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 | aravindhm/deep-goggle-master | experiment_shallow_quantitative.m | .m | deep-goggle-master/experiments/experiment_shallow_quantitative.m | 2,973 | utf_8 | d4877675fb7d85e8a7aa05917f69faed | function experiment_shallow_quantitative()
% Run some CNN experiments
experiment_setup;
images = experiment_get_dataset('imnet');
%images = {images{1:10}};
% -------------------------------------------------------------------------
% Setup experiments
% -------... |
github | aravindhm/deep-goggle-master | experiment_run.m | .m | deep-goggle-master/experiments/experiment_run.m | 5,990 | utf_8 | 31d8e27f5acb9e020a6fe186b0232898 | function experiment_run(exp)
if matlabpool('size') > 0
parfor i=1:numel(exp) % Easily run lots of experiments on a cluster
run_one(exp{i}) ;
end
else
for i=1:numel(exp)
ts = tic;
fprintf(1, 'Starting an expeirment');
run_one(exp{i}) ;
fprintf(1, 'done an expeirment');
toc(ts);
end
end
e... |
github | aravindhm/deep-goggle-master | dsift_net.m | .m | deep-goggle-master/experiments/networks/dsift_net.m | 3,249 | utf_8 | 79c1e14d5fe80ed6ff8d0b1f28d27069 | function net = dsift_net(binSize, varargin)
% Define a CNN equivalent to dense SIFT
opts.numOrientations = 4 ;
opts = vl_argparse(opts, varargin) ;
opts.binSize = binSize ;
% Spatial derivatives along NO directions
NO = opts.numOrientations ;
dx = [0 0 0 ; -1 0 1 ; 0 0 0]/2 ;
dy = dx' ;
for i=0:2*NO-1
t = (2*pi)/(... |
github | aravindhm/deep-goggle-master | hog_net.m | .m | deep-goggle-master/experiments/networks/hog_net.m | 8,690 | utf_8 | 6a80d594176002581a3052779234f6a4 | function net = hog_net(binSize, varargin)
% HOG_NET CNN-HOG
% NET = HOG_NET(BINSIZE) returns a CNN equivalent to the HOG feature
% extractor for the specified bin size.
%
% The implementation is numerically identical to VL_HOG(), which in turns
% is nearly exactly the same as UoCTTI HOG implementation (DPM V5... |
github | felipegb94/hdr_imaging-master | rgb_to_lab.m | .m | hdr_imaging-master/rgb_to_lab.m | 386 | utf_8 | c8eaa88b7289cc918354c0cc089c3f40 |
function [lab_img,l,a,b] = rgb_to_lab(src)
%load rgb image
rgb_img = imread(src);
%convert to lab
labTransformation = makecform('srgb2lab');
lab_img = applycform(rgb_img,labTransformation);
%seperate l,a,b
l = lab_img(:,:,1);
a = lab_img(:,:,2);
b = lab_img(:,:,3);
% figure(1), imshow(l);
% title('l');
% figure(2... |
github | felipegb94/hdr_imaging-master | reinhardLocal.m | .m | hdr_imaging-master/reinhardLocal.m | 2,040 | utf_8 | 3cab43e5ee03ccbacc7dd2de9fc28a39 | % Calculates the Reinhard local tone mapping of the HDR image
% algorithm taken from:
% http://www.cmap.polytechnique.fr/~peyre/cours/x2005signal/hdr_photographic.pdf
function [ pic ] = reinhardLocal( hdr, sat, eps, phi )
luminanceMap = luminance(hdr);
alpha = 1 / (2*sqrt(2));
key = 0.18;
v1 = zeros... |
github | felipegb94/hdr_imaging-master | weight.m | .m | hdr_imaging-master/weight.m | 282 | utf_8 | ccf55804e41c7f529fc186dc7aca0194 | % calculates the weight value
%
% assumes zmin = 0
% zmax = 255
%
% z = a pixel value from 0-255
function w = weight(z)
zmin = 0;
zmax = 255;
threshold = 0.5*(zmin+zmax);
if z <= threshold
w = (z - zmin)+1; % make sure it's never zero!
else
w = (zmax - z)+1;
end
|
github | felipegb94/hdr_imaging-master | getRadianceMap.m | .m | hdr_imaging-master/getRadianceMap.m | 1,242 | utf_8 | bd4a72f4f238cef55552d147b68f4655 | % Calculate the HDR image by using weighted averaging.
%
% Input:
%
% * g(z) = inverse response curve E, log exposure corresponding to pixel.
% Vector of size 256, each element corresponds to one pixel (0-255).
%
% * Z(i,j) = pixel values (either R G or B) of pixel at location i from
% image j. So the matrix dimensions... |
github | felipegb94/hdr_imaging-master | readImages.m | .m | hdr_imaging-master/readImages.m | 934 | utf_8 | d12f9dac8684e52eb4012f5d7f0a0d02 | % Create a list of all pictures in a directory and the exposure settings
%
% Enter the directory name to search for. Must include a .txt file named
% list.txt.
function [filenames, exposures, numExposures] = readImages(dirName)
% read the list.txt file and extract strings
file = fopen(strcat(dirName,'list.txt... |
github | felipegb94/hdr_imaging-master | reinhardGlobal.m | .m | hdr_imaging-master/reinhardGlobal.m | 665 | utf_8 | 1e9e9100d316e568d2a78d8fcfd0923d | % This function computes the Reinhard global tone mapping algorithm
function [ pic ] = reinhardGlobal( hdr, a, sat)
luminanceMap = luminance(hdr);
numPixels = size(hdr,1) * size(hdr,2);
% small delta to avoid taking log(0)
d = 0.00001;
% compute key
key = exp((1/numPixels)*(sum(sum(log(luminanceMap + d)))));
% sca... |
github | felipegb94/hdr_imaging-master | radianceWeights.m | .m | hdr_imaging-master/radianceWeights.m | 324 | utf_8 | 49927b1ef7381e0cf1862798244ece85 | %% returns the weights list for radiance map equation
function [ weights ] = radianceWeights(g)
weights = zeros(256, 1);
g2 = diff(g) + 0.1;
for i = 1:255
weights(i,1) = g(i) / g2(i);
if weights(i,1) < 0
weights(i,1) = exp(weights(i,1));
end
end
weights(256) = g(256) / g2(255);
weights = weights ... |
github | bithollow/ardupilot-master | RotToQuat.m | .m | ardupilot-master/libraries/AP_NavEKF/Models/Common/RotToQuat.m | 288 | utf_8 | 9239706354267c8f5f2a29f992c07de9 | % convert froma rotation vector in radians to a quaternion
function quaternion = RotToQuat(rotVec)
vecLength = sqrt(rotVec(1)^2 + rotVec(2)^2 + rotVec(3)^2);
if vecLength < 1e-6
quaternion = [1;0;0;0];
else
quaternion = [cos(0.5*vecLength); rotVec/vecLength*sin(0.5*vecLength)];
end |
github | bithollow/ardupilot-master | NormQuat.m | .m | ardupilot-master/libraries/AP_NavEKF/Models/Common/NormQuat.m | 198 | utf_8 | ed913e87efc9194a2c52b266fced8da7 | % normalise the quaternion
function quaternion = normQuat(quaternion)
quatMag = sqrt(quaternion(1)^2 + quaternion(2)^2 + quaternion(3)^2 + quaternion(4)^2);
quaternion(1:4) = quaternion / quatMag;
|
github | bithollow/ardupilot-master | QuatToEul.m | .m | ardupilot-master/libraries/AP_NavEKF/Models/Common/QuatToEul.m | 436 | utf_8 | c19c9235052d99b8b943a7157e83fc94 | % Convert from a quaternion to a 321 Euler rotation sequence in radians
function Euler = QuatToEul(quat)
Euler = zeros(3,1);
Euler(1) = atan2(2*(quat(3)*quat(4)+quat(1)*quat(2)), quat(1)*quat(1) - quat(2)*quat(2) - quat(3)*quat(3) + quat(4)*quat(4));
Euler(2) = -asin(2*(quat(2)*quat(4)-quat(1)*quat(3)));
Euler(3) =... |
github | mars920314/DeepFi-master | get_scaled_csi.m | .m | DeepFi-master/DeepFi/get_scaled_csi.m | 1,842 | utf_8 | 25f6ee30c68e10fbfaaeff35624ab758 | %GET_SCALED_CSI Converts a CSI struct to a channel matrix H.
%
% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>
%
function ret = get_scaled_csi(csi_st)
% Pull out CSI
csi = csi_st.csi;
% Calculate the scale factor between normalized CSI and RSSI (mW)
csi_sq = csi .* conj(csi);
csi_pwr =... |
github | mars920314/DeepFi-master | get_total_rss.m | .m | DeepFi-master/DeepFi/get_total_rss.m | 592 | utf_8 | 9f75c5f068248c64a969d4b65b5aa054 | %GET_TOTAL_RSS Calculates the Received Signal Strength (RSS) in dBm from
% a CSI struct.
%
% (c) 2011 Daniel Halperin <dhalperi@cs.washington.edu>
%
function ret = get_total_rss(csi_st)
error(nargchk(1,1,nargin));
% Careful here: rssis could be zero
rssi_mag = 0;
if csi_st.rssi_a ~= 0
rssi_mag ... |
github | mars920314/DeepFi-master | get_eff_SNRs.m | .m | DeepFi-master/DeepFi/get_eff_SNRs.m | 2,622 | utf_8 | ed7119854587f1abc99cab9b0ecd1312 | %GET_EFF_SNRS Compute the effective SNR values from a CSI matrix
% Note that the matrix is expected to have dimensions M x N x S, where
% M = # TX antennas
% N = # RX antennas
% S = # subcarriers
%
% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>,
% Wenjun Hu
%
function ret=ge... |
github | mars920314/DeepFi-master | CG_MNIST.m | .m | DeepFi-master/DeepFi/CG_MNIST.m | 2,727 | utf_8 | 4679a67a7470f0d5835c1cad7f2d0896 | % Version 1.000
%
% Code provided by Ruslan Salakhutdinov and Geoff Hinton
%
% Permission is granted for anyone to copy, use, modify, or distribute this
% program and accompanying programs and documents for any purpose, provided
% this copyright notice is retained and prominently displayed, along with
% a note saying t... |
github | mars920314/DeepFi-master | GaussianRBM.m | .m | DeepFi-master/DeepFi/GaussianRBM.m | 6,288 | utf_8 | f7b69160ff574b93d8b31c90140241cb | %{
===========================================================================
Code provided by Yichuan (Charlie) Tang
http://www.cs.toronto.edu/~tang
Permission is granted for anyone to copy, use, modify, or distribute this
program and accompanying programs and documents for any purpose, provided
this copyright noti... |
github | mars920314/DeepFi-master | read_bf_file.m | .m | DeepFi-master/DeepFi/read_bf_file.m | 2,577 | utf_8 | 3046107c2e85bb02155fda059099b086 | %READ_BF_FILE Reads in a file of beamforming feedback logs.
% This version uses the *C* version of read_bfee, compiled with
% MATLAB's MEX utility.
%
% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>
%
function ret = read_bf_file(filename)
%% Input check
error(nargchk(1,1,nargin));
%% Open file
f = fope... |
github | mars920314/DeepFi-master | dbinv.m | .m | DeepFi-master/DeepFi/dbinv.m | 145 | utf_8 | f3e0b99630ef3ad7fcc8ca3ac3daade3 | %DBINV Convert from decibels.
%
% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>
%
function ret = dbinv(x)
ret = 10.^(x/10);
end
|
github | mars920314/DeepFi-master | get_scaled_csi.m | .m | DeepFi-master/Deep Belief Networks/get_scaled_csi.m | 1,842 | utf_8 | 25f6ee30c68e10fbfaaeff35624ab758 | %GET_SCALED_CSI Converts a CSI struct to a channel matrix H.
%
% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>
%
function ret = get_scaled_csi(csi_st)
% Pull out CSI
csi = csi_st.csi;
% Calculate the scale factor between normalized CSI and RSSI (mW)
csi_sq = csi .* conj(csi);
csi_pwr =... |
github | mars920314/DeepFi-master | get_total_rss.m | .m | DeepFi-master/Deep Belief Networks/get_total_rss.m | 592 | utf_8 | 9f75c5f068248c64a969d4b65b5aa054 | %GET_TOTAL_RSS Calculates the Received Signal Strength (RSS) in dBm from
% a CSI struct.
%
% (c) 2011 Daniel Halperin <dhalperi@cs.washington.edu>
%
function ret = get_total_rss(csi_st)
error(nargchk(1,1,nargin));
% Careful here: rssis could be zero
rssi_mag = 0;
if csi_st.rssi_a ~= 0
rssi_mag ... |
github | mars920314/DeepFi-master | CG_MNIST.m | .m | DeepFi-master/Deep Belief Networks/CG_MNIST.m | 2,727 | utf_8 | 4679a67a7470f0d5835c1cad7f2d0896 | % Version 1.000
%
% Code provided by Ruslan Salakhutdinov and Geoff Hinton
%
% Permission is granted for anyone to copy, use, modify, or distribute this
% program and accompanying programs and documents for any purpose, provided
% this copyright notice is retained and prominently displayed, along with
% a note saying t... |
github | mars920314/DeepFi-master | CG_CLASSIFY.m | .m | DeepFi-master/Deep Belief Networks/CG_CLASSIFY.m | 1,853 | utf_8 | 6ed770942ea0c0f3a0f53cfe675bb5ff | % Version 1.000
%
% Code provided by Ruslan Salakhutdinov and Geoff Hinton
%
% Permission is granted for anyone to copy, use, modify, or distribute this
% program and accompanying programs and documents for any purpose, provided
% this copyright notice is retained and prominently displayed, along with
% a note saying t... |
github | mars920314/DeepFi-master | read_bf_file.m | .m | DeepFi-master/Deep Belief Networks/read_bf_file.m | 2,577 | utf_8 | 3046107c2e85bb02155fda059099b086 | %READ_BF_FILE Reads in a file of beamforming feedback logs.
% This version uses the *C* version of read_bfee, compiled with
% MATLAB's MEX utility.
%
% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>
%
function ret = read_bf_file(filename)
%% Input check
error(nargchk(1,1,nargin));
%% Open file
f = fope... |
github | mars920314/DeepFi-master | mnistdisp.m | .m | DeepFi-master/Deep Belief Networks/mnistdisp.m | 1,084 | utf_8 | fe0cdd3b44b770d51322d5c6e9f4fd91 | % Version 1.000
%
% Code provided by Ruslan Salakhutdinov and Geoff Hinton
%
% Permission is granted for anyone to copy, use, modify, or distribute this
% program and accompanying programs and documents for any purpose, provided
% this copyright notice is retained and prominently displayed, along with
% a note saying t... |
github | mars920314/DeepFi-master | dbinv.m | .m | DeepFi-master/Deep Belief Networks/dbinv.m | 145 | utf_8 | f3e0b99630ef3ad7fcc8ca3ac3daade3 | %DBINV Convert from decibels.
%
% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>
%
function ret = dbinv(x)
ret = 10.^(x/10);
end
|
github | mars920314/DeepFi-master | CG_CLASSIFY_INIT.m | .m | DeepFi-master/Deep Belief Networks/CG_CLASSIFY_INIT.m | 1,136 | utf_8 | 22b98fdbaa2f63132f19a95e73c35d22 | % Version 1.000
%
% Code provided by Ruslan Salakhutdinov and Geoff Hinton
%
% Permission is granted for anyone to copy, use, modify, or distribute this
% program and accompanying programs and documents for any purpose, provided
% this copyright notice is retained and prominently displayed, along with
% a note saying t... |
github | mars920314/DeepFi-master | mnistdisp.m | .m | DeepFi-master/Restricted Boltzmann Machines/mnistdisp.m | 1,136 | utf_8 | bdf11055312aa60255e74937dde95073 | % Version 1.000
%
% Code provided by Ruslan Salakhutdinov and Geoff Hinton
%
% Permission is granted for anyone to copy, use, modify, or distribute this
% program and accompanying programs and documents for any purpose, provided
% this copyright notice is retained and prominently displayed, along with
% a note saying t... |
github | kenders2000/BlindRT-master | Choose_signal.m | .m | BlindRT-master/Choose_signal.m | 4,495 | utf_8 | 183a22623d5b7fc61d871fbe054c864a | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This function is used to extracted the exponentional
% damping continuous segments of the speech signal
% Input:
% x_E ------ The envelope of the speech signal
% Fs ------ The sampling frequency
% Best_s_num ------ The n... |
github | jeffreyvarner/CHEME-2880-S15-MAB-MODEL-master | ObjectiveFunction.m | .m | CHEME-2880-S15-MAB-MODEL-master/ObjectiveFunction.m | 2,561 | utf_8 | 7924a9bf5cef8c3bb102e08f442bdd85 | % ------------------------------------------------------------------------------------- %
% Copyright (c) 2015 Varnerlab,
% School of Chemical and Biomolecular Engineering,
% Cornell University, Ithaca NY 14853 USA.
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software a... |
github | jeffreyvarner/CHEME-2880-S15-MAB-MODEL-master | BalanceEquations.m | .m | CHEME-2880-S15-MAB-MODEL-master/BalanceEquations.m | 3,601 | utf_8 | 6d44da49dc612f725d9eb588cfcd0550 | % ------------------------------------------------------------------------------------- %
% Copyright (c) 2015 Varnerlab,
% School of Chemical and Biomolecular Engineering,
% Cornell University, Ithaca NY 14853 USA.
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software a... |
github | jeffreyvarner/CHEME-2880-S15-MAB-MODEL-master | SolveBalanceEquations.m | .m | CHEME-2880-S15-MAB-MODEL-master/SolveBalanceEquations.m | 2,248 | utf_8 | 43e238bdd2feb4f4a7fb74b1f33dafb9 | % ------------------------------------------------------------------------------------- %
% Copyright (c) 2015 Varnerlab,
% School of Chemical and Biomolecular Engineering,
% Cornell University, Ithaca NY 14853 USA.
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software a... |
github | Guokr1991/ultratrack-master | do_dyna_scans.m | .m | ultratrack-master/do_dyna_scans.m | 9,979 | utf_8 | 9f6b063e42f91063198fc6484b96e5ef | function do_dyna_scans(PHANTOM_FILE,OUTPUT_FILE,PARAMS);
%
% do_dyna_scans(PHANTOM_FILE,OUTPUT_FILE,PARAMS);
%
% Function for doing ARFI scans with the URI/Field toolkit
%
% 11/11/04 Stephen McAleavey, U. Rochester BME
%
%
% PHANTOM_FILE Filename for phantom files - will look for
% everything w/ %03d number appende... |
github | feelus-disk/_programming-master | func.m | .m | _programming-master/lang_matlab/old_progs/func.m | 758 | utf_8 | 077cbbc6496a897be632929794464c7d | function func
global R L C
global w0 alpha wc
C=1e-6;
L=5;
ww=[];
R=100:50:500;
w0=(L.*C).^(-0.5);
alpha=R./(2*L);
wc=(w0.^2-alpha.^2).^0.5;
hold on;
for i=1:length(R)
data=[R(i) alpha(i) wc(i) ];
w=400:0.1:500;
Rw=(w.*L-(w.*C).^(-1)).^2 + R(i).^2;
plot(w,Rw);
... |
github | charmgil/CTBN-master | maxsum_forest.m | .m | CTBN-master/CTBN/maxsum_forest.m | 7,004 | utf_8 | 980db79dbcfdfbfd1e2998b3fdab8ed7 | % maxsum_forest: returns the joint assignment with the highest probability
% (wrapper for maxsum_tree)
function [ assn logprob ] = maxsum_forest( T )
% options
is_verbose = false;
% init
n_nodes = size(T, 2);
% traverse + examine T
lookup = nan(n_nodes, 1); % node# -> cell_index lookup table
max_node_i = -1;
for i... |
github | charmgil/CTBN-master | MAP_prediction_sw.m | .m | CTBN-master/CTBN/MAP_prediction_sw.m | 1,988 | utf_8 | 84ed46516d4964c380e2fbca12d8feba | % This is an implmentation of the prediction algorithm presented in [Batal, Hong, Hauskrecht 2013]
% I. Batal, C. Hong, and M. Hauskrecht.
% An efficient probabilistic framework for multi-dimensional classification.
% CIKM 2013, Burlingame, CA, USA. October 2013.
function [ Y_pred Y_log_prob] = MAP_p... |
github | charmgil/CTBN-master | compute_crossvalidation_loglikelihood_sw.m | .m | CTBN-master/CTBN/compute_crossvalidation_loglikelihood_sw.m | 3,049 | utf_8 | 2d5014062a6cab6a7dd5c8cae212e9b7 | %compute the likelihood of LR which learns from X to Y on the
%Crossvalidation splits in indices
function [ LL ] = compute_crossvalidation_loglikelihood_sw( X, Y, Y_parent, indices, k)
%save time
learn_cost=false;
for i=1:k
index_validation = find(indices==i);
index_train = find(indices~=i);
... |
github | charmgil/CTBN-master | learn_output_tree_sw.m | .m | CTBN-master/CTBN/learn_output_tree_sw.m | 2,046 | utf_8 | 1390f813fdd387b0db245bc6c53b0161 | % This is an implmentation of the prediction algorithm presented in [Batal, Hong, Hauskrecht 2013]
% I. Batal, C. Hong, and M. Hauskrecht.
% An efficient probabilistic framework for multi-dimensional classification.
% CIKM 2013, Burlingame, CA, USA. October 2013.
function [ T ] = learn_output_tree_sw... |
github | charmgil/CTBN-master | compute_crossvalidation_loglikelihood.m | .m | CTBN-master/CTBN/compute_crossvalidation_loglikelihood.m | 663 | utf_8 | 3019a9546545b61b77ef32b451676760 | %compute the likelihood of LR which learns from X to Y on the
%Crossvalidation splits in indices
function [ LL ] = compute_crossvalidation_loglikelihood( X, Y, indices, k)
%save time
learn_cost=false;
for i=1:k
index_validation = find(indices==i);
index_train = find(indices~=i);
Y_train=Y(i... |
github | charmgil/CTBN-master | evaluate_probability.m | .m | CTBN-master/auxiliary/evaluate_probability.m | 435 | utf_8 | 0599afdbb2864440511af7489ab72b01 | %T is the tree (orgnaized in breadth-first fashion), Y is the assignment to compute it probability
function [ log_prob ] = evaluate_probability( T, Y )
log_prob=0;
for i=1:length(T)
y_val=Y(T{i}.node);
if(isempty(T{i}.parent))
log_prob=log_prob+T{i}.log_potential(y_val+1);
else
y_... |
github | charmgil/CTBN-master | generate_all_combinations.m | .m | CTBN-master/auxiliary/generate_all_combinations.m | 1,507 | utf_8 | 9b165b84e11bf0864539fccd261a26ea | %all binary combinations of size n
function [ C ] = generate_all_combinations( Y )
d=size(Y,2);
if(d==1)
C=0:length(unique(Y(:,1)))-1;
end
if(d==2)
S1=0:length(unique(Y(:,1)))-1;
S2=0:length(unique(Y(:,2)))-1;
sets = {S1, S2};
[x y] = ndgrid(sets{:});
C = [x(:) y(:... |
github | charmgil/CTBN-master | check_directed_cycle.m | .m | CTBN-master/auxiliary/check_directed_cycle.m | 563 | utf_8 | 460dbeedcb5df54aab99fb3d38ab18fa | %check if node "from" can be reached from node "to": meaning there is a
%cycle
function [ err ] = check_directed_cycle( T, to, from)
%do a BFS starting from node to, if we encounter node from, then there is a
%cycle
err=false;
[ idx ] = search_tree( to, T );
%BFS
list=T{idx}.children;
while(~isempty(li... |
github | charmgil/CTBN-master | getMeasuresMLC.m | .m | CTBN-master/auxiliary/getMeasuresMLC.m | 2,227 | utf_8 | dd33394f227c1796fb947ba10bb2ec74 | %Y: true labels, Y_pred: predicted labels, Y_log_prob: log probability of the true class
%according to the model
function [ obj ] = getMeasuresMLC( Y, Y_pred, Y_log_prob )
[N d] = size(Y);
%% Compute Exact Matching rate and Hamming
cnt = 0;
cnt2 = 0;
for i=1:N
y = Y(i,:);
y_pred = Y_pred(... |
github | charmgil/CTBN-master | search_tree.m | .m | CTBN-master/auxiliary/search_tree.m | 226 | utf_8 | 33125937d299af06a3c6c659ef044bd8 | %search the tree for node n, return -1 if not found
function [ idx ] = search_tree( n, T )
%search the tree for the node
idx=-1;
for i=1:length(T)
if(T{i}.node==n)
idx=i;
break;
end
end
|
github | charmgil/CTBN-master | LR_predict.m | .m | CTBN-master/auxiliary/LR_predict.m | 451 | utf_8 | 0e66bf576a329d141e2062eafae1ffe6 | %return P(y=1|x,w)
function [ P ] = LR_predict( w,X )
n=size(X,1);
if size(w,1) == 1
for i=1:n
x=X(i,:);
z=dot(w,[1 x]);
P(i)=1.0 ./ (1.0 + exp(-z));
end
else % for multi-class
for i=1:n
for m = 1:size(w,1)
x=X(i,:);
z(m)=exp(do... |
github | charmgil/CTBN-master | LR_likelihood.m | .m | CTBN-master/auxiliary/LR_likelihood.m | 644 | utf_8 | 7c3302cf219343ab08fec002a51c8db4 | %compute the likelihood of Y under probabilities P
%P(i) is Prob(Y(i)=1)
function [ LL ] = LR_likelihood( P, Y )
[r,c] = size(P);
if min(r,c) == 1 || min(r,c) == 2
%LogLikelihood
LL=0;
n=length(Y);
for i=1:n
if(Y(i)==1)
LL=LL+log(P(i));
else
... |
github | charmgil/CTBN-master | LR_train.m | .m | CTBN-master/auxiliary/LR_train.m | 2,726 | utf_8 | 19db0adbf6bdd4b0d6ca983b94b1085a | %learn_cost is a boolean variable, if false, do not optimize over for
%learning the cost, use the standard 1 cost (save time)
function [ weights ] = LR_train( X, Y, varargin )
global LR_implementation;
k=length(unique(Y))-1;
if isequal(LR_implementation,'weighted_liblinear')
if(nargin<3)
fprint... |
github | horosproject/horosplugins-master | FAWebPostQuery.m | .m | horosplugins-master/MIRC Teaching File/FAWebPostQuery.m | 12,199 | utf_8 | 39900b69e48a1a1a20cf292ef410cd53 | //
// FAWebPostQuery.m
// TeachingFile
//
// Created by Lance Pysher on 3/8/07.
// Copyright 2007 __MyCompanyName__. All rights reserved.
//
#import "FAWebPostQuery.h"
#import "httpFlattening.h"
#define READ_SIZE 1024
NSString * const FAWebPostAlreadyPosted
= @"FAWebPostAlreadyPosted";... |
github | sbargoti/matlab-code-master | bin_save.m | .m | matlab-code-master/SuchetTrunkDetection/LoadData/bin_save.m | 5,910 | utf_8 | 002e9877ce4fbb4ca66a4fdb593ba53a | %bin_save Save data to headerless binary file (as used by comma).
% data = bin_save(DATA,FILENAME,FORMAT) saves data to file with raw data
% types specified by the format string.
%
% DATA The matrix of data to save to the file.
% that will contain the raw... |
github | sbargoti/matlab-code-master | bin_load.m | .m | matlab-code-master/SuchetTrunkDetection/LoadData/bin_load.m | 5,821 | utf_8 | 2622ec00c6dd17901dff405b483948ff | %bin_load Load headerless binary data (as used by comma) into workspace.
% data = bin_load(FILENAME,FORMAT) loads the variables from a file into a
% double-precision array, with raw data types specified by the format
% string.
%
% FILENAME The string file name for the file
% ... |
github | sbargoti/matlab-code-master | v2struct.m | .m | matlab-code-master/SuchetTrunkDetection/LoadData/v2struct.m | 16,311 | utf_8 | 77b042ae8b342c50880cf0e543b7bd27 | %% v2struct
% v2struct Pack/Unpack Variables to/from a scalar structure.
function varargout = v2struct(varargin)
%% Description
% v2struct has dual functionality in packing & unpacking variables into structures and
% vice versa, according to the syntax and inputs.
%
% Function features:
% * Pack... |
github | sbargoti/matlab-code-master | SplitVStructure.m | .m | matlab-code-master/SuchetTrunkDetection/SegmentPointCloud/SplitVStructure.m | 8,929 | utf_8 | c2d98c6e1466081cd017ecc26cd3c849 | function [frontFacePC,pcIdx] = SplitVStructure(points3D)
% function frontFacePC = SplitVStructure(PC)
% Given a point cloud post ground removal, separate out the V structure
disp('Splitting the V Structure');
PC = points3D;
% Cut off the top 20% of the point cloud - gets rid of the roof
% Cut the roof from the ex... |
github | sbargoti/matlab-code-master | RealignRowV2.m | .m | matlab-code-master/SuchetTrunkDetection/SegmentPointCloud/RealignRowV2.m | 5,551 | utf_8 | 23c19f57d9363d6cb7ac235d143281da | function [AlignedPC,PCIdx] = RealiginRowV2(x,y,z,row_type)
% Similar to RealignRow but takes in data rather than importing within
% itself
% Within AlignMajorPlane we are also separating two rows from each other
% (may need to look into this while working on i-structures)
% Rotate Data along dominant axes
% Rot... |
github | sbargoti/matlab-code-master | RealignRow.m | .m | matlab-code-master/SuchetTrunkDetection/SegmentPointCloud/RealignRow.m | 4,960 | utf_8 | 0ee7e8fc0cd15e9ba4af982b74705531 | function [AlignedPC,PCIdx,OriginalPC,originalPCTime] = RealignRow(CSVFileName)
% Import data
[Time,r,b,e,x,y,z,ref,scan] = ImportSickCSV(CSVFileName);
p = findstr(CSVFileName,'SickRow');
[originalPCTime,r,b,e,x2,y2,z2,ref,scan] = ImportSickCSV([CSVFileName(1:p+6),'Z',CSVFileName(p+7:end)]);
OriginalPC = [x2,y2,z... |
github | sbargoti/matlab-code-master | trunkSegmentationPart2Working.m | .m | matlab-code-master/SuchetTrunkDetection/TrunkDetection/trunkSegmentationPart2Working.m | 8,395 | utf_8 | 305839fd982f244ef83cc908a3db00c8 | % Second part of script containing trunk detection using images
% By this time, the mapping has been done in linux and the images have been
% classified using calvins code
function trunkSegmentationPart2(dataPath,modelPath,row_type)
% clear all; close all; clc;
%% Load data from face segmentation
% parentFolder... |
github | sbargoti/matlab-code-master | trunkSegmentationPart2.m | .m | matlab-code-master/SuchetTrunkDetection/TrunkDetection/trunkSegmentationPart2.m | 11,109 | utf_8 | d410693da05838d57ec74d4db01315ad | % Second part of script containing trunk detection using images
% By this time, the mapping has been done in linux and the images have been
% classified using calvins code
function trunkSegmentationPart2(dataPath,modelPath,row_type)
% clear all; close all; clc;
%% Load data from face segmentation
% parentFolder... |
github | sbargoti/matlab-code-master | trunkSegmentationPart1.m | .m | matlab-code-master/SuchetTrunkDetection/TrunkDetection/trunkSegmentationPart1.m | 2,839 | utf_8 | 537b7b146d6a07964e4d7430d6b60409 | % Main function for trunk detection using images
% In its current form, it jumps between linux scripts and matlab functions
% clear all; close all; clc;
function trunkSegmentationPart1(dataPath,row_number,row_type)
%% Load point cloud data - both original and rotated/flattened [preprocessing done in linux]
% paren... |
github | sbargoti/matlab-code-master | HoughLineFit.m | .m | matlab-code-master/SuchetTrunkDetection/Observations/HoughLineFit.m | 3,559 | utf_8 | d147d3de93ab65f6a08be69163a81070 | function [linePos, lineEnds, lineLength, lineAngles, linePointsIdx] = HoughLineFit(points2D,row_type)
% function [linePos, lineEnds, lineLength, lineAngles, linePointsIdx] = HoughLinesFit(points2D)
% Given a 2d point cloud, fit lines using the matlab build in hough
% transform function.
disp('Applying Hough Transf... |
github | sbargoti/matlab-code-master | iso2seconds.m | .m | matlab-code-master/SuchetTrunkDetection/Observations/iso2seconds.m | 487 | utf_8 | cfca2cec2fd79e5049e213e732aa4dc4 | % ISO2SECONDS Convert format of time from iso string to seconds since epoch
%
% time_as_seconds_since_epoch = ISO2SECONDS( time_as_iso_string ) converts
% between these two formats
%
% Example
% -------
% seconds2iso(1399970753.498385)
% seconds2iso(1.399970753498385e+009)
%
% See also: seconds2iso
%
% ... |
github | sbargoti/matlab-code-master | seconds2iso.m | .m | matlab-code-master/SuchetTrunkDetection/Observations/seconds2iso.m | 483 | utf_8 | ea7aaff165b79d8fc349219083a7da3d | % SECONDS2ISO Convert format of time from seconds since epoch to iso string
%
% time_as_iso_string = SECONDS2ISO( time_as_seconds_since_epoch ) converts
% between these two formats
%
% Example
% -------
% iso2seconds('20140513T084553.498385')
%
% See also: iso2seconds
%
% author James Underwood
function ... |
github | sbargoti/matlab-code-master | ModHoughLines.m | .m | matlab-code-master/SuchetTrunkDetection/Observations/ModHoughLines.m | 7,655 | utf_8 | aca61e68b8f3b9ea15a64f6b35c0adb4 | function lines = ModHoughLines(IMAGE,points2D,imageRes,theta,rho,peaks,fillgap,minlength)
%HOUGHLINES Extract line segments based on Hough transform.
% LINES = HOUGHLINES(BW, THETA, RHO, PEAKS, points2D) extracts line segments
% in the image BW associated with particular bins in a Hough
% transform. THETA a... |
github | sbargoti/matlab-code-master | fitplane.m | .m | matlab-code-master/SuchetTrunkDetection/ModelFitting/fitplane.m | 1,753 | utf_8 | 1e714564feeab6c8851f300c0745ec90 | % FITPLANE - solves coefficients of plane fitted to 3 or more points
%
% Usage: B = fitplane(XYZ)
%
% Where: XYZ - 3xNpts array of xyz coordinates to fit plane to.
% If Npts is greater than 3 a least squares solution
% is generated.
%
% Returns: B - 4x1 array of plane coe... |
github | sbargoti/matlab-code-master | iscolinear.m | .m | matlab-code-master/SuchetTrunkDetection/ModelFitting/iscolinear.m | 2,380 | utf_8 | b433c5fb9ce61e016fa92db1b88cefb9 | % ISCOLINEAR - are 3 points colinear
%
% Usage: r = iscolinear(p1, p2, p3, flag)
%
% Arguments:
% p1, p2, p3 - Points in 2D or 3D.
% flag - An optional parameter set to 'h' or 'homog'
% indicating that p1, p2, p3 are homogneeous
% coordinates with arb... |
github | sbargoti/matlab-code-master | ransac.m | .m | matlab-code-master/SuchetTrunkDetection/ModelFitting/ransac.m | 10,115 | utf_8 | 135d524e5b93f4d89ad73333ba730226 | % RANSAC - Robustly fits a model to data with the RANSAC algorithm
%
% Usage:
%
% [M, inliers] = ransac(x, fittingfn, distfn, degenfn s, t, feedback, ...
% maxDataTrials, maxTrials)
%
% Arguments:
% x - Data sets to which we are seeking to fit a model M
% It is... |
github | sbargoti/matlab-code-master | ransacfitplane.m | .m | matlab-code-master/SuchetTrunkDetection/ModelFitting/ransacfitplane.m | 4,390 | utf_8 | 68eaec53eaf67316fc1bd60520104cc0 | % RANSACFITPLANE - fits plane to 3D array of points using RANSAC
%
% Usage [B, P, inliers] = ransacfitplane(XYZ, t, feedback)
%
% This function uses the RANSAC algorithm to robustly fit a plane
% to a set of 3D data points.
%
% Arguments:
% XYZ - 3xNpts array of xyz coordinates to fit plane to.
% ... |
github | sbargoti/matlab-code-master | vl_compile.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/vl_compile.m | 5,060 | utf_8 | 978f5189bb9b2a16db3368891f79aaa6 | function vl_compile(compiler)
% VL_COMPILE Compile VLFeat MEX files
% VL_COMPILE() uses MEX() to compile VLFeat MEX files. This command
% works only under Windows and is used to re-build problematic
% binaries. The preferred method of compiling VLFeat on both UNIX
% and Windows is through the provided Makefile... |
github | sbargoti/matlab-code-master | vl_noprefix.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/vl_noprefix.m | 1,875 | utf_8 | 97d8755f0ba139ac1304bc423d3d86d3 | function vl_noprefix
% VL_NOPREFIX Create a prefix-less version of VLFeat commands
% VL_NOPREFIX() creats prefix-less stubs for VLFeat functions
% (e.g. SIFT for VL_SIFT). This function is seldom used as the stubs
% are included in the VLFeat binary distribution anyways. Moreover,
% on UNIX platforms, the stub... |
github | sbargoti/matlab-code-master | vl_pegasos.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/misc/vl_pegasos.m | 2,837 | utf_8 | d5e0915c439ece94eb5597a07090b67d | % VL_PEGASOS [deprecated]
% VL_PEGASOS is deprecated. Please use VL_SVMTRAIN() instead.
function [w b info] = vl_pegasos(X,Y,LAMBDA, varargin)
% Verbose not supported
if (sum(strcmpi('Verbose',varargin)))
varargin(find(strcmpi('Verbose',varargin),1))=[];
fprintf('Option VERBOSE is no longer supported.\n');
en... |
github | sbargoti/matlab-code-master | vl_svmpegasos.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/misc/vl_svmpegasos.m | 1,178 | utf_8 | 009c2a2b87a375d529ed1a4dbe3af59f | % VL_SVMPEGASOS [deprecated]
% VL_SVMPEGASOS is deprecated. Please use VL_SVMTRAIN() instead.
function [w b info] = vl_svmpegasos(DATA,LAMBDA, varargin)
% Verbose not supported
if (sum(strcmpi('Verbose',varargin)))
varargin(find(strcmpi('Verbose',varargin),1))=[];
fprintf('Option VERBOSE is no longer suppor... |
github | sbargoti/matlab-code-master | vl_override.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/misc/vl_override.m | 4,654 | utf_8 | e233d2ecaeb68f56034a976060c594c5 | function config = vl_override(config,update,varargin)
% VL_OVERRIDE Override structure subset
% CONFIG = VL_OVERRIDE(CONFIG, UPDATE) copies recursively the fileds
% of the structure UPDATE to the corresponding fields of the
% struture CONFIG.
%
% Usually CONFIG is interpreted as a list of paramters with their
... |
github | sbargoti/matlab-code-master | vl_quickvis.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/quickshift/vl_quickvis.m | 3,696 | utf_8 | 27f199dad4c5b9c192a5dd3abc59f9da | function [Iedge dists map gaps] = vl_quickvis(I, ratio, kernelsize, maxdist, maxcuts)
% VL_QUICKVIS Create an edge image from a Quickshift segmentation.
% IEDGE = VL_QUICKVIS(I, RATIO, KERNELSIZE, MAXDIST, MAXCUTS) creates an edge
% stability image from a Quickshift segmentation. RATIO controls the tradeoff
% bet... |
github | sbargoti/matlab-code-master | vl_demo_aib.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/demo/vl_demo_aib.m | 2,928 | utf_8 | 590c6db09451ea608d87bfd094662cac | function vl_demo_aib
% VL_DEMO_AIB Test Agglomerative Information Bottleneck (AIB)
D = 4 ;
K = 20 ;
randn('state',0) ;
rand('state',0) ;
X1 = randn(2,300) ; X1(1,:) = X1(1,:) + 2 ;
X2 = randn(2,300) ; X2(1,:) = X2(1,:) - 2 ;
X3 = randn(2,300) ; X3(2,:) = X3(2,:) + 2 ;
figure(1) ; clf ; hold on ;
vl_plotframe(X... |
github | sbargoti/matlab-code-master | vl_demo_alldist.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/demo/vl_demo_alldist.m | 5,460 | utf_8 | 6d008a64d93445b9d7199b55d58db7eb | function vl_demo_alldist
%
numRepetitions = 3 ;
numDimensions = 1000 ;
numSamplesRange = [300] ;
settingsRange = {{'alldist2', 'double', 'l2', }, ...
{'alldist', 'double', 'l2', 'nosimd'}, ...
{'alldist', 'double', 'l2' }, ...
{'alldist2', 's... |
github | sbargoti/matlab-code-master | vl_demo_ikmeans.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/demo/vl_demo_ikmeans.m | 774 | utf_8 | 17ff0bb7259d390fb4f91ea937ba7de0 | function vl_demo_ikmeans()
% VL_DEMO_IKMEANS
numData = 10000 ;
dimension = 2 ;
data = uint8(255*rand(dimension,numData)) ;
numClusters = 3^3 ;
[centers, assignments] = vl_ikmeans(data, numClusters);
figure(1) ; clf ; axis off ;
plotClusters(data, centers, assignments) ;
vl_demo_print('ikmeans_2d',0.6);
[tree, assig... |
github | sbargoti/matlab-code-master | vl_demo_svm.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/demo/vl_demo_svm.m | 1,235 | utf_8 | 7cf6b3504e4fc2cbd10ff3fec6e331a7 | % VL_DEMO_SVM Demo: SVM: 2D linear learning
function vl_demo_svm
y=[];X=[];
% Load training data X and their labels y
load('vl_demo_svm_data.mat')
Xp = X(:,y==1);
Xn = X(:,y==-1);
figure
plot(Xn(1,:),Xn(2,:),'*r')
hold on
plot(Xp(1,:),Xp(2,:),'*b')
axis equal ;
vl_demo_print('svm_training') ;
% Parameters
lambda =... |
github | sbargoti/matlab-code-master | vl_demo_kdtree_sift.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/demo/vl_demo_kdtree_sift.m | 6,832 | utf_8 | e676f80ac330a351f0110533c6ebba89 | function vl_demo_kdtree_sift
% VL_DEMO_KDTREE_SIFT
% Demonstrates the use of a kd-tree forest to match SIFT
% features. If FLANN is present, this function runs a comparison
% against it.
% AUTORIGHS
rand('state',0) ;
randn('state',0);
do_median = 0 ;
do_mean = 1 ;
% try to setup flann
if ~exist('flann_search'... |
github | sbargoti/matlab-code-master | vl_impattern.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/imop/vl_impattern.m | 6,876 | utf_8 | 1716a4d107f0186be3d11c647bc628ce | function im = vl_impattern(varargin)
% VL_IMPATTERN Generate an image from a stock pattern
% IM=VLPATTERN(NAME) returns an instance of the specified
% pattern. These stock patterns are useful for testing algoirthms.
%
% All generated patterns are returned as an image of class
% DOUBLE. Both gray-scale and colou... |
github | sbargoti/matlab-code-master | vl_tpsu.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/imop/vl_tpsu.m | 1,755 | utf_8 | 09f36e1a707c069b375eb2817d0e5f13 | function [U,dU,delta]=vl_tpsu(X,Y)
% VL_TPSU Compute the U matrix of a thin-plate spline transformation
% U=VL_TPSU(X,Y) returns the matrix
%
% [ U(|X(:,1) - Y(:,1)|) ... U(|X(:,1) - Y(:,N)|) ]
% [ ]
% [ U(|X(:,M) - Y(:,1)|) ... U(|X(:,M) - Y(:,N)|) ]
%
% where X... |
github | sbargoti/matlab-code-master | vl_xyz2lab.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/imop/vl_xyz2lab.m | 1,570 | utf_8 | 09f95a6f9ae19c22486ec1157357f0e3 | function J=vl_xyz2lab(I,il)
% VL_XYZ2LAB Convert XYZ color space to LAB
% J = VL_XYZ2LAB(I) converts the image from XYZ format to LAB format.
%
% VL_XYZ2LAB(I,IL) uses one of the illuminants A, B, C, E, D50, D55,
% D65, D75, D93. The default illuminatn is E.
%
% See also: VL_XYZ2LUV(), VL_HELP().
% Copyright ... |
github | sbargoti/matlab-code-master | vl_test_gmm.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/xtest/vl_test_gmm.m | 1,332 | utf_8 | 76782cae6c98781c6c38d4cbf5549d94 | function results = vl_test_gmm(varargin)
% VL_TEST_GMM
% Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
vl_test_init ;
end
function s = setup()
randn('st... |
github | sbargoti/matlab-code-master | vl_test_twister.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/xtest/vl_test_twister.m | 1,251 | utf_8 | 2bfb5a30cbd6df6ac80c66b73f8646da | function results = vl_test_twister(varargin)
% VL_TEST_TWISTER
vl_test_init ;
function test_illegal_args()
vl_assert_exception(@() vl_twister(-1), 'vl:invalidArgument') ;
vl_assert_exception(@() vl_twister(1, -1), 'vl:invalidArgument') ;
vl_assert_exception(@() vl_twister([1, -1]), 'vl:invalidArgument') ;
function te... |
github | sbargoti/matlab-code-master | vl_test_kdtree.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/xtest/vl_test_kdtree.m | 2,449 | utf_8 | 9d7ad2b435a88c22084b38e5eb5f9eb9 | function results = vl_test_kdtree(varargin)
% VL_TEST_KDTREE
vl_test_init ;
function s = setup()
randn('state',0) ;
s.X = single(randn(10, 1000)) ;
s.Q = single(randn(10, 10)) ;
function test_nearest(s)
for tmethod = {'median', 'mean'}
for type = {@single, @double}
conv = type{1} ;
tmethod = char(tmethod) ;... |
github | sbargoti/matlab-code-master | vl_test_imwbackward.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/xtest/vl_test_imwbackward.m | 514 | utf_8 | 33baa0784c8f6f785a2951d7f1b49199 | function results = vl_test_imwbackward(varargin)
% VL_TEST_IMWBACKWARD
vl_test_init ;
function s = setup()
s.I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
function test_identity(s)
xr = 1:size(s.I,2) ;
yr = 1:size(s.I,1) ;
[x,y] = meshgrid(xr,yr) ;
vl_assert_almost_equal(s.I, vl_imwbackward(xr,yr,s.I,... |
github | sbargoti/matlab-code-master | vl_test_alphanum.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/xtest/vl_test_alphanum.m | 1,624 | utf_8 | 2da2b768c2d0f86d699b8f31614aa424 | function results = vl_test_alphanum(varargin)
% VL_TEST_ALPHANUM
vl_test_init ;
function s = setup()
s.strings = ...
{'1000X Radonius Maximus','10X Radonius','200X Radonius','20X Radonius','20X Radonius Prime','30X Radonius','40X Radonius','Allegia 50 Clasteron','Allegia 500 Clasteron','Allegia 50B Clasteron','Al... |
github | sbargoti/matlab-code-master | vl_test_printsize.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/xtest/vl_test_printsize.m | 1,447 | utf_8 | 0f0b6437c648b7a2e1310900262bd765 | function results = vl_test_printsize(varargin)
% VL_TEST_PRINTSIZE
vl_test_init ;
function s = setup()
s.fig = figure(1) ;
s.usletter = [8.5, 11] ; % inches
s.a4 = [8.26772, 11.6929] ;
clf(s.fig) ; plot(1:10) ;
function teardown(s)
close(s.fig) ;
function test_basic(s)
for sigma = [1 0.5 0.2]
vl_printsize(s.fig, s... |
github | sbargoti/matlab-code-master | vl_test_cummax.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/xtest/vl_test_cummax.m | 838 | utf_8 | 5e98ee1681d4823f32ecc4feaa218611 | function results = vl_test_cummax(varargin)
% VL_TEST_CUMMAX
vl_test_init ;
function test_basic()
vl_assert_almost_equal(...
vl_cummax(1), 1) ;
vl_assert_almost_equal(...
vl_cummax([1 2 3 4], 2), [1 2 3 4]) ;
function test_multidim()
a = [1 2 3 4 3 2 1] ;
b = [1 2 3 4 4 4 4] ;
for k=1:6
dims = ones(1,6) ;
dim... |
github | sbargoti/matlab-code-master | vl_test_imintegral.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/xtest/vl_test_imintegral.m | 1,429 | utf_8 | 4750f04ab0ac9fc4f55df2c8583e5498 | function results = vl_test_imintegral(varargin)
% VL_TEST_IMINTEGRAL
vl_test_init ;
function state = setup()
state.I = ones(5,6) ;
state.correct = [ 1 2 3 4 5 6 ;
2 4 6 8 10 12 ;
3 6 9 12 15 18 ;
4 8 12 ... |
github | sbargoti/matlab-code-master | vl_test_sift.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/xtest/vl_test_sift.m | 1,318 | utf_8 | 806c61f9db9f2ebb1d649c9bfcf3dc0a | function results = vl_test_sift(varargin)
% VL_TEST_SIFT
vl_test_init ;
function s = setup()
s.I = im2single(imread(fullfile(vl_root,'data','box.pgm'))) ;
[s.ubc.f, s.ubc.d] = ...
vl_ubcread(fullfile(vl_root,'data','box.sift')) ;
function test_ubc_descriptor(s)
err = [] ;
[f, d] = vl_sift(s.I,...
... |
github | sbargoti/matlab-code-master | vl_test_binsum.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/xtest/vl_test_binsum.m | 1,377 | utf_8 | f07f0f29ba6afe0111c967ab0b353a9d | function results = vl_test_binsum(varargin)
% VL_TEST_BINSUM
vl_test_init ;
function test_three_args()
vl_assert_almost_equal(...
vl_binsum([0 0], 1, 2), [0 1]) ;
vl_assert_almost_equal(...
vl_binsum([1 7], -1, 1), [0 7]) ;
vl_assert_almost_equal(...
vl_binsum([1 7], -1, [1 2 2 2 2 2 2 2]), [0 0]) ;
function te... |
github | sbargoti/matlab-code-master | vl_test_lbp.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/xtest/vl_test_lbp.m | 892 | utf_8 | a79c0ce0c85e25c0b1657f3a0b499538 | function results = vl_test_lbp(varargin)
% VL_TEST_TWISTER
vl_test_init ;
function test_unfiorm_lbps(s)
% enumerate the 56 uniform lbps
q = 0 ;
for i=0:7
for j=1:7
I = zeros(3) ;
p = mod(s.pixels - i + 8, 8) + 1 ;
I(p <= j) = 1 ;
f = vl_lbp(single(I), 3) ;
q = q + 1 ;
vl_assert_equal(find(f... |
github | sbargoti/matlab-code-master | vl_test_colsubset.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/xtest/vl_test_colsubset.m | 828 | utf_8 | be0c080007445b36333b863326fb0f15 | function results = vl_test_colsubset(varargin)
% VL_TEST_COLSUBSET
vl_test_init ;
function s = setup()
s.x = [5 2 3 6 4 7 1 9 8 0] ;
function test_beginning(s)
vl_assert_equal(1:5, vl_colsubset(1:10, 5, 'beginning')) ;
vl_assert_equal(1:5, vl_colsubset(1:10, .5, 'beginning')) ;
function test_ending(s)
vl_assert_equa... |
github | sbargoti/matlab-code-master | vl_test_alldist.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/xtest/vl_test_alldist.m | 2,373 | utf_8 | 9ea1a36c97fe715dfa2b8693876808ff | function results = vl_test_alldist(varargin)
% VL_TEST_ALLDIST
vl_test_init ;
function s = setup()
vl_twister('state', 0) ;
s.X = 3.1 * vl_twister(10,10) ;
s.Y = 4.7 * vl_twister(10,7) ;
function test_null_args(s)
vl_assert_equal(...
vl_alldist(zeros(15,12), zeros(15,0), 'kl2'), ...
zeros(12,0)) ;
vl_assert_equa... |
github | sbargoti/matlab-code-master | vl_test_ihashsum.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/xtest/vl_test_ihashsum.m | 581 | utf_8 | edc283062469af62056b0782b171f5fc | function results = vl_test_ihashsum(varargin)
% VL_TEST_IHASHSUM
vl_test_init ;
function s = setup()
rand('state',0) ;
s.data = uint8(round(16*rand(2,100))) ;
sel = find(all(s.data==0)) ;
s.data(1,sel)=1 ;
function test_hash(s)
D = size(s.data,1) ;
K = 5 ;
h = zeros(1,K,'uint32') ;
id = zeros(D,K,'uint8');
next = zer... |
github | sbargoti/matlab-code-master | vl_test_grad.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/xtest/vl_test_grad.m | 434 | utf_8 | 4d03eb33a6a4f68659f868da95930ffb | function results = vl_test_grad(varargin)
% VL_TEST_GRAD
vl_test_init ;
function s = setup()
s.I = rand(150,253) ;
s.I_small = rand(2,2) ;
function test_equiv(s)
vl_assert_equal(gradient(s.I), vl_grad(s.I)) ;
function test_equiv_small(s)
vl_assert_equal(gradient(s.I_small), vl_grad(s.I_small)) ;
function test_equiv... |
github | sbargoti/matlab-code-master | vl_test_whistc.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/xtest/vl_test_whistc.m | 1,384 | utf_8 | 81c446d35c82957659840ab2a579ec2c | function results = vl_test_whistc(varargin)
% VL_TEST_WHISTC
vl_test_init ;
function test_acc()
x = ones(1, 10) ;
e = 1 ;
o = 1:10 ;
vl_assert_equal(vl_whistc(x, o, e), 55) ;
function test_basic()
x = 1:10 ;
e = 1:10 ;
o = ones(1, 10) ;
vl_assert_equal(histc(x, e), vl_whistc(x, o, e)) ;
x = linspace(-1,11,100) ;
o =... |
github | sbargoti/matlab-code-master | vl_test_roc.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/xtest/vl_test_roc.m | 1,019 | utf_8 | 9b2ae71c9dc3eda0fc54c65d55054d0c | function results = vl_test_roc(varargin)
% VL_TEST_ROC
vl_test_init ;
function s = setup()
s.scores0 = [5 4 3 2 1] ;
s.scores1 = [5 3 4 2 1] ;
s.labels = [1 1 -1 -1 -1] ;
function test_perfect_tptn(s)
[tpr,tnr] = vl_roc(s.labels,s.scores0) ;
vl_assert_almost_equal(tpr, [0 1 2 2 2 2] / 2) ;
vl_assert_almost_equal(tnr,... |
github | sbargoti/matlab-code-master | vl_test_dsift.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/xtest/vl_test_dsift.m | 2,048 | utf_8 | fbbfb16d5a21936c1862d9551f657ccc | function results = vl_test_dsift(varargin)
% VL_TEST_DSIFT
vl_test_init ;
function s = setup()
I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
s.I = rgb2gray(single(I)) ;
function test_fast_slow(s)
binSize = 4 ; % bin size in pixels
magnif = 3 ; % bin size / keypoint scale
scale = binSize... |
github | sbargoti/matlab-code-master | vl_test_alldist2.m | .m | matlab-code-master/vlfeat-0.9.20/toolbox/xtest/vl_test_alldist2.m | 2,284 | utf_8 | 89a787e3d83516653ae8d99c808b9d67 | function results = vl_test_alldist2(varargin)
% VL_TEST_ALLDIST
vl_test_init ;
% TODO: test integer classes
function s = setup()
vl_twister('state', 0) ;
s.X = 3.1 * vl_twister(10,10) ;
s.Y = 4.7 * vl_twister(10,7) ;
function test_null_args(s)
vl_assert_equal(...
vl_alldist2(zeros(15,12), zeros(15,0), 'kl2'), ...
... |
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