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 | wangmaoCS/oxford_RANSAC_LBP-master | config_oxford.m | .m | oxford_RANSAC_LBP-master/utils/config_oxford.m | 1,324 | utf_8 | e94661904a0e723553a679d95db23a4a | % Return a config object that contains all the paths for the Oxford dataset
% as well as the groundtruth
function cfg = config_oxford (params)
% Default parameters
cfg = config (params);
cfg.desc_nlearn = 5000000;
% Load groundtruth
cfg.gnd_fname = [cfg.dir_data 'gnd_oxford5k.mat'];
load (cfg.gnd_fname); ... |
github | wangmaoCS/oxford_RANSAC_LBP-master | my_nn.m | .m | oxford_RANSAC_LBP-master/utils/my_nn.m | 2,153 | utf_8 | 58c6b922d9a529a334ae24354ff2fb01 | % Return the k nearest neighbors of a set of query vectors
%
% Usage: [ids,dis] = nn(v, q, k, distype)
% v the dataset to be searched (one vector per column)
% q the set of queries (one query per column)
% k (default:1) the number of nearest neigbors we want
% distype d... |
github | wangmaoCS/oxford_RANSAC_LBP-master | yael_vecs_normalize.m | .m | oxford_RANSAC_LBP-master/utils/yael_vecs_normalize.m | 718 | utf_8 | 113b79aad4badd1d53937499f8078616 | % This function normalize a set of vectors
% Parameters:
% v the set of vectors to be normalized (column stored)
% nr the norm for which the normalization is performed (Default: Euclidean)
% rval replace value in case the vector is 0-norm
%
% Output:
% vout the normalized vector
% vnr the norms of ... |
github | wangmaoCS/oxford_RANSAC_LBP-master | config_oxfordq.m | .m | oxford_RANSAC_LBP-master/utils/config_oxfordq.m | 1,119 | utf_8 | e124775f4e2071aa206784348e89580d | % Creates config structure with filenames of data
% The function optionally take a path to specify where data is stord
function cfg = config_oxfordq (params)
% Default parameters
cfg = config (params);
cfg = struct;
cfg.desc_d = 128;
cfg.desc_nlearn = 5000000;
cfg.n = 55;
% Override the default paramet... |
github | wangmaoCS/oxford_RANSAC_LBP-master | compute_map.m | .m | oxford_RANSAC_LBP-master/utils/compute_map.m | 1,846 | utf_8 | 97ec2a86542fd2a35fd509732ae5f396 | % This function computes the mAP for a given set of returned results.
%
% Usage: map = compute_map (ranks, gnd);
%
% Notes:
% 1) ranks starts from 1, size(ranks) = db_size X #queries
% 2) The junk results (e.g., the query itself) should be declared in the gnd stuct array
function [map, aps] = compute_map (ranks, gnd, v... |
github | wangmaoCS/oxford_RANSAC_LBP-master | load_ext.m | .m | oxford_RANSAC_LBP-master/utils/load_ext.m | 2,715 | utf_8 | 2cec807c8443df302331fc4cd049076f | % Generic way to load files depending on the type (determined by extension)
%
function [X,Y] = load_ext (filename, nrows, bounds, verbose)
% Retrieve the extension of the file
ext = regexp (filename, '\.(\w)*$');
if length(ext) == 0
error ('The filename should have an extension');
end
ext = filename (ext:end);
nmin... |
github | wangmaoCS/oxford_RANSAC_LBP-master | config_holidays.m | .m | oxford_RANSAC_LBP-master/utils/config_holidays.m | 1,138 | utf_8 | 87237f43e1f728b63c23068e3dc9e00e | % Return a config object that contains all the paths for the Holidays dataset
% as well as the groundtruth
function cfg = config_holidays (params)
% Default parameters
cfg = config (params);
cfg.desc_nlearn = 5000000;
% Load groundtruth
cfg.gnd_fname = [cfg.dir_data 'gnd_holidays.mat'];
load (cfg.gnd_fname); % Retri... |
github | wangmaoCS/oxford_RANSAC_LBP-master | config_flickr60k.m | .m | oxford_RANSAC_LBP-master/utils/config_flickr60k.m | 566 | utf_8 | 976c3f98854869e42801bfe7675496fc | % Creates config structure with filenames of data
% The function optionally take a path to specify where data is stord
function cfg = config_flickr60k (params)
% Default parameters
cfg = config (params);
cfg.desc_nlearn = 5000000;
% Load groundtruth
cfg.gnd_fname = [cfg.dir_data 'gnd_flickr60k.mat'];
load (... |
github | wangmaoCS/oxford_RANSAC_LBP-master | fvecs_read.m | .m | oxford_RANSAC_LBP-master/utils/fvecs_read.m | 1,457 | utf_8 | 0318bd7f465153c725687e87ddd7c3ca | % Read a set of vectors stored in the fvec format (int + n * float)
% The function returns a set of output vector (one vector per column)
%
% Syntax:
% v = fvecs_read (filename) -> read all vectors
% v = fvecs_read (filename, n) -> read n vectors
% v = fvecs_read (filename, [a b]) -> read the vectors from ... |
github | mukamel-lab/CellSort-master | CellsortApplyFilter.m | .m | CellSort-master/CellsortApplyFilter.m | 2,096 | utf_8 | ebbca294cce8a613cf1357f1a66033cd | function cell_sig = CellsortApplyFilter(fn, ica_segments, flims, movm, subtractmean)
% cell_sig = CellsortApplyFilter(fn, ica_segments, flims, movm, subtractmean)
%
%CellsortApplyFilter
% Read in movie data and output signals corresponding to specified spatial
% filters
%
% Inputs:
% fn - file name of TIFF movie fi... |
github | mukamel-lab/CellSort-master | CellsortICAplot.m | .m | CellSort-master/CellsortICAplot.m | 15,621 | utf_8 | 004580121685777a03b07890b9a5b3fe | function CellsortICAplot(mode, ica_filters, ica_sig, f0, tlims, dt, ratebin, plottype, ICuse, spt, spc)
% CellsortICAplot(mode, ica_filters, ica_sig, f0, tlims, dt, ratebin, plottype, ICuse, spt, spc)
%
% Display the results of ICA analysis in the form of paired spatial filters
% and signal time courses
%
% Inputs:
% ... |
github | mukamel-lab/CellSort-master | CellsortPlotPCspectrum.m | .m | CellSort-master/CellsortPlotPCspectrum.m | 2,283 | utf_8 | 3dc571d694b0b9d38a412a3da327399b | function CellsortPlotPCspectrum(fn, CovEvals, PCuse)
% CellsortPlotPCspectrum(fn, CovEvals, PCuse)
%
% Plot the principal component (PC) spectrum and compare with the
% corresponding random-matrix noise floor
%
% Inputs:
% fn - movie file name. Must be in TIFF format.
% CovEvals - eigenvalues of the covariance matr... |
github | mukamel-lab/CellSort-master | CellsortChoosePCs.m | .m | CellSort-master/CellsortChoosePCs.m | 3,367 | utf_8 | a649279a6a4104cc3bae1f58eacfaa2c | function [PCuse] = CellsortChoosePCs(fn, mixedfilters)
% [PCuse] = CellsortChoosePCs(fn, mixedfilters)
%
% Allows the user to select which principal components will be kept
% following dimensional reduction.
%
% Inputs:
% fn - movie file name. Must be in TIFF format.
% mixedfilters - N x X matrix of N spatial signa... |
github | evilbinary/webrtc_lite-master | rtpAnalyze.m | .m | webrtc_lite-master/tools/matlab/rtpAnalyze.m | 7,892 | utf_8 | 46e63db0fa96270c14a0c205bbab42e4 | function rtpAnalyze( input_file )
%RTP_ANALYZE Analyze RTP stream(s) from a txt file
% The function takes the output from the command line tool rtp_analyze
% and analyzes the stream(s) therein. First, process your rtpdump file
% through rtp_analyze (from command line):
% $ out/Debug/rtp_analyze my_file.rtp my_f... |
github | evilbinary/webrtc_lite-master | apmtest.m | .m | webrtc_lite-master/webrtc/modules/audio_processing/test/apmtest.m | 9,470 | utf_8 | ad72111888b4bb4b7c4605d0bf79d572 | function apmtest(task, testname, filepath, casenumber, legacy)
%APMTEST is a tool to process APM file sets and easily display the output.
% APMTEST(TASK, TESTNAME, CASENUMBER) performs one of several TASKs:
% 'test' Processes the files to produce test output.
% 'list' Prints a list of cases in the test set,... |
github | evilbinary/webrtc_lite-master | plot_neteq_delay.m | .m | webrtc_lite-master/webrtc/modules/audio_coding/neteq/test/delay_tool/plot_neteq_delay.m | 5,563 | utf_8 | 8b6a66813477863da513b1e6971dbc97 | function [delay_struct, delayvalues] = plot_neteq_delay(delayfile, varargin)
% InfoStruct = plot_neteq_delay(delayfile)
% InfoStruct = plot_neteq_delay(delayfile, 'skipdelay', skip_seconds)
%
% Henrik Lundin, 2006-11-17
% Henrik Lundin, 2011-05-17
%
try
s = parse_delay_file(delayfile);
catch
error(lasterr);
e... |
github | ATM-HSW/mbed_target-master | setup_MbedTarget.m | .m | mbed_target-master/setup_MbedTarget.m | 3,847 | utf_8 | e238dcd44c8f0d375a1471ee1ccfe5ab | % MbedTarget Simulink target
% Copyright (c) 2014-2018 Dr.O.Hagendorf , HS Wismar
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
%
% http://www.apache.org/licenses/LICENSE-2.0
%
% ... |
github | ATM-HSW/mbed_target-master | mbed_grt_make_rtw_hook.m | .m | mbed_target-master/mbed/mbed_grt_make_rtw_hook.m | 12,008 | utf_8 | aaa09382df0b9cc40092accb2b2e96cb | % MbedTarget Simulink target
% Copyright (c) 2014-2017 Dr.O.Hagendorf , HS Wismar
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
%
% http://www.apache.org/licenses/LICENSE-2.0
%
% ... |
github | ATM-HSW/mbed_target-master | mbed_tlc_opencallback.m | .m | mbed_target-master/mbed/mbed_tlc_opencallback.m | 719 | utf_8 | f3e007b21b266965a1905759784599c9 | % MbedTarget Simulink target
% Copyright (c) 2014-2017 Dr.O.Hagendorf , HS Wismar
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
%
% http://www.apache.org/licenses/LICENSE-2.0
%
% ... |
github | ATM-HSW/mbed_target-master | mbed_tlc_callback.m | .m | mbed_target-master/mbed/mbed_tlc_callback.m | 1,768 | utf_8 | 656991988749567cf0d7c3f68976081c | % MbedTarget Simulink target
% Copyright (c) 2014-2017 Dr.O.Hagendorf , HS Wismar
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
%
% http://www.apache.org/licenses/LICENSE-2.0
%
% ... |
github | ATM-HSW/mbed_target-master | thinkspeakGetChannelFields.m | .m | mbed_target-master/mbed/thinkspeakGetChannelFields.m | 810 | utf_8 | b4a9afa570033cc1bed97d8f329cd17f | %import matlab.net.*
%import matlab.net.http.*
function [number, fnames] = thinkspeakGetChannelFields(channel, readkey)
import matlab.net.*
import matlab.net.http.*
number = [];
fnames = {};
r = RequestMessage;
uri = URI(['https://api.thingspeak.com/channels/' channel '/fields/2.json?api_key=' readkey '&results=0'])... |
github | ATM-HSW/mbed_target-master | mbed_mbedls.m | .m | mbed_target-master/mbed/mbed_mbedls.m | 1,008 | utf_8 | 4d446805c0cbae34476396a543fb7e08 | % MbedTarget Simulink target
% Copyright (c) 2014-2017 Dr.O.Hagendorf , HS Wismar
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
%
% http://www.apache.org/licenses/LICENSE-2.0
%
% ... |
github | ATM-HSW/mbed_target-master | mbed_getAppConfigs.m | .m | mbed_target-master/mbed/mbed_getAppConfigs.m | 1,101 | utf_8 | ad7a0447e7bf7908a9221a9828860a1c | % MbedTarget Simulink target
% Copyright (c) 2014-2018 Dr.O.Hagendorf , HS Wismar
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
%
% http://www.apache.org/licenses/LICENSE-2.0
%
% ... |
github | ATM-HSW/mbed_target-master | mbed_getDownloadMethod.m | .m | mbed_target-master/mbed/mbed_getDownloadMethod.m | 945 | utf_8 | f93694ca1bae9b27696668ee7947e2bf | % MbedTarget Simulink target
% Copyright (c) 2014-2017 Dr.O.Hagendorf , HS Wismar
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
%
% http://www.apache.org/licenses/LICENSE-2.0
%
% ... |
github | ATM-HSW/mbed_target-master | mbed_grt_wrap_make_cmd_hook.m | .m | mbed_target-master/mbed/mbed_grt_wrap_make_cmd_hook.m | 2,913 | utf_8 | 843ad34684fe16018c047541b811d478 | % MbedTarget Simulink target
% Copyright (c) 2014-2017 Dr.O.Hagendorf , HS Wismar
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
%
% http://www.apache.org/licenses/LICENSE-2.0
%
% ... |
github | ATM-HSW/mbed_target-master | mbed_getTargetRootPath.m | .m | mbed_target-master/mbed/mbed_getTargetRootPath.m | 1,262 | utf_8 | 4a467ae11c47beceed7d4b2d85076c7a | % MbedTarget Simulink target
% Copyright (c) 2014-2017 Dr.O.Hagendorf , HS Wismar
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
%
% http://www.apache.org/licenses/LICENSE-2.0
%
% ... |
github | ATM-HSW/mbed_target-master | wmicGet.m | .m | mbed_target-master/mbed/wmicGet.m | 7,174 | utf_8 | db1cbbc74795829f5173a71961696309 | function infos = wmicGet(classOrAlias, properties, wqlKeyWord, clauses)
%wmicGet Computer and operating system information on Windows.
% wmicGet returns computer and operating system informations on Windows
% platforms. It uses the Windows Management Instrumentation Command-line
% (WMIC).
%
% wmicGet(CLASS) ret... |
github | ATM-HSW/mbed_target-master | mbed_getIDEs.m | .m | mbed_target-master/mbed/mbed_getIDEs.m | 1,199 | utf_8 | bbb16cab5f3ff1514188547a0b844f48 | % MbedTarget Simulink target
% Copyright (c) 2014-2017 Dr.O.Hagendorf , HS Wismar
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
%
% http://www.apache.org/licenses/LICENSE-2.0
%
% ... |
github | ATM-HSW/mbed_target-master | mbed_getTargetDestFolder.m | .m | mbed_target-master/mbed/mbed_getTargetDestFolder.m | 926 | utf_8 | fbc1c9c81b0ad36512256fb5c4acd48f | % MbedTarget Simulink target
% Copyright (c) 2014-2017 Dr.O.Hagendorf , HS Wismar
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
%
% http://www.apache.org/licenses/LICENSE-2.0
%
% ... |
github | ATM-HSW/mbed_target-master | sl_customization.m | .m | mbed_target-master/mbed/sl_customization.m | 1,330 | utf_8 | a3c82847dad355eba6b60ff91d5c4893 | % MbedTarget Simulink target
% Copyright (c) 2014-2017 Dr.O.Hagendorf , HS Wismar
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
%
% http://www.apache.org/licenses/LICENSE-2.0
%
% ... |
github | ATM-HSW/mbed_target-master | mbed_grt_select_callback_handler.m | .m | mbed_target-master/mbed/mbed_grt_select_callback_handler.m | 1,814 | utf_8 | 8b4c3f9974a56a6b175ea6b156cc3fc4 | % MbedTarget Simulink target
% Copyright (c) 2014-2017 Dr.O.Hagendorf , HS Wismar
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
%
% http://www.apache.org/licenses/LICENSE-2.0
%
% ... |
github | ATM-HSW/mbed_target-master | mbed_getHelpFile.m | .m | mbed_target-master/mbed/mbed_getHelpFile.m | 8,125 | utf_8 | 3ccce6486ac1f9cd88993bc2d2a28738 | % MbedTarget Simulink target
% Copyright (c) 2014-2017 Dr.O.Hagendorf , HS Wismar
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
%
% http://www.apache.org/licenses/LICENSE-2.0
%
% ... |
github | ATM-HSW/mbed_target-master | mbed_select_callback_handler.m | .m | mbed_target-master/mbed/mbed_select_callback_handler.m | 2,193 | utf_8 | f54b6977efb2eaba03d92f4bf6046fc9 | % MbedTarget Simulink target
% Copyright (c) 2014-2017 Dr.O.Hagendorf , HS Wismar
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
%
% http://www.apache.org/licenses/LICENSE-2.0
%
% ... |
github | ATM-HSW/mbed_target-master | mbed_slx_preload.m | .m | mbed_target-master/mbed/mbed_slx_preload.m | 751 | utf_8 | 88d9b506c13672de98ea158f492b5bac | % MbedTarget Simulink target
% Copyright (c) 2014-2017 Dr.O.Hagendorf , HS Wismar
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
%
% http://www.apache.org/licenses/LICENSE-2.0
%
% ... |
github | ATM-HSW/mbed_target-master | mbed_getTargets.m | .m | mbed_target-master/mbed/mbed_getTargets.m | 1,007 | utf_8 | 40d48b47f142a4c85101cc672ca4a2ef | % MbedTarget Simulink target
% Copyright (c) 2014-2017 Dr.O.Hagendorf , HS Wismar
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
%
% http://www.apache.org/licenses/LICENSE-2.0
%
% ... |
github | ATM-HSW/mbed_target-master | mbed_wrap_make_cmd_hook.m | .m | mbed_target-master/mbed/mbed_wrap_make_cmd_hook.m | 2,740 | utf_8 | d8c30635d3e40c44a3d88d94c505f113 | % MbedTarget Simulink target
% Copyright (c) 2014-2017 Dr.O.Hagendorf , HS Wismar
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
%
% http://www.apache.org/licenses/LICENSE-2.0
%
% ... |
github | ATM-HSW/mbed_target-master | savejson.m | .m | mbed_target-master/mbed/jsonlab-1.5/savejson.m | 19,005 | utf_8 | 2abe93f113a0cff486589165c908511d | 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 | ATM-HSW/mbed_target-master | loadjson.m | .m | mbed_target-master/mbed/jsonlab-1.5/loadjson.m | 16,682 | ibm852 | 7eead0aa7db35c892d9233e6fa63cc05 | 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 (q.fang <at> neu.edu)
% created on 2011/09/09, including previous works from
%
% Nedial... |
github | ATM-HSW/mbed_target-master | loadubjson.m | .m | mbed_target-master/mbed/jsonlab-1.5/loadubjson.m | 13,272 | utf_8 | 6b84fc36f88b25a5db2515a93b6f23bc | 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 (q.fang <at> neu.edu)
% created on 2013/08/01
%
% $Id$
%
% input:
% fname: input file... |
github | ATM-HSW/mbed_target-master | saveubjson.m | .m | mbed_target-master/mbed/jsonlab-1.5/saveubjson.m | 17,757 | utf_8 | 865a3f4e074323a42f75910b868ff3fb | 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 | ATM-HSW/mbed_target-master | strmulticompare.m | .m | mbed_target-master/examples/serial/strmulticompare.m | 591 | utf_8 | ce1699a32a8dd3ce8c19ef0e12fc36bb | % function ret = strmulticompare1(dataline)
%
% commands = {'on', 'off'};
% ret = uint8(0);
%
% for i=1:size(commands,2)
% if contains(char(commands{i}), char(dataline(1:size(commands{i},2))))
% ret = uint8(i);
% return;
% end
% end
function ret = strmulticompare(dataline)
commands = {'on',... |
github | ATM-HSW/mbed_target-master | slblocks.m | .m | mbed_target-master/blocks/slx/slblocks.m | 540 | utf_8 | 05ab188a04b48060f73c8d35af0fbbf3 |
% Copyright 2010 The MathWorks, Inc.
% 2014 Dr. Olaf Hagendorf, HS Wismar
function blkStruct = slblocks
blkStruct.Name = 'Mbed Target'; %Display name
blkStruct.OpenFcn = 'mbed_lib'; %Library name
blkStruct.MaskDisplay = '';
Browser(1).Library = 'mbed_lib'; %Library name
Browser(... |
github | gissemari/KernelHashingFramework-master | knnsearch.m | .m | KernelHashingFramework-master/knnsearch.m | 380 | utf_8 | 38dceac4e8e96250afbce13d9feb5c77 | %Exhaustive search, think about other method if TIME is compared
function [val IDX] = knnsearch(X,Y)
for i=1:size(Y,1)
for j=1:size(X,1)
sum = 0;
for k=1:size(X,2)
sum = sum + (Y(i,k)-X(j,k))^2;
end
allDistances(i,j) = sum;
end
end
... |
github | gissemari/KernelHashingFramework-master | calculateSimilarity.m | .m | KernelHashingFramework-master/calculateSimilarity.m | 2,178 | utf_8 | c60966fc8d95473b4f0587d22a51f393 | % Generates the kernel matrix K that should be square
function [K newSigma] = calculateSimilarity(TSdatasetX, TSdatasetY, timeSteps, sigma)
m1 = size(TSdatasetX,3);
m2 = size(TSdatasetY,3);
K = zeros(m1,m2);
%%%%%%%%% Normalize
distanceMeasure = 0;
for i=1:m1
for j=1:m2
... |
github | yxie/Hidden-Markov-Model-master | HMM_backward.m | .m | Hidden-Markov-Model-master/HMM_backward.m | 402 | utf_8 | 02d686ecf55440a40a1faf7a31e6e844 | %backward algorithm
%pi: Nx1
%A: NxN
%B: NxM
%O: Tx1
function [po, beta] = HMM_backward(pi, A, B, O)
N = length(pi);
T = length(O);
beta = zeros(N,T);
beta(:,T) = ones(N,1);
%display(beta(:,T));
for t=T-1:-1:1
%display(t);
beta(:,t) = A * ( B(:,O(t+1)).* beta(:,t+1) ... |
github | yxie/Hidden-Markov-Model-master | HMM_forward.m | .m | Hidden-Markov-Model-master/HMM_forward.m | 385 | utf_8 | 90b70368943c439ef3c22d0f59334b60 | %forward algorithm
%pi: Nx1
%A: NxN
%B: NxM
%O: Tx1
function [po, alpha] = HMM_forward(pi, A, B, O)
N = length(pi);
T = length(O);
alpha = zeros(N,T);
alpha(:,1) = pi .* B(:,O(1));
%display(alpha(:,1));
for t=2:T
for j=1:N
alpha(j,t) = alpha(:,t-1)' * A(:,j) * B(j, O(t));
... |
github | yxie/Hidden-Markov-Model-master | HMM_viterbi.m | .m | Hidden-Markov-Model-master/HMM_viterbi.m | 733 | utf_8 | a47e7c1b1ebaa961fd07a4241617ebba | %viterbi algorithm
%pi: Nx1
%A: NxT
%B: NxM
%O: Tx1
function [p_star, q_star, delta, phi] = HMM_viterbi(pi, A, B, O)
N = length(pi);
T = length(O);
delta = zeros(N,T);
phi = zeros(N,T);
delta(:,1) = pi .* B(:, O(1));
phi(:,1) = 0;
%display(delta(:,1));
%display(phi(:,1));
for t = ... |
github | yxie/Hidden-Markov-Model-master | HMM_EM.m | .m | Hidden-Markov-Model-master/HMM_EM.m | 1,248 | utf_8 | 3515c12109bfd55ee750c4747dadf501 | %EM algorithm for parameter estimation
%pi: Nx1
%A: NxN
%B: NxM
%O: Tx1
function [pi_best, A_best, B_best] = HMM_EM(pi, A, B, O)
N = length(pi);
M = size(B,2);
T = length(O);
[po_f, alpha] = HMM_forward(pi, A, B, O);
[po_b, beta] = HMM_backward(pi, A, B, O);
assert(abs(po_f - po_b) < ... |
github | NREL/dynamo-master | inventory_adp.m | .m | dynamo-master/example/inventory/old/inventory_adp.m | 28,863 | utf_8 | 250a0e1242ff875596140f8b0d333df7 | function [orders, results, value_function_approx] = ...
inventory_adp(N, n_iter, inv_p, adp, restart_soln, ref_policy, tol)
%INVENTORY_ADP One product stochastic inventory approx. dynamic prog. (ADP)
%
% Usage: [orders, results,, value_function_approx] = ...
% inventory_adp(N, n_iter, inv_p, adp,... |
github | NREL/dynamo-master | inventory_inf_horiz.m | .m | dynamo-master/example/inventory/old/inventory_inf_horiz.m | 13,586 | utf_8 | e688630219a05abd48f1728d4448c373 | function [Orders, Values, n_iter, v_trace] = inventory_inf_horiz(max_inv, p_demand, ...
disc_rate, type, tol, guess, order_cost_fun, income_fun, hold_cost_fun)
% INVENTORY_INF_HORIZ Infinite Horizon Stochastic Inventory DP (ESD.862)
%
% Usage: [Orders, Values, n_iter, v_trace] = inventory_inf_horiz(max_inv, p_d... |
github | NREL/dynamo-master | doctest.m | .m | dynamo-master/extern/doctest/doctest.m | 6,659 | utf_8 | 704d1c7b8e6bedcdc44f51878b6dbfca | function [all_pass, n_pass, n_tests] = doctest(func_or_class, varargin)
% Run examples embedded in documentation
%
% doctest func_name
% doctest('func_name')
% doctest class_name
% doctest('class_name')
% [all_pass, n_pass, n_tests] = doctest( __ )
%
% Example:
% Say you have a function that adds 7 to things:
% fun... |
github | NREL/dynamo-master | doctest_run.m | .m | dynamo-master/extern/doctest/doctest_run.m | 2,769 | utf_8 | 3e1b3fb26a73c6e18028a99f980eac79 | function results = doctest_run(docstring)
%DOCTEST_RUN - used internally by doctest
%
% Usage:
% doctest_run(docstring)
% Runs all the examples in the given docstring and returns a
% structure with the results from running.
%
% The return value is a structure with the following fields:
%
% results.source:... |
github | andrejchenko/oop_matlab-master | Bhattacharyya_angle.m | .m | oop_matlab-master/@CombineClass/Bhattacharyya_angle.m | 585 | utf_8 | 95d53097b93128a7b024b14722cabd79 | %https://en.wikipedia.org/wiki/Bhattacharyya_angle
%https://en.wikipedia.org/wiki/Bhattacharyya_distance
function angles = Bhattacharyya_angle(cObj,prob,alphas)
angles = [];
for i = 1: size(prob,1)
v1 = prob(i,:);
v2 = alphas(i,:);
bc = 0;
for j = 1:size(v1,2)
... |
github | andrejchenko/oop_matlab-master | Bhattacharayya_distance_matlab.m | .m | oop_matlab-master/@CombineClass/Bhattacharayya_distance_matlab.m | 1,994 | utf_8 | 9727337611b433622653337b5ba92eb4 | %http://www.mathworks.com/matlabcentral/fileexchange/18662-bhattacharyya-distance-measure-for-pattern-recognition/content/bhattacharyya.m
%The m-file provides a tool to calculate the Bhattacharyya Distance Measure (BDM) between two classes of normal distributed data.
%The BDM is widely used in Pattern Recognition as a... |
github | andrejchenko/oop_matlab-master | kl_Divergence.m | .m | oop_matlab-master/@CombineClass/kl_Divergence.m | 974 | utf_8 | 590ea7b195cc2adf79582a48fc7c9bef | %Base P and Q on the same set of outcomes
%http://stats.stackexchange.com/questions/97938/calculate-the-kullback-leibler-divergence-in-practice
%Normalization of the KL Divergence values:
%http://math.stackexchange.com/questions/51482/can-i-normalize-kl-divergence-to-be-leq-1
function n_kl_divergences = kl_Divergence(c... |
github | andrejchenko/oop_matlab-master | Bhattacharayya_coefficient.m | .m | oop_matlab-master/@CombineClass/Bhattacharayya_coefficient.m | 177 | utf_8 | 0dd9c7e0802493ce13df5cfc9a101708 | %http://stackoverflow.com/questions/19972878/matlab-code-to-compare-two-histograms
function k = Bhattacharayya_coefficient(cObj,X1,X2)
k = sum(sqrt(X1(:)).*sqrt(X2(:)));
end |
github | andrejchenko/oop_matlab-master | selectXPixPerClass_IncludeXNeighbours.m | .m | oop_matlab-master/@Utils/selectXPixPerClass_IncludeXNeighbours.m | 12,799 | utf_8 | 252f2a7dcd90e86b3bfb1a149d1c2230 | function selectXPixPerClass_IncludeXNeighbours(obj,nObj)
% input: indian_pines,indian_pines_gt,numBands, numPix, numNeigh, numClasses
uniqueClasses = unique(obj.indian_pines_gt);
[width, height] = size(obj.indian_pines_gt);
classMatrix = zeros(obj.numClasses,width,height);
% Find the different class labeles from the ... |
github | andrejchenko/oop_matlab-master | MESMA_brute_small.m | .m | oop_matlab-master/lib/MESMA_brute_small.m | 2,501 | utf_8 | 4b99fc7c194781c59fdb0c068c9bb970 | function [idx, A, rec, minerr]=MESMA_brute_small (x,L)
% MESMA_BRUTE_SMALL Efficient brute force approach for MESMA problems with
% a low number of endmember libraries.
%
% input: x contains the mixed spectra (dimension x number of spectra)
% L contains the libraries as a cell array containing p matrices
% ... |
github | andrejchenko/oop_matlab-master | extract_class_endmembers.m | .m | oop_matlab-master/lib/extract_class_endmembers.m | 1,008 | utf_8 | 4b86e5a96e39cf2a900013cf191ab2a0 | function [E,I]=extract_class_endmembers(L)
% extract_class_endmembers
% Rob Heylen
% L is a cell array with p cells. Each cell contains a matrix with spectra
% listed columnwise. The algorithm will find the maximal simplex, with the
% constraint that one endmember has to be selected from each cell. The E
% matrix will ... |
github | andrejchenko/oop_matlab-master | MDPPI.m | .m | oop_matlab-master/lib/MDPPI.m | 6,336 | utf_8 | 74aa502fc7f2018a99f9601bed72a832 | function score=MDPPI(x,dim,numit,weighted)
% MDPPI Multi-dimensional PPI
%
% Input: x: [d,N] input data set containing N points of dimension d
% dim: positive integer, dimensionality of the projections
% numit: positive integer, number of iterations
% weighted: logical. Indic... |
github | andrejchenko/oop_matlab-master | scatterPlotting.m | .m | oop_matlab-master/lib/scatterPlotting.m | 505 | utf_8 | 714ccc32058371a99df29fede933a57b | function scatterPlotting()
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
allSet = obj.indian_pines(:,:,1:50);
YAll = reshape(allSet,145*145,50);
plotmatrix(YAll)
end
function [obj,nObj,svmObj,sunObj] = setExperimentParameters()
obj = Utils;
nObj = Neighb... |
github | andrejchenko/oop_matlab-master | multilogit.m | .m | oop_matlab-master/mlr/mlr_from_scratch/multilogit.m | 8,574 | utf_8 | d1fe801718b1df37874859e51ba147e8 | function results = multilogit(y,x,beta0,maxit,tol);
% PURPOSE: implements multinomial logistic regression
% Pr(y_i=j) = exp(x_i'beta_j)/sum_l[exp(x_i'beta_l)]
% where:
% i = 1,2,...,nobs
% j,l = 0,1,2,...,ncat
%-------------------------------------------------------------------------%
% USAGE: results = mul... |
github | andrejchenko/oop_matlab-master | combSVM_SUN_0NN_0_9lambda_pavia.m | .m | oop_matlab-master/Experiments/Combination/pavia_uni/combSVM_SUN_0NN_0_9lambda_pavia.m | 704 | utf_8 | 5f0c9d7a0024548fe50284d33387a2fe | function combSVM_SUN_0NN_0_9lambda_pavia()
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Pavia();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
if(obj.numNeigh > 0)
obj.assembleXTrainData(nObj);
end
cObj = CombineClass;
% cObj.combineMethods(obj,nObj,svmObj... |
github | andrejchenko/oop_matlab-master | stackingValTuning_svm_0NN_0_9lambda_pines.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/use validation data/stackingValTuning_svm_0NN_0_9lambda_pines.m | 1,555 | utf_8 | 5cfd62b4915751a947653d2ba015e8ec | function stackingValTuning_svm_0NN_0_9lambda_pines
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
obj.extractValidationData();
if(obj.numNeigh > 0)
obj.assembleXTrainData(nObj);
end
cObj = CombineClass... |
github | andrejchenko/oop_matlab-master | stacking_WithValSet_svm_sunsal_sum1_max_0NN_pines.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/use validation data/stacking_WithValSet_svm_sunsal_sum1_max_0NN_pines.m | 1,706 | utf_8 | ac68d9c2af7c531f6e7def1ba6508f79 | function stacking_WithValSet_svm_sunsal_sum1_max_0NN_pines
avgUnmixing = [];
iter = 100;
for a = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
obj.extractValidationData();
if(obj.nu... |
github | andrejchenko/oop_matlab-master | stacking_WithValSetForTrain_2Feat_svm_sunsal_sum1_sum_0NN_pines.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/use validation data/stacking_WithValSetForTrain_2Feat_svm_sunsal_sum1_sum_0NN_pines.m | 1,836 | utf_8 | 0cf2f42122472d38c235e895118f65e8 | function stacking_WithValSetForTrain_2Feat_svm_sunsal_sum1_sum_0NN_pines
avgUnmixing = [];
iter = 50;
for a = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
obj.extractValidationData();
... |
github | andrejchenko/oop_matlab-master | stacking_WithValSetForTrain_svm_sunsal_sum1_sum_0NN_pines.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/use validation data/stacking_WithValSetForTrain_svm_sunsal_sum1_sum_0NN_pines.m | 2,167 | utf_8 | 633daa21078764960ecb92aa83a1b823 | function stacking_WithValSetForTrain_svm_sunsal_sum1_sum_0NN_pines
avgUnmixing = [];
iter = 50;
for a = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
obj.extractValidationData();
if... |
github | andrejchenko/oop_matlab-master | stackingValTuning_svm_sunsal_Avg_0NN_0_9lambda_pines.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/use validation data/stackingValTuning_svm_sunsal_Avg_0NN_0_9lambda_pines.m | 2,360 | utf_8 | 5efbc0416a8db1dcc0917060ffc4b92b | function stackingValTuning_svm_sunsal_Max_0NN_0_9lambda_pines
avgUnmixing = 0;
iter = 100;
for a = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
obj.extractValidationData();
if(obj.... |
github | andrejchenko/oop_matlab-master | stacking_WithValSet_svm_sunsal_sum1_avg_0NN_pines.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/use validation data/stacking_WithValSet_svm_sunsal_sum1_avg_0NN_pines.m | 1,706 | utf_8 | 3daf2508696b48a232ddc93c157dcdce | function stacking_WithValSet_svm_sunsal_sum1_avg_0NN_pines
avgUnmixing = [];
iter = 100;
for a = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
obj.extractValidationData();
if(obj.nu... |
github | andrejchenko/oop_matlab-master | stackingValTuning_svm_sunsal_Max_0NN_0_9lambda_pines.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/use validation data/stackingValTuning_svm_sunsal_Max_0NN_0_9lambda_pines.m | 2,374 | utf_8 | fb867deed93de02cca8705f4c775099f | function stackingValTuning_svm_sunsal_Max_0NN_0_9lambda_pines
avgUnmixing = 0;
iter = 100;
for a = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
obj.extractValidationData();
if(obj.n... |
github | andrejchenko/oop_matlab-master | stacking_WithValSetForTrain_ARD_svm_sunsal_sum1_sum_0NN_pines.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/use validation data/stacking_WithValSetForTrain_ARD_svm_sunsal_sum1_sum_0NN_pines.m | 2,025 | utf_8 | c064b3a177bece4251b1d58d91b333ba | function stacking_WithValSetForTrain_ARD_svm_sunsal_sum1_sum_0NN_pines
avgUnmixing = [];
iter = 50;
for a = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
obj.extractValidationData();
... |
github | andrejchenko/oop_matlab-master | stacking_WithValSet_svm_sunsal_sum1_sum_0NN_pines.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/use validation data/stacking_WithValSet_svm_sunsal_sum1_sum_0NN_pines.m | 1,853 | utf_8 | f5cb157c09fa36ca171314a5e928343a | function stacking_WithValSet_svm_sunsal_sum1_sum_0NN_pines
avgUnmixing = [];
iter = 100;
for a = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
obj.extractValidationData();
if(obj.nu... |
github | andrejchenko/oop_matlab-master | combMaxBothNorm_AbunPosterior_5Pix_0NN.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/no validation data/combMaxBothNorm_AbunPosterior_5Pix_0NN.m | 1,500 | utf_8 | 567a02f8501a19a11764801e63472d93 | function combMaxBothNorm_AbunPosterior_5Pix_0NN
avgUnmixing = 0;
iter = 100;
for a = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
if(obj.numNeigh > 0)
obj.assembleXTrainDat... |
github | andrejchenko/oop_matlab-master | stacking_svm_Decision_Values_Normed_Sunsal_Sum1_5Pix_0NN_pines.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/no validation data/stacking_svm_Decision_Values_Normed_Sunsal_Sum1_5Pix_0NN_pines.m | 1,454 | utf_8 | 3b0081d44f0291fcde2ffb34c074cf96 | function stacking_svm_Normed_Distances_Values_Sunsal_Sum1_5Pix_0NN_pines
avgAccVec = [];
iter = 100;
for a = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
%obj.extractValidationData();
... |
github | andrejchenko/oop_matlab-master | stackingSvm_Sunsal_96F_0NN_0_9lambda_pines.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/no validation data/stackingSvm_Sunsal_96F_0NN_0_9lambda_pines.m | 1,838 | utf_8 | c5af28ac8fa4472bdf571e69ea2ab11a | function stackingSvm_Sunsal_96F_0NN_0_9lambda_pines
avgUnmixing = 0;
iter = 100;
for a = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
%obj.extractValidationData();
if(obj.numNei... |
github | andrejchenko/oop_matlab-master | stacking_unlabeledData_4NN_0_9lambda_pines.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/no validation data/stacking_unlabeledData_4NN_0_9lambda_pines.m | 1,055 | utf_8 | eb903cc8649c924e85ee17d43efde08f | function stacking_unlabeledData_4NN_0_9lambda_pines()
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
if(obj.numNeigh > 0)
%obj.assembleXTrainData(nObj);
nObj.spectralAngle(obj);
end
cObj = Comb... |
github | andrejchenko/oop_matlab-master | bayesClassifCombination_5Pix_0NN.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/no validation data/bayesClassifCombination_5Pix_0NN.m | 1,431 | utf_8 | 355667ab12177a67845590367e5c86ca | function bayesClassifCombination_Lee_5Pix_0NN()
avgAccVec = zeros(50,21);
iter = 50;
c = 1;
for w = 0:0.05:1 %exponential weight
for a = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours... |
github | andrejchenko/oop_matlab-master | stacking_svm_sunsal_NoValidation0NN_0_9lambda_pines.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/no validation data/stacking_svm_sunsal_NoValidation0NN_0_9lambda_pines.m | 1,823 | utf_8 | 8acb5b859ed00aedbf3a947dd5e91990 | function stacking_svm_sunsal_NoValidation0NN_0_9lambda_pines
avgUnmixing = 0;
iter = 100;
for a = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
%obj.extractValidationData();
if(o... |
github | andrejchenko/oop_matlab-master | stacking_svm_sunsal_0NN_0_9lambda_pines.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/no validation data/stacking_svm_sunsal_0NN_0_9lambda_pines.m | 1,423 | utf_8 | 45d808af9c61b25fe1d548bed9023cf7 | function stacking_svm_sunsal_0NN_0_9lambda_pines
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
obj.extractValidationData();
if(obj.numNeigh > 0)
obj.assembleXTrainData(nObj);
end
cObj = CombineCl... |
github | andrejchenko/oop_matlab-master | stackingSvm_sunsal_Sum_0NN_0_9lambda_pines.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/no validation data/stackingSvm_sunsal_Sum_0NN_0_9lambda_pines.m | 1,825 | utf_8 | 2c626ce57d2bb1d1df7314f0a1b7ffd4 | function stackingSvm_sunsal_Sum_0NN_0_9lambda_pines
avgUnmixing = 0;
iter = 100;
for a = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
%obj.extractValidationData();
if(obj.numNei... |
github | andrejchenko/oop_matlab-master | sumKernels.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/no validation data/sumKernels.m | 1,285 | utf_8 | 8f40e0e0e505ed20a1190c1e2dd4a0f7 | function sumKernels()
avgAcc = 0;
iter = 100;
tic;
for a = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
if(obj.numNeigh > 0)
obj.assembleXTrainData(nObj);
end
... |
github | andrejchenko/oop_matlab-master | stacking_svm_Distances_Sunsal_Sum1_5Pix_0NN_pines.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/no validation data/stacking_svm_Distances_Sunsal_Sum1_5Pix_0NN_pines.m | 1,405 | utf_8 | 5409f77a81e5b3a68e9d4dacb91317e9 | function stacking_svm_Distances_Sunsal_Sum1_5Pix_0NN_pines
avgAccVec = [];
iter = 100;
for a = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
%obj.extractValidationData();
if(obj.... |
github | andrejchenko/oop_matlab-master | combAvgAbun_Posterior_5Pix_0NN.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/no validation data/combAvgAbun_Posterior_5Pix_0NN.m | 1,483 | utf_8 | f6b4a7b92a650bd359150d52cbb6f8b5 | function combAvgAbun_Posterior_5Pix_0NN
avgUnmixing = 0;
iter = 100;
for a = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
if(obj.numNeigh > 0)
obj.assembleXTrainData(nObj);... |
github | andrejchenko/oop_matlab-master | consensusRule_5Pix_0NN_0_9lambda.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/no validation data/consensusRule_5Pix_0NN_0_9lambda.m | 1,581 | utf_8 | 40176aeaeeeeb9ac6dde5c3f9a256b48 | function consensusRule_5Pix_0NN_0_9lambda
avgUnmixing = 0;
iter = 100;
for w = 0:0.1:1
for a = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
if(obj.numNeigh > 0)... |
github | andrejchenko/oop_matlab-master | combSVM_SUN_0NN_0_9lambda_pines.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/no validation data/combSVM_SUN_0NN_0_9lambda_pines.m | 710 | utf_8 | 0700f067806cc38fae1ad3a849fb4295 | function combSVM_SUN_0NN_0_9lambda_pines
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
if(obj.numNeigh > 0)
obj.assembleXTrainData(nObj);
end
cObj = CombineClass;
% cObj.combineMethods(obj,nObj,s... |
github | andrejchenko/oop_matlab-master | stacking_GenerateMetaFeaturesByCrossValidation_Sum1_Sum5Pix_0NN.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/no validation data/stacking_GenerateMetaFeaturesByCrossValidation_Sum1_Sum5Pix_0NN.m | 2,995 | utf_8 | 41abf9009f828fd49523cfb6d051e086 | function stacking_GenerateMetaFeaturesByCrossValidation_Sum1_Sum5Pix_0NN
avgAccVec = zeros(50,1);
iter = 50;
for a = 1:iter
%% TRAINING PHASE
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);... |
github | andrejchenko/oop_matlab-master | stacking_svm_Distances_Sunsal_Sum1_Sum_5Pix_0NN_pines.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/no validation data/stacking_svm_Distances_Sunsal_Sum1_Sum_5Pix_0NN_pines.m | 1,397 | utf_8 | 48cd09f0038731ebf6ebeea91c5431ea | function stacking_svm_Distances_Sunsal_Sum1_Sum_5Pix_0NN_pines
avgAccVec = [];
iter = 100;
for a = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
%obj.extractValidationData();
if(... |
github | andrejchenko/oop_matlab-master | stackingSVM_SunsalSum1_Sum_0NN_NormExtData.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/no validation data/stackingSVM_SunsalSum1_Sum_0NN_NormExtData.m | 1,330 | utf_8 | 6189dd75be95ad8b22a046a7e3cdf05c | function stackingSVM_SunsalSum1_Sum_0NN_NormExtData
avgAccVec = [];
iter = 100;
for a = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
%obj.extractValidationData();
if(obj.numNeig... |
github | andrejchenko/oop_matlab-master | abundancesAsFeaturesToSVM.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/no validation data/abundancesAsFeaturesToSVM.m | 1,583 | utf_8 | 950a27604974e28aa90d8eb632603099 | function abundancesAsFeaturesToSVM()
avgAccVec = [];
iter = 100;
for a = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
cObj = CombineClass;
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
trainData = obj.trainData;
... |
github | andrejchenko/oop_matlab-master | stacking_svm_0NN_0_9lambda_pines.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/no validation data/stacking_svm_0NN_0_9lambda_pines.m | 1,362 | utf_8 | 7b268bf5fb7bf77f43bfee022ecd8192 | function stacking_svm_0NN_0_9lambda_pines
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
obj.extractValidationData();
if(obj.numNeigh > 0)
obj.assembleXTrainData(nObj);
end
cObj = CombineClass;
%cO... |
github | andrejchenko/oop_matlab-master | stacking_svmNormUnit_sunsalNormSum1Avg_NoVal_0NN_0_9lam_pines.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/no validation data/stacking_svmNormUnit_sunsalNormSum1Avg_NoVal_0NN_0_9lam_pines.m | 1,916 | utf_8 | 1e7c2728ad100816ca1dc6ab19a64aaf | function stacking_svmNormUnit_sunsalNormSum1Avg_NoVal_0NN_0_9lam_pines
avgUnmixing = 0;
iter = 100;
for a = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
%obj.extractValidationData();
... |
github | andrejchenko/oop_matlab-master | bayesClassifCombination_5Pix_0NN_usingOnlyTrainData.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/no validation data/bayesClassifCombination_5Pix_0NN_usingOnlyTrainData.m | 1,581 | utf_8 | 3429cc5ec9e8a8436a3f31ff655d3d9e | function bayesClassifCombination_5Pix_0NN_usingOnlyTrainData()
avgAccVec = [];
iter = 50;
for a = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
%obj.extractValidationData();
... |
github | andrejchenko/oop_matlab-master | stacking_svmNormUnit_sunsalNormSum1_NoVal_0NN_0_9lam_pines.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/no validation data/stacking_svmNormUnit_sunsalNormSum1_NoVal_0NN_0_9lam_pines.m | 1,917 | utf_8 | 39e00c172a6d06801b6a4ce71e8ae403 | function stacking_svmNormUnit_sunsalNormSum1_NoVal_0NN_0_9lam_pines
avgUnmixing = 0;
iter = 100;
for a = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
%obj.extractValidationData();
... |
github | andrejchenko/oop_matlab-master | consensusRule_AvgAbun_5Pix_0NN_0_9lambda.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/no validation data/consensusRule_AvgAbun_5Pix_0NN_0_9lambda.m | 1,608 | utf_8 | 39e89907e8dd3c789113aa56c97ee881 | function consensusRule_AvgAbun_5Pix_0NN_0_9lambda
avgUnmixing = 0;
iter = 100;
for w = 0:0.1:1
for a = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
if(obj.numNe... |
github | andrejchenko/oop_matlab-master | combMaxNormedAbun_Posterior_5Pix_0NN_0_9_lambda.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/no validation data/combMaxNormedAbun_Posterior_5Pix_0NN_0_9_lambda.m | 1,500 | utf_8 | df7b0651945b413827c849508861ddcf | function combMaxNormedAbun_Posterior_5Pix_0NN_0_9_lambda
avgUnmixing = 0;
iter = 100;
for a = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
if(obj.numNeigh > 0)
obj.assemble... |
github | andrejchenko/oop_matlab-master | stacking_svmNormUnit_sunsalNormSum1Max_NoVal_0NN_0_9lam_pines.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/no validation data/stacking_svmNormUnit_sunsalNormSum1Max_NoVal_0NN_0_9lam_pines.m | 1,916 | utf_8 | c0b77e3afe1ed0845178ed9eebae3996 | function stacking_svmNormUnit_sunsalNormSum1Max_NoVal_0NN_0_9lam_pines
avgUnmixing = 0;
iter = 100;
for a = 1:iter
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
%obj.extractValidationData();
... |
github | andrejchenko/oop_matlab-master | stacking_svm_sunsal_0NN_0_9lambda_pines_test.m | .m | oop_matlab-master/Experiments/Combination/indian_pines/no validation data/stacking_svm_sunsal_0NN_0_9lambda_pines_test.m | 1,190 | utf_8 | b2c35c619d9253e5ffe0a26ccff1c1ea | function stacking_svm_sunsal_0NN_0_9lambda_pines_test
[obj,nObj,svmObj,sunObj] = setExperimentParameters();
obj.load_Indian_Pines();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
if(obj.numNeigh > 0)
obj.assembleXTrainData(nObj);
end
cObj = CombineClass;
%cObj.baseClassific... |
github | andrejchenko/oop_matlab-master | Pix5_0NN_lambda_0_9_pavia.m | .m | oop_matlab-master/Experiments/Sunsal/pavia_uni/Pix5_0NN_lambda_0_9_pavia.m | 1,332 | utf_8 | e851aeeef767f292f64faad9664db6aa | function Pix5_0NN_lambda_0_9_pavia()
tic;
avgUnmixing = 0;
iter = 100;
for a = 1:100
[obj,nObj,sObj] = setExperimentParameters();
obj.load_Pavia();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
obj.assembleXTrainData(nObj);
sObj.unmixing(obj);
%acc = obj.ac... |
github | andrejchenko/oop_matlab-master | sunsal_without_positivity_pavia.m | .m | oop_matlab-master/Experiments/Sunsal/pavia_uni/sunsal_without_positivity_pavia.m | 778 | utf_8 | ac2f6561ff7dd6ff7254757ff3571455 | function sunsal_without_positivity_pavia()
iter = 100;
avgAccVec = zeros(iter,1);
for a = 1:iter
[obj,nObj,sObj] = setExperimentParameters();
obj.load_Pavia();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
if(obj.numNeigh > 0)
obj.assembleXTrainData(nObj);... |
github | andrejchenko/oop_matlab-master | minimumResiduals_pavia.m | .m | oop_matlab-master/Experiments/Sunsal/pavia_uni/minimumResiduals_pavia.m | 991 | utf_8 | e477851e9cc3115995c615e50977f175 | function minimumResiduals_pavia()
iter = 100;
avgAccVec = zeros(iter,1);
for a = 1:iter
[obj,nObj,sObj] = setExperimentParameters();
obj.load_Pavia();
obj.selectXPixPerClass_IncludeXNeighbours(nObj);
if(obj.numNeigh > 0)
obj.assembleXTrainData(nObj);
... |
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