Example usage for weka.core Utils getFlag

Introduction

In this page you can find the example usage for weka.core Utils getFlag.

Prototype

public static boolean getFlag(String flag, String[] options) throws Exception 

Source Link

Document

Checks if the given array contains the flag "-String".

Usage

From source file:Bilbo.java

License:Open Source License

/**
 * Parses a given list of options. <p/>
 *
 <!-- options-start -->/*from w w  w. j a va 2  s  . c o m*/
 * Valid options are: <p/>
 * 
 * <pre> -P
 *  Size of each bag, as a percentage of the
 *  training set size. (default 100)</pre>
 * 
 * <pre> -O
 *  Calculate the out of bag error.</pre>
 * 
 * <pre> -represent-copies-using-weights
 *  Represent copies of instances using weights rather than explicitly.</pre>
 * 
 * <pre> -S &lt;num&gt;
 *  Random number seed.
 *  (default 1)</pre>
 * 
 * <pre> -num-slots &lt;num&gt;
 *  Number of execution slots.
 *  (default 1 - i.e. no parallelism)</pre>
 * 
 * <pre> -I &lt;num&gt;
 *  Number of iterations.
 *  (default 10)</pre>
 * 
 * <pre> -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console</pre>
 * 
 * <pre> -W
 *  Full name of base classifier.
 *  (default: weka.classifiers.trees.REPTree)</pre>
 * 
 * <pre> 
 * Options specific to classifier weka.classifiers.trees.REPTree:
 * </pre>
 * 
 * <pre> -M &lt;minimum number of instances&gt;
 *  Set minimum number of instances per leaf (default 2).</pre>
 * 
 * <pre> -V &lt;minimum variance for split&gt;
 *  Set minimum numeric class variance proportion
 *  of train variance for split (default 1e-3).</pre>
 * 
 * <pre> -N &lt;number of folds&gt;
 *  Number of folds for reduced error pruning (default 3).</pre>
 * 
 * <pre> -S &lt;seed&gt;
 *  Seed for random data shuffling (default 1).</pre>
 * 
 * <pre> -P
 *  No pruning.</pre>
 * 
 * <pre> -L
 *  Maximum tree depth (default -1, no maximum)</pre>
 * 
 * <pre> -I
 *  Initial class value count (default 0)</pre>
 * 
 * <pre> -R
 *  Spread initial count over all class values (i.e. don't use 1 per value)</pre>
 * 
 <!-- options-end -->
 *
 * Options after -- are passed to the designated classifier.<p>
 *
 * @param options the list of options as an array of strings
 * @throws Exception if an option is not supported
 */
@Override
public void setOptions(String[] options) throws Exception {

    String bagSize = Utils.getOption('P', options);
    if (bagSize.length() != 0) {
        setBagSizePercent(Integer.parseInt(bagSize));
    } else {
        setBagSizePercent(100);
    }

    setCalcOutOfBag(Utils.getFlag('O', options));

    setRepresentCopiesUsingWeights(Utils.getFlag("represent-copies-using-weights", options));

    super.setOptions(options);

    Utils.checkForRemainingOptions(options);
}

---

From source file:PrincipalComponents.java

License:Open Source License

/**
 * Parses a given list of options.//from   w  w  w  . j a v a  2 s. co m
 * <p>
 * <p>
 * <!-- options-start --> Valid options are:
 * <p>
 * <p>
 * <pre>
 * -C
 *  Center (rather than standardize) the
 *  data and compute PCA using the covariance (rather
 *   than the correlation) matrix.
 * </pre>
 * <p>
 * <pre>
 * -R
 *  Retain enough PC attributes to account
 *  for this proportion of variance in the original data.
 *  (default = 0.95)
 * </pre>
 * <p>
 * <pre>
 * -O
 *  Transform through the PC space and
 *  back to the original space.
 * </pre>
 * <p>
 * <pre>
 * -A
 *  Maximum number of attributes to include in
 *  transformed attribute names. (-1 = include all)
 * </pre>
 * <p>
 * <!-- options-end -->
 *
 * @param options the list of options as an array of strings
 * @throws Exception if an option is not supported
 */
@Override
public void setOptions(String[] options) throws Exception {
    resetOptions();
    String optionString;

    optionString = Utils.getOption('R', options);
    if (optionString.length() != 0) {
        Double temp;
        temp = Double.valueOf(optionString);
        setVarianceCovered(temp.doubleValue());
    }
    optionString = Utils.getOption('A', options);
    if (optionString.length() != 0) {
        setMaximumAttributeNames(Integer.parseInt(optionString));
    }

    setTransformBackToOriginal(Utils.getFlag('O', options));
    setCenterData(Utils.getFlag('C', options));
}

---

From source file:TextDirectoryLoader.java

License:Open Source License

/** 
 * Parses a given list of options. <p/>
 *
 <!-- options-start -->//from w w  w  .  j a  v a2  s.c om
 * Valid options are: <p/>
 * 
 * <pre> -D
 *  Enables debug output.
 *  (default: off)</pre>
 * 
 * <pre> -F
 *  Stores the filename in an additional attribute.
 *  (default: off)</pre>
 * 
 * <pre> -dir &lt;directory&gt;
 *  The directory to work on.
 *  (default: current directory)</pre>
 * 
 <!-- options-end -->
 *
 * @param options the options
 * @throws Exception if options cannot be set
 */
public void setOptions(String[] options) throws Exception {
    setDebug(Utils.getFlag("D", options));

    setOutputFilename(Utils.getFlag("F", options));

    setDirectory(new File(Utils.getOption("dir", options)));

    String charSet = Utils.getOption("charset", options);
    m_charSet = "";
    if (charSet.length() > 0) {
        m_charSet = charSet;
    }
}

---

From source file:BaggingImprove.java

/**
 * Parses a given list of options./*w w w .ja  v  a 2s  .c  om*/
 * <p/>
 *
 * <!-- options-start -->
 * Valid options are:
 * <p/>
 *
 * <pre>
 * -P
 *  Size of each bag, as a percentage of the
 *  training set size. (default 100)
 * </pre>
 *
 * <pre>
 * -O
 *  Calculate the out of bag error.
 * </pre>
 *
 * <pre>
 * -S &lt;num&gt;
 *  Random number seed.
 *  (default 1)
 * </pre>
 *
 * <pre>
 * -I &lt;num&gt;
 *  Number of iterations.
 *  (default 10)
 * </pre>
 *
 * <pre>
 * -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
 * </pre>
 *
 * <pre>
 * -W
 *  Full name of base classifier.
 *  (default: weka.classifiers.trees.REPTree)
 * </pre>
 *
 * <pre>
 * Options specific to classifier weka.classifiers.trees.REPTree:
 * </pre>
 *
 * <pre>
 * -M &lt;minimum number of instances&gt;
 *  Set minimum number of instances per leaf (default 2).
 * </pre>
 *
 * <pre>
 * -V &lt;minimum variance for split&gt;
 *  Set minimum numeric class variance proportion
 *  of train variance for split (default 1e-3).
 * </pre>
 *
 * <pre>
 * -N &lt;number of folds&gt;
 *  Number of folds for reduced error pruning (default 3).
 * </pre>
 *
 * <pre>
 * -S &lt;seed&gt;
 *  Seed for random data shuffling (default 1).
 * </pre>
 *
 * <pre>
 * -P
 *  No pruning.
 * </pre>
 *
 * <pre>
 * -L
 *  Maximum tree depth (default -1, no maximum)
 * </pre>
 *
 * <!-- options-end -->
 *
 * Options after -- are passed to the designated classifier.
 * <p>
 *
 * @param options the list of options as an array of strings
 * @throws Exception if an option is not supported
 */
@Override
public void setOptions(String[] options) throws Exception {

    String bagSize = Utils.getOption('P', options);
    if (bagSize.length() != 0) {
        setBagSizePercent(Integer.parseInt(bagSize));
    } else {
        setBagSizePercent(100);
    }

    setCalcOutOfBag(Utils.getFlag('O', options));

    super.setOptions(options);
}

---

From source file:REPTree.java

License:Open Source License

/**
 * Parses a given list of options. <p/>
 * // w  w w.j ava  2s.com
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -M &lt;minimum number of instances&gt;
 *  Set minimum number of instances per leaf (default 2).</pre>
 * 
 * <pre> -V &lt;minimum variance for split&gt;
 *  Set minimum numeric class variance proportion
 *  of train variance for split (default 1e-3).</pre>
 * 
 * <pre> -N &lt;number of folds&gt;
 *  Number of folds for reduced error pruning (default 3).</pre>
 * 
 * <pre> -S &lt;seed&gt;
 *  Seed for random data shuffling (default 1).</pre>
 * 
 * <pre> -P
 *  No pruning.</pre>
 * 
 * <pre> -L
 *  Maximum tree depth (default -1, no maximum)</pre>
 * 
 <!-- options-end -->
 * 
 * @param options the list of options as an array of strings
 * @throws Exception if an option is not supported
 */
public void setOptions(String[] options) throws Exception {

    String minNumString = Utils.getOption('M', options);
    if (minNumString.length() != 0) {
        m_MinNum = (double) Integer.parseInt(minNumString);
    } else {
        m_MinNum = 2;
    }
    String minVarString = Utils.getOption('V', options);
    if (minVarString.length() != 0) {
        m_MinVarianceProp = Double.parseDouble(minVarString);
    } else {
        m_MinVarianceProp = 1e-3;
    }
    String numFoldsString = Utils.getOption('N', options);
    if (numFoldsString.length() != 0) {
        m_NumFolds = Integer.parseInt(numFoldsString);
    } else {
        m_NumFolds = 3;
    }
    String seedString = Utils.getOption('S', options);
    if (seedString.length() != 0) {
        m_Seed = Integer.parseInt(seedString);
    } else {
        m_Seed = 1;
    }
    m_NoPruning = Utils.getFlag('P', options);
    String depthString = Utils.getOption('L', options);
    if (depthString.length() != 0) {
        m_MaxDepth = Integer.parseInt(depthString);
    } else {
        m_MaxDepth = -1;
    }
    String initialCountString = Utils.getOption('I', options);
    if (initialCountString.length() != 0) {
        m_InitialCount = Double.parseDouble(initialCountString);
    } else {
        m_InitialCount = 0;
    }
    m_SpreadInitialCount = Utils.getFlag('R', options);

    Utils.checkForRemainingOptions(options);
}

---

From source file:Pair.java

License:Open Source License

public void setOptions(String[] options) throws Exception {

    String sourceFileName = Utils.getOption('S', options);
    if (sourceFileName.length() == 0) {
        throw new Exception("A filename must be specified with" + " the -S option.");
    } else {/*from   w w w  . jav  a2s.  c  om*/
        setSourceFile(new File(sourceFileName));
    }

    doFraction = (Utils.getFlag('F', options));
    doBagging = (Utils.getFlag('B', options));
    doUpsource = (Utils.getFlag('U', options));
    useMedian = (Utils.getFlag('M', options));
    resample = (Utils.getFlag('R', options));
    doSampleSize = Utils.getFlag("SS", options);
    fixedBeta = Utils.getFlag("FB", options);

    String optionString = Utils.getOption("TT", options);
    testData = new Instances(new BufferedReader(new FileReader(optionString)));
    testData.setClassIndex(testData.numAttributes() - 1);

    String r = Utils.getOption("Ratio", options);
    if (!r.equals(""))
        sourceRatio = Double.parseDouble(r);

    super.setOptions(options);

    r = Utils.getOption("II", options);
    if (!r.equals(""))
        sourceIterations = Integer.parseInt(r);
    else
        sourceIterations = m_NumIterations;

}

---

From source file:REPRandomTree.java

License:Open Source License

/**
 * Parses a given list of options. <p/>
 * //from w ww .j a  v a  2s.com
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -M &lt;minimum number of instances&gt;
 *  Set minimum number of instances per leaf (default 2).</pre>
 * 
 * <pre> -V &lt;minimum variance for split&gt;
 *  Set minimum numeric class variance proportion
 *  of train variance for split (default 1e-3).</pre>
 * 
 * <pre> -N &lt;number of folds&gt;
 *  Number of folds for reduced error pruning (default 3).</pre>
 * 
 * <pre> -S &lt;seed&gt;
 *  Seed for random data shuffling (default 1).</pre>
 * 
 * <pre> -P
 *  No pruning.</pre>
 * 
 * <pre> -L
 *  Maximum tree depth (default -1, no maximum)</pre>
 * 
 <!-- options-end -->
 * 
 * @param options the list of options as an array of strings
 * @throws Exception if an option is not supported
 */
public void setOptions(String[] options) throws Exception {

    String minNumString = Utils.getOption('M', options);
    if (minNumString.length() != 0) {
        m_MinNum = (double) Integer.parseInt(minNumString);
    } else {
        m_MinNum = 2;
    }
    String minVarString = Utils.getOption('V', options);
    if (minVarString.length() != 0) {
        m_MinVarianceProp = Double.parseDouble(minVarString);
    } else {
        m_MinVarianceProp = 1e-3;
    }
    String numFoldsString = Utils.getOption('N', options);
    if (numFoldsString.length() != 0) {
        m_NumFolds = Integer.parseInt(numFoldsString);
    } else {
        m_NumFolds = 3;
    }
    String seedString = Utils.getOption('S', options);
    if (seedString.length() != 0) {
        m_Seed = Integer.parseInt(seedString);
    } else {
        m_Seed = 1;
    }
    m_NoPruning = Utils.getFlag('P', options);
    String depthString = Utils.getOption('L', options);
    if (depthString.length() != 0) {
        m_MaxDepth = Integer.parseInt(depthString);
    } else {
        m_MaxDepth = -1;
    }
    String initialCountString = Utils.getOption('I', options);
    if (initialCountString.length() != 0) {
        m_InitialCount = Double.parseDouble(initialCountString);
    } else {
        m_InitialCount = 0;
    }
    m_SpreadInitialCount = Utils.getFlag('R', options);

    String featureFracString = Utils.getOption('F', options);
    if (featureFracString.length() != 0) {
        m_FeatureFrac = Double.parseDouble(featureFracString);
    } else {
        m_FeatureFrac = 1.0;
    }

    Utils.checkForRemainingOptions(options);
}

---

From source file:MultiClassClassifier.java

License:Open Source License

/**
 * Parses a given list of options. <p/>
 *
 <!-- options-start -->//from   www. ja  va2s .co m
 * Valid options are: <p/>
 * 
 * <pre> -M &lt;num&gt;
 *  Sets the method to use. Valid values are 0 (1-against-all),
 *  1 (random codes), 2 (exhaustive code), and 3 (1-against-1). (default 0)
 * </pre>
 * 
 * <pre> -R &lt;num&gt;
 *  Sets the multiplier when using random codes. (default 2.0)</pre>
 * 
 * <pre> -P
 *  Use pairwise coupling (only has an effect for 1-against1)</pre>
 * 
 * <pre> -S &lt;num&gt;
 *  Random number seed.
 *  (default 1)</pre>
 * 
 * <pre> -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console</pre>
 * 
 * <pre> -W
 *  Full name of base classifier.
 *  (default: weka.classifiers.functions.Logistic)</pre>
 * 
 * <pre> 
 * Options specific to classifier weka.classifiers.functions.Logistic:
 * </pre>
 * 
 * <pre> -D
 *  Turn on debugging output.</pre>
 * 
 * <pre> -R &lt;ridge&gt;
 *  Set the ridge in the log-likelihood.</pre>
 * 
 * <pre> -M &lt;number&gt;
 *  Set the maximum number of iterations (default -1, until convergence).</pre>
 * 
 <!-- options-end -->
 *
 * @param options the list of options as an array of strings
 * @throws Exception if an option is not supported
 */
public void setOptions(String[] options) throws Exception {

    String errorString = Utils.getOption('M', options);
    if (errorString.length() != 0) {
        setMethod(new SelectedTag(Integer.parseInt(errorString), TAGS_METHOD));
    } else {
        setMethod(new SelectedTag(METHOD_1_AGAINST_ALL, TAGS_METHOD));
    }

    String rfactorString = Utils.getOption('R', options);
    if (rfactorString.length() != 0) {
        setRandomWidthFactor((new Double(rfactorString)).doubleValue());
    } else {
        setRandomWidthFactor(2.0);
    }

    setUsePairwiseCoupling(Utils.getFlag('P', options));

    super.setOptions(options);
}

---

From source file:GainRatioAttributeEval1.java

License:Open Source License

/**
 * Parses a given list of options. <p/>
 *
 <!-- options-start -->// w  w  w . j  a va2  s .  co  m
 * Valid options are: <p/>
 * 
 * <pre> -M
 *  treat missing values as a seperate value.</pre>
 * 
 <!-- options-end -->
 *
 * @param options the list of options as an array of strings
 * @throws Exception if an option is not supported
 **/
public void setOptions(String[] options) throws Exception {
    resetOptions();
    setMissingMerge(!(Utils.getFlag('M', options)));
}

---

From source file:SMO.java

License:Open Source License

/**
 * Parses a given list of options. <p/>
 *
 <!-- options-start -->// ww  w. j a v  a  2  s  .  c  om
 * Valid options are: <p/>
 * 
 * <pre> -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console</pre>
 * 
 * <pre> -no-checks
 *  Turns off all checks - use with caution!
 *  Turning them off assumes that data is purely numeric, doesn't
 *  contain any missing values, and has a nominal class. Turning them
 *  off also means that no header information will be stored if the
 *  machine is linear. Finally, it also assumes that no instance has
 *  a weight equal to 0.
 *  (default: checks on)</pre>
 * 
 * <pre> -C &lt;double&gt;
 *  The complexity constant C. (default 1)</pre>
 * 
 * <pre> -N
 *  Whether to 0=normalize/1=standardize/2=neither. (default 0=normalize)</pre>
 * 
 * <pre> -L &lt;double&gt;
 *  The tolerance parameter. (default 1.0e-3)</pre>
 * 
 * <pre> -P &lt;double&gt;
 *  The epsilon for round-off error. (default 1.0e-12)</pre>
 * 
 * <pre> -M
 *  Fit logistic models to SVM outputs. </pre>
 * 
 * <pre> -V &lt;double&gt;
 *  The number of folds for the internal
 *  cross-validation. (default -1, use training data)</pre>
 * 
 * <pre> -W &lt;double&gt;
 *  The random number seed. (default 1)</pre>
 * 
 * <pre> -K &lt;classname and parameters&gt;
 *  The Kernel to use.
 *  (default: weka.classifiers.functions.supportVector.PolyKernel)</pre>
 * 
 * <pre> 
 * Options specific to kernel weka.classifiers.functions.supportVector.PolyKernel:
 * </pre>
 * 
 * <pre> -D
 *  Enables debugging output (if available) to be printed.
 *  (default: off)</pre>
 * 
 * <pre> -no-checks
 *  Turns off all checks - use with caution!
 *  (default: checks on)</pre>
 * 
 * <pre> -C &lt;num&gt;
 *  The size of the cache (a prime number), 0 for full cache and 
 *  -1 to turn it off.
 *  (default: 250007)</pre>
 * 
 * <pre> -E &lt;num&gt;
 *  The Exponent to use.
 *  (default: 1.0)</pre>
 * 
 * <pre> -L
 *  Use lower-order terms.
 *  (default: no)</pre>
 * 
 <!-- options-end -->
 *
 * @param options the list of options as an array of strings
 * @throws Exception if an option is not supported 
 */
public void setOptions(String[] options) throws Exception {
    String tmpStr;
    String[] tmpOptions;

    setChecksTurnedOff(Utils.getFlag("no-checks", options));

    tmpStr = Utils.getOption('C', options);
    if (tmpStr.length() != 0)
        setC(Double.parseDouble(tmpStr));
    else
        setC(1.0);

    tmpStr = Utils.getOption('L', options);
    if (tmpStr.length() != 0)
        setToleranceParameter(Double.parseDouble(tmpStr));
    else
        setToleranceParameter(1.0e-3);

    tmpStr = Utils.getOption('P', options);
    if (tmpStr.length() != 0)
        setEpsilon(Double.parseDouble(tmpStr));
    else
        setEpsilon(1.0e-12);

    tmpStr = Utils.getOption('N', options);
    if (tmpStr.length() != 0)
        setFilterType(new SelectedTag(Integer.parseInt(tmpStr), TAGS_FILTER));
    else
        setFilterType(new SelectedTag(FILTER_NORMALIZE, TAGS_FILTER));

    setBuildLogisticModels(Utils.getFlag('M', options));

    tmpStr = Utils.getOption('V', options);
    if (tmpStr.length() != 0)
        setNumFolds(Integer.parseInt(tmpStr));
    else
        setNumFolds(-1);

    tmpStr = Utils.getOption('W', options);
    if (tmpStr.length() != 0)
        setRandomSeed(Integer.parseInt(tmpStr));
    else
        setRandomSeed(1);

    tmpStr = Utils.getOption('K', options);
    tmpOptions = Utils.splitOptions(tmpStr);
    if (tmpOptions.length != 0) {
        tmpStr = tmpOptions[0];
        tmpOptions[0] = "";
        setKernel(Kernel.forName(tmpStr, tmpOptions));
    }

    super.setOptions(options);
}

---

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