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github | domingomery/Balu-master | Bft_norm.m | .m | Balu-master/FeatureTransformation/Bft_norm.m | 804 | utf_8 | d7c2538186847a2e75bfa851d560ea16 | % [Xnew,a,b] = Bft_norm(X,normtype)
%
% Toolbox: Balu
%
% Normalization of features X.
% normtype = 1 for variance = 1 and mean = 0
% normtype = 0 for max = 1, min = 0
%
% Xnew = a*X + b
%
% Example:
% load datareal
% X = f(:,1:2);
% figure(1); Bio_plotfeatures(X,d);
% Xnew = Bft_n... |
github | domingomery/Balu-master | Bft_pcr.m | .m | Balu-master/FeatureTransformation/Bft_pcr.m | 1,391 | utf_8 | 30f27448d41bb6e00f46ef5b118bc291 | % function [T,P,B,Y] = Bft_pcr(X,d,m)
%
% Toolbox: Balu
%
% Principal Component Regression.
%
% X: Input matrix with features
% d: Vector with ideal classifcation.
% m: Number of principal components to be selected.
% T: Loadings of X (m transformed features). Matrix T is Xo*W', where
% Xo is norm... |
github | domingomery/Balu-master | Bft_pca.m | .m | Balu-master/FeatureTransformation/Bft_pca.m | 1,746 | utf_8 | 750ebd79b2336b0b83e01909c9ce5b4e | % [Y,lambda,A,Xs,mx] = Bft_pca(X,m)
%
% Toolbox: Balu
% Principal component analysis
% X is the matrix feature.
% m number of selected components or the energy 0<m<=1 (in this case it
% will be selected the first d principal components that fulfill the
% condition sum(lambda(1:d))/sum(lambda) >= e... |
github | domingomery/Balu-master | Bft_sigmoid.m | .m | Balu-master/FeatureTransformation/Bft_sigmoid.m | 1,313 | utf_8 | 4c5a8af970182cb718bf5277b5182f09 | % function [param]=fitSigmoid2Scores(posFeats, negFeats, classifier, display)
% original de A.Soto
% nPosExamples=size(posFeats,1);
% nNegExamples=size(negFeats,1);
%
% [predict_label, accuracy, posScores]=svmpredict31EchoOff(ones(nPosExamples,1), posFeats, classifier);
% [predict_label, accuracy, negScores]=svmpredict... |
github | domingomery/Balu-master | Bim_segsliwin.m | .m | Balu-master/ImageProcessing/Bim_segsliwin.m | 6,154 | utf_8 | 545521d878ecc4ca840f00d7e532eed8 | % [Dmap,Dbin] = Bim_segsliwin(I,options)
%
% Toolbox: Balu
%
% Feature extraction of random sliding windows.
% This program select automatically detection windows sized mxm
% with label '1' and lable '0'. For each window
% Balu intensity features are extracted.
%
% Input:
% I original imag... |
github | domingomery/Balu-master | Bim_color2bwreg.m | .m | Balu-master/ImageProcessing/Bim_color2bwreg.m | 1,111 | utf_8 | 94582675f866cc524e2d69f113a3f4a1 | % Y = Bim_color2bwreg(I,R)
%
% Toolbox: Balu
% Convert only a region of a color image into a grayscale image.
%
% I is a color image, R is a binary image, I and R have the same size.
% This function converts to grayscale those pixels of I where R is equal
% to '1'. The rest of the pixels keep the same original... |
github | domingomery/Balu-master | Bim_segotsu.m | .m | Balu-master/ImageProcessing/Bim_segotsu.m | 823 | utf_8 | 1c19112fbef3468e8eb590a6a413cdb8 | % R = Bim_segotsu(I)
%
% Toolbox: Balu
%
% Otsu segmentation of a grayvalue. This function requires Image
% Processing Toolbox.
%
% Input data:
% I grayvalue image.
%
% Output:
% R: binary image.
%
% Example: Training & Test together:
% X = imread('testimg1.jpg');
% figure(1)
% ... |
github | domingomery/Balu-master | Bim_segsliwinsc.m | .m | Balu-master/ImageProcessing/Bim_segsliwinsc.m | 6,096 | utf_8 | 62929a65c9807ff1428adff5f0e81f1b | % [Dmap,Dbin] = Bim_segsliwin(I,options)
%
% Toolbox: Balu
%
% Feature extraction of random sliding windows.
% This program select automatically detection windows sized mxm
% with label '1' and lable '0'. For each window
% Balu intensity features are extracted.
%
% Input:
% I original imag... |
github | domingomery/Balu-master | Bim_segmowgli.m | .m | Balu-master/ImageProcessing/Bim_segmowgli.m | 1,796 | utf_8 | 8b61fab787ed384c4deabc9c005c15bd | % [F,m] = Bsegmowgli(J,R,Amin,sig)
%
% Toolbox: Balu
% Segmentation of regions in image J using LoG edge detection.
% R : binary image of same size of J that indicates the piexels where
% the segmentation will be performed. Default R = ones(size(J));
% Amin: minimum area of the segmented details.
%... |
github | domingomery/Balu-master | Bim_rgb2hcm.m | .m | Balu-master/ImageProcessing/Bim_rgb2hcm.m | 1,464 | utf_8 | e68066237af8f37b6aa8379073852c2a | % J = Bim_rgb2hcm(RGB)
%
% Toolbox: Balu
% Conversion RGB to high contrast image.
%
% RGB: color image
% J : hcm image
%
% See details in:
% Mery, D.; Pedreschi, F. (2005): Segmentation of Colour Food Images using
% a Robust Algorithm. Journal of Food Engineering 66(3): 353-360.
%
% Example:
... |
github | domingomery/Balu-master | Bim_segdefects.m | .m | Balu-master/ImageProcessing/Bim_segdefects.m | 5,483 | utf_8 | 33f3cf266a990a4a2cafab9aec713fa9 | % D.Mery
% Ago-2009
% Segmentacion de fallas hipoteticas
% input : X imagen de rayos X de entrada
% output: Y imagen binaria de salida con las fallas hipoteticas
% output: feat matriz de features extraidas ver ultimas lineas del codigo
function [Y,feat] = Bim_segdefects(X)
%
%Inicializacion de parametros:
... |
github | domingomery/Balu-master | Bim_inthist.m | .m | Balu-master/ImageProcessing/Bim_inthist.m | 1,153 | utf_8 | 79820a859a0bf1d712f6d98009a32408 | % function H = Bim_inthist(I,b)
%
%
% Toolbox: Balu
% Integral histogram.
%
% Input data:
% I grayvalue image (only for positive values)
% b number of bins
%
% Output:
% H Integral histogram (size = NxMxb, where [N,M] = size(I))
%
% Example:
% I = imread('rice.png');
% H = Bim... |
github | domingomery/Balu-master | Bim_rgb2lab.m | .m | Balu-master/ImageProcessing/Bim_rgb2lab.m | 1,036 | utf_8 | a3f4bba222f5bb85b6a7b333c9510c4f | % K = Bim_rgb2lab(I,M)
%
% Toolbox: Balu
% Conversion RGB->L*a*b according to parameters M estimated with
% function Bim_labparam.m after
% Leon,K.; Mery,D.; Pedreschi,F.;Leon,J.(2006): Color measurement in
% L*a*b* units from RGB digital images. Food Research International,
% 39(10):1084-1091.
... |
github | domingomery/Balu-master | Bim_performance.m | .m | Balu-master/ImageProcessing/Bim_performance.m | 2,500 | utf_8 | 766564a0d64922e9b4bc4b77c4a76dda | % [precision,recall] = Bim_performance(Xideal,Xtest,thfp,thtp,show)
%
% Toolbox: Balu
%
% Precision and recall rates using binary images Xideal (for the ideal
% detection) and Xtest (for the real detection).
%
% The computation of the False Positives are as follows: all pixels from
% Xtest that have a min... |
github | domingomery/Balu-master | Bim_fconversion.m | .m | Balu-master/ImageProcessing/Bim_fconversion.m | 671 | utf_8 | cd5a71f4baee94a8a955493db6cdf961 | % Bim_fconversion(fmt1,fmt2)
%
% Toolbox: Balu
% Format conversion of images
%
% It converts all images with format fmt1 into images with format fmt2.
% fmt1 and fmt2 are strings.
%
% D.Mery, PUC-DCC, Jun 2010
% http://dmery.ing.puc.cl
%
function Bim_fconversion(fmt1,fmt2)
d = dir(['*.' fmt1]);
n = length(d... |
github | domingomery/Balu-master | Bim_segbalu_reg.m | .m | Balu-master/ImageProcessing/Bim_segbalu_reg.m | 1,427 | utf_8 | c75081940d907639125d299b6a7cb98d | % [R,E,J] = Bim_segbalu(I,p)
%
% Toolbox: Balu
% Segmentation of an object with homogeneous background.
%
% I: input image
% p: threshold (default: p=-0.05) with p between -1 and 1.
% A positive value is used to dilate the segmentation,
% the negative to erode.
% R: binary image of the object
... |
github | domingomery/Balu-master | Bim_LUT.m | .m | Balu-master/ImageProcessing/Bim_LUT.m | 1,968 | utf_8 | 86de9d6b61c2d153e3e164f0c41768fd | % function Y = LUT(X,T,show)
%
% Toolbox: Balu
% Look Up Table transformation for grayvalue images.
%
% Input data:
% X grayvalue image.
% T look uo table
% show display results
%
% Output:
% Y transformed image
%
% Ejemplo:
% load clown
% T = 256*ones(256,1);
% ... |
github | domingomery/Balu-master | Bim_morphoreg_reg.m | .m | Balu-master/ImageProcessing/Bim_morphoreg_reg.m | 785 | utf_8 | 50686ce4da6f2746bfcbdcaeaf4d1227 | % [R,E] = Bmorphoreg(J,t);
% [R,E] = Bmorphoreg(Ro);
%
% Toolbox: Balu
% Morphology operations of binary image J>t (or Ro): remove isolate
% pixels and fill holes.
% R: binary image of the region
% E: binary image of the edge
%
% Example:
% I = imread('testimg2.jpg');
% figure(1);imshow... |
github | domingomery/Balu-master | Bim_segmaxfisher.m | .m | Balu-master/ImageProcessing/Bim_segmaxfisher.m | 2,173 | utf_8 | e23a468c51952f27e4afdcbd08bf4ad6 | % [R,E,J] = Bim_segmaxfisher(I,p)
%
% Toolbox: Balu
% Segmentation of an object with homogeneous background. The idea is to
% find a linear transformation from RGB to grayscale so that the Fisher
% discriminant is maximum.
%
% I: input image
% p: threshold (default: p=-0.05) with p between -1 and 1.
% ... |
github | domingomery/Balu-master | Bim_equalization.m | .m | Balu-master/ImageProcessing/Bim_equalization.m | 1,130 | utf_8 | 7786030d40a10636a43364a7e821c746 | % Y = Bim_equalization(X)
% Y = Bim_equalization(X,show)
%
% Toolbox: Balu
% Enhancement of a grayvalue image forcing an uniform histogram.
%
% Input data:
% X grayvalue image.
%
% Output:
% Y: enhanced image so that histogram of Y is perfectlty uniformed
% distributed. Y is uint8.... |
github | domingomery/Balu-master | Bim_regiongrow.m | .m | Balu-master/ImageProcessing/Bim_regiongrow.m | 3,573 | utf_8 | 30c4b7a6a6e4dc25c01b0d27cacf822b | % Dt = Bim_regiongrow(I,th)
%
% Toolbox: Balu
% Interactive selection of regions with similar grayvalues using growing
% region algorithm.
% I : grayscale image
% th : maximal difference of grayvalues in the region
% (default: th = 20)
% Dt : output binary image with segmented regions
%
% Example:
% ... |
github | domingomery/Balu-master | Bim_rgb2lab0.m | .m | Balu-master/ImageProcessing/Bim_rgb2lab0.m | 621 | utf_8 | 9a202ef96904986698081347d37c8142 | % Lab = Bim_rgb2lab0(RGB)
%
% Toolbox: Balu
% Conversion RGB to L*a*b using formulas.
%
% RGB: color image
% Lab : L*a*b*
%
% Example:
% I = imread('testimg2.jpg');
% J = Bim_rgb2lab0(I);
% figure(1)
% imshow(I); title('control image')
% figure(2); imshow(J(:,:,1),[]); title(... |
github | domingomery/Balu-master | Bim_regionseed.m | .m | Balu-master/ImageProcessing/Bim_regionseed.m | 1,026 | utf_8 | ab3cb931861befc57cf007b3f2defdf2 | % L = Bim_regionseed(I,seeds)
%
% Toolbox: Balu
% Growing region algorithm from seed pixels.
% I : grayscale image
% seeds: seed pixels
%
% Example:
% I = imread('X1.png');
% [fr,seeds] = Bim_segmser(I,[5 2500 0.7 0.2 10 0 1]);
% L = Bim_regionseed(I,seeds);
% figure;imshow(L,[])
%
% D.Mery, PUC... |
github | domingomery/Balu-master | Bim_lin.m | .m | Balu-master/ImageProcessing/Bim_lin.m | 891 | utf_8 | 9df70e811bbb70326cc182e4668f811c | % J = Bim_lin(I,t)
%
% Toolbox: Balu
% Lineal enhancement of image I from 0 to 255.
%
% Input data:
% I grayvalue image.
%
% Output:
% J: enhanced image so that
% J = m*I + b; where min(J) = 0, max(J) = 255.
% J is uint8.
%
% Example:
% X = imread('testimg2.jpg');... |
github | domingomery/Balu-master | Bim_labparam.m | .m | Balu-master/ImageProcessing/Bim_labparam.m | 2,037 | utf_8 | a485bf54f5ec3febbbb8a9819d522b05 | % [M,emean,estd,ecorr] = Bim_labparam(RGBmes,LABmes,model,show)
%
% Toolbox: Balu
% Estimate the parameters of the conversion RGB->L*a*b after
% Leon,K.; Mery,D.; Pedreschi,F.;Leon,J.(2006): Color measurement in
% L*a*b* units from RGB digital images. Food Research International,
% 39(10):1084-1091.
... |
github | domingomery/Balu-master | Bim_segmser.m | .m | Balu-master/ImageProcessing/Bim_segmser.m | 4,247 | utf_8 | a0352ffb09b37d892892711e54144916 | % [frames,seeds,M] = Bim_segmser(I,param)
%
% Toolbox: Balu
% Segmentation using MSER algorithm.
%
% I: input image
% param(1) = minimal area of the ellipse in pixels
% param(2) = maximal area of the ellipse in pixels
% param(3) = MinDiversity eg 0.7
% param(4) = MaxVariation eg 0.2
% param(5) ... |
github | domingomery/Balu-master | Bim_inthistread.m | .m | Balu-master/ImageProcessing/Bim_inthistread.m | 1,566 | utf_8 | 040086134380bd15bd726d33be582115 | % function h = Bim_inthistread(H,i1,j1,i2,j2,options)
%
%
% Toolbox: Balu
% Histogram of a part of an image using integral histograms.
%
% Input data:
% H integral histogram.
% (i1,j1,i2,j2) rectangle of the image
%
% Output:
% h histogram
%
% Example:
% I = imread('rice.png');
% ... |
github | domingomery/Balu-master | Bim_segbalu.m | .m | Balu-master/ImageProcessing/Bim_segbalu.m | 1,218 | utf_8 | b2cb42ed92c1aa68aa74db47f2b02619 | % [R,E,J] = Bim_segbalu(I,p)
%
% Toolbox: Balu
% Segmentation of an object with homogeneous background.
%
% I: input image
% p: threshold (default: p=-0.05) with p between -1 and 1.
% A positive value is used to dilate the segmentation,
% the negative to erode.
% R: binary image of the object
... |
github | domingomery/Balu-master | Bim_d2.m | .m | Balu-master/ImageProcessing/Bim_d2.m | 548 | utf_8 | 20119221a2ba011cb009233bf9450d50 | % J = Bimgd2(I)
%
% Toolbox: Balu
% Second derivative of image X.
%
% Input data:
% I grayvalue image.
%
% Output:
% J = conv2(I,[0 1 0;1 -4 1;0 1 0],'same');
%
% Example:
% X = imread('testimg2.jpg');
% I = rgb2gray(X);
% figure(1)
% imshow(I); title('ori... |
github | domingomery/Balu-master | Bim_d1.m | .m | Balu-master/ImageProcessing/Bim_d1.m | 1,027 | utf_8 | f3e9f396bd818d5942dffdd15e6c8127 | % function J = Bim_d1(I,m);
%
% Toolbox: Balu
% First derivative of image X using a m x m Gauss operator.
%
% Input data:
% I grayvalue image.
%
% Output:
% J first derivative of I
%
% Example:
% X = imread('testimg2.jpg');
% I = rgb2gray(X);
% figure(1)
% ... |
github | domingomery/Balu-master | Bim_deconvolution.m | .m | Balu-master/ImageProcessing/Bim_deconvolution.m | 2,127 | utf_8 | bbe896fe35ddb9d87cddfb9afc351a65 | % Fs = Bim_deconvolution(G,h,C,SNR2,a)
%
% Toolbox: Balu
% Image restoration using inverse filtering.
%
% ( H*(u,v) )a ( H*(u,v)') )1-a
% Inverse Filter = W(u,v) = (----------) (---------------------)
% (|H(u,v)|^2) (|H(u,v)|^2 + C/SNR^2 )
%
% S... |
github | domingomery/Balu-master | Bim_colorconv.m | .m | Balu-master/ImageProcessing/Bim_colorconv.m | 3,110 | utf_8 | 76678a4d0b2d54dd50986f2c160632ba | % J = Bim_colorconv(X,s1,s2)
%
% Toolbox: Balu
% Color conversion of image (in color space s1) into image Y
% (in color space s2).
%
%
% Example:
% I = imread('testimg2.jpg');
% J = Bim_colorconv(I,'rgb','hcm');
% figure(1)
% imshow(I); title('control image')
% figure(2)
% imshow(J); title... |
github | domingomery/Balu-master | Bim_segkmeans.m | .m | Balu-master/ImageProcessing/Bim_segkmeans.m | 1,862 | utf_8 | e68b3e0e1a8cf6611d75763594899bd4 | % [R,J] = Bim_segkmeans(I,k,r,show)
%
% Toolbox: Balu
%
% Segmentation of color images using kmeans.
%
% I: input image
% k: number of clusters
% r: resize image
% show: 1 means intermediate results will be displayed
%
% Example 1:
% I = imread('testimg1.jpg');
% [R,J] = Bim_segkmeans(I,2,1,1);
%... |
github | domingomery/Balu-master | Bim_resminio.m | .m | Balu-master/ImageProcessing/Bim_resminio.m | 1,267 | utf_8 | e6d1d3f523668feace139bcaa731a692 | % Fs = Bim_resminio(G,h,method)
%
% Toolbox: Balu
% Image restoration using MINIO criterium.
%
% G : blurred image
% h : PSF
% method = 1: minio ||f_N-g|| -> min (default)
% = 2: ||f|| -> min
% Fs: restored image
%
% See details in:
% Mery, D.; Filbert, D. (2000): A Fast Non-iterative Algorit... |
github | domingomery/Balu-master | Bim_segblops.m | .m | Balu-master/ImageProcessing/Bim_segblops.m | 4,014 | utf_8 | 9ca9089d040add608673a396e8355e9c | % D.Mery
% Octo-2010
% Segmentacion de blops
% input : X imagen de entrada
% output: Y imagen binaria de salida con los blops detectados
% output: feat matriz de features extraidas ver ultimas lineas del codigo
function [Y,feat] = Bim_segblops(X,pa,show)
if ~exist('show','var')
show = 0;
end
n ... |
github | domingomery/Balu-master | Bim_segpca.m | .m | Balu-master/ImageProcessing/Bim_segpca.m | 2,033 | utf_8 | dade6a52f8207c7f41493a0988f8c60f | % [R,E,J] = Bim_segpca(I,p)
%
% Toolbox: Balu
% Segmentation of an object with homogeneous background using the first
% principal component of I.
%
% I: input image
% p: threshold (default: p=-0.05) with p between -1 and 1.
% A positive value is used to dilate the segmentation,
% the negative to ... |
github | domingomery/Balu-master | Bim_morphoreg.m | .m | Balu-master/ImageProcessing/Bim_morphoreg.m | 765 | utf_8 | 931e79de37c3447f8bc71a43ec1ac216 | % [R,E] = Bmorphoreg(J,t);
% [R,E] = Bmorphoreg(Ro);
%
% Toolbox: Balu
% Morphology operations of binary image J>t (or Ro): remove isolate
% pixels and fill holes.
% R: binary image of the region
% E: binary image of the edge
%
% Example:
% I = imread('testimg2.jpg');
% figure(1);imshow... |
github | domingomery/Balu-master | Bim_rgb2pca.m | .m | Balu-master/ImageProcessing/Bim_rgb2pca.m | 588 | utf_8 | c666736b61f3b47076a5831d86c00e25 | % K = Bim_rgb2pca(I)
%
% Toolbox: Balu
% Converts a RGB image into a new color image where the channel i is
% the principal componet i of image I, for i=1,2,3.
%
% D.Mery, PUC-DCC, Apr. 2008
% http://dmery.ing.puc.cl
function K = Bim_rgb2pca(I)
I = double(I);
X1 = I(:,:,1);
X2 = I(:,:,2);
... |
github | domingomery/Balu-master | Bim_cssalient.m | .m | Balu-master/ImageProcessing/Bim_cssalient.m | 2,479 | utf_8 | 671cce636e66f8586f26c8f376354064 | % function [J_on, J_off] = Bim_cssalient( I, type, show )
%
% Toolbox: Balu
% compute center surround saliency image from and gray scale image follow
% mehtod mentioned in Montabone & Soto (2010). Method precompute an
% integral image to do all calculations.
%
% Inputs:
% I: gray scale image. If dep... |
github | domingomery/Balu-master | Bim_segmaxvar.m | .m | Balu-master/ImageProcessing/Bim_segmaxvar.m | 2,561 | utf_8 | 72a8df004e0fc243bf776e4a299fac37 | % [R,E,J] = Bim_segmaxvar(I,p)
%
% Toolbox: Balu
% Segmentation of an object with homogeneous background using as objective
% function the maximization of the variance of a linear transformation of
% the color image.
%
% I: input image
% p: threshold (default: p=-0.05) with p between -1 and 1.
% A po... |
github | awadell1/Datamaster-master | DataHash.m | .m | Datamaster-master/DataHash.m | 19,187 | utf_8 | b09507caf51eef3efdf7d6e389c55ec2 | function Hash = DataHash(Data, Opt)
% DATAHASH - Checksum for Matlab array of any type
% This function creates a hash value for an input of any type. The type and
% dimensions of the input are considered as default, such that UINT8([0,0]) and
% UINT16(0) have different hash values. Nested STRUCTs and CELLs are parsed
%... |
github | awadell1/Datamaster-master | sqlite.m | .m | Datamaster-master/@sqlite/sqlite.m | 2,130 | utf_8 | 5e8ef21f439606188e8e670783b3f64e | classdef sqlite
%Class for connecting to an sqlite database
properties
conn = [];
end
methods
function obj = sqlite(dbpath)
import py.sqlite3.*
obj.conn = py.sqlite3.connect(dbpath);
end
function execute(obj, SQLQuer... |
github | awadell1/Datamaster-master | getChannel.m | .m | Datamaster-master/@datasource/getChannel.m | 2,489 | utf_8 | a7bb341d1b017f9975366d282e5c4499 | function channel = getChannel(ds,chanName,varargin)
%Validate Channel Names
persistent p
if ~isempty(p) || true
p = inputParser;
p.FunctionName = 'getChannel';
p.addRequired('ds',@(x) isa(x,'datasource') && length(x)==1);
p.addRequired('chanName',@(x) ischar(x) || iscell(x));
p.addOptional('filter'... |
github | awadell1/Datamaster-master | CheckBuild.m | .m | Datamaster-master/TestSuite/CheckBuild.m | 1,503 | utf_8 | 41129bf5c62cbc5eca27d529d53e563b | %% DatamasterWiki Location
wiki = Datamaster.getConfigSetting('wiki');
%Get code to test
exampleCode = getExampleCode(wiki);
%Run Tests
runtests([pwd; exampleCode(:, 1)])
%Clean up
for i = 1:length(exampleCode)
delete(exampleCode{i, 1})
end
function exampleCode = getExampleCode(folder)
%Scan Files for matlab co... |
github | awadell1/Datamaster-master | getConfigSetting.m | .m | Datamaster-master/@Datamaster/getConfigSetting.m | 2,639 | utf_8 | a37fe3ae20b14fe6a137ba4da6f2c95b | function value = getConfigSetting(Key)
%getConfigSetting returns an iniConfig file for the current user
% Detailed explanation goes here
%Expected filename for config file
userConfig = fullfile(Datamaster.getPath, 'config.ini');
defaultConfig = fullfile(Datamaster.getPath, 'default.ini');
%Check for both config fil... |
github | awadell1/Datamaster-master | addEntry.m | .m | Datamaster-master/@Datamaster/addEntry.m | 4,681 | utf_8 | cbe8dc06e7ff0df61757d1298c32810c | function addEntry(dm, MoTeCFile, FinalHash, Details, channels)
try
%Check if the Log Date and Time were recorded
if isfield(Details, 'LogDate') && isfield(Details, 'LogTime')
try %#ok<TRYNC>
%Use date time found in details instead of file creation date
log... |
github | awadell1/Datamaster-master | checkDetails.m | .m | Datamaster-master/@DataReporter/checkDetails.m | 2,654 | utf_8 | 5acb5339452cb1f1d99363226113b65d | function checkDetails(dr,hash)
%Checks that the datasources (Given by hash) followed Standard Data
%Logging Practices
%Get Datasources
ds = dr.dm.getDatasource(hash);
Error = zeros(1,length(ds));
%Check Each Datasource
for i = 1:length(ds)
%%Report Current Log File
fprintf('L... |
github | awadell1/Datamaster-master | getDatasourceDrive.m | .m | Datamaster-master/@DataReporter/private/getDatasourceDrive.m | 1,767 | utf_8 | 616a5596864c5326557dc28d35ab703f | function [ldLoc, ldxLoc] = getDatasourceDrive(MoTeCFile,savePath)
% Polls Google Drive to download the requested MoTeC Log files
% MoTeCFile: Struct w/ the following fields
% ld: The file id of the .ld file to download
% ldx: The file id of the .ldx file to download
%
% ldLoc: filepath wher... |
github | Ishita10/Machine-Learning-master | submit.m | .m | Machine-Learning-master/SpamClassifier/submit.m | 16,836 | utf_8 | 40e4fb5817ab3ded1294f0db7921ef2c | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | Ishita10/Machine-Learning-master | porterStemmer.m | .m | Machine-Learning-master/SpamClassifier/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 | Ishita10/Machine-Learning-master | submitWeb.m | .m | Machine-Learning-master/SpamClassifier/submitWeb.m | 827 | utf_8 | bfb2fa08cac9d8d797e3071d3fdd7ca1 | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | Ishita10/Machine-Learning-master | submit.m | .m | Machine-Learning-master/Movie RecommenderSystem/submit.m | 17,515 | utf_8 | 26eeaed83f5060f08635dd64cf98d8f6 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | Ishita10/Machine-Learning-master | submitWeb.m | .m | Machine-Learning-master/Movie RecommenderSystem/submitWeb.m | 827 | utf_8 | bfb2fa08cac9d8d797e3071d3fdd7ca1 | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | Ishita10/Machine-Learning-master | submit.m | .m | Machine-Learning-master/HandWrittenDigitsRecognitionSystem/submit.m | 17,129 | utf_8 | 90fe6bfd432e59c0a145fb1240d34ce5 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | Ishita10/Machine-Learning-master | submitWeb.m | .m | Machine-Learning-master/HandWrittenDigitsRecognitionSystem/submitWeb.m | 827 | utf_8 | bfb2fa08cac9d8d797e3071d3fdd7ca1 | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | Ishita10/Machine-Learning-master | submit.m | .m | Machine-Learning-master/AnamolyDetectionSystem/submit.m | 17,515 | utf_8 | 26eeaed83f5060f08635dd64cf98d8f6 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | Ishita10/Machine-Learning-master | submitWeb.m | .m | Machine-Learning-master/AnamolyDetectionSystem/submitWeb.m | 827 | utf_8 | bfb2fa08cac9d8d797e3071d3fdd7ca1 | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | Ishita10/Machine-Learning-master | submit.m | .m | Machine-Learning-master/ImageCompressionUsingK-MeansClustering/submit.m | 16,958 | utf_8 | 9bbc174b62acd280557995ac15eeea31 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | Ishita10/Machine-Learning-master | submitWeb.m | .m | Machine-Learning-master/ImageCompressionUsingK-MeansClustering/submitWeb.m | 827 | utf_8 | bfb2fa08cac9d8d797e3071d3fdd7ca1 | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | cvlab-epfl/TILDE-master | vl_compile.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_noprefix.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_pegasos.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_svmpegasos.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_override.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_quickvis.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_demo_aib.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_demo_alldist.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_demo_ikmeans.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_demo_svm.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_demo_kdtree_sift.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_impattern.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_tpsu.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_xyz2lab.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_test_gmm.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_test_twister.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_test_kdtree.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_test_imwbackward.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_test_alphanum.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_test_printsize.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_test_cummax.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_test_imintegral.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_test_sift.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_test_binsum.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_test_lbp.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_test_colsubset.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_test_alldist.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_test_ihashsum.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_test_grad.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_test_whistc.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_test_roc.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_test_dsift.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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 | cvlab-epfl/TILDE-master | vl_test_alldist2.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/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'), ...
... |
github | cvlab-epfl/TILDE-master | vl_test_fisher.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/toolbox/xtest/vl_test_fisher.m | 2,097 | utf_8 | c9afd9ab635bd412cbf8be3c2d235f6b | function results = vl_test_fisher(varargin)
% VL_TEST_FISHER
vl_test_init ;
function s = setup()
randn('state',0) ;
dimension = 5 ;
numData = 21 ;
numComponents = 3 ;
s.x = randn(dimension,numData) ;
s.mu = randn(dimension,numComponents) ;
s.sigma2 = ones(dimension,numComponents) ;
s.prior = ones(1,numComponents) ;
s... |
github | cvlab-epfl/TILDE-master | vl_test_imsmooth.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/toolbox/xtest/vl_test_imsmooth.m | 1,837 | utf_8 | 718235242cad61c9804ba5e881c22f59 | function results = vl_test_imsmooth(varargin)
% VL_TEST_IMSMOOTH
vl_test_init ;
function s = setup()
I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
I = max(min(vl_imdown(I),1),0) ;
s.I = single(I) ;
function test_pad_by_continuity(s)
% Convolving a constant signal padded with continuity does not change... |
github | cvlab-epfl/TILDE-master | vl_test_svmtrain.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/toolbox/xtest/vl_test_svmtrain.m | 4,277 | utf_8 | 071b7c66191a22e8236fda16752b27aa | function results = vl_test_svmtrain(varargin)
% VL_TEST_SVMTRAIN
vl_test_init ;
end
function s = setup()
randn('state',0) ;
Np = 10 ;
Nn = 10 ;
xp = diag([1 3])*randn(2, Np) ;
xn = diag([1 3])*randn(2, Nn) ;
xp(1,:) = xp(1,:) + 2 + 1 ;
xn(1,:) = xn(1,:) - 2 + 1 ;
s.x = [xp xn] ;
s.y = [ones(1,Np) ... |
github | cvlab-epfl/TILDE-master | vl_test_phow.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/toolbox/xtest/vl_test_phow.m | 549 | utf_8 | f761a3bb218af855986263c67b2da411 | function results = vl_test_phow(varargin)
% VL_TEST_PHOPW
vl_test_init ;
function s = setup()
s.I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
s.I = single(s.I) ;
function test_gray(s)
[f,d] = vl_phow(s.I, 'color', 'gray') ;
assert(size(d,1) == 128) ;
function test_rgb(s)
[f,d] = vl_phow(s.I, 'color',... |
github | cvlab-epfl/TILDE-master | vl_test_kmeans.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/toolbox/xtest/vl_test_kmeans.m | 3,632 | utf_8 | 0e1d6f4f8101c8982a0e743e0980c65a | function results = vl_test_kmeans(varargin)
% VL_TEST_KMEANS
% 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 ;
function s = setup()
randn('sta... |
github | cvlab-epfl/TILDE-master | vl_test_hikmeans.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/toolbox/xtest/vl_test_hikmeans.m | 463 | utf_8 | dc3b493646e66316184e86ff4e6138ab | function results = vl_test_hikmeans(varargin)
% VL_TEST_IKMEANS
vl_test_init ;
function s = setup()
rand('state',0) ;
s.data = uint8(rand(2,1000) * 255) ;
function test_basic(s)
[tree, assign] = vl_hikmeans(s.data,3,100) ;
assign_ = vl_hikmeanspush(tree, s.data) ;
vl_assert_equal(assign,assign_) ;
function test_elka... |
github | cvlab-epfl/TILDE-master | vl_test_aib.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/toolbox/xtest/vl_test_aib.m | 1,277 | utf_8 | 78978ae54e7ebe991d136336ba4bf9c6 | function results = vl_test_aib(varargin)
% VL_TEST_AIB
vl_test_init ;
function s = setup()
s = [] ;
function test_basic(s)
Pcx = [.3 .3 0 0
0 0 .2 .2] ;
% This results in the AIB tree
%
% 1 - \
% 5 - \
% 2 - / \
% - 7
% 3 - \ /
% 6 - /
% 4 - /
%
% coded by the map [5 ... |
github | cvlab-epfl/TILDE-master | vl_test_plotbox.m | .m | TILDE-master/matlab/external/vlfeat-0.9.18/toolbox/xtest/vl_test_plotbox.m | 414 | utf_8 | aa06ce4932a213fb933bbede6072b029 | function results = vl_test_plotbox(varargin)
% VL_TEST_PLOTBOX
vl_test_init ;
function test_basic(s)
figure(1) ; clf ;
vl_plotbox([-1 -1 1 1]') ;
xlim([-2 2]) ;
ylim([-2 2]) ;
close(1) ;
function test_multiple(s)
figure(1) ; clf ;
randn('state', 0) ;
vl_plotbox(randn(4,10)) ;
close(1) ;
function test_style(s)
figure... |
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