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 | zakk0610/ML_study-master | loadjson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/machine-learning-ex6/ex6/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 | zakk0610/ML_study-master | loadubjson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/machine-learning-ex6/ex6/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 | zakk0610/ML_study-master | saveubjson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/machine-learning-ex6/ex6/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 | zakk0610/ML_study-master | submit.m | .m | ML_study-master/ML_course_by_Andrew_Ng/machine-learning-ex7/ex7/submit.m | 1,438 | utf_8 | 665ea5906aad3ccfd94e33a40c58e2ce | function submit()
addpath('./lib');
conf.assignmentSlug = 'k-means-clustering-and-pca';
conf.itemName = 'K-Means Clustering and PCA';
conf.partArrays = { ...
{ ...
'1', ...
{ 'findClosestCentroids.m' }, ...
'Find Closest Centroids (k-Means)', ...
}, ...
{ ...
'2', ...
... |
github | zakk0610/ML_study-master | submitWithConfiguration.m | .m | ML_study-master/ML_course_by_Andrew_Ng/machine-learning-ex7/ex7/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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 | zakk0610/ML_study-master | savejson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/machine-learning-ex7/ex7/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 | zakk0610/ML_study-master | loadjson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/machine-learning-ex7/ex7/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 | zakk0610/ML_study-master | loadubjson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/machine-learning-ex7/ex7/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 | zakk0610/ML_study-master | saveubjson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/machine-learning-ex7/ex7/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 | zakk0610/ML_study-master | submit.m | .m | ML_study-master/ML_course_by_Andrew_Ng/machine-learning-ex5/ex5/submit.m | 1,765 | utf_8 | b1804fe5854d9744dca981d250eda251 | function submit()
addpath('./lib');
conf.assignmentSlug = 'regularized-linear-regression-and-bias-variance';
conf.itemName = 'Regularized Linear Regression and Bias/Variance';
conf.partArrays = { ...
{ ...
'1', ...
{ 'linearRegCostFunction.m' }, ...
'Regularized Linear Regression Cost Fun... |
github | zakk0610/ML_study-master | submitWithConfiguration.m | .m | ML_study-master/ML_course_by_Andrew_Ng/machine-learning-ex5/ex5/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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 | zakk0610/ML_study-master | savejson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/machine-learning-ex5/ex5/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 | zakk0610/ML_study-master | loadjson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/machine-learning-ex5/ex5/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 | zakk0610/ML_study-master | loadubjson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/machine-learning-ex5/ex5/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 | zakk0610/ML_study-master | saveubjson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/machine-learning-ex5/ex5/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 | zakk0610/ML_study-master | submit.m | .m | ML_study-master/ML_course_by_Andrew_Ng/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 | zakk0610/ML_study-master | submitWithConfiguration.m | .m | ML_study-master/ML_course_by_Andrew_Ng/machine-learning-ex3/ex3/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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 | zakk0610/ML_study-master | savejson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/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 | zakk0610/ML_study-master | loadjson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/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 | zakk0610/ML_study-master | loadubjson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/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 | zakk0610/ML_study-master | saveubjson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/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 | zakk0610/ML_study-master | submit.m | .m | ML_study-master/ML_course_by_Andrew_Ng/machine-learning-ex8/ex8/submit.m | 2,135 | utf_8 | eebb8c0a1db5a4df20b4c858603efad6 | function submit()
addpath('./lib');
conf.assignmentSlug = 'anomaly-detection-and-recommender-systems';
conf.itemName = 'Anomaly Detection and Recommender Systems';
conf.partArrays = { ...
{ ...
'1', ...
{ 'estimateGaussian.m' }, ...
'Estimate Gaussian Parameters', ...
}, ...
{ ...... |
github | zakk0610/ML_study-master | submitWithConfiguration.m | .m | ML_study-master/ML_course_by_Andrew_Ng/machine-learning-ex8/ex8/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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 | zakk0610/ML_study-master | savejson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/machine-learning-ex8/ex8/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 | zakk0610/ML_study-master | loadjson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/machine-learning-ex8/ex8/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 | zakk0610/ML_study-master | loadubjson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/machine-learning-ex8/ex8/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 | zakk0610/ML_study-master | saveubjson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/machine-learning-ex8/ex8/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 | zakk0610/ML_study-master | submit.m | .m | ML_study-master/ML_course_by_Andrew_Ng/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 | zakk0610/ML_study-master | submitWithConfiguration.m | .m | ML_study-master/ML_course_by_Andrew_Ng/machine-learning-ex1/ex1/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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 | zakk0610/ML_study-master | savejson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/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 | zakk0610/ML_study-master | loadjson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/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 | zakk0610/ML_study-master | loadubjson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/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 | zakk0610/ML_study-master | saveubjson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/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 | zakk0610/ML_study-master | learn_perceptron.m | .m | ML_study-master/NN_course_by_Goeffrey_E._Hinton/assignment1/learn_perceptron.m | 6,069 | utf_8 | b1c01c7f0ac821c476cacea8f1f256c9 | %% 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 | zakk0610/ML_study-master | plot_perceptron.m | .m | ML_study-master/NN_course_by_Goeffrey_E._Hinton/assignment1/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 | zakk0610/ML_study-master | train.m | .m | ML_study-master/NN_course_by_Goeffrey_E._Hinton/assignment2/train.m | 9,291 | utf_8 | 3a6649b082f3822159703fef4accf2bd | % 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 | conght/BLMLink-SDK-master | echo_diagnostic.m | .m | BLMLink-SDK-master/third-party/pj-project/third_party/speex/libspeex/echo_diagnostic.m | 2,148 | utf_8 | 8a62f0a5a0a0d3cf27d90754d99a1157 | % Attempts to diagnose AEC problems from recorded samples
%
% out = echo_diagnostic(rec_file, play_file, out_file, tail_length)
%
% Computes the full matrix inversion to cancel echo from the
% recording 'rec_file' using the far end signal 'play_file' using
% a filter length of 'tail_length'. The output is saved... |
github | xprova/bisect-tau-master | readSpiceBin.m | .m | bisect-tau-master/octave/readSpiceBin.m | 1,205 | utf_8 | 35dccfe3c332a904c03670814b98badc | function simulation = readSpiceBin(binFile)
if nargin == 0
binFile = getOutputFile('spice-output.bin');
end
fid = fopen(binFile, 'r');
if fid == -1
error('could not open file %s', binFile);
end
variableSection = 0;
sigNames = {};
sigTypes = {};
while 1
str = fgetl(fid);
if startsWith(str, '... |
github | xprova/bisect-tau-master | runChecks.m | .m | bisect-tau-master/octave/runChecks.m | 2,573 | utf_8 | 86cba1a53f40e67fb4f3bd1a402aaa09 | function runChecks(dutFile)
checks = {
{'checking if ngspice is installed ...', @checkSpice}
{'checking if dut file exists ...', @() checkExist(dutFile)}
{'checking DUT behavior (test Case 1) ...', @() checkCase1DUT(dutFile)}
{'checking DUT behavior (test Case 2) ...', @() checkCase2DUT(dutFile)}
}... |
github | xprova/bisect-tau-master | runBisectionDeep.m | .m | bisect-tau-master/octave/runBisectionDeep.m | 2,299 | utf_8 | 74b799c1f95b57eec5e06643a83354d9 | function runBisectionDeep()
delete(getOutputFile('spice-deepstep-*.bin'));
bisectionResultsDeep = [];
load(getOutputFile('bisection-output.mat'));
initStepLow = 31;
initStepHigh = 32;
if bisectionResults(initStepLow, 4) ~= 0
error('initStepLow is not a low state');
end
if bisectionResults(initStepH... |
github | xprova/bisect-tau-master | genInitialConditions.m | .m | bisect-tau-master/octave/genInitialConditions.m | 1,773 | utf_8 | 87f70cf688f0f7f90cd2ad9f9708f73e | function genInitialConditions(sigNames, sigTypes, sigICs, tRestart)
if ~nargin
tRestart = 5.25e-9;
[t, signals, sigNames, sigTypes] = readSpiceBin(getOutputFile('spice-output.bin'));
k = find(t > tRestart, 1, 'first');
sigICs = signals(:, k);
end
nSigs = length(sigICs);
line... |
github | xprova/bisect-tau-master | bisectTau.m | .m | bisect-tau-master/octave/bisectTau.m | 1,916 | utf_8 | 482d1f51f33c93cdee481ea5f045b911 | % this function is called from the shell script wrapper ./bisect-tau
%
% it is passed the working dir (output of pwd) and a list of the command
% line options supplied by the user
%
% note about working directories:
%
% - octave will use the tool's home directory as its working directory
% - this will also be ngspice's... |
github | xprova/bisect-tau-master | runBisection.m | .m | bisect-tau-master/octave/runBisection.m | 2,248 | utf_8 | 48138bf0dfa0002ed0b19927f6b72581 | function runBisection(dutFile)
resFile = getOutputFile('bisection-output.mat');
if exist(resFile, 'file')
delete(resFile);
end
if exist(getOutputFile('spice-step-001.bin'), 'file')
delete(getOutputFile('spice-step-*.bin'));
end
% parameters
plotRange = [4.8 6] * 1e-9;
skipChecks = 0;
% lower and uppe... |
github | xprova/bisect-tau-master | simSpice.m | .m | bisect-tau-master/octave/simSpice.m | 1,348 | utf_8 | 2f1614867b001340769747cdd1b86c6f | function [sim, errMsg] = simSpice(testbenchCode, binFile, quiet)
if nargin < 3; quiet = 0; end
cmdFile = getOutputFile('runTestbench.cmd');
testbenchCirFile = getOutputFile('testbench.cir');
fid = fopen(testbenchCirFile, 'w');
if fid == -1
error('cannot output to file %s', testbenchCirFile)
else
for i=1... |
github | guevaracodina/ioi11-master | ioi_read_single_bin_image_old_format.m | .m | ioi11-master/ioi_read_single_bin_image_old_format.m | 1,150 | utf_8 | 96ee55e214141ccb774db96773130448 | % read anatomical binary image -- old format -- this only reads the first frame
function image_total = ioi_read_single_bin_image_old_format(fname)
% Open file containing a single volume
read_format = 'int16';
fidA = fopen(fname);
I = fread(fidA,2,read_format);
if all(I==[1;0]) % New format including image number
... |
github | guevaracodina/ioi11-master | ioi_read_bin_image_old_format.m | .m | ioi11-master/ioi_read_bin_image_old_format.m | 6,124 | utf_8 | 0a84ab96c92df4e185ee1e91581ebe9e | %read binary images
function [image_total fcount_out]= ioi_read_bin_image_old_format(fnames,...
fcount,indices,vx,n_frames,first_file,last_file,im_count)
%Example of usage to read a single volume:
% [image_total fcount_out]= ioi_read_bin_image_old_format('JJ000000.bin',0,1:83,[1 1 1],83,1,20,0);
%If frames a... |
github | guevaracodina/ioi11-master | barwitherr.m | .m | ioi11-master/barwitherr.m | 6,218 | utf_8 | 7ccb2db121015a05a56ec17d92b996e6 | %**************************************************************************
%
% This is a simple extension of the bar plot to include error bars. It
% is called in exactly the same way as bar but with an extra input
% parameter "errors" passed first.
%
% Parameters:
% errors - the errors to be plotted... |
github | guevaracodina/ioi11-master | ioi_get_data.m | .m | ioi11-master/ioi_get_data.m | 2,471 | utf_8 | 94a2d976689d81c4ee310aeb9e8b825e | function M = ioi_get_data(IOI,ROI,M,r1,s1)
%extract data for up to 3 modalities for session s1 at region-of-interest r1
cHbO = M.O.cHbO;
cHbR = M.O.cHbR;
cFlow = M.O.cFlow;
includeHbR = M.O.includeHbR;
includeHbT = M.O.includeHbT;
includeFlow = M.O.includeFlow;
%number of modalities
l = includeHbR + includeHbT... |
github | guevaracodina/ioi11-master | myNeuralNetworkFunction.m | .m | ioi11-master/myNeuralNetworkFunction.m | 28,860 | utf_8 | a948fa283277a71c923cec69dfe9c49c | function [y1] = myNeuralNetworkFunction(x1)
%MYNEURALNETWORKFUNCTION neural network simulation function.
%
% Generated by Neural Network Toolbox function genFunction, 07-Nov-2016 11:54:25.
%
% [y1] = myNeuralNetworkFunction(x1) takes these arguments:
% x = Qx56 matrix, input #1
% and returns:
% y = Qx1 matr... |
github | guevaracodina/ioi11-master | convnfft.m | .m | ioi11-master/convnfft.m | 6,549 | utf_8 | a8564c830f2165a5da2007b0cd9f6ef8 | function A = convnfft(A, B, shape, dims, options)
% CONVNFFT FFT-BASED N-dimensional convolution.
% C = CONVNFFT(A, B) performs the N-dimensional convolution of
% matrices A and B. If nak = size(A,k) and nbk = size(B,k), then
% size(C,k) = max([nak+nbk-1,nak,nbk]);
%
% C = CONVNFFT(A, B, SHAPE) controls... |
github | guevaracodina/ioi11-master | ioi_save_nifti.m | .m | ioi11-master/ioi_save_nifti.m | 437 | utf_8 | 6659bbed4d5fcf28f77dd1d6272b77c2 | % Save the data in nifti format
function ioi_save_nifti(image, fname, voxel_size)
% At this point we have our images, need to transform in nifti for further
% SPM processing
% Should we hardcode all of this or should it be the user's choice?
origin=[0 0 0];
datatype=16; % 'single'
%datatype = 4; %int16
descript... |
github | guevaracodina/ioi11-master | ioi_cine_display_GUI.m | .m | ioi11-master/ioi_cine_display_GUI.m | 10,730 | utf_8 | e4156ba7805170691598c091cb2c49ac | function varargout = ioi_cine_display_GUI(varargin)
% IOI_CINE_DISPLAY_GUI MATLAB code for ioi_cine_display_GUI.fig
% Last Modified by GUIDE v2.5 26-Dec-2011 13:11:07
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui... |
github | guevaracodina/ioi11-master | CombVec.m | .m | ioi11-master/CombVec.m | 1,291 | utf_8 | 15a67d86a57c2b890e90d16198f484cb | function out = CombVec(varargin)
%CombVec Generate all possible combinations of input vectors.
%
% CombVec(A1,A2,...) takes any number of inputs,
% A1 - Matrix of N1 (column) vectors.
% A2 - Matrix of N2 (column) vectors.
% ...
% and returns a matrix of (N1*N2*...) column vectors, where the c... |
github | guevaracodina/ioi11-master | ioi_filtfilt.m | .m | ioi11-master/ioi_filtfilt.m | 11,181 | utf_8 | 12bbdc376c916e52c91ef089ca1507d7 | function y = ioi_filtfilt(b,a,x)
%FILTFILT Zero-phase forward and reverse digital IIR filtering.
% Y = FILTFILT(B, A, X) filters the data in vector X with the filter
% described by vectors A and B to create the filtered data Y. The
% filter is described by the difference equation:
%
% a(1)*y(n) = b(1)*... |
github | guevaracodina/ioi11-master | ioi_read_time_vol.m | .m | ioi11-master/ioi_read_time_vol.m | 446 | utf_8 | b164ee49a4272a561f7e1ebc6e8c99c8 | % No check on dimensions since it is only called internally,
% Returns a temporal slice of a volume time series
function slice=ioi_read_time_vol(V,n)
spm_check_orientations(V);
%-Read in image data
%--------------------------------------------------------------------------
slice = zeros([V(1).dim(1:3)]); ... |
github | guevaracodina/ioi11-master | ioi_epsilon_pathlength.m | .m | ioi11-master/ioi_epsilon_pathlength.m | 3,505 | utf_8 | 3c2f578431cdb4cedd3d09fdc95e4900 | function eps_pathlength = ioi_epsilon_pathlength(lambda1,lambda2,npoints,...
whichSystem,whichCurve,baseline_hbt,baseline_hbo,baseline_hbr,debug)
% This function estimates epsilon * D, it takes into account the camera
% response, the leds spectra and uses a pathlength factor either set from Kohl
% or Dunn in the li... |
github | guevaracodina/ioi11-master | ByteSize.m | .m | ioi11-master/scripts/ByteSize.m | 1,011 | utf_8 | 5f3842c3f6fc1c401eda1e332993a093 | function ByteSize(in, fid)
% BYTESIZE writes the memory usage of the provide variable to the given file
% identifier. Output is written to screen if fid is 1, empty or not provided.
if nargin == 1 || isempty(fid)
fid = 1;
end
s = whos('in');
fprintf(fid,[Bytes2str(s.bytes) '\n']);
end
function str = ... |
github | guevaracodina/ioi11-master | script_group_bilateral_corr.m | .m | ioi11-master/scripts/script_group_bilateral_corr.m | 20,363 | utf_8 | 7c24f5c19f7c10fcef56eabca147d636 | %% Script to plot group correlation tests
function script_group_bilateral_corr(c1, dType)
switch dType
case 'filt'
fileName = 'group_corr_pair_seeds.mat';
case 'diff'
fileName = 'group_corr_pair_seeds_diff.mat';
case 'raw'
fileName = 'group_corr_pair_seed... |
github | shaharkov/AcceleratedQuadraticProxy-master | initialize.m | .m | AcceleratedQuadraticProxy-master/initialize.m | 970 | utf_8 | c5eef142546c1f710faa9c51d636c5b8 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Code implementing the paper "Accelerated Quadratic Proxy for Geometric Optimization", SIGGRAPH 2016.
% Disclaimer: The code is provided as-is for academic use only and without any guarantees.
% Please contact the author to report... |
github | shaharkov/AcceleratedQuadraticProxy-master | boundaryFaces.m | .m | AcceleratedQuadraticProxy-master/code/meshHelpers/boundaryFaces.m | 1,904 | utf_8 | f2daa42bd1ca92d7ae00d357a96a3e55 | % Code implementing the paper "Injective and Bounded Mappings in 3D".
% Disclaimer: The code is provided as-is and without any guarantees. Please contact the author to report any bugs.
% Written by Noam Aigerman, http://www.wisdom.weizmann.ac.il/~noamaig/
function [F,inds] = boundaryFaces(T,keep)
%compute the boundary... |
github | shaharkov/AcceleratedQuadraticProxy-master | getDistortionColormap.m | .m | AcceleratedQuadraticProxy-master/code/meshHelpers/getDistortionColormap.m | 452 | utf_8 | 7ef090d23276a321cdad1617e2f5a80b | % Code implementing the paper "Injective and Bounded Mappings in 3D".
% Disclaimer: The code is provided as-is and without any guarantees. Please contact the author to report any bugs.
% Written by Noam Aigerman, http://www.wisdom.weizmann.ac.il/~noamaig/
function cols = getDistortionColormap()
%set to out color map
C... |
github | shaharkov/AcceleratedQuadraticProxy-master | computeInjectiveStepSize.m | .m | AcceleratedQuadraticProxy-master/code/meshHelpers/computeInjectiveStepSize.m | 8,462 | utf_8 | 96df7430ec84df746a5fae2c8e1c2a17 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Code implementing the paper "Accelerated Quadratic Proxy for Geometric Optimization", SIGGRAPH 2016.
% Disclaimer: The code is provided as-is for academic use only and without any guarantees.
% Please contact the author to report... |
github | shaharkov/AcceleratedQuadraticProxy-master | indCoordsToLinearSystem.m | .m | AcceleratedQuadraticProxy-master/code/meshHelpers/indCoordsToLinearSystem.m | 800 | utf_8 | ea465596c3b2e5757f4af559f9088b6b | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Code implementing the paper "Accelerated Quadratic Proxy for Geometric Optimization", SIGGRAPH 2016.
% Disclaimer: The code is provided as-is for academic use only and without any guarantees.
% Please contact the author to report... |
github | shaharkov/AcceleratedQuadraticProxy-master | computeTutte.m | .m | AcceleratedQuadraticProxy-master/code/meshHelpers/computeTutte.m | 1,274 | utf_8 | da950eb23e7a961bc885d7fd3729b90d | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Code implementing the paper "Accelerated Quadratic Proxy for Geometric Optimization", SIGGRAPH 2016.
% Disclaimer: The code is provided as-is for academic use only and without any guarantees.
% Please contact the author to report... |
github | shaharkov/AcceleratedQuadraticProxy-master | getTensorTranspose.m | .m | AcceleratedQuadraticProxy-master/code/mathHelpers/getTensorTranspose.m | 861 | utf_8 | 985b13fee1b8b001e25e584d7eb59032 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Code implementing the paper "Accelerated Quadratic Proxy for Geometric Optimization", SIGGRAPH 2016.
% Disclaimer: The code is provided as-is for academic use only and without any guarantees.
% Please contact the author to report... |
github | shaharkov/AcceleratedQuadraticProxy-master | spdiag.m | .m | AcceleratedQuadraticProxy-master/code/mathHelpers/spdiag.m | 623 | utf_8 | 87864e9b78c7c6f42af5f397eb29d293 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Code implementing the paper "Accelerated Quadratic Proxy for Geometric Optimization", SIGGRAPH 2016.
% Disclaimer: The code is provided as-is for academic use only and without any guarantees.
% Please contact the author to report... |
github | shaharkov/AcceleratedQuadraticProxy-master | solveConstrainedLS.m | .m | AcceleratedQuadraticProxy-master/code/mathHelpers/solveConstrainedLS.m | 745 | utf_8 | a00affae758634e1618c6c8b2c546568 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Code implementing the paper "Accelerated Quadratic Proxy for Geometric Optimization", SIGGRAPH 2016.
% Disclaimer: The code is provided as-is for academic use only and without any guarantees.
% Please contact the author to report... |
github | shaharkov/AcceleratedQuadraticProxy-master | colStack.m | .m | AcceleratedQuadraticProxy-master/code/mathHelpers/colStack.m | 589 | utf_8 | e85ef3096b85b0abfc9b4b00dac7a44e | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Code implementing the paper "Accelerated Quadratic Proxy for Geometric Optimization", SIGGRAPH 2016.
% Disclaimer: The code is provided as-is for academic use only and without any guarantees.
% Please contact the author to report... |
github | zhanghang1989/visualResNet-master | visual.m | .m | visualResNet-master/visual.m | 3,075 | utf_8 | 1cc2bccaa5b9ca88d8abfeb20fd9f6a8 | function visual(varargin)
%% Look through ResNet
% Hang Zhang
addpath utils
run dependencies/matconvnet/matlab/vl_setupnn.m
run dependencies/vlfeat/toolbox/vl_setup.m
opts.model = 50;
opts.data = 'data/val';
opts.topN = 1;
opts.num = 30;
opts = vl_argparse(opts, varargin) ;
%% prepare image and the model
switch opts.... |
github | hurricanedjp/paper_code-master | makeLMfilters.m | .m | paper_code-master/PSO-CNN/util/makeLMfilters.m | 1,895 | utf_8 | 21950924882d8a0c49ab03ef0681b618 | function F=makeLMfilters
% Returns the LML filter bank of size 49x49x48 in F. To convolve an
% image I with the filter bank you can either use the matlab function
% conv2, i.e. responses(:,:,i)=conv2(I,F(:,:,i),'valid'), or use the
% Fourier transform.
SUP=49; % Support of the largest filter (must be... |
github | timattox/lammps_USER-DPD-master | lmp2cfg.m | .m | lammps_USER-DPD-master/tools/matlab/lmp2cfg.m | 8,711 | utf_8 | 343f9a061b25b2adf8f299708590a3f9 | function lmp2cfg(varargin)
% Converts LAMMPS dump file to Extended CFG Format (No velocity) to be used
% with AtomEye (http://164.107.79.177/Archive/Graphics/A/)
% Input :
% Necessary (in order)
% timestep,Natoms,x_bound,y_bound,z_bound,H,atom_data,mass,
% cfg file name, dumpfile name
% Optional
% ... |
github | apurvasijaria/Banking-networks-and-contagion-master | fails.m | .m | Banking-networks-and-contagion-master/fails.m | 487 | utf_8 | 57831650cba2a743dfc605c5f54fd263 | %Inclmplte though
function survival = fails(Sij,Tij,cij,dij,dom_node,int_node,firm_node,Bi,Di)
Nav=1000;
persistance V[Nav];
j=1;
i=1;
tot_node=dom_node+int_node+firm_node;
%Asset for ith bank is given by Ai = Xj?NDiSij+Xj?NIiSij+Xj?NFiSij+Xj?MFiTij+Bi,
SumSA=sum(Sij,2);
SumSL=sum(Sij);
SumT=sum(Tij,2);
A=sumSA+sumT+B... |
github | csdms-contrib/slepian_echo-master | inpolymoll.m | .m | slepian_echo-master/inpolymoll.m | 7,736 | utf_8 | 0e39938f1d3fe6016dbd7ea3ddd80976 | function varargout=inpolymoll(lonv,latv,degres)
% [v,xp,yp,xgr,ygr,degres]=INPOLYMOLL(lonv,latv,degres)
%
% Finds the indices of a Mollweide grid that fall within a polygon
% defined by longitude and latitude pairs in degrees
%
% INPUT:
%
% lonv,latv Same-size arrays to define pairs of longitude and latitude
% deg... |
github | czm0/Caffe-master | classification_demo.m | .m | Caffe-master/caffe-master/matlab/demo/classification_demo.m | 5,412 | utf_8 | 8f46deabe6cde287c4759f3bc8b7f819 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | toastpp/toastpp-master | mtoast2_install.m | .m | toastpp-master/mtoast2_install.m | 3,994 | utf_8 | cb66a8241c4c9c5992fe281a911fc378 | % This script adds the TOAST mex and script directories to the
% Matlab path.
% To add the path permanently, you can append the contents of this
% file to your startup.m file, located in our Matlab startup
% directory, or in <matlabroot>/toolbox/local.
function mtoast_install(varargin)
nargin = length(varargin);
nogu... |
github | toastpp/toastpp-master | fwd1_aniso.m | .m | toastpp-master/examples/matlab/fwd1_aniso.m | 3,478 | utf_8 | bab07f6623968e9c386f640d2feb42ea | % Simple 2D example for anisotropic diffusion using a diffusion tensor
function fwd1_aniso
close all
meshdir = '../../test/2D/meshes/';
hmesh = toastMesh([meshdir 'circle25_32.msh']);
hmesh.ReadQM([meshdir 'circle25_32x32.qm']);
refind = 1.4;
c0 = 0.3;
cm = c0/refind;
n = hmesh.NodeCount;
ne = hmesh.ElementCount;
... |
github | toastpp/toastpp-master | fwd1_aniso3D.m | .m | toastpp-master/examples/matlab/fwd1_aniso3D.m | 3,449 | utf_8 | de43ec0c8ae44617e37b2d801c374c6b | function tmp
close all
meshdir = '../../test/3D/meshes/';
hmesh = toastMesh([meshdir 'cyl3.msh']);
hmesh.ReadQM([meshdir 'cyl_3ring.qm']);
refind = 1.4;
c0 = 0.3;
cm = c0/refind;
n = hmesh.NodeCount;
ne = hmesh.ElementCount;
mua_homog = 0.01;
mus_homog = 1;
kap_homog = 1/(3*(mua_homog+mus_homog));
freq = 100;
qve... |
github | toastpp/toastpp-master | nonuniqueness.m | .m | toastpp-master/examples/matlab/nonuniqueness/nonuniqueness.m | 4,927 | utf_8 | b157f985b16f552129c6e960166a0d24 | % This example demonstrates a non-uniqueness condition in DOT:
% Transillumination amplitude data from a steady-state measurement at a
% single wavelength are not sufficient for reconstructing both absorption
% and scattering distributions.
%
% This is demonstrated by generating data from a model with homogeneous
... |
github | toastpp/toastpp-master | FDOT_recon_CW_Proj.m | .m | toastpp-master/examples/matlab/fluorescence/FDOT_recon_CW_Proj.m | 4,456 | utf_8 | 5f99635ab48dccec17df8d7068cd8dfc | % N.B. To run this code you need to install Stanford Wavelab and add it to your matlab path
% (http://www-stat.stanford.edu/~wavelab/)
%
function FDOT_recon_CW_Proj
close all
nproj=8;
projgrid = [128 128];
meshfile = 'cyl2.msh';
qmfile = 'circle200_8x8x3_z0.qm';
mesh = toastMesh(meshfile);
mesh.ReadQM(qmfile);
hprojl... |
github | toastpp/toastpp-master | regul_huber.m | .m | toastpp-master/script/matlab/gui/regul_huber.m | 5,428 | utf_8 | a754a52e2a5e6c4b43283f54a3c94c10 | function varargout = regul_huber(varargin)
% REGUL_HUBER M-file for regul_huber.fig
% REGUL_HUBER, by itself, creates a new REGUL_HUBER or raises the existing
% singleton*.
%
% H = REGUL_HUBER returns the handle to a new REGUL_HUBER or the handle to
% the existing singleton*.
%
% REGUL_HUBER('C... |
github | toastpp/toastpp-master | fwd_gui.m | .m | toastpp-master/script/matlab/gui/fwd_gui.m | 31,437 | utf_8 | 0893eca8e9f2421d86a34645ce5ca557 | function varargout = fwd_gui(varargin)
% FWD_GUI M-file for fwd_gui.fig
% FWD_GUI, by itself, creates a new FWD_GUI or raises the existing
% singleton*.
%
% H = FWD_GUI returns the handle to a new FWD_GUI or the handle to
% the existing singleton*.
%
% FWD_GUI('CALLBACK',hObject,eventData,handl... |
github | toastpp/toastpp-master | regul_prior.m | .m | toastpp-master/script/matlab/gui/regul_prior.m | 11,763 | utf_8 | f070c9cdfc8c55dcb308512fa184f55a | function varargout = regul_prior(varargin)
%REGUL_PRIOR M-file for regul_prior.fig
% REGUL_PRIOR, by itself, creates a new REGUL_PRIOR or raises the existing
% singleton*.
%
% H = REGUL_PRIOR returns the handle to a new REGUL_PRIOR or the handle to
% the existing singleton*.
%
% REGUL_PRIOR('Pr... |
github | toastpp/toastpp-master | regul_tv.m | .m | toastpp-master/script/matlab/gui/regul_tv.m | 5,368 | utf_8 | 3d2f0b8d1201f3742cb602407c4b074e | function varargout = regul_tv(varargin)
% REGUL_TV M-file for regul_tv.fig
% REGUL_TV, by itself, creates a new REGUL_TV or raises the existing
% singleton*.
%
% H = REGUL_TV returns the handle to a new REGUL_TV or the handle to
% the existing singleton*.
%
% REGUL_TV('CALLBACK',hObject,eventDa... |
github | toastpp/toastpp-master | recon_inv_gui.m | .m | toastpp-master/script/matlab/gui/recon_inv_gui.m | 12,979 | utf_8 | 9ba1ecf96d2eaa30e79ee4cdc76e12be | function varargout = recon_inv_gui(varargin)
% RECON_INV_GUI M-file for recon_inv_gui.fig
% RECON_INV_GUI, by itself, creates a new RECON_INV_GUI or raises the existing
% singleton*.
%
% H = RECON_INV_GUI returns the handle to a new RECON_INV_GUI or the handle to
% the existing singleton*.
%
% ... |
github | toastpp/toastpp-master | recon_data_gui.m | .m | toastpp-master/script/matlab/gui/recon_data_gui.m | 12,698 | utf_8 | 850f311a7357571798a99742c64a7bb6 | function varargout = recon_data_gui(varargin)
% RECON_DATA_GUI M-file for recon_data_gui.fig
% RECON_DATA_GUI, by itself, creates a new RECON_DATA_GUI or raises the existing
% singleton*.
%
% H = RECON_DATA_GUI returns the handle to a new RECON_DATA_GUI or the handle to
% the existing singleton*.
%
... |
github | toastpp/toastpp-master | regul_gui.m | .m | toastpp-master/script/matlab/gui/regul_gui.m | 8,033 | utf_8 | 29b3bc4ebd674ba05a3e73633bde62e3 | function varargout = regul_gui(varargin)
% REGUL_GUI M-file for regul_gui.fig
% REGUL_GUI, by itself, creates a new REGUL_GUI or raises the existing
% singleton*.
%
% H = REGUL_GUI returns the handle to a new REGUL_GUI or the handle to
% the existing singleton*.
%
% REGUL_GUI('CALLBACK',hObject... |
github | toastpp/toastpp-master | meas_gui.m | .m | toastpp-master/script/matlab/gui/meas_gui.m | 11,743 | utf_8 | 61a76483c3177ddf74270a9587845148 | function varargout = meas_gui(varargin)
% MEAS_GUI M-file for meas_gui.fig
% MEAS_GUI, by itself, creates a new MEAS_GUI or raises the existing
% singleton*.
%
% H = MEAS_GUI returns the handle to a new MEAS_GUI or the handle to
% the existing singleton*.
%
% MEAS_GUI('CALLBACK',hObject,eventDa... |
github | toastpp/toastpp-master | param_gui.m | .m | toastpp-master/script/matlab/gui/param_gui.m | 13,618 | utf_8 | 3b802c6fe7cca542071b30e116061a8e | function varargout = param_gui(varargin)
% PARAM_GUI M-file for param_gui.fig
% PARAM_GUI, by itself, creates a new PARAM_GUI or raises the existing
% singleton*.
%
% H = PARAM_GUI returns the handle to a new PARAM_GUI or the handle to
% the existing singleton*.
%
% PARAM_GUI('CALLBACK',hObject... |
github | toastpp/toastpp-master | fwdsolver_gui.m | .m | toastpp-master/script/matlab/gui/fwdsolver_gui.m | 7,809 | utf_8 | fd00f695503d3e2ead5717c2de8e8558 | function varargout = fwdsolver_gui(varargin)
% FWDSOLVER_GUI M-file for fwdsolver_gui.fig
% FWDSOLVER_GUI, by itself, creates a new FWDSOLVER_GUI or raises the existing
% singleton*.
%
% H = FWDSOLVER_GUI returns the handle to a new FWDSOLVER_GUI or the handle to
% the existing singleton*.
%
% ... |
github | toastpp/toastpp-master | recon_gui.m | .m | toastpp-master/script/matlab/gui/recon_gui.m | 17,075 | utf_8 | 2b46b3338ca3173122cbd41b486f4f47 | function varargout = recon_gui(varargin)
% RECON_GUI M-file for recon_gui.fig
% RECON_GUI, by itself, creates a new RECON_GUI or raises the existing
% singleton*.
%
% H = RECON_GUI returns the handle to a new RECON_GUI or the handle to
% the existing singleton*.
%
% RECON_GUI('CALLBACK',hObject... |
github | toastpp/toastpp-master | toastquad2linmesh.m | .m | toastpp-master/script/matlab/utilities/toastquad2linmesh.m | 1,089 | utf_8 | 3d69114e29c08844b8388a73e8b5eba2 |
function toastquad2linmesh(Filein, Fileout)
%
% read quadratic BEM mesh and convert to lin by splitting into 4.
%
% the mesh files
[ntype,QVertex,nreg,QTri,mua,diff,rind,ptype] = readtoastquadmeshsurf3d(Filein);
QNoV = size(QVertex,1);
QNoF = size(QTri,1);
disp(['minimum vertex ' num2str(min(min(QTri)))]);
LNoV = ... |
github | toastpp/toastpp-master | quad2linmesh.m | .m | toastpp-master/script/matlab/utilities/quad2linmesh.m | 994 | utf_8 | dec573b0534a61fd962205611a46ca5b |
function [QVertex,LTri] = quad2linmesh(QNoV, QNoF, Filein, Fileout)
%
% read quadratic BEM mesh and convert to lin by splitting into 4.
%
% the mesh files
fid = fopen(Filein) ;% opens the file for reading: external mesh
QTri = []; % holds original quadratic element indices
QVertex = []; % holds original quadratic no... |
github | toastpp/toastpp-master | toastWriteGmshMesh.m | .m | toastpp-master/script/matlab/utilities/toastWriteGmshMesh.m | 385 | utf_8 | a9801fd9ece0f1e32f47ac1d964f634a | % Write a Toast Mesh in Gmsh format
function toastWriteGmshMesh (mesh, fname, prm)
if nargin > 2 && strcmpi(prm,'surf')
[vtx,idx,eltp] = mesh.SurfData;
gmsh.d = 2;
else
[vtx,idx,eltp] = mesh.Data;
gmsh.d = size(vtx,2);
end
gmsh.n = size(vtx,2);
gmsh.nNodes = size(vtx,1);
gmsh.nodes = vtx;
gmsh.nElems ... |
github | toastpp/toastpp-master | toastReadGmshMesh.m | .m | toastpp-master/script/matlab/utilities/toastReadGmshMesh.m | 1,474 | utf_8 | 145e0bbc8734e05c3270bef1301e8e50 | % Convert a Gmsh mesh file to a toastMesh object
% Currently this works only for tetrahedral meshes
function mesh = toastReadGmshMesh (fname)
% Read the file, using the Gmsh-provided Matlab converter
gmsh = load_gmsh(fname);
eltp = 0;
% Extract vertices
vtx = gmsh.POS;
% Look for tetrahedral elements
%tet_idx = fin... |
github | toastpp/toastpp-master | plotmeshsol.m | .m | toastpp-master/script/matlab/utilities/plotmeshsol.m | 1,286 | utf_8 | db97cf919db80d9d27b1ae7c7fb29a77 | %
% plot function C2 on mesh (n_nodes, p)
%
function plotmeshsol(n_nodes, p, n_elements, node, C2, cmin, cmax)
scale = 1.0;
figure;
for i=1:n_nodes
x(i)=p(i,1);
y(i)=p(i,2);
z(i)=p(i,3);
end
for i=1:n_elements
ie=node(i,1)+1;
je=node(i,2)+1;
ke=node(i,3)+1;
le=node(i,4)+1;
... |
github | toastpp/toastpp-master | plot_surf_sol.m | .m | toastpp-master/script/matlab/utilities/plot_surf_sol.m | 638 | utf_8 | fa63d7a8e27e7305843872ce485861fe |
function plot_surf_sol(elem,n_elem,nodes,n_nodes,x_sol)
% draw the solution on the surface.
% picture of 3 nodes triangles
for i1=1:n_nodes
x(i1)=nodes(i1,1);
y(i1)=nodes(i1,2);
z(i1)=nodes(i1,3);
end
figure;
for j=1:n_elem %drawing the quadrilaterals
hold on;
ie=elem(j,1);
je=elem(j,2);
... |
github | toastpp/toastpp-master | readmesh.m | .m | toastpp-master/script/matlab/utilities/readmesh.m | 1,783 | utf_8 | 1c3a43c37697af89ead8ed7e131de2c6 |
function [nodes,elem] = readmesh(lou);
% read mesh
% the mesh files
fid = fopen(lou) ;% opens the file for reading: external mesh
%external mesh
NoV=fscanf(fid,'%i',1)
x0=zeros(NoV,1);
y0=zeros(NoV,1);
z0=zeros(NoV,1);
%Reading from the file
% x,y,z
for j=1:(NoV),
fscanf(fid,'%c',[2]);
a=fscan... |
github | toastpp/toastpp-master | readmesh_plot.m | .m | toastpp-master/script/matlab/utilities/readmesh_plot.m | 1,687 | utf_8 | ac2d969f7efed1c25e293990a2116339 |
function [nodes,elem] = readmesh(lou);
% read mesh
% the mesh files
fid = fopen(lou) ;% opens the file for reading: external mesh
%external mesh
NoV=fscanf(fid,'%i',1)
x0=zeros(NoV,1);
y0=zeros(NoV,1);
z0=zeros(NoV,1);
%Reading from the file
% x,y,z
for j=1:(NoV),
fscanf(fid,'%c',[2]);
a=fscanf(fi... |
github | toastpp/toastpp-master | plot_sol.m | .m | toastpp-master/script/matlab/utilities/plot_sol.m | 2,976 | utf_8 | 1310c1527624dd732cd1aa500a5e97de |
function plot_sol(elem,n_elem,nodes,n_nodes,x_sol,source)
% draw the solution on the surface.
% picture of 6 nodes triangles
for i1=1:n_nodes% turning ampitude to log scale
x(i1)=nodes(i1,1);
y(i1)=nodes(i1,2);
z(i1)=nodes(i1,3);
x_amp(i1)=log(abs(x_sol(i1)));
% x_pha(i1)=angle(x_sol(i1));
% ... |
github | toastpp/toastpp-master | plotmeshes.m | .m | toastpp-master/script/matlab/utilities/plotmeshes.m | 1,273 | utf_8 | 2f41e5c91f68daad4f59e8e3d72f9704 | %
% plot function C2 on mesh (n_nodes, p)
%
function plotmeshsol(n_nodes, p, n_elements, node, C2, cmin, cmax)
figure;
for i=1:n_nodes
x(i)=p(i,1);
y(i)=p(i,2);
z(i)=p(i,3);
end
for i=1:n_elements
ie=node(i,1)+1;
je=node(i,2)+1;
ke=node(i,3)+1;
le=node(i,4)+1;
me=node(... |
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