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‎BuildAndTest.m‎ renamed to ‎BuildMINT.m‎

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% updating MATLAB paths, and ensuring all necessary configurations are applied at MATLAB startup. Execute from the
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% MINT project root directory.
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function BuildAndTest()
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function BuildMINT()
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ecosdir = fullfile(pwd, 'extern', 'ecos-matlab-master','bin');
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configdir = fullfile(pwd, 'config');
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% MI_BuildAndTest(configdir, 0);
@@ -130,17 +130,6 @@ function PID_BuildAndTest(configdir, ecosdir)
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srcdir = fullfile(pwd, moduleFolder);
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find_and_replace_in(startupfiletemplate, startupfile, '$PID_MATLAB_ROOT', srcdir);
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add_to_startup(startupfile);
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% disp('Testing PID');
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% testFolder = fullfile(pwd, 'tests', 'PID');
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% testSuite = matlab.unittest.TestSuite.fromFolder(testFolder, 'IncludingSubfolders', true);
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% runner = matlab.unittest.TestRunner.withTextOutput;
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% result = run(runner, testSuite);
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% if any([result.Failed])
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% error('One or more tests have failed.');
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% else
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% disp('PID tests completed successfully.');
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% end
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end
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‎Documentation/MI/Extrapolation/Helpers/partition_X.md‎

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PARTITION_X Creates sub-partitions of response matrix for quadratic
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extrapolation bias correction.
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------
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SYNTAX
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------
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PARTITION_X can be used in two different ways
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Xpart = partition_X(X, nt, Npart, partIdx, ntPart);
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or
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[Xpart, ntPart, totNtPart] = partition_X(X, nt, Npart, partIdx);
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In the first case it is assumed that the number of trial per stimulus,
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ntPart, of the final partition matrix is known. In the second case it
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is assumed that ntPart is not know and needs to be computed by the
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function itself (see also the examples below). This dual behavior can
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be used to save time when different partitions corresponding to the
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same Npart value need to be computed.
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---------
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ARGUMENTS
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---------
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X - response matrix
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nt - number of trials per stimulus
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Npart - number of partitions
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partIdx - index of the partition to be extracted
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ntPart - number of trials per stimulus of the partition matrix. In
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Matlab's language it would be ntPart = floor(nt./Npart)
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------
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OUTPUT
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------
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Xpart - partition matrix
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npart - number of trials per stimulus of the partition matrix. In
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Matlab's language it would be ntPart = floor(nt./Npart)
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totNtPart - total number of trials of the partition matrix. In Matlab's
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language it would be totNtPart = sum(ntPart)
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--------
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EXAMPLES
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--------
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The command
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[Xpart, ntPart, totNtPart] = partition_X(X, nt, 4, 2);
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provides the second of the four possible partitions of X together with
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the number of trials per stimulus and the total number of trials of the
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partition matrix. Alternatively
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Xpart = partition_X(X, nt, 4, 2, ntPart);
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also provides the second of the four possible partitions of X. In this
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case, however, the array ntPart is provided as an input to speed up
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computations.
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Copyright (C) 2010 Cesare Magri
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Version: 1.0.2
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-------
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LICENSE
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-------
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This software is distributed free under the condition that:
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1. it shall not be incorporated in software that is subsequently sold;
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2. the authorship of the software shall be acknowledged and the following
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article shall be properly cited in any publication that uses results
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generated by the software:
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Magri C, Whittingstall K, Singh V, Logothetis NK, Panzeri S: A
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toolbox for the fast information analysis of multiple-site LFP, EEG
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and spike train recordings. BMC Neuroscience 2009 10(1):81;
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3. this notice shall remain in place in each source file.
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----------
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DISCLAIMER
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----------
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
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IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
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THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
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CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
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LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
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NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

‎Documentation/MI/Extrapolation/Helpers/xtrploop.md‎

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%%% *function [HX, HXY, HlX, HlXY, HiX, HiXY, ChiX, HshX, HshXY] = xtrploop(X, pars)*
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%%%
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%%% ### Description
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%%% Loops over iterations of the extrapolation procedure.
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%%%
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%%% ### Inputs:
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%%% - *X*: *nDimensions x nTrials x nStimuli* array including response data.
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%%% - *pars*: parameter structure to check generated by build_parameters_structure.
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%%%
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%%% ### Outputs:
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%%% - *HX*: estimate of H(X).
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%%% - *HXY*: estimate of H(X|Y).
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%%% - *HlX*: estimate of H_lin(X).
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%%% - *HlXY*: estimate of H_lin(X|Y).
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%%% - *HiX*: estimate of H_ind(X).
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%%% - *HiXY*: estimate of H_ind(X|Y).
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%%% - *ChiX*: estimate of Chi(X).
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%%% - *HshX*: estimate of H_shuffle(X).
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%%% - *HshXY*: estimate of H_shuffle(X|Y).
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%%%

‎Documentation/MI/Extrapolation/Quadratic/quadratic_extrapolation.md‎

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%%% *function [HX, HXY, HlX, HlXY, HiX, HiXY, ChiX, HshX, HshXY] = quadratic_extrapolation_v2(X, pars)*
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%%%
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%%% ### Description
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%%% Quadratic extrapolation procedure.
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%%%
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%%% ### Inputs:
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%%% - *X*: *nDimensions x nTrials x nStimuli* array including response data.
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%%% - *pars*: parameter structure to check generated by build_parameters_structure.
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%%% -
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%%%
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%%% ### Outputs:
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%%% - *HX*: estimate of H(X).
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%%% - *HXY*: estimate of H(X|Y).
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%%% - *HlX*: estimate of H_lin(X).
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%%% - *HlXY*: estimate of H_lin(X|Y).
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%%% - *HiX*: estimate of H_ind(X).
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%%% - *HiXY*: estimate of H_ind(X|Y).
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%%% - *ChiX*: estimate of Chi(X).
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%%% - *HshX*: estimate of H_shuffle(X).
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%%% - *HshXY*: estimate of H_shuffle(X|Y).
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%%%

‎Documentation/MI/Helpers/findtrial.md‎

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%%% *function ind = findtrial(Nt, maxNt, Ns)*
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%%%
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%%% ### Description
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%%% Index responses corresponding to trials.
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%%%
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%%% ### Inputs:
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%%% - *Nt*: number of trials per stimulus array.
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%%% - *maxNt*: max number of trials (must be equal to max(NT)).
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%%% - *Ns*: number of stimuli (must be equal to length(NT)).
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%%%
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%%% ### Outputs:
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%%% - *ind*: position of the trials in the response matrix R (see ENTROPY and INFORMATION) ignoring the first dimension (dimensionality of the response).
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%%%
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%%% ### Further notes:
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%%%
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%%% EXAMPLE
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%%% Let's consider the response matrix in the figure below (letters are
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%%% used to represent undefined values):
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%%%
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%%% -----------------
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%%% | q | s | - | - |
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%%% |---------------|
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%%% | r | t | - | - |
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%%% -----------------
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%%% ----------------- /
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%%% | i | m | o | - | /
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%%% |---------------| /
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%%% | l | n | p | - | /
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%%% ----------------- /
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%%% | ----------------- / S=3
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%%% | | a | c | e | g | /
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%%% L=2 | |---------------| /
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%%% | | b | d | f | h | /
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%%% | ----------------- /
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%%%
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%%% -----------------
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%%% T=4
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%%%
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%%% in this case L=2, T=4 and S=3. Four trials are availabe for the first
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%%% stimulus, two for the second and three for the third stimulus. We have
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%%% Nt = [4; 3; 2] and maxNt = 4 and thus
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%%%
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%%% ind = tridxs([4; 3; 2], 4, 3)
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%%%
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%%% ind =
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%%%
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%%% 1
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%%% 2
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%%% 3
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%%% 4
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%%% 5
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%%% 6
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%%% 7
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%%% 9
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%%% 10
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%%%
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%%% The indexes computed this way can be used to extract all the responses
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%%% corresponding to trials by means of the command
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%%%
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%%% R(:,ind) =
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%%%
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%%% a c e g i m o q s
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%%% b d f h l n p r t
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%%%
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%%% REMARKS
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%%% The inputs are obviously redundant since all the information required for
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%%% the computation is contained in NT. However, by passing the additional
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%%% arguments we can speed up things a bit.
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%%%
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%%%

‎Documentation/MI/InputParsing/additional_checks.md‎

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%%% *function additional_checks(X, Par, opts)*
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%%%
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%%% ### Description
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%%% Perform additional checks over input to ENTROPY.
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%%%
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%%% ### Inputs:
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%%% - *X*: *nDimensionsX x nTrials x nStimuli* array including response data.
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%%% - *Par*: parameter structure to check generated by build_parameters_structure.
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%%% - *opts*: options to compute the entropy.
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%%%

‎Documentation/MI/InputParsing/build_parameters_structure.md‎

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%%% *function pars = build_parameters_structure(X, opts, responseMatrixName, outputsList)*
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%%%
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%%% ### Description
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%%% Build parameters structure for ENTROPY.m
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%%%
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%%% ### Inputs:
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%%% - *X*: *nDimensionsX X nTrials X nY* 3D array including response data.
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%%% - *opts*: options to compute the entropy.
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%%% - *responseMatrixName*: name associated to the X matrix.
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%%% - *outputsList*: cell array of char arrays of strings specifying which quantities to compute.
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%%%
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%%% ### Further notes:
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%%%
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%%% Recap of the fields in the parameter structure
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%%% ----------------------------------------------
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%%%
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%%% X-related parameters:
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%%% - pars.Nc
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%%% - pars.Ns
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%%% - pars.Nt
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%%% - pars.maxNt
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%%% - pars.totNt
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%%%
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%%% TESTMODE-related parameters:
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%%% - pars.testmode
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%%%
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%%% METHOD-related parameters:
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%%% - pars.methodFunc
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%%% - pars.methodNum
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%%%
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%%% EXTRAPOLATION-related parameters
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%%% - pars.xtrp
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%%%
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%%% BIAS-related parameters:
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%%% - pars.biasCorrNum
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%%% - pars.biasCorrFuncName
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%%%
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%%% BOOTSTRAP-related parameters:
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%%% - pars.btsp
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%%%
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%%% OUTPUT-related parameters:
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%%% - pars.whereHX
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%%% - pars.whereHXY
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%%% - pars.whereHlX
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%%% - pars.whereHlXY
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%%% - pars.whereHiX
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%%% - pars.whereChiX
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%%% - pars.whereHshX
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%%% - pars.whereHshXY
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%%% - pars.whereHiXY
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%%% - pars.doHX
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%%% - pars.doHXY
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%%% - pars.doHlX
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%%% - pars.doHlXY
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%%% - pars.doHiX
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%%% - pars.doHiXY
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%%% - pars.doChiX
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%%% - pars.doHshX
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%%% - pars.doHshXY
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%%%
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%%% CHECKS-related parameters:
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%%% - pars.addChecks
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%%% - pars.numberOfSpecifiedOptions
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%%% - pars.Noutput
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%%%

‎Documentation/MI/Methods/DirectMethod/direct_method.md‎

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%%% DIRECT_METHOD Estimate entropy quantities with direct method.
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%%%
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%%% This function is the direct method engine of called by ENTROPY and its
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%%% related functions.
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%%%
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%%% USAGE
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%%%
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%%% [HX, HXY, HlX, HlXY, HiX, HiXY, ChiX, HshX, HshXY] = direct_method(X, nt, pars);
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%%%
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%%%
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%%% INPUTS
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%%%
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%%% X - response matrix
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%%% pars - parameters structure
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%%%
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%%% *function direct_method*
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%%%
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%%% ### Description
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%%% Estimate entropy quantities with direct method. This function is the direct method engine of called by ENTROPY and its related functions.
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%%%
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%%% ### Inputs:
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%%% - *X*: *nDimensions x nTrials x nStimuli* array including response data.
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%%% - *pars*: parameter structure to check generated by build_parameters_structure.
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%%%
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%%% ### Outputs:
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%%% - *HX*: direct estimate of H(X).
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%%% - *HXY*: direct estimate of H(X|Y).
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%%% - *HlX*: direct estimate of H_lin(X).
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%%% - *HlXY*: direct estimate of H_lin(X|Y).
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%%% - *HiX*: direct estimate of H_ind(X).
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%%% - *HiXY*: direct estimate of H_ind(X|Y).
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%%% - *ChiX*: direct estimate of Chi(X).
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%%% - *HshX*: direct estimate of H_shuffle(X).
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%%% - *HshXY*: direct estimate of H_shuffle(X|Y).
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%%%
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%%% ### Further notes:
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%%%
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%%% The parameters structure must include the following fields:
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%%%
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%%% =========================================================================
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%%% | FIELD NAME | TYPE | DESCRIPTION |
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%%% =========================================================================
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%%% | pars.nt | numeric array | number of trials per stimulus. |
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%%% |-----------------------------------------------------------------------|
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%%% | pars.biasCorrNum | scalar value | bias correction number: |
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%%% | | | |
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%%% | | | -1 : user-defined bias |
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%%% | | | correction routine |
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%%% | | | |
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%%% | | | 2 : Panzeri & Treves |
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%%% |-----------------------------------------------------------------------|
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%%% | pars.doHX | logical value | flag specifying whether H(X) needs |
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%%% | | | to be estimated |
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%%% |-----------------------------------------------------------------------|
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%%% | pars.doHXY | logical value | flag specifying whether H(X|Y) |
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%%% | | | needs to be estimated |
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%%% |-----------------------------------------------------------------------|
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%%% | pars.doHlX | logical value | flag specifying whether H_lin(X) |
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%%% | | | needs to be estimated |
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%%% |-----------------------------------------------------------------------|
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%%% | pars.doHlXY | logical value | flag specifying whether H_lin(X|Y) |
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%%% | | | needs to be estimated |
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%%% |-----------------------------------------------------------------------|
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%%% | pars.doHiX | logical value | flag specifying whether H_ind(X) |
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%%% | | | needs to be estimated |
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%%% |-----------------------------------------------------------------------|
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%%% | pars.doHiXY | logical value | flag specifying whether H_ind(X|Y) |
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%%% | | | needs to be estimated |
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%%% |-----------------------------------------------------------------------|
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%%% | pars.doChiX | logical value | flag specifying whether Chi(X) |
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%%% | | | needs to be estimated |
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%%% |-----------------------------------------------------------------------|
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%%% | pars.doHshX | logical value | flag specifying whether H_sh(X) |
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%%% | | | needs to be estimated |
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%%% |-----------------------------------------------------------------------|
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%%% | pars.doHshXY | logical value | flag specifying whether H_sh(X|Y) |
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%%% | | | needs to be estimated |
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%%% |-----------------------------------------------------------------------|
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%%% | pars.testMode | logical value | flag specifying whther test-mode |
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%%% | | | has been selected |
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%%% =========================================================================
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%%%
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%%% Zero is returned for all outputs corresponding to output options that were not selected by the user.
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%%%

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