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

org.apache.mahout.math.Matrix.aggregate()
Collects the results of a function applied to each element of a matrix and then aggregated. @param combiner A function that combines the results of the mapper. @param mapper A function to apply to each element. @return The result.

        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 = learningAlgorithm.getBest().getMappedParams()[0];
        mu = learningAlgorithm.getBest().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 UnaryFunction() {
          @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 UnaryFunction() {
          @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 UnaryFunction() {
          @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 = learningAlgorithm.getBest().getMappedParams()[0];
        mu = learningAlgorithm.getBest().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;
        }

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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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