Package org.apache.mahout.math.function

Examples of org.apache.mahout.math.function.SquareRootFunction

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    setNumObservations((long) getS0());
    setTotalObservations(getTotalObservations() + getNumObservations());
    setCenter(getS1().divide(getS0()));
    // compute the component stds
    if (getS0() > 1) {
      setRadius(getS2().times(getS0()).minus(getS1().times(getS1())).assign(new SquareRootFunction()).divide(getS0()));
    }
    setS0(0);
    setS1(center.like());
    setS2(center.like());
  }
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  @Override
  public void compute() {
    if (s0 != 0.0) {
      mean = s1.divide(s0);
      std = s2.times(s0).minus(s1.times(s1)).assign(new SquareRootFunction()).divide(s0);
    }
  }
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    return mean;
  }

  @Override
  public Vector getStd() {
    return variance.clone().assign(new SquareRootFunction());
  }
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      Vector delta = v.get().minus(sampleMean);
      sampleVar.assign(delta.times(delta), Functions.PLUS);
    }
    sampleVar = sampleVar.divide(sampleN - 1);
    sampleStd = sampleVar.clone();
    sampleStd.assign(new SquareRootFunction());
    log.info("Observing {} samples m=[{}, {}] sd=[{}, {}]",
             sampleN, sampleMean.get(0), sampleMean.get(1), sampleStd.get(0), sampleStd.get(1));
  }
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      Vector delta = v.get().minus(sampleMean);
      delta.times(delta).addTo(sampleVar);
    }
    sampleVar = sampleVar.divide(sampleN - 1);
    sampleStd = sampleVar.clone();
    sampleStd.assign(new SquareRootFunction());
    log.info("Observing {} samples m=[{}, {}] sd=[{}, {}]",
             new Object[] { sampleN, sampleMean.get(0), sampleMean.get(1), sampleStd.get(0), sampleStd.get(1) });
  }
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    numPoints = (int) s0;
    center = s1.divide(s0);
    // compute the component stds
    if (s0 > 1) {
      radius = s2.times(s0).minus(s1.times(s1))
          .assign(new SquareRootFunction()).divide(s0);
    }
    s0 = 0;
    s1 = null;
    s2 = null;
  }
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  @Override
  public void compute() {
    if (s0 != 0.0) {
      mean = s1.divide(s0);
      std = s2.times(s0).minus(s1.times(s1)).assign(new SquareRootFunction()).divide(s0);
    }
  }
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    setNumObservations((long) getS0());
    setTotalObservations(getTotalObservations() + getNumObservations());
    setCenter(getS1().divide(getS0()));
    // compute the component stds
    if (getS0() > 1) {
      setRadius(getS2().times(getS0()).minus(getS1().times(getS1())).assign(new SquareRootFunction()).divide(getS0()));
    }
    setS0(0);
    setS1(center.like());
    setS2(center.like());
  }
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      Vector delta = v.get().minus(sampleMean);
      sampleVar.assign(delta.times(delta), Functions.PLUS);
    }
    sampleVar = sampleVar.divide(sampleN - 1);
    sampleStd = sampleVar.clone();
    sampleStd.assign(new SquareRootFunction());
    log.info("Observing {} samples m=[{}, {}] sd=[{}, {}]",
             sampleN, sampleMean.get(0), sampleMean.get(1), sampleStd.get(0), sampleStd.get(1));
  }
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    setNumObservations((long) getS0());
    setTotalObservations(getTotalObservations() + getNumObservations());
    setCenter(getS1().divide(getS0()));
    // compute the component stds
    if (getS0() > 1) {
      setRadius(getS2().times(getS0()).minus(getS1().times(getS1())).assign(new SquareRootFunction()).divide(getS0()));
    }
    setS0(0);
    setS1(center.like());
    setS2(center.like());
  }
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  • org.apache.mahout.clustering.dirichlet.models.NormalModel
  • org.apache.mahout.clustering.fuzzykmeans.SoftCluster
  • org.apache.mahout.clustering.kmeans.Cluster
  • org.apache.mahout.clustering.OnlineGaussianAccumulator
  • org.apache.mahout.clustering.RunningSumsGaussianAccumulator
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