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 | minjiang/transferlearning-master | MyTCA.m | .m | transferlearning-master/code/MyTCA.m | 2,818 | utf_8 | 7aee1d32ebfb97f5974be024ce450ce1 | function [X_src_new,X_tar_new,A] = MyTCA(X_src,X_tar,options)
% Inputs: [dim is the dimension of features]
%%% X_src:source feature matrix, ns * dim
%%% X_tar:target feature matrix, nt * dim
%%% options:option struct
% Outputs:
%%% X_src_new:transformed source feature matrix, ns * dim_new
%%... |
github | minjiang/transferlearning-master | lapgraph.m | .m | transferlearning-master/code/MyARTL/lapgraph.m | 20,244 | utf_8 | cfed436191fe6a863089f6da80644260 | function [W, elapse] = lapgraph(fea,options)
% Usage:
% W = graph(fea,options)
%
% fea: Rows of vectors of data points. Each row is x_i
% options: Struct value in Matlab. The fields in options that can be set:
% Metric - Choices are:
% 'Euclidean' - Will use the Euclidean distance of two data... |
github | minjiang/transferlearning-master | MyARTL.m | .m | transferlearning-master/code/MyARTL/MyARTL.m | 3,503 | utf_8 | 91802921f23d322f2ffca0e311f9372a | function [acc,acc_ite,Alpha] = MyARTL(X_src,Y_src,X_tar,Y_tar,options)
% Inputs:
%%% X_src :source feature matrix, ns * m
%%% Y_src :source label vector, ns * 1
%%% X_tar :target feature matrix, nt * m
%%% Y_tar :target label vector, nt * 1
%%% options:option struct
% Outputs:
%%% ac... |
github | 100957264/WatchLauncher-master | echo_diagnostic.m | .m | WatchLauncher-master/NormalTools/studio/android/app/src/main/jni/libspeex/echo_diagnostic.m | 2,076 | utf_8 | 8d5e7563976fbd9bd2eda26711f7d8dc | % Attempts to diagnose AEC problems from recorded samples
%
% out = echo_diagnostic(rec_file, play_file, out_file, tail_length)
%
% Computes the full matrix inversion to cancel echo from the
% recording 'rec_file' using the far end signal 'play_file' using
% a filter length of 'tail_length'. The output is saved to 'o... |
github | jkjung-avt/py-faster-rcnn-master | voc_eval.m | .m | py-faster-rcnn-master/lib/datasets/VOCdevkit-matlab-wrapper/voc_eval.m | 1,332 | utf_8 | 3ee1d5373b091ae4ab79d26ab657c962 | function res = voc_eval(path, comp_id, test_set, output_dir)
VOCopts = get_voc_opts(path);
VOCopts.testset = test_set;
for i = 1:length(VOCopts.classes)
cls = VOCopts.classes{i};
res(i) = voc_eval_cls(cls, VOCopts, comp_id, output_dir);
end
fprintf('\n~~~~~~~~~~~~~~~~~~~~\n');
fprintf('Results:\n');
aps = [res(:... |
github | vkalogeiton/caffe-master | classification_demo.m | .m | caffe-master/matlab/demo/classification_demo.m | 5,466 | utf_8 | 45745fb7cfe37ef723c307dfa06f1b97 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | lederman/Prol-master | gpsf_report1_figures.m | .m | Prol-master/doc/figures/gpsf_report1_figures.m | 3,770 | utf_8 | e0676d6ce41e08e23c42591f0ce93954 | %
% prol
% Demosntration code for computing generalized prolate spheroidal functions.
% (Matlab(R) version)
%
% Author: Roy R. Lederman
% http://roy.lederman.name/
% http://github.com/lederman/prol
%
% This code generates the figures for the paper gpsf_report1.tex
%
function gpsf_report1_figures()
% run matl... |
github | lederman/Prol-master | prolate_ev.m | .m | Prol-master/src/matlab/prolate_ev.m | 647 | utf_8 | ea9b52e826d67d93d39a8d5ebe4dee78 |
function [v] = prolate_ev(prolate_dat, prolate_ids, xx)
%
% Evaluates the prolate functions.
%
% Input:
% * prolate_dat : precomputed data structure (prolate_crea).
% * prolate_ids : which prolates to compute. vector of ids between 0 and prolate_dat.num_prols-1.
% * xx : vector of points in the interval [0,1] w... |
github | lederman/Prol-master | prolate_crea.m | .m | Prol-master/src/matlab/prolate_crea.m | 6,511 | utf_8 | 5a20b52f509115bbf50f57247e82a50e |
function [prolate_dat, iserr , prolate_dat_tmp] = prolate_crea(c, D, N, minEigenvalRatio, matdim , prolate_crea_options)
%
% prolate_crea creates a data-structure for computing a family of
% generalized prolate spheroidal functions for dimension D and order N.
%
% Input:
% * c : prolate truncation frequency
% * D : ... |
github | lederman/Prol-master | matlab_addpath_prol_src.m | .m | Prol-master/src/matlab/matlab_addpath_prol_src.m | 224 | utf_8 | f61dd8a0f775bcd01a4600503a733dc1 | %
% Add to path
%
function matlab_addpath_prol_src()
path_to_pkg = fileparts(mfilename('fullpath'));
addp = @(d)(addpath(fullfile(path_to_pkg, d)));
addp('');
addp('polynomials');
addp('service');
end
|
github | lederman/Prol-master | prolate_analyticgam.m | .m | Prol-master/src/matlab/service/prolate_analyticgam.m | 1,598 | utf_8 | a8cef2c41c56b40443b2f172ac32a1a7 |
function gam = prolate_analyticgam(prolate_dat, n)
%
% Computation of the n-th eigenvalue of the integral operator.
% Uses the data structure prolate_dat created by prolate_crea.
%
% Generally speaking, this function should only be used for computing the
% eigenvalue for n=0 by prolate_crea.
%
% Input:
% * prolate_d... |
github | lederman/Prol-master | prolate_numericalgam.m | .m | Prol-master/src/matlab/service/prolate_numericalgam.m | 1,757 | utf_8 | 96fdcfafa9bf6765fd02db80606f0be4 |
function gam = prolate_numericalgam(prolate_dat, n)
%
% Numerical computation of the n-th eigenvalue of the integral operator.
% Uses the data structure prolate_dat created by prolate_crea.
%
% Generally speaking, this function should only be used for computing the
% eigenvalue for n=0 by prolate_crea, and should not... |
github | lederman/Prol-master | prolate_diffop_mat_tridiag.m | .m | Prol-master/src/matlab/service/prolate_diffop_mat_tridiag.m | 1,607 | utf_8 | f7308e7a142a5c14badff37d8e20c56c |
function [vdiag, voffdiag] = prolate_diffop_mat_tridiag(c,p,N,maxk)
%
% Computes the matrix representation of the differential operator,
% in the basis of Zernike polynomials.
%
% Input:
% * c,p,N : prolate parameters.
% * maxk : matrix truncations: the dimensionality of the matrix is k+1
% Output:
% * vdiag... |
github | lederman/Prol-master | prolate_diffop_mat_full.m | .m | Prol-master/src/matlab/service/prolate_diffop_mat_full.m | 834 | utf_8 | ca3ead2addc46e97c38d150ba121df6b |
function [mat, vdiag, voffdiag ] = prolate_diffop_mat_full(c,p,N,maxk)
%
% Computes the full matrix representation of the differential operator,
% in the basis of Zernike polynomials.
%
% Input:
% * c,p,N : prolate parameters.
% * maxk : matrix truncations: the dimensionality of the matrix is k+1
% Output:
% *... |
github | lederman/Prol-master | prolate_ZernikeNorm_ex.m | .m | Prol-master/src/matlab/polynomials/prolate_ZernikeNorm_ex.m | 796 | utf_8 | 32da78a7d24f02fd89e8f9c58e238999 |
function v = prolate_ZernikeNorm_ex(p,N,cfsvec,xx)
%
%
% Evaluates functions expanded in the basis of normalized Zernike
% polynomials.
%
% v(i,j) = \sum_{q=0}^{k-1} cfsvec(q,j) \hat{R}_{N,n,p}_q(x_i)
%
%
% Input:
% * p,N : the p,N parameters of the Zernike polynomials to use here.
% * cfsvec : k x m matrix.
% ... |
github | lederman/Prol-master | prolate_xdZernikeNorm_coef.m | .m | Prol-master/src/matlab/polynomials/prolate_xdZernikeNorm_coef.m | 1,054 | utf_8 | e1d65d8fdd68d9868f8e127161c4ed41 |
function dvec = prolate_xdZernikeNorm_coef(p,N,vec)
%
% Computes the expansion of xf'(x) in the basis of Zernike polynomials,
% where f(x) is given in the basis of Zernike polynomials.
%
% Input:
% * p,N : Prolate/Zernike parameters.
% * vec : vector (or multiple vectors in multiple columns) of the
% coefficients o... |
github | lederman/Prol-master | prolate_ZernikeNorm_ex_fromJacobi.m | .m | Prol-master/src/matlab/polynomials/prolate_ZernikeNorm_ex_fromJacobi.m | 1,221 | utf_8 | 4cfe0e16b432763a99f8566676137ce5 |
function v = prolate_ZernikeNorm_ex_fromJacobi(p,N,cfsvec,xx)
%
% Evaluates functions expanded in the basis of normalized Zernike polynomials
% using Jacobi polynomials.
%
% v(i,j) = \sum_{q=0}^{k-1} cfsvec(q,j) \hat{R}_{N,n,p}_q(x_i)
%
% Using Jacobi polynomials:
% \hat{R}_{N,n,p}_q(x_i) = (-1)^n \sqrt{2(2n+N+p/... |
github | ngcthuong/CSNet-master | Cal_PSNRSSIM.m | .m | CSNet-master/utilities/Cal_PSNRSSIM.m | 6,250 | utf_8 | 891b4e57ebcd097592850eecf97f150e | function [psnr_cur, ssim_cur] = Cal_PSNRSSIM(A,B,row,col)
[n,m,ch]=size(B);
A = A(row+1:n-row,col+1:m-col,:);
B = B(row+1:n-row,col+1:m-col,:);
A=double(A); % Ground-truth
B=double(B); %
e=A(:)-B(:);
mse=mean(e.^2);
psnr_cur=10*log10(255^2/mse);
if ch==1
[ssim_cur, ~] = ssim_index(A, B);
else
ssim_cur = -1;... |
github | ngcthuong/CSNet-master | test_network_v02.m | .m | CSNet-master/utilities/test_network_v02.m | 3,766 | utf_8 | 6abb3286637df8403f7e640f9b53db51 |
function net = CSNet_init
global featureSize noLayer blkSize subRate;
test = 1;
if test == 1
featureSize = 64;
noLayer = 7;
blkSize = 32;
subRate = 0.1;
end
noMeas = round(subRate * blkSize ^2);
%%% 17 layers
b_min = 0.025;
lr11 = [1 1];
lr10 = [1 0];
lr00 = [0 0];
weightDecay = [1 0];
meanva... |
github | ngcthuong/CSNet-master | Cal_PSNRSSIM.m | .m | CSNet-master/Data/utilities/Cal_PSNRSSIM.m | 6,250 | utf_8 | 891b4e57ebcd097592850eecf97f150e | function [psnr_cur, ssim_cur] = Cal_PSNRSSIM(A,B,row,col)
[n,m,ch]=size(B);
A = A(row+1:n-row,col+1:m-col,:);
B = B(row+1:n-row,col+1:m-col,:);
A=double(A); % Ground-truth
B=double(B); %
e=A(:)-B(:);
mse=mean(e.^2);
psnr_cur=10*log10(255^2/mse);
if ch==1
[ssim_cur, ~] = ssim_index(A, B);
else
ssim_cur = -1;... |
github | ngcthuong/CSNet-master | CSNet_init.m | .m | CSNet-master/TrainingCode/CSNet_v03/CSNet_init.m | 3,597 | utf_8 | f6f53c2bb1c1455b8cf8497263f2e338 | function net = CSNet_init
global featureSize noLayer blkSize subRate isLearnMtx;
test = 0;
if test == 1
featureSize = 64;
noLayer = 7;
blkSize = 32;
subRate = 0.1;
end
noMeas = round(subRate * blkSize ^2);
%%% 17 layers
b_min = 0.025;
lr11 = [1 1];
lr10 = [1 0];
lr00 = [0 0];
weightDecay = [1 ... |
github | ngcthuong/CSNet-master | CSNet_train.m | .m | CSNet-master/TrainingCode/CSNet_v03/CSNet_train.m | 12,946 | utf_8 | dbf0bbf2dc7f04221f1c4cec58d49787 | function [net, state] = CSNet_train(net, varargin)
% The function automatically restarts after each training epoch by
% checkpointing.
%
% The function supports training on CPU or on one or more GPUs
% (specify the list of GPU IDs in the `gpus` option).
% Copyright (C) 2014-16 Andrea Vedaldi.
% All rights... |
github | ngcthuong/CSNet-master | Cal_PSNRSSIM.m | .m | CSNet-master/TrainingCode/CSNet_v03/utilities/Cal_PSNRSSIM.m | 6,250 | utf_8 | 891b4e57ebcd097592850eecf97f150e | function [psnr_cur, ssim_cur] = Cal_PSNRSSIM(A,B,row,col)
[n,m,ch]=size(B);
A = A(row+1:n-row,col+1:m-col,:);
B = B(row+1:n-row,col+1:m-col,:);
A=double(A); % Ground-truth
B=double(B); %
e=A(:)-B(:);
mse=mean(e.^2);
psnr_cur=10*log10(255^2/mse);
if ch==1
[ssim_cur, ~] = ssim_index(A, B);
else
ssim_cur = -1;... |
github | ngcthuong/CSNet-master | test_network_v02.m | .m | CSNet-master/TrainingCode/CSNet_v03/utilities/test_network_v02.m | 3,766 | utf_8 | 6abb3286637df8403f7e640f9b53db51 |
function net = CSNet_init
global featureSize noLayer blkSize subRate;
test = 1;
if test == 1
featureSize = 64;
noLayer = 7;
blkSize = 32;
subRate = 0.1;
end
noMeas = round(subRate * blkSize ^2);
%%% 17 layers
b_min = 0.025;
lr11 = [1 1];
lr10 = [1 0];
lr00 = [0 0];
weightDecay = [1 0];
meanva... |
github | ngcthuong/CSNet-master | CSNet_init.m | .m | CSNet-master/TrainingCode/CSNet_v02/CSNet_init.m | 3,501 | utf_8 | ea6f159161352a1a5852a8f290a8e6e3 | function net = CSNet_init
global featureSize noLayer blkSize subRate isLearnMtx;
test = 0;
if test == 1
featureSize = 64;
noLayer = 7;
blkSize = 32;
subRate = 0.1;
end
noMeas = round(subRate * blkSize ^2);
%%% 17 layers
b_min = 0.025;
lr11 = [1 1];
lr10 = [1 0];
lr00 = [0 0];
weightDecay = [1 ... |
github | ngcthuong/CSNet-master | CSNet_train.m | .m | CSNet-master/TrainingCode/CSNet_v02/CSNet_train.m | 12,946 | utf_8 | dbf0bbf2dc7f04221f1c4cec58d49787 | function [net, state] = CSNet_train(net, varargin)
% The function automatically restarts after each training epoch by
% checkpointing.
%
% The function supports training on CPU or on one or more GPUs
% (specify the list of GPU IDs in the `gpus` option).
% Copyright (C) 2014-16 Andrea Vedaldi.
% All rights... |
github | cedricxie/MATLAB_Automated_Driving_Box-master | clusterDetections.m | .m | MATLAB_Automated_Driving_Box-master/Sensor_Fusion_Using_Synthetic_Radar_and_Vision_Data/clusterDetections.m | 2,042 | utf_8 | 6d58bf60e4d9920de8dcf76f50fc1911 | % clusterDetections
% This function merges multiple detections suspected to be of the same vehicle to a single detection.
% The function looks for detections that are closer than the size of a vehicle.
% Detections that fit this criterion are considered a cluster and are merged to a single detection
% at the centroid ... |
github | cedricxie/MATLAB_Automated_Driving_Box-master | createDemoDisplay.m | .m | MATLAB_Automated_Driving_Box-master/Sensor_Fusion_Using_Synthetic_Radar_and_Vision_Data/createDemoDisplay.m | 2,798 | utf_8 | 61b8d87959d894c7cb1233665f7ca089 | % createDemoDisplay
% This function creates a three-panel display:
% Top-left corner of display: A top view that follows the ego vehicle.
% Bottom-left corner of display: A chase-camera view that follows the ego vehicle.
% Right-half of display: A bird's-eye plot display.
function BEP = createDemoDisplay(egoCar, sen... |
github | cedricxie/MATLAB_Automated_Driving_Box-master | vehicleToImageROI.m | .m | MATLAB_Automated_Driving_Box-master/Visual_Perception_Using_Monocular_Camera/vehicleToImageROI.m | 653 | utf_8 | 9afa4c556cc9a400e9c90235a6468c54 | %%
% *vehicleToImageROI* converts ROI in vehicle coordinates to image coordinates
% in bird's-eye-view image.
function imageROI = vehicleToImageROI(birdsEyeConfig, vehicleROI)
vehicleROI = double(vehicleROI);
loc2 = abs(vehicleToImage(birdsEyeConfig, [vehicleROI(2) vehicleROI(4)]));
loc1 = abs(vehicleToImage(birdsEye... |
github | cedricxie/MATLAB_Automated_Driving_Box-master | takeSnapshot.m | .m | MATLAB_Automated_Driving_Box-master/Visual_Perception_Using_Monocular_Camera/takeSnapshot.m | 747 | utf_8 | 1791ab533aacfe782877dc4956d747f3 | %%
% *takeSnapshot* captures the output for the HTML publishing report.
function I = takeSnapshot(frame, sensor, sensorOut)
% Unpack the inputs
leftEgoBoundary = sensorOut.leftEgoBoundary;
rightEgoBoundary = sensorOut.rightEgoBoundary;
locations = sensorOut.vehicleLocations;
xVehiclePoints ... |
github | cedricxie/MATLAB_Automated_Driving_Box-master | validateBoundaryFcn.m | .m | MATLAB_Automated_Driving_Box-master/Visual_Perception_Using_Monocular_Camera/validateBoundaryFcn.m | 364 | utf_8 | 3899e015c1d26d20057a5c58c7b0d8d3 | %%
% *validateBoundaryFcn* rejects some of the lane boundary curves
% computed using the RANSAC algorithm.
function isGood = validateBoundaryFcn(params)
if ~isempty(params)
a = params(1);
% Reject any curve with a small 'a' coefficient, which makes it highly
% curved.
isGood = abs(a) < 0.003; % a from... |
github | cedricxie/MATLAB_Automated_Driving_Box-master | insertVehicleDetections.m | .m | MATLAB_Automated_Driving_Box-master/Visual_Perception_Using_Monocular_Camera/insertVehicleDetections.m | 480 | utf_8 | 3ab6baac14f95acdee8c09e73aa3706a | %%
% *insertVehicleDetections* inserts bounding boxes and displays
% [x,y] locations corresponding to returned vehicle detections.
function imgOut = insertVehicleDetections(imgIn, locations, bboxes)
imgOut = imgIn;
for i = 1:size(locations, 1)
location = locations(i, :);
bbox = bboxes(i, :);
label = ... |
github | cedricxie/MATLAB_Automated_Driving_Box-master | computeVehicleLocations.m | .m | MATLAB_Automated_Driving_Box-master/Visual_Perception_Using_Monocular_Camera/computeVehicleLocations.m | 1,058 | utf_8 | 7df6f836cb4cca22035ffe4fad5968d8 | %%
% *computeVehicleLocations* calculates the location of a vehicle
% in vehicle coordinates, given a bounding box returned by a detection
% algorithm in image coordinates. It returns the center location of the
% bottom of the bounding box in vehicle coordinates. Because a monocular
% camera sensor and a simple homogra... |
github | cedricxie/MATLAB_Automated_Driving_Box-master | classifyLaneTypes.m | .m | MATLAB_Automated_Driving_Box-master/Visual_Perception_Using_Monocular_Camera/classifyLaneTypes.m | 979 | utf_8 | 4fa6c4e6726da000a539c83cb0589b84 | %%
% *classifyLaneTypes* determines lane marker types as |solid|, |dashed|, etc.
function boundaries = classifyLaneTypes(boundaries, boundaryPoints)
for bInd = 1 : numel(boundaries)
vehiclePoints = boundaryPoints{bInd};
% Sort by x
vehiclePoints = sortrows(vehiclePoints, 1);
xVehicle = vehiclePoints(:... |
github | cedricxie/MATLAB_Automated_Driving_Box-master | visualizeSensorResults.m | .m | MATLAB_Automated_Driving_Box-master/Visual_Perception_Using_Monocular_Camera/visualizeSensorResults.m | 2,379 | utf_8 | 469fbc729ba3949f70d12fce4d77e690 | %% visualizeSensorResults displays core information and intermediate results from the monocular camera sensor simulation.
function isPlayerOpen = visualizeSensorResults(frame, sensor, sensorOut,...
intOut, closePlayers)
% Unpack the main inputs
leftEgoBoundary = sensorOut.leftEgoBoundary;
rightEgoBou... |
github | tjdodwell/matLam-master | makeMesh.m | .m | matLam-master/include/preProcessing/makeMesh.m | 4,803 | utf_8 | 6896a25bf75578e555cda0b40938ee2c | function msh = makeMesh(model)
% -----------------------------------------------------------------------
% This code is released under GNU LESSER GENERAL PUBLIC LICENSE v3 (LGPL)
%
% Details are provided in license.txt file in the main directory
%
% 1/8/14 - Dr T. J. Dodwell - University of Bath - tjd20@bath.ac.uk
% --... |
github | tjdodwell/matLam-master | makeABDH2.m | .m | matLam-master/include/FEM/makeABDH2.m | 7,495 | utf_8 | cfbbd48beb5d01237bbfee4326aab1c5 | function mat = makeABDH2(model)
switch lower(model.type)
case 'mindlin'
% upper and lower coordinates
z = zeros(1,model.numPly+1);
z(1) = 0;
for i = 2:model.numPly+1
z(i) = z(i-1) + model.t(i-1);
end
... |
github | tjdodwell/matLam-master | elementShapeFunctions.m | .m | matLam-master/include/FEM/elementShapeFunctions.m | 1,612 | utf_8 | 30bd5c1f46692a39f093d1514e018e26 | function [Ni,dNdX,detJ] = elementShapeFunctions(msh,ie,ip,integration_option)
switch lower(integration_option);
case 'full'
[IP_X,IP_W] = ip_quad;
[N, dNdu] = shapeFunctionQ4(IP_X);
Ni = N{ip}; dNdui = dNdu{ip};
case 'reduced'
... |
github | RWEISCHEDEL/University-of-Utah-Coursework-master | matchExposures.m | .m | University-of-Utah-Coursework-master/CS 6320 - Computer Vision/project_1/project_1/matchExposures.m | 2,853 | utf_8 | ae91ed3665fbf30805c02a26aedd688d | function [matchedImage] = matchExposures(images, transforms, performLoop)
numberImages = size(images, 4);
gammaList = ones(numberImages, 1);
for i = 2 : numberImages
gammaList(i) = matchImagePair(images(:, :, :, i - 1), images(:, :, :, i), transforms(:, :, i));
end
if performLoop
logGammaList = log(gam... |
github | RWEISCHEDEL/University-of-Utah-Coursework-master | CannyEdgeDetection.m | .m | University-of-Utah-Coursework-master/CS 6320 - Computer Vision/project_1/project_1/Functions/CannyEdgeDetection.m | 3,412 | utf_8 | 6f73fb6ab7f1dff7fd8e67c55ce58382 | imageMatrix1 = imread('lineDetect1.bmp');
imageMatrix2 = imread('lineDetect2.bmp');
imageMatrix3 = imread('lineDetect3.bmp');
outputImage1 = edgeDetection(imageMatrix1);
outputImage2 = edgeDetection(imageMatrix1);
outputImage3 = edgeDetection(imageMatrix1);
imwrite(outputImage1, 'Outputs/cannyedgedetection1.png', 'pn... |
github | RWEISCHEDEL/University-of-Utah-Coursework-master | StereoMatching.m | .m | University-of-Utah-Coursework-master/CS 6320 - Computer Vision/project_1/project_1/Functions/StereoMatching.m | 2,242 | utf_8 | 5ed51e493dc2bb8c52487d16eafb2b2c | left1 = imread('left1.png');
left2 = imread('left2.png');
left3 = imread('left3.bmp');
right1 = imread('right1.png');
right2 = imread('right2.png');
right3 = imread('right3.bmp');
outputImage1 = stereoMatch(left1, right1);
outputImage2 = stereoMatch(left2, right2);
outputImage3 = stereoMatch(left3, right3);
imwrite(... |
github | RWEISCHEDEL/University-of-Utah-Coursework-master | SimpleSkySegmentation.m | .m | University-of-Utah-Coursework-master/CS 6320 - Computer Vision/project_1/project_1/Functions/SimpleSkySegmentation.m | 1,708 | utf_8 | aac6dec37084561767bdb82c8459d3ed | imageMatrix1 = imread('detectSky1.bmp');
imageMatrix2 = imread('detectSky2.bmp');
imageMatrix3 = imread('detectSky3.bmp');
outputImage1 = segmentation(imageMatrix1);
outputImage2 = segmentation(imageMatrix2);
outputImage3 = segmentation(imageMatrix3);
imwrite(outputImage1, 'Outputs/simpleskydetection1.png', 'png');
i... |
github | RWEISCHEDEL/University-of-Utah-Coursework-master | savepgm.m | .m | University-of-Utah-Coursework-master/CS 6320 - Computer Vision/project_2/Functions/toolbox_calib/TOOLBOX_calib/savepgm.m | 447 | utf_8 | b8fe9ed33cbd68ea4b83271b431e3667 | %SAVEPGM Write a PGM format file
%
% SAVEPGM(filename, im)
%
% Saves the specified image array in a binary (P5) format PGM image file.
%
% SEE ALSO: loadpgm
%
% Copyright (c) Peter Corke, 1999 Machine Vision Toolbox for Matlab
% Peter Corke 1994
function savepgm(fname, im)
fid = fopen(fname, 'w');
[r,c] = size(... |
github | RWEISCHEDEL/University-of-Utah-Coursework-master | ginput4.m | .m | University-of-Utah-Coursework-master/CS 6320 - Computer Vision/project_2/Functions/toolbox_calib/TOOLBOX_calib/ginput4.m | 7,121 | utf_8 | 1d7231b0daed3533514a77f79f4e096a | function [out1,out2,out3] = ginput4(arg1)
[out1,out2,out3] = ginput(arg1);
return;
%GINPUT Graphical input from mouse.
% [X,Y] = GINPUT(N) gets N points from the current axes and returns
% the X- and Y-coordinates in length N vectors X and Y. The cursor
% can be positioned using a mouse (or by using the Ar... |
github | RWEISCHEDEL/University-of-Utah-Coursework-master | loadinr.m | .m | University-of-Utah-Coursework-master/CS 6320 - Computer Vision/project_2/Functions/toolbox_calib/TOOLBOX_calib/loadinr.m | 1,029 | utf_8 | ac39329cc5acba186f4c5ef4c62f3a33 | %LOADINR Load an INRIMAGE format file
%
% LOADINR(filename, im)
%
% Load an INRIA image format file and return it as a matrix
%
% SEE ALSO: saveinr
%
% Copyright (c) Peter Corke, 1999 Machine Vision Toolbox for Matlab
% Peter Corke 1996
function im = loadinr(fname, im)
fid = fopen(fname, 'r');
s = fgets(fid);
... |
github | RWEISCHEDEL/University-of-Utah-Coursework-master | saveppm.m | .m | University-of-Utah-Coursework-master/CS 6320 - Computer Vision/project_2/Functions/toolbox_calib/TOOLBOX_calib/saveppm.m | 722 | utf_8 | 9904ad3d075a120ca32bd9c10e019512 | %SAVEPPM Write a PPM format file
%
% SAVEPPM(filename, I)
%
% Saves the specified red, green and blue planes in a binary (P6)
% format PPM image file.
%
% SEE ALSO: loadppm
%
% Copyright (c) Peter Corke, 1999 Machine Vision Toolbox for Matlab
% Peter Corke 1994
function saveppm(fname, I)
I = double(I);
if size(I,... |
github | RWEISCHEDEL/University-of-Utah-Coursework-master | ginput3.m | .m | University-of-Utah-Coursework-master/CS 6320 - Computer Vision/project_2/Functions/toolbox_calib/TOOLBOX_calib/ginput3.m | 6,344 | utf_8 | 1cc27af57f9872f05bbf0d9b8a0fdbc9 | function [out1,out2,out3] = ginput2(arg1)
%GINPUT Graphical input from mouse.
% [X,Y] = GINPUT(N) gets N points from the current axes and returns
% the X- and Y-coordinates in length N vectors X and Y. The cursor
% can be positioned using a mouse (or by using the Arrow Keys on some
% systems). Data points a... |
github | RWEISCHEDEL/University-of-Utah-Coursework-master | ginput2.m | .m | University-of-Utah-Coursework-master/CS 6320 - Computer Vision/project_2/Functions/toolbox_calib/TOOLBOX_calib/ginput2.m | 6,105 | utf_8 | 983a72db9a079ba54ab084149ced6ae9 | function [out1,out2,out3] = ginput2(arg1)
%GINPUT Graphical input from mouse.
% [X,Y] = GINPUT(N) gets N points from the current axes and returns
% the X- and Y-coordinates in length N vectors X and Y. The cursor
% can be positioned using a mouse (or by using the Arrow Keys on some
% systems). Data points a... |
github | RWEISCHEDEL/University-of-Utah-Coursework-master | loadppm.m | .m | University-of-Utah-Coursework-master/CS 6320 - Computer Vision/project_2/Functions/toolbox_calib/TOOLBOX_calib/loadppm.m | 2,356 | utf_8 | 341aee7d75f529ff3425160291592356 | %LOADPPM Load a PPM image
%
% I = loadppm(filename)
%
% Returns a matrix containing the image loaded from the PPM format
% file filename. Handles ASCII (P3) and binary (P6) PPM file formats.
%
% If the filename has no extension, and open fails, a '.ppm' and
% '.pnm' extension will be tried.
%
% SEE ALSO: saveppm loadp... |
github | RWEISCHEDEL/University-of-Utah-Coursework-master | saveinr.m | .m | University-of-Utah-Coursework-master/CS 6320 - Computer Vision/project_2/Functions/toolbox_calib/TOOLBOX_calib/saveinr.m | 949 | utf_8 | a18df4fba021be006842fbc35166bc23 | %SAVEINR Write an INRIMAGE format file
%
% SAVEINR(filename, im)
%
% Saves the specified image array in a INRIA image format file.
%
% SEE ALSO: loadinr
%
% Copyright (c) Peter Corke, 1999 Machine Vision Toolbox for Matlab
% Peter Corke 1996
function saveinr(fname, im)
fid = fopen(fname, 'w');
[r,c] = size(im');
... |
github | RWEISCHEDEL/University-of-Utah-Coursework-master | stereo_gui.m | .m | University-of-Utah-Coursework-master/CS 6320 - Computer Vision/project_2/Functions/toolbox_calib/TOOLBOX_calib/stereo_gui.m | 6,208 | utf_8 | 6cc48675fdf9c8c36bc147da7d046d06 | % stereo_gui
% Stereo Camera Calibration Toolbox (two cameras, internal and external calibration):
%
% It is assumed that the two cameras (left and right) have been calibrated with the pattern at the same 3D locations, and the same points
% on the pattern (select the same grid points). Therefore, in particular, the sam... |
github | RWEISCHEDEL/University-of-Utah-Coursework-master | loadpgm.m | .m | University-of-Utah-Coursework-master/CS 6320 - Computer Vision/project_2/Functions/toolbox_calib/TOOLBOX_calib/loadpgm.m | 1,838 | utf_8 | 6ec18330c2633d5519c72eb2e6fe963b | %LOADPGM Load a PGM image
%
% I = loadpgm(filename)
%
% Returns a matrix containing the image loaded from the PGM format
% file filename. Handles ASCII (P2) and binary (P5) PGM file formats.
%
% If the filename has no extension, and open fails, a '.pgm' will
% be appended.
%
%
% Copyright (c) Peter Corke, 1999 Machin... |
github | RWEISCHEDEL/University-of-Utah-Coursework-master | Quaternion2R.m | .m | University-of-Utah-Coursework-master/CS 6320 - Computer Vision/project_2/Inputs/Assignment2_DATA/Assignment2_DATA/CODE/Quaternion2R.m | 342 | utf_8 | 0dc39a43367f00b5830e73144bf55f7c |
function R = Quaternion2R(q)
q = q / norm(q);
R = [
q(1)^2 + q(2)^2 - q(3)^2 - q(4)^2, 2*(q(2)*q(3) - q(1)*q(4)), 2*(q(2)*q(4) + q(1)*q(3));
2*(q(2)*q(3) + q(1)*q(4)), q(1)^2-q(2)^2 + q(3)^2 - q(4)^2, 2*(q(3)*q(4) - q(1)*q(2));
2*(q(2)*q(4) - q(1)*q(3)), 2*(q(3)*q(4) + q(1)*q(2)), q(1)^2 - q(2)^2 - q(... |
github | RWEISCHEDEL/University-of-Utah-Coursework-master | Register3DPointsQuaternion.m | .m | University-of-Utah-Coursework-master/CS 6320 - Computer Vision/project_2/Inputs/Assignment2_DATA/Assignment2_DATA/CODE/Register3DPointsQuaternion.m | 1,501 | utf_8 | 6535ceb941775580a6874cc4223f7f0c | % compute transformation from pointsA and poitnsB so that
% pointsB = R * pointsA + t
function finalTrans = Register3DPointsQuaternion(pointsA, pointsB)
% pointsA, pointsB - 3 x n matrices.
% clear all; close all; clc;
%
% pointsA = [5 6 8; 10 2 3; 18 9 10]';
%
% trueRotMat = RPY2Rot(10, 15, 30);
% trueTransVec = [1... |
github | RWEISCHEDEL/University-of-Utah-Coursework-master | appendimages.m | .m | University-of-Utah-Coursework-master/CS 6320 - Computer Vision/project_2/siftDemoV4/siftDemoV4/appendimages.m | 461 | utf_8 | a7ad42558236d4f7bd90dc6e72631d54 | % im = appendimages(image1, image2)
%
% Return a new image that appends the two images side-by-side.
function im = appendimages(image1, image2)
% Select the image with the fewest rows and fill in enough empty rows
% to make it the same height as the other image.
rows1 = size(image1,1);
rows2 = size(image2,1);
if (... |
github | RWEISCHEDEL/University-of-Utah-Coursework-master | showkeys.m | .m | University-of-Utah-Coursework-master/CS 6320 - Computer Vision/project_2/siftDemoV4/siftDemoV4/showkeys.m | 1,699 | utf_8 | 4e67466c0fd7739350cb2af5767e10a4 | % showkeys(image, locs)
%
% This function displays an image with SIFT keypoints overlayed.
% Input parameters:
% image: the file name for the image (grayscale)
% locs: matrix in which each row gives a keypoint location (row,
% column, scale, orientation)
function showkeys(image, locs)
disp('Drawin... |
github | RWEISCHEDEL/University-of-Utah-Coursework-master | sift.m | .m | University-of-Utah-Coursework-master/CS 6320 - Computer Vision/project_2/siftDemoV4/siftDemoV4/sift.m | 2,496 | utf_8 | 7cdcf3bcc06643a2ec205788c77ac597 | % [image, descriptors, locs] = sift(imageFile)
%
% This function reads an image and returns its SIFT keypoints.
% Input parameters:
% imageFile: the file name for the image.
%
% Returned:
% image: the image array in double format
% descriptors: a K-by-128 matrix, where each row gives an invariant
% ... |
github | RWEISCHEDEL/University-of-Utah-Coursework-master | Q3VT.m | .m | University-of-Utah-Coursework-master/CS 6320 - Computer Vision/project_2/siftDemoV4/siftDemoV4/Q3VT.m | 1,016 | utf_8 | 6fa94d5010a5e371dfcd7bd64490424c | % Q3 - Image Based Location with Vocab Tree
function VT
dataBaseDescriptors = [];
queryDescriptors = [];
dataBaseImgOrder = [];
queryImageOrder = [];
files = dir('D:/Matlab Projects/project_2/Inputs/Assignment2_DATA/Assignment2_DATA/database/*.png');
% Build the Codebase of Descriptors
for file = files'
f... |
github | RWEISCHEDEL/University-of-Utah-Coursework-master | match.m | .m | University-of-Utah-Coursework-master/CS 6320 - Computer Vision/project_2/siftDemoV4/siftDemoV4/match.m | 1,940 | utf_8 | e876f215400508c0507fd248db781333 | % num = match(image1, image2)
%
% This function reads two images, finds their SIFT features, and
% displays lines connecting the matched keypoints. A match is accepted
% only if its distance is less than distRatio times the distance to the
% second closest match.
% It returns the number of matches displayed.
%
%... |
github | RWEISCHEDEL/University-of-Utah-Coursework-master | Q3BOW.m | .m | University-of-Utah-Coursework-master/CS 6320 - Computer Vision/project_2/siftDemoV4/siftDemoV4/Q3BOW.m | 2,621 | utf_8 | 349eabe80816699c0b002a45c4951563 | % Q3 - Image Based Location with Bag of Words
function BOW
dataBaseDescriptors = [];
queryDescriptors = [];
dataBaseImgOrder = [];
queryImageOrder = [];
files = dir('D:/Matlab Projects/project_2/Inputs/Assignment2_DATA/Assignment2_DATA/database/*.png');
% Build the Codebase of Descriptors
for file = files'
... |
github | RWEISCHEDEL/University-of-Utah-Coursework-master | matchExposures.m | .m | University-of-Utah-Coursework-master/CS 6320 - Computer Vision/Panorama - Final Project/matchExposures.m | 2,853 | utf_8 | ae91ed3665fbf30805c02a26aedd688d | function [matchedImage] = matchExposures(images, transforms, performLoop)
numberImages = size(images, 4);
gammaList = ones(numberImages, 1);
for i = 2 : numberImages
gammaList(i) = matchImagePair(images(:, :, :, i - 1), images(:, :, :, i), transforms(:, :, i));
end
if performLoop
logGammaList = log(gam... |
github | albanie/mcnExtraLayers-master | setup_mcnExtraLayers.m | .m | mcnExtraLayers-master/setup_mcnExtraLayers.m | 1,383 | utf_8 | 027d96f5ef9ba1d0e9f6b49f6cb1bfe3 | function setup_mcnExtraLayers
%SETUP_MCNEXTRALAYERS Sets up mcnExtraLayers by adding its folders to the path
% add dependencies
check_dependency('autonn') ;
root = fileparts(mfilename('fullpath')) ;
addpath(root, [root '/matlab'], [root '/matlab/wrappers'], [root '/utils']) ;
% ------------------------------... |
github | albanie/mcnExtraLayers-master | findBestEpoch.m | .m | mcnExtraLayers-master/utils/findBestEpoch.m | 3,735 | utf_8 | b82068b1b1ed40c9b537adbec22bb03a | function bestEpoch = findBestEpoch(expDir, varargin)
%FINDBESTEPOCH finds the best epoch of training
% FINDBESTEPOCH(EXPDIR) evaluates the checkpoints
% (the `net-epoch-%d.mat` files created during
% training) in EXPDIR
%
% FINDBESTEPOCH(..., 'option', value, ...) accepts the following
% options:
%
% `prior... |
github | albanie/mcnExtraLayers-master | checkLearningParams.m | .m | mcnExtraLayers-master/utils/checkLearningParams.m | 9,611 | utf_8 | 0dea868bbdec5be853e0fb633f4309ff | function checkLearningParams(mcn_outs, opts)
%CHECKlEARNINGPARAMS compare parameters against caffe.
% Algo: we first parse the prototxt and build a set of basic "layer"
% objects to store parameters. These can then be directly compared against
% their mcn equivalents to reduced the risk of incorrect initiali... |
github | albanie/mcnExtraLayers-master | vl_nnaugdata.m | .m | mcnExtraLayers-master/matlab/vl_nnaugdata.m | 3,294 | utf_8 | 701424346f4149e883a403a5d675fc60 | function y = vl_nnaugdata(x, varargin)
% VL_NNAUGDATA data augmentation for visual data
% Y = VL_NNAUGDATA(X) randomly applies a set of data augmentation
% transformations to the HxWxCxN input tensor X to produce an
% augmented version of the data Y (of the same shape as X).
%
% VL_NNAUGDATA(..., 'option', valu... |
github | albanie/mcnExtraLayers-master | vl_nnnonorm.m | .m | mcnExtraLayers-master/matlab/vl_nnnonorm.m | 1,345 | utf_8 | 807c6f7dfff7d9811abb625348f1ea26 | function [y, dzdg, dzdb] = vl_nnnonorm(x, g, b, varargin)
%VL_NNNONORM applies weights and biases, but does no normalization
% Y = VL_NNNONORM(X,G,B) applies a set of gains and biases to
% the input X with shape HxWxCxN. "No normalization" is defined as:
%
% Y(i,j,k,t) = G(k') * X(i,j,k,t) + B(k')
%
% where
... |
github | albanie/mcnExtraLayers-master | vl_nngnorm.m | .m | mcnExtraLayers-master/matlab/vl_nngnorm.m | 3,180 | utf_8 | 823c3574250346a697bc9a4f1c6de84d | function [y, dzdg, dzdb] = vl_nngnorm(x, g, b, varargin)
%VL_NNGNORM CNN group normalization.
% Y = VL_NNGNORM(X,G,B) applies group normalization
% to the input X with shape HxWxCxN. Group normalization is defined as:
%
% Y(i,j,k,t) = G(k',t) * X_HAT(i,j,k,t) + B(k',t)
%
% where
% k' = group_idx(k,C,G),... |
github | albanie/mcnExtraLayers-master | vl_nnbrenorm.m | .m | mcnExtraLayers-master/matlab/vl_nnbrenorm.m | 3,216 | utf_8 | 5fdea2ededecb39d822efa787e95fe7c | function [y, dzdg, dzdb, m] = vl_nnbrenorm(x, g, b, m, clips, test, varargin)
%VL_NNBRENORM CNN batch renormalisation.
% Y = VL_NNBRENORM(X,G,B,M,CLIPS,TEST) applies batch renormalization
% to the input X. Batch renormalization is defined as:
%
% Y(i,j,k,t) = G(k) * X_HAT(i,j,k,t) + B(k)
%
% where
% X_H... |
github | albanie/mcnExtraLayers-master | vl_nnbrenorm_wrapper.m | .m | mcnExtraLayers-master/matlab/wrappers/vl_nnbrenorm_wrapper.m | 1,743 | utf_8 | cff657bd6caed20fcb40d29a091db700 | function [y, dzdg, dzdb, moments] = vl_nnbrenorm_wrapper(x, g, b, ...
moments, clips, test, varargin)
%VL_NNBRENORM_WRAPPER AutoNN wrapper for MatConvNet's vl_nnbrenorm
% VL_NNBRENORM has a non-standard interface (returns a derivative for the
% moments, even though they... |
github | albanie/mcnExtraLayers-master | nnslice.m | .m | mcnExtraLayers-master/matlab/xtest/suite/nnslice.m | 881 | utf_8 | 9094327e26df505701c1a9362932ec3c | classdef nnslice < nntest
methods (Test)
function basic(test)
sz = [3,3,5,4] ;
x = test.randn(sz) ;
dim = 4 ;
slicePoints = 1:dim - 1 ; % slice along fourth dim
y = vl_nnslice(x, dim, slicePoints, []) ;
% check derivatives with numerical approximation
dzdy... |
github | albanie/mcnExtraLayers-master | nntukeyloss.m | .m | mcnExtraLayers-master/matlab/xtest/suite/nntukeyloss.m | 1,350 | utf_8 | 977e5438454bf3ef4c0eb49b95fa5ec3 | classdef nntukeyloss < nntest
methods (Test)
function basic(test)
% We have to be a little bit devious when constructing the
% numerical check - if computing
% x(i) + delta
% changes the value of the median of the residuals, the MAD value
% will also change and there will appear ... |
github | g4idrijs/ultrasoundsim-master | off_axis_demo.m | .m | ultrasoundsim-master/demos/off_axis_demo.m | 2,827 | utf_8 | 5c1318c6b824864b8e1d0cbdc2bb87aa | % Demo of simulating off axis.
function [pw] = simulate_off_axis()
% Structured as a function so that we can write helper functions in the
% same file.
% Setup the transducer array.
width = 5e-5;
height = 5e-5;
elements_x = 800;
elements_y = 1;
kerf = 5e-5;
r_curv = 6e-2;
trans... |
github | g4idrijs/ultrasoundsim-master | titrate_size_spacing_combo_and_focus.m | .m | ultrasoundsim-master/demos/titrate_size_spacing_combo_and_focus.m | 3,710 | utf_8 | e87dc4285343cb1fca055bd56b79f452 | % Script to run through different spacings and focus.
function titrate_spacing_and_focus()
% Structured as a function so that we can write helper functions in the
% same file.
% Constant of 1 element in y-direction.
elements_y = 1;
% Curvature to match human skull.
r_curv = 6e-2;
% Defin... |
github | g4idrijs/ultrasoundsim-master | titrate_spacing_and_focus.m | .m | ultrasoundsim-master/demos/titrate_spacing_and_focus.m | 3,749 | utf_8 | 7c08022742b0c92d5a9bc139f1b16e5b | % Script to run through different spacings and focus.
function titrate_spacing_and_focus()
% Structured as a function so that we can write helper functions in the
% same file.
% Constant of 1 element in y-direction.
elements_y = 1;
% Curvature to match human skull.
r_curv = 6e-2;
% Defin... |
github | g4idrijs/ultrasoundsim-master | titrate_spacing_and_num_elements.m | .m | ultrasoundsim-master/demos/titrate_spacing_and_num_elements.m | 3,890 | utf_8 | cfc872229bb8c15d3bef27fb6f767c37 | % Script to run through different element spacings to observe effect.
% Titrate through combinations of num elements and spacing.
function titrate_spacing_and_num_elements()
% Structured as a function so that we can write helper functions in the
% same file.
% Constant of 1 element in y-direction.
ele... |
github | g4idrijs/ultrasoundsim-master | titrate_frequency.m | .m | ultrasoundsim-master/demos/titrate_frequency.m | 2,752 | utf_8 | 7955d432fdd68a56a0a038a40a0c6693 | % Script to run through different frequencies to observe effect.
function titrate_frequency()
% Structured as a function so that we can write helper functions in the
% same file.
% Setup the transducer array.
width = 5e-5;
height = 5e-5;
elements_x = 200;
elements_y = 1;
kerf = 5e-5;
... |
github | g4idrijs/ultrasoundsim-master | titrate_focus_position.m | .m | ultrasoundsim-master/demos/titrate_focus_position.m | 2,762 | utf_8 | 915696ae209fb87bf5994cc3c0f4f71c | % Script to run through different frequencies to observe effect.
function titrate_focus_position()
% Structured as a function so that we can write helper functions in the
% same file.
% Setup the transducer array.
width = 5e-4;
height = 5e-4;
elements_x = 100;
elements_y = 1;
kerf = 1e... |
github | Bladefidz/machine-learning-master | submit.m | .m | machine-learning-master/coursera/machine-learning-standford-univerity/machine-learning-ex2/ex2/submit.m | 1,605 | utf_8 | 9b63d386e9bd7bcca66b1a3d2fa37579 | function submit()
addpath('./lib');
conf.assignmentSlug = 'logistic-regression';
conf.itemName = 'Logistic Regression';
conf.partArrays = { ...
{ ...
'1', ...
{ 'sigmoid.m' }, ...
'Sigmoid Function', ...
}, ...
{ ...
'2', ...
{ 'costFunction.m' }, ...
'Logistic R... |
github | Bladefidz/machine-learning-master | submit.m | .m | machine-learning-master/coursera/machine-learning-standford-univerity/machine-learning-ex4/ex4/submit.m | 1,635 | utf_8 | ae9c236c78f9b5b09db8fbc2052990fc | function submit()
addpath('./lib');
conf.assignmentSlug = 'neural-network-learning';
conf.itemName = 'Neural Networks Learning';
conf.partArrays = { ...
{ ...
'1', ...
{ 'nnCostFunction.m' }, ...
'Feedforward and Cost Function', ...
}, ...
{ ...
'2', ...
{ 'nnCostFunct... |
github | Bladefidz/machine-learning-master | submit.m | .m | machine-learning-master/coursera/machine-learning-standford-univerity/machine-learning-ex6/ex6/submit.m | 1,318 | utf_8 | bfa0b4ffb8a7854d8e84276e91818107 | function submit()
addpath('./lib');
conf.assignmentSlug = 'support-vector-machines';
conf.itemName = 'Support Vector Machines';
conf.partArrays = { ...
{ ...
'1', ...
{ 'gaussianKernel.m' }, ...
'Gaussian Kernel', ...
}, ...
{ ...
'2', ...
{ 'dataset3Params.m' }, ...
... |
github | Bladefidz/machine-learning-master | porterStemmer.m | .m | machine-learning-master/coursera/machine-learning-standford-univerity/machine-learning-ex6/ex6/porterStemmer.m | 9,902 | utf_8 | 7ed5acd925808fde342fc72bd62ebc4d | function stem = porterStemmer(inString)
% Applies the Porter Stemming algorithm as presented in the following
% paper:
% Porter, 1980, An algorithm for suffix stripping, Program, Vol. 14,
% no. 3, pp 130-137
% Original code modeled after the C version provided at:
% http://www.tartarus.org/~martin/PorterStemmer/c.tx... |
github | Bladefidz/machine-learning-master | submit.m | .m | machine-learning-master/coursera/machine-learning-standford-univerity/machine-learning-ex7/ex7/submit.m | 1,438 | utf_8 | 665ea5906aad3ccfd94e33a40c58e2ce | function submit()
addpath('./lib');
conf.assignmentSlug = 'k-means-clustering-and-pca';
conf.itemName = 'K-Means Clustering and PCA';
conf.partArrays = { ...
{ ...
'1', ...
{ 'findClosestCentroids.m' }, ...
'Find Closest Centroids (k-Means)', ...
}, ...
{ ...
'2', ...
... |
github | Bladefidz/machine-learning-master | submit.m | .m | machine-learning-master/coursera/machine-learning-standford-univerity/machine-learning-ex5/ex5/submit.m | 1,765 | utf_8 | b1804fe5854d9744dca981d250eda251 | function submit()
addpath('./lib');
conf.assignmentSlug = 'regularized-linear-regression-and-bias-variance';
conf.itemName = 'Regularized Linear Regression and Bias/Variance';
conf.partArrays = { ...
{ ...
'1', ...
{ 'linearRegCostFunction.m' }, ...
'Regularized Linear Regression Cost Fun... |
github | Bladefidz/machine-learning-master | submit.m | .m | machine-learning-master/coursera/machine-learning-standford-univerity/machine-learning-ex3/ex3/submit.m | 1,567 | utf_8 | 1dba733a05282b2db9f2284548483b81 | function submit()
addpath('./lib');
conf.assignmentSlug = 'multi-class-classification-and-neural-networks';
conf.itemName = 'Multi-class Classification and Neural Networks';
conf.partArrays = { ...
{ ...
'1', ...
{ 'lrCostFunction.m' }, ...
'Regularized Logistic Regression', ...
}, ..... |
github | Bladefidz/machine-learning-master | submit.m | .m | machine-learning-master/coursera/machine-learning-standford-univerity/machine-learning-ex8/ex8/submit.m | 2,135 | utf_8 | eebb8c0a1db5a4df20b4c858603efad6 | function submit()
addpath('./lib');
conf.assignmentSlug = 'anomaly-detection-and-recommender-systems';
conf.itemName = 'Anomaly Detection and Recommender Systems';
conf.partArrays = { ...
{ ...
'1', ...
{ 'estimateGaussian.m' }, ...
'Estimate Gaussian Parameters', ...
}, ...
{ ...... |
github | Bladefidz/machine-learning-master | submit.m | .m | machine-learning-master/coursera/machine-learning-standford-univerity/machine-learning-ex1/ex1/submit.m | 1,876 | utf_8 | 8d1c467b830a89c187c05b121cb8fbfd | function submit()
addpath('./lib');
conf.assignmentSlug = 'linear-regression';
conf.itemName = 'Linear Regression with Multiple Variables';
conf.partArrays = { ...
{ ...
'1', ...
{ 'warmUpExercise.m' }, ...
'Warm-up Exercise', ...
}, ...
{ ...
'2', ...
{ 'computeCost.m... |
github | fuenwang/BiomedicalSound-master | saveFig.m | .m | BiomedicalSound-master/hw02/submit/saveFig.m | 225 | utf_8 | 1e79a8c1f6d13a39941aa0d64550e925 | %
% EE6265 Fu-En Wang 106061531 HW2 11/14/2017
%
function saveFig(fig, path)
fig.PaperPositionMode = 'auto';
fig_pos = fig.PaperPosition;
fig.PaperSize = [fig_pos(3) fig_pos(4)];
print(fig, path, '-dpdf')
end |
github | fuenwang/BiomedicalSound-master | cyst_phantom.m | .m | BiomedicalSound-master/hw02/submit/cyst_phantom.m | 1,094 | utf_8 | bb73536838617945fa437e231968c9b4 | %
% EE6265 Fu-En Wang 106061531 HW2 11/14/2017
%
function [pos, amp] = cyst_phantom (N, C)
x_size = 15/1000; % Width of phantom [mm]
y_size = 0; % Transverse width of phantom [mm]
z_size = 20/1000; % Height of phantom [mm]
z_start = 30/1000; % Start of phantom surface [mm];
% Creat the general scatterers... |
github | fuenwang/BiomedicalSound-master | getNewArray.m | .m | BiomedicalSound-master/hw02/submit/getNewArray.m | 306 | utf_8 | 0ed688092474e37118bd3155e3545c62 | %
% EE6265 Fu-En Wang 106061531 HW2 11/14/2017
%
function [new_data] = getNewArray(origin, M, N)
new_data = zeros(1, N);
for i = 1:N
if i * M <= 1000
index = (i-1)*M+1 : i*M;
else
index = (i-1)*M+1 : length(origin);
end
new_data(i) = sum(origin(index));
end
end |
github | fuenwang/BiomedicalSound-master | saveFig.m | .m | BiomedicalSound-master/hw02/code/saveFig.m | 225 | utf_8 | 1e79a8c1f6d13a39941aa0d64550e925 | %
% EE6265 Fu-En Wang 106061531 HW2 11/14/2017
%
function saveFig(fig, path)
fig.PaperPositionMode = 'auto';
fig_pos = fig.PaperPosition;
fig.PaperSize = [fig_pos(3) fig_pos(4)];
print(fig, path, '-dpdf')
end |
github | fuenwang/BiomedicalSound-master | cyst_phantom.m | .m | BiomedicalSound-master/hw02/code/cyst_phantom.m | 1,094 | utf_8 | bb73536838617945fa437e231968c9b4 | %
% EE6265 Fu-En Wang 106061531 HW2 11/14/2017
%
function [pos, amp] = cyst_phantom (N, C)
x_size = 15/1000; % Width of phantom [mm]
y_size = 0; % Transverse width of phantom [mm]
z_size = 20/1000; % Height of phantom [mm]
z_start = 30/1000; % Start of phantom surface [mm];
% Creat the general scatterers... |
github | fuenwang/BiomedicalSound-master | getNewArray.m | .m | BiomedicalSound-master/hw02/code/getNewArray.m | 306 | utf_8 | 0ed688092474e37118bd3155e3545c62 | %
% EE6265 Fu-En Wang 106061531 HW2 11/14/2017
%
function [new_data] = getNewArray(origin, M, N)
new_data = zeros(1, N);
for i = 1:N
if i * M <= 1000
index = (i-1)*M+1 : i*M;
else
index = (i-1)*M+1 : length(origin);
end
new_data(i) = sum(origin(index));
end
end |
github | fuenwang/BiomedicalSound-master | xdc_dynamic_focus.m | .m | BiomedicalSound-master/hw02/code/Field2/xdc_dynamic_focus.m | 1,324 | utf_8 | 5b19e1bc74874267f2480741a74b9a62 | % Procedure for using dynamic focusing for an aperture
%
% Calling: xdc_dynamic_focus (Th, time, dir_zx,dir_zy);
%
% Parameters: Th - Pointer to the transducer aperture.
% time - Time after which the dynamic focus is valid.
% dir_zx - Direction (angle) in radians for ... |
github | fuenwang/BiomedicalSound-master | xdc_focus.m | .m | BiomedicalSound-master/hw02/code/Field2/xdc_focus.m | 974 | utf_8 | 36043bd3d056fa7dd1245db340ed62d8 | % Procedure for creating a focus time line for an aperture
%
% Calling: xdc_focus (Th, times, points);
%
% Parameters: Th - Pointer to the transducer aperture.
% times - Time after which the associated focus is valid.
% points - Focus points. Vector with three columns (x,y,z)
... |
github | fuenwang/BiomedicalSound-master | xdc_triangles.m | .m | BiomedicalSound-master/hw02/code/Field2/xdc_triangles.m | 1,603 | utf_8 | 540861e828a1f99427c7a46c07cbcb70 | % Procedure for creating an aperture with a number
% of physical elements consisting of triangles
%
% Calling: Th = xdc_triangles (data, center, focus);
%
% data - Information about the triangles. One row
% for each triangle. The contents is:
%
% Index Variable Value
% ------------... |
github | fuenwang/BiomedicalSound-master | field_logo.m | .m | BiomedicalSound-master/hw02/code/Field2/field_logo.m | 393 | utf_8 | 74305dd23287025a2e56f3921eb0621a | % Function to display the logo for field
%
% Version 1.3, August 10, 2007 by Joergen Arendt Jensen
% Error in loading filr fixed
function res = field_logo
% Create a window and display the Field II logo
h=figure;
axes('position',[0 0 1 1]);
place=which ('logo_field.mat');
eval(['load ',place])
image... |
github | fuenwang/BiomedicalSound-master | xdc_linear_multirow.m | .m | BiomedicalSound-master/hw02/code/Field2/xdc_linear_multirow.m | 2,353 | utf_8 | 18208adff504f9015f3174ab59d46a54 | % Procedure for creating a linear array transducer
% with an number of rows (1.5D array)
%
% Calling: Th = xdc_linear_multirow (no_elem_x, width, no_ele_y, heights, kerf_x, kerf_y,
% no_sub_x, no_sub_y, focus);
%
% Parameters: no_elem_x - Number of physical elements in x... |
github | fuenwang/BiomedicalSound-master | calc_hhp.m | .m | BiomedicalSound-master/hw02/code/Field2/calc_hhp.m | 846 | utf_8 | b3e9ab563d3bca28df72800ae37fff6d | % Procedure for calculating the pulse echo field.
%
% Calling: [hhp, start_time] = calc_hhp(Th1, Th2, points);
%
% Parameters: Th1 - Pointer to the transmit aperture.
% Th2 - Pointer to the receive aperture.
% points - Field points. Vector with three columns (x,y,z)
% ... |
github | fuenwang/BiomedicalSound-master | field_debug.m | .m | BiomedicalSound-master/hw02/code/Field2/field_debug.m | 417 | utf_8 | b8b796a2dc96f73d1e1cb36de01190f2 | % Procedure for initialize the Field II debugging. This will print
% out various information about the programs inner working.
%
% Calling: field_debug(state)
%
% Parameters: State - 1: debugging, 0: no debugging.
%
% Return: nothing.
%
% Version 1.0, November 20, 1995 by Joergen Arendt Jensen
function res =... |
github | fuenwang/BiomedicalSound-master | ele_waveform.m | .m | BiomedicalSound-master/hw02/code/Field2/ele_waveform.m | 1,143 | utf_8 | 0573a5fbc90caa641825a0e8c53267e5 | % Procedure for setting the waveform of individual
% physical elements of the transducer
%
% Calling: ele_waveform (Th, element_no, samples);
%
% Parameters: Th - Pointer to the transducer aperture.
% element_no - Column vector with one integer for each physical
% ... |
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