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 | andrejchenko/oop_matlab-master | onImg_corrected_Pix5_0NN_lambda_0_9.m | .m | oop_matlab-master/Experiments/Sunsal/indian_pines/onImg_corrected_Pix5_0NN_lambda_0_9.m | 756 | utf_8 | e2d21bfbb0818cd30d43dda95d2e3c41 | function onImg_corrected_Pix5_0NN_lambda_0_9()
iter = 100;
avgAccVec = zeros(iter,1);
for a = 1:iter
[obj,nObj,sObj] = setExperimentParameters();
obj.load_Indian_Pines_corrected();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
if(obj.numNeigh > 0)
obj.ass... |
github | andrejchenko/oop_matlab-master | sunsal_without_positivity.m | .m | oop_matlab-master/Experiments/Sunsal/indian_pines/sunsal_without_positivity.m | 747 | utf_8 | 0915e9b8a1dc7ec0095fe1c15af140f7 | function sunsal_without_positivity()
iter = 100;
avgAccVec = zeros(iter,1);
for a = 1:iter
[obj,nObj,sObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
if(obj.numNeigh > 0)
obj.assembleXTrainData(nObj)... |
github | andrejchenko/oop_matlab-master | Div10000_NoUnitVecNorm_Pix5_0NN_lambda_0_9.m | .m | oop_matlab-master/Experiments/Sunsal/indian_pines/Div10000_NoUnitVecNorm_Pix5_0NN_lambda_0_9.m | 831 | utf_8 | 154553288ff3fa7e641e38c46fb91ea6 | function Div10000_NoUnitVecNorm_Pix5_0NN_lambda_0_9()
iter = 100;
avgAccVec = zeros(iter,1);
for a = 1:iter
[obj,nObj,sObj] = setExperimentParameters();
avgAccVec(a) = obj.acc;
end
obj.load_Indian_Pines();
%obj.load_Indian_Pines_no_normalization(); %temporarily ONLY
... |
github | andrejchenko/oop_matlab-master | rawImg_NonUnitvecNorm_pix5_0NN.m | .m | oop_matlab-master/Experiments/Sunsal/indian_pines/rawImg_NonUnitvecNorm_pix5_0NN.m | 819 | utf_8 | b49ec6e596a69d34668d3ab43bf353c4 | function rawImg_NonUnitvecNorm_pix5_0NN()
iter = 100;
avgAccVec = zeros(iter,1);
for a = 1:iter
[obj,nObj,sObj] = setExperimentParameters();
%obj.load_Indian_Pines();
obj.load_Indian_Pines_no_normalization(); %temporarily ONLY
obj.selectXPixPerClassNoUnitNorm_IncludeXNei... |
github | andrejchenko/oop_matlab-master | direct_reflectances_NonUnitVecNorm_Pix5_0NN.m | .m | oop_matlab-master/Experiments/Sunsal/indian_pines/direct_reflectances_NonUnitVecNorm_Pix5_0NN.m | 791 | utf_8 | 94e0b012e173fa6b5b22f02a04fc1581 | function direct_reflectances_NonUnitVecNorm_Pix5_0NN()
iter = 100;
avgAccVec = zeros(iter,1);
for a = 1:iter
[obj,nObj,sObj] = setExperimentParameters();
obj.load_Indian_Pines_direct_reflectance();
obj.selectXPixPerClassNoUnitNorm_IncludeXNeighbours(nObj);
if(obj.numNei... |
github | andrejchenko/oop_matlab-master | direct_reflectances_Pix5_0NN.m | .m | oop_matlab-master/Experiments/Sunsal/indian_pines/direct_reflectances_Pix5_0NN.m | 766 | utf_8 | 6fb0ac57cd1625f4b5cb5e342034c561 | function direct_reflectances_Pix5_0NN()
iter = 100;
avgAccVec = zeros(iter,1);
for a = 1:iter
[obj,nObj,sObj] = setExperimentParameters();
obj.load_Indian_Pines_direct_reflectance();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
if(obj.numNeigh > 0)
obj.a... |
github | andrejchenko/oop_matlab-master | trainAndValData_Pix5_0NN_lambda_0_9.m | .m | oop_matlab-master/Experiments/Sunsal/indian_pines/trainAndValData_Pix5_0NN_lambda_0_9.m | 946 | utf_8 | 178e5ada762e6db7ea9c2848e30e6cb8 | function trainAndValData_Pix5_0NN_lambda_0_9()
avgUnmixingVec = [];
iter = 50;
for a = 1:iter
[obj,nObj,sObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
if(obj.numNeigh > 0)
obj.assembleXTrainData(nObj);
... |
github | andrejchenko/oop_matlab-master | Pix5_0NN_lambda_0_9.m | .m | oop_matlab-master/Experiments/Sunsal/indian_pines/Pix5_0NN_lambda_0_9.m | 798 | utf_8 | a00c767f749955a4a352d5ba18f518ea | function Pix5_0NN_lambda_0_9()
iter = 100;
avgAccVec = zeros(iter,1);
for a = 1:iter
[obj,nObj,sObj] = setExperimentParameters();
obj.load_Indian_Pines();
%obj.load_Indian_Pines_no_normalization(); %temporarily ONLY
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
... |
github | andrejchenko/oop_matlab-master | TrainPixels10_forLearning_KSVD_dictionary.m | .m | oop_matlab-master/Experiments/Sunsal/indian_pines/usingLearnedDictionary/TrainPixels10_forLearning_KSVD_dictionary.m | 1,754 | utf_8 | 5a58d315e6df6e06ac91511ab09aba44 | function TrainPixels10_forLearning_KSVD_dictionary()
avgAccVec = [];
iter = 100;
for a = 1:iter
[obj,nObj,svmObj,sunObj,param] = setExperimentParameters();
%
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
D = obj.trainData'; % should ... |
github | andrejchenko/oop_matlab-master | minimalResiduals.m | .m | oop_matlab-master/Experiments/Sunsal/indian_pines/minimalResiduals/minimalResiduals.m | 960 | utf_8 | fbd11de3a549dc1da4e637c91e8bb592 | function minimalResiduals()
iter = 100;
avgAccVec = zeros(iter,1);
for a = 1:iter
[obj,nObj,sObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
if(obj.numNeigh > 0)
obj.assembleXTrainData(nObj);
... |
github | andrejchenko/oop_matlab-master | svm_5Pix_0NN_pavia.m | .m | oop_matlab-master/Experiments/SVM/pavia_uni/svm_5Pix_0NN_pavia.m | 1,239 | utf_8 | cbb6f1ca69c64008455f9f8b51a98bbf | function svm_5Pix_0NN_pavia()
avgAcc = 0;
iter = 100;
tic;
for a = 1:iter
[obj,nObj,svmObj] = setExperimentParameters();
obj.load_Pavia();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
obj.assembleXTrainData(nObj);
svmClassification(svmObj,obj);
str =... |
github | andrejchenko/oop_matlab-master | DivBy10000_NoUnitVecNorm_5Pix_0NN.m | .m | oop_matlab-master/Experiments/SVM/indian_pines/DivBy10000_NoUnitVecNorm_5Pix_0NN.m | 984 | utf_8 | 026661c8ef08f6f4c3c06212ba313912 | function DivBy10000_NoUnitVecNorm_5Pix_0NN()
avgAcc = 0;
iter = 100;
avgAccVec = zeros(iter,1);
tic;
for a = 1:iter
[obj,nObj,svmObj] = setExperimentParameters();
obj.load_Indian_Pines();
%obj.load_Indian_Pines_direct_reflectance(); %temporarily ONLY
obj.selectXPixPe... |
github | andrejchenko/oop_matlab-master | svm_test_5Pic.m | .m | oop_matlab-master/Experiments/SVM/indian_pines/svm_test_5Pic.m | 747 | utf_8 | 8a65d75ca3bdf64dd64ec1c15f6b3987 | function svm_5Pix_0NN()
[obj,nObj,svmObj] = setExperimentParameters();
%obj.load_Indian_Pines();
%obj.selectXPixPerClass_IncludeXNeighbours(nObj);
load('trainData');
load('trainLabels');
load('testData');
load('testLabels');
obj.trainData = trainData;
obj.trainLabels = tra... |
github | andrejchenko/oop_matlab-master | direct_reflectances_svm_5Pix_0NN.m | .m | oop_matlab-master/Experiments/SVM/indian_pines/direct_reflectances_svm_5Pix_0NN.m | 973 | utf_8 | 2b659f345d3e002f71f08185d4b1bc7e | function direct_reflectances_svm_5Pix_0NN()
avgAcc = 0;
iter = 100;
avgAccVec = zeros(iter,1);
tic;
for a = 1:iter
[obj,nObj,svmObj] = setExperimentParameters();
%obj.load_Indian_Pines();
obj.load_Indian_Pines_direct_reflectance(); %temporarily ONLY
obj.selectXPixPer... |
github | andrejchenko/oop_matlab-master | direct_reflectances_LibSVm_Norm_5Pix_00NN.m | .m | oop_matlab-master/Experiments/SVM/indian_pines/direct_reflectances_LibSVm_Norm_5Pix_00NN.m | 994 | utf_8 | c6f657d6c24a355bd4f548ab7ba7daa8 | function direct_reflectances_LibSVm_Norm_5Pix_00NN()
avgAcc = 0;
iter = 100;
avgAccVec = zeros(iter,1);
tic;
for a = 1:iter
[obj,nObj,svmObj] = setExperimentParameters();
%obj.load_Indian_Pines();
obj.load_Indian_Pines_direct_reflectance(); %temporarily ONLY
obj.sele... |
github | andrejchenko/oop_matlab-master | svm_libSVM_nom_5Pix_0NN.m | .m | oop_matlab-master/Experiments/SVM/indian_pines/svm_libSVM_nom_5Pix_0NN.m | 964 | utf_8 | 2d7c98fcc517320acdba8fa0805a3777 | function svm_libSVM_nom_5Pix_0NN()
avgAcc = 0;
iter = 100;
avgAccVec = zeros(iter,1);
tic;
for a = 1:iter
[obj,nObj,svmObj] = setExperimentParameters();
%obj.load_Indian_Pines();
obj.load_Indian_Pines_no_normalization(); %temporarily ONLY
obj.selectXPixelPerClass_16B... |
github | andrejchenko/oop_matlab-master | direct_reflectances_NoUnitNorm_svm_5Pix_0NN.m | .m | oop_matlab-master/Experiments/SVM/indian_pines/direct_reflectances_NoUnitNorm_svm_5Pix_0NN.m | 994 | utf_8 | 42d5cfd849e1193355a9baa9b5525148 | function direct_reflectances_NoUnitNorm_svm_5Pix_0NN()
avgAcc = 0;
iter = 100;
avgAccVec = zeros(iter,1);
tic;
for a = 1:iter
[obj,nObj,svmObj] = setExperimentParameters();
%obj.load_Indian_Pines();
obj.load_Indian_Pines_direct_reflectance(); %temporarily ONLY
obj.se... |
github | andrejchenko/oop_matlab-master | svm_5Pix_0NN.m | .m | oop_matlab-master/Experiments/SVM/indian_pines/svm_5Pix_0NN.m | 941 | utf_8 | 28c86447307030e3d52c538b470a1e5a | function svm_5Pix_0NN()
avgAcc = 0;
iter = 100;
avgAccVec = zeros(iter,1);
tic;
for a = 1:iter
[obj,nObj,svmObj] = setExperimentParameters();
obj.load_Indian_Pines();
%obj.load_Indian_Pines_no_normalization(); %temporarily ONLY
obj.selectXPixPerClass_IncludeXNeighbou... |
github | andrejchenko/oop_matlab-master | rawImg_NoUnitVecNorm_5Pix_0NN.m | .m | oop_matlab-master/Experiments/SVM/indian_pines/rawImg_NoUnitVecNorm_5Pix_0NN.m | 1,031 | utf_8 | 873d7a8eb26c9fccd0d3486c31c1c8b5 | function rawImg_NoUnitVecNorm_5Pix_0NN()
avgAcc = 0;
iter = 100;
avgAccVec = zeros(iter,1);
tic;
for a = 1:iter
[obj,nObj,svmObj] = setExperimentParameters();
obj.load_Indian_Pines_no_normalization();
%obj.load_Indian_Pines();
%obj.load_Indian_Pines_direct_reflectanc... |
github | andrejchenko/oop_matlab-master | DivBy10000_LibSVM_Norm_5Pix_0NN.m | .m | oop_matlab-master/Experiments/SVM/indian_pines/DivBy10000_LibSVM_Norm_5Pix_0NN.m | 984 | utf_8 | 40e94521746e8236271d1271f611326d | function DivBy10000_LibSVM_Norm_5Pix_0NN()
avgAcc = 0;
iter = 100;
avgAccVec = zeros(iter,1);
tic;
for a = 1:iter
[obj,nObj,svmObj] = setExperimentParameters();
obj.load_Indian_Pines();
%obj.load_Indian_Pines_direct_reflectance(); %temporarily ONLY
obj.selectXPixelPe... |
github | andrejchenko/oop_matlab-master | extractDataFrom100Runs.m | .m | oop_matlab-master/Experiments/SVM/indian_pines/extractDataFrom100Runs.m | 446 | utf_8 | 714c28fed58f7b92fba9ee3c9651914c | function extractDataFrom100Runs()
iter = 100;
tic;
for a = 1:iter
[obj,nObj,svmObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
end
end
function [obj,nObj,svmObj] = setExperimentParameters()
obj = Utils;
... |
github | andrejchenko/oop_matlab-master | svm_5Pix_0NN_onImg_corrected.m | .m | oop_matlab-master/Experiments/SVM/indian_pines/svm_5Pix_0NN_onImg_corrected.m | 967 | utf_8 | 6be1e2bd3917ca3a3edfa4b5f1bb29f9 | function svm_5Pix_0NN_onImg_corrected()
avgAcc = 0;
iter = 100;
avgAccVec = zeros(iter,1);
tic;
for a = 1:iter
[obj,nObj,svmObj] = setExperimentParameters();
obj.load_Indian_Pines_corrected();
%obj.load_Indian_Pines_no_normalization(); %temporarily ONLY
obj.selectXPi... |
github | andrejchenko/oop_matlab-master | trainAndValData_5Pix_0NN.m | .m | oop_matlab-master/Experiments/SVM/indian_pines/trainAndValData_5Pix_0NN.m | 938 | utf_8 | ebafa113ce9c564e609aa687b34a32e7 | function trainAndValData_5Pix_0NN
avgUnmixing = [];
iter = 50;
for a = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
obj.extractValidationData();
if(obj.numNeigh > 0)
ob... |
github | andrejchenko/oop_matlab-master | svmKernelClassify.m | .m | oop_matlab-master/Experiments/SVM/indian_pines/svmKernelClassify.m | 1,105 | utf_8 | 77a9954c523572fe01af73431e076944 | function svmKernelClassify()
avgAcc = 0;
iter = 100;
tic;
for a = 1:iter
[obj,nObj,svmObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
svmObj.svmKernelClassify(obj);
svmAccuracy = svmObj.acc... |
github | andrejchenko/oop_matlab-master | ls_svm_5Pix.m | .m | oop_matlab-master/Experiments/ls_svm/indian_pines/ls_svm_5Pix.m | 1,263 | utf_8 | b9a877825aa5668adcfe9a4cc285618d | function ls_svm_5Pix()
avgAccVec = [];
iter = 100;
for i = 1:iter
type = 'classification';
L_fold = 10;
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
model = initlssvm(obj.trainData,obj.trainLabels,type,[],[],'... |
github | andrejchenko/oop_matlab-master | reflectanceImg_mlr_Sub_Classify_5Pix_0NN.m | .m | oop_matlab-master/Experiments/MLR/indian_pines/reflectanceImg_mlr_Sub_Classify_5Pix_0NN.m | 882 | utf_8 | 8750a13c1519fb4044c6211dd3286d5e | function reflectanceImg_mlr_Sub_Classify_5Pix_0NN()
iter = 100;
avgAccVec = zeros(iter,1);
o_acc_vec = zeros(iter,1);
for i = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines_direct_reflectance();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);... |
github | andrejchenko/oop_matlab-master | mlr_Sub_Classify_5Pix_0NN.m | .m | oop_matlab-master/Experiments/MLR/indian_pines/mlr_Sub_Classify_5Pix_0NN.m | 848 | utf_8 | ccd3fe8f4844035ae5d36ff758603b37 | function mlr_Sub_Classify_5Pix_0NN()
iter = 100;
avgAccVec = zeros(iter,1);
o_acc_vec = zeros(iter,1);
for i = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
mObj = MLR;
... |
github | andrejchenko/oop_matlab-master | mlr_classify_5Pix_0NN.m | .m | oop_matlab-master/Experiments/MLR/indian_pines/mlr_classify_5Pix_0NN.m | 650 | utf_8 | 3632a6a28f3845275609fd9b73c30e23 | function mlr_classify_5Pix_0NN()
avgAccVec = [];
iter = 100;
for i = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
mObj = MLR;
mObj.trainMLR(obj);
load mlrModel
... |
github | andrejchenko/oop_matlab-master | complementarity_p.m | .m | oop_matlab-master/Experiments/Complementarity/pavia/complementarity_p.m | 1,497 | utf_8 | bb5b1cdf87d4e3a6d380a472f39f49b6 | function complementarity()
iter = 100;
cObj = CombineClass;
tic;
for a = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Pavia();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
svmClassification(svmObj,obj);
sunObj.unmixing(obj);
... |
github | andrejchenko/oop_matlab-master | complementarity_mlr.m | .m | oop_matlab-master/Experiments/Complementarity/indian_pines_mlr/complementarity_mlr.m | 1,700 | utf_8 | e3d4400d35d849f05a0446448228e391 | function complementarity_mlr()
iter = 100;
cObj = CombineClass;
tic;
for a = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
load('predicted');
sunObj.unmixing(obj);
... |
github | andrejchenko/oop_matlab-master | complementarity.m | .m | oop_matlab-master/Experiments/Complementarity/indian_pines/complementarity.m | 2,007 | utf_8 | 84a16089824e596ea39405793f81c380 | function complementarity()
avgAcc = 0;
iter = 100;
cObj = CombineClass;
tic;
for a = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
svmClassification(svmObj,obj);
... |
github | shashank215r/Computer-Vision-shadowRemoval-master | createH.m | .m | Computer-Vision-shadowRemoval-master/FinalProject/createH.m | 604 | utf_8 | 59ee98c2f3cbe594b3b66b0449024d47 | function H = createH(lambda, gamma, numPoints)
Hc = createPartH(lambda, gamma, numPoints);
Ht1 = createPartH(lambda, 1 - gamma, numPoints);
Ht2 = createPartH(lambda, 1 - gamma, numPoints);
zeroMatrix = zeros(numPoints);
H = [Hc zeroMatrix zeroMatrix; zeroMatrix Ht1 zeroMatrix; zeroMatrix zeroMatr... |
github | shashank215r/Computer-Vision-shadowRemoval-master | GradientSynthesisH.m | .m | Computer-Vision-shadowRemoval-master/FinalProject/ImageRecH_V11/GradientSynthesisH.m | 3,927 | utf_8 | 40df290a164ff9564dbc5440770386e5 | % GradientSynthesisH.m
% Performs the synthesis of gradient decomposition generated by GradientAnalysisH.m
% Inputs:
% D = decomposition output of gradient analysis step;
% X, Y = filtered and downsampled parts of the gradient, output of
% gradient analysis step;
% averageData = scalar, should be ... |
github | shashank215r/Computer-Vision-shadowRemoval-master | OneChannelRec.m | .m | Computer-Vision-shadowRemoval-master/FinalProject/ImageRecH_V11/OneChannelRec.m | 1,941 | utf_8 | c58a71c0535a4d55513403c1247a9aa4 | % Program OneChannelRec.m
% -------------------------------------------------------------------
% This function reconstructs a *one channel* (i.e., grayscale) image
% from a given *Hudgin* gradient using the Hampton Haar wavelet based algorithm;
% (please see Sec.2.2 in (1) for Hudgin gradient discretization Equat... |
github | shashank215r/Computer-Vision-shadowRemoval-master | GradientAnalysis.m | .m | Computer-Vision-shadowRemoval-master/FinalProject/ImageRecH_V11/GradientAnalysis.m | 8,848 | utf_8 | 72ecf84b98d004b12252d56c15019555 | % GradientAnalysis.m
% Performs the analysis of gradient data generated by getGradient.m
% D is the decomposition
% X and Y are multigrid representations of the filtered X and Y data that
% has use in the smoothing portion of synthesis.
% Reference:
% P. Hampton, P. Agathoklis, C. Bradley, "A New Wave-F... |
github | shashank215r/Computer-Vision-shadowRemoval-master | splitRGB.m | .m | Computer-Vision-shadowRemoval-master/FinalProject/ImageRecH_V11/splitRGB.m | 1,214 | utf_8 | 9fc03d0e23fbf122155b0c6d222d485d | % Program splitRGB.m
% Given a multichannel image, this function separates it in its R, G and B channels
% and returns each matrix as an output, and the mean value in each channel.
%
% Inputs:
% I = color image
% display_channels = optional input; should be set to 1 if we want to
% se... |
github | shashank215r/Computer-Vision-shadowRemoval-master | diffmeasure.m | .m | Computer-Vision-shadowRemoval-master/FinalProject/ImageRecH_V11/diffmeasure.m | 2,973 | utf_8 | 56312e6d2ca2f654dfeee83a97514624 | % function s = diffmeasure(Orig, Approx, mea)
%
% Computes different measures to allow comparison between an original and
% approximated version of a signal.
%
% Input:
% Orig = Original data
% Approx = approximated data
% Can be vectors or matrices, but must be of the same size
% ... |
github | jte0419/Rocket_Nozzle_Design-master | PM_EQUATION.m | .m | Rocket_Nozzle_Design-master/PM_EQUATION.m | 1,530 | utf_8 | 0e0deaddfff4dbb9d59efd86ec4304e3 | % SOLVE PRANDTL-MEYER EQUATION
% Written by: JoshTheEngineer
% YouTube : www.youtube.com/JoshTheEngineer
% Website : www.joshtheengineer.com
% Started: 12/07/15
% Updated: 12/07/15 - Started code
% - Works as intended
% 11/??/17 - Updated to faster Mach number solver
%
% PURPOSE... |
github | MITgcm/gcmfaces-master | diags_driver_tex.m | .m | gcmfaces-master/gcmfaces_diags/diags_driver_tex.m | 9,397 | utf_8 | 226e8b3142b93b86758d48765ad81218 | function []=diags_driver_tex(dirMat,setDiags,dirTex,nameTex);
% DIAGS_DRIVER_TEX(dirMat,setDiags,dirTex,nameTex)
%
% displays multiple sets of diagnostics (setDiags={'profiles',
% 'cost','A','B','C','MLD','D'} by default) from the results
% stored in dirMat and outputs the plots to tex
% ([dirTex nameTex '... |
github | MITgcm/gcmfaces-master | diags_grid_parms.m | .m | gcmfaces-master/gcmfaces_diags/diags_grid_parms.m | 8,609 | utf_8 | 58c4934e05bb8af8a3795774ca68ab37 | function []=diags_grid_parms(dirModel,listTimes,doInteractive);
%object : load grid, set params, and save myparms to dirMat
%input : dirModel is the model output directory
% listTimes is the time list obtained from diags_list_times
% doInteractive=1 allows users to specify parameter... |
github | MITgcm/gcmfaces-master | gcmfaces_remap_2d.m | .m | gcmfaces-master/gcmfaces_calc/gcmfaces_remap_2d.m | 4,758 | utf_8 | 2f263d1294bcf71671093dd84c67fa22 | function [fld]=gcmfaces_remap_2d(lon,lat,fld,nDblRes,varargin);
%object: use bin average to remap ONE lat-lon grid FIELD to a gcmfaces grid
%input: lon,lat,fld are the gridded product arrays
% nDblRes is the number of times the input field
% resolution should be doubled before bin averag... |
github | MITgcm/gcmfaces-master | gcmfaces_remap_3d.m | .m | gcmfaces-master/gcmfaces_calc/gcmfaces_remap_3d.m | 3,234 | utf_8 | e284af4eb0cb8a41accbef0c74c10eda | function [fldOut]=gcmfaces_remap_3d(lon,lat,depIn,fldIn,nDblRes);
%object: use bin average to remap a lat-lon-depth field to mygrid.mskC
%input: lon,lat,dep,fld are the gridded product arrays (2D,2D,1D,3D)
%assumption:fld should show NaN for missing values
gcmfaces_global;
%initiate triangulation:
gcmfaces_bin... |
github | MITgcm/gcmfaces-master | m_map_gcmfaces.m | .m | gcmfaces-master/gcmfaces_maps/m_map_gcmfaces.m | 12,642 | utf_8 | d084ebcc4558cfb2b1d12d35f6e20694 | function []=m_map_gcmfaces(fld,varargin);
%object: gcmfaces front end to m_map
%inputs: fld is the 2D field to be mapped, or a cell (see below).
%optional: proj is either the index (integer; 0 by default) of pre-defined
% projection(s) or parameters to pass to m_proj (cell)
%more so: other option... |
github | MITgcm/gcmfaces-master | cost_sst.m | .m | gcmfaces-master/ecco_v4/cost_sst.m | 7,178 | utf_8 | 9d6ae3fe16d2f9a4f8f9624e12e4101d | function []=cost_sst(dirModel,dirMat,doComp,dirTex,nameTex);
%object: compute cost function term for sst data
%inputs: dimodel is the model directory
% dirMat is the directory where diagnozed .mat files will be saved
% -> set it to '' to use the default [dirModel 'mat/']
... |
github | MITgcm/gcmfaces-master | rads_noice_mad.m | .m | gcmfaces-master/ecco_v4/rads_noice_mad.m | 7,087 | utf_8 | ff4f2e29d09bca9d211964ceff423bd4 | function []=rads_noice_mad(choiceData,l0,L0,doTesting,doNoice);
%object : outlier (& ice if doNoice) data flagging for altimetry
%inputs : choiceData is 'topexfile','ersfile' or 'gfofile'
% l0 is the box longitude (l0+-10 will be treated)
% L0 is the box latitude (L0+-10 will be treated)
% doTes... |
github | MITgcm/gcmfaces-master | cost_altimeter.m | .m | gcmfaces-master/ecco_v4/cost_altimeter.m | 15,057 | utf_8 | f2102dbe716746a7cdfbb8f1fc804dac | function []=cost_altimeter(dirModel,dirMat);
%object: compute or plot the various sea level statistics
% (std model-obs, model, obs, leading to cost function terms)
%inputs: dirModel is the model run directory
% dirMat is the directory where diagnozed .mat files will be saved
% ... |
github | MITgcm/gcmfaces-master | process2interp.m | .m | gcmfaces-master/gcmfaces_IO/process2interp.m | 6,089 | utf_8 | 50ba7a95bda03a24c01154eb46438f2d | function [listInterp,listNot]=process2interp(dirDiags,fileDiags,varargin);
% [listInterp,listNot]=PROCESS2INTERP(dirDiags,fileDiags);
% []=PROCESS2INTERP(dirDiags,fileDiags,listInterp);
%
% Either computes listInterp and listNot (if nargin==2)
% Or interpolates and ouput fields in listInterp (if nargin==3)
%
% ... |
github | MITgcm/gcmfaces-master | rdmds.m | .m | gcmfaces-master/gcmfaces_IO/rdmds.m | 15,841 | utf_8 | e6a1a66954de77d11371ec5ff33ac325 | function [AA,itrs,MM] = rdmds(fnamearg,varargin)
% RDMDS Read MITgcmUV meta/data files
%
% A = RDMDS(FNAME)
% A = RDMDS(FNAME,ITER)
% A = RDMDS(FNAME,[ITER1 ITER2 ...])
% A = RDMDS(FNAME,NaN)
% A = RDMDS(FNAME,Inf)
% [A,ITS,M] = RDMDS(FNAME,[...])
% A = RDMDS(FNAME,[...],'rec',RECNUM)
%
% A = RDMDS(FNAME) reads data... |
github | MITgcm/gcmfaces-master | process2nctiles.m | .m | gcmfaces-master/gcmfaces_IO/process2nctiles.m | 17,500 | utf_8 | 8414748d7c857918e18354ddd8b50df2 | function []=process2nctiles(dirDiags,fileDiags,selectFld,tileSize);
%process2nctiles(dirDiags,fileDiags);
% object : convert MITgcm binary output to netcdf files (tiled)
% inputs : dirDiags is the directory containing binary model output from
% MITgcm/pkg/diagnostics; dirDiags and its subdirectories will
% ... |
github | MITgcm/gcmfaces-master | struct2nctiles.m | .m | gcmfaces-master/gcmfaces_IO/struct2nctiles.m | 3,421 | utf_8 | 459e431bfa6cb7b99bd1ae4b6e2f34a2 | function []=struct2nctiles(dirModel,fileOut,structIn,tileSize);
%process2nctiles(dirModel);
%object : convert MITgcm binary output to netcdf files (tiled)
%inputs : dirModel is the MITgcm run directory
% It is expected to contain binaries in
% 'diags/STATE/', 'diags/TRSP/', etc. as well
% ... |
github | saikatbsk/bagOfFeatures-master | extractFeatures.m | .m | bagOfFeatures-master/extractFeatures.m | 1,891 | utf_8 | f8986fe70908ff4b580b4152ade51c78 | %% ========================================================================
%% Extract SURF features from images.
%% NOTE: Requires OpenSURF_version1c/ to be in current path.
%%
%% Parameters:
%% image_set - Holds paths to images. M*N cell array, M = Number of
%% image categories, N = Sa... |
github | saikatbsk/bagOfFeatures-master | createKmeanClusters.m | .m | bagOfFeatures-master/createKmeanClusters.m | 2,699 | utf_8 | 9b558b4b05ccb923cc34b638c0455ae4 | %% ========================================================================
%% K-means clustering.
%%
%% Parameters:
%% all_des - All the SURF descriptors: m*64 or m*128.
%% all_des_sample - All the SURF descriptors per sample: 1*n cell.
%% N - Number of clusters.
%%
%% Returns:
%% ... |
github | saikatbsk/bagOfFeatures-master | euclideanDistance.m | .m | bagOfFeatures-master/euclideanDistance.m | 858 | utf_8 | b1355b4eb261e9e0bc46bad7acb81d82 | %% ========================================================================
%% Computes Euclidean distance matrix.
%%
%% Parameters:
%% a - (M*D) matrix.
%% b - (N*D) matrix.
%%
%% Returns:
%% d - Distance matrix (M*N).
%%
%% Description :
%% Fully vectorized computation of the Euclidean distance be... |
github | saikatbsk/bagOfFeatures-master | buildHist_train.m | .m | bagOfFeatures-master/buildHist_train.m | 1,252 | utf_8 | 6ce0f30dbe669ddaf76938b200b3ae95 | %% ========================================================================
%% Build histogram from input classes provided in training phase.
%%
%% Parameters:
%% centers - Cluster centers. N*M matrix.
%% all_des - All the SURF descriptors (m*64).
%% class_label - Class label for each surf descri... |
github | saikatbsk/bagOfFeatures-master | kNearestNeighbors.m | .m | bagOfFeatures-master/kNearestNeighbors.m | 1,442 | utf_8 | 564f1cd9d90fb29faa8e446f39e2c7bb | %% ========================================================================
%% Find the k - nearest neighbors (kNN) within a set of points.
%% Distance metric used: Euclidean distance.
%%
%% Parameters:
%% dataMatrix - N*D data matrix.
%% queryMatrix - M*D query matrix.
%% k - Number of neares... |
github | saikatbsk/bagOfFeatures-master | classify.m | .m | bagOfFeatures-master/classify.m | 1,696 | utf_8 | 60b1bc514f50eb3c7581c677de2f7ade | %% ========================================================================
%% Assign each test image to one of the possible classes by comparing its
%% histogram to histograms of all classes.
%%
%% Parameters:
%% hists - Histograms from training classes
%% hists_test - Histograms from test classes... |
github | saikatbsk/bagOfFeatures-master | buildHist_test.m | .m | bagOfFeatures-master/buildHist_test.m | 1,258 | utf_8 | 9723043be0b942a7340ae810c710a62a | %% ========================================================================
%% Build histogram from input classes provided in testing phase.
%%
%% Parameters:
%% centers - Cluster centers. N*M matrix.
%% all_des_sample - All the SURF descriptors per sample (1*n cell).
%% THRESH - Threshold... |
github | saikatbsk/bagOfFeatures-master | imageRead.m | .m | bagOfFeatures-master/imageRead.m | 1,015 | utf_8 | 4ef7c47f20824ee0039941a2c408b028 | %% ========================================================================
%% Reads a dataset of images(JPG). Sub folders containing different classes
%% of images are kept within a root folder.
%%
%% Parameters:
%% rootpath - Path to root folder.
%% subfolders - Sub folders within root folder.
%... |
github | saikatbsk/bagOfFeatures-master | affine_warp.m | .m | bagOfFeatures-master/OpenSURF_version1c/WarpFunctions/affine_warp.m | 9,721 | utf_8 | c917c5470b273f501abcdc82b040105c | function Iout=affine_warp(Iin,M,mode)
% Affine transformation function (Rotation, Translation, Resize)
% This function transforms a volume with a 3x3 transformation matrix
%
% Iout=affine_warp(Iin,Minv,mode)
%
% inputs,
% Iin: The input image
% Minv: The (inverse) 3x3 transformation matrix
% mode: If 0:... |
github | saikatbsk/bagOfFeatures-master | FastHessian_interpolateExtremum.m | .m | bagOfFeatures-master/OpenSURF_version1c/SubFunctions/FastHessian_interpolateExtremum.m | 2,461 | utf_8 | f093fd8f3df30968b3ab1d108f270261 | function [ipts, np]=FastHessian_interpolateExtremum(r, c, t, m, b, ipts, np)
% This function FastHessian_interpolateExtremum will ..
%
% [ipts,np] = FastHessian_interpolateExtremum( r,c,t,m,b,ipts,np )
%
% inputs,
% r :
% c :
% t :
% m :
% b :
% ipts :
% np :
%
% outputs,
% ... |
github | saikatbsk/bagOfFeatures-master | SurfDescriptor_GetDescriptor.m | .m | bagOfFeatures-master/OpenSURF_version1c/SubFunctions/SurfDescriptor_GetDescriptor.m | 3,636 | utf_8 | d178d192dd3143adae54ecbef4fa51da | function descriptor=SurfDescriptor_GetDescriptor(ip, bUpright, bExtended, img, verbose)
% This function SurfDescriptor_GetDescriptor will ..
%
% [descriptor] = SurfDescriptor_GetDescriptor( ip,bUpright,bExtended,img )
%
% inputs,
% ip : Interest Point (x,y,scale, orientation)
% bUpright : If true not ro... |
github | saikatbsk/bagOfFeatures-master | FastHessian_isExtremum.m | .m | bagOfFeatures-master/OpenSURF_version1c/SubFunctions/FastHessian_isExtremum.m | 1,680 | utf_8 | e9b565701ffc73f2596419b431e52352 | function an=FastHessian_isExtremum(r, c, t, m, b,FastHessianData)
% This function FastHessian_isExtremum will ..
%
% [an] = FastHessian_isExtremum( r,c,t,m,b,FastHessianData )
%
% inputs,
% r :
% c :
% t :
% m :
% b :
% FastHessianData :
%
% outputs,
% an :
%
% Fun... |
github | cbaldassano/Parcellating-connectivity-master | ft_write_cifti.m | .m | Parcellating-connectivity-master/matlab/cifti-matlab-master/ft_write_cifti.m | 32,017 | utf_8 | 809e6701afddbf795b508d25fd6c5bce | function ft_write_cifti(filename, source, varargin)
% FT_WRITE_CIFTI writes functional data or functional connectivity to a cifti-2
% file. The geometrical description of the brainordinates can consist of
% triangulated surfaces or voxels in a regular 3-D volumetric grid. The functional
% data can consist of a dense o... |
github | cbaldassano/Parcellating-connectivity-master | getdimord.m | .m | Parcellating-connectivity-master/matlab/cifti-matlab-master/private/getdimord.m | 13,605 | utf_8 | 7a04c781b55a48a6316b1d74bf986540 | function dimord = getdimord(data, field, varargin)
% GETDIMORD
%
% Use as
% dimord = getdimord(data, field)
%
% See also GETDIMSIZ
if strncmp(field, 'avg.', 4)
field = field(5:end); % strip the avg
data.(field) = data.avg.(field);
data = rmfield(data, 'avg');
end
if ~isfield(data, field)
error('field "%s" ... |
github | cbaldassano/Parcellating-connectivity-master | ft_datatype_sens.m | .m | Parcellating-connectivity-master/matlab/cifti-matlab-master/private/ft_datatype_sens.m | 20,793 | utf_8 | 1a5161e33bdd52cc9dc548f1382cb532 | function [sens] = ft_datatype_sens(sens, varargin)
% FT_DATATYPE_SENS describes the FieldTrip structure that represents
% an EEG, ECoG, or MEG sensor array. This structure is commonly called
% "elec" for EEG and "grad" for MEG, or more general "sens" for either
% one.
%
% The structure for MEG gradiometers and/or magn... |
github | cbaldassano/Parcellating-connectivity-master | ft_convert_units.m | .m | Parcellating-connectivity-master/matlab/cifti-matlab-master/private/ft_convert_units.m | 9,048 | utf_8 | b0b47d21a2d75a5138e1d3499af2bf96 | function [obj] = ft_convert_units(obj, target, varargin)
% FT_CONVERT_UNITS changes the geometrical dimension to the specified SI unit.
% The units of the input object is determined from the structure field
% object.unit, or is estimated based on the spatial extend of the structure,
% e.g. a volume conduction model of... |
github | cbaldassano/Parcellating-connectivity-master | ft_datatype.m | .m | Parcellating-connectivity-master/matlab/cifti-matlab-master/private/ft_datatype.m | 9,080 | utf_8 | 6dccfe99b0a6ac35d2eaf644219df092 | function [type, dimord] = ft_datatype(data, desired)
% FT_DATATYPE determines the type of data represented in a FieldTrip data
% structure and returns a string with raw, freq, timelock source, comp,
% spike, source, volume, dip.
%
% Use as
% [type, dimord] = ft_datatype(data)
% [status] = ft_datatype(data, d... |
github | cbaldassano/Parcellating-connectivity-master | individual2sn.m | .m | Parcellating-connectivity-master/matlab/cifti-matlab-master/private/individual2sn.m | 5,003 | utf_8 | 41e80c4e5da6a907ba8391ae1d245c29 | function [warped]= individual2sn(P, input)
% INDIVIDUAL2SN warps the input coordinates (defined as Nx3 matrix) from
% individual headspace coordinates into normalised MNI coordinates, using the
% (inverse of the) warp parameters defined in the structure spmparams.
%
% this is code inspired by nutmeg and spm: nut_mri2m... |
github | cbaldassano/Parcellating-connectivity-master | ft_write_cifti.m | .m | Parcellating-connectivity-master/matlab/cifti-matlab-master/private/ft_write_cifti.m | 32,017 | utf_8 | 809e6701afddbf795b508d25fd6c5bce | function ft_write_cifti(filename, source, varargin)
% FT_WRITE_CIFTI writes functional data or functional connectivity to a cifti-2
% file. The geometrical description of the brainordinates can consist of
% triangulated surfaces or voxels in a regular 3-D volumetric grid. The functional
% data can consist of a dense o... |
github | cbaldassano/Parcellating-connectivity-master | read_ply.m | .m | Parcellating-connectivity-master/matlab/cifti-matlab-master/private/read_ply.m | 5,986 | utf_8 | e7e4d22b778a98c951a9972c41d4dea3 | function [vert, face] = read_ply(fn)
% READ_PLY reads triangles, tetraheders or hexaheders from a Stanford *.ply file
%
% Use as
% [vert, face, prop, face_prop] = read_ply(filename)
%
% Documentation is provided on
% http://paulbourke.net/dataformats/ply/
% http://en.wikipedia.org/wiki/PLY_(file_format)
%
% See ... |
github | cbaldassano/Parcellating-connectivity-master | ft_read_header.m | .m | Parcellating-connectivity-master/matlab/cifti-matlab-master/private/ft_read_header.m | 81,838 | utf_8 | 5537dc846bb5b1a40badb1d99623cd90 | function [hdr] = ft_read_header(filename, varargin)
% FT_READ_HEADER reads header information from a variety of EEG, MEG and LFP
% files and represents the header information in a common data-independent
% format. The supported formats are listed below.
%
% Use as
% hdr = ft_read_header(filename, ...)
%
% Additional... |
github | cbaldassano/Parcellating-connectivity-master | ft_hastoolbox.m | .m | Parcellating-connectivity-master/matlab/cifti-matlab-master/private/ft_hastoolbox.m | 24,838 | utf_8 | 382beea7de26842c6c19d52d97f3f607 | function [status] = ft_hastoolbox(toolbox, autoadd, silent)
% FT_HASTOOLBOX tests whether an external toolbox is installed. Optionally
% it will try to determine the path to the toolbox and install it
% automatically.
%
% Use as
% [status] = ft_hastoolbox(toolbox, autoadd, silent)
%
% autoadd = 0 means that it will ... |
github | cbaldassano/Parcellating-connectivity-master | read_asa.m | .m | Parcellating-connectivity-master/matlab/cifti-matlab-master/private/read_asa.m | 3,811 | utf_8 | 4248bc5cdc1a9912319002dbdf89ecbc | function [val] = read_asa(filename, elem, format, number, token)
% READ_ASA reads a specified element from an ASA file
%
% val = read_asa(filename, element, type, number)
%
% where the element is a string such as
% NumberSlices
% NumberPositions
% Rows
% Columns
% etc.
%
% and format specifies the datatype a... |
github | cbaldassano/Parcellating-connectivity-master | ft_read_mri.m | .m | Parcellating-connectivity-master/matlab/cifti-matlab-master/private/ft_read_mri.m | 15,468 | utf_8 | a921a393da75a56940c3100fde50e10a | function [mri] = ft_read_mri(filename, varargin)
% FT_READ_MRI reads anatomical and functional MRI data from different
% file formats. The output data is structured in such a way that it is
% comparable to a FieldTrip source reconstruction.
%
% Use as
% [mri] = ft_read_mri(filename)
%
% Additional options should be ... |
github | cbaldassano/Parcellating-connectivity-master | ft_struct2double.m | .m | Parcellating-connectivity-master/matlab/cifti-matlab-master/private/ft_struct2double.m | 2,878 | utf_8 | 8b9a93199ee501c885aa1902dcdb0dee | function [x] = ft_struct2double(x, maxdepth)
% FT_STRUCT2DOUBLE converts all single precision numeric data in a structure
% into double precision. It will also convert plain matrices and
% cell-arrays.
%
% Use as
% x = ft_struct2double(x);
%
% Starting from MATLAB 7.0, you can use single precision data in your
% c... |
github | cbaldassano/Parcellating-connectivity-master | read_yokogawa_header_new.m | .m | Parcellating-connectivity-master/matlab/cifti-matlab-master/private/read_yokogawa_header_new.m | 8,897 | utf_8 | a0fde39f5b058a98548696afa7c4ab2c | function hdr = read_yokogawa_header_new(filename)
% READ_YOKOGAWA_HEADER_NEW reads the header information from continuous,
% epoched or averaged MEG data that has been generated by the Yokogawa
% MEG system and software and allows that data to be used in combination
% with FieldTrip.
%
% Use as
% [hdr] = read_yokoga... |
github | cbaldassano/Parcellating-connectivity-master | getdimsiz.m | .m | Parcellating-connectivity-master/matlab/cifti-matlab-master/private/getdimsiz.m | 986 | utf_8 | b5f9904d46ff48092b677b2df409bd0a | function dimsiz = getdimsiz(data, field)
% GETDIMSIZ
%
% Use as
% dimsiz = getdimsiz(data, field)
%
% See also GETDIMORD
if strncmp(field, 'avg.', 4)
field = field(5:end); % strip the avg
data.(field) = data.avg.(field);
end
if ~isfield(data, field)
error('field "%s" not present in data', field);
end
dimsiz... |
github | cbaldassano/Parcellating-connectivity-master | read_yokogawa_header.m | .m | Parcellating-connectivity-master/matlab/cifti-matlab-master/private/read_yokogawa_header.m | 8,282 | utf_8 | 417f8357a0739467d030be8b62b135cf | function hdr = read_yokogawa_header(filename)
% READ_YOKOGAWA_HEADER reads the header information from continuous,
% epoched or averaged MEG data that has been generated by the Yokogawa
% MEG system and software and allows that data to be used in combination
% with FieldTrip.
%
% Use as
% [hdr] = read_yokogawa_heade... |
github | cbaldassano/Parcellating-connectivity-master | read_stl.m | .m | Parcellating-connectivity-master/matlab/cifti-matlab-master/private/read_stl.m | 4,026 | utf_8 | f71794de6bea12bd26ff2989429b9001 | function [pnt, tri, nrm] = read_stl(filename);
% READ_STL reads a triangulation from an ascii or binary *.stl file, which
% is a file format native to the stereolithography CAD software created by
% 3D Systems.
%
% Use as
% [pnt, tri, nrm] = read_stl(filename)
%
% The format is described at http://en.wikipedia.org/w... |
github | cbaldassano/Parcellating-connectivity-master | ft_filetype.m | .m | Parcellating-connectivity-master/matlab/cifti-matlab-master/private/ft_filetype.m | 62,414 | utf_8 | fd47d76e314e08fad5dbf0391b9e877b | function [type] = ft_filetype(filename, desired, varargin)
% FT_FILETYPE determines the filetype of many EEG/MEG/MRI data files by
% looking at the name, extension and optionally (part of) its contents.
% It tries to determine the global type of file (which usually
% corresponds to the manufacturer, the recording syst... |
github | cbaldassano/Parcellating-connectivity-master | delete.m | .m | Parcellating-connectivity-master/matlab/cifti-matlab-master/@xmltree/delete.m | 1,186 | utf_8 | c889a736d28f8f035d669d259c092503 | function tree = delete(tree,uid)
% XMLTREE/DELETE Delete (delete a subtree given its UID)
%
% tree - XMLTree object
% uid - array of UID's of subtrees to be deleted
%__________________________________________________________________________
%
% Delete a subtree given its UID
% The tree parameter must be in ... |
github | cbaldassano/Parcellating-connectivity-master | save.m | .m | Parcellating-connectivity-master/matlab/cifti-matlab-master/@xmltree/save.m | 5,111 | utf_8 | acc42f2362b69cf039403d3f03a7dea5 | function varargout = save(tree, filename)
% XMLTREE/SAVE Save an XML tree in an XML file
% FORMAT varargout = save(tree,filename)
%
% tree - XMLTree
% filename - XML output filename
% varargout - XML string
%__________________________________________________________________________
%
% Convert an XML tree into a ... |
github | cbaldassano/Parcellating-connectivity-master | branch.m | .m | Parcellating-connectivity-master/matlab/cifti-matlab-master/@xmltree/branch.m | 1,747 | utf_8 | 0c6688994b914c764780a0090df744dd | function subtree = branch(tree,uid)
% XMLTREE/BRANCH Branch Method
% FORMAT uid = parent(tree,uid)
%
% tree - XMLTree object
% uid - UID of the root element of the subtree
% subtree - XMLTree object (a subtree from tree)
%__________________________________________________________________________
%
% Return a su... |
github | cbaldassano/Parcellating-connectivity-master | flush.m | .m | Parcellating-connectivity-master/matlab/cifti-matlab-master/@xmltree/flush.m | 1,410 | utf_8 | 9b6dd180b94631d5e618de841d26ba7d | function tree = flush(tree,uid)
% XMLTREE/FLUSH Flush (Clear a subtree given its UID)
%
% tree - XMLTree object
% uid - array of UID's of subtrees to be cleared
% Default is root
%__________________________________________________________________________
%
% Clear a subtree given its UID (remove... |
github | cbaldassano/Parcellating-connectivity-master | copy.m | .m | Parcellating-connectivity-master/matlab/cifti-matlab-master/@xmltree/copy.m | 1,629 | utf_8 | 8fcc6e508ac15a7768328d715653060d | function tree = copy(tree,subuid,uid)
% XMLTREE/COPY Copy Method (copy a subtree in another branch)
% FORMAT tree = copy(tree,subuid,uid)
%
% tree - XMLTree object
% subuid - UID of the subtree to copy
% uid - UID of the element where the subtree must be duplicated
%______________________________________... |
github | cbaldassano/Parcellating-connectivity-master | convert.m | .m | Parcellating-connectivity-master/matlab/cifti-matlab-master/@xmltree/convert.m | 5,598 | utf_8 | 9b155d3069239d701614c92051242c35 | function s = convert(tree,uid)
% XMLTREE/CONVERT Converter an XML tree in a Matlab structure
%
% tree - XMLTree object
% uid - uid of the root of the subtree, if provided.
% Default is root
% s - converted structure
%_______________________________________________________________________... |
github | cbaldassano/Parcellating-connectivity-master | editor.m | .m | Parcellating-connectivity-master/matlab/cifti-matlab-master/@xmltree/editor.m | 12,418 | utf_8 | 2f5acbc76538e911f4f851e19cef5ec7 | function editor(tree)
%XMLTREE/EDITOR A Graphical User Interface for an XML tree
% EDITOR(TREE) opens a new Matlab figure displaying the xmltree
% object TREE.
% H = EDITOR(TREE) also returns the figure handle H.
%
% This is a beta version of <xmltree/view> successor
%
% See also XMLTREE
%_________________________... |
github | cbaldassano/Parcellating-connectivity-master | find.m | .m | Parcellating-connectivity-master/matlab/cifti-matlab-master/@xmltree/find.m | 5,898 | utf_8 | 9bb7e64b57330d8791eceab30b953f06 | function list = find(varargin)
% XMLTREE/FIND Find elements in a tree with specified characteristics
% FORMAT list = find(varargin)
%
% tree - XMLTree object
% xpath - string path with specific grammar (XPath)
% uid - lists of root uid's
% parameter/value - pair of pattern
% list - list of uid's of matched elements... |
github | cbaldassano/Parcellating-connectivity-master | xml_parser.m | .m | Parcellating-connectivity-master/matlab/cifti-matlab-master/@xmltree/private/xml_parser.m | 16,171 | utf_8 | bcf7c402ac267b5824a54a902847f311 | function tree = xml_parser(xmlstr)
% XML (eXtensible Markup Language) Processor
% FORMAT tree = xml_parser(xmlstr)
%
% xmlstr - XML string to parse
% tree - tree structure corresponding to the XML file
%__________________________________________________________________________
%
% xml_parser.m is an XML 1.0 (http:/... |
github | cbaldassano/Parcellating-connectivity-master | save.m | .m | Parcellating-connectivity-master/matlab/cifti-matlab-master/@gifti/save.m | 21,085 | utf_8 | 2ec4f8188c5e09dccf5497c576178df1 | function save(this,filename,encoding)
% Save GIfTI object in a GIfTI format file
% FORMAT save(this,filename)
% this - GIfTI object
% filename - name of GIfTI file to be created [Default: 'untitled.gii']
% encoding - optional argument to specify encoding format, among
% ASCII, Base64Binary, GZipBase6... |
github | cbaldassano/Parcellating-connectivity-master | gifti.m | .m | Parcellating-connectivity-master/matlab/cifti-matlab-master/@gifti/gifti.m | 3,771 | utf_8 | b31122a44e5e66b0735375f09d6ba7b5 | function this = gifti(varargin)
% GIfTI Geometry file format class
% Geometry format under the Neuroimaging Informatics Technology Initiative
% (NIfTI):
% http://www.nitrc.org/projects/gifti/
% http://nifti.nimh.nih.gov/
%_____________________________________________________________... |
github | cbaldassano/Parcellating-connectivity-master | read_gifti_file.m | .m | Parcellating-connectivity-master/matlab/cifti-matlab-master/@gifti/private/read_gifti_file.m | 6,657 | utf_8 | 89eef9fcf93140dad7d7f00423927c60 | function this = read_gifti_file(filename, this)
% Low level reader of GIfTI 1.0 files
% FORMAT this = read_gifti_file(filename, this)
% filename - XML GIfTI filename
% this - structure with fields 'metaData', 'label' and 'data'.
%__________________________________________________________________________
% Cop... |
github | cbaldassano/Parcellating-connectivity-master | isintent.m | .m | Parcellating-connectivity-master/matlab/cifti-matlab-master/@gifti/private/isintent.m | 2,523 | utf_8 | 41d1fde0526143827c0f739bf10dc55b | function [a, b] = isintent(this,intent)
% Correspondance between fieldnames and NIfTI intent codes
% FORMAT ind = isintent(this,intent)
% this - GIfTI object
% intent - fieldnames
% a - indices of found intent(s)
% b - indices of dataarrays of found intent(s)
%_______________________________________... |
github | cbaldassano/Parcellating-connectivity-master | LinkageConstrained.m | .m | Parcellating-connectivity-master/matlab/clustering/LinkageConstrained.m | 3,187 | utf_8 | e8fae47c7d6c38655705ec453c0dea86 | % Modification of Matlab "LINKAGEOLD" function to only merge elements
% that are spatially adjacent, as specified by adj_list
function Z = LinkageConstrained(D, adj_list)
if (CheckSymApprox(D))
X = D;
else
X = [D D'];
end
% Compute squared euclidean distance Y between rows
Qx = repmat(dot(X,X,2),1,size(X,1))... |
github | cbaldassano/Parcellating-connectivity-master | WardClustering.m | .m | Parcellating-connectivity-master/matlab/clustering/WardClustering.m | 480 | utf_8 | 25f365f1c6ad6f878263a18058f36379 | % Computes the Ward clustering linkage matrix Z for a given connectivity
% matrix D and spatial adjacency specified by adj_list, gives the
% specific clustering z for number of clusters n_clust
function [z Z] = WardClustering(D, adj_list, n_clust, vox_to_clust)
if (nargin == 4)
unclust = ~ismember(1:size(D,1),v... |
github | cbaldassano/Parcellating-connectivity-master | LocalSimilarity.m | .m | Parcellating-connectivity-master/matlab/clustering/LocalSimilarity.m | 1,398 | utf_8 | d628b6c48c59e2f9094c25a8986005fc | function z = LocalSimilarity(D, adj_list, n_clust, vox_to_clust)
if (nargin < 4)
vox_to_clust = 1:size(D,1);
end
N = size(D,1);
W = inf(size(D));
if (CheckSymApprox(D))
for i = 1:size(D,1)
for j = adj_list{i}
W(i,j) = norm(D(i, (1:N ~= i) & (1:N ~= j)) - ...
D(j, (... |
github | cbaldassano/Parcellating-connectivity-master | LogProbWC.m | .m | Parcellating-connectivity-master/matlab/ddCRP/LogProbWC.m | 1,030 | utf_8 | 54adc3439a74e186f4f77e32fc864180 | % Compute probability of each Ward clustering (from Z) of a matrix D at
% various sizes, using our model with hyperparameters alpha, kappa, nu, sigsq
function logp = LogProbWC(D, Z, sizes, alpha, kappa, nu, sigsq)
hyp = ComputeCachedLikelihoodTerms(kappa, nu, sigsq);
logp = zeros(length(sizes),1);
for i = 1:length(s... |
github | cbaldassano/Parcellating-connectivity-master | FullProbabilityddCRP.m | .m | Parcellating-connectivity-master/matlab/ddCRP/FullProbabilityddCRP.m | 2,202 | utf_8 | 0c214e1bfa0f149f778073836d274803 | % Compute full probability of a given parcellation of D, specified both in terms
% of voxel links c and list of arrays of element indices "parcels".
% Hyperparmeters as specified as alpha and vectorized hyp, and whether D is
% symmetric is given by the boolean sym.
% Note that this is very slow for large matric... |
github | cbaldassano/Parcellating-connectivity-master | InitializeAndRunddCRP.m | .m | Parcellating-connectivity-master/matlab/ddCRP/InitializeAndRunddCRP.m | 1,381 | utf_8 | 853f5f6196bf15875f92790e9b2efe9c | % Initializes our method using a Ward clustering linkage matrix
% Z, a (normalized) connectivity matrix D_norm, and adjacency list defining
% spatial adjacency, the possible numbers of parcels to consider for
% initialization ("sizes"), hyperparameters alpha, kappa, nu, and sigsq, the
% number of passes over th... |
github | cbaldassano/Parcellating-connectivity-master | ComputeCachedLikelihoodTerms.m | .m | Parcellating-connectivity-master/matlab/ddCRP/ComputeCachedLikelihoodTerms.m | 322 | utf_8 | bd37d62be9e3c342c51871422cdb780e | % Precompute and package hyperparameter expressions into vector form
function cached = ComputeCachedLikelihoodTerms( kappa, nu, sigsq )
cached = [ ...
0 ...
kappa ...
nu ...
sigsq ...
nu * sigsq ...
-gammaln(nu/2) + (1/2)*log(kappa) + (nu/2)*log(nu*sigsq)];
cached = cast(cached, 'single');
end... |
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