/** 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());
}
}
/** 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));
}
/**
* 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();
}
/**
* 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();
}
