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github | utvisionlab/mixest-master | spdffactory.m | .m | mixest-master/mixest/auxiliary/manopt_manifolds/positivedefinite/spdffactory.m | 6,888 | utf_8 | 3bb958b787e7527d9fa7a606835ae8fb | %% |spdfastfactory|
% Returns a manifold structure to optimize over symmetric positive definite
% matrices
%
% *Syntax*
%
% M = spdfastfactory(n)
%
% *Description*
%
% |M = spdfastfactory(n)| returns |M|, a structure describing the
% Riemmanian manifold of symmetric |n-by-n| positive definite matrices.
%
% The retra... |
github | utvisionlab/mixest-master | sqrtm_fast.m | .m | mixest-master/mixest/auxiliary/manopt_manifolds/positivedefinite/sqrtm_fast.m | 1,005 | utf_8 | 1cf2de88a13ccf3b36f0c64ecd669d91 | %% |sqrtm_fast|
% Calculate matrix square root using Schur decomposition
%
% *Syntax*
%
% As = sqrtm_fast(A)
%
% Copyright 2015 Reshad Hosseini and Mohamadreza Mash'al
% This file is part of MixEst: visionlab.ut.ac.ir/mixest
%
% Original author: Reshad Hosseini
%
% Change log:
%
function As=sqrtm_fast(A)
% ToDO:... |
github | utvisionlab/mixest-master | positivefactory.m | .m | mixest-master/mixest/auxiliary/manopt_manifolds/positivedefinite/positivefactory.m | 2,165 | utf_8 | 3360224214bc02e6853e2464ea3abd25 | %% |positivefactory|
% Returns a manifold struct to optimize over manifold of positive numbers
%
% *Syntax*
%
% M = positivefactory(n)
%
% *Description*
%
% |M = positivefactory(n)| returns |M|, a structure describing the positive
% manifold.
%
% Copyright 2015 Reshad Hosseini and Mohamadreza Mash'al
% This file is ... |
github | utvisionlab/mixest-master | linesearch_wolfe_euc.m | .m | mixest-master/mixest/auxiliary/manopt_manifolds/linesearch/linesearch_wolfe_euc.m | 10,316 | utf_8 | fcd312fd8d7a8e444a385efd8c539c2d | function [stepsize newx storedb lsmem lsstats] = ...
linesearch_wolfe_euc(problem, x, d, f0, df0, options, storedb, lsmem)
% Adaptive line search algorithm (step size selection) for descent methods.
%
% function [stepsize newx storedb lsmem lsstats] =
% linesearch_adaptive(problem, x, d, f0, df0, options, ... |
github | utvisionlab/mixest-master | linesearch_wolfe.m | .m | mixest-master/mixest/auxiliary/manopt_manifolds/linesearch/linesearch_wolfe.m | 11,360 | utf_8 | b48002be663b4b3fd9f47cbf37ebe3de | function [stepsize newx storedb lsmem lsstats] = ...
linesearch_wolfe(problem, x, d, f0, df0, options, storedb, lsmem, grad)
% Adaptive line search algorithm (step size selection) for descent methods.
%
% function [stepsize newx storedb lsmem lsstats] =
% linesearch_adaptive(problem, x, d, f0, df0, options... |
github | utvisionlab/mixest-master | linesearch_wolfe_old.m | .m | mixest-master/mixest/auxiliary/manopt_manifolds/linesearch/linesearch_wolfe_old.m | 10,118 | utf_8 | 7d08bc3b907bfcea8c0d969033c50951 | function [stepsize newx storedb lsmem lsstats] = ...
linesearch_wolfe(problem, x, d, f0, df0, options, storedb, lsmem)
% Adaptive line search algorithm (step size selection) for descent methods.
%
% function [stepsize newx storedb lsmem lsstats] =
% linesearch_adaptive(problem, x, d, f0, df0, options, stor... |
github | utvisionlab/mixest-master | sphere2factory.m | .m | mixest-master/mixest/auxiliary/manopt_manifolds/sphere/sphere2factory.m | 2,250 | utf_8 | 13e4da349d04b85e9571c6da85190205 | %% |positivefactory|
% Returns a manifold struct to optimize over manifold of positive numbers
%
% *Syntax*
%
% M = positivefactory(n)
%
% *Description*
%
% |M = positivefactory(n)| returns |M|, a structure describing the positive
% manifold.
%
% Copyright 2015 Reshad Hosseini and Mohamadreza Mash'al
% This file is ... |
github | utvisionlab/mixest-master | check_grad.m | .m | mixest-master/mixest/auxiliary/checking/check_grad.m | 1,403 | utf_8 | 1cae3ce9031fbb7a2c8fcb1440fbecb0 | %% |check_grad|
% *Note:* This is a private function.
%
% Checking gradient derivation of log-likelihood
%
% *Syntax*
%
% check_grad(D, theta, data)
%
% Copyright 2015 Reshad Hosseini and Mohamadreza Mash'al
% This file is part of MixEst: visionlab.ut.ac.ir/mixest
%
% Contributors:
% Reshad Hosseini
% Mohamadreza ... |
github | utvisionlab/mixest-master | obj2vec.m | .m | mixest-master/mixest/auxiliary/checking/obj2vec.m | 1,007 | utf_8 | 5e06e2677fa5d01dd52c224b919ba193 | %% |obj2vec|
% *Note:* This is a private function.
%
% Convert the object into a vector
%
% *Syntax*
%
% vecOut = obj2vec(obj)
%
% Copyright 2015 Reshad Hosseini and Mohamadreza Mash'al
% This file is part of MixEst: visionlab.ut.ac.ir/mixest
%
% Contributors:
% Reshad Hosseini
% Mohamadreza Mash'al
%
% Change log... |
github | utvisionlab/mixest-master | check_kl.m | .m | mixest-master/mixest/auxiliary/checking/check_kl.m | 1,019 | utf_8 | 11e1df39fd6762a15a8e82d6103eff2a | %% |check_kl|
% *Note:* This is a private function.
%
% Checking kl-divergence derivation
%
% *Syntax*
%
% check_kl(D, p1, p2)
%
% Copyright 2015 Reshad Hosseini and Mohamadreza Mash'al
% This file is part of MixEst: visionlab.ut.ac.ir/mixest
%
% Contributors:
% Reshad Hosseini
% Mohamadreza Mash'al
%
% Change log... |
github | utvisionlab/mixest-master | vec2obj.m | .m | mixest-master/mixest/auxiliary/checking/vec2obj.m | 993 | utf_8 | 53dc3a2ad6c4cbbce716e7bf325f2255 | %% |vec2obj|
% *Note:* This is a private function.
%
% Convert the vector |vec| into an object like the sample object |obj|
%
% *Syntax*
%
% obj = vec2obj(obj, vec)
%
% Copyright 2015 Reshad Hosseini and Mohamadreza Mash'al
% This file is part of MixEst: visionlab.ut.ac.ir/mixest
%
% Contributors:
% Reshad Hosseini... |
github | utvisionlab/mixest-master | objlen.m | .m | mixest-master/mixest/auxiliary/checking/objlen.m | 678 | utf_8 | 822618885d4e062e3a70af3311941b03 | %% |objlen|
% *Note:* This is a private function.
%
% Calculate the number of numerical elements in obj
%
% *Syntax*
%
% l = objlen(obj)
%
% Copyright 2015 Reshad Hosseini and Mohamadreza Mash'al
% This file is part of MixEst: visionlab.ut.ac.ir/mixest
%
% Contributors:
% Reshad Hosseini
% Mohamadreza Mash'al
%
% ... |
github | utvisionlab/mixest-master | desc_dir_cal.m | .m | mixest-master/mixest/auxiliary/manopt_solvers/lbfgs/desc_dir_cal.m | 2,878 | utf_8 | 7a86126ebae493ad89e325b679e556ac | %% |desc_dir_cal|
% *Note:* This is a private function.
%
% Copyright 2015 Reshad Hosseini and Mohamadreza Mash'al
% This file is part of MixEst: visionlab.ut.ac.ir/mixest
%
% Original author: Reshad Hosseini
%
% Change log:
%
function p_desc = desc_dir_cal(p, M, grad_diff_all, desc_dir_all, ...
x_all, ddgd, gd,... |
github | utvisionlab/mixest-master | lbfgs.m | .m | mixest-master/mixest/auxiliary/manopt_solvers/lbfgs/lbfgs.m | 9,736 | utf_8 | 18a00b66540e80e46de589228daff0ef | %% |lbfgs|
% Manifold LBFGS minimization algorithm for Manopt
%
% Copyright 2015 Reshad Hosseini and Mohamadreza Mash'al
% This file is part of MixEst: visionlab.ut.ac.ir/mixest
%
% Original author: Reshad Hosseini, Aug. 30, 2013.
%
% Change log:
% Reshad Hosseini, Jun.26,2013: Improving speed when "transpf" is pre... |
github | utvisionlab/mixest-master | lbfgs_update.m | .m | mixest-master/mixest/auxiliary/manopt_solvers/lbfgs/lbfgs_update.m | 2,660 | utf_8 | b044c04edb8f62c612fd5b4e42e00701 | %% |lbfgs_update|
% *Note:* This is a private function.
%
% Copyright 2015 Reshad Hosseini and Mohamadreza Mash'al
% This file is part of MixEst: visionlab.ut.ac.ir/mixest
%
% Original author: Reshad Hosseini
%
% Change log:
%
function [grad_diff_all, desc_dir_all, x_all, gd_all, ddgd_all, Hdiag, ...
Expc_all, ... |
github | utvisionlab/mixest-master | sgd_old.m | .m | mixest-master/mixest/auxiliary/manopt_solvers/sgd/sgd_old.m | 6,130 | utf_8 | 9e82a86fe0084439437172f8fa70b6a9 | %% |sgd|
% Manifold Stochastic Gradient Descent(SGD) minimization algorithm for Manopt
%
% Copyright 2015 Reshad Hosseini and Mohamadreza Mash'al
% This file is part of MixEst: visionlab.ut.ac.ir/mixest
%
% Original author: Reshad Hosseini, Aug. 30, 2013.
%
% Change log:
% Reshad Hosseini, Jun.26,2013: Improving sp... |
github | utvisionlab/mixest-master | sgd.m | .m | mixest-master/mixest/auxiliary/manopt_solvers/sgd/sgd.m | 10,158 | utf_8 | affa134ece1cdd37b24988a001bb424e | %% |sgd|
% Manifold Stochastic Gradient Descent(SGD) minimization algorithm for Manopt
%
% Copyright 2015 Reshad Hosseini and Mohamadreza Mash'al
% This file is part of MixEst: visionlab.ut.ac.ir/mixest
%
% Original author: Reshad Hosseini, Aug. 30, 2013.
%
% Change log:
% Reshad Hosseini, Jun.26,2013: Improving sp... |
github | utvisionlab/mixest-master | TestRunDisplay.m | .m | mixest-master/thirdparty/matlab-xunit-master/src/TestRunDisplay.m | 9,772 | utf_8 | 1c65aa22432e7ebea518594b5df800a4 | classdef TestRunDisplay < TestRunMonitor
%TestRunDisplay Print test suite execution results.
% TestRunDisplay is a subclass of TestRunMonitor. If a TestRunDisplay
% object is passed to the run method of a TestComponent, such as a
% TestSuite or a TestCase, it will print information to the Command
% Window (or ... |
github | utvisionlab/mixest-master | runxunit.m | .m | mixest-master/thirdparty/matlab-xunit-master/src/runxunit.m | 5,594 | utf_8 | 271abc6aaf06b2e64f251931f10f37cd | function out = runxunit(varargin)
%runxunit Run unit tests
% runxunit runs all the test cases that can be found in the current directory
% and summarizes the results in the Command Window.
%
% Test cases can be found in the following places in the current directory:
%
% * An M-file function whose name start... |
github | utvisionlab/mixest-master | xml_write.m | .m | mixest-master/thirdparty/matlab-xunit-master/src/xml_write.m | 18,325 | utf_8 | 24bd3dc683e5a0a0ad4080deaa6a93a5 | function DOMnode = xml_write(filename, tree, RootName, Pref)
%XML_WRITE Writes Matlab data structures to XML file
%
% DESCRIPTION
% xml_write( filename, tree) Converts Matlab data structure 'tree' containing
% cells, structs, numbers and strings to Document Object Model (DOM) node
% tree, then saves it to XML file 'fi... |
github | utvisionlab/mixest-master | TestSuite.m | .m | mixest-master/thirdparty/matlab-xunit-master/src/TestSuite.m | 13,145 | utf_8 | a2f83a7c15ba0ad13fa330aa59d1451a | %TestSuite Collection of TestComponent objects
% The TestSuite class defines a collection of TestComponent objects.
%
% TestSuite methods:
% TestSuite - Constructor
% add - Add test component to test suite
% print - Display test suite summary to Comman... |
github | utvisionlab/mixest-master | xml_read.m | .m | mixest-master/thirdparty/matlab-xunit-master/src/xml_read.m | 23,858 | utf_8 | d68b7e27ad197bc94b445c3a833b9f23 | function [tree, RootName, DOMnode] = xml_read(xmlfile, Pref)
%XML_READ reads xml files and converts them into Matlab's struct tree.
%
% DESCRIPTION
% tree = xml_read(xmlfile) reads 'xmlfile' into data structure 'tree'
%
% tree = xml_read(xmlfile, Pref) reads 'xmlfile' into data structure 'tree'
% according to your pref... |
github | utvisionlab/mixest-master | isTestCaseSubclass.m | .m | mixest-master/thirdparty/matlab-xunit-master/src/+xunit/+utils/isTestCaseSubclass.m | 894 | utf_8 | 5c7e3f1d9b1eb3e2148cad3cde93c5c7 | function tf = isTestCaseSubclass(name)
%isTestCaseSubclass True for name of a TestCase subclass
% tf = isTestCaseSubclass(name) returns true if the string name is the name of
% a TestCase subclass on the MATLAB path.
% Steven L. Eddins
% Copyright 2008-2009 The MathWorks, Inc.
tf = false;
class_meta = meta.c... |
github | utvisionlab/mixest-master | arrayToString.m | .m | mixest-master/thirdparty/matlab-xunit-master/src/+xunit/+utils/arrayToString.m | 2,972 | utf_8 | 173fdeb3960985834f7a252c7916fa32 | function s = arrayToString(A)
%arrayToString Convert array to string for display.
% S = arrayToString(A) converts the array A into a string suitable for
% including in assertion messages. Small arrays are converted using disp(A).
% Large arrays are displayed similar to the way structure field values display
% ... |
github | utvisionlab/mixest-master | compareFloats.m | .m | mixest-master/thirdparty/matlab-xunit-master/src/+xunit/+utils/compareFloats.m | 4,376 | utf_8 | f77141ccf2a6b351c309eb9be4211065 | function result = compareFloats(varargin)
%compareFloats Compare floating-point arrays using tolerance.
% result = compareFloats(A, B, compare_type, tol_type, tol, floor_tol)
% compares the floating-point arrays A and B using a tolerance. compare_type
% is either 'elementwise' or 'vector'. tol_type is either 'r... |
github | utvisionlab/mixest-master | maxcut.m | .m | mixest-master/thirdparty/manopt/examples/maxcut.m | 11,837 | utf_8 | 24c2f0dec952e2573770b3c7fff0d956 | function [x cutvalue cutvalue_upperbound Y] = maxcut(L, r)
% Algorithm to (try to) compute a maximum cut of a graph, via SDP approach.
%
% function x = maxcut(L)
% function [x cutvalue cutvalue_upperbound Y] = maxcut(L, r)
%
% L is the Laplacian matrix describing the graph to cut. The Laplacian of a
% graph is L = D -... |
github | utvisionlab/mixest-master | maxcut_octave.m | .m | mixest-master/thirdparty/manopt/examples/maxcut_octave.m | 10,225 | utf_8 | 1e3291732d3df9c409ac7e3c656bbef1 | function [x cutvalue cutvalue_upperbound Y] = maxcut_octave(L, r)
% Algorithm to (try to) compute a maximum cut of a graph, via SDP approach.
%
% function x = maxcut_octave(L)
% function [x cutvalue cutvalue_upperbound Y] = maxcut_octave(L, r)
%
% See examples/maxcut.m for help about the math behind this example. This... |
github | utvisionlab/mixest-master | sparse_pca.m | .m | mixest-master/thirdparty/manopt/examples/sparse_pca.m | 6,374 | utf_8 | e4d16571861fa419aa8e0dca17d55711 | function [Z, P, X, A] = sparse_pca(A, m, gamma)
% Sparse principal component analysis based on optimization over Stiefel.
%
% [Z, P, X] = sparse_pca(A, m, gamma)
%
% We consider sparse PCA applied to a data matrix A of size pxn, where p is
% the number of samples (observations) and n is the number of variables
% (featu... |
github | utvisionlab/mixest-master | grassmannfactory.m | .m | mixest-master/thirdparty/manopt/manopt/manifolds/grassmann/grassmannfactory.m | 7,982 | utf_8 | a5f66f1bc2f60bdfc2ff8af9bd8ccacb | function M = grassmannfactory(n, p, k)
% Returns a manifold struct to optimize over the space of vector subspaces.
%
% function M = grassmannfactory(n, p)
% function M = grassmannfactory(n, p, k)
%
% Grassmann manifold: each point on this manifold is a collection of k
% vector subspaces of dimension p embedded in R^n.
... |
github | utvisionlab/mixest-master | elliptopefactory.m | .m | mixest-master/thirdparty/manopt/manopt/manifolds/symfixedrank/elliptopefactory.m | 7,271 | utf_8 | a13695f7011e4c9135781f05485d1f4d | function M = elliptopefactory(n, k)
% Manifold of n-by-n PSD matrices of rank k with unit diagonal elements.
%
% function M = elliptopefactory(n, k)
%
% The geometry is based on the paper,
% M. Journee, P.-A. Absil, F. Bach and R. Sepulchre,
% "Low-Rank Optimization on the Cone of Positive Semidefinite Matrices",
% SIO... |
github | utvisionlab/mixest-master | spectrahedronfactory.m | .m | mixest-master/thirdparty/manopt/manopt/manifolds/symfixedrank/spectrahedronfactory.m | 3,805 | utf_8 | bdbd9b8978e11013cdad8ed7158d5795 | function M = spectrahedronfactory(n, k)
% Manifold of n-by-n symmetric positive semidefinite natrices of rank k
% with trace (sum of diagonal elements) being 1.
%
% function M = spectrahedronfactory(n, k)
%
% The goemetry is based on the paper,
% M. Journee, P.-A. Absil, F. Bach and R. Sepulchre,
% "Low-Rank Optinizati... |
github | utvisionlab/mixest-master | sympositivedefinitefactory.m | .m | mixest-master/thirdparty/manopt/manopt/manifolds/symfixedrank/sympositivedefinitefactory.m | 5,348 | utf_8 | bc81f3e772ade05cd2256d29dd289730 | function M = sympositivedefinitefactory(n)
% Manifold of n-by-n symmetric positive definite matrices with
% the bi-invariant geometry.
%
% function M = sympositivedefinitefactory(n)
%
% A point X on the manifold is represented as a symmetric positive definite
% matrix X (nxn).
%
% The following material is referenced f... |
github | utvisionlab/mixest-master | symfixedrankYYfactory.m | .m | mixest-master/thirdparty/manopt/manopt/manifolds/symfixedrank/symfixedrankYYfactory.m | 3,496 | utf_8 | 82bc0919d4729ab40c5a55c2d6f19947 | function M = symfixedrankYYfactory(n, k)
% Manifold of n-by-n symmetric positive semidefinite matrices of rank k.
%
% function M = symfixedrankYYfactory(n, k)
%
% The geometry is based on the paper,
% M. Journee, P.-A. Absil, F. Bach and R. Sepulchre,
% "Low-Rank Optimization on the Cone of Positive Semidefinite Matric... |
github | utvisionlab/mixest-master | complexcirclefactory.m | .m | mixest-master/thirdparty/manopt/manopt/manifolds/complexcircle/complexcirclefactory.m | 3,558 | utf_8 | dde7799b7c8e18feed5ecb1baa3beb88 | function M = complexcirclefactory(n)
% Returns a manifold struct to optimize over unit-modulus complex numbers.
%
% function M = complexcirclefactory()
% function M = complexcirclefactory(n)
%
% Description of vectors z in C^n (complex) such that each component z(i)
% has unit modulus. The manifold structure is the Rie... |
github | utvisionlab/mixest-master | fixedrankfactory_3factors_preconditioned.m | .m | mixest-master/thirdparty/manopt/manopt/manifolds/fixedrank/fixedrankfactory_3factors_preconditioned.m | 11,365 | utf_8 | 586646939b844c902e69840302ddf7ad | function M = fixedrankfactory_3factors_preconditioned(m, n, k)
% Manifold of m-by-n matrices of rank k with polar quotient geometry.
%
% function M = fixedrankLSRquotientfactory(m, n, k)
%
% A point X on the manifold is represented as a structure with three
% fields: L, S and R. The matrices L (mxk) and R (nxk) are ort... |
github | utvisionlab/mixest-master | fixedrankfactory_2factors_subspace_projection.m | .m | mixest-master/thirdparty/manopt/manopt/manifolds/fixedrank/fixedrankfactory_2factors_subspace_projection.m | 6,050 | utf_8 | b93294f6129f3147f02cec96edb756b2 | function M = fixedrankfactory_2factors_subspace_projection(m, n, k)
% Manifold of m-by-n matrices of rank k with quotient geometry.
%
% function M = fixedrankfactory_2factors_subspace_projection(m, n, k)
%
% This follows the quotient geometry described in the following paper:
% B. Mishra, G. Meyer, S. Bonnabel and R. S... |
github | utvisionlab/mixest-master | fixedrankfactory_2factors_preconditioned.m | .m | mixest-master/thirdparty/manopt/manopt/manifolds/fixedrank/fixedrankfactory_2factors_preconditioned.m | 5,639 | utf_8 | 32c031047b7b6e7aae3ad320770bf054 | function M = fixedrankfactory_2factors_preconditioned(m, n, k)
% Manifold of m-by-n matrices of rank k with new balanced quotient geometry
%
% function M = fixedrankfactory_2factors_preconditioned(m, n, k)
%
% This follows the quotient geometry described in the following paper:
% B. Mishra, K. Adithya Apuroop and R. Se... |
github | utvisionlab/mixest-master | fixedrankembeddedfactory.m | .m | mixest-master/thirdparty/manopt/manopt/manifolds/fixedrank/fixedrankembeddedfactory.m | 10,556 | utf_8 | 53c6143e605b4b40dd275f13525aced2 | function M = fixedrankembeddedfactory(m, n, k)
% Manifold struct to optimize fixed-rank matrices w/ an embedded geometry.
%
% function M = fixedrankembeddedfactory(m, n, k)
%
% Manifold of m-by-n real matrices of fixed rank k. This follows the
% geometry described in this paper (which for now is the documentation):
% B... |
github | utvisionlab/mixest-master | fixedrankfactory_3factors.m | .m | mixest-master/thirdparty/manopt/manopt/manifolds/fixedrank/fixedrankfactory_3factors.m | 5,846 | utf_8 | e63dc014f3c7509346e84937620a5c46 | function M = fixedrankfactory_3factors(m, n, k)
% Manifold of m-by-n matrices of rank k with polar quotient geometry.
%
% function M = fixedrankfactory_3factors(m, n, k)
%
% Follows the polar quotient geometry described in the following paper:
% G. Meyer, S. Bonnabel and R. Sepulchre,
% "Linear regression under fixed-r... |
github | utvisionlab/mixest-master | fixedrankMNquotientfactory.m | .m | mixest-master/thirdparty/manopt/manopt/manifolds/fixedrank/fixedrankMNquotientfactory.m | 4,311 | utf_8 | 5e88218a79dd6351eac77678b441a4ed | function M = fixedrankMNquotientfactory(m, n, k)
% Manifold of m-by-n matrices of rank k with quotient geometry.
%
% function M = fixedrankMNquotientfactory(m, n, k)
%
% This follows the quotient geometry described in the following paper:
% P.-A. Absil, L. Amodei and G. Meyer,
% "Two Newton methods on the manifold of f... |
github | utvisionlab/mixest-master | fixedrankfactory_2factors.m | .m | mixest-master/thirdparty/manopt/manopt/manifolds/fixedrank/fixedrankfactory_2factors.m | 5,627 | utf_8 | b32ff8702d64bb8e4d61ada797f20a33 | function M = fixedrankfactory_2factors(m, n, k)
% Manifold of m-by-n matrices of rank k with balanced quotient geometry.
%
% function M = fixedrankfactory_2factors(m, n, k)
%
% This follows the balanced quotient geometry described in the following paper:
% G. Meyer, S. Bonnabel and R. Sepulchre,
% "Linear regression un... |
github | utvisionlab/mixest-master | obliquefactory.m | .m | mixest-master/thirdparty/manopt/manopt/manifolds/oblique/obliquefactory.m | 6,363 | utf_8 | 584a49adde66f63d54f6ea9115f1436a | function M = obliquefactory(n, m, transposed)
% Returns a manifold struct to optimize over matrices w/ unit-norm columns.
%
% function M = obliquefactory(n, m)
% function M = obliquefactory(n, m, transposed)
%
% Oblique manifold: deals with matrices of size n x m such that each column
% has unit 2-norm, i.e., is a poin... |
github | utvisionlab/mixest-master | stiefelfactory.m | .m | mixest-master/thirdparty/manopt/manopt/manifolds/stiefel/stiefelfactory.m | 4,826 | utf_8 | ae1077c390f151fc955fc2d88c6d8a68 | function M = stiefelfactory(n, p, k)
% Returns a manifold structure to optimize over orthonormal matrices.
%
% function M = stiefelfactory(n, p)
% function M = stiefelfactory(n, p, k)
%
% The Stiefel manifold is the set of orthonormal nxp matrices. If k
% is larger than 1, this is the Cartesian product of the Stiefel m... |
github | utvisionlab/mixest-master | rotationsfactory.m | .m | mixest-master/thirdparty/manopt/manopt/manifolds/rotations/rotationsfactory.m | 4,696 | utf_8 | 98b73af2eb2c31fd21f041479f887efc | function M = rotationsfactory(n, k)
% Returns a manifold structure to optimize over rotation matrices.
%
% function M = rotationsfactory(n)
% function M = rotationsfactory(n, k)
%
% Special orthogonal group (the manifold of rotations): deals with matrices
% R of size n x n x k (or n x n if k = 1, which is the default)... |
github | utvisionlab/mixest-master | spherecomplexfactory.m | .m | mixest-master/thirdparty/manopt/manopt/manifolds/sphere/spherecomplexfactory.m | 3,145 | utf_8 | 9936c34e8f183316ea93ebb7aedd8681 | function M = spherecomplexfactory(n, m)
% Returns a manifold struct to optimize over unit-norm complex matrices.
%
% function M = spherecomplexfactory(n)
% function M = spherecomplexfactory(n, m)
%
% Manifold of n-by-m complex matrices of unit Frobenius norm.
% By default, m = 1, which corresponds to the unit sphere in... |
github | utvisionlab/mixest-master | spherefactory.m | .m | mixest-master/thirdparty/manopt/manopt/manifolds/sphere/spherefactory.m | 3,295 | utf_8 | 1e285f6b26d327bc90eb44661e9c2725 | function M = spherefactory(n, m)
% Returns a manifold struct to optimize over unit-norm vectors or matrices.
%
% function M = spherefactory(n)
% function M = spherefactory(n, m)
%
% Manifold of n-by-m real matrices of unit Frobenius norm.
% By default, m = 1, which corresponds to the unit sphere in R^n. The
% metric is... |
github | utvisionlab/mixest-master | trustregions.m | .m | mixest-master/thirdparty/manopt/manopt/solvers/trustregions/trustregions.m | 26,920 | utf_8 | cb07446ec8178a1cd80d29676652319c | function [x, cost, info, options] = trustregions(problem, x, options)
% Riemannian trust-regions solver for optimization on manifolds.
%
% function [x, cost, info, options] = trustregions(problem)
% function [x, cost, info, options] = trustregions(problem, x0)
% function [x, cost, info, options] = trustregions(problem,... |
github | utvisionlab/mixest-master | linesearch_adaptive.m | .m | mixest-master/thirdparty/manopt/manopt/solvers/linesearch/linesearch_adaptive.m | 5,179 | utf_8 | 975c584abd6b97fc0f9acd9ef06883d7 | function [stepsize newx storedb lsmem lsstats] = ...
linesearch_adaptive(problem, x, d, f0, df0, options, storedb, lsmem)
% Adaptive line search algorithm (step size selection) for descent methods.
%
% function [stepsize newx storedb lsmem lsstats] =
% linesearch_adaptive(problem, x, d, f0, df0, options, s... |
github | utvisionlab/mixest-master | example1.m | .m | mixest-master/examples/example1.m | 1,099 | utf_8 | 66e904eb4afc7abafa2b8c25383cafe7 | %% Example 1
%%
% This example visualizes the estimation process of a mixture of three
% Gaussians on sample 2-D data.
%
function example1
clc
clear
close all
load data2d
num = 3;
D = mixturefactory(mvn2factory(2), num);
% graphical visualization
figure('Units', 'normalized', 'OuterPosition', [0 0 1 1])
options... |
github | utvisionlab/mixest-master | example3.m | .m | mixest-master/examples/example3.m | 2,009 | utf_8 | 4fb1332dd52938ed0e9136c4ac173d49 | %% Example 3
%%
% This example visualizes the estimation process of a mixture of von
% Mises-Fisher distributions on sample data.
%
function example3
clc
clear
close all
num = 5;
D = mixturefactory(vmffactory(3), num);
theta = D.randparam();
for k = 1 : num
theta.D{k}.kappa = theta.D{k}.kappa*10 +10;
end
data... |
github | utvisionlab/mixest-master | example4.m | .m | mixest-master/examples/example4.m | 1,095 | utf_8 | 2f8bcb478e239fe8629a56be4a6dce13 | %% Example 4
%%
% This example visualizes the estimation process of a modified version of
% the Competitive EM (CEM) algorithm (Zhang et al., 2004) on sample 2-D
% data.
%
function example4
clc
clear
close all
f = load('data_sm');
data = f.data;
% visualization and plotting options
figure('Units', 'normalized', '... |
github | utvisionlab/mixest-master | example2.m | .m | mixest-master/examples/example2.m | 1,232 | utf_8 | 08d0e4928e9ffd00531970617e86beaf | %% Example 2
%%
% This example uses a mixture of multinomial logistic experts to classify
% Fisher's iris data.
%
function example2
clc
clear
%close all
load data_iris
% random permute the iris data
index = randperm(150);
% load rndprm150
data = data(:, index);
% Use 120 data for training
data_train = data(:, 1:1... |
github | utvisionlab/mixest-master | test_mvnfactory.m | .m | mixest-master/tests/test_mvnfactory.m | 4,731 | utf_8 | 83a1c01e27b997d15fba0963f9fad135 | function test_suite = test_mvnfactory %#ok<STOUT>
initTestSuite;
function s = setup %#ok<*DEFNU>
% This is run before running each test case and the output is passed to the
% test case.
s.D = mvnfactory(2);
% theta generated by: theta = D.randparam()
s.theta.mu = [0.537667139546100;1.833885014595087];
s.theta.sigma... |
github | utvisionlab/mixest-master | test_mixturefactory.m | .m | mixest-master/tests/test_mixturefactory.m | 5,775 | utf_8 | b1e643fe88c752d98666bdd3c6b0e111 | function test_suite = test_mixturefactory %#ok<STOUT>
initTestSuite;
function s = setup %#ok<*DEFNU>
% This is run before running each test case and the output is passed to the
% test case.
s.D = mixturefactory(mvnfactory(1), 5);
s.theta.D{1} = struct('mu',1 ,'sigma',1);
s.theta.D{2} = struct('mu',2 ,'sigma',2);
s.t... |
github | utvisionlab/mixest-master | test_gammafactory.m | .m | mixest-master/tests/test_gammafactory.m | 2,074 | utf_8 | 6fb2f3d75dfd695b3d9e474403c27703 | function test_suite = test_gammafactory %#ok<STOUT>
initTestSuite;
function s = setup %#ok<*DEFNU>
% This is run before running each test case and the output is passed to the
% test case.
s.D = gammafactory();
% theta generated by: theta = D.randparam()
s.theta.a = 0.126986816293506;
s.theta.b = 0.913375856139019;
... |
github | utvisionlab/mixest-master | test_simplex.m | .m | mixest-master/tests/highlevel/test_simplex.m | 322 | utf_8 | f827e0d9097a31ab02ab2cc28b3798f9 | function test_simplex
profile on
q= 10;
M = simplexKfactory(q,2);
problem.M = M;
disp('Checking ll gradient using egrad2rgrad');
problem.costgrad = @(x)costgrad(M,x);
figure(1),checkgradient(problem)
profile viewer
profile off
function [lik,dll] = costgrad(M,x)
lik = sum(x.^3);
dll = 3*x.^2;
dll = M.egrad2rgrad(x,dll... |
github | utvisionlab/mixest-master | test_plotting.m | .m | mixest-master/tests/highlevel/test_plotting.m | 739 | utf_8 | aad9e5d25e52c8e983356a234c06b701 | function test_plotting
clc
clear
close all
data = 1 + 3.*randn(1, 10000);
D = mvnfactory(1);
options.solver = 'cg';
% options.plotcost = true;
% options.plotgradnorm = true;
options.plotitercount = Inf;
options.plotavg = 5;
figure
options.plotcost.axes = subplot(1,2,1);
options.plotgradnorm.axes = subplot(1,2,2);
... |
github | utvisionlab/mixest-master | test_functions.m | .m | mixest-master/tests/highlevel/test_functions.m | 2,657 | utf_8 | d58056420e73d3b4586f516324d5f751 | function thetaO = test_functions(D, nrs)
profile on
% Generating random samples
theta = D.randparam();
if isscalar(nrs)
data = D.sample(theta, nrs);
else
data = nrs;
end
%figure(5), plot(data(1,:),data(2,:),'.');
if isfield(D, 'penalizercost')
% Checking penalizer
disp('Checking penalizer');
Ppa... |
github | utvisionlab/mixest-master | test_custom_splitinit.m | .m | mixest-master/tests/highlevel/test_custom_splitinit.m | 1,113 | utf_8 | 70da0a3ec35bc8859e9108548ff5f2d7 | function test_custom_splitinit
clc
clear
close all
load data2d
D = mixturefactory(mvnfactory(2), 1);
theta = D.init(data);
options.sm.splitinit = @splitinit;
D.visualize(theta, struct('data', data));
[newD, newtheta] = D.split(1, theta, options, data);
newD.visualize(n... |
github | Nek5000-deprecated/Nek5000-deprecated-master | xxt_test.m | .m | Nek5000-deprecated-master/jl/xxt_test.m | 2,311 | utf_8 | 1b4747c4dcb41ff5f4013264a8a9728d | function xxt_test
Al0=[8 -1; -1 4];
Ac0=[-2 -2 -2; 0 -2 -1];
As0=[4 -1 0; -1 8 -1; 0 -1 4];
Al1=[4];
Ac1=[-1 -1 -2];
As1=[4 -2 -1; -2 4 -1; -1 -1 4];
Al2=[4];
Ac2=[-1 -2 -1];
As2=[4 -1 -2; -1 4 -1; -2 -1 4];
A0=[Al0 Ac0; Ac0' As0];
A1=[Al1 Ac1; Ac1' As1];
A2=[Al2 Ac2; Ac2' As2];
Il=eye(4); Is=eye(4);
gI=eye(8);
Rl... |
github | Nek5000-deprecated/Nek5000-deprecated-master | xxt_test2.m | .m | Nek5000-deprecated-master/jl/xxt_test2.m | 1,743 | utf_8 | d77fba63152f473361f4dbec5f787af5 | %p = [4 3 2 1 3 6 1 5 6 5 ]
%inv(A)(p,p)
function M=bdiag(A,B,C)
[ra ca]=size(A);
[rb cb]=size(B);
[rc cc]=size(C);
M = [ A zeros(ra,cb) zeros(ra,cc)
zeros(rb,ca) B zeros(rb,cc)
zeros(rc,ca) zeros(rc,cb) C ];
end
Al0=[];
Ac0=zeros(2)([],:);
As0=[1 -.5; -.5 1];
Al1=[ 2 -.5 -... |
github | msyamkumar/captioned_image_retrieval_CS766_final-master | display_results.m | .m | captioned_image_retrieval_CS766_final-master/caffenet/display_results.m | 2,178 | utf_8 | 56d8711c002a37be00bdae95b1d97e74 | function display_results()
filenames = {'caffenet/finetune_iter_9000_results.txt', ...
'caffenet/caffenet_results.txt'};
filenames = {'prefine_fullcrop.txt', 'postfine_fullcrop.txt'};
% filenames = {'finetune_iter_9000_results.txt', 'postfine_fullcrop.txt'};
map1 = getQuery2Result(filenames{1});
map2 = getQuery2Re... |
github | msyamkumar/captioned_image_retrieval_CS766_final-master | featureCombination.m | .m | captioned_image_retrieval_CS766_final-master/combinedModel/featureCombination.m | 6,331 | utf_8 | 1e7d2600246efdf9d349b5ac88f3be1c | function [ output_args ] = featureCombination( ldaMatFile, NNMatFile )
%UNTITLED2 Summary of this function goes here
% Detailed explanation goes here
%% Load filenames of train/test set
% Load image filenames of train/dev/test sets
filenames = {'Flickr_8k.trainImages.txt', ...
'Flickr_8k.devImages.txt', ...
... |
github | msyamkumar/captioned_image_retrieval_CS766_final-master | display_joint_model_results.m | .m | captioned_image_retrieval_CS766_final-master/combinedModel/display_joint_model_results.m | 1,865 | utf_8 | 8f5ee95e207358358e6f396f4235f882 | function display_joint_model_results()
load('text_further_inds.mat', 'text_further_inds');
load('visual_further_inds.mat', 'visual_further_inds');
load('equal_inds.mat', 'equal_inds');
%% Load image filenames of train/dev/test sets
filenames = {'Flickr_8k.trainImages.txt', ...
'Flickr_8k.devImages.txt', ...
'... |
github | msyamkumar/captioned_image_retrieval_CS766_final-master | sp_dense_sift.m | .m | captioned_image_retrieval_CS766_final-master/SpatialPyramid/sp_dense_sift.m | 4,103 | utf_8 | c56a4af8fe7c5c5f975ca04a0cb6a974 | function [sift_arr, grid_x, grid_y] = sp_dense_sift(I, grid_spacing, patch_size)
% Original script by Svetlana Lazebnick
% Adapted by Antonio Torralba: modified using convolutions to speed up the computations.
% And brought back into Svetlana's library
if(~exist('grid_spacing','var'))
grid_spacing = 1;
end
... |
github | msyamkumar/captioned_image_retrieval_CS766_final-master | sp_find_sift_grid.m | .m | captioned_image_retrieval_CS766_final-master/SpatialPyramid/sp_find_sift_grid.m | 4,187 | utf_8 | 029daeb5a3d4d49bb26b0e1a64cc4b97 | function sift_arr = sp_find_sift_grid(I, grid_x, grid_y, patch_size, sigma_edge)
% parameters
num_angles = 8;
num_bins = 4;
num_samples = num_bins * num_bins;
alpha = 9;
if nargin < 5
sigma_edge = 1;
end
angle_step = 2 * pi / num_angles;
angles = 0:angle_step:2*pi;
angles(num_angles+1) = []; % bin ... |
github | polarch/Spherical-Harmonic-Transform-master | w3j_stirling.m | .m | Spherical-Harmonic-Transform-master/w3j_stirling.m | 2,347 | utf_8 | 83c6c8c96f356c010b8b3722c5ef4cd3 | function w = w3j_stirling(j1, j2, j3, m1, m2, m3)
% W3J_STIRLING Computes Wigner-3j symbols using Stirling's approximation.
%
% W3J_STIRLING computes the Wigner 3j symbol through the Racah formula found
% in
% http://mathworld.wolfram.com/Wigner3j-Symbol.html, Eq.7.
%
% with the difference that it uses ... |
github | polarch/Spherical-Harmonic-Transform-master | sym_w3j.m | .m | Spherical-Harmonic-Transform-master/sym_w3j.m | 2,219 | utf_8 | 064ae516c01c2c5ff2061c83c5975292 | function w = sym_w3j(j1, j2, j3, m1, m2, m3)
% SYM_W3J Returns the Wigner-3j symbols in symbolic form.
%
% W3J computes the Wigner 3j symbol through the Racah formula in
% symbolic (exact) form. It is extremely slow, useful however for
% testing of numerical results.
%
% http://mathworld.wolfram.com/Wigner3j... |
github | polarch/Spherical-Harmonic-Transform-master | getSHrotMtx.m | .m | Spherical-Harmonic-Transform-master/getSHrotMtx.m | 5,690 | utf_8 | 23fd02205c71cc28c952c77ae8f8625f | function R = getSHrotMtx(Rxyz, L, basisType)
%GETSHROTMTX Rotation matrices of real/complex spherical harmonics
% GETSHROTMTX computes the rotation matrices for real spherical
% harmonics according to the recursive method of Ivanic and Ruedenberg,
% as can be found in
%
% Ivanic, J., Ruedenberg, K. (1... |
github | haefnerlab/monkey-learning-master | Convert_Sampling_Output.m | .m | monkey-learning-master/Convert_Sampling_Output.m | 4,192 | utf_8 | f91b8fd16dccfb4683b78ea72e39618c | function [ pops_task ] = Convert_Sampling_Output(e, n_pops, n_neurons, n_trials, n_contrasts_task)
%CONVERT_SAMPLING_OUTPUT convert from the struct output from the sampling
%model to a populations struct that looks like pops_task and pops_fix as
%used elsewhere in this repository
%
% [ pops_task ] = CONVERT_SAMPLING_OU... |
github | haefnerlab/monkey-learning-master | Compute_fPrime_stimulus_means.m | .m | monkey-learning-master/Compute_fPrime_stimulus_means.m | 1,123 | utf_8 | af0e10e0443b93242aea9a6cbd22c4d2 | function [ populations ] = Compute_fPrime_stimulus_means( populations, recompute )
%Compute_fPrime compute f' (change in mean response with change in
%stimulus) and return modified populations with new .fprime_stimulus_mean vector
%
%Also computes sensitivity; the p-value that each f' value is different
%from zero is s... |
github | haefnerlab/monkey-learning-master | Match_Corresponding_Populations.m | .m | monkey-learning-master/Match_Corresponding_Populations.m | 2,321 | utf_8 | 25581efd2a2de164c336d37363fcffdd | function [ ptasks, pfixs ] = Match_Corresponding_Populations( ptasks, pfixs )
%MATCH_CORRESPONDING_POPULATIONS align 'task' and 'fixation' populations
%
% I.E. we keep only sessions and cells shared across both fixation and task
% datasets
%
% NOTE none of the 'additional' fields are filtered here like
% 'tuning_vm' or... |
github | haefnerlab/monkey-learning-master | Compute_fPrime_bestfit.m | .m | monkey-learning-master/Compute_fPrime_bestfit.m | 753 | utf_8 | e854b5c27769034ed53537d38e3e872c | function [ populations_task ] = Compute_fPrime_bestfit(populations_task, populations_fixation, recompute)
%Compute_fPrime_bestfit compute f' as change-in-tuning-curve-values at the
% two stimulus values.
%
% note: Fit_Population_TuningCurves takes a long time. Use parpool if
% possible
if nargin < 3, recompute = fals... |
github | haefnerlab/monkey-learning-master | Compute_Choice_Probabilities.m | .m | monkey-learning-master/Compute_Choice_Probabilities.m | 1,443 | utf_8 | 6c8b1a8487b496744766a55399f13010 | function [ pops_task ] = Compute_Choice_Probabilities( pops_task, zscore )
%COMPUTE_CHOICE_PROBABILITY compute a 'cp' field for each population: an
%array with one element per neuron, the choice probability of that neuron
%
%choice probability indirectly measures how correlated a single neuron is
%with the subject's *c... |
github | haefnerlab/monkey-learning-master | Split_Conditions.m | .m | monkey-learning-master/Split_Conditions.m | 2,671 | utf_8 | 442acc7fa5aadbea3f74d8090ef36437 | function [ pops_task, pops_fix ] = Split_Conditions( pops_task, pops_fix )
%SPLIT_CONDITIONS split each population's spike Rates into seven
%conditions:
% - pop.spikeCounts_stimA = spike counts on trials with one sign stim
% - pop.spikeCounts_stimB = spike counts on trials with other sign stim
% - pop.spikeCounts_stim0... |
github | haefnerlab/monkey-learning-master | test_sampling_prediction.m | .m | monkey-learning-master/test_sampling_prediction.m | 2,267 | utf_8 | a0f5959360e111e10eca2df9553e9e2f | function params = test_sampling_prediction(delta, n, params)
% generate predictions from sampling simulation
% load preprocessed sampling data
filename = fullfile('data', sprintf('SIM_d%.3f_v%d', delta, n), 'preprocessed.mat');
if ~exist(filename, 'file')
error('no sampling model prediction d=%f n=%d', delta, n);
... |
github | haefnerlab/monkey-learning-master | ndtriu.m | .m | monkey-learning-master/+Util/ndtriu.m | 583 | utf_8 | 26d25aacd7e3f62210205c498e7f7d89 | function [ indices ] = ndtriu( size, k )
%NDTRIU Get 'upper triangular' indices for an nd-array of the given size
if nargin < 2, k=0; end
ndims = length(size);
if ndims == 1
indices = 1:size(1);
else
indices = arrayfun(@(i) is_unique_index(size,i,k), 1:prod(size));
end
end
function [ unq ] = is_unique_inde... |
github | haefnerlab/monkey-learning-master | analyze_task_offset.m | .m | monkey-learning-master/+scripts/analyze_task_offset.m | 14,585 | utf_8 | 295268e74b77abf7c7ed45aa6b6d0474 | function [all_correlations, all_pvalues, rot_sym_offsets] = analyze_task_offset( params, memo_file, recompute, figpath )
%ANALYZE_TASK_OFFSET complementary to analyze_scatter_moments,
% this function compares a "choice-triggered" (zero-stimulus) moment with
% f' for different notions of f' to see if the statistical mom... |
github | haefnerlab/monkey-learning-master | fitVonMises.m | .m | monkey-learning-master/+TuningCurves/fitVonMises.m | 3,999 | utf_8 | 3fea9ad96e9b33c9ec8c533d9eed57a4 | function [ best_params, curve, best_map, worst_params, worst_map ] = fitVonMises( orientations, spikeCounts, use_map, n_init, trialDurSecs )
%FITVONMISES ML or MAP fit a von Mises tuning curve (see vonMises()) to
% (orientation,rate) data. Assumed poisson variability and some
% mostly-arbitrary priors on parameters
%
%... |
github | isetbio/isetbio-master | ieReadSmallMatrix.m | .m | isetbio-master/isettools/gui/ieReadSmallMatrix.m | 6,510 | utf_8 | 589dfb0455893cebcef34b8ab03732c7 | function gMat = ieReadSmallMatrix(mSize, defMatrix, fmt, prompt, ...
yxPosition, outVarName, cList)
% A GUI to read a small matrix in a structured way
%
% Syntax:
% m = ieReadSmallMatrix(mSize)
%
% Description:
% We create a figure, gMat, that contains edit boxes where the user can
% enter matrix data.
%
% ... |
github | isetbio/isetbio-master | ieFontChangeSize.m | .m | isetbio-master/isettools/gui/font/ieFontChangeSize.m | 5,072 | utf_8 | 72c30468cbe46df5eb0c452b566ebfd3 | function dSize = ieFontChangeSize(fig, dSize)
% Change and apply the font size preference used in an ISET window
%
% Syntax:
% dSize = ieFontChangeSize(fig, [dSize])
%
% Description:
% The font size preference information is stored using the Matlab
% setpref/getpref mechanism. Hence, the size is remembered acro... |
github | isetbio/isetbio-master | ieFontSizeSet.m | .m | isetbio-master/isettools/gui/font/ieFontSizeSet.m | 4,019 | utf_8 | 7ddeb01a6705ee568f3835c1b50202d2 | function fSize = ieFontSizeSet(fig, fSize)
% Set the font size of all the text in the window objects
%
% Syntax:
% fSize = ieFontSizeSet(fig, fSize);
%
% Description:
% Set the font size for all of the text in the window object(s).
%
% The font size is set to all the text in the window. The first textbox
% ... |
github | isetbio/isetbio-master | bipolarWindow.m | .m | isetbio-master/isettools/bipolar/bipolarWindow.m | 13,173 | utf_8 | 4e65203a2e630a3c37a4fd8ba8ab6c2b | function varargout = bipolarWindow(varargin)
% BIPOLARWINDOW MATLAB code for bipolarwindow.fig
% BIPOLARWINDOW, by itself, creates a new BIPOLARWINDOW or raises the existing
% singleton*.
%
% H = BIPOLARWINDOW returns the handle to a new BIPOLARWINDOW or the handle to
% the existing singleton*.
%
% ... |
github | isetbio/isetbio-master | bipolarLayerWindow.m | .m | isetbio-master/isettools/bipolar/bipolarLayerWindow.m | 13,990 | utf_8 | e65524aa7f7553176f57fc72e512c52c | function varargout = bipolarLayerWindow(varargin)
% BIPOLARLAYERWINDOW MATLAB code for bipolarlayerwindow.fig
% BIPOLARLAYERWINDOW, by itself, creates a new BIPOLARLAYERWINDOW or raises the existing
% singleton*.
%
% H = BIPOLARLAYERWINDOW returns the handle to a new BIPOLARLAYERWINDOW or the handle to
%... |
github | isetbio/isetbio-master | emCreate.m | .m | isetbio-master/isettools/eyemovement/emCreate.m | 5,844 | utf_8 | d6367b55e42ff8fad995136f6c4a2fe3 | function em = emCreate(params, varargin)
% Create eye movement structure
%
% Syntax:
% em = emCreate(params, [varargin])
%
% Description:
% Create the structure to contain the eye movement.
%
% Inputs:
% params - eye-movement parameters
% .emFlag - 3x1 flag vector, indicating whether or not to include
... |
github | isetbio/isetbio-master | emGet.m | .m | isetbio-master/isettools/eyemovement/emGet.m | 10,762 | utf_8 | 3f3040cd77c8502b5723405c5efe70bf | function val = emGet(em, params, varargin)
% Get eye movement properties
%
% Syntax:
% val = emGet(em, params, [varargin])
%
% Description:
% The model for generating an eye movement sequence is defined in
% @coneMosaic.emGenSequence.
%
% Retrieve the parameter specified in params.
%
% Inputs:
% em ... |
github | isetbio/isetbio-master | midgetRGCMosaicGeneratorApp.m | .m | isetbio-master/isettools/ganglioncells/midgetRGCMosaicGeneratorApp.m | 22,397 | utf_8 | 84d80f45f806419f6e2118ff3d14b053 | classdef midgetRGCMosaicGeneratorApp < handle
properties (GetAccess=public, SetAccess=private)
% GUI components
mainView;
% Current action to perform
currentPipeline;
% State (mosaic and optics params)
simulation;
end
properties (GetAccess=private, SetAc... |
github | isetbio/isetbio-master | testProductionReadyMRGCmosaic.m | .m | isetbio-master/isettools/ganglioncells/testProductionReadyMRGCmosaic.m | 12,894 | utf_8 | e8ca6573ca400d23da74e12f524ee6f4 | function testProductionReadyMRGCmosaic
% Retrieve the source RGC mosaic
theSourceMidgetRGCMosaic = retrieveSourceRGCMosaic();
% Instantiate a compute-ready optimized mRGCMosaic located
% at (x,y) = (1,0.5), with width = 0.4 degs and height = 0.2 degs
mySmallMRGCmosaic = mRGCMosaic(theSourceMidget... |
github | isetbio/isetbio-master | testRTVF.m | .m | isetbio-master/isettools/ganglioncells/testRTVF.m | 8,457 | utf_8 | 7111d03fb850c7f3d35c91d9784fec8c | function testRTVF
computedRTVFobjectExportDirectory = '/Volumes/SSDdisk/MATLAB/toolboxes/isetbio/isettools/ganglioncells/tmp';
% Compute everything
computeRTVF = false;
updateLconeRTVF = true;
updateMconeRTVF = false;
usePreviousRetinalConePoolingParams = true;
positionDegs = [1 ... |
github | isetbio/isetbio-master | visualizeSpatialRFmaps.m | .m | isetbio-master/isettools/ganglioncells/+midgetRGCMosaicInspector/visualizeSpatialRFmaps.m | 7,474 | utf_8 | 2da4954ca41ecdf16dfbd2301f3d0d09 | function visualizeSpatialRFmaps(mosaicCenterParams, maxRGCsNum)
midgetRGCMosaicInspector.say('Visualizing spatial receptive field maps');
% Generate mosaic filename and directory
[mosaicFileName, mosaicDirectory] = midgetRGCMosaicInspector.mosaicFileName(...
mosaicCenterParams);
% Load t... |
github | isetbio/isetbio-master | fitMosaicSTFs.m | .m | isetbio-master/isettools/ganglioncells/+midgetRGCMosaicInspector/fitMosaicSTFs.m | 10,681 | utf_8 | 4233ce05cb9c1164e1f8378f158577c3 | function fitMosaicSTFs(mosaicCenterParams, rfModelParams, opticsParams, maxRGCsNum)
midgetRGCMosaicInspector.say('Fitting mosaic STFs using the difference of Gaussians model');
% Generate the frozen mosaic filename
frozenMosaicFileName = midgetRGCMosaicInspector.frozenMosaicFileName(...
mosaicCen... |
github | isetbio/isetbio-master | renderSTFfitPlots.m | .m | isetbio-master/isettools/ganglioncells/+midgetRGCMosaicInspector/renderSTFfitPlots.m | 10,100 | utf_8 | 6e9aee17999e39ce682086a848d04f04 | function [hFigRcDegs, hFigRsRcRatios, hFigSCintSensRatios] = renderSTFfitPlots(hFigRcDegs, hFigRsRcRatios, hFigSCintSensRatios, ...
theMidgetRGCMosaic, theMeridianAngles, iMeridianAngle, theMeridianFits)
if (iMeridianAngle == 1)
hFigRcDegs = figure(1); clf;
set(hFigRcDegs, 'Position', [10 1... |
github | isetbio/isetbio-master | compareRTVFmodelSTFtoMeasuredSTF.m | .m | isetbio-master/isettools/ganglioncells/+midgetRGCMosaicInspector/compareRTVFmodelSTFtoMeasuredSTF.m | 8,345 | utf_8 | ab92206e7a629c34db2c61fbfe842042 | function compareRTVFmodelSTFtoMeasuredSTF(mosaicCenterParams, rfModelParams, opticsParams)
% Generate mosaic filename and directory
mosaicFileName = midgetRGCMosaicInspector.mosaicFileName(mosaicCenterParams);
% Load the center-connected mosaic
load(mosaicFileName, 'theMidgetRGCmosaic');
theU... |
github | isetbio/isetbio-master | computeRetinalRFcenterMaps.m | .m | isetbio-master/isettools/ganglioncells/@midgetRGCMosaic/computeRetinalRFcenterMaps.m | 3,903 | utf_8 | 0e2f5f637eccafec9349c2158e0a7029 | function retinalRFcenterMaps = computeRetinalRFcenterMaps(obj, marginDegs, spatialSupportSamplesNum, varargin)
% Parse input
p = inputParser;
p.addParameter('forRGCindices', [], @(x)(isempty(x)||isnumeric(x)));
p.parse(varargin{:});
% Which RGC indices to compute retinal RFcenter maps for
... |
github | isetbio/isetbio-master | R2VFTobjectsOLD.m | .m | isetbio-master/isettools/ganglioncells/@midgetRGCMosaic/R2VFTobjectsOLD.m | 10,891 | utf_8 | 3b3a7702939e9eb7dc2ba3990bb2dde6 | function [RTVFTobjList, ...
theSamplingPositionGrid, ...
theConesNumPooledByTheRFcenterGrid, ...
theVisualSTFSurroundToCenterRcRatioGrid, ...
theVisualSTFSurroundToCenterIntegratedSensitivityRatioGrid] = R2VFTobjectsOLD(...
RTVobjIndicesToBeComputed, ...
... |
github | isetbio/isetbio-master | visualizeSingleRetinalRF.m | .m | isetbio-master/isettools/ganglioncells/@midgetRGCMosaic/visualizeSingleRetinalRF.m | 3,435 | utf_8 | a9072cfb49e7aa9d396d02a450380fed | function visualizeSingleRetinalRF(obj,theRGCindex, varargin)
% Parse input
p = inputParser;
p.addParameter('figureHandle', [], @(x)(isempty(x)||isa(x, 'handle')));
p.addParameter('axesHandle', [], @(x)(isempty(x)||isa(x, 'handle')));
p.addParameter('fontSize', 16, @isscalar);
p.addParameter('plo... |
github | isetbio/isetbio-master | visualizeSpatialRFs.m | .m | isetbio-master/isettools/ganglioncells/@midgetRGCMosaic/visualizeSpatialRFs.m | 24,449 | utf_8 | 4ac0e083162149eb64b21cea3f51e8fb | function [hFig, allAxes] = visualizeSpatialRFs(obj, varargin)
% Parse input
p = inputParser;
p.addParameter('maxVisualizedRFs', 18, @(x)(isempty(x) || isscalar(x)));
p.addParameter('visualizedRFspatialExtent', [], @(x)(isempty(x) || isscalar(x)));
p.addParameter('onlyForRGCwithIndex', [], @(x)(isem... |
github | isetbio/isetbio-master | visualizeRetinalRFs.m | .m | isetbio-master/isettools/ganglioncells/@midgetRGCMosaic/visualizeRetinalRFs.m | 17,618 | utf_8 | a1e158f9800d5bcb7942341c41bcb043 | function visualizeRetinalRFs(obj, varargin)
% Parse input
p = inputParser;
p.addParameter('exportGraphicForEachRF', false, @islogical);
p.addParameter('maxExportedGraphs', 15, @isnumeric);
p.addParameter('spatialSupportSamplesNum', 256, @isnumeric);
p.addParameter('showInputConeMosaic', true, @i... |
github | isetbio/isetbio-master | computeResponsesByPoolingConeResponses.m | .m | isetbio-master/isettools/ganglioncells/@midgetRGCMosaic/computeResponsesByPoolingConeResponses.m | 4,441 | utf_8 | 7b81a66fe518eeb3526fc423f4274ee5 | function [midgetRGCresponses, midgetRGCresponseTemporalSupport] = computeResponsesByPoolingConeResponses(...
obj, coneMosaicResponses, coneMosaicResponseTemporalSupport)
assert(ndims(coneMosaicResponses) == 3, ...
'midgetRGCMosaic.computeResponsesByPoolingConeResponses: coneMosaicResponses must hav... |
github | isetbio/isetbio-master | generateCenterSurroundSpatialPoolingRFs.m | .m | isetbio-master/isettools/ganglioncells/@midgetRGCMosaic/generateCenterSurroundSpatialPoolingRFs.m | 16,491 | utf_8 | 05fed773ce2676a342414fec79c4e53a | function generateCenterSurroundSpatialPoolingRFs(obj, theRTVFobjList , ...
theSamplingPositionGrid, theConesNumPooledByTheRFcenterGrid, ...
theVisualSTFSurroundToCenterRcRatioGrid, ...
theVisualSTFSurroundToCenterIntegratedSensitivityRatioGrid)
obj.theRTVFobjList = theRTVFobjLis... |
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