List of downsample() Examples

Examples of downsample()

net.opentsdb.core.Query.downsample()
Downsamples the results by specifying a fixed interval between points.
Technically, downsampling means reducing the sampling interval. Here the idea is similar. Instead of returning every single data point that matched the query, we want one data point per fixed time interval. The way we get this one data point is by aggregating all the data points of that interval together using an {@link Aggregator}. This enables you to compute things like the 5-minute average or 10 minute 99th percentile. @param interval Number of seconds wanted between each data point. @param downsampler Aggregation function to use to group data pointswithin an interval.
org.encog.platformspecific.j2se.data.image.ImageMLDataSet.downsample()
Downsample all images and generate training data. @param theHeight The height to downsample to. @param theWidth the width to downsample to.
org.encog.platformspecific.j2se.data.image.ImageNeuralData.downsample()
Downsample, and copy, the image contents into the data of this object. Calling this method has no effect on the image, as the same image can be downsampled multiple times to different resolutions. @param downsampler The downsampler object to use. @param findBounds Should the bounds be located and cropped. @param height The height to downsample to. @param width The width to downsample to. @param hi The high value to normalize to. @param lo The low value to normalize to.
org.encog.platformspecific.j2se.data.image.ImageNeuralDataSet.downsample()
Downsample all images and generate training data. @param theHeight The height to downsample to. @param theWidth the width to downsample to.

Examples of net.opentsdb.core.Query.downsample()

        } catch (NoSuchElementException e) {
          throw new BadRequestException("No such downsampling function: "
                                        + parts[1].substring(dash + 1));
        }
        final long interval = DateTime.parseDuration(parts[1].substring(0, dash));
        tsdbquery.downsample(interval, downsampler);
      } else {
        tsdbquery.downsample(1000, agg);
      }
      tsdbqueries[nqueries++] = tsdbquery;
    }

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Examples of net.opentsdb.core.Query.downsample()

                                        + parts[1].substring(dash + 1));
        }
        final long interval = DateTime.parseDuration(parts[1].substring(0, dash));
        tsdbquery.downsample(interval, downsampler);
      } else {
        tsdbquery.downsample(1000, agg);
      }
      tsdbqueries[nqueries++] = tsdbquery;
    }
    return tsdbqueries;
  }

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Examples of net.opentsdb.core.Query.downsample()

      if (end_ts > 0) {
        query.setEndTime(end_ts);
      }
      query.setTimeSeries(metric, tags, agg, rate, rate_options);
      if (downsample) {
        query.downsample(interval, sampler);
      }
      queries.add(query);
    }
  }

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Examples of org.encog.platformspecific.j2se.data.image.ImageMLDataSet.downsample()

    Downsample downsample = new SimpleIntensityDownsample();
    ImageMLDataSet set = new ImageMLDataSet(downsample,true,-1,1);
    BasicMLData ideal = new BasicMLData(1);
    ImageMLData input = new ImageMLData(image);
    set.add(input,ideal);
    set.downsample(2,2);
    Iterator<MLDataPair> itr = set.iterator();
    MLDataPair pair = (MLDataPair)itr.next();
    MLData data = pair.getInput();
    double[] d = data.getData();
    //Assert.assertEquals(d[0],-1.0, 0.1);

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Examples of org.encog.platformspecific.j2se.data.image.ImageNeuralData.downsample()

  public void processWhatIs() throws IOException {
    final String filename = getArg("image");
    final File file = new File(filename);
    final Image img = ImageIO.read(file);
    final ImageNeuralData input = new ImageNeuralData(img);
    input.downsample(this.downsample, false, this.downsampleHeight,
        this.downsampleWidth, 1, -1);
    final int winner = this.network.winner(input);
    System.out.println("What is: " + filename + ", it seems to be: "
        + this.neuron2identity.get(winner));
  }

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Examples of org.encog.platformspecific.j2se.data.image.ImageNeuralDataSet.downsample()

    Downsample downsample = new SimpleIntensityDownsample();
    ImageNeuralDataSet set = new ImageNeuralDataSet(downsample,true,-1,1);
    BasicMLData ideal = new BasicMLData(1);
    ImageNeuralData input = new ImageNeuralData(image);
    set.add(input,ideal);
    set.downsample(2,2);
    Iterator<MLDataPair> itr = set.iterator();
    MLDataPair pair = (MLDataPair)itr.next();
    MLData data = pair.getInput();
    double[] d = data.getData();
    //Assert.assertEquals(d[0],-1.0, 0.1);

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