Package org.apache.mahout.math

Examples of org.apache.mahout.math.DenseVector.divide()

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          Vector v = SAMPLE_DATA.get(vix).get();
          count++;
          v.addTo(center);
          DisplayClustering.plotRectangle(g2, v, dv);
        }
        center = center.divide(count);
        DisplayClustering.plotEllipse(g2, center, dv1);
        DisplayClustering.plotEllipse(g2, center, dv2);
      }
    }
  }
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    throws IOException, InterruptedException {
    Vector pi = new DenseVector(clusters.size());
    for (int i = 0; i < clusters.size(); i++) {
      pi.set(i, clusters.get(i).getModel().pdf(vector));
    }
    pi = pi.divide(pi.zSum());
    if (emitMostLikely) {
      emitMostLikelyCluster(vector, clusters, pi, context);
    } else {
      emitAllClusters(vector, clusters, pi, context);
    }
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    Vector pi = new DenseVector(clusters.size());
    for (int i = 0; i < clusters.size(); i++) {
      double pdf = clusters.get(i).getModel().pdf(vector);
      pi.set(i, pdf);
    }
    pi = pi.divide(pi.zSum());
    if (emitMostLikely) {
      emitMostLikelyCluster(vector, clusters, pi, writer);
    } else {
      emitAllClusters(vector, clusters, pi, writer);
    }
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          Vector v = SAMPLE_DATA.get(vix).get();
          count++;
          center.assign(v, Functions.PLUS);
          DisplayClustering.plotRectangle(g2, v, dv);
        }
        center = center.divide(count);
        DisplayClustering.plotEllipse(g2, center, dv1);
        DisplayClustering.plotEllipse(g2, center, dv2);
      }
    }
  }
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    throws IOException, InterruptedException {
    Vector pi = new DenseVector(clusters.size());
    for (int i = 0; i < clusters.size(); i++) {
      pi.set(i, clusters.get(i).getModel().pdf(vector));
    }
    pi = pi.divide(pi.zSum());
    if (emitMostLikely) {
      emitMostLikelyCluster(vector, clusters, pi, context);
    } else {
      emitAllClusters(vector, clusters, pi, context);
    }
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    Vector pi = new DenseVector(clusters.size());
    for (int i = 0; i < clusters.size(); i++) {
      double pdf = clusters.get(i).getModel().pdf(vector);
      pi.set(i, pdf);
    }
    pi = pi.divide(pi.zSum());
    if (emitMostLikely) {
      emitMostLikelyCluster(vector, clusters, pi, writer);
    } else {
      emitAllClusters(vector, clusters, pi, writer);
    }
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          Vector v = SAMPLE_DATA.get(vix).get();
          count++;
          center.assign(v, Functions.PLUS);
          DisplayClustering.plotRectangle(g2, v, dv);
        }
        center = center.divide(count);
        DisplayClustering.plotEllipse(g2, center, dv1);
        DisplayClustering.plotEllipse(g2, center, dv2);
      }
    }
  }
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    Vector sum = new DenseVector(2);
    for (VectorWritable v : sampleData) {
      sum.assign(v.get(), Functions.PLUS);
      sampleN++;
    }
    sampleMean = sum.divide(sampleN);

    Vector sampleVar = new DenseVector(2);
    for (VectorWritable v : sampleData) {
      Vector delta = v.get().minus(sampleMean);
      sampleVar.assign(delta.times(delta), Functions.PLUS);
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    Vector sampleVar = new DenseVector(2);
    for (VectorWritable v : sampleData) {
      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 v = SAMPLE_DATA.get(vix).get();
          count++;
          v.addTo(center);
          DisplayClustering.plotRectangle(g2, v, dv);
        }
        center = center.divide(count);
        DisplayClustering.plotEllipse(g2, center, dv1);
        DisplayClustering.plotEllipse(g2, center, dv2);
      }
    }
  }
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