mirror of https://github.com/jlizier/jidt
100 lines
5.3 KiB
Matlab
Executable File
100 lines
5.3 KiB
Matlab
Executable File
%%
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%% Java Information Dynamics Toolkit (JIDT)
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%% Copyright (C) 2012, 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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% Example 6 - Mutual information calculation with dynamic specification of calculator
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% This example shows how to write Matlab/Octave code to take advantage of the
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% common interfaces defined for various information-theoretic calculators.
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% Here, we use the common form of the infodynamics.measures.continuous.MutualInfoCalculatorMultiVariate
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% interface (which is never named here) to write common code into which we can plug
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% one of three concrete implementations (kernel estimator, Kraskov estimator or
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% linear-Gaussian estimator) by dynamically supplying the class name of
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% the concrete implementation.
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% Change location of jar to match yours:
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javaaddpath('../../infodynamics.jar');
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%---------------------
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% 1. Properties for the calculation (these are dynamically changeable, you could
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% load them in from another properties file):
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% The name of the data file (relative to this directory)
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datafile = '../data/4ColsPairedNoisyDependence-1.txt';
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% List of column numbers for univariate time seres 1 and 2:
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% (you can select any columns you wish to be contained in each variable)
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univariateSeries1Column = 1; % array indices start from 1 in octave/matlab
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univariateSeries2Column = 3;
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% List of column numbers for joint variables 1 and 2:
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% (you can select any columns you wish to be contained in each variable)
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jointVariable1Columns = [1,2]; % array indices start from 1 in octave/matlab
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jointVariable2Columns = [3,4];
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% The name of the concrete implementation of the interface
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% infodynamics.measures.continuous.MutualInfoCalculatorMultiVariate
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% which we wish to use for the calculation.
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% Note that one could use any of the following calculators (try them all!):
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% implementingClass = 'infodynamics.measures.continuous.kraskov.MutualInfoCalculatorMultiVariateKraskov1'; % MI(1;3) as 0.10044, MI([1,2], [3,4]) = 0.36353 (NATS not bits)
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% implementingClass = 'infodynamics.measures.continuous.kernel.MutualInfoCalculatorMultiVariateKernel';
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% implementingClass = 'infodynamics.measures.continuous.gaussian.MutualInfoCalculatorMultiVariateGaussian';
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implementingClass = 'infodynamics.measures.continuous.kraskov.MutualInfoCalculatorMultiVariateKraskov1';
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%---------------------
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% 2. Load in the data
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data = load(datafile);
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% Pull out the columns from the data set for a univariate MI calculation:
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univariateSeries1 = data(:, univariateSeries1Column);
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univariateSeries2 = data(:, univariateSeries2Column);
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% Pull out the columns from the data set for a multivariate MI calculation:
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jointVariable1 = data(:, jointVariable1Columns);
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jointVariable2 = data(:, jointVariable2Columns);
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%---------------------
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% 3. Dynamically instantiate an object of the given class:
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% (in fact, all java object creation in octave/matlab is dynamic - it has to be,
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% since the languages are interpreted. This makes our life slightly easier at this
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% point than it is in demos/java/example6 where we have to handle this manually)
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miCalc = javaObject(implementingClass);
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%---------------------
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% 4. Start using the MI calculator, paying attention to only
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% call common methods defined in the interface type
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% infodynamics.measures.continuous.MutualInfoCalculatorMultiVariate
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% not methods only defined in a given implementation class.
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% a. Initialise the calculator for a univariate calculation:
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miCalc.initialise(1, 1);
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% b. Supply the observations to compute the PDFs from:
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miCalc.setObservations(octaveToJavaDoubleArray(univariateSeries1), octaveToJavaDoubleArray(univariateSeries2));
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% c. Make the MI calculation:
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miUnivariateValue = miCalc.computeAverageLocalOfObservations();
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%---------------------
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% 5. Continue onto a multivariate calculation, still
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% only calling common methods defined in the interface type.
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% a. Initialise the calculator for a multivariate calculation
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% to use the required number of dimensions for each variable:
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miCalc.initialise(length(jointVariable1Columns), length(jointVariable2Columns));
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% b. Supply the observations to compute the PDFs from:
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% (Note the different method call octaveToJavaDoubleMatrix to convert a *multivariate*
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% time-series to Java format here)
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miCalc.setObservations(octaveToJavaDoubleMatrix(jointVariable1), octaveToJavaDoubleMatrix(jointVariable2));
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% c. Make the MI calculation:
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miJointValue = miCalc.computeAverageLocalOfObservations();
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fprintf('MI calculator %s\ncomputed the univariate MI(%d;%d) as %.5f and joint MI([%s];[%s]) as %.5f\n', ...
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implementingClass, univariateSeries1Column, univariateSeries2Column, miUnivariateValue, ...
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sprintf('%d,', jointVariable1Columns), sprintf('%d,', jointVariable2Columns), miJointValue);
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