Package org.apache.mahout.math

Examples of org.apache.mahout.math.Matrix.aggregate()

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      OnlineLogisticRegression model = state.getModels().get(0);
      // finish off pending regularization
      model.close();

      Matrix beta = model.getBeta();
      maxBeta = beta.aggregate(Functions.MAX, Functions.ABS);
      nonZeros = beta.aggregate(Functions.PLUS, new DoubleFunction() {
        @Override
        public double apply(double v) {
          return Math.abs(v) > 1.0e-6 ? 1 : 0;
        }
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      // finish off pending regularization
      model.close();

      Matrix beta = model.getBeta();
      maxBeta = beta.aggregate(Functions.MAX, Functions.ABS);
      nonZeros = beta.aggregate(Functions.PLUS, new DoubleFunction() {
        @Override
        public double apply(double v) {
          return Math.abs(v) > 1.0e-6 ? 1 : 0;
        }
      });
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        @Override
        public double apply(double v) {
          return Math.abs(v) > 1.0e-6 ? 1 : 0;
        }
      });
      positive = beta.aggregate(Functions.PLUS, new DoubleFunction() {
        @Override
        public double apply(double v) {
          return v > 0 ? 1 : 0;
        }
      });
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        @Override
        public double apply(double v) {
          return v > 0 ? 1 : 0;
        }
      });
      norm = beta.aggregate(Functions.PLUS, Functions.ABS);

      lambda = best.getMappedParams()[0];
      mu = best.getMappedParams()[1];
    } else {
      maxBeta = 0;
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        @Override
        public double apply(double v) {
          return v > 0 ? 1 : 0;
        }
      });
      norm = beta.aggregate(Functions.PLUS, Functions.ABS);

      lambda = best.getMappedParams()[0];
      mu = best.getMappedParams()[1];
    } else {
      maxBeta = 0;
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      OnlineLogisticRegression model = state.getModels().get(0);
      // finish off pending regularization
      model.close();

      Matrix beta = model.getBeta();
      maxBeta = beta.aggregate(Functions.MAX, Functions.ABS);
      nonZeros = beta.aggregate(Functions.PLUS, new DoubleFunction() {
        @Override
        public double apply(double v) {
          return Math.abs(v) > 1.0e-6 ? 1 : 0;
        }
View Full Code Here
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      // finish off pending regularization
      model.close();

      Matrix beta = model.getBeta();
      maxBeta = beta.aggregate(Functions.MAX, Functions.ABS);
      nonZeros = beta.aggregate(Functions.PLUS, new DoubleFunction() {
        @Override
        public double apply(double v) {
          return Math.abs(v) > 1.0e-6 ? 1 : 0;
        }
      });
View Full Code Here
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        @Override
        public double apply(double v) {
          return Math.abs(v) > 1.0e-6 ? 1 : 0;
        }
      });
      positive = beta.aggregate(Functions.PLUS, new DoubleFunction() {
        @Override
        public double apply(double v) {
          return v > 0 ? 1 : 0;
        }
      });
View Full Code Here
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