/** tests the commandline operation of the saver. */
  public void testSaverCommandlineArgs() {
    String[] options;

    options = getCommandlineOptions(false);

    try {
      ((OptionHandler) m_Saver).setOptions(options);
    } catch (Exception e) {
      e.printStackTrace();
      fail("Command line test failed ('" + Utils.arrayToString(options) + "'): " + e.toString());
    }
  }
Exemplo n.º 2
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 /** uses the low level approach */
 protected static void useLowLevel(Instances data) throws Exception {
   System.out.println("\n3. Low-level");
   AttributeSelection attsel = new AttributeSelection();
   CfsSubsetEval eval = new CfsSubsetEval();
   GreedyStepwise search = new GreedyStepwise();
   search.setSearchBackwards(true);
   attsel.setEvaluator(eval);
   attsel.setSearch(search);
   attsel.SelectAttributes(data);
   int[] indices = attsel.selectedAttributes();
   System.out.println(
       "selected attribute indices (starting with 0):\n" + Utils.arrayToString(indices));
 }
Exemplo n.º 3
0
  /**
   * Returns a string that describes the filter as source. The filter will be contained in a class
   * with the given name (there may be auxiliary classes), and will contain two methods with these
   * signatures:
   *
   * <pre><code>
   * // converts one row
   * public static Object[] filter(Object[] i);
   * // converts a full dataset (first dimension is row index)
   * public static Object[][] filter(Object[][] i);
   * </code></pre>
   *
   * where the array <code>i</code> contains elements that are either Double, String, with missing
   * values represented as null. The generated code is public domain and comes with no warranty.
   *
   * @param className the name that should be given to the source class.
   * @param data the dataset used for initializing the filter
   * @return the object source described by a string
   * @throws Exception if the source can't be computed
   */
  public String toSource(String className, Instances data) throws Exception {
    StringBuffer result;
    boolean[] process;
    int i;

    result = new StringBuffer();

    // determine what attributes were processed
    process = new boolean[data.numAttributes()];
    for (i = 0; i < data.numAttributes(); i++) {
      process[i] = (data.attribute(i).isNumeric() && (i != data.classIndex()));
    }

    result.append("class " + className + " {\n");
    result.append("\n");
    result.append("  /** lists which attributes will be processed */\n");
    result.append(
        "  protected final static boolean[] PROCESS = new boolean[]{"
            + Utils.arrayToString(process)
            + "};\n");
    result.append("\n");
    result.append("  /** the computed means */\n");
    result.append(
        "  protected final static double[] MEANS = new double[]{"
            + Utils.arrayToString(m_Means)
            + "};\n");
    result.append("\n");
    result.append("  /**\n");
    result.append("   * filters a single row\n");
    result.append("   * \n");
    result.append("   * @param i the row to process\n");
    result.append("   * @return the processed row\n");
    result.append("   */\n");
    result.append("  public static Object[] filter(Object[] i) {\n");
    result.append("    Object[] result;\n");
    result.append("\n");
    result.append("    result = new Object[i.length];\n");
    result.append("    for (int n = 0; n < i.length; n++) {\n");
    result.append("      if (PROCESS[n] && (i[n] != null))\n");
    result.append("        result[n] = ((Double) i[n]) - MEANS[n];\n");
    result.append("      else\n");
    result.append("        result[n] = i[n];\n");
    result.append("    }\n");
    result.append("\n");
    result.append("    return result;\n");
    result.append("  }\n");
    result.append("\n");
    result.append("  /**\n");
    result.append("   * filters multiple rows\n");
    result.append("   * \n");
    result.append("   * @param i the rows to process\n");
    result.append("   * @return the processed rows\n");
    result.append("   */\n");
    result.append("  public static Object[][] filter(Object[][] i) {\n");
    result.append("    Object[][] result;\n");
    result.append("\n");
    result.append("    result = new Object[i.length][];\n");
    result.append("    for (int n = 0; n < i.length; n++) {\n");
    result.append("      result[n] = filter(i[n]);\n");
    result.append("    }\n");
    result.append("\n");
    result.append("    return result;\n");
    result.append("  }\n");
    result.append("}\n");

    return result.toString();
  }
Exemplo n.º 4
0
  /**
   * Returns a string that describes the filter as source. The filter will be contained in a class
   * with the given name (there may be auxiliary classes), and will contain two methods with these
   * signatures:
   *
   * <pre><code>
   * // converts one row
   * public static Object[] filter(Object[] i);
   * // converts a full dataset (first dimension is row index)
   * public static Object[][] filter(Object[][] i);
   * </code></pre>
   *
   * where the array <code>i</code> contains elements that are either Double, String, with missing
   * values represented as null. The generated code is public domain and comes with no warranty.
   *
   * @param className the name that should be given to the source class.
   * @param data the dataset used for initializing the filter
   * @return the object source described by a string
   * @throws Exception if the source can't be computed
   */
  public String toSource(String className, Instances data) throws Exception {
    StringBuffer result;
    boolean[] process;
    int i;

    result = new StringBuffer();

    // determine what attributes were processed
    process = new boolean[data.numAttributes()];
    for (i = 0; i < data.numAttributes(); i++)
      process[i] = (data.attribute(i).isNumeric() && (i != data.classIndex()));

    result.append("class " + className + " {\n");
    result.append("\n");
    result.append("  /** lists which attributes will be processed */\n");
    result.append(
        "  protected final static boolean[] PROCESS = new boolean[]{"
            + Utils.arrayToString(process)
            + "};\n");
    result.append("\n");
    result.append("  /** the minimum values for numeric values */\n");
    result.append(
        "  protected final static double[] MIN = new double[]{"
            + Utils.arrayToString(m_MinArray).replaceAll("NaN", "Double.NaN")
            + "};\n");
    result.append("\n");
    result.append("  /** the maximum values for numeric values */\n");
    result.append(
        "  protected final static double[] MAX = new double[]{"
            + Utils.arrayToString(m_MaxArray)
            + "};\n");
    result.append("\n");
    result.append("  /** the scale factor */\n");
    result.append("  protected final static double SCALE = " + m_Scale + ";\n");
    result.append("\n");
    result.append("  /** the translation */\n");
    result.append("  protected final static double TRANSLATION = " + m_Translation + ";\n");
    result.append("\n");
    result.append("  /**\n");
    result.append("   * filters a single row\n");
    result.append("   * \n");
    result.append("   * @param i the row to process\n");
    result.append("   * @return the processed row\n");
    result.append("   */\n");
    result.append("  public static Object[] filter(Object[] i) {\n");
    result.append("    Object[] result;\n");
    result.append("\n");
    result.append("    result = new Object[i.length];\n");
    result.append("    for (int n = 0; n < i.length; n++) {\n");
    result.append("      if (PROCESS[n] && (i[n] != null)) {\n");
    result.append("        if (Double.isNaN(MIN[n]) || (MIN[n] == MAX[n]))\n");
    result.append("          result[n] = 0;\n");
    result.append("        else\n");
    result.append(
        "          result[n] = (((Double) i[n]) - MIN[n]) / (MAX[n] - MIN[n]) * SCALE + TRANSLATION;\n");
    result.append("      }\n");
    result.append("      else {\n");
    result.append("        result[n] = i[n];\n");
    result.append("      }\n");
    result.append("    }\n");
    result.append("\n");
    result.append("    return result;\n");
    result.append("  }\n");
    result.append("\n");
    result.append("  /**\n");
    result.append("   * filters multiple rows\n");
    result.append("   * \n");
    result.append("   * @param i the rows to process\n");
    result.append("   * @return the processed rows\n");
    result.append("   */\n");
    result.append("  public static Object[][] filter(Object[][] i) {\n");
    result.append("    Object[][] result;\n");
    result.append("\n");
    result.append("    result = new Object[i.length][];\n");
    result.append("    for (int n = 0; n < i.length; n++) {\n");
    result.append("      result[n] = filter(i[n]);\n");
    result.append("    }\n");
    result.append("\n");
    result.append("    return result;\n");
    result.append("  }\n");
    result.append("}\n");

    return result.toString();
  }