本文整理了Java中edu.illinois.cs.cogcomp.sl.util.WeightVector.<init>()方法的一些代码示例，展示了WeightVector.<init>()的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。WeightVector.<init>()方法的具体详情如下：
包路径：edu.illinois.cs.cogcomp.sl.util.WeightVector
类名称：WeightVector
方法名：<init>

WeightVector.<init>介绍

暂无

代码示例

代码示例来源：origin: edu.illinois.cs.cogcomp/IllinoisSL-core

public InferenceThread(
    AbstractInferenceSolver infSolver,
    StructuredInstanceWithAlphas[] subset, WeightVector wv, int threadId, Parameters parameters) {
  this.infSolver = infSolver;
  this.alphaInsList = subset;
  this.threadId = threadId;
  this.wv = new WeightVector(wv);
  this.parameters = parameters;
  logger.trace("Thread:" + threadId + " handles "
        + subset.length + " instances!");
}

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-sl

public InferenceThread(
    AbstractInferenceSolver infSolver,
    StructuredInstanceWithAlphas[] subset, WeightVector wv, int threadId, SLParameters parameters) {
  this.infSolver = infSolver;
  this.alphaInsList = subset;
  this.threadId = threadId;
  this.wv = new WeightVector(wv);
  this.parameters = parameters;
  logger.trace("Thread:" + threadId + " handles "
        + subset.length + " instances!");
}

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-sl-core

public InferenceThread(
    AbstractInferenceSolver infSolver,
    StructuredInstanceWithAlphas[] subset, WeightVector wv, int threadId, SLParameters parameters) {
  this.infSolver = infSolver;
  this.alphaInsList = subset;
  this.threadId = threadId;
  this.wv = new WeightVector(wv);
  this.parameters = parameters;
  logger.trace("Thread:" + threadId + " handles "
        + subset.length + " instances!");
}

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-sl

/**
 * To train with the default choice(zero vector) of initial weight vector. 
 * Often this suffices.
 * @param problem    The structured problem on which the perceptron should be trained
 * @return w    The weight vector learnt from the training
 * @throws Exception
 */
@Override
public WeightVector train(SLProblem problem) throws Exception {
  WeightVector init = new WeightVector(10000);
  return train(problem, init);
}
/**

代码示例来源：origin: edu.illinois.cs.cogcomp/IllinoisSL-core

/**
 * To train with the default choice(zero vector) of initial weight vector. 
 * Often this suffices.
 * @param problem    The structured problem on which the perceptron should be trained
 * @return w    The weight vector learnt from the training
 * @throws Exception
 */
@Override
public WeightVector train(StructuredProblem problem) throws Exception {
  WeightVector init = new WeightVector(10000);
  return train(problem, init);
}
/**

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-sl-core

/**
 * To train with the default choice(zero vector) of initial weight vector. 
 * Often this suffices.
 * @param problem    The structured problem on which the perceptron should be trained
 * @return w    The weight vector learnt from the training
 * @throws Exception
 */
@Override
public WeightVector train(SLProblem problem, SLParameters params) throws Exception {
  WeightVector init = new WeightVector(10000);
  return train(problem, params, init);
}
/**

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-sl

@Override
public WeightVector train(SLProblem sp) throws Exception {
  return train(sp, new WeightVector(10000));
}

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-sl-core

/**
 * To train with the default choice(zero vector) of initial weight vector. 
 * Often this suffices.
 * @param problem    The structured problem on which the perceptron should be trained
 * @return w    The weight vector learnt from the training
 * @throws Exception
 */
@Override
public WeightVector train(SLProblem problem, SLParameters params) throws Exception {
  WeightVector init = new WeightVector(10000);
  return train(problem, params, init);
}
/**

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-sl-core

@Override
public WeightVector train(SLProblem sp) throws Exception {
  return train(sp, new WeightVector(10000));
}

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-sl-core

public WeightVector train(SLProblem problem) throws Exception {
  return train(problem, new WeightVector(10000));
}

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-sl-core

/**
 * To train with the default choice(zero vector) of initial weight vector. 
 * Often this suffices.
 * @param problem    The structured problem on which the perceptron should be trained
 * @return w    The weight vector learnt from the training
 * @throws Exception
 */
@Override
public WeightVector train(SLProblem problem) throws Exception {
  WeightVector init = new WeightVector(10000);
  return train(problem, init);
}
/**

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-sl-core

public static WeightVector readFromFile(Lexiconer lm, String filepath) throws IOException {
    List<String> lines = Files.readAllLines(Paths.get(filepath), Charset.defaultCharset());
    float[] input = new float[lines.size()];
    for(String line:lines)
    {
      String[] parts = line.split("\\s+");
      assert parts.length==2 : "weight file corrupted";
      String fstr = parts[0];
      if(fstr.equals("null"))
        continue;
      float val = Float.parseFloat(parts[1]);
      input[lm.getFeatureId(fstr)]=val;
    }
    WeightVector wv = new WeightVector(input);
    return wv;
  }
}

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-sl

@Override
public void run(WeightVector w, AbstractInferenceSolver inference)
    throws Exception {
  float [] array = new float[w.getInternalArray().length];
  for(int i=0 ;i< array.length;i++)
    array[i] = w.getInternalArray()[i];
    
  WeightVector wv = new WeightVector(array);
  wvList.add(wv);
  runningTime.add(System.currentTimeMillis() - startTime);
}
public void postEvaluation(SLProblem sp, AbstractInferenceSolver infSolver) throws Exception{

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-sl

public static WeightVector readFromFile(Lexiconer lm, String filepath) throws FileNotFoundException {
    List<String> lines = LineIO.read(filepath);
    float[] input = new float[lines.size()];
    for(String line:lines)
    {
      String[] parts = line.split("\\s+");
      assert parts.length==2 : "weight file corrupted";
      String fstr = parts[0];
      if(fstr.equals("null"))
        continue;
      float val = Float.parseFloat(parts[1]);
      input[lm.getFeatureId(fstr)]=val;
    }
    WeightVector wv = new WeightVector(input);
    return wv;
  }
}

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-sl-core

public static WeightVector load(String fileName) {
  try {
    GZIPInputStream zipin = new GZIPInputStream(new FileInputStream(fileName));
    BufferedReader reader = new BufferedReader(new InputStreamReader(zipin));
    String line;
    line = reader.readLine().trim();
    if (!line.equals("WeightVector")) {
      reader.close();
      throw new IOException("Invalid model file.");
    }
    line = reader.readLine().trim();
    int size = Integer.parseInt(line);
    WeightVector w = new WeightVector(size);
    while ((line = reader.readLine()) != null) {
      line = line.trim();
      String[] parts = line.split(":");
      int index = Integer.parseInt(parts[0]);
      float value = Float.parseFloat(parts[1]);
      w.setElement(index, value);
    }
    zipin.close();
    return w;
  } catch (Exception e) {
    log.error("Error loading model file {}", fileName);
    System.exit(-1);
  }
  return null;
}

代码示例来源：origin: CogComp/cogcomp-nlp

public static WeightVector load(String fileName) {
  try {
    GZIPInputStream zipin = new GZIPInputStream(new FileInputStream(fileName));
    BufferedReader reader = new BufferedReader(new InputStreamReader(zipin));
    String line;
    line = reader.readLine().trim();
    if (!line.equals("WeightVector")) {
      reader.close();
      throw new IOException("Invalid model file.");
    }
    line = reader.readLine().trim();
    int size = Integer.parseInt(line);
    WeightVector w = new WeightVector(size);
    while ((line = reader.readLine()) != null) {
      line = line.trim();
      String[] parts = line.split(":");
      int index = Integer.parseInt(parts[0]);
      float value = Float.parseFloat(parts[1]);
      w.setElement(index, value);
    }
    zipin.close();
    return w;
  } catch (Exception e) {
    log.error("Error loading model file {}", fileName);
    System.exit(-1);
  }
  return null;
}

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-sl-core

/**
 * The function for the users to call for the structured SVM
 * 
 * @param sp
 *            Structured Labeled Dataset
 * @param params
 *            parameters
 * @return
 * @throws Exception
 */
@Override
public WeightVector train(final SLProblem sp, SLParameters params) throws Exception {
  WeightVector wv = null;
  
  // +1 because wv.u[0] stores the bias term
  if(params.TOTAL_NUMBER_FEATURE >0){
    wv = new WeightVector(params.TOTAL_NUMBER_FEATURE + 1);
    wv.setExtendable(false);
  } else {
    wv = new WeightVector(8192);
    wv.setExtendable(true);
  }
  return train(sp,params,wv);
}

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-sl

@Deprecated
public static WeightVector getWeightVectorBySumAlpahFv(
    StructuredInstanceWithAlphas[] alphaInsList, boolean isExtendable,
    int numIns) {
  int numFeatures = -1;
  for (int i = 0; i < numIns; i++) {
    int currentMaxIdx = alphaInsList[i].getMaxIdx();
    if (currentMaxIdx > numFeatures)
      numFeatures = currentMaxIdx;
  }
  logger.info("number of features: " + numFeatures);
  WeightVector currentWv = new WeightVector(numFeatures + 1);
  currentWv.setExtendable(isExtendable);
  // float[] cur_w = new float[max_n + 1];
  for (int i = 0; i < numIns; i++) {
    alphaInsList[i].fillWeightVector(currentWv);
  }
  return currentWv;
}

代码示例来源：origin: edu.illinois.cs.cogcomp/IllinoisSL-core

@Deprecated
public static WeightVector getWeightVectorBySumAlpahFv(
    StructuredInstanceWithAlphas[] alphaInsList, boolean isExtendable,
    int numIns) {
  int numFeatures = -1;
  for (int i = 0; i < numIns; i++) {
    int currentMaxIdx = alphaInsList[i].getMaxIdx();
    if (currentMaxIdx > numFeatures)
      numFeatures = currentMaxIdx;
  }
  logger.info("number of features: " + numFeatures);
  WeightVector currentWv = new WeightVector(numFeatures + 1);
  currentWv.setExtendable(isExtendable);
  // float[] cur_w = new float[max_n + 1];
  for (int i = 0; i < numIns; i++) {
    alphaInsList[i].fillWeightVector(currentWv);
  }
  return currentWv;
}

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-sl-core

@Deprecated
public static WeightVector getWeightVectorBySumAlpahFv(
    StructuredInstanceWithAlphas[] alphaInsList, boolean isExtendable,
    int numIns) {
  int numFeatures = -1;
  for (int i = 0; i < numIns; i++) {
    int currentMaxIdx = alphaInsList[i].getMaxIdx();
    if (currentMaxIdx > numFeatures)
      numFeatures = currentMaxIdx;
  }
  logger.info("number of features: " + numFeatures);
  WeightVector currentWv = new WeightVector(numFeatures + 1);
  currentWv.setExtendable(isExtendable);
  // float[] cur_w = new float[max_n + 1];
  for (int i = 0; i < numIns; i++) {
    alphaInsList[i].fillWeightVector(currentWv);
  }
  return currentWv;
}