Sparkling Water: Spark+H2O #5

Published on 2018年10月28日 by anesth

前回のH2O DeepLearningモデル構築で最終的にDeepLearning_model.javaが出力される。

/*
  Licensed under the Apache License, Version 2.0
    http://www.apache.org/licenses/LICENSE-2.0.html

  AUTOGENERATED BY H2O at 2018-10-28T16:28:21.908+09:00
  3.10.5.4
  
  Standalone prediction code with sample test data for DeepLearningModel named DeepLearning_model_1540691693623_1

  How to download, compile and execute:
      mkdir tmpdir
      cd tmpdir
      curl http:/macbook-pro-5/***.***.***.***:54321/3/h2o-genmodel.jar > h2o-genmodel.jar
      curl http:/macbook-pro-5/***.***.***.***:54321/3/Models.java/DeepLearning_model_1540691693623_1 > DeepLearning_model_1540691693623_1.java
      javac -cp h2o-genmodel.jar -J-Xmx2g -J-XX:MaxPermSize=128m DeepLearning_model_1540691693623_1.java

     (Note:  Try java argument -XX:+PrintCompilation to show runtime JIT compiler behavior.)
*/
import java.util.Map;
import hex.genmodel.GenModel;
import hex.genmodel.annotations.ModelPojo;

@ModelPojo(name="DeepLearning_model_1540691693623_1", algorithm="deeplearning")
public class DeepLearning_model_1540691693623_1 extends GenModel {
  public hex.ModelCategory getModelCategory() { return hex.ModelCategory.Binomial; }
  public boolean isSupervised() { return true; }
  public int nfeatures() { return 28; }
  public int nclasses() { return 2; }
  // Thread-local storage for input neuron activation values.
  final double[] NUMS = new double[28];
  static class NORMMUL implements java.io.Serializable {
    public static final double[] VALUES = new double[28];
    static {
      NORMMUL_0.fill(VALUES);
    }
    static final class NORMMUL_0 implements java.io.Serializable {
      static final void fill(double[] sa) {
        sa[0] = 1.7687666758901264;
        sa[1] = 0.9913821723096016;
        sa[2] = 0.9936853236221495;
        sa[3] = 1.6666383037503143;
        sa[4] = 0.9937488864153216;
        sa[5] = 2.105437242840467;
        sa[6] = 0.9908361123051243;
        sa[7] = 0.9940879578054577;
        sa[8] = 0.9729178470795813;
        sa[9] = 2.0007082148981183;
        sa[10] = 0.9908480722586916;
        sa[11] = 0.9938660006765665;
        sa[12] = 0.9528949618561464;
        sa[13] = 2.0508575278770245;
        sa[14] = 0.9912345684749029;
        sa[15] = 0.9937069184546701;
        sa[16] = 0.8377686617849256;
        sa[17] = 1.9768892962453906;
        sa[18] = 0.9926596305568416;
        sa[19] = 0.9937642381155177;
        sa[20] = 0.7141691868612743;
        sa[21] = 1.4816770107928496;
        sa[22] = 2.62612559064267;
        sa[23] = 6.074042423656259;
        sa[24] = 2.5162755660661866;
        sa[25] = 1.9042873091548915;
        sa[26] = 2.7391280258211443;
        sa[27] = 3.1929106654741926;
      }
    }
}
  static class NORMSUB implements java.io.Serializable {
    public static final double[] VALUES = new double[28];
    static {
      NORMSUB_0.fill(VALUES);
    }
    static final class NORMSUB_0 implements java.io.Serializable {
      static final void fill(double[] sa) {
        sa[0] = 0.991609543911382;
        sa[1] = -6.172673222856738E-5;
        sa[2] = 2.1854967778855427E-4;
        sa[3] = 0.9984508227147362;
        sa[4] = 2.7703720484945748E-5;
        sa[5] = 0.9908871828929133;
        sa[6] = -8.849292707005366E-5;
        sa[7] = -1.54437948956086E-4;
        sa[8] = 0.9999179533939873;
        sa[9] = 0.9927406409576117;
        sa[10] = -1.6275628806378383E-4;
        sa[11] = 7.234230467006848E-5;
        sa[12] = 1.0002384149332444;
        sa[13] = 0.992170569563542;
        sa[14] = 4.1840079250049434E-5;
        sa[15] = 3.615676062458686E-5;
        sa[16] = 0.999843197801924;
        sa[17] = 0.9861267051297294;
        sa[18] = 3.768967267508312E-5;
        sa[19] = 1.6822728241595495E-4;
        sa[20] = 1.0001443952117024;
        sa[21] = 1.0343311905419041;
        sa[22] = 1.0247880896606023;
        sa[23] = 1.0505599836914452;
        sa[24] = 1.0096686212679917;
        sa[25] = 0.9729231063653536;
        sa[26] = 1.0329730880581618;
        sa[27] = 0.9597638460111044;
      }
    }
}
  // Workspace for categorical offsets.
  public static final int[] CATOFFSETS = {0};
  // Hidden layer dropout ratios.
  public static final double[] HIDDEN_DROPOUT_RATIOS = {0.5,0.5,0.5};
  // Number of neurons for each layer.
  public static final int[] NEURONS = {28,500,500,500,2};
    // Thread-local storage for neuron activation values.
    final double[][] ACTIVATION = new double[][] {
      /* Input */ DeepLearning_model_1540691693623_1_Activation_0.VALUES,
      /* RectifierDropout */ DeepLearning_model_1540691693623_1_Activation_1.VALUES,
      /* RectifierDropout */ DeepLearning_model_1540691693623_1_Activation_2.VALUES,
      /* RectifierDropout */ DeepLearning_model_1540691693623_1_Activation_3.VALUES,
      /* Softmax */ DeepLearning_model_1540691693623_1_Activation_4.VALUES
    };
    // Neuron bias values.
    public static final double[][] BIAS = new double[][] {
      /* Input */ DeepLearning_model_1540691693623_1_Bias_0.VALUES,
      /* RectifierDropout */ DeepLearning_model_1540691693623_1_Bias_1.VALUES,
      /* RectifierDropout */ DeepLearning_model_1540691693623_1_Bias_2.VALUES,
      /* RectifierDropout */ DeepLearning_model_1540691693623_1_Bias_3.VALUES,
      /* Softmax */ DeepLearning_model_1540691693623_1_Bias_4.VALUES
    };
    // Connecting weights between neurons.
    public static final float[][] WEIGHT = new float[][] {
      /* Input */ DeepLearning_model_1540691693623_1_Weight_0.VALUES,
      /* RectifierDropout */ DeepLearning_model_1540691693623_1_Weight_1.VALUES,
      /* RectifierDropout */ DeepLearning_model_1540691693623_1_Weight_2.VALUES,
      /* RectifierDropout */ DeepLearning_model_1540691693623_1_Weight_3.VALUES,
      /* Softmax */ DeepLearning_model_1540691693623_1_Weight_4.VALUES
    };

  // Names of columns used by model.
  public static final String[] NAMES = NamesHolder_DeepLearning_model_1540691693623_1.VALUES;
  // Number of output classes included in training data response column.
  public static final int NCLASSES = 2;

  // Column domains. The last array contains domain of response column.
  public static final String[][] DOMAINS = new String[][] {
    /* features0 */ null,
    /* features1 */ null,
    /* features2 */ null,
    /* features3 */ null,
    /* features4 */ null,
    /* features5 */ null,
    /* features6 */ null,
    /* features7 */ null,
    /* features8 */ null,
    /* features9 */ null,
    /* features10 */ null,
    /* features11 */ null,
    /* features12 */ null,
    /* features13 */ null,
    /* features14 */ null,
    /* features15 */ null,
    /* features16 */ null,
    /* features17 */ null,
    /* features18 */ null,
    /* features19 */ null,
    /* features20 */ null,
    /* features21 */ null,
    /* features22 */ null,
    /* features23 */ null,
    /* features24 */ null,
    /* features25 */ null,
    /* features26 */ null,
    /* features27 */ null,
    /* label */ DeepLearning_model_1540691693623_1_ColInfo_28.VALUES
  };
  // Prior class distribution
  public static final double[] PRIOR_CLASS_DISTRIB = {0.4701428794437793,0.5298571205562207};
  // Class distribution used for model building
  public static final double[] MODEL_CLASS_DISTRIB = null;

  public DeepLearning_model_1540691693623_1() { super(NAMES,DOMAINS); }
  public String getUUID() { return Long.toString(-5314795359721044940L); }

  // Pass in data in a double[], pre-aligned to the Model's requirements.
  // Jam predictions into the preds[] array; preds[0] is reserved for the
  // main prediction (class for classifiers or value for regression),
  // and remaining columns hold a probability distribution for classifiers.
  public final double[] score0( double[] data, double[] preds ) {
    java.util.Arrays.fill(preds,0);
    java.util.Arrays.fill(NUMS,0);
    int i = 0, ncats = 0;
    final int n = data.length;
    for(; i<n; ++i) {
      NUMS[i] = Double.isNaN(data[i]) ? 0 : (data[i] - NORMSUB.VALUES[i])*NORMMUL.VALUES[i];
    }
    java.util.Arrays.fill(ACTIVATION[0],0);
    for (i=0; i<NUMS.length; ++i) {
      ACTIVATION[0][CATOFFSETS[CATOFFSETS.length-1] + i] = Double.isNaN(NUMS[i]) ? 0 : NUMS[i];
    }
    for (i=1; i<ACTIVATION.length; ++i) {
      java.util.Arrays.fill(ACTIVATION[i],0);
      int cols = ACTIVATION[i-1].length;
      int rows = ACTIVATION[i].length;
      int extra=cols-cols%8;
      int multiple = (cols/8)*8-1;
      int idx = 0;
      float[] a = WEIGHT[i];
      double[] x = ACTIVATION[i-1];
      double[] y = BIAS[i];
      double[] res = ACTIVATION[i];
      for (int row=0; row<rows; ++row) {
        double psum0 = 0, psum1 = 0, psum2 = 0, psum3 = 0, psum4 = 0, psum5 = 0, psum6 = 0, psum7 = 0;
        for (int col = 0; col < multiple; col += 8) {
          int off = idx + col;
          psum0 += a[off    ] * x[col    ];
          psum1 += a[off + 1] * x[col + 1];
          psum2 += a[off + 2] * x[col + 2];
          psum3 += a[off + 3] * x[col + 3];
          psum4 += a[off + 4] * x[col + 4];
          psum5 += a[off + 5] * x[col + 5];
          psum6 += a[off + 6] * x[col + 6];
          psum7 += a[off + 7] * x[col + 7];
        }
        res[row] += psum0 + psum1 + psum2 + psum3;
        res[row] += psum4 + psum5 + psum6 + psum7;
        for (int col = extra; col < cols; col++)
          res[row] += a[idx + col] * x[col];
        res[row] += y[row];
        idx += cols;
      }
      if (i<ACTIVATION.length-1) {
        for (int r=0; r<ACTIVATION[i].length; ++r) {
          ACTIVATION[i][r] = Math.max(0, ACTIVATION[i][r]);
          if (i<ACTIVATION.length-1) {
            ACTIVATION[i][r] *= 1 - HIDDEN_DROPOUT_RATIOS[i-1];
          }
        }
      }
      if (i == ACTIVATION.length-1) {
        double max = ACTIVATION[i][0];
        for (int r=1; r<ACTIVATION[i].length; r++) {
          if (ACTIVATION[i][r]>max) max = ACTIVATION[i][r];
        }
        double scale = 0;
        for (int r=0; r<ACTIVATION[i].length; r++) {
          ACTIVATION[i][r] = Math.exp(ACTIVATION[i][r] - max);
          scale += ACTIVATION[i][r];
        }
        for (int r=0; r<ACTIVATION[i].length; r++) {
          if (Double.isNaN(ACTIVATION[i][r]))
            throw new RuntimeException("Numerical instability, predicted NaN.");
          ACTIVATION[i][r] /= scale;
          preds[r+1] = ACTIVATION[i][r];
        }
      }
    }
    preds[0] = hex.genmodel.GenModel.getPrediction(preds, PRIOR_CLASS_DISTRIB, data, 0.44319463906785384);
    return preds;
  }
}
// Neuron activation values for Input layer
class DeepLearning_model_1540691693623_1_Activation_0 implements java.io.Serializable {
  public static final double[] VALUES = new double[28];
  static {
    DeepLearning_model_1540691693623_1_Activation_0_0.fill(VALUES);
  }
  static final class DeepLearning_model_1540691693623_1_Activation_0_0 implements java.io.Serializable {
    static final void fill(double[] sa) {
      sa[0] = 0.0;
      sa[1] = 0.0;
      sa[2] = 0.0;
      sa[3] = 0.0;
      sa[4] = 0.0;
      sa[5] = 0.0;
      sa[6] = 0.0;
      sa[7] = 0.0;
      sa[8] = 0.0;
      sa[9] = 0.0;
      sa[10] = 0.0;
      sa[11] = 0.0;
      sa[12] = 0.0;
      sa[13] = 0.0;
      sa[14] = 0.0;
      sa[15] = 0.0;
      sa[16] = 0.0;
      sa[17] = 0.0;
      sa[18] = 0.0;
      sa[19] = 0.0;
      sa[20] = 0.0;
      sa[21] = 0.0;
      sa[22] = 0.0;
      sa[23] = 0.0;
      sa[24] = 0.0;
      sa[25] = 0.0;
      sa[26] = 0.0;
      sa[27] = 0.0;
    }
  }
}
// Neuron activation values for RectifierDropout layer
class DeepLearning_model_1540691693623_1_Activation_1 implements java.io.Serializable {
  public static final double[] VALUES = new double[500];
  static {
    DeepLearning_model_1540691693623_1_Activation_1_0.fill(VALUES);
  }
  static final class DeepLearning_model_1540691693623_1_Activation_1_0 implements java.io.Serializable {
    static final void fill(double[] sa) {
      sa[0] = 0.0;
      sa[1] = 0.0;
      sa[2] = 0.0;
      sa[3] = 0.0;
      sa[4] = 0.0;
      sa[5] = 0.0;
......
      sa[499] = 0.0;
    }
  }
}
// Neuron activation values for RectifierDropout layer
class DeepLearning_model_1540691693623_1_Activation_2 implements java.io.Serializable {
  public static final double[] VALUES = new double[500];
  static {
    DeepLearning_model_1540691693623_1_Activation_2_0.fill(VALUES);
  }
  static final class DeepLearning_model_1540691693623_1_Activation_2_0 implements java.io.Serializable {
    static final void fill(double[] sa) {
      sa[0] = 0.0;
      sa[1] = 0.0;
      sa[2] = 0.0;
      sa[3] = 0.0;
      sa[4] = 0.0;
      sa[5] = 0.0;
......
      sa[499] = 0.0;
    }
  }
}
// Neuron activation values for RectifierDropout layer
class DeepLearning_model_1540691693623_1_Activation_3 implements java.io.Serializable {
  public static final double[] VALUES = new double[500];
  static {
    DeepLearning_model_1540691693623_1_Activation_3_0.fill(VALUES);
  }
  static final class DeepLearning_model_1540691693623_1_Activation_3_0 implements java.io.Serializable {
    static final void fill(double[] sa) {
      sa[0] = 0.0;
      sa[1] = 0.0;
      sa[2] = 0.0;
      sa[3] = 0.0;
      sa[4] = 0.0;
      sa[5] = 0.0;
......
      sa[499] = 0.0;
    }
  }
}
// Neuron activation values for Softmax layer
class DeepLearning_model_1540691693623_1_Activation_4 implements java.io.Serializable {
  public static final double[] VALUES = new double[2];
  static {
    DeepLearning_model_1540691693623_1_Activation_4_0.fill(VALUES);
  }
  static final class DeepLearning_model_1540691693623_1_Activation_4_0 implements java.io.Serializable {
    static final void fill(double[] sa) {
      sa[0] = 0.0;
      sa[1] = 0.0;
    }
  }
}
// Neuron bias values for Input layer
class DeepLearning_model_1540691693623_1_Bias_0 implements java.io.Serializable {
  public static final double[] VALUES = null;
}
// Neuron bias values for RectifierDropout layer
class DeepLearning_model_1540691693623_1_Bias_1 implements java.io.Serializable {
  public static final double[] VALUES = new double[500];
  static {
    DeepLearning_model_1540691693623_1_Bias_1_0.fill(VALUES);
  }
  static final class DeepLearning_model_1540691693623_1_Bias_1_0 implements java.io.Serializable {
    static final void fill(double[] sa) {
      sa[0] = -0.6082208509410506;
      sa[1] = -0.7866761395746675;
      sa[2] = -0.7902771837144533;
      sa[3] = -1.078600190576397;
      sa[4] = -1.1325559100264349;
      sa[5] = -1.3141680956557276;
......
      sa[499] = -1.1605339237438876;
    }
  }
}
// Neuron bias values for RectifierDropout layer
class DeepLearning_model_1540691693623_1_Bias_2 implements java.io.Serializable {
  public static final double[] VALUES = new double[500];
  static {
    DeepLearning_model_1540691693623_1_Bias_2_0.fill(VALUES);
  }
  static final class DeepLearning_model_1540691693623_1_Bias_2_0 implements java.io.Serializable {
    static final void fill(double[] sa) {
      sa[0] = 0.02034537567535967;
      sa[1] = 0.2056398687484795;
      sa[2] = -0.6811208002656562;
      sa[3] = 0.34906780212313926;
      sa[4] = 0.1994718812435918;
      sa[5] = -0.4389162411792007;
......
      sa[499] = -0.19825444966679512;
    }
  }
}
// Neuron bias values for RectifierDropout layer
class DeepLearning_model_1540691693623_1_Bias_3 implements java.io.Serializable {
  public static final double[] VALUES = new double[500];
  static {
    DeepLearning_model_1540691693623_1_Bias_3_0.fill(VALUES);
  }
  static final class DeepLearning_model_1540691693623_1_Bias_3_0 implements java.io.Serializable {
    static final void fill(double[] sa) {
      sa[0] = -0.37334331766490575;
      sa[1] = -0.2192842820590019;
      sa[2] = -0.48037506449229245;
      sa[3] = -0.27829081354485186;
      sa[4] = -0.5348837641304954;
........
      sa[499] = -0.38094144337852304;
    }
  }
}
// Neuron bias values for Softmax layer
class DeepLearning_model_1540691693623_1_Bias_4 implements java.io.Serializable {
  public static final double[] VALUES = new double[2];
  static {
    DeepLearning_model_1540691693623_1_Bias_4_0.fill(VALUES);
  }
  static final class DeepLearning_model_1540691693623_1_Bias_4_0 implements java.io.Serializable {
    static final void fill(double[] sa) {
      sa[0] = -0.11833918432774448;
      sa[1] = 0.08029026554573974;
    }
  }
}
class DeepLearning_model_1540691693623_1_Weight_0 implements java.io.Serializable {
  public static final float[] VALUES = null;
}

// Neuron weights connecting Input and RectifierDropout layer
class DeepLearning_model_1540691693623_1_Weight_1 implements java.io.Serializable {
  public static final float[] VALUES = new float[14000];
  static {
    DeepLearning_model_1540691693623_1_Weight_1_0.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_1_1.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_1_2.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_1_3.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_1_4.fill(VALUES);
  }
  static final class DeepLearning_model_1540691693623_1_Weight_1_0 implements java.io.Serializable {
    static final void fill(float[] sa) {
      sa[0] = -0.06823527f;
      sa[1] = 0.01967861f;
      sa[2] = -0.03716086f;
      sa[3] = -0.011376046f;
      sa[4] = -0.019753613f;
      sa[5] = -0.19310619f;
......
      sa[13999] = -0.12823965f;
    }
  }
}
// Neuron weights connecting RectifierDropout and RectifierDropout layer
class DeepLearning_model_1540691693623_1_Weight_2 implements java.io.Serializable {
  public static final float[] VALUES = new float[250000];
  static {
    DeepLearning_model_1540691693623_1_Weight_2_0.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_1.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_2.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_3.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_4.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_5.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_6.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_7.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_8.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_9.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_10.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_11.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_12.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_13.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_14.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_15.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_16.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_17.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_18.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_19.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_20.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_21.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_22.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_23.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_24.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_25.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_26.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_27.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_28.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_29.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_30.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_31.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_32.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_33.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_34.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_35.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_36.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_37.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_38.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_39.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_40.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_41.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_42.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_43.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_44.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_45.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_46.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_47.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_48.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_49.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_50.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_51.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_52.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_53.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_54.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_55.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_56.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_57.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_58.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_59.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_60.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_61.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_62.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_63.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_64.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_65.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_66.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_67.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_68.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_69.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_70.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_71.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_72.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_73.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_74.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_75.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_76.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_77.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_78.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_79.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_80.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_81.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_82.fill(VALUES);
    DeepLearning_model_1540691693623_1_Weight_2_83.fill(VALUES);
  }
  static final class DeepLearning_model_1540691693623_1_Weight_2_0 implements java.io.Serializable {
    static final void fill(float[] sa) {
      sa[0] = -0.24805546f;
      sa[1] = -0.06499913f;
      sa[2] = -0.14989536f;
      sa[3] = -0.06383444f;
      sa[4] = 0.14076568f;
      sa[5] = -0.16730806f;
......
      sa[249999] = 0.005610808f;
    }
  }
}
// Neuron weights connecting RectifierDropout and Softmax layer
class DeepLearning_model_1540691693623_1_Weight_4 implements java.io.Serializable {
  public static final float[] VALUES = new float[1000];
  static {
    DeepLearning_model_1540691693623_1_Weight_4_0.fill(VALUES);
  }
  static final class DeepLearning_model_1540691693623_1_Weight_4_0 implements java.io.Serializable {
    static final void fill(float[] sa) {
      sa[0] = 0.2789825f;
      sa[1] = 0.4297707f;
      sa[2] = -0.28039044f;
      sa[3] = -0.13956442f;
      sa[4] = -0.07300115f;
      sa[5] = 0.13860723f;
........
      sa[999] = 0.043791886f;
    }
  }
}
// The class representing training column names
class NamesHolder_DeepLearning_model_1540691693623_1 implements java.io.Serializable {
  public static final String[] VALUES = new String[28];
  static {
    NamesHolder_DeepLearning_model_1540691693623_1_0.fill(VALUES);
  }
  static final class NamesHolder_DeepLearning_model_1540691693623_1_0 implements java.io.Serializable {
    static final void fill(String[] sa) {
      sa[0] = "features0";
      sa[1] = "features1";
      sa[2] = "features2";
      sa[3] = "features3";
      sa[4] = "features4";
      sa[5] = "features5";
      sa[6] = "features6";
      sa[7] = "features7";
      sa[8] = "features8";
      sa[9] = "features9";
      sa[10] = "features10";
      sa[11] = "features11";
      sa[12] = "features12";
      sa[13] = "features13";
      sa[14] = "features14";
      sa[15] = "features15";
      sa[16] = "features16";
      sa[17] = "features17";
      sa[18] = "features18";
      sa[19] = "features19";
      sa[20] = "features20";
      sa[21] = "features21";
      sa[22] = "features22";
      sa[23] = "features23";
      sa[24] = "features24";
      sa[25] = "features25";
      sa[26] = "features26";
      sa[27] = "features27";
    }
  }
}
// The class representing column label
class DeepLearning_model_1540691693623_1_ColInfo_28 implements java.io.Serializable {
  public static final String[] VALUES = new String[2];
  static {
    DeepLearning_model_1540691693623_1_ColInfo_28_0.fill(VALUES);
  }
  static final class DeepLearning_model_1540691693623_1_ColInfo_28_0 implements java.io.Serializable {
    static final void fill(String[] sa) {
      sa[0] = "0";
      sa[1] = "1";
    }
  }
}

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Licensed under the Apache License, Version 2.0

http://www.apache.org/licenses/LICENSE-2.0.html

AUTOGENERATED BY H2O at 2018-10-28T16:28:21.908+09:00

3.10.5.4

Standalone prediction code with sample test data for DeepLearningModel named DeepLearning_model_1540691693623_1

How to download, compile and execute:

mkdir tmpdir

cd tmpdir

curl http:/macbook-pro-5/***.***.***.***:54321/3/h2o-genmodel.jar > h2o-genmodel.jar

curl http:/macbook-pro-5/***.***.***.***:54321/3/Models.java/DeepLearning_model_1540691693623_1 > DeepLearning_model_1540691693623_1.java

javac -cp h2o-genmodel.jar -J-Xmx2g -J-XX:MaxPermSize=128m DeepLearning_model_1540691693623_1.java

(Note: Try java argument -XX:+PrintCompilation to show runtime JIT compiler behavior.)

import java.util.Map;

import hex.genmodel.GenModel;

import hex.genmodel.annotations.ModelPojo;

@ModelPojo(name="DeepLearning_model_1540691693623_1", algorithm="deeplearning")

public class DeepLearning_model_1540691693623_1 extends GenModel {

public hex.ModelCategory getModelCategory() { return hex.ModelCategory.Binomial; }

public boolean isSupervised() { return true; }

public int nfeatures() { return 28; }

public int nclasses() { return 2; }

// Thread-local storage for input neuron activation values.

final double[] NUMS = new double[28];

static class NORMMUL implements java.io.Serializable {

public static final double[] VALUES = new double[28];

static {

NORMMUL_0.fill(VALUES);

}

static final class NORMMUL_0 implements java.io.Serializable {

static final void fill(double[] sa) {

sa[0] = 1.7687666758901264;

sa[1] = 0.9913821723096016;

sa[2] = 0.9936853236221495;

sa[3] = 1.6666383037503143;

sa[4] = 0.9937488864153216;

sa[5] = 2.105437242840467;

sa[6] = 0.9908361123051243;

sa[7] = 0.9940879578054577;

sa[8] = 0.9729178470795813;

sa[9] = 2.0007082148981183;

sa[10] = 0.9908480722586916;

sa[11] = 0.9938660006765665;

sa[12] = 0.9528949618561464;

sa[13] = 2.0508575278770245;

sa[14] = 0.9912345684749029;

sa[15] = 0.9937069184546701;

sa[16] = 0.8377686617849256;

sa[17] = 1.9768892962453906;

sa[18] = 0.9926596305568416;

sa[19] = 0.9937642381155177;

sa[20] = 0.7141691868612743;

sa[21] = 1.4816770107928496;

sa[22] = 2.62612559064267;

sa[23] = 6.074042423656259;

sa[24] = 2.5162755660661866;

sa[25] = 1.9042873091548915;

sa[26] = 2.7391280258211443;

sa[27] = 3.1929106654741926;

}

static class NORMSUB implements java.io.Serializable {

public static final double[] VALUES = new double[28];

static {

NORMSUB_0.fill(VALUES);

}

static final class NORMSUB_0 implements java.io.Serializable {

static final void fill(double[] sa) {

sa[0] = 0.991609543911382;

sa[1] = -6.172673222856738E-5;

sa[2] = 2.1854967778855427E-4;

sa[3] = 0.9984508227147362;

sa[4] = 2.7703720484945748E-5;

sa[5] = 0.9908871828929133;

sa[6] = -8.849292707005366E-5;

sa[7] = -1.54437948956086E-4;

sa[8] = 0.9999179533939873;

sa[9] = 0.9927406409576117;

sa[10] = -1.6275628806378383E-4;

sa[11] = 7.234230467006848E-5;

sa[12] = 1.0002384149332444;

sa[13] = 0.992170569563542;

sa[14] = 4.1840079250049434E-5;

sa[15] = 3.615676062458686E-5;

sa[16] = 0.999843197801924;

sa[17] = 0.9861267051297294;

sa[18] = 3.768967267508312E-5;

sa[19] = 1.6822728241595495E-4;

sa[20] = 1.0001443952117024;

sa[21] = 1.0343311905419041;

sa[22] = 1.0247880896606023;

sa[23] = 1.0505599836914452;

sa[24] = 1.0096686212679917;

sa[25] = 0.9729231063653536;

sa[26] = 1.0329730880581618;

sa[27] = 0.9597638460111044;

}

// Workspace for categorical offsets.

public static final int[] CATOFFSETS = {0};

// Hidden layer dropout ratios.

public static final double[] HIDDEN_DROPOUT_RATIOS = {0.5,0.5,0.5};

// Number of neurons for each layer.

public static final int[] NEURONS = {28,500,500,500,2};

// Thread-local storage for neuron activation values.

final double[][] ACTIVATION = new double[][] {

/* Input */ DeepLearning_model_1540691693623_1_Activation_0.VALUES,

/* RectifierDropout */ DeepLearning_model_1540691693623_1_Activation_1.VALUES,

/* RectifierDropout */ DeepLearning_model_1540691693623_1_Activation_2.VALUES,

/* RectifierDropout */ DeepLearning_model_1540691693623_1_Activation_3.VALUES,

/* Softmax */ DeepLearning_model_1540691693623_1_Activation_4.VALUES

};

// Neuron bias values.

public static final double[][] BIAS = new double[][] {

/* Input */ DeepLearning_model_1540691693623_1_Bias_0.VALUES,

/* RectifierDropout */ DeepLearning_model_1540691693623_1_Bias_1.VALUES,

/* RectifierDropout */ DeepLearning_model_1540691693623_1_Bias_2.VALUES,

/* RectifierDropout */ DeepLearning_model_1540691693623_1_Bias_3.VALUES,

/* Softmax */ DeepLearning_model_1540691693623_1_Bias_4.VALUES

};

// Connecting weights between neurons.

public static final float[][] WEIGHT = new float[][] {

/* Input */ DeepLearning_model_1540691693623_1_Weight_0.VALUES,

/* RectifierDropout */ DeepLearning_model_1540691693623_1_Weight_1.VALUES,

/* RectifierDropout */ DeepLearning_model_1540691693623_1_Weight_2.VALUES,

/* RectifierDropout */ DeepLearning_model_1540691693623_1_Weight_3.VALUES,

/* Softmax */ DeepLearning_model_1540691693623_1_Weight_4.VALUES

};

// Names of columns used by model.

public static final String[] NAMES = NamesHolder_DeepLearning_model_1540691693623_1.VALUES;

// Number of output classes included in training data response column.

public static final int NCLASSES = 2;

// Column domains. The last array contains domain of response column.

public static final String[][] DOMAINS = new String[][] {

/* features0 */ null,

/* features1 */ null,

/* features2 */ null,

/* features3 */ null,

/* features4 */ null,

/* features5 */ null,

/* features6 */ null,

/* features7 */ null,

/* features8 */ null,

/* features9 */ null,

/* features10 */ null,

/* features11 */ null,

/* features12 */ null,

/* features13 */ null,

/* features14 */ null,

/* features15 */ null,

/* features16 */ null,

/* features17 */ null,

/* features18 */ null,

/* features19 */ null,

/* features20 */ null,

/* features21 */ null,

/* features22 */ null,

/* features23 */ null,

/* features24 */ null,

/* features25 */ null,

/* features26 */ null,

/* features27 */ null,

/* label */ DeepLearning_model_1540691693623_1_ColInfo_28.VALUES

};

// Prior class distribution

public static final double[] PRIOR_CLASS_DISTRIB = {0.4701428794437793,0.5298571205562207};

// Class distribution used for model building

public static final double[] MODEL_CLASS_DISTRIB = null;

public DeepLearning_model_1540691693623_1() { super(NAMES,DOMAINS); }

public String getUUID() { return Long.toString(-5314795359721044940L); }

// Pass in data in a double[], pre-aligned to the Model's requirements.

// Jam predictions into the preds[] array; preds[0] is reserved for the

// main prediction (class for classifiers or value for regression),

// and remaining columns hold a probability distribution for classifiers.

public final double[] score0( double[] data, double[] preds ) {

java.util.Arrays.fill(preds,0);

java.util.Arrays.fill(NUMS,0);

int i = 0, ncats = 0;

final int n = data.length;

for(; i<n; ++i) {

NUMS[i] = Double.isNaN(data[i]) ? 0 : (data[i] - NORMSUB.VALUES[i])*NORMMUL.VALUES[i];

}

java.util.Arrays.fill(ACTIVATION[0],0);

for (i=0; i<NUMS.length; ++i) {

ACTIVATION[0][CATOFFSETS[CATOFFSETS.length-1] + i] = Double.isNaN(NUMS[i]) ? 0 : NUMS[i];

}

for (i=1; i<ACTIVATION.length; ++i) {

java.util.Arrays.fill(ACTIVATION[i],0);

int cols = ACTIVATION[i-1].length;

int rows = ACTIVATION[i].length;

int extra=cols-cols%8;

int multiple = (cols/8)*8-1;

int idx = 0;

float[] a = WEIGHT[i];

double[] x = ACTIVATION[i-1];

double[] y = BIAS[i];

double[] res = ACTIVATION[i];

for (int row=0; row<rows; ++row) {

double psum0 = 0, psum1 = 0, psum2 = 0, psum3 = 0, psum4 = 0, psum5 = 0, psum6 = 0, psum7 = 0;

for (int col = 0; col < multiple; col += 8) {

int off = idx + col;

psum0 += a[off ] * x[col ];

psum1 += a[off + 1] * x[col + 1];

psum2 += a[off + 2] * x[col + 2];

psum3 += a[off + 3] * x[col + 3];

psum4 += a[off + 4] * x[col + 4];

psum5 += a[off + 5] * x[col + 5];

psum6 += a[off + 6] * x[col + 6];

psum7 += a[off + 7] * x[col + 7];

}

res[row] += psum0 + psum1 + psum2 + psum3;

res[row] += psum4 + psum5 + psum6 + psum7;

for (int col = extra; col < cols; col++)

res[row] += a[idx + col] * x[col];

res[row] += y[row];

idx += cols;

}

if (i<ACTIVATION.length-1) {

for (int r=0; r<ACTIVATION[i].length; ++r) {

ACTIVATION[i][r] = Math.max(0, ACTIVATION[i][r]);

if (i<ACTIVATION.length-1) {

ACTIVATION[i][r] *= 1 - HIDDEN_DROPOUT_RATIOS[i-1];

}

if (i == ACTIVATION.length-1) {

double max = ACTIVATION[i][0];

for (int r=1; r<ACTIVATION[i].length; r++) {

if (ACTIVATION[i][r]>max) max = ACTIVATION[i][r];

}

double scale = 0;

for (int r=0; r<ACTIVATION[i].length; r++) {

ACTIVATION[i][r] = Math.exp(ACTIVATION[i][r] - max);

scale += ACTIVATION[i][r];

}

for (int r=0; r<ACTIVATION[i].length; r++) {

if (Double.isNaN(ACTIVATION[i][r]))

throw new RuntimeException("Numerical instability, predicted NaN.");

ACTIVATION[i][r] /= scale;

preds[r+1] = ACTIVATION[i][r];

}

preds[0] = hex.genmodel.GenModel.getPrediction(preds, PRIOR_CLASS_DISTRIB, data, 0.44319463906785384);

return preds;

}

// Neuron activation values for Input layer

class DeepLearning_model_1540691693623_1_Activation_0 implements java.io.Serializable {

public static final double[] VALUES = new double[28];

static {

DeepLearning_model_1540691693623_1_Activation_0_0.fill(VALUES);

}

static final class DeepLearning_model_1540691693623_1_Activation_0_0 implements java.io.Serializable {

static final void fill(double[] sa) {

sa[0] = 0.0;

sa[1] = 0.0;

sa[2] = 0.0;

sa[3] = 0.0;

sa[4] = 0.0;

sa[5] = 0.0;

sa[6] = 0.0;

sa[7] = 0.0;

sa[8] = 0.0;

sa[9] = 0.0;

sa[10] = 0.0;

sa[11] = 0.0;

sa[12] = 0.0;

sa[13] = 0.0;

sa[14] = 0.0;

sa[15] = 0.0;

sa[16] = 0.0;

sa[17] = 0.0;

sa[18] = 0.0;

sa[19] = 0.0;

sa[20] = 0.0;

sa[21] = 0.0;

sa[22] = 0.0;

sa[23] = 0.0;

sa[24] = 0.0;

sa[25] = 0.0;

sa[26] = 0.0;

sa[27] = 0.0;

}

// Neuron activation values for RectifierDropout layer

class DeepLearning_model_1540691693623_1_Activation_1 implements java.io.Serializable {

public static final double[] VALUES = new double[500];

static {

DeepLearning_model_1540691693623_1_Activation_1_0.fill(VALUES);

}

static final class DeepLearning_model_1540691693623_1_Activation_1_0 implements java.io.Serializable {

static final void fill(double[] sa) {

sa[0] = 0.0;

sa[1] = 0.0;

sa[2] = 0.0;

sa[3] = 0.0;

sa[4] = 0.0;

sa[5] = 0.0;

......

sa[499] = 0.0;

}

// Neuron activation values for RectifierDropout layer

class DeepLearning_model_1540691693623_1_Activation_2 implements java.io.Serializable {

public static final double[] VALUES = new double[500];

static {

DeepLearning_model_1540691693623_1_Activation_2_0.fill(VALUES);

}

static final class DeepLearning_model_1540691693623_1_Activation_2_0 implements java.io.Serializable {

static final void fill(double[] sa) {

sa[0] = 0.0;

sa[1] = 0.0;

sa[2] = 0.0;

sa[3] = 0.0;

sa[4] = 0.0;

sa[5] = 0.0;

......

sa[499] = 0.0;

}

// Neuron activation values for RectifierDropout layer

class DeepLearning_model_1540691693623_1_Activation_3 implements java.io.Serializable {

public static final double[] VALUES = new double[500];

static {

DeepLearning_model_1540691693623_1_Activation_3_0.fill(VALUES);

}

static final class DeepLearning_model_1540691693623_1_Activation_3_0 implements java.io.Serializable {

static final void fill(double[] sa) {

sa[0] = 0.0;

sa[1] = 0.0;

sa[2] = 0.0;

sa[3] = 0.0;

sa[4] = 0.0;

sa[5] = 0.0;

......

sa[499] = 0.0;

}

// Neuron activation values for Softmax layer

class DeepLearning_model_1540691693623_1_Activation_4 implements java.io.Serializable {

public static final double[] VALUES = new double[2];

static {

DeepLearning_model_1540691693623_1_Activation_4_0.fill(VALUES);

}

static final class DeepLearning_model_1540691693623_1_Activation_4_0 implements java.io.Serializable {

static final void fill(double[] sa) {

sa[0] = 0.0;

sa[1] = 0.0;

}

// Neuron bias values for Input layer

class DeepLearning_model_1540691693623_1_Bias_0 implements java.io.Serializable {

public static final double[] VALUES = null;

}

// Neuron bias values for RectifierDropout layer

class DeepLearning_model_1540691693623_1_Bias_1 implements java.io.Serializable {

public static final double[] VALUES = new double[500];

static {

DeepLearning_model_1540691693623_1_Bias_1_0.fill(VALUES);

}

static final class DeepLearning_model_1540691693623_1_Bias_1_0 implements java.io.Serializable {

static final void fill(double[] sa) {

sa[0] = -0.6082208509410506;

sa[1] = -0.7866761395746675;

sa[2] = -0.7902771837144533;

sa[3] = -1.078600190576397;

sa[4] = -1.1325559100264349;

sa[5] = -1.3141680956557276;

......

sa[499] = -1.1605339237438876;

}

// Neuron bias values for RectifierDropout layer

class DeepLearning_model_1540691693623_1_Bias_2 implements java.io.Serializable {

public static final double[] VALUES = new double[500];

static {

DeepLearning_model_1540691693623_1_Bias_2_0.fill(VALUES);

}

static final class DeepLearning_model_1540691693623_1_Bias_2_0 implements java.io.Serializable {

static final void fill(double[] sa) {

sa[0] = 0.02034537567535967;

sa[1] = 0.2056398687484795;

sa[2] = -0.6811208002656562;

sa[3] = 0.34906780212313926;

sa[4] = 0.1994718812435918;

sa[5] = -0.4389162411792007;

......

sa[499] = -0.19825444966679512;

}

// Neuron bias values for RectifierDropout layer

class DeepLearning_model_1540691693623_1_Bias_3 implements java.io.Serializable {

public static final double[] VALUES = new double[500];

static {

DeepLearning_model_1540691693623_1_Bias_3_0.fill(VALUES);

}

static final class DeepLearning_model_1540691693623_1_Bias_3_0 implements java.io.Serializable {

static final void fill(double[] sa) {

sa[0] = -0.37334331766490575;

sa[1] = -0.2192842820590019;

sa[2] = -0.48037506449229245;

sa[3] = -0.27829081354485186;

sa[4] = -0.5348837641304954;

........

sa[499] = -0.38094144337852304;

}

// Neuron bias values for Softmax layer

class DeepLearning_model_1540691693623_1_Bias_4 implements java.io.Serializable {

public static final double[] VALUES = new double[2];

static {

DeepLearning_model_1540691693623_1_Bias_4_0.fill(VALUES);

}

static final class DeepLearning_model_1540691693623_1_Bias_4_0 implements java.io.Serializable {

static final void fill(double[] sa) {

sa[0] = -0.11833918432774448;

sa[1] = 0.08029026554573974;

}

class DeepLearning_model_1540691693623_1_Weight_0 implements java.io.Serializable {

public static final float[] VALUES = null;

}

// Neuron weights connecting Input and RectifierDropout layer

class DeepLearning_model_1540691693623_1_Weight_1 implements java.io.Serializable {

public static final float[] VALUES = new float[14000];

static {

DeepLearning_model_1540691693623_1_Weight_1_0.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_1_1.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_1_2.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_1_3.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_1_4.fill(VALUES);

}

static final class DeepLearning_model_1540691693623_1_Weight_1_0 implements java.io.Serializable {

static final void fill(float[] sa) {

sa[0] = -0.06823527f;

sa[1] = 0.01967861f;

sa[2] = -0.03716086f;

sa[3] = -0.011376046f;

sa[4] = -0.019753613f;

sa[5] = -0.19310619f;

......

sa[13999] = -0.12823965f;

}

// Neuron weights connecting RectifierDropout and RectifierDropout layer

class DeepLearning_model_1540691693623_1_Weight_2 implements java.io.Serializable {

public static final float[] VALUES = new float[250000];

static {

DeepLearning_model_1540691693623_1_Weight_2_0.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_1.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_2.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_3.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_4.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_5.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_6.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_7.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_8.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_9.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_10.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_11.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_12.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_13.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_14.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_15.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_16.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_17.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_18.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_19.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_20.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_21.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_22.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_23.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_24.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_25.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_26.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_27.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_28.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_29.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_30.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_31.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_32.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_33.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_34.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_35.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_36.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_37.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_38.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_39.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_40.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_41.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_42.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_43.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_44.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_45.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_46.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_47.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_48.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_49.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_50.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_51.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_52.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_53.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_54.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_55.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_56.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_57.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_58.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_59.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_60.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_61.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_62.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_63.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_64.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_65.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_66.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_67.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_68.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_69.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_70.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_71.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_72.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_73.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_74.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_75.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_76.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_77.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_78.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_79.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_80.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_81.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_82.fill(VALUES);

DeepLearning_model_1540691693623_1_Weight_2_83.fill(VALUES);

}

static final class DeepLearning_model_1540691693623_1_Weight_2_0 implements java.io.Serializable {

static final void fill(float[] sa) {

sa[0] = -0.24805546f;

sa[1] = -0.06499913f;

sa[2] = -0.14989536f;

sa[3] = -0.06383444f;

sa[4] = 0.14076568f;

sa[5] = -0.16730806f;

......

sa[249999] = 0.005610808f;

}

// Neuron weights connecting RectifierDropout and Softmax layer

class DeepLearning_model_1540691693623_1_Weight_4 implements java.io.Serializable {

public static final float[] VALUES = new float[1000];

static {

DeepLearning_model_1540691693623_1_Weight_4_0.fill(VALUES);

}

static final class DeepLearning_model_1540691693623_1_Weight_4_0 implements java.io.Serializable {

static final void fill(float[] sa) {

sa[0] = 0.2789825f;

sa[1] = 0.4297707f;

sa[2] = -0.28039044f;

sa[3] = -0.13956442f;

sa[4] = -0.07300115f;

sa[5] = 0.13860723f;

........

sa[999] = 0.043791886f;

}

// The class representing training column names

class NamesHolder_DeepLearning_model_1540691693623_1 implements java.io.Serializable {

public static final String[] VALUES = new String[28];

static {

NamesHolder_DeepLearning_model_1540691693623_1_0.fill(VALUES);

}

static final class NamesHolder_DeepLearning_model_1540691693623_1_0 implements java.io.Serializable {

static final void fill(String[] sa) {

sa[0] = "features0";

sa[1] = "features1";

sa[2] = "features2";

sa[3] = "features3";

sa[4] = "features4";

sa[5] = "features5";

sa[6] = "features6";

sa[7] = "features7";

sa[8] = "features8";

sa[9] = "features9";

sa[10] = "features10";

sa[11] = "features11";

sa[12] = "features12";

sa[13] = "features13";

sa[14] = "features14";

sa[15] = "features15";

sa[16] = "features16";

sa[17] = "features17";

sa[18] = "features18";

sa[19] = "features19";

sa[20] = "features20";

sa[21] = "features21";

sa[22] = "features22";

sa[23] = "features23";

sa[24] = "features24";

sa[25] = "features25";

sa[26] = "features26";

sa[27] = "features27";

}

// The class representing column label

class DeepLearning_model_1540691693623_1_ColInfo_28 implements java.io.Serializable {

public static final String[] VALUES = new String[2];

static {

DeepLearning_model_1540691693623_1_ColInfo_28_0.fill(VALUES);

}

static final class DeepLearning_model_1540691693623_1_ColInfo_28_0 implements java.io.Serializable {

static final void fill(String[] sa) {

sa[0] = "0";

sa[1] = "1";

}

Sparkling Water: Spark+H2O #4

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MacBook-Pro-5:spark-2.3.1-bin-hadoop2.7 $ ${SPARK_HOME}/bin/spark-shell --master local[*] --driver-memory 6g
2018-10-09 12:49:43 WARN  NativeCodeLoader:62 - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context Web UI available at http://************:4040
Spark context available as 'sc' (master = local[*], app id = local-1539056991319).
Spark session available as 'spark'.
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 2.3.1
      /_/
         
Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_181)
Type in expressions to have them evaluated.
Type : help for more information.

scala> import org.apache.spark.mllib.linalg._
import org.apache.spark.mllib.linalg._

scala> import org.apache.spark.mllib.regression._
import org.apache.spark.mllib.evaluation._

scala> val rawData = sc.textFile("hdfs://localhost/user/*******/ds/covtype.data")
rawData: org.apache.spark.rdd.RDD[String] = hdfs://localhost/user/*******/ds/covtype.data MapPartitionsRDD[7] at textFile at <console>:33

scala> rawData.first
res9: String = 2596,51,3,258,0,510,221,232,148,6279,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,5

scala> val data = rawData.map { line =>
     | val values = line.split(',').map(_.toDouble)
     | val featureVector = Vectors.dense(values.init)
     | val label = values.last - 1
     | LabeledPoint(label, featureVector)
     | }
data: org.apache.spark.rdd.RDD[org.apache.spark.mllib.regression.LabeledPoint] = MapPartitionsRDD[8] at map at <console>:34

scala> data.first
res10: org.apache.spark.mllib.regression.LabeledPoint = (4.0,[2596.0,51.0,3.0,258.0,0.0,510.0,221.0,232.0,148.0,6279.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])

scala> val Array(trainData, cvData, testData) =
     | data.randomSplit(Array(0.8, 0.1, 0.1))
trainData: org.apache.spark.rdd.RDD[org.apache.spark.mllib.regression.LabeledPoint] = MapPartitionsRDD[9] at randomSplit at <console>:35
cvData: org.apache.spark.rdd.RDD[org.apache.spark.mllib.regression.LabeledPoint] = MapPartitionsRDD[10] at randomSplit at <console>:35
testData: org.apache.spark.rdd.RDD[org.apache.spark.mllib.regression.LabeledPoint] = MapPartitionsRDD[11] at randomSplit at <console>:35

scala> trainData.cache()
res11: trainData.type = MapPartitionsRDD[9] at randomSplit at <console>:35

scala> cvData.cache()
res12: cvData.type = MapPartitionsRDD[10] at randomSplit at <console>:35

scala> testData.cache()
res13: testData.type = MapPartitionsRDD[11] at randomSplit at <console>:35

MacBook-Pro-5:spark-2.3.1-bin-hadoop2.7 $ ${SPARK_HOME}/bin/spark-shell --master local[*] --driver-memory 6g

2018-10-09 12:49:43 WARN NativeCodeLoader:62 - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

Setting default log level to "WARN".

To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).

Spark context Web UI available at http://************:4040

Spark context available as 'sc' (master = local[*], app id = local-1539056991319).

Spark session available as 'spark'.

Welcome to

____ __

/ __/__ ___ _____/ /__

_\ \/ _ \/ _ `/ __/ '_/

/___/ .__/\_,_/_/ /_/\_\ version 2.3.1

/_/

Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_181)

Type in expressions to have them evaluated.

Type : help for more information.

scala> import org.apache.spark.mllib.linalg._

import org.apache.spark.mllib.linalg._

scala> import org.apache.spark.mllib.regression._

import org.apache.spark.mllib.evaluation._

scala> val rawData = sc.textFile("hdfs://localhost/user/*******/ds/covtype.data")

rawData: org.apache.spark.rdd.RDD[String] = hdfs://localhost/user/*******/ds/covtype.data MapPartitionsRDD[7] at textFile at <console>:33

scala> rawData.first

res9: String = 2596,51,3,258,0,510,221,232,148,6279,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,5

scala> val data = rawData.map { line =>

| val values = line.split(',').map(_.toDouble)

| val featureVector = Vectors.dense(values.init)

| val label = values.last - 1

| LabeledPoint(label, featureVector)

| }

data: org.apache.spark.rdd.RDD[org.apache.spark.mllib.regression.LabeledPoint] = MapPartitionsRDD[8] at map at <console>:34

scala> data.first

res10: org.apache.spark.mllib.regression.LabeledPoint = (4.0,[2596.0,51.0,3.0,258.0,0.0,510.0,221.0,232.0,148.0,6279.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])

scala> val Array(trainData, cvData, testData) =

| data.randomSplit(Array(0.8, 0.1, 0.1))

trainData: org.apache.spark.rdd.RDD[org.apache.spark.mllib.regression.LabeledPoint] = MapPartitionsRDD[9] at randomSplit at <console>:35

cvData: org.apache.spark.rdd.RDD[org.apache.spark.mllib.regression.LabeledPoint] = MapPartitionsRDD[10] at randomSplit at <console>:35

testData: org.apache.spark.rdd.RDD[org.apache.spark.mllib.regression.LabeledPoint] = MapPartitionsRDD[11] at randomSplit at <console>:35

scala> trainData.cache()

res11: trainData.type = MapPartitionsRDD[9] at randomSplit at <console>:35

scala> cvData.cache()

res12: cvData.type = MapPartitionsRDD[10] at randomSplit at <console>:35

scala> testData.cache()

res13: testData.type = MapPartitionsRDD[11] at randomSplit at <console>:35

[crayon-6637b25bd75132 […]

MLST: eBURST

Published on 2018年10月8日2018年10月9日 by anesth

研究プロジェクトの関連で、ちょっと横道に逸れます。MLSTとeBURST Studyについて、復習を進めます。ーーーーーーーーーーーーーーーーーーーーーーーーーーーーー eBURST3のサイトhttp://eburst […]

Science To Medicine

Just My Daily Study Note by ts.anesth.kpum

Month: 10月 2018

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MLST: eBURST