Examples of org.apache.mahout.math.Vector.viewPart()

org.apache.mahout.math.Vector.viewPart()
Return a new matrix containing the subset of the recipient @param offset an int offset into the receiver @param length the cardinality of the desired result @return a new Vector @throws CardinalityException if the length is greater than the cardinality of the receiver @throws IndexException if the offset is negative or the offset+length is outside of the receiver

              : rowCount, 1);
      } else if (aCols[col].size() < rowCount) {
        Vector newVec =
          new SequentialAccessSparseVector(rowCount + blockHeight,
                                           aCols[col].getNumNondefaultElements() << 1);
        newVec.viewPart(0, aCols[col].size()).assign(aCols[col]);
        aCols[col] = newVec;
      }
    }


    @Override

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                                           1);
      } else if (aCols[col].size() < rowCount) {
        Vector newVec =
          new SequentialAccessSparseVector(rowCount << 1,
                                           aCols[col].getNumNondefaultElements() << 1);
        newVec.viewPart(0, aCols[col].size()).assign(aCols[col]);
        aCols[col] = newVec;
      }
    }


    @Override

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    input = readCsv("sgd.csv");


    // regenerate the target variable
    Vector target = new DenseVector(60);
    target.assign(0);
    target.viewPart(30, 30).assign(1);
    return target;
  }


  private static void train(Matrix input, Vector target, OnlineLearner lr) {
    RandomUtils.useTestSeed();

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    Vector result = classifyNoLink(instance);
    // Find the max value's index.
    int max = result.maxValueIndex();
    result.assign(0);
    result.setQuick(max, 1.0);
    return result.viewPart(1, result.size() - 1);
  }


  @Override
  public Vector classifyNoLink(Vector instance) {
    DenseVector hidden = inputToHidden(instance);

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    input = readCsv("sgd.csv");


    // regenerate the target variable
    Vector target = new DenseVector(60);
    target.assign(0);
    target.viewPart(30, 30).assign(1);
    return target;
  }


  static void train(Matrix input, Vector target, OnlineLearner lr) {
    RandomUtils.useTestSeed();

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    // Convert to probabilities by exponentiation.
    double max = result.maxValue();
    result.assign(Functions.minus(max)).assign(Functions.EXP);
    result = result.divide(result.norm(1));


    return result.viewPart(1, result.size() - 1);
  }


  @Override
  public Vector classifyNoLink(Vector instance) {
    Vector result = new DenseVector(weights.numRows());

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              : rowCount, 1);
      } else if (aCols[col].size() < rowCount) {
        Vector newVec =
          new SequentialAccessSparseVector(rowCount + blockHeight,
                                           aCols[col].getNumNondefaultElements() << 1);
        newVec.viewPart(0, aCols[col].size()).assign(aCols[col]);
        aCols[col] = newVec;
      }
    }


    @Override

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                                           1);
      } else if (aCols[col].size() < rowCount) {
        Vector newVec =
          new SequentialAccessSparseVector(rowCount << 1,
                                           aCols[col].getNumNondefaultElements() << 1);
        newVec.viewPart(0, aCols[col].size()).assign(aCols[col]);
        aCols[col] = newVec;
      }
    }


    @Override

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    input = readCsv("sgd.csv");


    // regenerate the target variable
    Vector target = new DenseVector(60);
    target.assign(0);
    target.viewPart(30, 30).assign(1);
    return target;
  }


  private static void train(Matrix input, Vector target, OnlineLearner lr) {
    RandomUtils.useTestSeed();

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    List<Vector> outputCache = getOutputInternal(instanceWithBias);
    // return the output of the last layer
    Vector result = outputCache.get(outputCache.size() - 1);
    // remove bias
    return result.viewPart(1, result.size() - 1);
  }


  /**
   * Calculate output internally, the intermediate output of each layer will be
   * stored.

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