Ejemplo n.º 1
0
 public ClassifierTheta(int FeatureLength, int CatSize) {
   CatSize--;
   this.FeatureLength = FeatureLength;
   this.CatSize = CatSize;
   W = DoubleMatrix.rand(FeatureLength, CatSize).subi(0.5);
   b = DoubleMatrix.rand(CatSize, 1).subi(0.5);
   Theta = new double[FeatureLength * CatSize + CatSize];
   flatten();
 }
  public static void main(String argv[]) {
    modshogun.init_shogun_with_defaults();
    int dim = 7;

    DoubleMatrix data = DoubleMatrix.rand(dim, dim);
    RealFeatures feats = new RealFeatures(data);
    DoubleMatrix data_T = data.transpose();
    DoubleMatrix symdata = data.add(data_T);

    int cols = (1 + dim) * dim / 2;
    DoubleMatrix lowertriangle = DoubleMatrix.zeros(1, cols);
    int count = 0;
    for (int i = 0; i < dim; i++) {
      for (int j = 0; j < dim; j++) {
        if (j <= i) {
          lowertriangle.put(0, count++, symdata.get(i, j));
        }
      }
    }

    CustomKernel kernel = new CustomKernel();
    kernel.set_triangle_kernel_matrix_from_triangle(lowertriangle);
    DoubleMatrix km_triangletriangle = kernel.get_kernel_matrix();

    kernel.set_triangle_kernel_matrix_from_full(symdata);
    DoubleMatrix km_fulltriangle = kernel.get_kernel_matrix();

    kernel.set_full_kernel_matrix_from_full(data);
    DoubleMatrix km_fullfull = kernel.get_kernel_matrix();

    modshogun.exit_shogun();
  }
  protected void applyDropOutIfNecessary(DoubleMatrix input) {
    if (dropOut > 0) {
      this.doMask = DoubleMatrix.rand(input.rows, input.columns).gt(dropOut);
    } else this.doMask = DoubleMatrix.ones(input.rows, input.columns);

    // actually apply drop out
    input.muli(doMask);
  }
Ejemplo n.º 4
0
  /** 训练模型 */
  private void train() {
    printInfo("训练数据载入完毕,开始训练模型………………训练数据规模" + train_set.rows + "x" + train_set.columns);
    // 输入矩阵初始化 横向即每列为一组输入
    /*
     *    4 8 …… N
     *
     *    1 5 …… N
     *    2 6 …… N
     *    3 7 …… N
     */
    P = new DoubleMatrix(NumberofInputNeurons, train_data_Count);
    // 测试机输出矩阵 横向
    T = new DoubleMatrix(NumberofOutputNeurons, train_data_Count);
    // 初始化
    for (int i = 0; i < train_data_Count; i++) {

      for (int j = 0; j < totalColCount; j++) {
        if (j < NumberofInputNeurons) {
          // 输入部分
          P.put(j, i, train_set.get(i, j));
        } else {
          T.put(j - NumberofInputNeurons, i, train_set.get(i, j));
        }
      }
    }
    printInfo("训练矩阵初始化完毕………………开始隐含层神经元权重与阈值");
    // end 初始化
    long start_time_train = System.currentTimeMillis();
    // 随机初始化输入权值矩阵
    // 行数为隐含层神经元个数,列数为输入层神经元
    /**
     * W11 W12 W13(第一个隐含神经元对应各输入的权值) W21 W22 W23(第二个隐含神经元对应各输入的权值) ………………………………(第N个隐含神经元对应各输入的权值)
     */
    InputWeight = DoubleMatrix.rand(NumberofHiddenNeurons, NumberofInputNeurons);

    // 初始化阈值
    BiasofHiddenNeurons = DoubleMatrix.rand(NumberofHiddenNeurons, 1);
    printInfo("隐含层神经元权重与阈值初始化完毕………………开始计算输入权重");
    DoubleMatrix tmpH = new DoubleMatrix(NumberofHiddenNeurons, train_data_Count);
    tmpH = InputWeight.mmul(P);
    printInfo("输入矩阵计算权重完毕………………开始计算输入权重");
    // 加阈值
    // 注意横向问题
    for (int i = 0; i < NumberofHiddenNeurons; i++) {
      for (int j = 0; j < train_data_Count; j++) {
        tmpH.put(i, j, tmpH.get(i, j) + BiasofHiddenNeurons.get(i, 0));
      }
    }
    printInfo("输入矩阵计算阈值完毕………………开始计算输入激活函数");
    // 输出矩阵
    DoubleMatrix H = new DoubleMatrix(NumberofHiddenNeurons, train_data_Count);

    if (func.startsWith("sig")) {
      for (int i = 0; i < NumberofHiddenNeurons; i++) {
        for (int j = 0; j < train_data_Count; j++) {
          double tmp = tmpH.get(i, j);
          tmp = 1.0f / (1 + Math.exp(-tmp));
          H.put(i, j, tmp);
        }
      }
    } else {
      throw new IllegalArgumentException("不支持的激活函数类型");
    }
    printInfo("输出矩阵计算激活函数完毕,开始求解广义逆………………矩阵规模" + H.columns + "x" + H.rows);
    // 将H转置
    DoubleMatrix Ht = H.transpose();

    // 求广义逆
    DoubleMatrix Ht_MP = getMPMatrix(Ht);
    printInfo("输出矩阵广义逆求解完毕………………开始求解输出矩阵权重");
    // 隐含层输出权重矩阵= Ht_MP * Tt
    OutputWeight = Ht_MP.mmul(T.transpose());
    printInfo("输出矩阵权重求解完毕………………");
    long end_time_train = System.currentTimeMillis();
    TrainingTime = (end_time_train - start_time_train) * 1.0f / 1000;

    printInfo("模型训练完毕………………开始评估模型");

    // 误差评估
    DoubleMatrix Yt = new DoubleMatrix(train_data_Count, NumberofOutputNeurons);
    Yt = Ht.mmul(OutputWeight);
    double MSE = 0;
    printInfo("共有" + NumberofOutputNeurons + "个输出值");
    for (int out = 0; out < NumberofOutputNeurons; out++) {
      printInfo("计算第" + (out + 1) + "个输出值");
      double mseTem = 0.0;
      for (int i = 0; i < train_data_Count; i++) {
        System.out.println(Yt.get(i, out) + " " + T.get(out, i));
        mseTem += (Yt.get(i, out) - T.get(out, i)) * (Yt.get(i, out) - T.get(out, i));
      }
      printInfo(
          "第" + NumberofOutputNeurons + "个输出值计算完毕,MSE=" + Math.sqrt(mseTem / train_data_Count));
      MSE += mseTem;
    }

    TrainingAccuracy = Math.sqrt(MSE / train_data_Count);
    printInfo("模型评估完毕………………");
  }