Package org.apache.hama.ml.math

Examples of org.apache.hama.ml.math.DoubleVector

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      // if our cache is enabled but empty, we have to read it from disk first
      if (cache.isEmpty()) {
        final NullWritable value = NullWritable.get();
        final VectorWritable key = new VectorWritable();
        while (peer.readNext(key, value)) {
          DoubleVector deepCopy = key.getVector().deepCopy();
          cache.add(deepCopy);
          // but do the assignment directly
          assignCentersInternal(newCenterArray, summationCount, deepCopy);
        }
      } else {
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  }

  private void assignCentersInternal(final DoubleVector[] newCenterArray,
      final int[] summationCount, final DoubleVector key) {
    final int lowestDistantCenter = getNearestCenter(key);
    final DoubleVector clusterCenter = newCenterArray[lowestDistantCenter];
    if (clusterCenter == null) {
      newCenterArray[lowestDistantCenter] = key;
    } else {
      // add the vector to the center
      newCenterArray[lowestDistantCenter] = newCenterArray[lowestDistantCenter]
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      HashMap<Integer, DoubleVector> centerMap = KMeansBSP.readOutput(conf,
          out, centerOut, fs);
      System.out.println(centerMap);
      assertEquals(1, centerMap.size());
      DoubleVector doubleVector = centerMap.get(0);
      assertTrue(doubleVector.get(0) >= 50 && doubleVector.get(0) < 51);
      assertTrue(doubleVector.get(1) >= 50 && doubleVector.get(1) < 51);
    } finally {
      fs.delete(new Path("/tmp/clustering"), true);
    }
  }
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    // read an item
    KeyValuePair<VectorWritable, DoubleWritable> kvp;
    while ((kvp = peer.readNext()) != null) {
      // calculate cost for given input
      double y = kvp.getValue().get();
      DoubleVector x = kvp.getKey().getVector();
      double costForX = regressionModel.calculateCostForItem(x, y, m, theta);

      // adds to local cost
      localCost += costForX;
    }
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      BSPPeer<VectorWritable, DoubleWritable, VectorWritable, DoubleWritable, VectorWritable> peer)
      throws IOException {
    KeyValuePair<VectorWritable, DoubleWritable> kvp;
    double[] thetaDelta = new double[theta.getLength()];
    while ((kvp = peer.readNext()) != null) {
      DoubleVector x = kvp.getKey().getVector();
      double y = kvp.getValue().get();
      double difference = regressionModel.applyHypothesis(theta, x) - y;
      for (int j = 0; j < theta.getLength(); j++) {
        thetaDelta[j] += difference * x.get(j);
      }
    }
    return thetaDelta;
  }
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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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    }

    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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    matrices[0] = new DenseDoubleMatrix(5, 3, 0.5);
    matrices[1] = new DenseDoubleMatrix(1, 6, 0.5);
    ann.setWeightMatrices(matrices);

    double[] arr = new double[] { 0, 1 };
    DoubleVector training = new DenseDoubleVector(arr);
    DoubleVector result = ann.getOutput(training);
    assertEquals(1, result.getDimension());
    // assertEquals(3, result.get(0), 0.000001);

    // second network
    SmallLayeredNeuralNetwork ann2 = new SmallLayeredNeuralNetwork();
    ann2.addLayer(2, false, FunctionFactory.createDoubleFunction("Sigmoid"));
    ann2.addLayer(3, false, FunctionFactory.createDoubleFunction("Sigmoid"));
    ann2.addLayer(1, true, FunctionFactory.createDoubleFunction("Sigmoid"));
    ann2.setCostFunction(FunctionFactory
        .createDoubleDoubleFunction("SquaredError"));
    ann2.setLearningRate(0.3);
    // intentionally initialize all weights to 0.5
    DoubleMatrix[] matrices2 = new DenseDoubleMatrix[2];
    matrices2[0] = new DenseDoubleMatrix(3, 3, 0.5);
    matrices2[1] = new DenseDoubleMatrix(1, 4, 0.5);
    ann2.setWeightMatrices(matrices2);

    double[] test = { 0, 0 };
    double[] result2 = { 0.807476 };

    DoubleVector vec = ann2.getOutput(new DenseDoubleVector(test));
    assertArrayEquals(result2, vec.toArray(), 0.000001);

    SmallLayeredNeuralNetwork ann3 = new SmallLayeredNeuralNetwork();
    ann3.addLayer(2, false, FunctionFactory.createDoubleFunction("Sigmoid"));
    ann3.addLayer(3, false, FunctionFactory.createDoubleFunction("Sigmoid"));
    ann3.addLayer(1, true, FunctionFactory.createDoubleFunction("Sigmoid"));
    ann3.setCostFunction(FunctionFactory
        .createDoubleDoubleFunction("SquaredError"));
    ann3.setLearningRate(0.3);
    // intentionally initialize all weights to 0.5
    DoubleMatrix[] initMatrices = new DenseDoubleMatrix[2];
    initMatrices[0] = new DenseDoubleMatrix(3, 3, 0.5);
    initMatrices[1] = new DenseDoubleMatrix(1, 4, 0.5);
    ann3.setWeightMatrices(initMatrices);

    double[] instance = { 0, 1 };
    DoubleVector output = ann3.getOutput(new DenseDoubleVector(instance));
    assertEquals(0.8315410, output.get(0), 0.000001);
  }
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        ann.updateWeightMatrices(matrices);
      }
    }

    for (int i = 0; i < instances.length; ++i) {
      DoubleVector input = new DenseDoubleVector(instances[i]).slice(2);
      // the expected output is the last element in array
      double result = instances[i][2];
      double actual = ann.getOutput(input).get(0);
      if (result < 0.5 && actual >= 0.5 || result >= 0.5 && actual < 0.5) {
        Log.info("Neural network failes to lear the XOR.");
      }
    }

    // write model into file and read out
    String modelPath = "/tmp/testSmallLayeredNeuralNetworkXORLocal";
    ann.setModelPath(modelPath);
    try {
      ann.writeModelToFile();
    } catch (IOException e) {
      e.printStackTrace();
    }
    SmallLayeredNeuralNetwork annCopy = new SmallLayeredNeuralNetwork(modelPath);
    // test on instances
    for (int i = 0; i < instances.length; ++i) {
      DoubleVector input = new DenseDoubleVector(instances[i]).slice(2);
      // the expected output is the last element in array
      double result = instances[i][2];
      double actual = annCopy.getOutput(input).get(0);
      if (result < 0.5 && actual >= 0.5 || result >= 0.5 && actual < 0.5) {
        Log.info("Neural network failes to lear the XOR.");
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Related Classes of org.apache.hama.ml.math.DoubleVector

  • 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
  • org.apache.hama.ml.perception.SmallMultiLayerPerceptron
  • org.apache.hama.ml.perception.TestSmallMultiLayerPerceptron
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