Esempio n. 1
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 public final double lgrad(double u, double a, Loss loss) {
   assert loss.isForNumeric() : "Loss function " + loss + " not applicable to numerics";
   switch (loss) {
     case Quadratic:
       return 2 * (u - a);
     case Absolute:
       return Math.signum(u - a);
     case Huber:
       return Math.abs(u - a) <= 1 ? u - a : Math.signum(u - a);
     case Poisson:
       assert a >= 0 : "Poisson loss L(u,a) requires variable a >= 0";
       return Math.exp(u) - a;
     case Hinge:
       // return a*u <= 1 ? -a : 0;
       return a == 0
           ? (-u <= 1 ? 1 : 0)
           : (u <= 1 ? -1 : 0); // Booleans are coded as {0,1} instead of {-1,1}
     case Logistic:
       // return -a/(1+Math.exp(a*u));
       return a == 0
           ? 1 / (1 + Math.exp(-u))
           : -1 / (1 + Math.exp(u)); // Booleans are coded as {0,1} instead of {-1,1}
     case Periodic:
       return ((2 * Math.PI) / _period) * Math.sin((a - u) * (2 * Math.PI) / _period);
     default:
       throw new RuntimeException("Unknown loss function " + loss);
   }
 }
Esempio n. 2
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 public final double loss(double u, double a, Loss loss) {
   assert loss.isForNumeric() : "Loss function " + loss + " not applicable to numerics";
   switch (loss) {
     case Quadratic:
       return (u - a) * (u - a);
     case Absolute:
       return Math.abs(u - a);
     case Huber:
       return Math.abs(u - a) <= 1 ? 0.5 * (u - a) * (u - a) : Math.abs(u - a) - 0.5;
     case Poisson:
       assert a >= 0 : "Poisson loss L(u,a) requires variable a >= 0";
       return Math.exp(u)
           + (a == 0 ? 0 : -a * u + a * Math.log(a) - a); // Since \lim_{a->0} a*log(a) = 0
     case Hinge:
       // return Math.max(1-a*u,0);
       return Math.max(1 - (a == 0 ? -u : u), 0); // Booleans are coded {0,1} instead of {-1,1}
     case Logistic:
       // return Math.log(1 + Math.exp(-a * u));
       return Math.log(
           1 + Math.exp(a == 0 ? u : -u)); // Booleans are coded {0,1} instead of {-1,1}
     case Periodic:
       return 1 - Math.cos((a - u) * (2 * Math.PI) / _period);
     default:
       throw new RuntimeException("Unknown loss function " + loss);
   }
 }
Esempio n. 3
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 public static int mimpute(double[] u, Loss multi_loss) {
   assert multi_loss.isForCategorical()
       : "Loss function " + multi_loss + " not applicable to categoricals";
   switch (multi_loss) {
     case Categorical:
     case Ordinal:
       double[] cand = new double[u.length];
       for (int a = 0; a < cand.length; a++) cand[a] = mloss(u, a, multi_loss);
       return ArrayUtils.minIndex(cand);
     default:
       throw new RuntimeException("Unknown multidimensional loss function " + multi_loss);
   }
 }
Esempio n. 4
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 public static double impute(double u, Loss loss) {
   assert loss.isForNumeric() : "Loss function " + loss + " not applicable to numerics";
   switch (loss) {
     case Quadratic:
     case Absolute:
     case Huber:
     case Periodic:
       return u;
     case Poisson:
       return Math.exp(u) - 1;
     case Hinge:
     case Logistic:
       return u > 0 ? 1 : 0; // Booleans are coded as {0,1} instead of {-1,1}
     default:
       throw new RuntimeException("Unknown loss function " + loss);
   }
 }
Esempio n. 5
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    public static double[] mlgrad(double[] u, int a, Loss multi_loss) {
      assert multi_loss.isForCategorical()
          : "Loss function " + multi_loss + " not applicable to categoricals";
      if (a < 0 || a > u.length - 1)
        throw new IllegalArgumentException(
            "Index must be between 0 and " + String.valueOf(u.length - 1));

      double[] grad = new double[u.length];
      switch (multi_loss) {
        case Categorical:
          for (int i = 0; i < u.length; i++) grad[i] = (1 + u[i] > 0) ? 1 : 0;
          grad[a] = (1 - u[a] > 0) ? -1 : 0;
          return grad;
        case Ordinal:
          for (int i = 0; i < u.length - 1; i++) grad[i] = (a > i && 1 - u[i] > 0) ? -1 : 0;
          return grad;
        default:
          throw new RuntimeException("Unknown multidimensional loss function " + multi_loss);
      }
    }
Esempio n. 6
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    public static double mloss(double[] u, int a, Loss multi_loss) {
      assert multi_loss.isForCategorical()
          : "Loss function " + multi_loss + " not applicable to categoricals";
      if (a < 0 || a > u.length - 1)
        throw new IllegalArgumentException(
            "Index must be between 0 and " + String.valueOf(u.length - 1));

      double sum = 0;
      switch (multi_loss) {
        case Categorical:
          for (int i = 0; i < u.length; i++) sum += Math.max(1 + u[i], 0);
          sum += Math.max(1 - u[a], 0) - Math.max(1 + u[a], 0);
          return sum;
        case Ordinal:
          for (int i = 0; i < u.length - 1; i++) sum += Math.max(a > i ? 1 - u[i] : 1, 0);
          return sum;
        default:
          throw new RuntimeException("Unknown multidimensional loss function " + multi_loss);
      }
    }