r); } else { cvIter = null; } String[] trainerNames = new String[numTrainers]; for (int trialIndex = 0; trialIndex < numTrials; trialIndex++) { System.out.println("\n-------------------- Trial " + trialIndex + " --------------------\n"); InstanceList[] ilists; BitSet unlabeledIndices = null; if (!separateIlists) { if (crossValidation.wasInvoked()) { InstanceList[] cvSplit = cvIter.next(); ilists = new InstanceList[3]; ilists[0] = cvSplit[0]; ilists[1] = cvSplit[1]; ilists[2] = cvSplit[0].cloneEmpty(); } else { ilists = ilist.split(r, new double[] { t, 1 - t - v, v }); } } else { ilists = new InstanceList[3]; ilists[0] = trainingFileIlist; ilists[1] = testFileIlist; ilists[2] = validationFileIlist; } if (unlabeledProportionOption.value > 0) unlabeledIndices = new cc.mallet.util.Randoms(r.nextInt()).nextBitSet(ilists[0].size(), unlabeledProportionOption.value); // InfoGain ig = new InfoGain (ilists[0]); // int igl = Math.min (10, ig.numLocations()); // for (int i = 0; i < igl; i++) // System.out.println // ("InfoGain["+ig.getObjectAtRank(i)+"]="+ig.getValueAtRank(i)); // ig.print(); // FeatureSelection selectedFeatures = new FeatureSelection (ig, // 8000); // ilists[0].setFeatureSelection (selectedFeatures); // OddsRatioFeatureInducer orfi = new OddsRatioFeatureInducer // (ilists[0]); // orfi.induceFeatures (ilists[0], false, true); // System.out.println // ("Training with "+ilists[0].size()+" instances"); long time[] = new long[numTrainers]; for (int c = 0; c < numTrainers; c++) { time[c] = System.currentTimeMillis(); ClassifierTrainer trainer = getTrainer(classifierTrainerStrings.get(c)); trainer.setValidationInstances(ilists[2]); // ConsoleView.writeInConsole("Trial " + trialIndex + " Training " + trainer + " with " + ilists[0].size() + " instances"); ConsoleView.printlInConsoleln("Training " + trainer + " with " + ilists[0].size() + " instances"); if (unlabeledProportionOption.value > 0) ilists[0].hideSomeLabels(unlabeledIndices); Classifier classifier = trainer.train(ilists[0]); if (unlabeledProportionOption.value > 0) ilists[0].unhideAllLabels(); //ConsoleView.writeInConsole("Trial " + trialIndex + " Training " + trainer.toString() + " finished"); ConsoleView.printlInConsoleln("Training " + trainer.toString() + " finished"); time[c] = System.currentTimeMillis() - time[c]; Trial trainTrial = new Trial(classifier, ilists[0]); // assert (ilists[1].size() > 0); Trial testTrial = new Trial(classifier, ilists[1]); Trial validationTrial = new Trial(classifier, ilists[2]); // gdruck - only perform evaluation if requested in report // options if (ReportOptions[ReportOption.train][ReportOption.confusion] && ilists[0].size() > 0) trainConfusionMatrix[c][trialIndex] = new ConfusionMatrix(trainTrial).toString(); if (ReportOptions[ReportOption.test][ReportOption.confusion] && ilists[1].size() > 0) testConfusionMatrix[c][trialIndex] = new ConfusionMatrix(testTrial).toString(); if (ReportOptions[ReportOption.validation][ReportOption.confusion] && ilists[2].size() > 0) validationConfusionMatrix[c][trialIndex] = new ConfusionMatrix(validationTrial).toString(); // gdruck - only perform evaluation if requested in report // options if (ReportOptions[ReportOption.train][ReportOption.accuracy]) trainAccuracy[c][trialIndex] = trainTrial.getAccuracy(); if (ReportOptions[ReportOption.test][ReportOption.accuracy]) testAccuracy[c][trialIndex] = testTrial.getAccuracy(); if (ReportOptions[ReportOption.validation][ReportOption.accuracy]) validationAccuracy[c][trialIndex] = validationTrial.getAccuracy(); if (outputFile.wasInvoked()) { String filename = outputFile.value; if (numTrainers > 1) filename = filename + trainer.toString(); if (numTrials > 1) filename = filename + ".trial" + trialIndex; try { ObjectOutputStream oos = new ObjectOutputStream(new FileOutputStream(filename)); oos.writeObject(classifier); oos.close(); } catch (Exception e) { e.printStackTrace(); throw new IllegalArgumentException("Couldn't write classifier to filename " + filename); } } // New Reporting // raw output if (ReportOptions[ReportOption.train][ReportOption.raw]) { System.out.println("Trial " + trialIndex + " Trainer " + trainer.toString()); System.out.println(" Raw Training Data"); printTrialClassification(trainTrial); } if (ReportOptions[ReportOption.test][ReportOption.raw]) { System.out.println("Trial " + trialIndex + " Trainer " + trainer.toString()); System.out.println(" Raw Testing Data"); printTrialClassification(testTrial); //System.out.println("Report Option :"+(ReportOptions[ReportOption.test][ReportOption.raw])); } if (ReportOptions[ReportOption.validation][ReportOption.raw]) { System.out.println("Trial " + trialIndex + " Trainer " + trainer.toString()); System.out.println(" Raw Validation Data"); printTrialClassification(validationTrial); } System.out.println( "Bino test vars size " + ilists[1].size() + "and accuracy + " + testTrial.getAccuracy() + " then success " + (int) testTrial.getAccuracy() * ilists[1].size()); BinomialTest binomtest = new BinomialTest(); double p = 0.5; // train if (ReportOptions[ReportOption.train][ReportOption.confusion]) { //ConsoleView.writeInConsole("Trial " + trialIndex + " Trainer " + trainer.toString() + " Training Data Confusion Matrix"); ConsoleView.printlInConsoleln(trainer.toString() + " Training Data Confusion Matrix"); if (ilists[0].size() > 0) ConsoleView.printlInConsoleln(trainConfusionMatrix[c][trialIndex]); } if (ReportOptions[ReportOption.train][ReportOption.accuracy]) { pvalue = binomtest.binomialTest(ilists[0].size(), (int) (trainTrial.getAccuracy() * ilists[0].size()), p, AlternativeHypothesis.TWO_SIDED); if (pvalue != 0) { if (pvalue > 0.5) pvalue = Math.abs(pvalue - 1); ConsoleView.printlInConsoleln("Binomial 2-Sided P value = " + pvalue + "\n"); } //ConsoleView.writeInConsole("Trial " + trialIndex + " Trainer " + trainer.toString() + " training data accuracy= " + trainAccuracy[c][trialIndex]); ConsoleView.printlInConsoleln( trainer.toString() + " training data accuracy= " + trainAccuracy[c][trialIndex]); } if (ReportOptions[ReportOption.train][ReportOption.f1]) { String label = ReportOptionArgs[ReportOption.train][ReportOption.f1]; //ConsoleView.writeInConsole("Trial " + trialIndex + " Trainer "+ trainer.toString() + " training data F1(" + label + ") = " + trainTrial.getF1(label)); ConsoleView.printlInConsoleln( trainer.toString() + " training data F1(" + label + ") = " + trainTrial.getF1(label)); } // validation if (ReportOptions[ReportOption.validation][ReportOption.confusion]) { // ConsoleView.writeInConsole("Trial " + trialIndex + " Trainer " + trainer.toString() + " Validation Data Confusion Matrix"); ConsoleView.printlInConsoleln(trainer.toString() + " Validation Data Confusion Matrix"); if (ilists[2].size() > 0) ConsoleView.printlInConsoleln(validationConfusionMatrix[c][trialIndex]); } if (ReportOptions[ReportOption.validation][ReportOption.accuracy]) { //ConsoleView.writeInConsole("Trial " + trialIndex + " Trainer " + trainer.toString() + " validation data accuracy= " + validationAccuracy[c][trialIndex]); ConsoleView.printlInConsoleln( trainer.toString() + " validation data accuracy= " + validationAccuracy[c][trialIndex]); } if (ReportOptions[ReportOption.validation][ReportOption.f1]) { String label = ReportOptionArgs[ReportOption.validation][ReportOption.f1]; //ConsoleView.writeInConsole("Trial " + trialIndex + " Trainer " + trainer.toString() + " validation data F1(" + label + ") = " + validationTrial.getF1(label)); ConsoleView.printlInConsoleln(trainer.toString() + " validation data F1(" + label + ") = " + validationTrial.getF1(label)); } // test if (ReportOptions[ReportOption.test][ReportOption.confusion]) { //ConsoleView.writeInConsole("Trial " + trialIndex + " Trainer " + trainer.toString() + " Test Data Confusion Matrix"); ConsoleView.printlInConsoleln(trainer.toString() + " Test Data Confusion Matrix"); if (ilists[1].size() > 0) ConsoleView.printlInConsoleln(testConfusionMatrix[c][trialIndex]); } if (ReportOptions[ReportOption.test][ReportOption.accuracy]) { pvalue = binomtest.binomialTest(ilists[1].size(), (int) (testTrial.getAccuracy() * ilists[1].size()), 0.5, AlternativeHypothesis.TWO_SIDED); if (pvalue != 0) { if (pvalue > 0.5) pvalue = Math.abs(pvalue - 1); ConsoleView.printlInConsoleln("Binomial 2-Sided P value = " + pvalue + " \n"); } //ConsoleView.writeInConsole("Trial " + trialIndex + " Trainer " + trainer.toString() + " test data accuracy= " + testAccuracy[c][trialIndex]); ConsoleView.printlInConsoleln( trainer.toString() + " test data accuracy= " + testAccuracy[c][trialIndex]); } if (ReportOptions[ReportOption.test][ReportOption.f1]) { String label = ReportOptionArgs[ReportOption.test][ReportOption.f1]; //ConsoleView.writeInConsole("Trial " + trialIndex + " Trainer " + trainer.toString() + " test data F1(" + label + ") = " + testTrial.getF1(label)); ConsoleView.printlInConsoleln( trainer.toString() + " test data F1(" + label + ") = " + testTrial.getF1(label)); } if (trialIndex == 0) trainerNames[c] = trainer.toString(); } // end for each trainer } // end for each trial // New reporting // "[train|test|validation]:[accuracy|f1|confusion|raw]" for (int c = 0; c < numTrainers; c++) { ConsoleView.printlInConsole("\n" + trainerNames[c].toString() + "\n"); if (ReportOptions[ReportOption.train][ReportOption.accuracy]) { /*ConsoleView.printlInConsoleln("Summary. train accuracy mean = " + MatrixOps.mean(trainAccuracy[c]) + " stddev = " + MatrixOps.stddev(trainAccuracy[c]) + " stderr = " + MatrixOps.stderr(trainAccuracy[c])); */ String trainResult = ""; if (pvalue != 0) trainResult += "Summary. train accuracy = " + MatrixOps.mean(trainAccuracy[c]); else trainResult += "Summary. train accuracy = " + MatrixOps.mean(trainAccuracy[c]); if (numTrials > 1) { trainResult += " stddev = " + MatrixOps.stddev(trainAccuracy[c]) + " stderr = " + MatrixOps.stderr(trainAccuracy[c]); } ConsoleView.printlInConsoleln(trainResult); } if (ReportOptions[ReportOption.validation][ReportOption.accuracy]) { /* ConsoleView.printlInConsoleln("Summary. validation accuracy mean = " + MatrixOps.mean(validationAccuracy[c]) + " stddev = " + MatrixOps.stddev(validationAccuracy[c]) + " stderr = " + MatrixOps.stderr(validationAccuracy[c]));*/ String validationResult = ""; if (pvalue != 0) validationResult += "Summary. validation accuracy = " + MatrixOps.mean(validationAccuracy[c]); else validationResult += "Summary. validation accuracy = " + MatrixOps.mean(validationAccuracy[c]); if (numTrials > 1) { validationResult += " stddev = " + MatrixOps.stddev(validationAccuracy[c]) + " stderr = " + MatrixOps.stderr(validationAccuracy[c]); } ConsoleView.printlInConsoleln(validationResult); } if (ReportOptions[ReportOption.test][ReportOption.accuracy]) { String testResult = ""; if (pvalue != 0) testResult += "Summary. test accuracy = " + MatrixOps.mean(testAccuracy[c]) + " Binomial 2-Sided Pvalue = " + pvalue; else testResult += "Summary. test accuracy = " + MatrixOps.mean(testAccuracy[c]) + " Pvalue < 10^(-1022)\n"; if (numTrials > 1) { testResult += " stddev = " + MatrixOps.stddev(testAccuracy[c]) + " stderr = " + MatrixOps.stderr(testAccuracy[c]); } ConsoleView.printlInConsoleln(testResult); /* if (pvalue != 0) ConsoleView.printlInConsoleln("Summary. test accuracy mean = " + MatrixOps.mean(testAccuracy[c]) + " stddev = " + MatrixOps.stddev(testAccuracy[c]) + " stderr = " + MatrixOps.stderr(testAccuracy[c]) + " pvalue = " + pvalue); else ConsoleView.printlInConsoleln("Summary. test accuracy mean = " + MatrixOps.mean(testAccuracy[c]) + " stddev = " + MatrixOps.stddev(testAccuracy[c]) + " stderr = " + MatrixOps.stderr(testAccuracy[c]) + " P value < 10^(-1022)\n"); */ } // If we are testing the classifier with two folders, result will be // empty - no report is generated if (result.isEmpty()) { if (pvalue != 0) result.add("Summary. test accuracy = " + MatrixOps.mean(testAccuracy[c]) + " Binomial 2-Sided Pvalue = " + pvalue); else result.add("Summary. test accuracy = " + MatrixOps.mean(testAccuracy[c]) + " Pvalue < 10^(-1022)\n"); if (numTrials > 1) { result.add(" stddev = " + MatrixOps.stddev(testAccuracy[c]) + " stderr = " + MatrixOps.stderr(testAccuracy[c])); } } } // end for each trainer return result; }
From source file:com.yahoo.labs.yamall.local.Yamall.java
public static void main(String[] args) { String[] remainingArgs = null; String inputFile = null;//from ww w . j av a2s . c om String predsFile = null; String saveModelFile = null; String initialModelFile = null; String lossName = null; String parserName = null; String linkName = null; String invertHashName = null; double learningRate = 1; String minPredictionString = null; String maxPredictionString = null; String fmNumberFactorsString = null; int bitsHash; int numberPasses; int holdoutPeriod = 10; boolean testOnly = false; boolean exponentialProgress; double progressInterval; options.addOption("h", "help", false, "displays this help"); options.addOption("t", false, "ignore label information and just test"); options.addOption(Option.builder().hasArg(false).required(false).longOpt("binary") .desc("reports loss as binary classification with -1,1 labels").build()); options.addOption( Option.builder().hasArg(false).required(false).longOpt("solo").desc("uses SOLO optimizer").build()); options.addOption(Option.builder().hasArg(false).required(false).longOpt("pcsolo") .desc("uses Per Coordinate SOLO optimizer").build()); options.addOption(Option.builder().hasArg(false).required(false).longOpt("pistol") .desc("uses PiSTOL optimizer").build()); options.addOption(Option.builder().hasArg(false).required(false).longOpt("kt") .desc("(EXPERIMENTAL) uses KT optimizer").build()); options.addOption(Option.builder().hasArg(false).required(false).longOpt("pckt") .desc("(EXPERIMENTAL) uses Per Coordinate KT optimizer").build()); options.addOption(Option.builder().hasArg(false).required(false).longOpt("pccocob") .desc("(EXPERIMENTAL) uses Per Coordinate COCOB optimizer").build()); options.addOption(Option.builder().hasArg(false).required(false).longOpt("cocob") .desc("(EXPERIMENTAL) uses COCOB optimizer").build()); options.addOption( Option.builder().hasArg(false).required(false).longOpt("fm").desc("Factorization Machine").build()); options.addOption(Option.builder("f").hasArg(true).required(false).desc("final regressor to save") .type(String.class).longOpt("final_regressor").build()); options.addOption(Option.builder("p").hasArg(true).required(false).desc("file to output predictions to") .longOpt("predictions").type(String.class).build()); options.addOption( Option.builder("i").hasArg(true).required(false).desc("initial regressor(s) to load into memory") .longOpt("initial_regressor").type(String.class).build()); options.addOption(Option.builder().hasArg(true).required(false).desc( "specify the loss function to be used. Currently available ones are: absolute, squared (default), hinge, logistic") .longOpt("loss_function").type(String.class).build()); options.addOption(Option.builder().hasArg(true).required(false).desc( "specify the link function used in the output of the predictions. Currently available ones are: identity (default), logistic") .longOpt("link").type(String.class).build()); options.addOption(Option.builder().hasArg(true).required(false) .desc("output human-readable final regressor with feature names").longOpt("invert_hash") .type(String.class).build()); options.addOption( Option.builder("l").hasArg(true).required(false).desc("set (initial) learning Rate, default = 1.0") .longOpt("learning_rate").type(String.class).build()); options.addOption(Option.builder("b").hasArg(true).required(false) .desc("number of bits in the feature table, default = 18").longOpt("bit_precision") .type(String.class).build()); options.addOption(Option.builder("P").hasArg(true).required(false) .desc("progress update frequency, integer: additive; float: multiplicative, default = 2.0") .longOpt("progress").type(String.class).build()); options.addOption(Option.builder().hasArg(true).required(false) .desc("smallest prediction to output, before the link function, default = -50") .longOpt("min_prediction").type(String.class).build()); options.addOption(Option.builder().hasArg(true).required(false) .desc("smallest prediction to output, before the link function, default = 50") .longOpt("max_prediction").type(String.class).build()); options.addOption(Option.builder().hasArg(true).required(false) .desc("ignore namespaces beginning with the characters in <arg>").longOpt("ignore") .type(String.class).build()); options.addOption(Option.builder().hasArg(true).required(false).desc("number of training passes") .longOpt("passes").type(String.class).build()); options.addOption( Option.builder().hasArg(true).required(false).desc("holdout period for test only, default = 10") .longOpt("holdout_period").type(String.class).build()); options.addOption(Option.builder().hasArg(true).required(false) .desc("number of factors for Factorization Machines default = 8").longOpt("fmNumberFactors") .type(String.class).build()); options.addOption(Option.builder().hasArg(true).required(false) .desc("specify the parser to use. Currently available ones are: vw (default), libsvm, tsv") .longOpt("parser").type(String.class).build()); options.addOption(Option.builder().hasArg(true).required(false).desc("schema file for the TSV input") .longOpt("schema").type(String.class).build()); CommandLineParser parser = new DefaultParser(); CommandLine cmd = null; try { cmd = parser.parse(options, args); } catch (ParseException e) { System.out.println("Unrecognized option"); help(); } if (cmd.hasOption("h")) help(); if (cmd.hasOption("t")) testOnly = true; if (cmd.hasOption("binary")) { binary = true; System.out.println("Reporting binary loss"); } initialModelFile = cmd.getOptionValue("i"); predsFile = cmd.getOptionValue("p"); lossName = cmd.getOptionValue("loss_function", "squared"); linkName = cmd.getOptionValue("link", "identity"); saveModelFile = cmd.getOptionValue("f"); learningRate = Double.parseDouble(cmd.getOptionValue("l", "1.0")); bitsHash = Integer.parseInt(cmd.getOptionValue("b", "18")); invertHashName = cmd.getOptionValue("invert_hash"); minPredictionString = cmd.getOptionValue("min_prediction", "-50"); maxPredictionString = cmd.getOptionValue("max_prediction", "50"); fmNumberFactorsString = cmd.getOptionValue("fmNumberFactors", "8"); parserName = cmd.getOptionValue("parser", "vw"); numberPasses = Integer.parseInt(cmd.getOptionValue("passes", "1")); System.out.println("Number of passes = " + numberPasses); if (numberPasses > 1) { holdoutPeriod = Integer.parseInt(cmd.getOptionValue("holdout_period", "10")); System.out.println("Holdout period = " + holdoutPeriod); } remainingArgs = cmd.getArgs(); if (remainingArgs.length == 1) inputFile = remainingArgs[0]; InstanceParser instanceParser = null; if (parserName.equals("vw")) instanceParser = new VWParser(bitsHash, cmd.getOptionValue("ignore"), (invertHashName != null)); else if (parserName.equals("libsvm")) instanceParser = new LIBSVMParser(bitsHash, (invertHashName != null)); else if (parserName.equals("tsv")) { String schema = cmd.getOptionValue("schema"); if (schema == null) { System.out.println("TSV parser requires a schema file."); System.exit(0); } else { String spec = null; try { spec = new String(Files.readAllBytes(Paths.get(schema))); } catch (IOException e) { System.out.println("Error reading the TSV schema file."); e.printStackTrace(); System.exit(0); } instanceParser = new TSVParser(bitsHash, cmd.getOptionValue("ignore"), (invertHashName != null), spec); } } else { System.out.println("Unknown parser."); System.exit(0); } System.out.println("Num weight bits = " + bitsHash); // setup progress String progress = cmd.getOptionValue("P", "2.0"); if (progress.indexOf('.') >= 0) { exponentialProgress = true; progressInterval = (double) Double.parseDouble(progress); } else { exponentialProgress = false; progressInterval = (double) Integer.parseInt(progress); } // min and max predictions minPrediction = (double) Double.parseDouble(minPredictionString); maxPrediction = (double) Double.parseDouble(maxPredictionString); // number of factors for Factorization Machines fmNumberFactors = (int) Integer.parseInt(fmNumberFactorsString); // configure the learner Loss lossFnc = null; LinkFunction link = null; if (initialModelFile == null) { if (cmd.hasOption("kt")) { learner = new KT(bitsHash); } else if (cmd.hasOption("pckt")) { learner = new PerCoordinateKT(bitsHash); } else if (cmd.hasOption("pcsolo")) { learner = new PerCoordinateSOLO(bitsHash); } else if (cmd.hasOption("solo")) { learner = new SOLO(bitsHash); } else if (cmd.hasOption("pccocob")) { learner = new PerCoordinateCOCOB(bitsHash); } else if (cmd.hasOption("cocob")) { learner = new COCOB(bitsHash); } else if (cmd.hasOption("pistol")) { learner = new PerCoordinatePiSTOL(bitsHash); } else if (cmd.hasOption("fm")) { learner = new SGD_FM(bitsHash, fmNumberFactors); } else learner = new SGD_VW(bitsHash); } else { learner = IOLearner.loadLearner(initialModelFile); } // setup link function if (linkName.equals("identity")) { link = new IdentityLinkFunction(); } else if (linkName.equals("logistic")) { link = new LogisticLinkFunction(); } else { System.out.println("Unknown link function."); System.exit(0); } // setup loss function if (lossName.equals("squared")) { lossFnc = new SquareLoss(); } else if (lossName.equals("hinge")) { lossFnc = new HingeLoss(); } else if (lossName.equals("logistic")) { lossFnc = new LogisticLoss(); } else if (lossName.equals("absolute")) { lossFnc = new AbsLoss(); } else { System.out.println("Unknown loss function."); System.exit(0); } learner.setLoss(lossFnc); learner.setLearningRate(learningRate); // maximum range predictions System.out.println("Max prediction = " + maxPrediction + ", Min Prediction = " + minPrediction); // print information about the learner System.out.println(learner.toString()); // print information about the link function System.out.println(link.toString()); // print information about the parser System.out.println(instanceParser.toString()); // print information about ignored namespaces System.out.println("Ignored namespaces = " + cmd.getOptionValue("ignore", "")); long start = System.nanoTime(); FileInputStream fstream; try { BufferedReader br = null; if (inputFile != null) { fstream = new FileInputStream(inputFile); System.out.println("Reading datafile = " + inputFile); br = new BufferedReader(new InputStreamReader(fstream)); } else { System.out.println("Reading from console"); br = new BufferedReader(new InputStreamReader(System.in)); } File fout = null; FileOutputStream fos = null; BufferedWriter bw = null; if (predsFile != null) { fout = new File(predsFile); fos = new FileOutputStream(fout); bw = new BufferedWriter(new OutputStreamWriter(fos)); } try { System.out.println("average example current current current"); System.out.println("loss counter label predict features"); int iter = 0; double cumLoss = 0; double weightedSampleSum = 0; double sPlus = 0; double sMinus = 0; Instance sample = null; boolean justPrinted = false; int pass = 0; ObjectOutputStream ooutTr = null; ObjectOutputStream ooutHO = null; ObjectInputStream oinTr = null; double pred = 0; int limit = 1; double hError = Double.MAX_VALUE; double lastHError = Double.MAX_VALUE; int numTestSample = 0; int numTrainingSample = 0; int idx = 0; if (numberPasses > 1) { ooutTr = new ObjectOutputStream(new FileOutputStream("cache_training.bin")); ooutHO = new ObjectOutputStream(new FileOutputStream("cache_holdout.bin")); oinTr = new ObjectInputStream(new FileInputStream("cache_training.bin")); } do { while (true) { double score; if (pass > 0 && numberPasses > 1) { Instance tmp = (Instance) oinTr.readObject(); if (tmp != null) sample = tmp; else break; } else { String strLine = br.readLine(); if (strLine != null) sample = instanceParser.parse(strLine); else break; } justPrinted = false; idx++; if (numberPasses > 1 && pass == 0 && idx % holdoutPeriod == 0) { // store the current sample for the holdout set ooutHO.writeObject(sample); ooutHO.reset(); numTestSample++; } else { if (numberPasses > 1 && pass == 0) { ooutTr.writeObject(sample); ooutTr.reset(); numTrainingSample++; } iter++; if (testOnly) { // predict the sample score = learner.predict(sample); } else { // predict the sample and update the classifier using the sample score = learner.update(sample); } score = Math.min(Math.max(score, minPrediction), maxPrediction); pred = link.apply(score); if (!binary) cumLoss += learner.getLoss().lossValue(score, sample.getLabel()) * sample.getWeight(); else if (Math.signum(score) != sample.getLabel()) cumLoss += sample.getWeight(); weightedSampleSum += sample.getWeight(); if (sample.getLabel() > 0) sPlus = sPlus + sample.getWeight(); else sMinus = sMinus + sample.getWeight(); // output predictions to file if (predsFile != null) { bw.write(String.format("%.6f %s", pred, sample.getTag())); bw.newLine(); } // print statistics to screen if (iter == limit) { justPrinted = true; System.out.printf("%.6f %12d % .4f % .4f %d\n", cumLoss / weightedSampleSum, iter, sample.getLabel(), pred, sample.getVector().size()); if (exponentialProgress) limit *= progressInterval; else limit += progressInterval; } } } if (numberPasses > 1) { if (pass == 0) { // finished first pass of many // write a null at the end of the files ooutTr.writeObject(null); ooutHO.writeObject(null); ooutTr.flush(); ooutHO.flush(); ooutTr.close(); ooutHO.close(); System.out.println("finished first epoch"); System.out.println(numTrainingSample + " training samples"); System.out.println(numTestSample + " holdout samples saved"); } lastHError = hError; hError = evalHoldoutError(); } if (numberPasses > 1) { System.out.printf("Weighted loss on holdout on epoch %d = %.6f\n", pass + 1, hError); oinTr.close(); oinTr = new ObjectInputStream(new FileInputStream("cache_training.bin")); if (hError > lastHError) { System.out.println("Early stopping"); break; } } pass++; } while (pass < numberPasses); if (justPrinted == false) { System.out.printf("%.6f %12d % .4f % .4f %d\n", cumLoss / weightedSampleSum, iter, sample.getLabel(), pred, sample.getVector().size()); } System.out.println("finished run"); System.out.println(String.format("average loss best constant predictor: %.6f", lossFnc.lossConstantBinaryLabels(sPlus, sMinus))); if (saveModelFile != null) IOLearner.saveLearner(learner, saveModelFile); if (invertHashName != null) IOLearner.saveInvertHash(learner.getWeights(), instanceParser.getInvertHashMap(), invertHashName); } catch (IOException e) { // TODO Auto-generated catch block e.printStackTrace(); } catch (ClassNotFoundException e) { // TODO Auto-generated catch block e.printStackTrace(); } // close the input stream try { br.close(); } catch (IOException e) { // TODO Auto-generated catch block e.printStackTrace(); } // close the output stream if (predsFile != null) { try { bw.close(); } catch (IOException e) { // TODO Auto-generated catch block e.printStackTrace(); } } long millis = System.nanoTime() - start; System.out.printf("Elapsed time: %d min, %d sec\n", TimeUnit.NANOSECONDS.toMinutes(millis), TimeUnit.NANOSECONDS.toSeconds(millis) - 60 * TimeUnit.NANOSECONDS.toMinutes(millis)); } catch ( FileNotFoundException e) { System.out.println("Error opening the input file"); e.printStackTrace(); } }
From source file:Main.java
static byte[] serialize(Serializable obj) throws IOException { ByteArrayOutputStream b = new ByteArrayOutputStream(); ObjectOutputStream o = new ObjectOutputStream(b); o.writeObject(obj); return b.toByteArray(); }
From source file:Main.java
private static void writeToFile(String filename, Object object) throws Exception { ObjectOutputStream oos = new ObjectOutputStream(new FileOutputStream(new File(filename))); oos.writeObject(object); oos.flush();/*from w w w. j a va2s .c o m*/ oos.close(); }
From source file:KeyTools.java
public static void writeToFile(Key key, File file) throws IOException { FileOutputStream fileoutputstream = new FileOutputStream(file); ObjectOutputStream objectoutputstream = new ObjectOutputStream(fileoutputstream); objectoutputstream.writeObject(key); objectoutputstream.close();//from w w w .j ava 2s . com }
From source file:Main.java
static public byte[] getObjectBytes(Object o) throws IOException { ByteArrayOutputStream baos = new ByteArrayOutputStream(); ObjectOutputStream oos = new ObjectOutputStream(baos); oos.writeObject(o); oos.close();/*ww w . j a v a 2s . c om*/ return baos.toByteArray(); }
From source file:Main.java
public static void serializar(String path, Object obj) throws Exception { FileOutputStream outFile = new FileOutputStream(path); ObjectOutputStream s = new ObjectOutputStream(outFile); s.writeObject(obj); s.close();//w ww . ja v a 2s. c o m }
From source file:Main.java
/** * Serialize an object into bytes./*from ww w .ja v a 2 s . c o m*/ * @param o Object to be serialized. * @return Serialized stream of bytes. * @throws IOException */ public static byte[] objectToBytes(Serializable o) throws IOException { ByteArrayOutputStream out = new ByteArrayOutputStream(); ObjectOutputStream oo = new ObjectOutputStream(out); oo.writeObject(o); oo.close(); return out.toByteArray(); }