public void checkModelType() throws IOException { String modelType = readUTF(); switch (modelType) { case "Perceptron": delegateModelReader = new PerceptronModelReader(this.dataReader); break; case "GIS": delegateModelReader = new GISModelReader(this.dataReader); break; case "QN": delegateModelReader = new QNModelReader(this.dataReader); break; case "NaiveBayes": delegateModelReader = new NaiveBayesModelReader(this.dataReader); break; default: throw new IOException("Unknown model format: " + modelType); } }
/** * Retrieve a model from disk. It assumes that models are saved in the * following sequence: * * <br>Perceptron (model type identifier) * <br>1. # of parameters (int) * <br>2. # of outcomes (int) * <br> * list of outcome names (String) * <br>3. # of different types of outcome patterns (int) * <br> * list of (int int[]) * <br> [# of predicates for which outcome pattern is true] [outcome pattern] * <br>4. # of predicates (int) * <br> * list of predicate names (String) * * <p>If you are creating a reader for a format which won't work with this * (perhaps a database or xml file), override this method and ignore the * other methods provided in this abstract class. * * @return The PerceptronModel stored in the format and location specified to * this PerceptronModelReader (usually via its the constructor). */ public AbstractModel constructModel() throws IOException { String[] outcomeLabels = getOutcomes(); int[][] outcomePatterns = getOutcomePatterns(); String[] predLabels = getPredicates(); Context[] params = getParameters(outcomePatterns); return new PerceptronModel(params, predLabels, outcomeLabels); }
public void checkModelType() throws java.io.IOException { String modelType = readUTF(); if (!modelType.equals("Perceptron")) System.out.println("Error: attempting to load a " + modelType + " model as a Perceptron model." + " You should expect problems."); } }
/** * Retrieve a model from disk. It assumes that models are saved in the * following sequence: * * <br>Perceptron (model type identifier) * <br>1. # of parameters (int) * <br>2. # of outcomes (int) * <br> * list of outcome names (String) * <br>3. # of different types of outcome patterns (int) * <br> * list of (int int[]) * <br> [# of predicates for which outcome pattern is true] [outcome pattern] * <br>4. # of predicates (int) * <br> * list of predicate names (String) * * <p>If you are creating a reader for a format which won't work with this * (perhaps a database or xml file), override this method and ignore the * other methods provided in this abstract class. * * @return The PerceptronModel stored in the format and location specified to * this PerceptronModelReader (usually via its the constructor). */ public AbstractModel constructModel() throws IOException { String[] outcomeLabels = getOutcomes(); int[][] outcomePatterns = getOutcomePatterns(); String[] predLabels = getPredicates(); Context[] params = getParameters(outcomePatterns); return new PerceptronModel(params, predLabels, outcomeLabels); }
public void checkModelType() throws java.io.IOException { String modelType = readUTF(); if (!modelType.equals("Perceptron")) System.out.println("Error: attempting to load a " + modelType + " model as a Perceptron model." + " You should expect problems."); } }
public void checkModelType() throws IOException { String modelType = readUTF(); switch (modelType) { case "Perceptron": delegateModelReader = new PerceptronModelReader(this.dataReader); break; case "GIS": delegateModelReader = new GISModelReader(this.dataReader); break; case "QN": delegateModelReader = new QNModelReader(this.dataReader); break; case "NaiveBayes": delegateModelReader = new NaiveBayesModelReader(this.dataReader); break; default: throw new IOException("Unknown model format: " + modelType); } }
/** * Retrieve a model from disk. It assumes that models are saved in the * following sequence: * * <br>Perceptron (model type identifier) * <br>1. # of parameters (int) * <br>2. # of outcomes (int) * <br> * list of outcome names (String) * <br>3. # of different types of outcome patterns (int) * <br> * list of (int int[]) * <br> [# of predicates for which outcome pattern is true] [outcome pattern] * <br>4. # of predicates (int) * <br> * list of predicate names (String) * * <p>If you are creating a reader for a format which won't work with this * (perhaps a database or xml file), override this method and ignore the * other methods provided in this abstract class. * * @return The PerceptronModel stored in the format and location specified to * this PerceptronModelReader (usually via its the constructor). */ public AbstractModel constructModel() throws IOException { String[] outcomeLabels = getOutcomes(); int[][] outcomePatterns = getOutcomePatterns(); String[] predLabels = getPredicates(); Context[] params = getParameters(outcomePatterns); return new PerceptronModel(params, predLabels, outcomeLabels); }
public void checkModelType() throws java.io.IOException { String modelType = readUTF(); if (!modelType.equals("Perceptron")) System.out.println("Error: attempting to load a " + modelType + " model as a Perceptron model." + " You should expect problems."); } }
public void checkModelType() throws IOException { String modelType = readUTF(); switch (modelType) { case "Perceptron": delegateModelReader = new PerceptronModelReader(this.dataReader); break; case "GIS": delegateModelReader = new GISModelReader(this.dataReader); break; case "QN": delegateModelReader = new QNModelReader(this.dataReader); break; case "NaiveBayes": delegateModelReader = new NaiveBayesModelReader(this.dataReader); break; default: throw new IOException("Unknown model format: " + modelType); } }