void saveUrl(String s) { SharedPreferences prefs = getSharedPreferences(getString(R.string.preference_file_key), Context.MODE_PRIVATE); ArrayList<String> urls = new ArrayList<String>(); try { urls = (ArrayList<String>) ObjectSerializer.deserialize( prefs.getString( getString(R.string.preference_file_key), ObjectSerializer.serialize(new ArrayList<String>()))); } catch (IOException e) { e.printStackTrace(); } if (!urls.contains(s)) urls.add(s); // save the url list to preferences SharedPreferences.Editor editor = prefs.edit(); try { editor.putString(getString(R.string.preference_file_key), ObjectSerializer.serialize(urls)); } catch (IOException e) { e.printStackTrace(); } editor.commit(); }
private static String train( SQLContext sqlContext, DataFrame positives, DataFrame negatives, String modelFileName) { // combine data sets DataFrame all = positives.unionAll(negatives); // split into training and test sets DataFrame[] split = all.randomSplit(new double[] {.80, .20}, 1); DataFrame training = split[0].cache(); DataFrame test = split[1].cache(); // fit logistic regression model PipelineModel model = fitLogisticRegressionModel(training); try { ObjectSerializer.serialize(model, modelFileName); } catch (IOException e1) { // TODO Auto-generated catch block e1.printStackTrace(); } // predict on training data to evaluate goodness of fit DataFrame trainingResults = model.transform(training).cache(); // predict on test set to evaluate goodness of fit DataFrame testResults = model.transform(test).cache(); // predict on unassigned data mentions StringBuilder sb = new StringBuilder(); sb.append(getMetrics(trainingResults, "Training\n")); sb.append(getMetrics(testResults, "Testing\n")); return sb.toString(); }