plateform stringclasses 1
value | repo_name stringlengths 13 113 | name stringlengths 3 74 | ext stringclasses 1
value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | lijunzh/fd_elastic-master | L1GeneralOrthantWise.m | .m | fd_elastic-master/src/PQN/L1General/L1GeneralOrthantWise.m | 5,512 | utf_8 | 5e4e4772d674902f60c0bf7ef517fd4f | function [w,fEvals] = L1GeneralOrthantWise(gradFunc,w,lambda,params,varargin)
%
% computes argmin_w: gradFunc(w,varargin) + sum lambda.*abs(w)
%
% Method used:
% Orthant-Wise Regression
%
% Parameters
% gradFunc - function of the form gradFunc(w,varargin{:})
% w - initial guess
% lambda - scale of L1 ... |
github | lijunzh/fd_elastic-master | L1GeneralProjection.m | .m | fd_elastic-master/src/PQN/L1General/L1GeneralProjection.m | 5,083 | utf_8 | 268e1936eb38842e70a15acc9b06b47f | function [w,fEvals] = L1GeneralProjection(gradFunc,w,lambda,params,varargin)
%
% computes argmin_w: gradFunc(w,varargin) + sum lambda.*abs(w)
%
% Method used:
% Two-Metric Projection method w/ non-negative variables
%
% Parameters
% gradFunc - function of the form gradFunc(w,varargin{:})
% w - initial gu... |
github | lijunzh/fd_elastic-master | logdetFunction.m | .m | fd_elastic-master/src/PQN/GGM/logdetFunction.m | 442 | utf_8 | e089e4642ff4aaaf50516b6488a257a1 | function [f,g] = logdetFunction(w,sigma)
n = size(sigma,1);
w = reshape(w,[n,n]);
f = -logdet(sigma + w,-Inf);
if ~isinf(f)
g = -inv(sigma + w);
else
g = zeros(size(sigma));
end
g = g(:);
global trace
if trace == 1
global fValues
fValues(end+1,1) = -f;
drawnow
end
end
funct... |
github | lijunzh/fd_elastic-master | drawGraph.m | .m | fd_elastic-master/src/PQN/KPM/drawGraph.m | 46,847 | utf_8 | d2429b94526ebcbca9649f90cd0a6b9d | function drawGraph(adj, varargin)
% drawGraph Automatic graph layout: interface to Neato (see http://www.graphviz.org/)
%
% drawGraph(adjMat, ...) draws a graph in a matlab figure
%
% Optional arguments (string/value pair) [default in brackets]
%
% labels - labels{i} is a *string* for node i [1:n]
% removeSelf... |
github | lijunzh/fd_elastic-master | process_options.m | .m | fd_elastic-master/src/PQN/KPM/process_options.m | 4,394 | utf_8 | 483b50d27e3bdb68fd2903a0cab9df44 | % PROCESS_OPTIONS - Processes options passed to a Matlab function.
% This function provides a simple means of
% parsing attribute-value options. Each option is
% named by a unique string and is given a default
% value.
%
% Usage: [var1, var2, ...... |
github | lijunzh/fd_elastic-master | UGM_Sample_Gibbs.m | .m | fd_elastic-master/src/PQN/UGM/sample/UGM_Sample_Gibbs.m | 1,407 | utf_8 | ada59ec0acf3f3aa12d629909aa9ba4b | function [samples] = UGM_Sample_Gibbs(nodePot,edgePot,edgeStruct,burnIn,y)
% Single Site Gibbs Sampling
if nargin < 5
% Initialize
[junk y] = max(nodePot,[],2);
end
if edgeStruct.useMex
samples = UGM_Sample_GibbsC(nodePot,edgePot,int32(edgeStruct.edgeEnds),int32(edgeStruct.nStates),int32(edgeStruct.V),int32(edgeS... |
github | lijunzh/fd_elastic-master | UGM_Sample_Exact.m | .m | fd_elastic-master/src/PQN/UGM/sample/UGM_Sample_Exact.m | 1,876 | utf_8 | 3bca2622a6fc60f6cb4da8101e748893 | function [samples] = UGM_Sample_Exact(nodePot,edgePot,edgeStruct)
% Exact sampling
[nNodes,maxState] = size(nodePot);
nEdges = size(edgePot,3);
edgeEnds = edgeStruct.edgeEnds;
nStates = edgeStruct.nStates;
maxIter= edgeStruct.maxIter;
samples = zeros(nNodes,0);
Z = computeZ(nodePot,edgePot,edgeEnds,nStates);
for s ... |
github | lijunzh/fd_elastic-master | UGM_Infer_Exact.m | .m | fd_elastic-master/src/PQN/UGM/infer/UGM_Infer_Exact.m | 1,746 | utf_8 | 36e8e9290900e570414b692a29509b72 | function [nodeBel, edgeBel, logZ] = UGM_Infer_Exact(nodePot, edgePot, edgeStruct)
% INPUT
% nodePot(node,class)
% edgePot(class,class,edge) where e is referenced by V,E (must be the same
% between feature engine and inference engine)
%
% OUTPUT
% nodeBel(node,class) - marginal beliefs
% edgeBel(class,class,e) ... |
github | lijunzh/fd_elastic-master | UGM_makeCRFedgePotentials.m | .m | fd_elastic-master/src/PQN/UGM/sub/UGM_makeCRFedgePotentials.m | 1,989 | utf_8 | 5ad22bbbc0fc345892b37a8ef4e8e102 | function [edgePot] = UGM_makeEdgePotentials(Xedge,v,edgeStruct,infoStruct)
% Makes pairwise class potentials for each node
%
% Xedge(1,feature,edge)
% v(feature,variable,variable) - edge weights
% nStates - number of States per node
%
% edgePot(class1,class2,edge)
if edgeStruct.useMex
% Mex Code
edgeP... |
github | lijunzh/fd_elastic-master | UGM_makeCRFNodePotentials.m | .m | fd_elastic-master/src/PQN/UGM/sub/UGM_makeCRFNodePotentials.m | 1,028 | utf_8 | 6558b6ec74648c9b18d6851c2ae6f7ca | function [nodePot] = UGM_makeCRFnodePotentials(X,w,edgeStruct,infoStruct)
% Makes class potentials for each node
%
% X(1,feature,node)
% w(feature,variable,variable) - node weights
% nStates - number of states per node
%
% nodePot(node,class)
if edgeStruct.useMex
% Mex Code
nNodes = size(X,3);
nSt... |
github | lijunzh/fd_elastic-master | UGM_initWeights.m | .m | fd_elastic-master/src/PQN/UGM/sub/UGM_initWeights.m | 572 | utf_8 | b52adde4da67db63cf469d1eefd6d65f | function [w,v,wLinInd,vLinInd] = UGM_initWeights(infoStruct,initFunc)
% [w,v,wLinInd,vLinInd] = UGM_initWeights(infoStruct,initFunc)
%
% Generates an initial weight vector
%
% X(instance,feature,node)
% Xedge(instance,feature,edge)
% infoStruct: structure containing nStates, tied, and ising
% type - 'random' or 'zero'
... |
github | lijunzh/fd_elastic-master | UGM_MRFLoss.m | .m | fd_elastic-master/src/PQN/UGM/train/UGM_MRFLoss.m | 5,348 | utf_8 | 23a3516c53c625c2451571adbbe4d5e7 | function [f,g] = UGM_MRFLoss(wv,y,edgeStruct,infoStruct,inferFunc,varargin)
% wv(variable)
% X(instance,feature,node)
% Xedge(instance,feature,edge)
% y(instance,node)
% edgeStruct
% inferFunc
% varargin - additional parameters of inferFunc
nNodeFeatures = 1;
nEdgeFeatures = 1;
[nInstances,nNodes] = size(y);
nFeatures... |
github | lijunzh/fd_elastic-master | UGM_PseudoLoss.m | .m | fd_elastic-master/src/PQN/UGM/train/UGM_PseudoLoss.m | 9,070 | utf_8 | fb2b963f6e701ed3ee560190279f043a | function [f,g,H] = UGM_loss(wv,X,Xedge,y,edgeStruct,infoStruct)
% wv(variable)
% X(instance,feature,node)
% Xedge(instance,feature,edge)
% y(instance,node)
% edgeStruct
% inferFunc
% tied
% Form weights
[w,v] = UGM_splitWeights(wv,infoStruct);
% Make Potentials
nodePot = UGM_makeNodePotentials(X,w,edgeStruct,infoStru... |
github | lijunzh/fd_elastic-master | UGM_CRFpseudoLoss.m | .m | fd_elastic-master/src/PQN/UGM/train/UGM_CRFpseudoLoss.m | 9,857 | utf_8 | 4f2c7be5956dc41f8c3d0ffeb9e3cb83 | function [f,g,H] = UGM_loss(wv,X,Xedge,y,edgeStruct,infoStruct)
% wv(variable)
% X(instance,feature,node)
% Xedge(instance,feature,edge)
% y(instance,node)
% edgeStruct
% inferFunc
% tied
% Form weights
[w,v] = UGM_splitWeights(wv,infoStruct);
% Make Potentials
nodePot = UGM_makeCRFNodePotentials(X,w,edgeStruct,infoS... |
github | lijunzh/fd_elastic-master | UGM_makeNodePotentials.m | .m | fd_elastic-master/src/PQN/UGM/potentials/UGM_makeNodePotentials.m | 1,046 | utf_8 | 5a2e8c508b9eacbbf1d0dbcafff5fa91 | function [nodePot] = makeNodePotentials(X,w,edgeStruct,infoStruct)
% Makes class potentials for each node
%
% X(1,feature,node)
% w(feature,variable,variable) - node weights
% nStates - number of states per node
%
% nodePot(node,class)
if edgeStruct.useMex
% Mex Code
nNodes = size(X,3);
nStates = ... |
github | lijunzh/fd_elastic-master | WolfeLineSearch.m | .m | fd_elastic-master/src/PQN/minFunc/WolfeLineSearch.m | 11,395 | utf_8 | 3d2acf1139093fe11df95ccdf888aab8 | function [t,f_new,g_new,funEvals,H] = WolfeLineSearch(...
x,t,d,f,g,gtd,c1,c2,LS,maxLS,tolX,debug,doPlot,saveHessianComp,funObj,varargin)
%
% Bracketing Line Search to Satisfy Wolfe Conditions
%
% Inputs:
% x: starting location
% t: initial step size
% d: descent direction
% f: function value at st... |
github | lijunzh/fd_elastic-master | minFunc_processInputOptions.m | .m | fd_elastic-master/src/PQN/minFunc/minFunc_processInputOptions.m | 3,704 | utf_8 | b1c1b56fb4cf20e8a7b44b359a27a792 |
function [verbose,verboseI,debug,doPlot,maxFunEvals,maxIter,tolFun,tolX,method,...
corrections,c1,c2,LS_init,LS,cgSolve,qnUpdate,cgUpdate,initialHessType,...
HessianModify,Fref,useComplex,numDiff,LS_saveHessianComp,...
DerivativeCheck,Damped,HvFunc,bbType,cycle,...
HessianIter,outputFcn,useMex,use... |
github | EdwardDixon/facenet-master | detect_face_v1.m | .m | facenet-master/tmp/detect_face_v1.m | 7,954 | utf_8 | 678c2105b8d536f8bbe08d3363b69642 | % MIT License
%
% Copyright (c) 2016 Kaipeng Zhang
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, including without limitation the rights
% to use, copy, modify, mer... |
github | EdwardDixon/facenet-master | detect_face_v2.m | .m | facenet-master/tmp/detect_face_v2.m | 9,016 | utf_8 | 0c963a91d4e52c98604dd6ca7a99d837 | % MIT License
%
% Copyright (c) 2016 Kaipeng Zhang
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, including without limitation the rights
% to use, copy, modify, mer... |
github | nikos-kekatos/SpaceEx-tutorials-master | plotting_template_bball.m | .m | SpaceEx-tutorials-master/Files/Plotting/MATLAB/examples/Bouncing_Ball/plotting_template_bball.m | 811 | utf_8 | 95ab77bfe873660cdd96c11ff9c9fab8 | % ========================================================================
% This example illustrates the use of Matlab functions for plotting
% flowpipes obtained with SpaceEx. There are two functions:
%
% USAGE:
%
% >> addpath('../../src/')
% >> plotting_template_bball('bball_timed.gen')
%
% If you want to use the si... |
github | nikos-kekatos/SpaceEx-tutorials-master | plotting_template_pendulum.m | .m | SpaceEx-tutorials-master/Files/Plotting/MATLAB/examples/pendulum/plotting_template_pendulum.m | 2,716 | utf_8 | b469a023b66b8639d8cdcaa56d881595 | % ========================================================================
% INTRODUCTION:
%
% This example illustrates the use of Matlab functions for plotting
% flowpipes obtained with SpaceEx. There are two functions:
%
% (i) `gen_to_mat.m` : save the plot as a .mat file.
%
% (ii) `plot_flowpipe.m` : plot the flowpi... |
github | nikos-kekatos/SpaceEx-tutorials-master | plot_flowpipe.m | .m | SpaceEx-tutorials-master/Files/Plotting/MATLAB/src/plot_flowpipe.m | 4,462 | utf_8 | dc6dfc2df1f0aadd10e9543fa0596d96 | function plot_flowpipe(fname, options)
% PLOT_FLOWPIPE Approximate plot of a large sequence of polygons by
% sampling.
%
% INPUT:
%
% "fname" - gen file name if it is in the same folder of this script,
% or the full path otherwise.
%
% "options" - see the code.
%
% EXAMPLES:
%
% Export the gen data to a... |
github | ALandauer/qDIC-master | areaMapping_2D.m | .m | qDIC-master/areaMapping_2D.m | 3,277 | utf_8 | 20e26769dd3d161a11287ddad4a11366 | function I = areaMapping_2D(varargin)
% I = areaMapping(I0,m,u0) symmetrically warps undeformed
% and deformed 2-D images by the displacement field from previous iteration
% using trilinear interpolation.
%
% INPUTS
% -------------------------------------------------------------------------
% I0: cell containing the ... |
github | ALandauer/qDIC-master | flagOutliers_2D.m | .m | qDIC-master/flagOutliers_2D.m | 3,702 | utf_8 | ddb4ec41cea2100a2ee76b4279621524 | function [cc,normFluctValues] = flagOutliers_2D(u,cc,thr,epsilon)
% u = flagOutliers(u,cc,thr,epsilon) removes outliers using the universal
% outlier test based on
%
% J. Westerweel and F. Scarano. Universal outlier detection for PIV data.
% Exp. Fluids, 39(6):1096{1100, August 2005. doi: 10.1007/s00348-005-0016-6
%
% ... |
github | ALandauer/qDIC-master | mirt2D_mexinterp.m | .m | qDIC-master/mirt2D_mexinterp.m | 1,421 | utf_8 | dfd88fbc1da9b7dd1ebdebd7550e746e | %MIRT2D_MEXINTERP Fast 2D linear interpolation
%
% ZI = mirt2D_mexinterp(Z,XI,YI) interpolates 2D image Z at the points with coordinates XI,YI.
% Z is assumed to be defined at regular spaced points 1:N, 1:M, where [M,N]=size(Z).
% If XI,YI values are outside the image boundaries, put NaNs in ZI.
%
% The performance... |
github | ALandauer/qDIC-master | funIDIC.m | .m | qDIC-master/funIDIC.m | 6,999 | utf_8 | 497a1c899f871561f66a8339663f6529 | function [u, cc, dm, m, tSwitch] = funIDIC(varargin)
% u = funIDIC(filename, sSize, sSizeMin, runMode) is the main function that performs
% IDIC on a time series of images.
%
% INPUTS
% -------------------------------------------------------------------------
% filename: string for the filename prefix for the images ... |
github | ALandauer/qDIC-master | filterDisplacements_2D.m | .m | qDIC-master/filterDisplacements_2D.m | 2,096 | utf_8 | 4981dbc2aeac0aabf27a93a89aaf7701 | function u = filterDisplacements_2D(u0,filterSize,z)
% I = filterDisplacements(I0,filterSize,z) applies a low-pass convolution
% filter to the displacement field to mitigate divergence based on
%
% F. F. J. Schrijer and F. Scarano. Effect of predictor corrector filtering
% on the stability and spatial resolution of ite... |
github | ALandauer/qDIC-master | IDIC.m | .m | qDIC-master/IDIC.m | 9,851 | utf_8 | b58b5a6a6108a0052bc17280a07fe1f1 | function [u, cc, dm, mFinal, decorrFlag] = IDIC(varargin)
%
% INPUTS
% -------------------------------------------------------------------------
% I0: cell containing the undeformed, I0{1}, and deformed, I0{2} images
% sSize: interrogation window (subset) size
% sSizeMin: interrogation window (subset) minimum siz... |
github | ALandauer/qDIC-master | DIC.m | .m | qDIC-master/DIC.m | 15,845 | utf_8 | c948e88b9c8f469ad981db5b79bd89cb | function [u, cc] = DIC(varargin)
% [du, cc] = DIC(I,sSize,sSpacing,ccThreshold) estimates
% displacements between two images through digital image
% correlation.
%
% INPUTS
% -------------------------------------------------------------------------
% I: cell containing the undeformed, I{1}, and deformed, I{2} 2-D ima... |
github | SwanLab/Swan-master | test3d_micro_cube.m | .m | Swan-master/test3d_micro_cube.m | 203,004 | utf_8 | f411079847d1d58632af11c52d4c4fc2 | %==================================================================
% General Data File
% Title: Default_title
% Units: SI
% Dimensions: 3D
% Type of problem: Plane_Stress
% Type of Phisics: ELASTIC
% Micro/Macro: MICRO
%
%==================================================================
clc
clo... |
github | SwanLab/Swan-master | PlaneOfResiduals.m | .m | Swan-master/Vigdergauz/Understunding/PlaneOfResiduals.m | 1,518 | utf_8 | 1ef438b0307f2ba91420175d20cca1a9 | function PlaneOfResiduals
rhoOptimal = 0.2;%0.61;
tanXiV = tan(20*pi/80);
%tanXiV = 0.9;
r1_0 = 0.97;
r2_0 = 0.99;
r2o = 0.998709640937773;
r1o = 0.998709640937773;
r0 = [r2o;r1o]
computeResidual(r0,rhoOptimal,tanXiV)
eps = 1e-13;
n1 = 20;
n2 = 20;
r1V = linspace(0.8,1-eps,n1);
r2V = linspace(0.8,1-eps,n2);
fo... |
github | SwanLab/Swan-master | understundingVigdergauz.m | .m | Swan-master/Vigdergauz/Understunding/understundingVigdergauz.m | 7,777 | utf_8 | d1788294d4b548df453add891f916e25 | function understundingVigdergauz
x0 = [0.8,0.8];
fun = @(x) computeEquations(x);
mesh = createMesh;
% problem.objective = fun;
% problem.x0 = x0;
% problem.solver = 'fsolve';
% problem.lb = [0,0];
% problem.ub = [1,1];
% problem.options = optimset('Display','iter');
%
% [x,fsol] = fsolve(problem)
r2o = 0.998709640937... |
github | SwanLab/Swan-master | interpolate3mat.m | .m | Swan-master/Multimaterial/interpolate3mat.m | 1,386 | utf_8 | ac8bc29cc989139c740e7a48298013b5 | function interpolate3mat
X(:,1) = [1 0 0];
X(:,2) = [0 1 0];
X(:,3) = [0 0 1];
xi1Init = 0.4;
xi2Init = 0.01;
alpha1Init = 17;
alpha2Init = 2;
alpha3Init = 4;
rho(1,1) = xi1Init;
rho(2,1) = xi2Init;
rho(3,1) = 1 - rho(1) - rho(2);
gamma(1,1) = alpha1Init;
gamma(2,1) = alpha2Init;
gamma(3,1) = alpha3Init;
figure('... |
github | SwanLab/Swan-master | ExperimentingGraph.m | .m | Swan-master/Graph/ExperimentingGraph.m | 1,017 | utf_8 | 7bdb9a17e749e4ee39708c048c0d1f4f | function ExperimentingGraph
nmax = 280;
G = graph();
G = addnode(G,nmax);
plot(G)
allEdges = nchoosek(1:nmax,2);
allEdgesN = allEdges;
nEdgeMax = size(allEdges,1);
sparsity = 0.05;
nEdge = round(sparsity*nEdgeMax);
for i = 1:nEdge
nPosibleEdges = size(allEdgesN,1);
newEdge = randi(nPosibleEdges);
edge =... |
github | SwanLab/Swan-master | compareInterations.m | .m | Swan-master/Topology Optimization/Applications/compareInterations.m | 1,267 | utf_8 | 898b2a9d8cf583ae62e1bffeb21a0298 | function compareInterations
fCase{1} = 'LatticeExperimentInputCantileverSymmetricMeshSuperEllipsePDE';
folder{1} = '/media/alex/My Passport/LatticeResults/CantileverSymmetricMeshSuperEllipsePDEGradientEpsilonH';
fCase{2} = 'LatticeExperimentInputCantileverSymmetricMeshSuperEllipsePDE';
folder{2} = '/media/alex/My Pa... |
github | SwanLab/Swan-master | compareInterations2.m | .m | Swan-master/Topology Optimization/Applications/compareInterations2.m | 1,151 | utf_8 | 08f836d5383d17f1087d9a83511f384e | function compareInterations2
fCase{1} = 'ExperimentingPlot';
folder{1} = '/media/alex/My Passport/LatticeResults/StressNormSuperEllipseRotation';
fCase{2} = 'ExperimentingPlot';
folder{2} = '/media/alex/My Passport/LatticeResults/StressNormRectangleRotation';
%fCase{3} = 'LatticeExperimentInputCantileverSymmetricM... |
github | SwanLab/Swan-master | PlottinMaxWithPNorm.m | .m | Swan-master/Topology Optimization/Applications/LatticeExperiments/PlottinMaxWithPNorm.m | 2,404 | utf_8 | 3e3e82396177aa8ba1af2ed9930eaf55 | function PlottinMaxWithPNorm
p = 64;
%nIteration = 498;
%fCase = 'ExperimentingPlot';
%folder = ['/home/alex/git-repos/Swan/Output/',fCase];
%nIteration = 545;
%fCase = 'LatticeExperimentInputCantileverSymmetricMeshSuperEllipsePDE';
%folder = '/media/alex/My Passport/LatticeResults/CantileverSymmetricMeshSuperEllips... |
github | SwanLab/Swan-master | plotSuperVsRectangleOptim.m | .m | Swan-master/Topology Optimization/Applications/LatticeExperiments/plotSuperVsRectangleOptim.m | 1,157 | utf_8 | 67ddc0dd208b2a5f59f6e43def2d9d02 | function plotSuperVsRectangleOptim
mCase = 'Middle';
folderR = ['/home/alex/Desktop/ExperimentingPlotRectangle',mCase,'/'];
[iter,cost,maxStress] = obtainCostShapes(folderR);
f = figure();
h{1} = plot(iter,[cost;maxStress]','b');
hline = findobj(gcf, 'type', 'line');
set(hline(1),'LineStyle','--')
folderS = ['/home/... |
github | SwanLab/Swan-master | plotPerimeterComplianceCost.m | .m | Swan-master/Topology Optimization/Applications/PerimeterExperiments/plotPerimeterComplianceCost.m | 992 | utf_8 | 1541075216b1ca60d25a199b97dc5dec | function plotPerimeterComplianceCost
%perCase = 'Total';
perCase = 'Relative';
plotCost(perCase);
end
function plotCost(perCase)
folder = ['/media/alex/My Passport/PerimeterResults/FineMesh/',perCase,'Perimeter01/'];
[iter,compliance,perimeter] = obtainCostShapes(folder);
f = figure();
hold on
pN{1} = plot(iter,0... |
github | SwanLab/Swan-master | MaterialDesignApproachComparison.m | .m | Swan-master/Topology Optimization/Applications/MaterialDesign/MaterialDesignApproachComparison.m | 1,242 | utf_8 | 24c6edbd0f8363c1de5fc2ae22889c67 | function MaterialDesignApproachComparison
matCase = 'Horizontal';
path = '/media/alex/My Passport/MaterialDesign/';
element = {'Tri','Quad'};
desVar = {'Density','LevelSet'};
filter = {'P1';'PDE'};
fCase{1} = 'CompositeMaterialDesign';
folder{1} = fullfile(path,matCase,element,desVar,filter);
fCase{2} = 'Composit... |
github | SwanLab/Swan-master | fixIndex.m | .m | Swan-master/Topology Optimization/Benchmarks/Maintenance/fixIndex.m | 583 | utf_8 | 7d0f4e250640e6f9903ef1790848a787 | function new_name = fixIndex(name,destination_folderpath)
index = 1;
list = updateList(destination_folderpath);
try_name = assembleName(name,index);
for i = 1:length(list)
if strcmpi(try_name,list(i).name)
index = index + 1;
try_name = assembleName(name,index);
... |
github | SwanLab/Swan-master | ImportCases.m | .m | Swan-master/Topology Optimization/Benchmarks/Maintenance/ImportCases.m | 1,368 | utf_8 | e7755aeae6ea77e29d878e9c8f55f7f4 | clear; close all; clc;
%% *************************** IMPORT CASES **************************** %%
% Move cases from an origin folder to a destination folder considering case
% indexes.
origin_folderpath = uigetdir;
destination_superfolderpath = uigetdir;
list = updateList(origin_folderpath);
for i = 1:length(list)
... |
github | SwanLab/Swan-master | removePatternFromFilename.m | .m | Swan-master/Topology Optimization/Benchmarks/Maintenance/removePatternFromFilename.m | 746 | utf_8 | eb96fd35e0b68d8987eefe0d32fbb89c | clear; close all; clc;
%% ***************** REMOVE PATTERN FROM FILENAME CASES **************** %%
% Remove a certain pattern from the name of a file.
folderpath = uigetdir;
list = updateList(folderpath);
pattern_to_remove = 'ORIOL_';
for i = 1:length(list)
old_name = list(i).name;
if contains(old_name,patt... |
github | SwanLab/Swan-master | RunningOneMicrostructureVademecum.m | .m | Swan-master/Topology Optimization/Homogenization/Sources/VadamecumCalculator/RunningOneMicrostructureVademecum.m | 752 | utf_8 | 76b1d3aa121b8db9aafec4290bb1be2d | function RunningOneMicrostructureVademecum
%dSmooth = obtainSettings('SquareMesh4b','Rectangle');
dSmooth = obtainSettings('SmallCircleQ2','SmoothRectangle');
vc = VademecumCellVariablesCalculator(dSmooth);
vc.computeVademecumData()
end
function d = obtainSettings(prefix,freeFemFile)
d = SettingsVademecumCellVariab... |
github | SwanLab/Swan-master | ComparingSymmetry.m | .m | Swan-master/Topology Optimization/Homogenization/Sources/VadamecumCalculator/ComparingSymmetry.m | 770 | utf_8 | 6bce34d7d895f1eb38c40ab27a731a88 | function ComparingSymmetry
incPhi = pi/180;
fileName = 'StressSymmetryTraction';
sPnormT = computeExperiment(incPhi,fileName);
fileName = 'StressSymmetryCompression';
sPnormC = computeExperiment(-incPhi,fileName);
end
function sPnorm = computeExperiment(incPhi,fileName)
txi = pi/2 - 0.2;%1083;
rho = 0.9;
q = 4;... |
github | SwanLab/Swan-master | StressMeshVariation.m | .m | Swan-master/Topology Optimization/Homogenization/Sources/VadamecumCalculator/StressMeshVariation.m | 732 | utf_8 | a969aa16faf878fb7fb8cab9ad3ff016 | function StressMeshVariation
hMesh = [0.1, 0.05, 0.0025, 0.00125];
for imesh = 1:length(hMesh)
dSmooth = obtainSettings(['RectangleStressMeshDependency',num2str((imesh))],'Rectangle',hMesh(imesh));
computeVademecum(dSmooth);
end
end
function computeVademecum(d)
vc = VademecumCellVariablesCalculator(d);
vc.computeVad... |
github | SwanLab/Swan-master | RunningVademecum.m | .m | Swan-master/Topology Optimization/Homogenization/Sources/VadamecumCalculator/RunningVademecum.m | 1,060 | utf_8 | c087a7f4123f17683e191c603552a2c8 | function RunningVademecum
%
% dSmooth = obtainSettings('SuperEllipseQMax');
% dSmooth.smoothingExponentSettings.type = 'Given';
% dSmooth.smoothingExponentSettings.q = 32;
% computeVademecum(dSmooth);
%
% dSmooth = obtainSettings('SuperEllipseQ2');
% dSmooth.smoothingExponentSettings.type = 'Given';
% dSmooth.smooth... |
github | SwanLab/Swan-master | findingVolumeMatch.m | .m | Swan-master/Topology Optimization/Homogenization/Sources/VadamecumCalculator/findingVolumeMatch.m | 422 | utf_8 | 228e5717e73d0b12bb85e910b6d51067 | function findingVolumeMatch
vS = findVolume('SmoothRectangle');
vR = findVolume('Rectangle');
end
function volV = findVolume(micro)
d = load(['/home/alex/git-repos/SwanLab/Swan/Output/',micro,'/',micro,'.mat']);
var = d.d.variables;
mxV = d.d.domVariables.mxV;
myV = d.d.domVariables.myV;
for imx = 1:l... |
github | SwanLab/Swan-master | runningOptimalSuperEllipseExponent.m | .m | Swan-master/Topology Optimization/Homogenization/Sources/VadamecumCalculator/runningOptimalSuperEllipseExponent.m | 475 | utf_8 | 31302449ab211ee6c0af81872433cc84 | function runningOptimalSuperEllipseExponent
s.samplePoints = createSamplePoints();
s.fileName = 'OptimalSuperEllipseExponentDataFromFixedRho';
exponentComputer = OptimalExponentComputer(s);
exponentComputer.compute();
end
function sample = createSamplePoints()
s.type = 'FromMxMy';
sample = SamplePointsCreatorForOpti... |
github | SwanLab/Swan-master | AnalyticalVsNumerical.m | .m | Swan-master/Topology Optimization/Homogenization/Sources/VadamecumCalculator/SmoothingExponentComputer/AnalyticalVsNumerical.m | 1,394 | utf_8 | b57cbc83d117e1270badaadba4371921 | function AnalyticalVsNumerical
v = VademecumReader();
s.vademecum = v;
sE = PonderatedOptimalSuperEllipseComputer(s);
sE.compute();
%t = abs(v.mxV(:,1) - v.myV(:,1)) < 1e-6;
m1 = v.mxV(:,1);
m2 = v.myV(:,1);
q = sE.qMean;
thet = 45;
theta = thet*pi/180;
m1L = @(x) sqrt(x^2*(1+tan(theta)^2));
m2L = @(y) sqrt(y^2*(... |
github | SwanLab/Swan-master | runningSimpleTopOpt.m | .m | Swan-master/SimpleTopOpt/runningSimpleTopOpt.m | 498 | utf_8 | dd5f5b99ad2c9facf9a1b431261ba41a | %% Simple topology optimization example
% Note that the beta term
function runningSimpleTopOpt
s.maxIter = 100;
s.TOL = 1e-12;
s.topOptProblem = createFullTopOptProblem();
solver = SimpleShapeOptimizationSolver(s);
solver.solve();
end
function t = createFullTopOptProblem()
settings = Settings('Example1');
translator... |
github | SwanLab/Swan-master | subsolv.m | .m | Swan-master/TopOptEig/OptimalBucklingColumn/subsolv.m | 5,887 | utf_8 | 4d6cc3eb2f01df75cb6feae665525c62 | % This is the file subsolv.m
%
function [xmma,ymma,zmma,lamma,xsimma,etamma,mumma,zetmma,smma] = ...
subsolv(m,n,epsimin,low,upp,alfa,beta,p0,q0,P,Q,a0,a,b,c,d);
%
% Written in May 1999 by
% Krister Svanberg <krille@math.kth.se>
% Department of Mathematics
% SE-10044 Stockholm, Sweden.
%
% This... |
github | SwanLab/Swan-master | mmasub.m | .m | Swan-master/TopOptEig/OptimalBucklingColumn/mmasub.m | 5,946 | utf_8 | 58dc1410125aaae8d671b426a85f3608 | % This is the file mmasub.m
%
function [xmma,ymma,zmma,lam,xsi,eta,mu,zet,s,low,upp] = ...
mmasub(m,n,iter,xval,xmin,xmax,xold1,xold2, ...
f0val,df0dx,df0dx2,fval,dfdx,dfdx2,low,upp,a0,a,c,d);
%
% Written in May 1999 by
% Krister Svanberg <krille@math.kth.se>
% Department of Mathematics
% SE-100... |
github | SwanLab/Swan-master | bound.m | .m | Swan-master/TopOptEig/OptimalBucklingColumn/bound.m | 6,682 | utf_8 | 813de3962b18bd8c013d954ff9690fc3 |
function [x] = bound(N)
% This algorithm maximizes the least eigenvalue of a clamped-clamped
% column, accounting for the presence of simple or multiple eigenvalues.
% It also illustrates the best strongest profile of that column against
% buckling as well as it gives its buckling modes. Its input variable is N... |
github | SwanLab/Swan-master | vert2lcon.m | .m | Swan-master/polytopes_2017_10_04_v1.9/vert2lcon.m | 5,773 | utf_8 | 3c50975c970cc4f5961715a166e01407 | function [A,b,Aeq,beq]=vert2lcon(V,tol)
%An extension of Michael Kleder's vert2con function, used for finding the
%linear constraints defining a polyhedron in R^n given its vertices. This
%wrapper extends the capabilities of vert2con to also handle cases where the
%polyhedron is not solid in R^n, i.e., where the... |
github | SwanLab/Swan-master | lcon2vert.m | .m | Swan-master/polytopes_2017_10_04_v1.9/lcon2vert.m | 15,372 | utf_8 | adab98b0f5c2f025ba76f9c88f0c8c64 | function [V,nr,nre]=lcon2vert(A,b,Aeq,beq,TOL,checkbounds)
%An extension of Michael Kleder's con2vert function, used for finding the
%vertices of a bounded polyhedron in R^n, given its representation as a set
%of linear constraints. This wrapper extends the capabilities of con2vert to
%also handle cases where the ... |
github | SwanLab/Swan-master | rayTracer.m | .m | Swan-master/gypsilabModified/openRay/rayTracer.m | 3,683 | utf_8 | 1770ad508b72889ee203feed1ff679a2 | function ray = rayTracer(ray,ord,rMax)
%+========================================================================+
%| |
%| OPENRAY - LIBRARY FOR TRI-DIMENSIONAL RAY TRACING |
%| openRay is part of the GYPSILAB toolbox ... |
github | SwanLab/Swan-master | integralEbd.m | .m | Swan-master/gypsilabModified/openEbd/integralEbd.m | 4,100 | utf_8 | fde80394e93a08ea414a874b5f3cdc4a | function [I,loc] = integralEbd(Xdom,Ydom,u,green,k,v,tol)
%+========================================================================+
%| |
%| OPENDOM - LIBRARY FOR NUMERICAL INTEGRATION |
%| openDom is part of th... |
github | SwanLab/Swan-master | besselJRobinzeros.m | .m | Swan-master/gypsilabModified/openEbd/utils/besselJRobinzeros.m | 1,431 | utf_8 | 2743c8f9d8c380a9fa5622efd4db0470 | function [zs] = besselJRobinzeros(c,k,N,freqCenter)
if ~exist('freqCenter','var')
freqCenter = 0;
end
xmin = max(freqCenter - pi*N,0);
xmax = freqCenter + 2*pi*N; % Using zn ~ pi*n
if c == Inf
% Returns the N first zeros of the function
% Jk(x)
zs = AllZeros(@(x)(besselj(k,x)),xmin,xmax,5*(N+freqCente... |
github | SwanLab/Swan-master | RadialQuadrature.m | .m | Swan-master/gypsilabModified/openEbd/radialQuad/RadialQuadrature.m | 12,212 | utf_8 | 4d0c3e0a8e95bc4506e6e8bf419e7105 | classdef RadialQuadrature
% Approximation of a function as a series of the functions (e_i)
% where e_i is the i-th normalized (in H10 norm) eigenfunction of the
% Laplace operator with Dirichlet boundary conditions.
% The approximation takes place on the set a(1) < |x| < a(2) of R^2
% and writes
... |
github | SwanLab/Swan-master | sphereMaxwell.m | .m | Swan-master/gypsilabModified/miscellaneous/sphereMaxwell.m | 5,141 | utf_8 | c2edb73ded59a527e93e4ed4ea6309c0 | function [esTheta, esPhi] = sphereMaxwell(radius, frequency, theta, phi, nMax)
% Compute the complex-value scattered electric far field of a perfectly
% conducting sphere using the mie series. Follows the treatment in
% Chapter 3 of
%
% Ruck, et. al. "Radar Cross Section Handbook", Plenum Press, 1970.
%
% The incide... |
github | SwanLab/Swan-master | mshClean.m | .m | Swan-master/gypsilabModified/openMsh/mshClean.m | 5,217 | utf_8 | ad930c538762f562dbac6b0dabb0bc02 | function mesh = mshClean(mesh,dst)
%+========================================================================+
%| |
%| OPENMSH - LIBRARY FOR MESH MANAGEMENT |
%| openMsh is part of the GYPSILAB toolbox for ... |
github | SwanLab/Swan-master | mshReadPly.m | .m | Swan-master/gypsilabModified/openMsh/mshReadPly.m | 17,258 | utf_8 | d02bba04d191d09e5e18680d30103adf | function [vertex,face] = mshReadPly(filename)
% read_ply - read data from PLY file.
%
% [vertex,face] = read_ply(filename);
%
% 'vertex' is a 'nb.vert x 3' array specifying the position of the vertices.
% 'face' is a 'nb.face x 3' array specifying the connectivity of the mesh.
%
% IMPORTANT: works only for tria... |
github | SwanLab/Swan-master | mshReadStl.m | .m | Swan-master/gypsilabModified/openMsh/mshReadStl.m | 3,520 | utf_8 | 68055d9984fbde771248884d606df172 | function [vtx,elt] = mshReadStl(file)
% STLREAD imports geometry from an STL file into MATLAB.
% FV = STLREAD(FILENAME) imports triangular faces from the ASCII or binary
% STL file idicated by FILENAME, and returns the patch struct FV, with fields
% 'faces' and 'vertices'.
%
% [F,V] = STLREAD(FILENAME) retu... |
github | SwanLab/Swan-master | mshTree.m | .m | Swan-master/gypsilabModified/openMsh/mshTree.m | 7,421 | utf_8 | 4adb5dc5a797e4fae6a0167bb58ed1d3 | function tree = mshTree(mesh,typ,Nlf,fig)
%+========================================================================+
%| |
%| OPENMSH - LIBRARY FOR MESH MANAGEMENT |
%| openMsh is part of the GYPSILAB toolb... |
github | SwanLab/Swan-master | mshMidpoint.m | .m | Swan-master/gypsilabModified/openMsh/mshMidpoint.m | 6,741 | utf_8 | dddacc98db280b2469202b171e440e64 | function [meshr,Ir] = mshMidpoint(mesh,I)
%+========================================================================+
%| |
%| OPENMSH - LIBRARY FOR MESH MANAGEMENT |
%| openMsh is part of the GYPSILAB toolb... |
github | SwanLab/Swan-master | mshReadMsh.m | .m | Swan-master/gypsilabModified/openMsh/mshReadMsh.m | 4,296 | utf_8 | bb060ec4ccb725d2837eac69deaaa6b8 | function [vtx,elt,col,data] = mshReadMsh(filename)
%+========================================================================+
%| |
%| OPENMSH - LIBRARY FOR MESH MANAGEMENT |
%| openMsh is part of the GYPSI... |
github | SwanLab/Swan-master | ffmInteractionsSparse.m | .m | Swan-master/gypsilabModified/openFfm/ffmInteractionsSparse.m | 6,847 | utf_8 | 24a505db7ba4f09c90852a9030e47f92 | function MV = ffmInteractionsSparse(X,Xbox,Y,Ybox,V,Ibox,green,k,edg,tol)
%+========================================================================+
%| |
%| OPENFFM - LIBRARY FOR FAST AND FREE MEMORY CONVOLUTION |
%| openF... |
github | SwanLab/Swan-master | ffmInteractionsSparseHF.m | .m | Swan-master/gypsilabModified/openFfm/ffmInteractionsSparseHF.m | 7,864 | utf_8 | 6e507eb2dc266efb27bf9fd99d6e0304 | function MV = ffmInteractionsSparseHF(X,Xbox,Y,Ybox,V,Ibox,green,k,edg,tol)
%+========================================================================+
%| |
%| OPENFFM - LIBRARY FOR FAST AND FREE MEMORY CONVOLUTION |
%| ope... |
github | SwanLab/Swan-master | domSemiAnalyticInt2D.m | .m | Swan-master/gypsilabModified/openDom/domSemiAnalyticInt2D.m | 3,515 | utf_8 | 2da8f9735e072de96405ed6bcae8e470 | function [logR,rlogR,gradlogR] = domSemiAnalyticInt2D(X,S,n,tau)
%+========================================================================+
%| |
%| OPENDOM - LIBRARY FOR NUMERICAL INTEGRATION |
%| openDom is pa... |
github | SwanLab/Swan-master | createSTL.m | .m | Swan-master/PostProcess/STL/createSTL.m | 1,760 | utf_8 | 52f2112d13e434c8770602ea429328c4 | function createSTL
pathTcl = '/home/alex/Desktop/tclFiles/';
gidPath = '/home/alex/GiDx64/15.0.1/';
resultsFile = '/home/alex/git-repos/FEM-MAT-OO/Output/GrippingTriangleFine_Case_1_1_1/GrippingTriangleFine_Case_1_1_1_12.flavia.res';
writeTclFile(pathTcl,gidPath,resultsFile)
writeExportTclFile(pathTcl,gidPath)
command ... |
github | SwanLab/Swan-master | getSubclasses.m | .m | Swan-master/Other/getSubclasses.m | 7,309 | utf_8 | 47de174f369d7029c2615f02e92929fd | function tb = getSubclasses(rootclass,rootpath)
% GETSUBCLASSES Display all subclasses
%
% GETSUBCLASSES(ROOTCLASS, [ROOTPATH])
% Lists all subclasses of ROOTCLASS and their node dependency.
% ROOTCLASS can be a string with the name of the class, an
% object or a meta.class().
%
% It looks for... |
github | SwanLab/Swan-master | genop.m | .m | Swan-master/Other/Utilities/Multiprod_2009/Testing/genop.m | 3,837 | utf_8 | 2c087f1f1c6d8843c6f5198716d04526 | function z = genop(op,x,y)
%GENOP Generalized array operations.
% GENOP(OP, X, Y) applies the function OP to the arguments X and Y where
% singleton dimensions of X and Y have been expanded so that X and Y are
% the same size, but this is done without actually copying any data.
%
% OP must be a function h... |
github | SwanLab/Swan-master | arraylab133.m | .m | Swan-master/Other/Utilities/Multiprod_2009/Testing/arraylab133.m | 2,056 | utf_8 | 46c91102f1666d2e8a3f0accd7d809ed | function c = arraylab133(a,b,d1,d2)
% Several adjustments to ARRAYLAB13:
% 1) Adjustment used in ARRAYLAB131 was not used here.
% 2) Nested statement used in ARRAYLAB132 was used here.
% 3) PERMUTE in subfunction MBYV was substituted with RESHAPE
% (faster by one order of magnitude!).
ndimsA ... |
github | SwanLab/Swan-master | timing_MX.m | .m | Swan-master/Other/Utilities/Multiprod_2009/Testing/timing_MX.m | 1,472 | utf_8 | 7db26cc2c4954f1026e93f2d0c44139a | function timing_MX
% TIMING_MX Speed of MX as performed by MULTIPROD and by a nested loop.
% TIMING_MX compares the speed of matrix expansion as performed by
% MULTIPROD and an equivalent nested loop. The results are shown in the
% manual (fig. 2).
% Notice that MULTIPROD enables array expansion which... |
github | SwanLab/Swan-master | timing_matlab_commands.m | .m | Swan-master/Other/Utilities/Multiprod_2009/Testing/timing_matlab_commands.m | 7,975 | utf_8 | 5384e23295d7b37d3318825a1d5c3dfe | function timing_matlab_commands
% TIMING_MATLAB_COMMANDS Testing for speed different MATLAB commands.
%
% Main conclusion: RESHAPE and * (i.e. MTIMES) are very quick!
% Paolo de Leva
% University of Rome, Foro Italico, Rome, Italy
% 2008 Dec 24
clear all
% Checking whether needed software exists
if ~exist('bsxfun'... |
github | SwanLab/Swan-master | arraylab13.m | .m | Swan-master/Other/Utilities/Multiprod_2009/Testing/arraylab13.m | 1,913 | utf_8 | 942e4a25270936f264b83f4367d9b7fa | function c = arraylab13(a,b,d1,d2)
% This is the engine used in MULTIPROD 1.3 for these cases:
% PxQ IN A - Rx1 IN B
% PxQ IN A - RxS IN B (slowest)
ndimsA = ndims(a); % NOTE - Since trailing singletons are removed,
ndimsB = ndims(b); % not always NDIMSB = NDIMSA
NsA = d2 - ndimsA; % Number of added trailing si... |
github | SwanLab/Swan-master | ExperimentingAcceleratedShapeOpt.m | .m | Swan-master/ImageProcessing/ExperimentingAccelerationForShapeOptimization/ExperimentingAcceleratedShapeOpt.m | 1,304 | utf_8 | c32b58cf71f0c4f321fc2d30bec70b69 | function ExperimentingAcceleratedShapeOpt()
exp1 = computeStandardCase();
exp2 = computeMomentumCase();
exp3 = computeConstantOne();
nFigures = 5;
fh = figure('units', 'pixels');
hold on
fh.set('Position',[4000 1500 3000 500])
plotSubFigure(nFigures,1,'Cost',exp1.JV,exp2.JV,exp3.JV)
plotSubFigure(nFigures,2,'Li... |
github | SwanLab/Swan-master | learningConvergence.m | .m | Swan-master/ImageProcessing/Old/learningConvergence.m | 2,079 | utf_8 | eda54498760dd18ec103fc1b2b0bf942 | function learningConvergence
k = 10;
iter = 1:200;
lblRate = LowerBoundLipshitzFirstOrder(iter);
lbscRate = lowerBoundStronglyConvexFirstOrder(iter,k);
sRate = subgradientConvergence(iter);
nRate = stronglyConvexNesterovConvergence(iter,k);
qRate = quadraticConvergence(iter,1/k);
lgRate = LipschitzGradientConvergenc... |
github | ha-ha-ha-han/NeuromicsCellDetection-master | registerSixPoints.m | .m | NeuromicsCellDetection-master/+neuroReg/registerSixPoints.m | 4,847 | utf_8 | 84d89f2c880829628c40d20530726a1d | function M_1 = registerSixPoints(pts_1,pts_2)
% This function calculate the M that let the equation
% point_set_2 = M^(-1) * point_set_1 best holds.
% x and y should be 3-by-6 arrays.
% in the neuroReg case, x(2,:) is assumed as zero.
% Because it is 1am and this is the 4th day that I work until 1am during the
% week ... |
github | ha-ha-ha-han/NeuromicsCellDetection-master | cutVolume.m | .m | NeuromicsCellDetection-master/+neuroReg/cutVolume.m | 4,191 | utf_8 | c21ab95b4df9ef6cce89c058b2ca584b | function [data_out,b_plane,b_list_volume] = cutVolume(data,cut_grid,M,d)
% cutVolume cut a slice from the data.
% data_out.x, data_out.y, data_out.value is the output slice.
% b_list is the boundary polygon (5-by-2) in the plane coordination
% b_list1 is the boundary polygon (5-by-3) in the volume coordination
% The sl... |
github | ha-ha-ha-han/NeuromicsCellDetection-master | rotationCorr3.m | .m | NeuromicsCellDetection-master/+neuroReg/rotationCorr3.m | 12,671 | utf_8 | ae693186f8640431e3d90302a603c363 | function TransTable = rotationCorr3(pt_list_slice,pt_list_vol,data_slice,AngleRange,Option,ex_list)
% rotationCorr3 returns the transformation parameters and the corresponding
% correlation functions.
% TransTable = ...
% rotationCorr3(pt_list_slice,pt_list_vol,data_slice,AngleRange,Option,ex_list)
% The transfermation... |
github | ha-ha-ha-han/NeuromicsCellDetection-master | plotTransform.m | .m | NeuromicsCellDetection-master/+neuroReg/plotTransform.m | 7,273 | utf_8 | 346f38d7e1f14d32aee85c590a274023 | function plotTransform(h,TransTable,DataSets,pt_list_vol,pt_list_slice,Option,ex_list,M_icp_s2v,M_icp_v2s,UseMe)
% plotTransform visualize the registration from dataZ to data_slice.
% h specifies the Figure handle to plot in.
% TransTable is a 1-by-7 table. It can be one row from the output of
% rotationCorr3. It recor... |
github | ha-ha-ha-han/NeuromicsCellDetection-master | detectCells2.m | .m | NeuromicsCellDetection-master/+neuroReg/detectCells2.m | 4,844 | utf_8 | 11a89cd603ebc1b64185caae30a5f639 | function [pt_list,pt_area] = detectCells2(data,Option,ex_list)
% detectCells2 detects cells from a slice data.
% [data_out,pt_list] = detectCells2(data,Option,ex_list)
% INPUT:
% data: a slice data with data.x, data.y, data.value (2D only).
% For 3D data, use bwCell3.
% Option: see neuroReg.setOption
% ex_list: points ... |
github | ha-ha-ha-han/NeuromicsCellDetection-master | detectCells3.m | .m | NeuromicsCellDetection-master/+neuroReg/detectCells3.m | 7,994 | utf_8 | 7eba0e222e182ece0386402da4122839 | function [pt_list,pt_area] = detectCells3(data,Option)
% detectCells3 detects cell positions from a ZStack data.
% [pt_list,pt_area] = detectCells3(data,Option)
% ---------
% OUTPUT:
% pt_list: positions of the detected cells (3-by-N array)
% pt_area: volume of each detected cells (1-by-N array)
% ---------
% INPUT
% d... |
github | ha-ha-ha-han/NeuromicsCellDetection-master | rotationCorr3_20170727.m | .m | NeuromicsCellDetection-master/+neuroReg/rotationCorr3_20170727.m | 14,541 | utf_8 | f20cc8f00c80e34760d981c40f935455 | function PeakTable = rotationCorr3(pt_list,data_slice,AngleRange,Option,ex_list)
% rotationCorr3 returns the transformation parameters and the corresponding
% correlation functions.
% The transfermation matrix is from volume to slice.
% OUTPUT:
% PeakTable record the possible transformation parameters and the
% coorela... |
github | ha-ha-ha-han/NeuromicsCellDetection-master | rotationHist3.m | .m | NeuromicsCellDetection-master/+neuroReg/rotationHist3.m | 13,151 | utf_8 | aadff69f7a896a683b1b400a671d2cda | function PeakTable = rotationHist3(pt_list_slice,pt_list_vol,data_slice,AngleRange,Option,ex_list)
% rotationHist3 returns the transformation parameters and the corresponding
% correlation functions using histogram method..
% The transfermation matrix is from volume to slice.
% OUTPUT:
% PeakTable record the possible t... |
github | ha-ha-ha-han/NeuromicsCellDetection-master | correction.m | .m | NeuromicsCellDetection-master/+neuroReg/@PlotSlices/correction.m | 7,284 | utf_8 | 692c480b324e30dab5e20d08685a7fbb | function correction(obj,varargin)
% Set the Upper and Lower limit. It is changeable, but now
% disabled.
% tll=1; %Tolerance lower limit.
%Max of tolerance is 1 and zero tolerance is forbidden.
%Choose this value in between.
% tul=1; %Tolerance upper limit.
%Max of tolerance is 1 and zero tolera... |
github | ha-ha-ha-han/NeuromicsCellDetection-master | area_secant_ph.m | .m | NeuromicsCellDetection-master/+neuroReg/@PlotSlices/private/area_secant_ph.m | 1,303 | utf_8 | 6481a9d3042db7acdb53481253370c1d | function [x,y,r] = area_secant_ph( x_grid , y_grid, x0, y0, x1, y1 )
% Area_secant_ph returns the coodinates of points of a secant of a
%rectangular area defined by x_grid and y_grid.
%% determine the line
k=(y1-y0)/(x1-x0);
reverse_flag=0;
if k>10e4 % in case the line is vertical to x-axis
[x_grid,y_grid]=e... |
github | thk2dth/LocalSmoothing-master | Proposed.m | .m | LocalSmoothing-master/Proposed.m | 3,481 | utf_8 | 579e28d64f4fd10c4032d2e3c3027495 | function [ nrbsPos, nrbsOri] = Proposed( wcs, pe, oe, c )
% Proposed method smooths the tool position and tool
% orientation both in WCS.
% Input:
% wcs, cutter data in WCS.
% pe, position error, in mm.
% oe, orientation error, in rad.
% c, d1/d2. c=0.25, by default.
% Output:
% nrbsPos, inserted B-splines du... |
github | thk2dth/LocalSmoothing-master | Yang.m | .m | LocalSmoothing-master/Yang.m | 2,907 | utf_8 | e6d6d8da4fbb32ac58939ba066de274e | function [ nrbsPos, nrbsRot] = Yang( mcs, pe, oe, mp )
% Yang's method smooths the tool position in WCS and tool
% orientation in MCS.
% Input:
% mcs, cutter data in MCS.
% pe, position error in WCS.
% oe, orientation error in WCS.
% mp, geometric property of machine tool.
% Output:
% nrbsPos, inserted B-spli... |
github | thk2dth/LocalSmoothing-master | Bi.m | .m | LocalSmoothing-master/Bi.m | 2,686 | utf_8 | dfb6f6b7f590a9f043fb61ef5bd1b555 | function [ nrbsTrans, nrbsRot] = Bi( mcs, pe, oe, mp )
% Bi's method smooths the tool position and tool
% orientation both in MCS.
% Input:
% mcs, cutter data in MCS.
% pe, position error in WCS.
% oe, orientation error in WCS.
% mp, geometric property of machine tool.
% Output:
% nrbsTrans, inserted B-spline... |
github | thk2dth/LocalSmoothing-master | SuccessRate.m | .m | LocalSmoothing-master/SuccessRate.m | 2,362 | utf_8 | 3f9c393aa7956fc7ec048dbccd6c7b4f | function [ra, rm, ea, em] = SuccessRate( nrbs, oe, isWCS, cornerIndex )
% Evaluate the success rate of the transition method.
% If the orientation error is under the specified value, i.e., oe,
% the method succeedes.
% Input:
% nrbs, inserted B-splines during orientation smoothing.
% oe, specified orientation error... |
github | thk2dth/LocalSmoothing-master | bspdegelev.m | .m | LocalSmoothing-master/nurbs_toolbox/bspdegelev.m | 20,232 | utf_8 | f3f726254444c07038e088dfbb120a32 | function [ic,ik] = bspdegelev(d,c,k,t)
%
% Function Name:
%
% bspdegevel - Degree elevate a univariate B-Spline.
%
% Calling Sequence:
%
% [ic,ik] = bspdegelev(d,c,k,t)
%
% Parameters:
%
% d : Degree of the B-Spline.
%
% c : Control points, matrix of size (dim,nc).
%
% k : Knot sequenc... |
github | evanrussek/Predictive-Representations-PLOS-CB-2017-master | model_SRDYNA.m | .m | Predictive-Representations-PLOS-CB-2017-master/agents/model_SRDYNA.m | 4,145 | utf_8 | b9bde41bec4a7e50ff64602bbf08bed6 | function model = model_SRDYNA()
model.init = @modelinit;
model.update = @modelupdate;
model.eval = @modeleval;
model.choose = @modelchoose;
model.dyna_update = @dyna_update;
model.qlearn_update = @qlearn_update;
function model = modelinit(model,game,params_in)
n_sa = length(game.sa_to_nextstate);
H = eye(n_s... |
github | evanrussek/Predictive-Representations-PLOS-CB-2017-master | model_SRMB.m | .m | Predictive-Representations-PLOS-CB-2017-master/agents/model_SRMB.m | 4,385 | utf_8 | ca063c120e201d37da8b2964a3a6eaba | function model = model_SRMB()
model.init = @modelinit;
model.update = @modelupdate;
model.eval = @modeleval;
model.choose = @modelchoose;
model.dyna_update = @dyna_update;
model.qlearn_update = @qlearn_update;
function model = modelinit(model,game, params_in)
if nargin < 3
% parameters
model.param.epsilon = .1... |
github | evanrussek/Predictive-Representations-PLOS-CB-2017-master | model_dynaQ3.m | .m | Predictive-Representations-PLOS-CB-2017-master/agents/model_dynaQ3.m | 3,212 | utf_8 | 65c8ca122cbbf62b77b3917514daccc9 | function model = model_dynaQ()
model.init = @modelinit;
model.update = @modelupdate;
model.eval = @modeleval;
model.choose = @modelchoose;
model.dyna_update = @dyna_update;
model.q_update = @q_update;
% store samples, but
function model = modelinit(model,game,params_in)
n_sa = length(game.sa_to_nextstate);
Q = ze... |
github | evanrussek/Predictive-Representations-PLOS-CB-2017-master | model_Qsr_r.m | .m | Predictive-Representations-PLOS-CB-2017-master/agents/model_Qsr_r.m | 4,016 | utf_8 | fb07da7010d4fd773602b1dc76111181 | function model = model_QSR()
model.init = @modelinit;
model.update = @modelupdate;
model.eval = @modeleval;
model.choose = @modelchoose;
model.dyna_update = @dyna_update;
model.qlearn_update = @qlearn_update;
function model = modelinit(model,game)
n_sa = length(game.sa_to_nextstate);
H = eye(n_sa);
H(n_sa,:) = 0;... |
github | evanrussek/Predictive-Representations-PLOS-CB-2017-master | model_SRTD.m | .m | Predictive-Representations-PLOS-CB-2017-master/agents/model_SRTD.m | 2,428 | utf_8 | 5621145bdb915387e3db6b3a711936dc | function model = model_SRTD()
model.init = @modelinit;
model.update = @modelupdate;
model.eval = @modeleval;
model.choose = @modelchoose;
function model = modelinit(model,game, params_in)
if nargin < 3
% parameters
model.param.epsilon = .1;
model.param.sr_alpha = .25;
model.param.w_alpha = .25;
model.... |
github | evanrussek/Predictive-Representations-PLOS-CB-2017-master | model_Vsr_r.m | .m | Predictive-Representations-PLOS-CB-2017-master/agents/model_Vsr_r.m | 2,142 | utf_8 | cd3291fd57ade21cafd7ca1516478814 | function model = model_Vsr()
model.init = @modelinit;
model.update = @modelupdate;
model.eval = @modeleval;
model.choose = @modelchoose;
function model = modelinit(model,game,params_in)
if nargin < 3
% parameters
model.param.epsilon = .1;
model.param.sr_alpha = .25;
model.param.w_alpha = .25;
model.pa... |
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