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 | lifeng9472/IBCCF-master | getVarReceptiveFields.m | .m | IBCCF-master/external_libs/matconvnet/matlab/+dagnn/@DagNN/getVarReceptiveFields.m | 3,635 | utf_8 | 6d61896e475e64e9f05f10303eee7ade | function rfs = getVarReceptiveFields(obj, var)
%GETVARRECEPTIVEFIELDS Get the receptive field of a variable
% RFS = GETVARRECEPTIVEFIELDS(OBJ, VAR) gets the receptivie fields RFS of
% all the variables of the DagNN OBJ into variable VAR. VAR is a variable
% name or index.
%
% RFS has one entry for each variable... |
github | lifeng9472/IBCCF-master | rebuild.m | .m | IBCCF-master/external_libs/matconvnet/matlab/+dagnn/@DagNN/rebuild.m | 3,243 | utf_8 | e368536d9e70c805d8424cdd6b593960 | function rebuild(obj)
%REBUILD Rebuild the internal data structures of a DagNN object
% REBUILD(obj) rebuilds the internal data structures
% of the DagNN obj. It is an helper function used internally
% to update the network when layers are added or removed.
varFanIn = zeros(1, numel(obj.vars)) ;
varFanOut = zero... |
github | lifeng9472/IBCCF-master | print.m | .m | IBCCF-master/external_libs/matconvnet/matlab/+dagnn/@DagNN/print.m | 15,032 | utf_8 | 7da4e68e624f559f815ee3076d9dd966 | function str = print(obj, inputSizes, varargin)
%PRINT Print information about the DagNN object
% PRINT(OBJ) displays a summary of the functions and parameters in the network.
% STR = PRINT(OBJ) returns the summary as a string instead of printing it.
%
% PRINT(OBJ, INPUTSIZES) where INPUTSIZES is a cell array of ... |
github | lifeng9472/IBCCF-master | fromSimpleNN.m | .m | IBCCF-master/external_libs/matconvnet/matlab/+dagnn/@DagNN/fromSimpleNN.m | 7,258 | utf_8 | 83f914aec610125592263d74249f54a7 | function obj = fromSimpleNN(net, varargin)
% FROMSIMPLENN Initialize a DagNN object from a SimpleNN network
% FROMSIMPLENN(NET) initializes the DagNN object from the
% specified CNN using the SimpleNN format.
%
% SimpleNN objects are linear chains of computational layers. These
% layers exchange information th... |
github | lifeng9472/IBCCF-master | vl_simplenn_display.m | .m | IBCCF-master/external_libs/matconvnet/matlab/simplenn/vl_simplenn_display.m | 12,455 | utf_8 | 65bb29cd7c27b68c75fdd27acbd63e2b | function [info, str] = vl_simplenn_display(net, varargin)
%VL_SIMPLENN_DISPLAY Display the structure of a SimpleNN network.
% VL_SIMPLENN_DISPLAY(NET) prints statistics about the network NET.
%
% INFO = VL_SIMPLENN_DISPLAY(NET) returns instead a structure INFO
% with several statistics for each layer of the netw... |
github | lifeng9472/IBCCF-master | vl_test_economic_relu.m | .m | IBCCF-master/external_libs/matconvnet/matlab/xtest/vl_test_economic_relu.m | 790 | utf_8 | 35a3dbe98b9a2f080ee5f911630ab6f3 | % VL_TEST_ECONOMIC_RELU
function vl_test_economic_relu()
x = randn(11,12,8,'single');
w = randn(5,6,8,9,'single');
b = randn(1,9,'single') ;
net.layers{1} = struct('type', 'conv', ...
'filters', w, ...
'biases', b, ...
'stride', 1, ...
... |
github | lifeng9472/IBCCF-master | get_subwindow.m | .m | IBCCF-master/utility/get_subwindow.m | 858 | utf_8 | dff8bc269574f16ee9c269250d675e7e | function out = get_subwindow(im, pos, sz)
%GET_SUBWINDOW Obtain sub-window from image, with replication-padding.
% Returns sub-window of image IM centered at POS ([y, x] coordinates),
% with size SZ ([height, width]). If any pixels are outside of the image,
% they will replicate the values at the borders.
%
% J... |
github | fudanxu/CV-CNN-master | calculate_acc.m | .m | CV-CNN-master/Test Demo/calculate_acc.m | 15,446 | utf_8 | b92eeffb33cb272289b888c460b69cb9 | %*****************************************************************
%Description: classification accuracy and confusion matrix
% e.g. accuracy1 refers to the accuracy of the 1st class.
% m1_2 refers to the probability of misclassifying the 1st class into the 2nd class.
%input: class predict from... |
github | fudanxu/CV-CNN-master | test_imaging.m | .m | CV-CNN-master/Test Demo/test_imaging.m | 3,971 | utf_8 | 2439c8a8fc85ed630752b9d26968ea9b | %*****************************************************************
%Description: classification result based on CV-CNN
%input: test result from CV_CNN--test_img_oo.mat
%output: classification result:class_img.mat
% classification image: ImageRGB.mat
%Note: This code is taking Flevoland dataset as an example.
%*... |
github | devraj89/GCDL---Generalized-Coupled-Dictionary-Learning-Algorithm-master | coupled_DL_recoupled_CCCA_mod.m | .m | GCDL---Generalized-Coupled-Dictionary-Learning-Algorithm-master/coupled_DL_recoupled_CCCA_mod.m | 5,504 | utf_8 | 83b762ff0b7da4e2075ebe020121adef | % Main Function of Coupled Dictionary Learning
% Input:
% Alphap,Alphas: Initial sparse coefficient of two domains
% Xh ,Xl : Image Data Pairs of two domains
% Dh ,Dl : Initial Dictionaries
% Wh ,Wl : Initial Projection Matrix
% par : Parameters
%
%
% Output
% Alphap,Alphas: Output... |
github | hsiboy/Talkie-master | lpcQuantise.m | .m | Talkie-master/Talkie/encoder/freemat/lpcQuantise.m | 5,008 | utf_8 | e6e0b41b2161f2a3fbf580c689b7a207 | % Talkie library
% Copyright 2011 Peter Knight
% This code is released under GPLv2 license.
%
% Quantise model coefficients, and generate bit codings
function [pitchq,energyq,kq,fields]=lpcQuantise(pitch,energy,k)
fields = zeros(1,13);
energyList = [0,2,3,4,5,7,10,15,20,32,41,57,81,114,161] / 255;
err = 99... |
github | hsiboy/Talkie-master | autocorrelate.m | .m | Talkie-master/Talkie/encoder/freemat/autocorrelate.m | 293 | utf_8 | 9bf054c24809b876c95db0b19919c14c | % Talkie library
% Copyright 2011 Peter Knight
% This code is released under GPLv2 license.
%
% Calculate autocorrelation of speech segment
function r = autocorrelate(w,len)
r = zeros(1,len);
wlen = length(w);
for n=1:len
r(n) = sum( w(1:wlen-n+1) .* w(n:wlen) );
end
|
github | hsiboy/Talkie-master | levinsonDurbin.m | .m | Talkie-master/Talkie/encoder/freemat/levinsonDurbin.m | 703 | utf_8 | 020b390fac9fc7ceee90ca98470f9271 | % Talkie library
% Copyright 2011 Peter Knight
% This code is released under GPLv2 license.
%
% Calculate LPC reflection coefficients
function [k,g] = levinsonDurbin(r,poles)
k(1)=1;
a=zeros(1,poles+1);
at=zeros(1,poles+1);
e=r(1);
for s=1:poles
k(s+1)=-r(s+1);
for t=1:s-1
... |
github | hsiboy/Talkie-master | lpcSynth.m | .m | Talkie-master/Talkie/encoder/freemat/lpcSynth.m | 1,081 | utf_8 | 1aecd505bd5ad580fec19b230c563c0b | % Talkie library
% Copyright 2011 Peter Knight
% This code is released under GPLv2 license.
%
% Synthesise model parameters
function samples=lpcSynth(pitch,energy,coefficients,length,poles,sampleRate)
samples = zeros(1,length);
u = zeros(1,poles+1);
x = zeros(1,poles+1);
% Generate excitation
... |
github | hsiboy/Talkie-master | bitEmit.m | .m | Talkie-master/Talkie/encoder/freemat/bitEmit.m | 327 | utf_8 | 42d645f5918bde340c98110841d7c429 | % Talkie library
% Copyright 2011 Peter Knight
% This code is released under GPLv2 license.
%
% Emit a parameter as bits
function bitEmit(val,bits)
bitpos = 2^(bits-1);
for b = 1:bits
if bitand(val,bitpos)
printf('1');
else
printf('0');
end
val = val*2;
... |
github | hsiboy/Talkie-master | pitchRefine.m | .m | Talkie-master/Talkie/encoder/freemat/pitchRefine.m | 535 | utf_8 | 72503d815958f9c6ad873c1c6ffbefff | % Talkie library
% Copyright 2011 Peter Knight
% This code is released under GPLv2 license.
%
% Home in on best fit pitch
function [pitch,score] = pitchRefine(w,pitchGuess,pitchRange,sampleRate)
score = 0;
phase = (1:length(w))*2*pi/sampleRate;
for (newGuess = pitchGuess-pitchRange:pitchRange/10:pitchGuess... |
github | OperationSmallKat/SmallKat_V2-master | LegFPK.m | .m | SmallKat_V2-master/Kinematics/LegFPK.m | 1,474 | utf_8 | fa6b8372f1493b284a4333f709837c29 | %Function takes in angles and returns the tip of the quadruped leg in XYZ
%cordinates in base frame
function Tip = LegFPK(p)
%Forward Kinematics
%DH Table for leg
% _________________________________________
% | Link | a | alpha | d | theta |
% | Base | 0 | -90 | 0 | 0 |
% | 1 | .161 | 90... |
github | kishore3229/Matlab-Code-master | Line.m | .m | Matlab-Code-master/Line.m | 20,312 | utf_8 | b842e2fb9fcb2c8e33d588abb27a18c8 | function varargout = Line(varargin)
% LINE M-file for Line.fig
% LINE, by itself, creates a new LINE or raises the existing
% singleton*.
%
% H = LINE returns the handle to a new LINE or the handle to
% the existing singleton*.
%
% LINE('CALLBACK',hObject,eventData,handles,...) calls the local
... |
github | EnstaBretagneClubRobo/Cordeliere-master | variogramfit.m | .m | Cordeliere-master/Codes_groupe_Krigeage/Kriging_3D/variogramfit.m | 18,298 | utf_8 | ecb3f120bcbef7510fc82ec9051f75d6 | function [a,c,n,S] = variogramfit(h,gammaexp,a0,c0,numobs,varargin)
% fit a theoretical variogram to an experimental variogram
%
% Syntax:
%
% [a,c,n] = variogramfit(h,gammaexp,a0,c0)
% [a,c,n] = variogramfit(h,gammaexp,a0,c0,numobs)
% [a,c,n] = variogramfit(h,gammaexp,a0,c0,numobs,'pn','pv',...)
... |
github | EnstaBretagneClubRobo/Cordeliere-master | fminsearchbnd.m | .m | Cordeliere-master/Codes_groupe_Krigeage/Kriging_3D/fminsearchbnd.m | 8,444 | utf_8 | 91711f07f16ddb2b2ecad857de119996 | function [x,fval,exitflag,output] = fminsearchbnd(fun,x0,LB,UB,options,varargin)
% FMINSEARCHBND: FMINSEARCH, but with bound constraints by transformation
% usage: x=FMINSEARCHBND(fun,x0)
% usage: x=FMINSEARCHBND(fun,x0,LB)
% usage: x=FMINSEARCHBND(fun,x0,LB,UB)
% usage: x=FMINSEARCHBND(fun,x0,LB,UB,options)
% us... |
github | linwh8/Machine-Learning-master | submit.m | .m | Machine-Learning-master/machine-learning-ex2/ex2/submit.m | 1,605 | utf_8 | 9b63d386e9bd7bcca66b1a3d2fa37579 | function submit()
addpath('./lib');
conf.assignmentSlug = 'logistic-regression';
conf.itemName = 'Logistic Regression';
conf.partArrays = { ...
{ ...
'1', ...
{ 'sigmoid.m' }, ...
'Sigmoid Function', ...
}, ...
{ ...
'2', ...
{ 'costFunction.m' }, ...
'Logistic R... |
github | linwh8/Machine-Learning-master | submitWithConfiguration.m | .m | Machine-Learning-master/machine-learning-ex2/ex2/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | linwh8/Machine-Learning-master | savejson.m | .m | Machine-Learning-master/machine-learning-ex2/ex2/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | linwh8/Machine-Learning-master | loadjson.m | .m | Machine-Learning-master/machine-learning-ex2/ex2/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | linwh8/Machine-Learning-master | loadubjson.m | .m | Machine-Learning-master/machine-learning-ex2/ex2/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | linwh8/Machine-Learning-master | saveubjson.m | .m | Machine-Learning-master/machine-learning-ex2/ex2/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | linwh8/Machine-Learning-master | submit.m | .m | Machine-Learning-master/machine-learning-ex3/ex3/submit.m | 1,567 | utf_8 | 1dba733a05282b2db9f2284548483b81 | function submit()
addpath('./lib');
conf.assignmentSlug = 'multi-class-classification-and-neural-networks';
conf.itemName = 'Multi-class Classification and Neural Networks';
conf.partArrays = { ...
{ ...
'1', ...
{ 'lrCostFunction.m' }, ...
'Regularized Logistic Regression', ...
}, ..... |
github | linwh8/Machine-Learning-master | submitWithConfiguration.m | .m | Machine-Learning-master/machine-learning-ex3/ex3/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | linwh8/Machine-Learning-master | savejson.m | .m | Machine-Learning-master/machine-learning-ex3/ex3/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | linwh8/Machine-Learning-master | loadjson.m | .m | Machine-Learning-master/machine-learning-ex3/ex3/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | linwh8/Machine-Learning-master | loadubjson.m | .m | Machine-Learning-master/machine-learning-ex3/ex3/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | linwh8/Machine-Learning-master | saveubjson.m | .m | Machine-Learning-master/machine-learning-ex3/ex3/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | linwh8/Machine-Learning-master | submit.m | .m | Machine-Learning-master/machine-learning-ex1/ex1/submit.m | 1,876 | utf_8 | 8d1c467b830a89c187c05b121cb8fbfd | function submit()
addpath('./lib');
conf.assignmentSlug = 'linear-regression';
conf.itemName = 'Linear Regression with Multiple Variables';
conf.partArrays = { ...
{ ...
'1', ...
{ 'warmUpExercise.m' }, ...
'Warm-up Exercise', ...
}, ...
{ ...
'2', ...
{ 'computeCost.m... |
github | linwh8/Machine-Learning-master | submitWithConfiguration.m | .m | Machine-Learning-master/machine-learning-ex1/ex1/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | linwh8/Machine-Learning-master | savejson.m | .m | Machine-Learning-master/machine-learning-ex1/ex1/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | linwh8/Machine-Learning-master | loadjson.m | .m | Machine-Learning-master/machine-learning-ex1/ex1/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | linwh8/Machine-Learning-master | loadubjson.m | .m | Machine-Learning-master/machine-learning-ex1/ex1/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | linwh8/Machine-Learning-master | saveubjson.m | .m | Machine-Learning-master/machine-learning-ex1/ex1/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | danfortunato/fast-poisson-solvers-master | test_poisson_solid_sphere.m | .m | fast-poisson-solvers-master/code/tests/test_poisson_solid_sphere.m | 1,126 | utf_8 | 6ed07b270536537bf30ee650b8657fa6 | function pass = test_poisson_solid_sphere( )
% Test the fast Poisson solver for the solid sphere.
tol = 1e-13;
pass = [];
% Test the Fourier modes from -3 to 3
n1 = 21; n2 = 22; n3 = 24;
r = chebpts( n1 );
th = pi*trigpts( n2 );
lam = pi*trigpts( n3 );
[rr, tt, ll] = ndgrid( r, th, lam );
for k = -3:3
exact = @(r... |
github | danfortunato/fast-poisson-solvers-master | test_poisson_cylinder.m | .m | fast-poisson-solvers-master/code/tests/test_poisson_cylinder.m | 1,519 | utf_8 | f9543dd46c7b1087eb78685bd60ccdbd | function pass = test_poisson_cylinder( )
% Test the fast Poisson solver for the solid cylinder.
tol = 1e-13;
pass = [];
% Test the Fourier modes from -3 to 3
n1 = 21; n2 = 22; n3 = 24;
r = chebpts( n1 );
th = pi*trigpts( n2 );
z = chebpts( n3 );
[rr, tt, zz] = ndgrid( r, th, z );
for k = -3:3
exact = @(r, th, z) ... |
github | danfortunato/fast-poisson-solvers-master | cylinderplot.m | .m | fast-poisson-solvers-master/code/vis/cylinderplot.m | 1,544 | utf_8 | 2d67bc792ceef9447aed1ca8ea5fc8c8 | function cylinderplot( X, type )
if ( nargin == 1 )
type = 'coeffs';
end
switch type
case 'coeffs'
ff = coeffs2vals( X );
case 'vals'
ff = X;
otherwise
error('Unknown input type.');
end
if ( ~isreal(ff) )
ff = real( ff );
end
[n1, n2, n3] = size( X );
r = chebpts( n1 );... |
github | danfortunato/fast-poisson-solvers-master | sphereplot.m | .m | fast-poisson-solvers-master/code/vis/sphereplot.m | 1,582 | utf_8 | 3a5c2ce9a787a82b96537ab3ad728363 | function sphereplot( X, type )
if ( nargin == 1 )
type = 'coeffs';
end
switch type
case 'coeffs'
ff = coeffs2vals( X );
case 'vals'
ff = X;
otherwise
error('Unknown input type.');
end
if ( ~isreal(ff) )
ff = real( ff );
end
[n1, n2, n3] = size( X );
r = chebpts( n1 );
... |
github | danfortunato/fast-poisson-solvers-master | makeFigures.m | .m | fast-poisson-solvers-master/code/vis/makeFigures.m | 8,407 | utf_8 | 17263bf3508fea3a8bab732b39db2f3a | function makeFigures( name, writeToDisk )
%MAKEFIGURES Make figures for the paper.
%
% name: The name of the figure to generate.
% Options are 'all' (default) or one of the following:
% - 'FiniteDifferenceTimings'
% - 'SquareSolution'
% - 'SquareTimings'
% - 'CylinderSolution... |
github | danfortunato/fast-poisson-solvers-master | poisson_cube.m | .m | fast-poisson-solvers-master/code/cube/poisson_cube.m | 7,599 | utf_8 | 248560894cdfa152de45a95b4f04d275 | function X = poisson_cube( F, tol )
%POISSON_CUBE Fast Poisson solver for the cube.
% POISSON_CUBE( F ) solves laplacian(U) = F on [-1,1]x[-1,1]x[-1,1] with
% zero Dirichlet boundary conditions. That is, U satisfies
%
% U_{x,x} + U_{y,y} + U_{z,z} = F, on [-1,1]^3 U = 0 on boundary
%
% F is input as an M... |
github | danfortunato/fast-poisson-solvers-master | poisson_rectangle.m | .m | fast-poisson-solvers-master/code/rectangle/poisson_rectangle.m | 6,949 | utf_8 | a25dca19c5ab567ed3de01eb824d80fd | function X = poisson_rectangle( F, varargin )
%POISSON_RECTANGLE Fast Poisson solver for the rectangle.
% POISSON_RECTANGLE( F ) solves laplacian(U) = F on [-1,1]x[-1,1] with
% zero Dirichlet boundary conditions. That is, U satisfies
%
% U_{x,x} + U_{y,y} = F, on [-1,1]x[-1,1] U = 0 on boundary
%
% F is ... |
github | andregouws/mrMeshPy-master | meshBuild_mrMeshPy.m | .m | mrMeshPy-master/matlabRoutines/meshBuild_mrMeshPy.m | 7,428 | utf_8 | cd89bff29e221fafb60854fb3e73728c | function [vw,newMeshNum] = meshBuild_mrMeshPy(vw,hemisphere)
% COPY OF ORIGINAL meshBuild adapted for mrMeshPy - AG 2017
% Build a 3D mesh for visualization and analysis
%
% [vw,newMeshNum,meshBuildPath] = meshBuild(vw,[hemisphere],meshBuildPath);
%
% Using mrVista data, build a mesh, save it in a file in the anatom... |
github | andregouws/mrMeshPy-master | gui_3dWindow_MeshPy.m | .m | mrMeshPy-master/matlabRoutines/gui_3dWindow_MeshPy.m | 13,690 | utf_8 | 76cdbce38b30bf8891fc8e73e619c3e5 | function varargout = gui_3dWindow_MeshPy(varargin)
% GUI_3DWINDOW_MESHPY MATLAB code for gui_3dWindow_MeshPy.fig
% GUI_3DWINDOW_MESHPY, by itself, creates a new GUI_3DWINDOW_MESHPY or raises the existing
% singleton*.
%
% H = GUI_3DWINDOW_MESHPY returns the handle to a new GUI_3DWINDOW_MESHPY or the hand... |
github | andregouws/mrMeshPy-master | meshAmplitudeMaps.m | .m | mrMeshPy-master/legacy/mrMesh/meshviewer/meshAmplitudeMaps.m | 13,889 | utf_8 | 97f639d9be02689fcb8ab234637239b1 | function [images, mapVals] = meshAmplitudeMaps(V, dialogFlag, varargin);
% Produce images of response amplitudes (estimated one of a number of
% different ways) for different event-related conditions on a mesh.
%
% NOTE: this will modify the 'map' field of the view.
%
% USAGE:
% [images mapVals] = meshAmplitudeMaps(g... |
github | andregouws/mrMeshPy-master | meshMovie.m | .m | mrMeshPy-master/legacy/mrMesh/meshviewer/meshMovie.m | 6,594 | utf_8 | e3b919c3ceea47b03a0b613e1ad149cc | function M = meshMovie(V, roiFlag, movieFileName, timeSteps, plotFlag)
%
% M = meshMovie([gray view], [roiFlag=-1], [movieFileName], [timeSteps=12], [plotFlag=1])
%
%Author: Wandell
%Purpose:
% Create a movie of the fundamental component of the time series based on
%
% the coherence and phase measurement.
% Th... |
github | andregouws/mrMeshPy-master | meshMultiAngle2.m | .m | mrMeshPy-master/legacy/mrMesh/meshviewer/meshMultiAngle2.m | 7,661 | utf_8 | 271092ab39b79bd252b57cbb985160e0 | function img = meshMultiAngle2(msh, settings, savePath, cbarFlag, msz);
% Takes a picture of a mesh at multiple camera settings, and saves as a
% .png in a directory or pastes in a PowerPoint file.
%
% img = meshMultiAngle2([mesh], [settings], [save directory or .ppt file],
% [cbarFlag], [montageS... |
github | andregouws/mrMeshPy-master | meshDrawROIs2.m | .m | mrMeshPy-master/legacy/mrMesh/meshviewer/meshDrawROIs2.m | 7,206 | utf_8 | bf2f73591282aa2e2af042d3e8b98fa6 | function [colors, msh, roiMask] = meshDrawROIs2(msh, rois, nodes, colors, update, edges);
% 'Draw' ROIs on a mesh as colors over different nodes,
% returning the updated mesh colors. Version for mrVista2.
%
% colors = meshDrawROIs2(msh, rois, <oldColors=mesh curvature>, ...
% <perim=1>, <updat... |
github | andregouws/mrMeshPy-master | meshBuild.m | .m | mrMeshPy-master/legacy/mrMesh/meshviewer/meshBuild.m | 6,910 | utf_8 | 98300007e74e9e1d8dcfc43a7ea8dc09 | function [vw,newMeshNum] = meshBuild(vw,hemisphere)
% Build a 3D mesh for visualization and analysis
%
% [vw,newMeshNum] = meshBuild(vw,[hemisphere]);
%
% Using mrVista data, build a mesh, save it in a file in the anatomy
% directory, and add the mesh to the 3D Control Window pull down
% options.
%
% vw: ... |
github | andregouws/mrMeshPy-master | meshGrowROI.m | .m | mrMeshPy-master/legacy/mrMesh/meshviewer/meshGrowROI.m | 5,510 | utf_8 | 0ac103a1d2853114720625acaa6a451e | function view = meshGrowROI(view, name, startCoord, mask);
% Grow an ROI along the cortical surface, starting at the mesh cursor
% position and extending along a contiguous patch defined by the data in
% mask.
%
% view = meshGrowROI([view], [name], [startCoord=cursor position], [mask=data overlay mask]);
%
% IN... |
github | andregouws/mrMeshPy-master | meshMontageMovie.m | .m | mrMeshPy-master/legacy/mrMesh/meshviewer/meshMontageMovie.m | 6,503 | utf_8 | 5bd24d78c37a9d01aee79e87c3a06f69 | function M = meshMontageMovie(V, whichMeshes, movieFileName, timeSteps, plotFlag, stimImages)
%
% M = meshMontageMovie([gray view], [whichMeshes], [movieFileName], [timeSteps=12], [plotFlag=1], [stimImages])
%
%Author: Wandell
%Purpose:
% Create a movie consisting of a montage of mesh images, each showing
% the fun... |
github | andregouws/mrMeshPy-master | meshParameterMaps.m | .m | mrMeshPy-master/legacy/mrMesh/meshviewer/meshParameterMaps.m | 10,149 | utf_8 | a013f41388613da4c5349a825895ff4c | function [images mapVals] = meshParameterMaps(V, dialogFlag, varargin);
% Produce images of parameter maps on a mesh.
%
% NOTE: this will modify the 'map' field of the view.
%
% USAGE:
% [images mapVals] = meshAmplitudeMaps(grayView, [dialogFlag], [options]);
%
% INPUTS:
% grayView: mrVista gray view, with a me... |
github | andregouws/mrMeshPy-master | meshCreate.m | .m | mrMeshPy-master/legacy/mrMesh/meshviewer/meshCreate.m | 2,381 | utf_8 | 3347f53de70bc2acf5d665b899d4a65c | function msh = meshCreate(mshType)
% mrMesh creation routine
%
% msh = meshCreate(mshType);
%
% We only create a vistaMesh type. In the future we may design additional
% mesh structures. See notes below about the properties of the msh fields.
%
% See also: meshSet/Get and mrmSet/Get
%
% Example:
% ... |
github | andregouws/mrMeshPy-master | meshCompareScans.m | .m | mrMeshPy-master/legacy/mrMesh/meshviewer/meshCompareScans.m | 9,112 | utf_8 | 51777b74f032d5d0b3596a22d29f830e | function [images pics] = meshCompareScans(V, scans, dts, settings, savePath, leg);
%
% Create mosaic images showing data from different scans / data
% types superimposed on the same mesh and view angles.
%
% [images pics] = meshCompareScans(<view, scans, dts, settings, savePath, leg>);
%
% This code requires that you... |
github | andregouws/mrMeshPy-master | meshMatchSettings.m | .m | mrMeshPy-master/legacy/mrMesh/meshviewer/meshMatchSettings.m | 4,335 | utf_8 | 009f6a9398b567be79bc3cb4d8f6c0b9 | function settings = meshMatchSettings(src, tgt, varargin);
% Adjust the view settings on one or more target meshes to match that on
% the source mesh.
%
% settings = meshMatchSettings([sourceMesh=current mesh], [targetMeshes], [gray/volume view]);
%
% INPUTS:
% sourceMesh: mesh whose view settings you want to ... |
github | andregouws/mrMeshPy-master | mrmSet.m | .m | mrMeshPy-master/legacy/mrMesh/mrm/mrmSet.m | 21,644 | utf_8 | cc937f4caa7c19408dfdea0353f9ab69 | function [msh, ret] = mrmSet(msh,param,varargin)
% General interface for communicating with mrMesh parameters.
%
% [msh, ret] = mrmSet(msh,param,varargin)
%
% This routine keeps track of what we need to do to adjust different types
% of visual properties of the image.
%
% The routine tries to update the msh stru... |
github | andregouws/mrMeshPy-master | mrmLoadOffFile.m | .m | mrMeshPy-master/legacy/mrMesh/mrm/mrmLoadOffFile.m | 2,197 | utf_8 | 8921617d9bfdc2f7e17d3aef4bf2fae0 | function msh = mrmLoadOffFile(offFile, origin)
% Buils a basic mrm structure given an OFF format mesh file.
% mrm = mrmLoadOffFile(offFile, origin)
%
% 2007.06.08 RFD wrote it.
if(~exist('offFile','var')||isempty(offFile))
[f,p] = uigetfile({'*.off';'*.*'},'Select the OFF file...');
if(isnumeric(f)); disp('use... |
github | andregouws/mrMeshPy-master | mrmConvertEMSEMesh.m | .m | mrMeshPy-master/legacy/mrMesh/mrm/mrmConvertEMSEMesh.m | 7,409 | utf_8 | b1b775d767cc4e8aed9c6e10bf5ac683 | function [msh,lights,tenseMsh] = mrmConvertEMSEMesh(fileName,mmPerVox, host, id, varargin);
%
% [msh,lights,tenseMsh] = mrmConvertEMSEMesh(fileName,mmPerVox, host, id, varargin);
%
%
% Author: ARW (based on RFD's mrmBuildMesh)
% Purpose:
% Take a mesh in EMSE's .wfr format and convert it into a mrLoadRet /
% mrMesh-... |
github | andregouws/mrMeshPy-master | mrmViewer.m | .m | mrMeshPy-master/legacy/mrMesh/mrm/mrmViewer.m | 9,185 | utf_8 | d0d0d67ec4da80e3dea9f568c4cdae55 | function varargout = mrmViewer(varargin)
% MRMVIEWER M-file for mrmViewer.fig
% MRMVIEWER, by itself, creates a new MRMVIEWER or raises the existing
% singleton*.
%
% H = MRMVIEWER returns the handle to a new MRMVIEWER or the handle to
% the existing singleton*.
%
% MRMVIEWER('CALLBACK',hObject... |
github | andregouws/mrMeshPy-master | mrmGet.m | .m | mrMeshPy-master/legacy/mrMesh/mrm/mrmGet.m | 9,887 | utf_8 | e8dd0d99fd2d7c23b47d0386a3f73bb4 | function val = mrmGet(msh,param,varargin)
% Communicate parameter values with a mrMesh window.
%
% val = mrmGet(msh,param,varargin)
%
% The general object mesh, typically a brain surface, contains various
% important parameters. These include the identity of the host computer
% running the mrMesh server (usually 'l... |
github | andregouws/mrMeshPy-master | mrmMakeMovie.m | .m | mrMeshPy-master/legacy/mrMesh/mrm/mrmMakeMovie.m | 2,415 | utf_8 | 6a016c342fcc99acc4d928e88c9c3074 | function mrmMakeMovie(id,rotBegin,rotEnd)
%
%
% Function to make a movie of brain rotating left to ventral view. This
% should really be a more general function, but this is a start.
%
% written by amr Jun 2010
%
if ~exist('id','var'), id = 500; end
if ~exist('rotBegin','var')
mrmRotateCamera(id,'left')
[r... |
github | andregouws/mrMeshPy-master | mrmMakeMovieGUI_Waypoint.m | .m | mrMeshPy-master/legacy/mrMesh/mrm/mrmMakeMovieGUI/mrmMakeMovieGUI_Waypoint.m | 4,744 | utf_8 | 29d4ffefd0edeea9b598da7c883a409a | function varargout = mrmMakeMovieGUI_Waypoint(varargin)
% MRMMAKEMOVIEGUI_WAYPOINT M-file for mrmMakeMovieGUI_Waypoint.fig
% MRMMAKEMOVIEGUI_WAYPOINT, by itself, creates a new MRMMAKEMOVIEGUI_WAYPOINT or raises the existing
% singleton*.
%
% H = MRMMAKEMOVIEGUI_WAYPOINT returns the handle to a new MRMMAK... |
github | andregouws/mrMeshPy-master | mrmMakeMovieGUI_Transition.m | .m | mrMeshPy-master/legacy/mrMesh/mrm/mrmMakeMovieGUI/mrmMakeMovieGUI_Transition.m | 4,452 | utf_8 | 40a785eea9283100dd17a89bc98eac89 | function varargout = mrmMakeMovieGUI_Transition(varargin)
% MRMMAKEMOVIEGUI_TRANSITION M-file for mrmMakeMovieGUI_Transition.fig
% MRMMAKEMOVIEGUI_TRANSITION, by itself, creates a new MRMMAKEMOVIEGUI_TRANSITION or raises the existing
% singleton*.
%
% H = MRMMAKEMOVIEGUI_TRANSITION returns the handle to ... |
github | andregouws/mrMeshPy-master | mrmMakeMovieGUI.m | .m | mrMeshPy-master/legacy/mrMesh/mrm/mrmMakeMovieGUI/mrmMakeMovieGUI.m | 22,903 | utf_8 | fc4be7c7cb86bd358de9c6648a54e8e7 | function varargout = mrmMakeMovieGUI(varargin)
% mrmMakeMovieGUI(meshID)
% Given a mesh ID #, opens a GUI allowing you to set up a series of
% events to be displayed in a .avi file.
%
% Events consist of waypoints, transitions, and pauses. All pauses must
% follow waypoints. Transitions must appear with waypo... |
github | andregouws/mrMeshPy-master | mrmMakeMovieGUI_Pause.m | .m | mrMeshPy-master/legacy/mrMesh/mrm/mrmMakeMovieGUI/mrmMakeMovieGUI_Pause.m | 3,455 | utf_8 | 2bb67f063f553fbaefb2021090109889 | function varargout = mrmMakeMovieGUI_Pause(varargin)
% MRMMAKEMOVIEGUI_PAUSE M-file for mrmMakeMovieGUI_Pause.fig
% MRMMAKEMOVIEGUI_PAUSE, by itself, creates a new MRMMAKEMOVIEGUI_PAUSE or raises the existing
% singleton*.
%
% H = MRMMAKEMOVIEGUI_PAUSE returns the handle to a new MRMMAKEMOVIEGUI_PAUSE or... |
github | andregouws/mrMeshPy-master | dtiSplitFourImages.m | .m | mrMeshPy-master/legacy/mrMesh/mrDiffusion/dtiSplitFourImages.m | 4,601 | utf_8 | aa0b90778b09e27998fda42d59bfe848 | function [images,imOrigin] = dtiSplitFourImages(handles,xIm,yIm,zIm)
%
% [images,imOrigin1,imOrigin2,imOrigin3,imOrigin4] = ...
% dtiSplitFourImages(handles,xIm,yIm,zIm,imOrigin)
%
% Splits each of the images xIm, yIm, and zIm into four images stored in
% the images structure. This is done so that tra... |
github | andregouws/mrMeshPy-master | dtiMrMeshOrigin.m | .m | mrMeshPy-master/legacy/mrMesh/mrDiffusion/dtiMrMeshOrigin.m | 2,730 | utf_8 | f21f253f859590893778a3ad4ea15e07 | function origin = dtiMrMeshOrigin(handles)
% Computes the (x,y,z) origin of the image plane data
%
% origin = dtiMrMeshOrigin(handles)
%
% NOTE: This routine was extracted from dtiMrMesh3AxisImage so we could
% build independent mrMesh outputs. I think it may be computing the origin
% of the quarter images from dtiS... |
github | winswang/comp_holo_video-master | TwIST.m | .m | comp_holo_video-master/3D/TwIST.m | 22,842 | utf_8 | 64ee75349f89f520998ec0d7afd15ac0 | function [x,x_debias,objective,times,debias_start,mses,max_svd] = ...
TwIST(y,A,tau,varargin)
%
% Usage:
% [x,x_debias,objective,times,debias_start,mses] = TwIST(y,A,tau,varargin)
%
% This function solves the regularization problem
%
% arg min_x = 0.5*|| y - A x ||_2^2 + tau phi( x ),
%
% where A is a ge... |
github | winswang/comp_holo_video-master | MyTVphi.m | .m | comp_holo_video-master/3D/MyTVphi.m | 281 | utf_8 | 9b938a61469a38963950ef4d40953c71 | function y=MyTVphi(x,Nvx,Nvy,Nvz)
% x = x(1:length(x)/2) + 1i*x(length(x)/2+1:end);
X=reshape(x,Nvx,Nvy,Nvz);
[y,dif]=MyTVnorm(X);
% re = real(y); im = imag(y);
% y = [re;im];
function [y,dif]=MyTVnorm(x)
TV=MyTV3D_conv(x);
dif=sqrt(sum(TV.*conj(TV),4));
y=sum(dif(:));
end
end |
github | winswang/comp_holo_video-master | TwIST.m | .m | comp_holo_video-master/Resolution/TwIST.m | 22,842 | utf_8 | 64ee75349f89f520998ec0d7afd15ac0 | function [x,x_debias,objective,times,debias_start,mses,max_svd] = ...
TwIST(y,A,tau,varargin)
%
% Usage:
% [x,x_debias,objective,times,debias_start,mses] = TwIST(y,A,tau,varargin)
%
% This function solves the regularization problem
%
% arg min_x = 0.5*|| y - A x ||_2^2 + tau phi( x ),
%
% where A is a ge... |
github | winswang/comp_holo_video-master | MyTVphi.m | .m | comp_holo_video-master/Resolution/MyTVphi.m | 281 | utf_8 | 9b938a61469a38963950ef4d40953c71 | function y=MyTVphi(x,Nvx,Nvy,Nvz)
% x = x(1:length(x)/2) + 1i*x(length(x)/2+1:end);
X=reshape(x,Nvx,Nvy,Nvz);
[y,dif]=MyTVnorm(X);
% re = real(y); im = imag(y);
% y = [re;im];
function [y,dif]=MyTVnorm(x)
TV=MyTV3D_conv(x);
dif=sqrt(sum(TV.*conj(TV),4));
y=sum(dif(:));
end
end |
github | winswang/comp_holo_video-master | TwIST.m | .m | comp_holo_video-master/4D/TwIST.m | 22,842 | utf_8 | 64ee75349f89f520998ec0d7afd15ac0 | function [x,x_debias,objective,times,debias_start,mses,max_svd] = ...
TwIST(y,A,tau,varargin)
%
% Usage:
% [x,x_debias,objective,times,debias_start,mses] = TwIST(y,A,tau,varargin)
%
% This function solves the regularization problem
%
% arg min_x = 0.5*|| y - A x ||_2^2 + tau phi( x ),
%
% where A is a ge... |
github | winswang/comp_holo_video-master | MyTVphi.m | .m | comp_holo_video-master/4D/MyTVphi.m | 281 | utf_8 | 9b938a61469a38963950ef4d40953c71 | function y=MyTVphi(x,Nvx,Nvy,Nvz)
% x = x(1:length(x)/2) + 1i*x(length(x)/2+1:end);
X=reshape(x,Nvx,Nvy,Nvz);
[y,dif]=MyTVnorm(X);
% re = real(y); im = imag(y);
% y = [re;im];
function [y,dif]=MyTVnorm(x)
TV=MyTV3D_conv(x);
dif=sqrt(sum(TV.*conj(TV),4));
y=sum(dif(:));
end
end |
github | chinmaydas96/Neural-Networks-for-Machine-Learning-master | learn_perceptron.m | .m | Neural-Networks-for-Machine-Learning-master/week-3/Assignment1/Octave/learn_perceptron.m | 6,030 | utf_8 | 71f226b260465cb3ec3b5c82e3519382 | %% Learns the weights of a perceptron and displays the results.
function [w] = learn_perceptron(neg_examples_nobias,pos_examples_nobias,w_init,w_gen_feas)
%%
% Learns the weights of a perceptron for a 2-dimensional dataset and plots
% the perceptron at each iteration where an iteration is defined as one
% full pass th... |
github | chinmaydas96/Neural-Networks-for-Machine-Learning-master | plot_perceptron.m | .m | Neural-Networks-for-Machine-Learning-master/week-3/Assignment1/Octave/plot_perceptron.m | 3,409 | utf_8 | 808099ac46c6f636fa74de07abbcc8bb | %% Plots information about a perceptron classifier on a 2-dimensional dataset.
function plot_perceptron(neg_examples, pos_examples, mistakes0, mistakes1, num_err_history, w, w_dist_history)
%%
% The top-left plot shows the dataset and the classification boundary given by
% the weights of the perceptron. The negative ex... |
github | chinmaydas96/Neural-Networks-for-Machine-Learning-master | train.m | .m | Neural-Networks-for-Machine-Learning-master/week-5/assignment2/train.m | 8,675 | utf_8 | eb006271f1479b68936e2aeb9c7222f8 | % This function trains a neural network language model.
function [model] = train(epochs)
% Inputs:
% epochs: Number of epochs to run.
% Output:
% model: A struct containing the learned weights and biases and vocabulary.
if size(ver('Octave'),1)
OctaveMode = 1;
warning('error', 'Octave:broadcast');
start_time... |
github | andrewganjinrui/CrackInspection_Matlab-master | bilateralFilter.m | .m | CrackInspection_Matlab-master/bilateralFilter.m | 7,195 | utf_8 | 820768914dac0bf852cd298cf3112a76 | %
% original src: http://people.csail.mit.edu/jiawen/software/bilateralFilter.m
% original author: Jiawen (Kevin) Chen
% <jiawen@csail.mit.edu>
% http://people.csail.mit.edu/jiawen/
%
% output = bilateralFilter( data, edge, ...
% edgeMin, edgeMax, ...
% ... |
github | wanghan0501/convolutional_sparse_coding-master | colorspace.m | .m | convolutional_sparse_coding-master/Main/Bilateral Filtering/colorspace.m | 13,590 | utf_8 | b1a9eb973fa39950345a1df707b5d2c8 | function varargout = colorspace(Conversion,varargin)
%COLORSPACE Convert a color image between color representations.
% B = COLORSPACE(S,A) converts the color representation of image A
% where S is a string specifying the conversion. S tells the
% source and destination color spaces, S = 'dest<-src', or
% alt... |
github | wanghan0501/convolutional_sparse_coding-master | bfilter2.m | .m | convolutional_sparse_coding-master/Main/Bilateral Filtering/bfilter2.m | 4,694 | utf_8 | c4212b1b128af24576bc68f8b7f09b2b |
% BFILTER2 Two dimensional bilateral filtering.
% This function implements 2-D bilateral filtering using
% the method outlined in:
%
% C. Tomasi and R. Manduchi. Bilateral Filtering for
% Gray and Color Images. In Proceedings of the IEEE
% International Conference on Computer Vision, 1998.
%... |
github | wanghan0501/convolutional_sparse_coding-master | deconvtvl2.m | .m | convolutional_sparse_coding-master/Main/deconvtv_v1/private/deconvtvl2.m | 6,403 | utf_8 | 217f733df269e458bcfcea143f50d64f | function out = deconvtvl2(g, H, mu, opts)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% out = deconvtv(g, H, mu, opts)
% deconvolves image g by solving the following TV minimization problem
%
% min (mu/2) || Hf - g ||^2 + ||f||_TV
%
% where ||f||_TV = sqrt( a||Dxf||^2 + b||Dyf||^2 c||Dtf||^2),
% Dxf = f(x+1,y, t) - f(x,y,t... |
github | wanghan0501/convolutional_sparse_coding-master | deconvtvl1.m | .m | convolutional_sparse_coding-master/Main/deconvtv_v1/private/deconvtvl1.m | 6,551 | utf_8 | 4edd96e4d2a766f65612a717b003a24e | function out = deconvtvl1(g, H, mu, opts)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% out = deconvtvl1(g, H, mu, opts)
% deconvolves image g by solving the following TV minimization problem
%
% min (mu/2) || Hf - g ||_1 + ||f||_TV
%
% where ||f||_TV = sqrt( a||Dxf||^2 + b||Dyf||^2 c||Dtf||^2),
% Dxf = f(x+1,y, t) - f(x,y... |
github | wanghan0501/convolutional_sparse_coding-master | spectrum.m | .m | convolutional_sparse_coding-master/Main/MCA/Wavelab850/spectrum.m | 13,263 | utf_8 | 5b5df172751851ed1f1f118f129ea9b0 | function [Spec,f] = spectrum(varargin)
%SPECTRUM Power spectrum estimate of one or two data sequences.
% P=SPECTRUM(X,NFFT,NOVERLAP,WIND) estimates the Power Spectral Density of
% signal vector X using Welch's averaged periodogram method. The signal X
% is divided into overlapping sections, each of which is detre... |
github | wanghan0501/convolutional_sparse_coding-master | getlength.m | .m | convolutional_sparse_coding-master/Main/MCA/Wavelab850/Utilities/getlength.m | 638 | utf_8 | 320d2a4b6bcb9556c70ea5aefc3ff7d4 | % method of class @signal
%
% INPUT VALUES:
%
% RETURN VALUE:
%
%
% (c) 2003, University of Cambridge, Medical Research Council
% Stefan Bleeck (stefan@bleeck.de)
% http://www.mrc-cbu.cam.ac.uk/cnbh/aimmanual
% $Date: 2003/01/17 16:57:43 $
% $Revision: 1.3 $
function res =getlength(sig)
% returns the length i... |
github | wanghan0501/convolutional_sparse_coding-master | spectrum.m | .m | convolutional_sparse_coding-master/Main/MCA/Wavelab850/Browsers/One-D/spectrum.m | 10,815 | utf_8 | 2465d1a507f9362e9c0f5bda718e3f80 | function [Spec,f] = spectrum(varargin)
%SPECTRUM Power spectrum estimate of one or two data sequences.
% SPECTRUM has been replaced by SPECTRUM.WELCH. SPECTRUM still works but
% may be removed in the future. Use SPECTRUM.WELCH (or its functional
% form PWELCH) instead. Type help SPECTRUM/WELCH for details.
%
% ... |
github | wanghan0501/convolutional_sparse_coding-master | def_signal.m | .m | convolutional_sparse_coding-master/Main/MCA/Wavelab850/Browsers/One-D/def_signal.m | 822 | utf_8 | 3d205982ba291fa4a1490bbc0d3c5032 | % def_signal -- Called by WLBrowser
% Usage
% def_signal
%
function x = def_signal(i)
do_global; global nsig;
signal_name = Signals_entries( i,: );
while signal_name( length(signal_name) ) == ' '
signal_name( length(signal_name) ) = [];
end
if ~exist('nsig') | nsig == [] | nsig == 0
nsig = 2^8;
end
... |
github | wanghan0501/convolutional_sparse_coding-master | def_data.m | .m | convolutional_sparse_coding-master/Main/MCA/Wavelab850/Browsers/One-D/def_data.m | 881 | utf_8 | dd6c7b1f269745e207c2f0b62b9125e0 | % def_data -- Called by WLBrowser
% Usage
% def_data
%
% Description
% Load WaveLab datasets
%
function x = def_data(i)
do_global
data_name = Data____entries( i+1, : );
while data_name( length(data_name) ) == ' '
data_name( length(data_name) ) = [];
end
if i < 7
x = ReadSignal(data_name);
signal_name... |
github | wanghan0501/convolutional_sparse_coding-master | AdaptDemo.m | .m | convolutional_sparse_coding-master/Main/MCA/Wavelab850/Papers/Adapt/AdaptDemo.m | 11,368 | utf_8 | 180001bdfcbd44bcd54b8cf9120dc5f0 | %********************************************************
function AdaptDemo(action)
%Usage: AdaptDemo
%Description: Demo for paper Adapting to Unknown Smoothness via Wavelet
%Shrinkage
%Date: August 1, 2005
%********************************************************
global plotOffset
global LastFigureNo
global PaperName... |
github | wanghan0501/convolutional_sparse_coding-master | CSpinDemo.m | .m | convolutional_sparse_coding-master/Main/MCA/Wavelab850/Papers/SpinCycle/CSpinDemo.m | 11,707 | utf_8 | bc47e62d03b6b2a5bf926f80cd1ea94f | %********************************************************
function CSpinDemo(action)
%Usage: CSpinDemo
%Description: Demo for paper Translation-Invariant DeNoisin
%Date: August 1, 2005
%********************************************************
global plotOffset
global LastFigureNo
global PaperName
global MakeFigureFileP... |
github | wanghan0501/convolutional_sparse_coding-master | MESDemo.m | .m | convolutional_sparse_coding-master/Main/MCA/Wavelab850/Papers/MinEntSeg/MESDemo.m | 12,121 | utf_8 | 10c38e6038829379a0e22866c7656a78 | %********************************************************
function MESDemo(action)
%Usage: MESDemo
%Description: Demo for paper Minimum Entropy Segmentation
%Date: August 1, 2005
%********************************************************
global plotOffset
global LastFigureNo
global PaperName
global MakeFigureFilePrefix
... |
github | wanghan0501/convolutional_sparse_coding-master | RiskDemo.m | .m | convolutional_sparse_coding-master/Main/MCA/Wavelab850/Papers/RiskAnalysis/RiskDemo.m | 11,626 | utf_8 | 2a233fe2f8463d8c4804916c4bba3f16 | %********************************************************
function RiskDemo(action)
%Usage: RiskDemo
%Description: Demo for paper EXACT RISK ANALYSIS OF WAVELET REGRESSION
%Date: August 1, 2005
%********************************************************
global plotOffset
global LastFigureNo
global PaperName
global MakeFi... |
github | wanghan0501/convolutional_sparse_coding-master | SCDemo.m | .m | convolutional_sparse_coding-master/Main/MCA/Wavelab850/Papers/ShortCourse/SCDemo.m | 11,628 | utf_8 | 2cfe2fd8ea91b4f70eb21adfc3f1722c | %********************************************************
function SCDemo(action)
%Usage: SCDemo
%Description: Demo for Short Course
%Date: August 1, 2005
%********************************************************
global plotOffset
global LastFigureNo
global PaperName
global MakeFigureFilePrefix
global IfNewWindow
globa... |
github | wanghan0501/convolutional_sparse_coding-master | BlockyDemo.m | .m | convolutional_sparse_coding-master/Main/MCA/Wavelab850/Papers/Blocky/BlockyDemo.m | 11,446 | utf_8 | eca745f65c369f6596e97b9b376401b3 | %********************************************************
function BockyDemo(action)
%Usage: BlockyDemo
%Description: Demo for paper Smooth Wavelet Decompositions with Blocky
%Coefficient Kernels
%Date: August 1, 2005
%********************************************************
global plotOffset
global LastFigureNo
global... |
github | wanghan0501/convolutional_sparse_coding-master | VdLDemo.m | .m | convolutional_sparse_coding-master/Main/MCA/Wavelab850/Papers/VillardDeLans/VdLDemo.m | 11,692 | utf_8 | de5771598e9111e4276abf5d9395a4ce | %********************************************************
function VdLDemo(action)
%Usage: VdLDemo
%Description: Demo for paper WaveLab and Reproducible Research
%Date: August 1, 2005
%********************************************************
global plotOffset
global LastFigureNo
global PaperName
global MakeFigureFilePr... |
github | wanghan0501/convolutional_sparse_coding-master | TourDemo.m | .m | convolutional_sparse_coding-master/Main/MCA/Wavelab850/Papers/Tour/TourDemo.m | 11,732 | utf_8 | 81919bc6b7d0842b12e02c40a909e027 | %********************************************************
function TourDemo(action)
%Usage: TourDemo
%Description: Demo for paper Wavelet Shrinkage and W.V.D.: A Ten-Minute
%Tour
%Date: August 1,2005
%********************************************************
global plotOffset
global LastFigureNo
global PaperName
global ... |
github | wanghan0501/convolutional_sparse_coding-master | AsympDemo.m | .m | convolutional_sparse_coding-master/Main/MCA/Wavelab850/Papers/Asymp/AsympDemo.m | 11,354 | utf_8 | 81c6f0550a19608ea51b03a62b22b423 | %********************************************************
function AsympDemo(action)
%Usage: AsympDemo
%Description: Demo for paper Wavelet Shrinkage: Asymptopia?
%Date: August 1, 2005
%********************************************************
global plotOffset
global LastFigureNo
global PaperName
global MakeFigureFileP... |
github | wanghan0501/convolutional_sparse_coding-master | cbpdngr.m | .m | convolutional_sparse_coding-master/SparseCode/cbpdngr.m | 11,600 | utf_8 | 2e39147180340593eaaa5c1950090018 | function [Y, optinf] = cbpdngr(D, S, lambda, mu, opt)
% cbpdngr -- Convolutional Basis Pursuit DeNoising with Gradient Regularization
%
% argmin_{x_k} (1/2)||\sum_k d_k * x_k - s||_2^2 +
% lambda \sum_k ||x_k||_1 +
% (mu/2) \sum_k ||G_r x_k||_2^2 +
% ... |
github | wanghan0501/convolutional_sparse_coding-master | bpdn.m | .m | convolutional_sparse_coding-master/SparseCode/bpdn.m | 8,693 | utf_8 | 16adec7a3bdc2618ca15b5a38b5a5e0f | function [Y, optinf] = bpdn(D, S, lambda, opt)
% bpdn -- Basis Pursuit DeNoising
%
% argmin_x (1/2)||D*x - s||_2^2 + lambda*||x||_1
%
% The solution is computed using the ADMM approach (see
% boyd-2010-distributed for details).
%
% Usage:
% [Y, optinf] = bpdn(D, S, lambda, opt)
%
% Input:... |
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