Package org.apache.hama.commons.math

Examples of org.apache.hama.commons.math.DoubleVector$DoubleVectorElement

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      }
    }

    double relativeError = 0;
    for (int i = 0; i < instances.length; ++i) {
      DoubleVector test = new DenseDoubleVector(instances[i]);
      double expected = test.get(test.getDimension() - 1);
      test = test.slice(test.getDimension() - 1);
      double actual = regression.getOutput(test).get(0);
      relativeError += Math.abs((expected - actual) / expected);
    }

    relativeError /= instances.length;
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    }

    double relativeError = 0.0;
    // calculate the error on test instance
    for (double[] testInstance : testInstances) {
      DoubleVector instance = new DenseDoubleVector(testInstance);
      double expected = instance.get(instance.getDimension() - 1);
      instance = instance.slice(instance.getDimension() - 1);
      double actual = regression.getOutput(instance).get(0);
      if (expected == 0) {
        expected = 0.0000001;
      }
      relativeError += Math.abs((expected - actual) / expected);
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        conf, out, centerOut, fs);
    System.out.println(centerMap);

    assertEquals(1, centerMap.size()); // because k = 1

    DoubleVector doubleVector = centerMap.get(0);
    assertEquals(Double.valueOf(50), doubleVector.get(0));
    assertEquals(Double.valueOf(50), doubleVector.get(1));
  }
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public class LogisticRegressionModelTest {

  @Test
  public void testCorrectCostCalculation() throws Exception {
    LogisticRegressionModel logisticRegressionModel = new LogisticRegressionModel();
    DoubleVector x = new DenseDoubleVector(new double[]{2, 3, 4});
    double y = 1;
    DoubleVector theta = new DenseDoubleVector(new double[]{1, 1, 1});
    BigDecimal cost = logisticRegressionModel.calculateCostForItem(x, y, 2, theta);
    assertEquals("wrong cost calculation for logistic regression", 6.170109486162941E-5d, cost.doubleValue(), 0.000001);
  }
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public class LinearRegressionModelTest {

  @Test
  public void testCorrectCostCalculation() throws Exception {
    LinearRegressionModel linearRegressionModel = new LinearRegressionModel();
    DoubleVector x = new DenseDoubleVector(new double[]{2, 3, 4});
    double y = 1;
    DoubleVector theta = new DenseDoubleVector(new double[]{1, 1, 1});
    BigDecimal cost = linearRegressionModel.calculateCostForItem(x, y, 2, theta);
    assertEquals("wrong cost calculation for linear regression", BigDecimal.valueOf(16d), cost);
  }
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    }

    double errorRate = 0;
    // calculate the error on test instance
    for (double[] testInstance : testInstances) {
      DoubleVector instance = new DenseDoubleVector(testInstance);
      double expected = instance.get(instance.getDimension() - 1);
      DoubleVector features = instance.slice(instance.getDimension() - 1);
      double actual = regression.getOutput(features).get(0);
      if (actual < 0.5 && expected >= 0.5 || actual >= 0.5 && expected < 0.5) {
        ++errorRate;
      }
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    for (int recordsRead = 0; recordsRead < batchSize; ++recordsRead) {
      if (!peer.readNext(key, value)) {
        peer.reopenInput();
        peer.readNext(key, value);
      }
      DoubleVector trainingInstance = value.getVector();
      SmallLayeredNeuralNetwork.matricesAdd(weightUpdates,
          this.inMemoryModel.trainByInstance(trainingInstance));
      avgTrainingError += this.inMemoryModel.trainingError;
    }
    avgTrainingError /= batchSize;
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   *
   * @param inputInstance
   * @return The compressed information.
   */
  private DoubleVector transform(DoubleVector inputInstance, int inputLayer) {
    DoubleVector internalInstance = new DenseDoubleVector(inputInstance.getDimension() + 1);
    internalInstance.set(0, 1);
    for (int i = 0; i < inputInstance.getDimension(); ++i) {
      internalInstance.set(i + 1, inputInstance.get(i));
    }
    DoubleFunction squashingFunction = model
        .getSquashingFunction(inputLayer);
    DoubleMatrix weightMatrix = null;
    if (inputLayer == 0) {
      weightMatrix = this.getEncodeWeightMatrix();
    } else {
      weightMatrix = this.getDecodeWeightMatrix();
    }
    DoubleVector vec = weightMatrix.multiplyVectorUnsafe(internalInstance);
    vec = vec.applyToElements(squashingFunction);
    return vec;
  }
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    double[] trainingFeature = this.featureTransformer.transform(
        trainingInstance.sliceUnsafe(0, this.layerSizeArray[0] - 1)).toArray();
    double[] trainingLabels = Arrays.copyOfRange(trainingVec,
        this.layerSizeArray[0], trainingVec.length);

    DoubleVector trainingFeatureVec = new DenseDoubleVector(trainingFeature);
    List<double[]> outputCache = this.outputInternal(trainingFeatureVec);

    // calculate the delta of output layer
    double[] delta = new double[this.layerSizeArray[this.layerSizeArray.length - 1]];
    double[] outputLayerOutput = outputCache.get(outputCache.size() - 1);
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    Preconditions.checkArgument(this.layerSizeList.get(0) - 1 == instance
        .getDimension(), String.format(
        "The dimension of input instance should be %d.",
        this.layerSizeList.get(0) - 1));
    // transform the features to another space
    DoubleVector transformedInstance = this.featureTransformer
        .transform(instance);
    // add bias feature
    DoubleVector instanceWithBias = new DenseDoubleVector(
        transformedInstance.getDimension() + 1);
    instanceWithBias.set(0, 0.99999); // set bias to be a little bit less than
                                      // 1.0
    for (int i = 1; i < instanceWithBias.getDimension(); ++i) {
      instanceWithBias.set(i, transformedInstance.get(i - 1));
    }

    List<DoubleVector> outputCache = getOutputInternal(instanceWithBias);
    // return the output of the last layer
    DoubleVector result = outputCache.get(outputCache.size() - 1);
    // remove bias
    return result.sliceUnsafe(1, result.getDimension() - 1);
  }
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Related Classes of org.apache.hama.commons.math.DoubleVector$DoubleVectorElement

  • org.apache.hama.commons.io.PipesVectorWritable
  • org.apache.hama.commons.io.VectorWritable
  • org.apache.hama.examples.NeuralNetwork
  • org.apache.hama.ml.ann.AutoEncoder
  • org.apache.hama.ml.ann.SmallLayeredNeuralNetwork
  • org.apache.hama.ml.ann.SmallLayeredNeuralNetworkTrainer
  • org.apache.hama.ml.ann.TestAutoEncoder
  • org.apache.hama.ml.ann.TestSmallLayeredNeuralNetwork
  • org.apache.hama.ml.kmeans.KMeansBSP
  • org.apache.hama.ml.kmeans.TestKMeansBSP
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