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 | satwikkottur/VisualWord2Vec-master | fetchVPTaskData.m | .m | VisualWord2Vec-master/word2vecVisual/utils/vp/fetchVPTaskData.m | 3,747 | utf_8 | fc8d454939cde0142fc8ba49afdec09a | % Script to extract all the relevant data to perform VP task
function fetchVPTaskData(vpPath, matPath, savePath)
% Input:
% vpPath - path to the VP dataset
% matPath - path to matfile 'vp_txt_features_mat'
% savePath - path to save all the files
%--------------- Saving the ground truth ---------------
fp... |
github | satwikkottur/VisualWord2Vec-master | saveFeatures.m | .m | VisualWord2Vec-master/word2vecVisual/utils/vp/saveFeatures.m | 686 | utf_8 | 1151ca17a23232a8c18be9775b23cb98 | % Script to save a feature matrix to a file given pointer
function saveFeatures(fileId, featMat)
% First line is number of features
fprintf(fileId, '%d\n', size(featMat, 2));
reverseStr = '';
for i = 1:size(featMat, 1)
% Display the progress
if rem(i, 10) == 0
percentDone = ... |
github | satwikkottur/VisualWord2Vec-master | saveVPTrainSentences.m | .m | VisualWord2Vec-master/word2vecVisual/utils/vp/saveVPTrainSentences.m | 936 | utf_8 | 00f71fac6d85011683b37d2474da1ca2 | % Script to save the VP trained sentences for tokenization and lemmatization
function saveVPTrainSentences(vpPath, savePath)
% Load the data and split
data = load(fullfile(vpPath, 'data/visual_paraphrasing', 'dataset.mat'));
load(fullfile(vpPath, 'data/visual_paraphrasing', 'split.mat'));
% Get sentenc... |
github | OrangeOwlSolutions/Miscellaneous-master | Jacobi_PN0n.m | .m | Miscellaneous-master/NFFF/Jacobi_PN0n.m | 2,166 | utf_8 | fb5f2e38d8f95e00773b869d71471cb7 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% Calculates Jacobi polynomials P_n^(N,0)(z) for 0 <= n <= nmax %
% ... |
github | OrangeOwlSolutions/Miscellaneous-master | KnabWindow.m | .m | Miscellaneous-master/BP_FBP_FFBP/KnabWindow.m | 1,558 | utf_8 | 8b9d0cb2441f8ad6d162a06f87d62115 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% CALCULATING THE INTERPOLATION WINDOW %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% --- Calculates the interpolation window G according to J. Selva,
% "Interpolation of Bounded Bandlimited Signals and Applications", see eq. (12).
% The interpolation window is the convolution, in... |
github | OrangeOwlSolutions/Miscellaneous-master | IntervalIntersection.m | .m | Miscellaneous-master/BP_FBP_FFBP/IntervalIntersection.m | 581 | utf_8 | f27fb6cf9fc0e69554ed620d77bb4910 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% INTERVAL INTERSECTION FOR CALCULATING THE CONVOLUTION %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [IInt,RelAInd,RelBInd] = IntervalIntersection(IA,IB)
if IA(2) < IB(1) || IB(2) < IA(1) % --- No overlap case.
IInt = [];
RelAInd... |
github | OrangeOwlSolutions/Miscellaneous-master | NFFT_Based_Selvas_Approach_for_Signal_Interp_1D.m | .m | Miscellaneous-master/BP_FBP_FFBP/NFFT_Based_Selvas_Approach_for_Signal_Interp_1D.m | 892 | utf_8 | 0208c1ba007296c1319090b77abdc6f3 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% NFFT BASED SELVA'S APPROACH TO BANDLIMITED SIGNAL INTERPOLATION %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function sUp = NFFT_Based_Selvas_Approach_for_Signal_Interp_1D(s, T, a, G, kg, xn)
N = length(s);
sF = fft(s); ... |
github | OrangeOwlSolutions/Miscellaneous-master | knab1D.m | .m | Miscellaneous-master/BP_FBP_FFBP/knab1D.m | 814 | utf_8 | e1edc8de81fffd267bfc8396000040af | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% TIME DOMAIN INTERPOLATION WITH APPROXIMATE PROLATE SPHEROIDAL WAVEFUNCTION WINDOW %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function result = knab1D(x, y, xn, B)
% --- Mean sampling ste... |
github | OrangeOwlSolutions/Miscellaneous-master | ConvWindow.m | .m | Miscellaneous-master/BP_FBP_FFBP/ConvWindow.m | 391 | utf_8 | feccf2e231e904119d030db101a6b769 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% APPROXIMATE PROLATE SPHEROIDAL WAVEFUNCTION %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% --- See page 5470, J. Selva, Convolution-Based Trigonometric Interpolation of Band-Limited Signals
function W = ConvWindow(f, B, T)
W = zeros(size(f));
I = abs(f) < B/2;
W... |
github | OrangeOwlSolutions/Miscellaneous-master | knabNFFT1D.m | .m | Miscellaneous-master/BP_FBP_FFBP/knabNFFT1D.m | 1,381 | utf_8 | 5077f254685567e9933ea6c008ba0b1a | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% FREQUENCY DOMAIN INTERPOLATION WITH APPROXIMATE PROLATE SPHEROIDAL WAVEFUNCTION WINDOW %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function result = knabNFFT1D(x, y, xn, B)
% --... |
github | OrangeOwlSolutions/Miscellaneous-master | rectNFFT1D.m | .m | Miscellaneous-master/BP_FBP_FFBP/rectNFFT1D.m | 2,247 | utf_8 | 8042899a6b3f9021d077245f7eb6b4e9 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% FREQUENCY DOMAIN INTERPOLATION WITH RECTANGULAR WINDOW %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function result = rectNFFT1D(x, y, xn, B)
% --- B: Bandwidth of the signal to be interpolated
% --- x: input sampling points
% -... |
github | OrangeOwlSolutions/Miscellaneous-master | knab2D.m | .m | Miscellaneous-master/BP_FBP_FFBP/knab2D.m | 2,045 | utf_8 | fa1e0221847c08258336061a80bba183 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% TIME DOMAIN INTERPOLATION WITH APPROXIMATE PROLATE SPHEROIDAL WAVEFUNCTION WINDOW %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function result = knab2D(ExtrapVal, RHO_IN, COSALPHA_IN, INPUT... |
github | OrangeOwlSolutions/Miscellaneous-master | NFFT_Based_Selvas_Approach_for_Signal_Interp_2D.m | .m | Miscellaneous-master/BP_FBP_FFBP/NFFT_Based_Selvas_Approach_for_Signal_Interp_2D.m | 1,471 | utf_8 | 29587124b45c2d0c81950e8fe3fdffbb | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% NUFFT BASED SELVA'S APPROACH TO BANDLIMITED SIGNAL INTERPOLATION %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function sUp = NFFT_Based_Selvas_Approach_for_Signal_Interp_2D(INPUT_DATA, T1, T2, a, b, G, kgn, kgm, RHO_OUT_SHI... |
github | sallyblockchain/yelp-image-classification-master | color_histogram.m | .m | yelp-image-classification-master/color_histogram.m | 5,224 | utf_8 | b0583916c9449a3fa03d0ff54a3980b5 | function confusion_matrix = color_histogram()
load('class_data.mat');
load('consolidated_data.mat');
num_training_examples = 800;
num_testing_examples = 200;
num_pyramid_level = 3; % for this MP
food_examples = find(label == 1);
non_food_examples = find(label ~= 1);
labe... |
github | tjssmy/QuadTreeCode-master | SetQTGMat.m | .m | QuadTreeCode-master/SetQTGMat.m | 1,540 | utf_8 | 1f8a7303c469edba358dbbe697636066 | function SetQTGMat(LeftBC,RightBC,UpBC,DownBC)
QTGlobals
for b=1:nBlocks
SetQTGMatBlock(b,RightBC,LeftBC,UpBC,DownBC);
end
end
function SetQTGMatBlock(b,RightBC,LeftBC,UpBC,DownBC)
QTGlobals
dx = Blocks{b}.dx;
dy = Blocks{b}.dy;
k1 = Blocks{b}.data(KVAL);
n = Blocks{b}.k;
Gtot = 0;
for br = 1:MAX_NUM_BR
... |
github | tjssmy/QuadTreeCode-master | RefineQTBlock.m | .m | QuadTreeCode-master/RefineQTBlock.m | 3,461 | utf_8 | ac996d632fe1d4409bb3efdc202e6a4a | function RefineQTblocks(b,kThresh)
QTGlobals
block = Blocks{b};
dx2 = block.dx/2;
dy2 = block.dy/2;
x = block.x;
y = block.y;
[i1,i2,j1,j2] = GetQTKfctRange(x,y,dx2,dy2);
kMap = kQTFct(i1:i2,j1:j2);
maxK = max(max(kMap));
minK = min(min(kMap));
delK = maxK-minK;
sVal = min(min(QTSmap(i1:i2,j1:j2)));
if (delK... |
github | tjssmy/QuadTreeCode-master | SetQTTransBVec.m | .m | QuadTreeCode-master/SetQTTransBVec.m | 843 | utf_8 | bbe40d3ec5d71d3125cfb07a4c524c22 | function [IntBC] = SetQTTransBVec(IntBC,t)
QTGlobals
for i=1:length(IntBC)
if IntBC(i).trans
for j=1:length(IntBC(i).blocks)
b = IntBC(i).blocks(j);
SetQTBTransVecBlock(Blocks{b},IntBC(i),t);
end
end
end
end
function SetQTBTransVecBlock(block,IntBC,t)
QTGlobals
n = bl... |
github | tjssmy/QuadTreeCode-master | AddQTBlockSurfacePnts.m | .m | QuadTreeCode-master/AddQTBlockSurfacePnts.m | 2,952 | utf_8 | 64876a4e168349d60dbb0ddebd6d1fd8 |
function [x,y,v] = AddQTBlockSurfacePnts(block, x,y,v,var)
QTGlobals
if ~block.br(RIGHT)
x(end+1) = block.x + block.dx/2;
y(end+1) = block.y;
if isnan(block.bc(RIGHT,var)) % floating
v(end+1) = block.data(var);
else
v(end+1) = block.bc(RIGHT,var);
end
end
if ~block.br(LEFT)
... |
github | tjssmy/QuadTreeCode-master | GetQTFct.m | .m | QuadTreeCode-master/GetQTFct.m | 1,114 | utf_8 | dce8d141740e9460e56a4ca9896f9ac4 | function GetQTFct(var,mode,drawfig,tle)
QTGlobals
fprintf('Generating Block Surface Values (%s,%i) : ',mode,var);
for b=1:nBlocks
if mod(b,500) == 0, fprintf('... %i',b); end
if mod(b,3500) == 0, fprintf('\n\t\t'); end
SetQTFctBlock(Blocks{b},var,mode);
end
fprintf('\n');
if drawfig
figure
... |
github | tjssmy/QuadTreeCode-master | GetQTBlockSurfaces.m | .m | QuadTreeCode-master/GetQTBlockSurfaces.m | 1,219 | utf_8 | 575acc902286386a8078eb187952d781 | function GetQTBlockSurfaces(var)
QTGlobals
fprintf('Generating Block Surfaces (%i): ',var);
for b=1:nBlocks
if mod(b,500) == 0, fprintf('... %i',b); end
Blocks{b} = GetQTBlockSurface(Blocks{b},var);
end
fprintf('\n');
end
function [block] = GetQTBlockSurface(block,var)
QTGlobals
dx2 = block.dx/2;
dy2 = ... |
github | tjssmy/QuadTreeCode-master | SetQTBVec.m | .m | QuadTreeCode-master/SetQTBVec.m | 2,223 | utf_8 | f67e3739c4ec623a6eb61995b4e1f6de | function [IntBC] = SetQTBVec(LeftBC,RightBC,UpBC,DownBC,IntBC)
QTGlobals
for b=1:nBlocks
IntBC = SetQTBVecBlock(Blocks{b},RightBC,LeftBC,UpBC,DownBC,IntBC);
end
end
function [IntBC] = SetQTBVecBlock(block,RightBC,LeftBC,UpBC,DownBC,IntBC)
QTGlobals
dx = block.dx;
dy = block.dy;
k1 = block.data(KVAL);
n = bloc... |
github | csdms-contrib/Auto_marsh-master | auto_marsh.m | .m | Auto_marsh-master/auto_marsh.m | 18,960 | utf_8 | bcd1be0f475c2c6601aa1bc1e04b827f | %--------------------------------------------------------------------------
% Author: Nicoletta Leonardi email: nicleona@bu.edu
% Copyrigth 2015 (C) Nicoletta Leonardi
%--------------------------------------------------------------------------
% START of copying permission statement
%-----------------------... |
github | jamt9000/matconvnet-rcnn-master | cnn_cifar.m | .m | matconvnet-rcnn-master/examples/cnn_cifar.m | 5,552 | utf_8 | 47f4bcb2198834a3904e7a0ebbca93a6 | function [net, info] = cnn_cifar(varargin)
% CNN_CIFAR Demonstrates MatConvNet on CIFAR
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', 'matlab', 'vl_setupnn.m')) ;
opts.dataDir = fullfile('data','cifar') ;
opts.expDir = fullfile('data','cifar-baseline') ;
opts.imdbPath = fullfile(opts.expDir, 'imdb.mat'... |
github | jamt9000/matconvnet-rcnn-master | cnn_imagenet.m | .m | matconvnet-rcnn-master/examples/cnn_imagenet.m | 9,528 | utf_8 | ae78900a71011c47ba0f6e3a27de7d20 | function cnn_imagenet(varargin)
% CNN_IMAGENET Demonstrates training a CNN on ImageNet
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', 'matlab', 'vl_setupnn.m')) ;
opts.dataDir = fullfile('data','imagenet12') ;
opts.expDir = fullfile('data','imagenet12-baseline') ;
[opts, varargin] = vl_argparse(opts, va... |
github | jamt9000/matconvnet-rcnn-master | cnn_mnist.m | .m | matconvnet-rcnn-master/examples/cnn_mnist.m | 4,985 | utf_8 | 51ee14dbfb7e624c3975087aeaacee71 | function [net, info] = cnn_mnist(varargin)
% CNN_MNIST Demonstrated MatConNet on MNIST
run(fullfile(fileparts(mfilename('fullpath')),...
'..', 'matlab', 'vl_setupnn.m')) ;
opts.dataDir = fullfile('data','mnist') ;
opts.expDir = fullfile('data','mnist-baseline') ;
opts.imdbPath = fullfile(opts.expDir, 'imdb.mat');
... |
github | jamt9000/matconvnet-rcnn-master | cnn_train.m | .m | matconvnet-rcnn-master/examples/cnn_train.m | 9,895 | utf_8 | 75f6886ec6428d420bddf8b3377bfb12 | function [net, info] = cnn_train(net, imdb, getBatch, varargin)
% CNN_TRAIN Demonstrates training a CNN
% CNN_TRAIN() is an example learner implementing stochastic gradient
% descent with momentum to train a CNN for image classification.
% It can be used with different datasets by providing a suitable
% g... |
github | jamt9000/matconvnet-rcnn-master | cnn_imagenet_evaluate.m | .m | matconvnet-rcnn-master/examples/cnn_imagenet_evaluate.m | 2,726 | utf_8 | 2f3d1847fbe22a45f35687ae096a0376 | function info = cnn_imagenet_evaluate(varargin)
% CNN_IMAGENET_EVALUATE Evauate MatConvNet models on ImageNet
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', 'matlab', 'vl_setupnn.m')) ;
opts.dataDir = fullfile('data', 'imagenet12') ;
opts.expDir = fullfile('data', 'imagenet12-eval-vgg-f') ;
opts.imdbPat... |
github | jamt9000/matconvnet-rcnn-master | vl_compilenn.m | .m | matconvnet-rcnn-master/matlab/vl_compilenn.m | 21,728 | utf_8 | 6e80bd21008f3090a4fee2f42ca25d6e | function vl_compilenn( varargin )
% VL_COMPILENN Compile the MatConvNet toolbox
% The `vl_compilenn()` function compiles the MEX files in the
% MatConvNet toolbox. See below for the requirements for compiling
% CPU and GPU code, respectively.
%
% `vl_compilenn('OPTION', ARG, ...)` accepts the following opt... |
github | jamt9000/matconvnet-rcnn-master | vl_simplenn_display.m | .m | matconvnet-rcnn-master/matlab/vl_simplenn_display.m | 5,140 | utf_8 | d5d591422b0f719d77dc2b206fabef82 | function info = vl_simplenn_display(net, res)
% VL_SIMPLENN_DISPLAY Simple CNN statistics
% VL_SIMPLENN_DISPLAY(NET) prints statistics about the network NET.
% Copyright (C) 2014 Andrea Vedaldi.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD li... |
github | jonasalmeida/json4mat-master | json2mat.m | .m | json4mat-master/json2mat.m | 4,667 | utf_8 | cacea0ef527d8a4f7a6c3075c88f1109 | function M=json2mat(J,tag)
%JSON2MAT converts a javscript data object (JSON) into a Matlab structure
% using s recursive approach. J can also be a file name.
%
%Example: lala=json2mat('{lele:2,lili:4,lolo:[1,2,{lulu:5,bubu:[[1,2],[3,4],[5,6]]}]}')
% notice lala.lolo{3}.bubu is read as a 2D matrix... |
github | nicolepaul/cee287-matlab-master | NewmarkAverageAccelerationCy.m | .m | cee287-matlab-master/NewmarkAverageAccelerationCy.m | 4,350 | utf_8 | 9b2ae184faf1bc1a8c4d34a520f4d065 | function [u, v, a, Sd, Sv, Sa, PSv, PSa, Fs, mu] = NewmarkAverageAccelerationCy(Tn, E, A, dt, u0, v0, Cy)
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% AVERAGE ACCELERATION INTEGRATION
% Performs numerical integration using average acceleration approach
% Assumes acceleration input to be i... |
github | nicolepaul/cee287-matlab-master | NewmarkAvgAccAlpha_Cy.m | .m | cee287-matlab-master/NewmarkAvgAccAlpha_Cy.m | 4,885 | utf_8 | dea461dc8c7d74124c7887896ea724c0 | function [u, v, a, Sd, Sv, Sa, PSv, PSa, Fs, mu] = NewmarkAvgAccAlpha_Cy(Tn, E, A, dt, u0, v0, Cy, alpha)
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% AVERAGE ACCELERATION INTEGRATION
% Performs numerical integration using average acceleration approach
% Assumes acceleration input to be i... |
github | nicolepaul/cee287-matlab-master | NewmarkAverageAcceleration.m | .m | cee287-matlab-master/NewmarkAverageAcceleration.m | 4,354 | utf_8 | 3e34fa89f59279d716b0c99b6ca75cfa | function [u, v, a, Sd, Sv, Sa, PSv, PSa, Fs, mu] = NewmarkAverageAcceleration(Tn, E, A, dt, u0, v0, uy)
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% AVERAGE ACCELERATION INTEGRATION
% Performs numerical integration using average acceleration approach
% Assumes acceleration input to be in ... |
github | nicolepaul/cee287-matlab-master | FindMu2.m | .m | cee287-matlab-master/FindMu2.m | 4,878 | utf_8 | 9bc7a61f48c040836e2c7cae44b20192 | function mu = FindMu2(Tn, E, A, dt, u0, v0, Cy, alpha)
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% AVERAGE ACCELERATION INTEGRATION
% Performs numerical integration using average acceleration approach
% Assumes acceleration input to be in g
% Calculates outputs in g, in/s, and in
%
% In... |
github | nicolepaul/cee287-matlab-master | FindMu.m | .m | cee287-matlab-master/FindMu.m | 4,408 | utf_8 | 26096a2cad84693950cd33611a4d46bb | function mu = FindMu(Tn, E, A, dt, u0, v0, uy)
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% AVERAGE ACCELERATION INTEGRATION
% Performs numerical integration using average acceleration approach
% Assumes acceleration input to be in g
% Calculates outputs in g, in/s, and in
%
% Inputs:
% ... |
github | nicolepaul/cee287-matlab-master | eigenvalueAnalysis.m | .m | cee287-matlab-master/homework 8/functions/eigenvalueAnalysis.m | 2,115 | utf_8 | 6f1b3615c687c7b79e8d1ada42b0f81e | function [w,T,sphi,Gamma] = eigenvalueAnalysis(nfloors,nmodes,mass,stiffness)
% Compute periods of vibration, mode shapes and participation factors
% @nfloors: The number of storys in the building
% @nfloors: The number of modes to consider
% @stiffness: Either the stiffness at every floor (float) or a vector of ... |
github | nicolepaul/cee287-matlab-master | eigenvalueAnalysis.m | .m | cee287-matlab-master/Homework5/eigenvalueAnalysis.m | 1,910 | utf_8 | 83bb737388a230b864e90dce7b72ea10 | function [w,T,sphi,Gamma] = eigenvalueAnalysis(nfloors,mass,stiffness)
% Compute periods of vibration, mode shapes and participation factors
% @nfloors: The number of storeys in the building
% @stiffness: Either the stiffness at every floor (float) or a vector of stiffnesses
% @mass: Either the mass at every floo... |
github | nicolepaul/cee287-matlab-master | equivalentLateralForce.m | .m | cee287-matlab-master/Homework5/equivalentLateralForce.m | 3,099 | utf_8 | be5af059b31d725ffe4f6b065d3b49a2 | function equivalentLateralForce(Tn,Cs,M,K,Hi)
% Complete the equivalent lateral force procedure
% @Tn - The natural period of the structure
% @Cs - The seismic response coefficient
% @M - The mass matrix [kips/g]
% @K - The stiffness matrix [kips/in]
% @Hi - The height of each floor [ft]
%... |
github | TerryC78/Footstep-Planning-of-Walking-Robots-Project-master | HighLevelPlan.m | .m | Footstep-Planning-of-Walking-Robots-Project-master/Matlab Simmechanics Simulator/HighLevelPlan/HighLevelPlan.m | 3,275 | utf_8 | 2e7419542fb84434f279001ab1e24492 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% High level A* planning which returns the body path of the robot
%
% Auther: Tianyu Chen
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [ rob_path_new ] = HighLevelPlan(CostMap)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if na... |
github | TerryC78/Footstep-Planning-of-Walking-Robots-Project-master | Train_cost_func.m | .m | Footstep-Planning-of-Walking-Robots-Project-master/Matlab Simmechanics Simulator/Learning the Terrain/Train_cost_func.m | 5,030 | utf_8 | 8b996627425da7fd190e8faaf8c3c3aa | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Train cost function
%
% Author: Tianyu Chen
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [CostElement, CostMap, LearnTerrain] = Train_cost_func(TerrainFile, Options)
fprintf('[Learn Terrain] Learning...\n')
global Classifier
global TemplateData
global T... |
github | dragontt/proj_alzheimers-master | GUI_Panel.m | .m | proj_alzheimers-master/yk_VascularTracing/functions/GUI_Panel.m | 27,590 | utf_8 | dab5b010061ebb887aff7101b93bcd15 | function varargout = GUI_Panel(varargin)
% GUI_PANEL MATLAB code for GUI_Panel.fig
% GUI_PANEL, by itself, creates a new GUI_PANEL or raises the existing
% singleton*.
%
% H = GUI_PANEL returns the handle to a new GUI_PANEL or the handle to
% the existing singleton*.
%
% GUI_PANEL('CALLBACK',hO... |
github | dragontt/proj_alzheimers-master | backgndsubtraction.m | .m | proj_alzheimers-master/yk_VascularTracing/functions/backgndsubtraction.m | 1,492 | utf_8 | 2d1065f770bf93897809b13ee1490534 | function imOut=backgndsubtraction(im,method,param)
% This function (background subtraction) uses scale space approach to
% eliminate lumination variance of image background.
%
% Input: im - input image
% method - options are 'ScaleSpace' and 'TopHat' (default)
% param - if 'ScaleSpace', times of sca... |
github | dragontt/proj_alzheimers-master | FixStructure.m | .m | proj_alzheimers-master/yk_VascularTracing/functions/FixStructure.m | 5,490 | utf_8 | 72f4f2b5902c29fc16488b4af9eb1b4a | function S=FixStructure(S)
% This function fixes errors of node-vessel connectivity and vessel
% numeratiion. It reindexes node and vessel number; reconnect vessel to node
% according to the smallest distance, if connection missed; then reassign
% the connection of node to vessel. The vessel to node connection is
% or... |
github | dragontt/proj_alzheimers-master | MergeStructNodes.m | .m | proj_alzheimers-master/yk_VascularTracing/functions/MergeStructNodes.m | 3,916 | utf_8 | d22070a3235610ccf3bae7b735d96466 | function SS=MergeStructNodes(SS,threshold)
% This function merges cluster of nodes within certain distance.
if nargin<2
threshold=5;
end
NodeList=zeros(SS.NodesCount,3);
for i=1:SS.NodesCount
for j=1:3
NodeList(i,j)=SS.Nodes{i}.location{j};
end
end
% find cluster of nodes within short radius
mtxD... |
github | dragontt/proj_alzheimers-master | stlwrite.m | .m | proj_alzheimers-master/yk_VascularTracing/functions/stlwrite.m | 8,302 | utf_8 | 82e7e5431b0da5beca25a48c697e5afa | function stlwrite(filename, varargin)
%STLWRITE Write STL file from patch or surface data.
%
% STLWRITE(FILE, FV) writes a stereolithography (STL) file to FILE for a
% triangulated patch defined by FV (a structure with fields 'vertices'
% and 'faces').
%
% STLWRITE(FILE, FACES, VERTICES) takes faces an... |
github | dragontt/proj_alzheimers-master | myaa.m | .m | proj_alzheimers-master/yk_VascularTracing/functions/myaa.m | 11,137 | utf_8 | 37e5fe8747de9b34dc8864aee344df56 | function [varargout] = myaa(varargin)
%MYAA Render figure with anti-aliasing.
% MYAA
% Anti-aliased rendering of the current figure. This makes graphics look
% a lot better than in a standard matlab figure, which is useful for
% publishing results on the web or to better see the fine details in a
% comple... |
github | dragontt/proj_alzheimers-master | ScaleSpace.m | .m | proj_alzheimers-master/yk_VascularTracing/functions/ScaleSpace.m | 793 | utf_8 | 798fb0b99b054fd335d40c38b510f678 | function imOut=ScaleSpace(im,xScale)
% This function (background subtraction) uses scale space approach to
% eliminate lumination variance of image background.
%
% Input: im = input image
% xScale = times of scale needed
% widthBoundary = width of the image boundary not to be subtracted
% Output: imOut =... |
github | dragontt/proj_alzheimers-master | GUI_Image.m | .m | proj_alzheimers-master/yk_VascularTracing/functions/GUI_Image.m | 4,324 | utf_8 | 29515099b61d1f0807a2667dfa5e0b52 | function varargout = GUI_Image(varargin)
% GUI_IMAGE MATLAB code for GUI_Image.fig
% GUI_IMAGE, by itself, creates a new GUI_IMAGE or raises the existing
% singleton*.
%
% H = GUI_IMAGE returns the handle to a new GUI_IMAGE or the handle to
% the existing singleton*.
%
% GUI_IMAGE('CALLBACK',hO... |
github | dragontt/proj_alzheimers-master | goTrace_toVectorized.m | .m | proj_alzheimers-master/yk_VascularTracing/functions/goTrace_toVectorized.m | 4,621 | utf_8 | cbc510e3532e7100e7651c0a848fe4e5 | function v= goTrace_toVectorized(s)
%Converts a structure exported from go tracer into a vectorized structure
%with the same format as the output of vida vectorized structure v differs
%from the structure of vida output in the following ways:
% v.Vertices has dummmy fields AllLabels AllConfidences AllNot... |
github | dragontt/proj_alzheimers-master | fixPlistStructure.m | .m | proj_alzheimers-master/yk_VascularTracing/functions/fixPlistStructure.m | 1,463 | utf_8 | 88348023c36b54697c52e3525274a524 | function S=fixPlistStructure(S)
for i=1:S.VesselsCount
charStart=[num2str(S.Vessels{i}.startPoint{1},'%10.6f'),', ',...
num2str(S.Vessels{i}.startPoint{2},'%10.6f'),', ',...
num2str(S.Vessels{i}.startPoint{3},'%10.6f')];
charEnd=[num2str(S.Vessels{i}.endPoint{1},'%10.6f'),', ',...
num2s... |
github | dragontt/proj_alzheimers-master | imthreshold3.m | .m | proj_alzheimers-master/yk_VascularTracing/functions/imthreshold3.m | 667 | utf_8 | 997a4a66f385b140629a38aa2b8a7748 | function imOut=imthreshold3(im,windowSize,offset)
% This function uses adaptive thresholding algorithm.
if nargin<2
windowSize=85;
offset=-9;
elseif nargin<3
offset=-9;
end
dim=size(im);
imOut=zeros(dim);
if length(dim)==2
imOut=AdaptiveThresholding(im,windowSize,offset);
elseif length(dim)==3
for... |
github | dragontt/proj_alzheimers-master | goTrace_toVectorized.m | .m | proj_alzheimers-master/yk_Convert/goTrace_toVectorized.m | 4,435 | utf_8 | d8abb0c28278f2602eaaaf908745b2b6 | function v= goTrace_toVectorized(s)
%Converts a structure exported from go tracer into a vectorized structure
%with the same format as the output of vida vectorized structure v differs
%from the structure of vida output in the following ways:
% v.Vertices has dummmy fields AllLabels AllConfidences AllNot... |
github | dragontt/proj_alzheimers-master | removeBadTracings.m | .m | proj_alzheimers-master/yk_Convert/removeBadTracings.m | 5,821 | utf_8 | 97b318c7c34526b6aa76577fdb880e13 | function [VessLengths,newStruct]= removeBadTracings(badStruct,Openfile)
%remove all of the bad vessels and nodes in the vectorized structure bad
%Struct which is an output strucuture of an automated tracing code, namely
%VIDA, Openfile is the filename with which it is saved.
tic
[vertsI,dim]=size(badStruct.Vertices.All... |
github | dragontt/proj_alzheimers-master | GUI_Panel.m | .m | proj_alzheimers-master/yk_PlugAnalysis/GUI_Panel.m | 24,840 | utf_8 | ab6eabe43ee2a97c40f335b75a4835f0 | function varargout = GUI_Panel(varargin)
% GUI_PANEL MATLAB code for GUI_Panel.fig
% GUI_PANEL, by itself, creates a new GUI_PANEL or raises the existing
% singleton*.
%
% H = GUI_PANEL returns the handle to a new GUI_PANEL or the handle to
% the existing singleton*.
%
% GUI_PANEL('CALLBACK',hO... |
github | dragontt/proj_alzheimers-master | backgndsubtraction.m | .m | proj_alzheimers-master/yk_PlugAnalysis/backgndsubtraction.m | 1,492 | utf_8 | 2d1065f770bf93897809b13ee1490534 | function imOut=backgndsubtraction(im,method,param)
% This function (background subtraction) uses scale space approach to
% eliminate lumination variance of image background.
%
% Input: im - input image
% method - options are 'ScaleSpace' and 'TopHat' (default)
% param - if 'ScaleSpace', times of sca... |
github | dragontt/proj_alzheimers-master | ScaleSpace.m | .m | proj_alzheimers-master/yk_PlugAnalysis/ScaleSpace.m | 793 | utf_8 | 798fb0b99b054fd335d40c38b510f678 | function imOut=ScaleSpace(im,xScale)
% This function (background subtraction) uses scale space approach to
% eliminate lumination variance of image background.
%
% Input: im = input image
% xScale = times of scale needed
% widthBoundary = width of the image boundary not to be subtracted
% Output: imOut =... |
github | dragontt/proj_alzheimers-master | GUI_Image.m | .m | proj_alzheimers-master/yk_PlugAnalysis/GUI_Image.m | 4,266 | utf_8 | 6a87773d188722dad443ee86f3f60435 | function varargout = GUI_Image(varargin)
% GUI_IMAGE MATLAB code for GUI_Image.fig
% GUI_IMAGE, by itself, creates a new GUI_IMAGE or raises the existing
% singleton*.
%
% H = GUI_IMAGE returns the handle to a new GUI_IMAGE or the handle to
% the existing singleton*.
%
% GUI_IMAGE('CALLBACK',hO... |
github | dragontt/proj_alzheimers-master | imthreshold3.m | .m | proj_alzheimers-master/yk_PlugAnalysis/imthreshold3.m | 612 | utf_8 | fc0bbfc3fc5f8474b530453b5dc1627d | function imOut=imthreshold3(im,windowSize,offset)
% This function uses adaptive thresholding algorithm.
if nargin<2
windowSize=85;
offset=-9;
elseif nargin<3
offset=-9;
end
dim=size(im);
imOut=zeros(dim);
if length(dim)==2
imOut=AdaptiveThresholding(im,windowSize,offset);
elseif length(dim)==3
for... |
github | dragontt/proj_alzheimers-master | freezeColors.m | .m | proj_alzheimers-master/yk_VascularAnalysis/functions/freezeColors.m | 9,815 | utf_8 | 2068d7a4f7a74d251e2519c4c5c1c171 | function freezeColors(varargin)
% freezeColors Lock colors of plot, enabling multiple colormaps per figure. (v2.3)
%
% Problem: There is only one colormap per figure. This function provides
% an easy solution when plots using different colomaps are desired
% in the same figure.
%
% freezeColors freeze... |
github | dragontt/proj_alzheimers-master | backgndsubtraction.m | .m | proj_alzheimers-master/yk_VascularAnalysis/functions/backgndsubtraction.m | 1,492 | utf_8 | 2d1065f770bf93897809b13ee1490534 | function imOut=backgndsubtraction(im,method,param)
% This function (background subtraction) uses scale space approach to
% eliminate lumination variance of image background.
%
% Input: im - input image
% method - options are 'ScaleSpace' and 'TopHat' (default)
% param - if 'ScaleSpace', times of sca... |
github | dragontt/proj_alzheimers-master | FixStructure.m | .m | proj_alzheimers-master/yk_VascularAnalysis/functions/FixStructure.m | 5,276 | utf_8 | 2362f03c8372cc2f8f8760025ff46404 | function S=FixStructure(S)
% This function fixes errors of node-vessel connectivity and vessel
% numeratiion. It reindexes node and vessel number; reconnect vessel to node
% according to the smallest distance, if connection missed; then reassign
% the connection of node to vessel. The vessel to node connection is
% or... |
github | dragontt/proj_alzheimers-master | myaa.m | .m | proj_alzheimers-master/yk_VascularAnalysis/functions/myaa.m | 11,137 | utf_8 | 37e5fe8747de9b34dc8864aee344df56 | function [varargout] = myaa(varargin)
%MYAA Render figure with anti-aliasing.
% MYAA
% Anti-aliased rendering of the current figure. This makes graphics look
% a lot better than in a standard matlab figure, which is useful for
% publishing results on the web or to better see the fine details in a
% comple... |
github | dragontt/proj_alzheimers-master | GUI_Image.m | .m | proj_alzheimers-master/yk_VascularAnalysis/functions/GUI_Image.m | 4,324 | utf_8 | 29515099b61d1f0807a2667dfa5e0b52 | function varargout = GUI_Image(varargin)
% GUI_IMAGE MATLAB code for GUI_Image.fig
% GUI_IMAGE, by itself, creates a new GUI_IMAGE or raises the existing
% singleton*.
%
% H = GUI_IMAGE returns the handle to a new GUI_IMAGE or the handle to
% the existing singleton*.
%
% GUI_IMAGE('CALLBACK',hO... |
github | dragontt/proj_alzheimers-master | imthreshold2.m | .m | proj_alzheimers-master/yk_VascularAnalysis/functions/imthreshold2.m | 1,333 | utf_8 | 5a5b68c9cd6a6d14cfe2f3eedef45d4f | function imOut=imthreshold2(im,weight,windowSize)
% This function uses local adaptive algorithm to threshold image pixel by
% pixel. For individual pixel, compute the mean intensity and standard
% deviation (std) of all neighbor pixels with in certain window.
% Then do math: threshold = mean + std * weight
if nargin<2... |
github | dragontt/proj_alzheimers-master | imthreshold3.m | .m | proj_alzheimers-master/yk_VascularAnalysis/functions/imthreshold3.m | 615 | utf_8 | 41e2385c7ea44ced4dda3dd98c6438c1 | function imOut=imthreshold3(im,windowSize,offset)
% This function uses adaptive thresholding algorithm.
if nargin<2
windowSize=85;
offset=-9;
elseif nargin<3
offset=-9;
end
dim=size(im);
imOut=zeros(dim);
if length(dim)==2
imOut=AdaptiveThresholding(im,windowSize,offset);
elseif length(dim)==3
par... |
github | danbar/murphy_line_draw-master | murphy_line_draw.m | .m | murphy_line_draw-master/murphy_line_draw.m | 4,504 | utf_8 | 4f5baca547d46e86e18c619b7ba2dbb1 | function bitmap = murphy_line_draw(bitmap, pt0, pt1, thickness)
% A modified version of Bresenham's line algorithm for drawing lines of
% arbitrary thickness.
%
%
% Documentation
% -------------
% The function implements Murphy's line algorithm [1] to draw lines of
% arbitrary thickness. It is a modified version of Br... |
github | Xiaomi2008/Caffe_3D_FF-master | col2im_gpu.m | .m | Caffe_3D_FF-master/matlab_tool/col2im_gpu.m | 3,037 | utf_8 | 2e0bb5d51ee5afcedeb298e8f32de0de | function [data_im, da_all_idx]= col2im_gpu(data_col, channels, height, width, depth, ksize, pad, stride)
height_col = floor((height + 2 * pad - ksize) / stride) + 1;
width_col = floor((width + 2 * pad - ksize) / stride) + 1;
depth_col = floor((depth + 2 * pad - ksize) / stride) + 1;
num_kernels = channels * ... |
github | Xiaomi2008/Caffe_3D_FF-master | drawOverall_all_report.m | .m | Caffe_3D_FF-master/matlab_tool/drawOverall_all_report.m | 10,902 | utf_8 | be913ea0530ae8fdc65f5e6d4ef00941 | function drawOverall_all_report()
if ispc
addpath('Z:\ABA\automatedGeneLabelingProject\code');
else
addpath('/home/tzeng/ABA/automatedGeneLabelingProject/code');
end
%result_dir=translatePath('Z:\ABA\autoGenelable_multi_lables_proj\report\result');
result_dir=translatePath('Z:\ABA\autoGenelable_multi_l... |
github | Xiaomi2008/Caffe_3D_FF-master | train_CAFFE_linearSVM_All.m | .m | Caffe_3D_FF-master/matlab_tool/train_CAFFE_linearSVM_All.m | 5,271 | utf_8 | 29f32477e068f9a82d18f1830a131f26 | function train_CAFFE_linearSVM_All(Age,mat_file)
addpath('/home/tzeng/ABA/automatedGeneLabelingProject/code');
data_dir=translatePath('Z:\ABA\autoGenelable_multi_lables_proj\data');
temp_dir=translatePath('Z:\ABA\autoGenelable_multi_lables_proj\temp');
% if nargin <6
% host ='hpcd';
% else
% host ='hpcq';
% end
caffe_m... |
github | Xiaomi2008/Caffe_3D_FF-master | col2im_gpu_kernel.m | .m | Caffe_3D_FF-master/matlab_tool/col2im_gpu_kernel.m | 2,341 | utf_8 | 3e17f4d41fbb06015ecd070ec676abad | function col2im_gpu(data_col, channels, height, width, depth, ksize, pad, stride, data_im)
height_col = foolr(height + 2 * pad - ksize) / stride) + 1;
width_col = floor((width + 2 * pad - ksize) / stride) + 1;
depth_col = floor((depth + 2 * pad - ksize) / stride) + 1;
num_kernels = channels * height * width *... |
github | Xiaomi2008/Caffe_3D_FF-master | readCaffeTrainTestImageListFromMatFile.m | .m | Caffe_3D_FF-master/matlab_tool/readCaffeTrainTestImageListFromMatFile.m | 2,196 | utf_8 | e3808e8420aaeef70d28c104a769446a | function [trainData,valData,testData,remainData] = readCaffeTrainTestImageListFromMatFile(age,ontology_level,ImageType)
addpath(translatePath('Z:\ABA\automatedGeneLabelingProject\code'));
numOfsectionForEachSerial =10;
data_dir=translatePath('Z:\ABA\autoGenelable_multi_lables_proj\data');
data_dir_cur=translatePath... |
github | Xiaomi2008/Caffe_3D_FF-master | train_CAFFE_linear_on_All_MPI.m | .m | Caffe_3D_FF-master/matlab_tool/train_CAFFE_linear_on_All_MPI.m | 5,995 | utf_8 | c4bf16655e8bb2522ce898758c14ce4a | function train_CAFFE_linear_on_All_MPI(Age,layer,mat_file,featuresampleMod,fineTune)
addpath('/home/tzeng/ABA/automatedGeneLabelingProject/code');
data_dir=translatePath('Z:\ABA\autoGenelable_multi_lables_proj\data');
temp_dir=translatePath('Z:\ABA\autoGenelable_multi_lables_proj\temp');
% if nargin <6
% host ='hp... |
github | Xiaomi2008/Caffe_3D_FF-master | generate_txtFile_for_Caffe_input_using_balanced_preSelectedIDX.m | .m | Caffe_3D_FF-master/matlab_tool/generate_txtFile_for_Caffe_input_using_balanced_preSelectedIDX.m | 8,511 | utf_8 | d3cc5f5ab72ae12693ffce062b4451ad | function generate_txtFile_for_Caffe_input_using_balanced_preSelectedIDX( age, numOfImagesForEachSerial,ontology_level,mask_image,classType)
%[level_labs,bowmatrix]=findFirstConnatAnnotatedLabel(level_labs_pattern,norm_matrix);
if nargin <4
mask_image =false;
end
addpath(translatePath('Z:\ABA\automatedGeneLabelingPro... |
github | Xiaomi2008/Caffe_3D_FF-master | col2im_gpul.m | .m | Caffe_3D_FF-master/matlab_tool/col2im_gpul.m | 2,342 | utf_8 | b32a959eaee393e53790102e80a7b963 | function col2im_gpu(data_col, channels, height, width, depth, ksize, pad, stride, data_im)
height_col = foolr(height + 2 * pad - ksize) / stride) + 1;
width_col = floor((width + 2 * pad - ksize) / stride) + 1;
depth_col = floor((depth + 2 * pad - ksize) / stride) + 1;
num_kernels = channels * height * width *... |
github | Xiaomi2008/Caffe_3D_FF-master | SNEMI3D_metrics.m | .m | Caffe_3D_FF-master/matlab_tool/SNEMI3D_metrics.m | 1,554 | utf_8 | 3e56a0fa7f15f9150ae27ff4e20b00bf | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SNEMI3D challenge: 3D segmentation of neurites in EM images
%
% Script to calculate the segmentation error between some 3D
% original labels and their corresponding proposed labels.
%
% The evaluation metric is:
% - Rand error: 1 - F-score of adapt... |
github | Xiaomi2008/Caffe_3D_FF-master | caffe_feature_extraction.m | .m | Caffe_3D_FF-master/matlab_tool/caffe_feature_extraction.m | 6,818 | utf_8 | 13de18beeb9c1f3224aebf9e96bf9b51 | function caffe_feature_extraction(Age,net_layer_name, deviceID,model_def_file,model_file,ImageType,featureType)
addpath(translatePath('Z:\ABA\autoGenelable_multi_lables_proj\code\caffe\matlab\caffe'));
if nargin==0
Age='E11.5';
end
if nargin <2
net_layer_name ='conv5_1';
deviceID =2;
end... |
github | Xiaomi2008/Caffe_3D_FF-master | drawOverallAUCreport.m | .m | Caffe_3D_FF-master/matlab_tool/drawOverallAUCreport.m | 5,983 | utf_8 | 3078e7b9911e68b8a69e40fe443933d9 | function drawOverallAUCreport()
if ispc
addpath('Z:\tzeng\ABA\automatedGeneLabelingProject\code');
else
addpath('/home/tzeng/ABA/automatedGeneLabelingProject/code');
end
%result_dir=translatePath('Z:\ABA\autoGenelable_multi_lables_proj\report\result');
result_dir=translatePath('Z:\ABA\autoGenelable_mul... |
github | Xiaomi2008/Caffe_3D_FF-master | build_Caffe_Matrix.m | .m | Caffe_3D_FF-master/matlab_tool/build_Caffe_Matrix.m | 5,695 | utf_8 | 1f0944e4a45e34e5aedf77c679d42f87 | function build_Caffe_Matrix(model_name,Age,Layer,partition_mode, feature_sample_mode,featureType)
if ispc
addpath('Z:\tzeng\ABA\automatedGeneLabelingProject\code');
else
addpath('/home/tzeng/ABA/automatedGeneLabelingProject/code');
end
if nargin <5
partition_mode= 'SagiPartition';
feature_sample_mode ... |
github | Xiaomi2008/Caffe_3D_FF-master | generate_txtFile_for_Caffe_input.m | .m | Caffe_3D_FF-master/matlab_tool/generate_txtFile_for_Caffe_input.m | 4,885 | utf_8 | 8aa189e63a3a220d001fb0669eb06f71 | function generate_txtFile_for_Caffe_input( age, numOfImagesForEachSerial,train_ratio,val_ratio)
addpath('/home/tzeng/ABA/automatedGeneLabelingProject/code');
[serial_section_info ,max_sagital_sectionPos]= getSerial_Info_ByAge(age);
num_serail=length(serial_section_info);
%'train_validataion_test_set_for_caffeCN... |
github | Xiaomi2008/Caffe_3D_FF-master | generateCNN_multilabel_multiclass_protoFile.m | .m | Caffe_3D_FF-master/matlab_tool/generateCNN_multilabel_multiclass_protoFile.m | 2,412 | utf_8 | fb66822aecc6b5e2f571203b8cca7eb0 |
function generateCNN_multilabel_multiclass_protoFile(outputFileName)
bottom_blob_name='fc7';
top_blob_name='fc8'
full_connection_layer_name=top_blob_name
fileID = fopen(outputFileName,'w');
blobs_lr_1=3;
blobs_lr_2=6;
num_class =4;
label_name ='L';
num_labels=81;
% for i=1:num_labels
% fprintf(fileID,'%s{\n','laye... |
github | Xiaomi2008/Caffe_3D_FF-master | caffeData_linear_trainPredict_ontology_5_MPI.m | .m | Caffe_3D_FF-master/matlab_tool/caffeData_linear_trainPredict_ontology_5_MPI.m | 16,708 | utf_8 | 1aa94c11d98ee7fab189d45f7ac28479 | function caffeData_linear_trainPredict_ontology_5_MPI()
addpath('/home/tzeng/ABA/automatedGeneLabelingProject/code');
temp_dir =translatePath('Z:\ABA\autoGenelable_multi_lables_proj\temp');
data_dir =translatePath('Z:\ABA\autoGenelable_multi_lables_proj\data');
ovf_data_dir =translatePath('Z:\ABA\automatedGeneLabelingP... |
github | Xiaomi2008/Caffe_3D_FF-master | set_run_all_param.m | .m | Caffe_3D_FF-master/matlab_tool/set_run_all_param.m | 1,529 | utf_8 | 0758e2b58570d94311c900f380e05816 | %% This is vgg_16 layer info
% 0('data', (10, 3, 224, 224))
% 1('conv1_1', (10, 64, 224, 224))
% 2('conv1_2', (10, 64, 224, 224))
% 3('pool1', (10, 64, 112, 112))
% 4('conv2_1', (10, 128, 112, 112))
% 5('conv2_2', (10, 128, 112, 112))
% 6('pool2', (10, 128, 56, 56))
% 7('conv3_1', (10, 256, 56, 56))
% 8('conv3_2', (10,... |
github | Xiaomi2008/Caffe_3D_FF-master | buildSectionCaffeFeature.m | .m | Caffe_3D_FF-master/matlab_tool/buildSectionCaffeFeature.m | 5,693 | utf_8 | d8a1dff84f6bea22178a900f9dac28e6 | function buildSectionCaffeFeature(ImageType,model_name,subDir,Layer,feature_mode,part_mode)
%
% function : build combined features from a serial secction images (subDir) , saved it to one single file in each section images folder.
%
%dirStruct =dir(subDir);
%dir_files =dirStruct(~[ dirStruct.isdir]);
%bow_files_idx... |
github | Xiaomi2008/Caffe_3D_FF-master | caffe_feature_extraction_sliceImages.m | .m | Caffe_3D_FF-master/matlab_tool/caffe_feature_extraction_sliceImages.m | 3,097 | utf_8 | c1b018e13580b47dd404867ce61ad1ac | function featureOut=caffe_feature_extraction_sliceImages(Age,net_layer_name, deviceID,model_def_file,model_file,ImageType)
addpath(translatePath('Z:\ABA\autoGenelable_multi_lables_proj\code\caffe\matlab\caffe'));
if nargin==0
Age='E11.5';
end
[trainData,valData,testData ] = readCaffeTrainTestImageListFro... |
github | Xiaomi2008/Caffe_3D_FF-master | prepare_batch.m | .m | Caffe_3D_FF-master/matlab/caffe/prepare_batch.m | 1,338 | utf_8 | b9a4920badcde685889596bc1f0b2c1e | % ------------------------------------------------------------------------
function images = prepare_batch(image_files,IMAGE_MEAN,batch_size)
% ------------------------------------------------------------------------
if nargin < 2
d = load('ilsvrc_2012_mean');
IMAGE_MEAN = d.image_mean;
end
num_images =... |
github | Xiaomi2008/Caffe_3D_FF-master | matcaffe_demo_backward.m | .m | Caffe_3D_FF-master/matlab/caffe/matcaffe_demo_backward.m | 4,409 | utf_8 | 01c13811e553bcbe1dccd13c518e2fa8 | function [scores, gradients] = matcaffe_demo_backward(im, use_gpu)
% scores = matcaffe_demo(im, use_gpu)
% By default uses cpu, recomeded gpu
% Demo of the matlab wrapper using the ILSVRC network.
%
% input
% im color image as uint8 HxWx3
% use_gpu 1 to use the GPU, 0 to use the CPU
%
% output
% ... |
github | Xiaomi2008/Caffe_3D_FF-master | matcaffe_demo.m | .m | Caffe_3D_FF-master/matlab/caffe/matcaffe_demo.m | 6,037 | utf_8 | b8221198b31bfed63710ba281f88b749 | function [scores, maxlabel] = matcaffe_demo(im, use_gpu)
% scores = matcaffe_demo(im, use_gpu)
% By default uses cpu
%
% Demo of the matlab wrapper using the ILSVRC network.
%
% input
% im color image as uint8 HxWx3
% use_gpu 1 to use the GPU, 0 to use the CPU
%
% output
% scores 1000-dimensio... |
github | Xiaomi2008/Caffe_3D_FF-master | matcaffe_demo_vgg_mean_pix.m | .m | Caffe_3D_FF-master/matlab/caffe/matcaffe_demo_vgg_mean_pix.m | 3,169 | utf_8 | 7713ebe0395bc21a037ca4dcfa9e9b9f | function scores = matcaffe_demo_vgg_mean_pix(im, use_gpu, model_def_file, model_file)
% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file)
%
% Demo of the matlab wrapper based on the networks used for the "VGG" entry
% in the ILSVRC-2014 competition and described in the tech. report
% "Very Deep ... |
github | Xiaomi2008/Caffe_3D_FF-master | vol3d.m | .m | Caffe_3D_FF-master/matlab/caffe/vol3d.m | 7,609 | utf_8 | 5746b9360eca710bdf9c8cbe6053c65a | function [model] = vol3d(varargin)
%H = VOL3D Volume render 3-D data.
% VOL3D uses the orthogonal plane 2-D texture mapping technique for
% volume rending 3-D data in OpenGL. Use the 'texture' option to fine
% tune the texture mapping technique. This function is best used with
% fast OpenGL hardware.
%
% vol3... |
github | ms2666/Sailboat-master | quivermc.m | .m | Sailboat-master/Testing/netCDF testing/quivermc.m | 44,890 | utf_8 | 8f3bbf968e2478ea396ce0ef0e58eef4 | function [hvectors,cb] = quivermc(lat,lon,u,v,varargin)
% QUIVERMC is an adapted version of Andrew Roberts' ncquiverref.
% Dr. Roberts' function and this function fix a couple of problems with Matlab's quiverm function.
% The two primary issues with quiverm are as follows:
%
% 1. Matlab's quiverm confoundingly mixes... |
github | ms2666/Sailboat-master | landmask.m | .m | Sailboat-master/Testing/learning/3D Navigation 2/landmask.m | 6,909 | utf_8 | 23d927a20c675e250d886dc30d43be0e | function land = landmask(lat,lon,varargin)
% landmask returns a logical array describing the landness of
% any given lat/lon arrays. Requires Matlab's Mapping Toolbox.
%
% This function uses Matlab's built-in coast.mat file with inpolygons
% to determing whether input lat/lons are inside or outside perimeters of
% l... |
github | ms2666/Sailboat-master | landmask.m | .m | Sailboat-master/Testing/learning/3D Navigation 3/landmask.m | 6,909 | utf_8 | 23d927a20c675e250d886dc30d43be0e | function land = landmask(lat,lon,varargin)
% landmask returns a logical array describing the landness of
% any given lat/lon arrays. Requires Matlab's Mapping Toolbox.
%
% This function uses Matlab's built-in coast.mat file with inpolygons
% to determing whether input lat/lons are inside or outside perimeters of
% l... |
github | ms2666/Sailboat-master | landmask.m | .m | Sailboat-master/Testing/learning/3D Navigation/landmask.m | 6,909 | utf_8 | 23d927a20c675e250d886dc30d43be0e | function land = landmask(lat,lon,varargin)
% landmask returns a logical array describing the landness of
% any given lat/lon arrays. Requires Matlab's Mapping Toolbox.
%
% This function uses Matlab's built-in coast.mat file with inpolygons
% to determing whether input lat/lons are inside or outside perimeters of
% l... |
github | ms2666/Sailboat-master | landmask.m | .m | Sailboat-master/Navigation/landmask.m | 6,909 | utf_8 | 23d927a20c675e250d886dc30d43be0e | function land = landmask(lat,lon,varargin)
% landmask returns a logical array describing the landness of
% any given lat/lon arrays. Requires Matlab's Mapping Toolbox.
%
% This function uses Matlab's built-in coast.mat file with inpolygons
% to determing whether input lat/lons are inside or outside perimeters of
% l... |
github | ms2666/Sailboat-master | drawGlobe.m | .m | Sailboat-master/Navigation/drawGlobe.m | 1,026 | utf_8 | 0786991ea98f89373af681a69b8a4593 | function [] = drawGlobe()
% Set up figure
figure('Renderer','opengl')
axesm('globe')
axis equal off
view(3)
shading interp
lighting flat
camlight right
camlight left
material metal
% Lower the samplefactor, the higher the resolution
samplefactor = 15;
[Z, zleg] = etopo('/Volumes/Macintosh Extension/Documents Extensio... |
github | ChenglongChen/caffe-windows-master | prepare_batch.m | .m | caffe-windows-master/matlab/caffe/prepare_batch.m | 1,298 | utf_8 | 68088231982895c248aef25b4886eab0 | % ------------------------------------------------------------------------
function images = prepare_batch(image_files,IMAGE_MEAN,batch_size)
% ------------------------------------------------------------------------
if nargin < 2
d = load('ilsvrc_2012_mean');
IMAGE_MEAN = d.image_mean;
end
num_images = length... |
github | ChenglongChen/caffe-windows-master | matcaffe_demo.m | .m | caffe-windows-master/matlab/caffe/matcaffe_demo.m | 3,344 | utf_8 | 669622769508a684210d164ac749a614 | function [scores, maxlabel] = matcaffe_demo(im, use_gpu)
% scores = matcaffe_demo(im, use_gpu)
%
% Demo of the matlab wrapper using the ILSVRC network.
%
% input
% im color image as uint8 HxWx3
% use_gpu 1 to use the GPU, 0 to use the CPU
%
% output
% scores 1000-dimensional ILSVRC score vector
%
% You m... |
github | Urmish/Scene-Classification-master | sp_dense_sift.m | .m | Scene-Classification-master/SpatialPyramid/sp_dense_sift.m | 3,961 | utf_8 | c05f842a89e07b32c7bf846f21e1451a | function [sift_arr, grid_x, grid_y] = sp_dense_sift(I, grid_spacing, patch_size)
% Original script by Svetlana Lazebnick
% Adapted by Antonio Torralba: modified using convolutions to speed up the computations.
% And brought back into Svetlana's library
if(~exist('grid_spacing','var'))
grid_spacing = 1;
end
if(~exi... |
github | Urmish/Scene-Classification-master | sp_find_sift_grid.m | .m | Scene-Classification-master/SpatialPyramid/sp_find_sift_grid.m | 4,053 | utf_8 | 439b3883808ded16d0a8f689abc38078 | function sift_arr = sp_find_sift_grid(I, grid_x, grid_y, patch_size, sigma_edge)
% parameters
num_angles = 8;
num_bins = 4;
num_samples = num_bins * num_bins;
alpha = 9;
if nargin < 5
sigma_edge = 1;
end
angle_step = 2 * pi / num_angles;
angles = 0:angle_step:2*pi;
angles(num_angles+1) = []; % bin centers
[hgt ... |
github | gopmc/SRD-master | quantalph.m | .m | SRD-master/BookFiles/quantalph.m | 458 | utf_8 | ab221152115b9dc14d54085f505854dc | % y=quantalph(x,alphabet)
%
% quantize the input signal x to the alphabet
% using nearest neighbor method
% input x - vector to be quantized
% alphabet - vector of discrete values that y can take on
% sorted in ascending order
% output y - quantized vector
function y=quantalph(x,alphabet)
alphabe... |
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