Java Code Examples for weka.core.Utils#getOptionPos()
The following examples show how to use
weka.core.Utils#getOptionPos() .
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Example 1
Source File: StrUtils.java From tsml with GNU General Public License v3.0 | 6 votes |
public static boolean isOption(String flag, String[] options) { try { flag = unflagify(flag); int i = Utils.getOptionPos(flag, options); if(i < 0 || i + 1 >= options.length) { return false; } String option = options[i + 1]; if(isFlag(option)) { return false; } return true; } catch (Exception e) { return false; } }
Example 2
Source File: ARAMNetworkfast.java From meka with GNU General Public License v3.0 | 6 votes |
/** * Parses a given list of options. Valid options are:<p> * * -K <br> * Use kernel estimation for modelling numeric attributes rather than * a single normal distribution.<p> * * -D <br> * Use supervised discretization to process numeric attributes. * * @param options the list of options as an array of strings * @exception Exception if an option is not supported */ public void setOptions(String[] options) throws Exception { //These are just examples, modify to suit your algorithm // boolean k = Utils.getFlag('K', options); // boolean d = Utils.getFlag('D', options); // if (k && d) { // throw new IllegalArgumentException( // "Can't use both kernel density estimation and discretization!"); // } // setUseSupervisedDiscretization(d); // setUseKernelEstimator(k); roa = (Utils.getOptionPos("P",options) >= 0) ? Double.parseDouble(Utils.getOption("P", options)) : roa; m_userankstoclass= (Utils.getOptionPos("K",options) >= 0); activity_report = (Utils.getOptionPos("Rt",options) >= 0) ? Utils.getOption("Rt", options) : ""; super.setOptions(options); }
Example 3
Source File: ARAMNetworkSparseH.java From meka with GNU General Public License v3.0 | 5 votes |
/** * Parses a given list of options. Valid options are:<p> * * -K <br> * Use kernel estimation for modelling numeric attributes rather than * a single normal distribution.<p> * * -D <br> * Use supervised discretization to process numeric attributes. * * @param options the list of options as an array of strings * @exception Exception if an option is not supported */ public void setOptions(String[] options) throws Exception { //These are just examples, modify to suit your algorithm // boolean k = Utils.getFlag('K', options); // boolean d = Utils.getFlag('D', options); // if (k && d) { // throw new IllegalArgumentException( // "Can't use both kernel density estimation and discretization!"); // } // setUseSupervisedDiscretization(d); // setUseKernelEstimator(k); roa = (Utils.getOptionPos("P",options) >= 0) ? Double.parseDouble(Utils.getOption("P", options)) : roa; m_userankstoclass= (Utils.getOptionPos("K",options) >= 0); super.setOptions(options); }
Example 4
Source File: ARAMNetwork.java From meka with GNU General Public License v3.0 | 5 votes |
/** * Parses a given list of options. Valid options are:<p> * * -K <br> * Use kernel estimation for modelling numeric attributes rather than * a single normal distribution.<p> * * -D <br> * Use supervised discretization to process numeric attributes. * * @param options the list of options as an array of strings * @exception Exception if an option is not supported */ public void setOptions(String[] options) throws Exception { //These are just examples, modify to suit your algorithm // boolean k = Utils.getFlag('K', options); // boolean d = Utils.getFlag('D', options); // if (k && d) { // throw new IllegalArgumentException( // "Can't use both kernel density estimation and discretization!"); // } // setUseSupervisedDiscretization(d); // setUseKernelEstimator(k); roa = (Utils.getOptionPos("P",options) >= 0) ? Double.parseDouble(Utils.getOption("P", options)) : roa; m_userankstoclass= (Utils.getOptionPos("K",options) >= 0); super.setOptions(options); }
Example 5
Source File: ARAMNetworkSparse.java From meka with GNU General Public License v3.0 | 5 votes |
/** * Parses a given list of options. Valid options are:<p> * * -K <br> * Use kernel estimation for modelling numeric attributes rather than * a single normal distribution.<p> * * -D <br> * Use supervised discretization to process numeric attributes. * * @param options the list of options as an array of strings * @exception Exception if an option is not supported */ public void setOptions(String[] options) throws Exception { //These are just examples, modify to suit your algorithm // boolean k = Utils.getFlag('K', options); // boolean d = Utils.getFlag('D', options); // if (k && d) { // throw new IllegalArgumentException( // "Can't use both kernel density estimation and discretization!"); // } // setUseSupervisedDiscretization(d); // setUseKernelEstimator(k); roa = (Utils.getOptionPos("P",options) >= 0) ? Double.parseDouble(Utils.getOption("P", options)) : roa; m_userankstoclass= (Utils.getOptionPos("K",options) >= 0); super.setOptions(options); }
Example 6
Source File: ARAMNetworkSparseV.java From meka with GNU General Public License v3.0 | 5 votes |
/** * Parses a given list of options. Valid options are:<p> * * -K <br> * Use kernel estimation for modelling numeric attributes rather than * a single normal distribution.<p> * * -D <br> * Use supervised discretization to process numeric attributes. * * @param options the list of options as an array of strings * @exception Exception if an option is not supported */ public void setOptions(String[] options) throws Exception { //These are just examples, modify to suit your algorithm // boolean k = Utils.getFlag('K', options); // boolean d = Utils.getFlag('D', options); // if (k && d) { // throw new IllegalArgumentException( // "Can't use both kernel density estimation and discretization!"); // } // setUseSupervisedDiscretization(d); // setUseKernelEstimator(k); roa = (Utils.getOptionPos("P",options) >= 0) ? Double.parseDouble(Utils.getOption("P", options)) : roa; m_userankstoclass= (Utils.getOptionPos("K",options) >= 0); super.setOptions(options); }
Example 7
Source File: ARAMNetworkSparseHT_Strange.java From meka with GNU General Public License v3.0 | 5 votes |
/** * Parses a given list of options. Valid options are:<p> * * -K <br> * Use kernel estimation for modelling numeric attributes rather than * a single normal distribution.<p> * * -D <br> * Use supervised discretization to process numeric attributes. * * @param options the list of options as an array of strings * @exception Exception if an option is not supported */ public void setOptions(String[] options) throws Exception { //These are just examples, modify to suit your algorithm // boolean k = Utils.getFlag('K', options); // boolean d = Utils.getFlag('D', options); // if (k && d) { // throw new IllegalArgumentException( // "Can't use both kernel density estimation and discretization!"); // } // setUseSupervisedDiscretization(d); // setUseKernelEstimator(k); roa = (Utils.getOptionPos("P",options) >= 0) ? Double.parseDouble(Utils.getOption("P", options)) : roa; m_userankstoclass= (Utils.getOptionPos("K",options) >= 0); super.setOptions(options); }
Example 8
Source File: ARAMNetworkSparseHT.java From meka with GNU General Public License v3.0 | 5 votes |
/** * Parses a given list of options. Valid options are:<p> * * -K <br> * Use kernel estimation for modelling numeric attributes rather than * a single normal distribution.<p> * * -D <br> * Use supervised discretization to process numeric attributes. * * @param options the list of options as an array of strings * @exception Exception if an option is not supported */ public void setOptions(String[] options) throws Exception { //These are just examples, modify to suit your algorithm // boolean k = Utils.getFlag('K', options); // boolean d = Utils.getFlag('D', options); // if (k && d) { // throw new IllegalArgumentException( // "Can't use both kernel density estimation and discretization!"); // } // setUseSupervisedDiscretization(d); // setUseKernelEstimator(k); roa = (Utils.getOptionPos("P",options) >= 0) ? Double.parseDouble(Utils.getOption("P", options)) : roa; m_userankstoclass= (Utils.getOptionPos("K",options) >= 0); super.setOptions(options); }
Example 9
Source File: WARAM.java From meka with GNU General Public License v3.0 | 5 votes |
/** * Parses a given list of options. Valid options are:<p> * * -K <br> * Use kernel estimation for modelling numeric attributes rather than * a single normal distribution.<p> * * -D <br> * Use supervised discretization to process numeric attributes. * * @param options the list of options as an array of strings * @exception Exception if an option is not supported */ public void setOptions(String[] options) throws Exception { //These are just examples, modify to suit your algorithm // boolean k = Utils.getFlag('K', options); // boolean d = Utils.getFlag('D', options); // if (k && d) { // throw new IllegalArgumentException( // "Can't use both kernel density estimation and discretization!"); // } // setUseSupervisedDiscretization(d); // setUseKernelEstimator(k); roa = (Utils.getOptionPos("P",options) >= 0) ? Double.parseDouble(Utils.getOption("P", options)) : roa; m_userankstoclass= (Utils.getOptionPos("K",options) >= 0); super.setOptions(options); }
Example 10
Source File: OptionUtils.java From meka with GNU General Public License v3.0 | 3 votes |
/** * Parses an array option, returns all the occurrences of the option as a string array. * * @param options the option array to use * @param option the option to look for in the options array (no leading dash) * @return the parsed value (or default value if option not present) * @throws Exception if parsing of value fails */ public static String[] parse(String[] options, String option) throws Exception { List<String> result = new ArrayList<>(); while (Utils.getOptionPos(option, options) > -1) result.add(Utils.getOption(option, options)); return result.toArray(new String[result.size()]); }