Java TrainingExample Example

说明

java trainingexample示例是从最受好评的开源项目中提取的实现代码,你可以参考下面示例的使用方式。

编程语言: Java

类/类型: TrainingExample

示例#1
文件: LogisticRegression.java项目: peterklipfel/AutoponicsVision

  /**
   * @param lr is the learning rate.
   * @param ts is the training set.
   */
  public LogisticRegression(double lr, double rp, TrainingExample[] ts) {
    learningRate = lr;
    regularizationParam = rp;
    trainingSet = ts;

    // Construct the correct number of hypothesis given
    // the number of features in the trainingSet
    // and the number of different classifications
    ArrayList<Integer> classifications = new ArrayList<Integer>();
    for (TrainingExample t : trainingSet) {
      Integer c = t.getAnswer();
      if (!classifications.contains(c)) classifications.add(c);
    }

    hypothesis = new Hypothesis[classifications.size() - 0];
    int s = trainingSet[0].getInput().length + 1;

    for (int i = 0; i < classifications.size() - 0; ++i) {
      hypothesis[i] = new Hypothesis(s, classifications.get(i));
    }

    try {
      costFunction = this.getClass().getMethod("defaultCostFunction", Hypothesis.class);
    } catch (Exception e) {
      e.printStackTrace();
    }
  }

示例#2
文件: LogisticRegression.java项目: peterklipfel/AutoponicsVision

    /**
     * Runs gradient decent on the hypothesis
     *
     * @param tSet the training set to be used
     */
    private void gradientDecent(TrainingExample[] tSet) {
      double h, val, newVal;
      int answer;
      double lm = LogisticRegression.this.learningRate / tSet.length;

      for (int i = 0; i < numFeatures; ++i) {
        val = 0;
        for (TrainingExample t : tSet) {
          answer = t.getAnswer();
          h = predict(t.getInput());
          if (i == 0) val += (h - answer);
          else val += (h - answer) * t.getInput()[i - 1];
        }

        newVal = parameter.get(i, 0) * (1 - lm * LogisticRegression.this.getRegularizationParam());
        newVal -= lm * val;

        parameter.set(i, 0, newVal);
      }
    }

示例#3
文件: LogisticRegression.java项目: peterklipfel/AutoponicsVision

  /**
   * Runs gradient decent to tune the parameters of each hypothesis.
   *
   * @param iterations the number of times to run gradient decent
   */
  public void tune(int iterations) {
    for (Hypothesis h : hypothesis) {
      // construct a new training set using One vs. Rest
      // if the training example has the same value as the
      // hypothesis then set the answer to 1
      // otherwise set the answer to 0.
      TrainingExample[] tSet = new TrainingExample[trainingSet.length];
      int answer;
      int i = 0;
      for (TrainingExample t : trainingSet) {
        if (t.getAnswer() == h.getClassification()) answer = 1;
        else answer = 0;

        tSet[i] = new TrainingExample(t.getInput(), answer);
        ++i;
      }

      for (i = 0; i < iterations; ++i) {
        h.gradientDecent(tSet);
      }
    }
  }

示例#4
文件: LogisticRegression.java项目: peterklipfel/AutoponicsVision

  /**
   * Calculates the cost of the <code>trainingSet.
   *
   * @param hyp the hypothesis to use in calculating the cost.
   * @return the cost associated with the hypothesis.
   */
  public double defaultCostFunction(Hypothesis hyp) {
    double error = 0;
    double h;
    int answer;
    for (TrainingExample t : trainingSet) {
      try {
        h = (Double) hyp.predict(t.getInput());
      } catch (Exception e) {
        e.printStackTrace();
        continue;
      }
      answer = t.getAnswer();
      error -= answer * log(h) + (1 - answer) * log(1 - h);
    }

    double regError = 0;
    for (int i = 0; i < hyp.getNumFeatures(); ++i) {
      regError += pow(hyp.getParameter(i), 2);
    }
    error += regError / regularizationParam;

    return error / (2 * trainingSet.length);
  }

示例#5
文件: KFoldCrossValidation.java项目: rohitpujar/KFoldCrossValidation

  public void labelGrids(int k) throws FileNotFoundException {
    // Initializing the global arraylist
    trainingData = new ArrayList<>();

    int count = 0;
    Scanner input = new Scanner(new File("data.txt"));
    int noOfRows = input.nextInt();
    int noOfColumns = input.nextInt();
    input.nextLine();
    String[] row;

    for (int i = 0; i < noOfRows; i++) {
      row = input.nextLine().split(" ");
      for (int j = 0; j < row.length; j++) {
        if (row[j].equals("+")) {
          TrainingExample newExample = new TrainingExample();
          newExample.x2 = i;
          newExample.x1 = j;
          newExample.y = '+';
          newExample.ex_no = count;
          count++;
          trainingData.add(newExample);
        } else if (row[j].equals("-")) {
          TrainingExample newExample = new TrainingExample();
          newExample.x2 = i;
          newExample.x1 = j;
          newExample.y = '-';
          newExample.ex_no = count;
          count++;
          trainingData.add(newExample);
        } else {
          TrainingExample newExample = new TrainingExample();
          newExample.x2 = i;
          newExample.x1 = j;
          newExample.y = '.';
          newExample.ex_no = count;
          count++;
          trainingData.add(newExample);
        }
      }
    }

    for (int i = 0; i < trainingData.size(); i++) {
      kNN(trainingData, i, 1, k, false, true);
    }

    labelPrint(trainingData, noOfRows, noOfColumns);
  }

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