mirror of https://github.com/jlizier/jidt
443 lines
18 KiB
Java
443 lines
18 KiB
Java
/*
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* Java Information Dynamics Toolkit (JIDT)
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* Copyright (C) 2015, Joseph T. Lizier
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*
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* This program is free software: you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* This program is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with this program. If not, see <http://www.gnu.org/licenses/>.
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*/
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package infodynamics.demos.autoanalysis;
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import infodynamics.measures.continuous.ActiveInfoStorageCalculator;
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import infodynamics.measures.continuous.ActiveInfoStorageCalculatorViaMutualInfo;
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import infodynamics.measures.continuous.InfoMeasureCalculatorContinuous;
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import infodynamics.measures.continuous.gaussian.ActiveInfoStorageCalculatorGaussian;
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import infodynamics.measures.continuous.gaussian.MutualInfoCalculatorMultiVariateGaussian;
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import infodynamics.measures.continuous.kernel.ActiveInfoStorageCalculatorKernel;
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import infodynamics.measures.continuous.kernel.ActiveInfoStorageCalculatorMultiVariateKernel;
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import infodynamics.measures.continuous.kernel.MutualInfoCalculatorMultiVariateKernel;
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import infodynamics.measures.continuous.kraskov.ActiveInfoStorageCalculatorKraskov;
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import infodynamics.measures.continuous.kraskov.MutualInfoCalculatorMultiVariateKraskov;
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import infodynamics.measures.discrete.ActiveInformationCalculatorDiscrete;
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import infodynamics.measures.discrete.InfoMeasureCalculatorDiscrete;
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import infodynamics.utils.MatrixUtils;
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import java.util.Vector;
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import javax.swing.JOptionPane;
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/**
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* This class provides a GUI to build a simple calculation
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* of active information storage,
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* and supply the code to execute it.
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*
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*
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* @author Joseph Lizier
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*
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*/
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public class AutoAnalyserAIS extends AutoAnalyser {
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/**
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* Need serialVersionUID to be serializable
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*/
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private static final long serialVersionUID = 1L;
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protected static final String DISCRETE_PROPNAME_K = "k_HISTORY";
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// Property names for specific continuous calculators:
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protected String[] gaussianProperties;
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protected String[] gaussianPropertiesFieldNames;
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protected String[] gaussianPropertyDescriptions;
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protected String[][] gaussianPropertyValueChoices;
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protected String[] kernelProperties;
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protected String[] kernelPropertiesFieldNames;
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protected String[] kernelPropertyDescriptions;
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protected String[][] kernelPropertyValueChoices;
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protected String[] kraskovProperties;
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protected String[] kraskovPropertiesFieldNames;
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protected String[] kraskovPropertyDescriptions;
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protected String[][] kraskovPropertyValueChoices;
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public AutoAnalyserAIS() {
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super();
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}
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public AutoAnalyserAIS(String pathToAutoAnalyserDir) {
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super(pathToAutoAnalyserDir);
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}
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/**
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* Constructor to initialise the GUI for a channel calculator
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*/
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protected void makeSpecificInitialisations() {
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numVariables = 1;
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variableColNumLabels = new String[] {"Variable"};
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useAllCombosCheckBox = true;
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useStatSigCheckBox = true;
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wordForCombinations = "variables";
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variableRelationshipFormatString = "col_%d";
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disableVariableColTextFieldsForAllCombos = new boolean[] {true};
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indentsForAllCombos = 1;
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// Set up the properties for Entropy:
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measureAcronym = "AIS";
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appletTitle = "JIDT Active Information Storage Auto-Analyser";
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calcTypes = new String[] {
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CALC_TYPE_DISCRETE, CALC_TYPE_BINNED, CALC_TYPE_GAUSSIAN,
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CALC_TYPE_KRASKOV, CALC_TYPE_KERNEL};
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unitsForEachCalc = new String[] {"bits", "bits", "nats", "nats", "bits"};
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// Discrete:
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discreteClass = ActiveInformationCalculatorDiscrete.class;
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discreteProperties = new String[] {
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DISCRETE_PROPNAME_BASE,
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DISCRETE_PROPNAME_K
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};
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discretePropertyDefaultValues = new String[] {
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"2",
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"1"
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};
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discretePropertyDescriptions = new String[] {
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"Number of discrete states available for each variable (i.e. 2 for binary)",
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"History embedding length (k_HISTORY)"
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};
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discretePropertyValueChoices = new String[][] {
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null,
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null
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};
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// Continuous:
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abstractContinuousClass = ActiveInfoStorageCalculator.class;
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// Common properties for all continuous calcs:
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commonContPropertyNames = new String[] {
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ActiveInfoStorageCalculator.K_PROP_NAME,
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ActiveInfoStorageCalculator.TAU_PROP_NAME,
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ActiveInfoStorageCalculatorViaMutualInfo.PROP_AUTO_EMBED_METHOD,
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ActiveInfoStorageCalculatorViaMutualInfo.PROP_K_SEARCH_MAX,
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ActiveInfoStorageCalculatorViaMutualInfo.PROP_TAU_SEARCH_MAX,
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};
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commonContPropertiesFieldNames = new String[] {
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"K_PROP_NAME",
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"TAU_PROP_NAME",
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"ActiveInfoStorageCalculatorViaMutualInfo.PROP_AUTO_EMBED_METHOD",
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"ActiveInfoStorageCalculatorViaMutualInfo.PROP_K_SEARCH_MAX",
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"ActiveInfoStorageCalculatorViaMutualInfo.PROP_TAU_SEARCH_MAX",
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};
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commonContPropertyDescriptions = new String[] {
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"History embedding length (k_HISTORY)",
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"History embedding delay (k_TAU)",
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"Method to automatically determine embedding length (k_HISTORY)<br/> and delay (k_TAU) for " +
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"the samples. Default is \"" + ActiveInfoStorageCalculatorKraskov.AUTO_EMBED_METHOD_NONE +
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"\" meaning values are set manually; other values include: <br/> -- \"" + ActiveInfoStorageCalculatorKraskov.AUTO_EMBED_METHOD_RAGWITZ +
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"\" for use of the Ragwitz criteria for both source and destination (searching up to \"" + ActiveInfoStorageCalculatorKraskov.PROP_K_SEARCH_MAX +
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"\" and \"" + ActiveInfoStorageCalculatorKraskov.PROP_TAU_SEARCH_MAX + "\"); <br/> -- \"" + ActiveInfoStorageCalculatorKraskov.AUTO_EMBED_METHOD_MAX_CORR_AIS +
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"\" for maximising the (bias corrected) Active Info Storage (searching up to \"" + ActiveInfoStorageCalculatorKraskov.PROP_K_SEARCH_MAX +
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"\" and \"" + ActiveInfoStorageCalculatorKraskov.PROP_TAU_SEARCH_MAX + "\"); <br/>Use of values other than \"" + ActiveInfoStorageCalculatorKraskov.AUTO_EMBED_METHOD_NONE +
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"\" leads to any previous settings for embedding lengths and delays to be overwritten after observations are supplied",
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"Max. embedding length to search to <br/>if auto embedding (as determined by " + ActiveInfoStorageCalculatorKraskov.PROP_AUTO_EMBED_METHOD + ")",
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"Max. embedding delay to search to <br/>if auto embedding (as determined by " + ActiveInfoStorageCalculatorKraskov.PROP_AUTO_EMBED_METHOD + ")",
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};
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commonContPropertyValueChoices = new String[][] {
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null,
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null,
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{ActiveInfoStorageCalculatorViaMutualInfo.AUTO_EMBED_METHOD_NONE,
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ActiveInfoStorageCalculatorViaMutualInfo.AUTO_EMBED_METHOD_RAGWITZ,
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ActiveInfoStorageCalculatorViaMutualInfo.AUTO_EMBED_METHOD_MAX_CORR_AIS},
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null,
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null,
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};
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// Gaussian properties:
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gaussianProperties = new String[] {
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MutualInfoCalculatorMultiVariateGaussian.PROP_BIAS_CORRECTION,
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ActiveInfoStorageCalculatorGaussian.PROP_MAX_CORR_AIS_NUM_SURROGATES
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};
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gaussianPropertiesFieldNames = new String[] {
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"MutualInfoCalculatorMultiVariateGaussian.PROP_BIAS_CORRECTION",
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"ActiveInfoStorageCalculatorGaussian.PROP_MAX_CORR_AIS_NUM_SURROGATES"
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};
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gaussianPropertyDescriptions = new String[] {
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"Whether the analytically determined bias (as the mean of the<br/>" +
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"surrogate distribution) will be subtracted from all" +
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"calculated values. Default is false.",
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"Number of surrogates to use in computing the bias correction<br/>if required for " +
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ActiveInfoStorageCalculatorKraskov.AUTO_EMBED_METHOD_MAX_CORR_AIS + " auto-embedding method.<br/>" +
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"(default is 0, meaning to use analytic method -- recommended)"
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};
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gaussianPropertyValueChoices = new String[][] {
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{"true", "false"},
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null
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};
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// Kernel:
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kernelProperties = new String[] {
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MutualInfoCalculatorMultiVariateKernel.KERNEL_WIDTH_PROP_NAME,
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MutualInfoCalculatorMultiVariateKernel.DYN_CORR_EXCL_TIME_NAME,
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MutualInfoCalculatorMultiVariateKernel.NORMALISE_PROP_NAME,
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ActiveInfoStorageCalculatorMultiVariateKernel.PROP_MAX_CORR_AIS_NUM_SURROGATES
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};
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kernelPropertiesFieldNames = new String[] {
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"MutualInfoCalculatorMultiVariateKernel.KERNEL_WIDTH_PROP_NAME",
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"MutualInfoCalculatorMultiVariateKernel.DYN_CORR_EXCL_TIME_NAME",
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"MutualInfoCalculatorMultiVariateKernel.NORMALISE_PROP_NAME",
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"ActiveInfoStorageCalculatorMultiVariateKernel.PROP_MAX_CORR_AIS_NUM_SURROGATES"
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};
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kernelPropertyDescriptions = new String[] {
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"Kernel width to be used in the calculation. <br/>If the property " +
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MutualInfoCalculatorMultiVariateKernel.NORMALISE_PROP_NAME +
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" is set, then this is a number of standard deviations; " +
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"otherwise it is an absolute value.",
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"Dynamic correlation exclusion time or <br/>Theiler window (see Kantz and Schreiber); " +
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"0 (default) means no dynamic exclusion window",
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"(boolean) whether to normalise <br/>each incoming time-series to mean 0, standard deviation 1, or not (default true, recommended)",
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"Number of surrogates to use in computing the bias correction<br/>if required for " +
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ActiveInfoStorageCalculatorKraskov.AUTO_EMBED_METHOD_MAX_CORR_AIS + " auto-embedding method.<br/>" +
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"(default is 20)"
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};
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kernelPropertyValueChoices = new String[][] {
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null,
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null,
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{"true", "false"},
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null
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};
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// KSG (Kraskov):
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kraskovProperties = new String[] {
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MutualInfoCalculatorMultiVariateKraskov.PROP_NORMALISE,
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MutualInfoCalculatorMultiVariateKraskov.PROP_K,
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MutualInfoCalculatorMultiVariateKraskov.PROP_ADD_NOISE,
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MutualInfoCalculatorMultiVariateKraskov.PROP_DYN_CORR_EXCL_TIME,
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MutualInfoCalculatorMultiVariateKraskov.PROP_NORM_TYPE,
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MutualInfoCalculatorMultiVariateKraskov.PROP_NUM_THREADS,
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MutualInfoCalculatorMultiVariateKraskov.PROP_USE_GPU,
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ActiveInfoStorageCalculatorKraskov.PROP_KRASKOV_ALG_NUM,
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ActiveInfoStorageCalculatorKraskov.PROP_RAGWITZ_NUM_NNS,
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};
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kraskovPropertiesFieldNames = new String[] {
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"MutualInfoCalculatorMultiVariateKraskov.PROP_NORMALISE",
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"MutualInfoCalculatorMultiVariateKraskov.PROP_K",
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"MutualInfoCalculatorMultiVariateKraskov.PROP_ADD_NOISE",
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"MutualInfoCalculatorMultiVariateKraskov.PROP_DYN_CORR_EXCL_TIME",
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"MutualInfoCalculatorMultiVariateKraskov.PROP_NORM_TYPE",
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"MutualInfoCalculatorMultiVariateKraskov.PROP_NUM_THREADS",
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"MutualInfoCalculatorMultiVariateKraskov.PROP_USE_GPU",
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"PROP_KRASKOV_ALG_NUM",
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"PROP_RAGWITZ_NUM_NNS"
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};
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kraskovPropertyDescriptions = new String[] {
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"(boolean) whether to normalise <br/>each incoming time-series to mean 0, standard deviation 1, or not (recommended)",
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"Number of k nearest neighbours to use <br/>in the full joint kernel space in the KSG algorithm",
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"Standard deviation for an amount <br/>of random Gaussian noise to add to each variable, " +
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"to avoid having neighbourhoods with artificially large counts. <br/>" +
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"(\"false\" may be used to indicate \"0\".). The amount is added in after any normalisation.",
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"Dynamic correlation exclusion time or <br/>Theiler window (see Kantz and Schreiber); " +
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"0 (default) means no dynamic exclusion window",
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"<br/>Norm type to use in KSG algorithm between the points in each marginal space. <br/>Options are: " +
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"\"MAX_NORM\" (default), otherwise \"EUCLIDEAN\" or \"EUCLIDEAN_SQUARED\" (both equivalent here)",
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"Number of parallel threads to use <br/>in computation: an integer > 0 or \"USE_ALL\" " +
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"(default, to indicate to use all available processors)",
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"Whether to enable the GPU module (number of threads then has no bearing); boolean, default false",
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"Which KSG algorithm to use (1 or 2)",
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"Number of k nearest neighbours for <br/>Ragwitz auto embedding (if used; defaults to match property \"k\")"
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};
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kraskovPropertyValueChoices = new String[][] {
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{"true", "false"},
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null,
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null,
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null,
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{"MAX_NORM", "EUCLIDEAN", "EUCLIDEAN_SQUARED"},
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null,
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{"true", "false"},
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{"1", "2"},
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null,
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};
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}
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@Override
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protected void fillOutAllCombinations(Vector<int[]> variableCombinations) {
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// All combinations here means all variables
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for (int s = 0; s < dataColumns; s++) {
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variableCombinations.add(new int[] {s});
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}
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}
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@Override
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protected String[] setUpLoopsForAllCombos(StringBuffer javaCode,
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StringBuffer pythonCode, StringBuffer matlabCode) {
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// Set up loops in the code:
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// 1. Java code
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javaCode.append(" \n");
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javaCode.append(" // Compute for all variables:\n");
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javaCode.append(" for (int v = 0; v < " + dataColumns +
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"; v++) {\n");
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String javaPrefix = " ";
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javaCode.append(javaPrefix + "// For each variable:\n");
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// 2. Python code
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pythonCode.append("\n");
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pythonCode.append("# Compute for all variables:\n");
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pythonCode.append("for v in range(" + dataColumns + "):\n");
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String pythonPrefix = " ";
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pythonCode.append(pythonPrefix+ "# For each variable:\n");
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// 3. Matlab code
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matlabCode.append("\n");
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matlabCode.append("% Compute for all variables:\n");
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matlabCode.append("for v = 1:" + dataColumns + "\n");
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String matlabPrefix = "\t";
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matlabCode.append(matlabPrefix + "% For each variable:\n");
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// Return the variables to index each column:
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return new String[] {"v"};
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}
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@Override
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protected void finaliseLoopsForAllCombos(StringBuffer javaCode,
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StringBuffer pythonCode, StringBuffer matlabCode) {
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// 1. Java code
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javaCode.append(" }\n");
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// 2. Python code
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// Nothing to do
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// 3. Matlab code
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matlabCode.append("end\n");
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}
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@Override
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protected String formatStringWithColumnNumbers(String formatStr, int[] columnNumbers) {
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// We format the variable number into the
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// return string here:
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return String.format(formatStr,
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columnNumbers[0]);
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}
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@Override
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protected boolean skipColumnCombo(int[] columnCombo) {
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// No reason to skip any columns here
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return false;
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}
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@Override
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protected void setObservations(InfoMeasureCalculatorDiscrete calcDiscrete,
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InfoMeasureCalculatorContinuous calcContinuous,
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int[] columnCombo) throws Exception {
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String selectedCalcType = (String)
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calcTypeComboBox.getSelectedItem();
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int variableColumn = columnCombo[0];
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// Set observations
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if (selectedCalcType.equalsIgnoreCase(CALC_TYPE_DISCRETE)) {
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ActiveInformationCalculatorDiscrete calc = (ActiveInformationCalculatorDiscrete) calcDiscrete;
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calc.addObservations(
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MatrixUtils.selectColumn(dataDiscrete, variableColumn));
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} else if (selectedCalcType.equalsIgnoreCase(CALC_TYPE_BINNED)) {
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ActiveInformationCalculatorDiscrete calc = (ActiveInformationCalculatorDiscrete) calcDiscrete;
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calc.addObservations(
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MatrixUtils.discretise(
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MatrixUtils.selectColumn(data, variableColumn),
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// Should be no parse error on the alphabet size by now
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Integer.parseInt(propertyValues.get(DISCRETE_PROPNAME_BASE))));
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} else {
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ActiveInfoStorageCalculator calc = (ActiveInfoStorageCalculator) calcContinuous;
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calc.setObservations(
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MatrixUtils.selectColumn(data, variableColumn));
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}
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}
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protected CalcProperties assignCalcProperties(String selectedCalcType)
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throws Exception {
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// Let the super class handle discrete calculators
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CalcProperties calcProperties = super.assignCalcProperties(selectedCalcType);
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if (calcProperties == null) {
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// We need to assign properties for a continuous calculator
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calcProperties = new CalcProperties();
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calcProperties.calc = assignCalcObjectContinuous(selectedCalcType);
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calcProperties.calcClass = calcProperties.calc.getClass();
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if (selectedCalcType.equalsIgnoreCase(CALC_TYPE_GAUSSIAN)) {
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calcProperties.classSpecificPropertyNames = gaussianProperties;
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calcProperties.classSpecificPropertiesFieldNames = gaussianPropertiesFieldNames;
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calcProperties.classSpecificPropertyDescriptions = gaussianPropertyDescriptions;
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calcProperties.classSpecificPropertyValueChoices = gaussianPropertyValueChoices;
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} else if (selectedCalcType.equalsIgnoreCase(CALC_TYPE_KRASKOV)) {
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calcProperties.classSpecificPropertyNames = kraskovProperties;
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calcProperties.classSpecificPropertiesFieldNames = kraskovPropertiesFieldNames;
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calcProperties.classSpecificPropertyDescriptions = kraskovPropertyDescriptions;
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calcProperties.classSpecificPropertyValueChoices = kraskovPropertyValueChoices;
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} else if (selectedCalcType.equalsIgnoreCase(CALC_TYPE_KERNEL)) {
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calcProperties.classSpecificPropertyNames = kernelProperties;
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calcProperties.classSpecificPropertiesFieldNames = kernelPropertiesFieldNames;
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calcProperties.classSpecificPropertyDescriptions = kernelPropertyDescriptions;
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calcProperties.classSpecificPropertyValueChoices = kernelPropertyValueChoices;
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} else {
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calcProperties = null;
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throw new Exception("No recognised calculator selected: " +
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selectedCalcType);
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}
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}
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return calcProperties;
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}
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/**
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* Method to assign and initialise our continuous calculator class
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*/
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@Override
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protected ActiveInfoStorageCalculator assignCalcObjectContinuous(String selectedCalcType) throws Exception {
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if (selectedCalcType.equalsIgnoreCase(CALC_TYPE_GAUSSIAN)) {
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return new ActiveInfoStorageCalculatorGaussian();
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} else if (selectedCalcType.equalsIgnoreCase(CALC_TYPE_KRASKOV)) {
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return new ActiveInfoStorageCalculatorKraskov();
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} else if (selectedCalcType.equalsIgnoreCase(CALC_TYPE_KERNEL)) {
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return new ActiveInfoStorageCalculatorKernel();
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} else {
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throw new Exception("No recognised continuous calculator selected: " +
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selectedCalcType);
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}
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}
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/**
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* Method to assign and initialise our discrete calculator class
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*/
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protected DiscreteCalcAndArguments assignCalcObjectDiscrete() throws Exception {
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int base, k;
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try {
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String basePropValueStr = propertyValues.get(DISCRETE_PROPNAME_BASE);
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base = Integer.parseInt(basePropValueStr);
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} catch (Exception ex) {
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JOptionPane.showMessageDialog(this,
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ex.getMessage());
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resultsLabel.setText("Cannot read a value for property " + DISCRETE_PROPNAME_BASE);
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return null;
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}
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try {
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String kPropValueStr = propertyValues.get(DISCRETE_PROPNAME_K);
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k = Integer.parseInt(kPropValueStr);
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} catch (Exception ex) {
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JOptionPane.showMessageDialog(this,
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ex.getMessage());
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resultsLabel.setText("Cannot read a value for property " + DISCRETE_PROPNAME_K);
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return null;
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}
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return new DiscreteCalcAndArguments(
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new ActiveInformationCalculatorDiscrete(base, k),
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base,
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base + ", " + k);
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}
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/**
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* @param args
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*/
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public static void main(String[] args) {
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new AutoAnalyserAIS();
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}
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}
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