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 | jhalakpatel/AI-ML-DL-master | loadubjson.m | .m | AI-ML-DL-master/AndrewNg_MachineLearning/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 | jhalakpatel/AI-ML-DL-master | saveubjson.m | .m | AI-ML-DL-master/AndrewNg_MachineLearning/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 | jhalakpatel/AI-ML-DL-master | submit.m | .m | AI-ML-DL-master/AndrewNg_MachineLearning/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 | jhalakpatel/AI-ML-DL-master | submitWithConfiguration.m | .m | AI-ML-DL-master/AndrewNg_MachineLearning/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 | jhalakpatel/AI-ML-DL-master | savejson.m | .m | AI-ML-DL-master/AndrewNg_MachineLearning/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 | jhalakpatel/AI-ML-DL-master | loadjson.m | .m | AI-ML-DL-master/AndrewNg_MachineLearning/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 | jhalakpatel/AI-ML-DL-master | loadubjson.m | .m | AI-ML-DL-master/AndrewNg_MachineLearning/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 | jhalakpatel/AI-ML-DL-master | saveubjson.m | .m | AI-ML-DL-master/AndrewNg_MachineLearning/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 | jhalakpatel/AI-ML-DL-master | submit.m | .m | AI-ML-DL-master/AndrewNg_MachineLearning/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 | jhalakpatel/AI-ML-DL-master | submitWithConfiguration.m | .m | AI-ML-DL-master/AndrewNg_MachineLearning/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 | jhalakpatel/AI-ML-DL-master | savejson.m | .m | AI-ML-DL-master/AndrewNg_MachineLearning/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 | jhalakpatel/AI-ML-DL-master | loadjson.m | .m | AI-ML-DL-master/AndrewNg_MachineLearning/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 | jhalakpatel/AI-ML-DL-master | loadubjson.m | .m | AI-ML-DL-master/AndrewNg_MachineLearning/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 | jhalakpatel/AI-ML-DL-master | saveubjson.m | .m | AI-ML-DL-master/AndrewNg_MachineLearning/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 | SeRViCE-Lab/FormationControl-master | detector.m | .m | FormationControl-master/sphero_ros/detector.m | 6,914 | utf_8 | d28e558faed25d1343ebb11ae9439023 | % Version 1.4:
% - Replaces centroids by median of upper edge of the bbox.
% this provides a more stable representation for the
% location of the spheros
%
% Version 1.3:
% - Sends back the run time as a parameter
%
% Version 1.2:
% -... |
github | panji530/EDSC-master | hungarian.m | .m | EDSC-master/hungarian.m | 11,781 | utf_8 | 294996aeeca4dadfc427da4f81f8b99d | function [C,T]=hungarian(A)
%HUNGARIAN Solve the Assignment problem using the Hungarian method.
%
%[C,T]=hungarian(A)
%A - a square cost matrix.
%C - the optimal assignment.
%T - the cost of the optimal assignment.
%s.t. T = trace(A(C,:)) is minimized over all possible assignments.
% Adapted from the FORTRAN ... |
github | panji530/EDSC-master | dataProjection.m | .m | EDSC-master/dataProjection.m | 733 | utf_8 | 608c1dd2735280c008ffa8c973aff3d2 | %--------------------------------------------------------------------------
% This function takes the D x N data matrix with columns indicating
% different data points and project the D dimensional data into a r
% dimensional subspace using PCA.
% X: D x N matrix of N data points
% r: dimension of the PCA projection, i... |
github | voquocduy/Pedestrian-Detection-using-Hog-Svm-Matab-master | chuongtrinh.m | .m | Pedestrian-Detection-using-Hog-Svm-Matab-master/chuongtrinh.m | 4,326 | utf_8 | 9aa6e0fba6419280402c840c53ddc448 | function varargout = chuongtrinh(varargin)
% CHUONGTRINH MATLAB code for chuongtrinh.fig
% CHUONGTRINH, by itself, creates a new CHUONGTRINH or raises the existing
% singleton*.
%
% H = CHUONGTRINH returns the handle to a new CHUONGTRINH or the handle to
% the existing singleton*.
%
% CHUONGTRI... |
github | voquocduy/Pedestrian-Detection-using-Hog-Svm-Matab-master | plot_DETcurve.m | .m | Pedestrian-Detection-using-Hog-Svm-Matab-master/plot_DETcurve.m | 5,510 | utf_8 | 6c913ccc7db9a1ed012aa94ead1116cd | function plot_DETcurve(models, model_names,pos_path, neg_path)
% PLOT_DETCURVE function to compute de DET plot given a set of models
%
% INPUT:
% models: SVM models to test (as a row vector)
% model_names: names of the models to use it in the DET_plot legends
% (as cell array)
% pos/neg path: path to pos/... |
github | voquocduy/Pedestrian-Detection-using-Hog-Svm-Matab-master | draw_sliding_window.m | .m | Pedestrian-Detection-using-Hog-Svm-Matab-master/draw_sliding_window.m | 3,630 | utf_8 | 2577c102d36999695fc68a9d8324fe2e | function draw_sliding_window(I, model)
% DRAW_SLIDING_WINDOW function that given an image and a model scans
% exhaustively over a scale-space pyramid the image for pedestrians
% drawing the sliding detection window and the confidence probability.
%
% INPUT:
% model: model to test
% I: image to scan
%
%
... |
github | voquocduy/Pedestrian-Detection-using-Hog-Svm-Matab-master | compute_level0_coordinates.m | .m | Pedestrian-Detection-using-Hog-Svm-Matab-master/compute_level0_coordinates.m | 1,067 | utf_8 | d65b971929cc232aad3dc34827e93fe2 |
%% Aux function to compute the windows coordiantes at level 0 pyramid image
function [bb_size, new_cords] = compute_level0_coordinates(wxl, coordinates, inds, scale)
% Consts
bb_width = 64;
bb_height = 128;
% Vars
new_cords = zeros(size(inds,2),2);
bb_size = zeros(size(inds,2),2... |
github | voquocduy/Pedestrian-Detection-using-Hog-Svm-Matab-master | test_svm.m | .m | Pedestrian-Detection-using-Hog-Svm-Matab-master/test_svm.m | 11,053 | utf_8 | 9bfbc961a2df8136aa2b0eb74f485b1d | function statistics = test_svm(model,paths)
% TEST_SVM Tests a (lib)SVM classifier from the specified images paths
%
% INPUT:
% model: SVMmodel to use
% threshold: positive confidence threshold
% paths: positive / negative images_path to test
% //
% windows, descriptor and test parameter configuration is read from the... |
github | voquocduy/Pedestrian-Detection-using-Hog-Svm-Matab-master | test_svm_PCA.m | .m | Pedestrian-Detection-using-Hog-Svm-Matab-master/test_svm_PCA.m | 11,226 | utf_8 | 515c2df08059e5339874b04bb212cf82 | function statistics = test_svm_PCA(model,Ureduce, paths)
% TEST_SVM_PCA Tests a (lib)SVM classifier from the specified images paths
% reducing first each hog matrix to a dimensionality reduced
% version.
%
% INPUT:
% model: SVMmodel to use
% threshold: positive confidence threshold
% paths:... |
github | voquocduy/Pedestrian-Detection-using-Hog-Svm-Matab-master | non_max_suppression.m | .m | Pedestrian-Detection-using-Hog-Svm-Matab-master/non_max_suppression.m | 1,983 | utf_8 | f929c2cfe27c04ea18291377d6a6c143 | function max_indices = non_max_suppression(coords, probs, bb_sizes)
% NON_MAX_SUPRESION applies non maximum suppression to get the
% most confident detections over a proximity area.
% Input: window coordiantes, window classification probabilities and
% window size referenced to the level 0 pyramid layer.
% Out... |
github | voquocduy/Pedestrian-Detection-using-Hog-Svm-Matab-master | static_detector.m | .m | Pedestrian-Detection-using-Hog-Svm-Matab-master/static_detector.m | 5,315 | utf_8 | c2e656c452e2addd5dc511b90b999441 | function static_detector(I,model)
% STATIC_DETECTOR given a folder containing PNG or JPG images applies
% the specified libSVM model to scan through every image
% for pedestrians in a sliding window basis.
%
% All the parameters are hard coded to guaratee independence from
% external ... |
github | voquocduy/Pedestrian-Detection-using-Hog-Svm-Matab-master | get_negative_windows.m | .m | Pedestrian-Detection-using-Hog-Svm-Matab-master/get_negative_windows.m | 1,915 | utf_8 | ecdfa7fe0e0ffad38158346f78fed842 |
function get_negative_windows(num_random_windows, num_images)
% GET_NEGATIVE_WINDOWS retrieves random windows from the original negative
% image set and saves the window in the specified
% folder when prompted.
% INPUT:
% num_random_windows: random window samples per... |
github | SkoltechRobotics/pcl-master | plot_camera_poses.m | .m | pcl-master/gpu/kinfu/tools/plot_camera_poses.m | 3,407 | utf_8 | d210c150da98c3f4667f2c1e8d4eb6d2 | % Copyright (c) 2014-, Open Perception, Inc.
% All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions
% are met:
%
% * Redistributions of source code must retain the above copyright
% notice, this list of ... |
github | LarsonLab/UTEMRI_Brain-master | precon_3dute_pfile_bartv300_allec.m | .m | UTEMRI_Brain-master/ImageReconstruction/precon_3dute_pfile_bartv300_allec.m | 13,757 | utf_8 | 78e61bbb10ca3b8c81f5817eb027c508 | function [im, header] = precon_3dute_pfile_bartv300_allec(pfile, ...
coils, undersamp, ...
skip, freq_shift, echoes,reg_coe, skip_calib_coil, cc_coil, rNecho,ind_echo_recon, espirit_recon);
% [im, header, rhuser, data, data_grid] = recon_3dute_pfile(pfile,
% coils, undersamp, skip, freq_shift, echoes)
%
% Recon... |
github | LarsonLab/UTEMRI_Brain-master | ute_dicom.m | .m | UTEMRI_Brain-master/ImageReconstruction/ute_dicom.m | 3,971 | utf_8 | 3ad8fd96e99a55aeb5c47fbaf7ba76f0 | function ute_dicom(finalImage, pfile_name, output_image, image_option, scaleFactor, seriesNumberOffset)
% Convert matlab 3D matrix to dicom for UTE sequences
% resolution is fixed in the recon - FOV/readout(from scanner), isotropic
% matrix size is determined in the recon
% Inputs:
% finalImage: 3D image matrix
% p... |
github | LarsonLab/UTEMRI_Brain-master | get_TE.m | .m | UTEMRI_Brain-master/ImageReconstruction/get_TE.m | 1,517 | utf_8 | d8688efc7a911afd02528f7c5f87b3b3 | %% Import data from text file.
% Script for importing data from the following text file:
%
% /data/larson/brain_uT2/2017-09-29_3T-volunteer/multi_utes.dat
%
% To extend the code to different selected data or a different text file,
% generate a function instead of a script.
% Auto-generated by MATLAB on 2017/09/29 1... |
github | longcw/pytorch-faster-rcnn-master | voc_eval.m | .m | pytorch-faster-rcnn-master/lib/datasets/VOCdevkit-matlab-wrapper/voc_eval.m | 1,332 | utf_8 | 3ee1d5373b091ae4ab79d26ab657c962 | function res = voc_eval(path, comp_id, test_set, output_dir)
VOCopts = get_voc_opts(path);
VOCopts.testset = test_set;
for i = 1:length(VOCopts.classes)
cls = VOCopts.classes{i};
res(i) = voc_eval_cls(cls, VOCopts, comp_id, output_dir);
end
fprintf('\n~~~~~~~~~~~~~~~~~~~~\n');
fprintf('Results:\n');
aps = [res(:... |
github | dkouzoup/hanging-chain-acado-master | plot_partial_condensing.m | .m | hanging-chain-acado-master/code/utils/plot_partial_condensing.m | 1,721 | utf_8 | 306d4177769297aab60fa87db66c8a40 | function FHANDLE = plot_partial_condensing(logs)
%% process data
solver = logs{1}.solver(1:strfind(logs{1}.solver,'_')-1);
NMASS = size(logs, 1);
BS = size(logs,2);
FS = 24;
CPUTIMES = zeros(NMASS, BS);
BLOCKSIZE = zeros(NMASS, BS);
for ii = 1:NMASS
for jj = 1:BS
if ~contains(logs{ii, jj}.solv... |
github | dkouzoup/hanging-chain-acado-master | plot_logs.m | .m | hanging-chain-acado-master/code/utils/plot_logs.m | 4,222 | utf_8 | 76a079fac6d034835eeb007d55e22c64 | function [FHANDLE] = plot_logs(logs, FADED, LOGSCALE, FHANDLE, xlims, ylims)
% PLOT_LOGS plot performance of QP solvers as a function of prediction
% horizon N.
%
% INPUTS:
%
% logs logged data from simulation (cell array)
% FADED set to true to plot solver curves faded (boolean)
% F... |
github | shane-nichols/smn-thesis-master | muellerData.m | .m | smn-thesis-master/muellerData.m | 52,836 | utf_8 | c342735994beb5434aef01012c5eb83e | classdef (InferiorClasses = {?matlab.graphics.axis.Axes}) muellerData
properties
Label % string
Value % 4,4,M,N,... array of Mueller matrix values
ErValue % 4,4,M,N,... array of Mueller matrix error values
Size % size of Value
Dims % cell array of length n... |
github | shane-nichols/smn-thesis-master | MPlot3D.m | .m | smn-thesis-master/MPlot3D.m | 15,677 | utf_8 | 1a8b8381948165ef6054487ebde73297 | classdef (InferiorClasses = {?matlab.graphics.axis.Axes}) MPlot3D < handle
properties
uniquezero = true
palette = 'HotCold Bright'
gs = 0
width
fontsize = 14
limz = 1e-3
norm = true
hSpacing = 3;
vSpacing = 3;
cbw = 10;
... |
github | shane-nichols/smn-thesis-master | genop.m | .m | smn-thesis-master/dependencies/Multiprod_2009/Testing/genop.m | 3,837 | utf_8 | 2c087f1f1c6d8843c6f5198716d04526 | function z = genop(op,x,y)
%GENOP Generalized array operations.
% GENOP(OP, X, Y) applies the function OP to the arguments X and Y where
% singleton dimensions of X and Y have been expanded so that X and Y are
% the same size, but this is done without actually copying any data.
%
% OP must be a function h... |
github | shane-nichols/smn-thesis-master | arraylab133.m | .m | smn-thesis-master/dependencies/Multiprod_2009/Testing/arraylab133.m | 2,056 | utf_8 | 46c91102f1666d2e8a3f0accd7d809ed | function c = arraylab133(a,b,d1,d2)
% Several adjustments to ARRAYLAB13:
% 1) Adjustment used in ARRAYLAB131 was not used here.
% 2) Nested statement used in ARRAYLAB132 was used here.
% 3) PERMUTE in subfunction MBYV was substituted with RESHAPE
% (faster by one order of magnitude!).
ndimsA ... |
github | shane-nichols/smn-thesis-master | timing_MX.m | .m | smn-thesis-master/dependencies/Multiprod_2009/Testing/timing_MX.m | 1,472 | utf_8 | 7db26cc2c4954f1026e93f2d0c44139a | function timing_MX
% TIMING_MX Speed of MX as performed by MULTIPROD and by a nested loop.
% TIMING_MX compares the speed of matrix expansion as performed by
% MULTIPROD and an equivalent nested loop. The results are shown in the
% manual (fig. 2).
% Notice that MULTIPROD enables array expansion which... |
github | shane-nichols/smn-thesis-master | timing_matlab_commands.m | .m | smn-thesis-master/dependencies/Multiprod_2009/Testing/timing_matlab_commands.m | 7,975 | utf_8 | 5384e23295d7b37d3318825a1d5c3dfe | function timing_matlab_commands
% TIMING_MATLAB_COMMANDS Testing for speed different MATLAB commands.
%
% Main conclusion: RESHAPE and * (i.e. MTIMES) are very quick!
% Paolo de Leva
% University of Rome, Foro Italico, Rome, Italy
% 2008 Dec 24
clear all
% Checking whether needed software exists
if ~exist('bsxfun'... |
github | shane-nichols/smn-thesis-master | arraylab13.m | .m | smn-thesis-master/dependencies/Multiprod_2009/Testing/arraylab13.m | 1,913 | utf_8 | 942e4a25270936f264b83f4367d9b7fa | function c = arraylab13(a,b,d1,d2)
% This is the engine used in MULTIPROD 1.3 for these cases:
% PxQ IN A - Rx1 IN B
% PxQ IN A - RxS IN B (slowest)
ndimsA = ndims(a); % NOTE - Since trailing singletons are removed,
ndimsB = ndims(b); % not always NDIMSB = NDIMSA
NsA = d2 - ndimsA; % Number of added trailing si... |
github | shane-nichols/smn-thesis-master | materialLib.m | .m | smn-thesis-master/materialLib/materialLib.m | 6,718 | utf_8 | 6395189afc14d401689fa1ea8dc486c6 | function [epsilon,alpha,mu] = materialLib(material, wavelengths, varargin)
% small library of optical functions for anisotropic materials
Nwl = length(wavelengths);
epsilon = zeros(3,3,Nwl);
mu = setDiag(ones(3,Nwl));
alpha = 0;
switch material
case 'rubrene'
data = load('rubreneOptfun.mat');
... |
github | shane-nichols/smn-thesis-master | MPlot4D.m | .m | smn-thesis-master/misc_utilities/MPlot4D.m | 14,618 | utf_8 | 8b22ef4fe9c79bd9e96465da950b8e1c | classdef (InferiorClasses = {?matlab.graphics.axis.Axes}) MPlot4D < handle
% this is a more powerful but less polished version of MPlot3D. It can
% accept arrays of dimension 5 and make videos that run over the 5th
% dimension. The constructor requires an array xData, which is the physical
% values ascr... |
github | shane-nichols/smn-thesis-master | MMgetp.m | .m | smn-thesis-master/misc_utilities/MMgetp.m | 8,872 | utf_8 | 65bcc2600efa3ab7e9f30ba08f232327 | function out = MMgetp(M,parameter)
% This function contains many parameters that one can compute from a
% Mueller matrix (M). In general, M is assumed to be an
% experimental one. Hence, a Mueller-Jones matrix or even a physical M is
% not assumed. For most parameters, M is first converted to its closest
% Mueller-Jo... |
github | shane-nichols/smn-thesis-master | PEMphaseVoltCali.m | .m | smn-thesis-master/misc_utilities/4PEM/PEMphaseVoltCali.m | 1,767 | utf_8 | f8a6fe7fc71e93e6c0a55a006b32a851 | function [p_out,phase_out] = PEMphaseVoltCali(t,f,p)
% p_out = [m,b,s] array of fitting values.
% phase_out = phase of the PEM
% this function demostrates how to find a linear relation relating the
% PEM voltage to the amplitude of modulation.
volts = 0:0.01:2; % create an array of voltages to apply to the PEM
Amps = ... |
github | Saswati18/projectile_motion_matlab-master | quadDiff.m | .m | projectile_motion_matlab-master/quadDiff.m | 111 | utf_8 | 237d26155a5fb105383e2b0461272448 | %% Equation of motion
function xdot = mo(t, x, u)
% xdotdot = a
xdot = [0 1; 0 0]*x + [0 ; 1]*u ;
end |
github | Saswati18/projectile_motion_matlab-master | mo.m | .m | projectile_motion_matlab-master/mo.m | 117 | utf_8 | 8378ff91202eb369f2cf3029b7a10bea | %% Equation of motion
function xdot = motion(t, x, u)
% xdotdot = a
xdot = [0 1; 0 0].*x + [0 ; 1].*u ;
end |
github | emsr/maths_burkhardt-master | bivnor.m | .m | maths_burkhardt-master/bivnor.m | 4,663 | utf_8 | aeeb07fb4759e7959064b8129dc6a95f | function value = bivnor ( ah, ak, r )
%*****************************************************************************80
%
%% BIVNOR computes the bivariate normal CDF.
%
% Discussion:
%
% BIVNOR computes the probability for two normal variates X and Y
% whose correlation is R, that AH <= X and AK <= Y.
%
% Licen... |
github | bsxfan/meta-embeddings-master | SGME_MXE.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/SGME_MXE.m | 2,114 | utf_8 | 829ff4b78c816bad28ac1dd5db3afbb8 | function [y,back] = SGME_MXE(A,B,D,As,Bs,labels,logPrior)
if nargin==0
test_this();
return;
end
dA = zeros(size(A));
dB = zeros(size(B));
dD = zeros(size(D));
dAs = zeros(size(As));
dBs = zeros(size(Bs));
[LEc,back1] = SGME_logexpectation(A,B,D);
[LEs,ba... |
github | bsxfan/meta-embeddings-master | SGME_train.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/SGME_train.m | 2,349 | utf_8 | 875c864d98e47717be58a0d88a2550ab | function model = SGME_train(R,labels,nu,zdim,niters,test)
if nargin==0
test_this();
return;
end
[rdim,n] = size(R);
m = max(labels);
blocks = sparse(labels,1:n,true,m+1,n);
num = find(blocks(:));
%Can we choose maximum likelihood prior parameters, given labels... |
github | bsxfan/meta-embeddings-master | scaled_GME_precision.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/scaled_GME_precision.m | 2,566 | utf_8 | 59c037444c1e57e933d5346bc36263b6 | function [SGMEP,meand] = scaled_GME_precision(B)
if nargin==0
test_this();
return;
end
dim = size(B,1);
[V,D] = eig(B); % B = VDV'
d = diag(D);
meand = mean(d);
%D = sparse(D);
%I = speye(dim);
SGMEP.logdet = @logdet;
SGMEP.solve = @solve;
functi... |
github | bsxfan/meta-embeddings-master | dsolve.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/dsolve.m | 980 | utf_8 | 8734dea4d3f28af88579fef7b106d892 | function [Y,back] = dsolve(RHS,A)
% SOLVE: Y= A\RHS, with backpropagation into both arguments
%
% This is mostly for debugging purposes. It can be done more efficiently
% by caching a matrix factorization to re-use for derivative (and also for
% the determinant if needed).
if nargin==0
test_this();
... |
github | bsxfan/meta-embeddings-master | labels2blocks.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/labels2blocks.m | 1,058 | utf_8 | 4c8472730d7214ee98dda298830f8849 | function [subsets,counts] = labels2blocks(labels)
% Inputs:
% labels: n-vector with elements in 1..m, maps each of n customers to a
% table number. There are m tables. Empty tables not allowed.
%
% Ouputs:
% subsets: n-by-m logical, with one-hot rows
% counts: m-vector, maps table number to customer co... |
github | bsxfan/meta-embeddings-master | create_BXE_calculator.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/create_BXE_calculator.m | 2,055 | utf_8 | 494fcd9ff939f75d131309b403080ae5 | function calc = create_BXE_calculator(log_expectations,prior,poi)
calc.BXE = @BXE;
calc.get_tar_non = @get_tar_non;
n = length(poi);
spoi = sparse(poi);
tar = bsxfun(@eq,spoi,spoi.');
ntar = 0;
nnon = 0;
for k=1:n-1
jj = k+1:n;
tari = full(tar(k,jj));
ntari = s... |
github | bsxfan/meta-embeddings-master | PLDA_mixture_responsibilities.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/PLDA_mixture_responsibilities.m | 1,346 | utf_8 | 78dfbb4de92f575f08845cbc7e0010fb | function P = PLDA_mixture_responsibilities(w,F,W,R)
if nargin==0
P = test_this();
return
end
K = length(w);
if iscell(F)
[D,d] = size(F{1});
else
[D,d] = size(F);
end
N = size(R,2);
P = zeros(K,N);
Id = eye(d);
for k=1:K
if is... |
github | bsxfan/meta-embeddings-master | create_partition_posterior_calculator.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/create_partition_posterior_calculator.m | 4,076 | utf_8 | 32fda68f00bdccc246e56e3db2e0babe | function calc = create_partition_posterior_calculator(log_expectations,prior,poi)
% Inputs:
% log_expectations: function handle, maps matrices of additive natural
% parameters to log-expectations
% prior: Exchangeable prior over partitions, for example CRP. It needs to
% implement prio... |
github | bsxfan/meta-embeddings-master | SGME_train_BXE.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/SGME_train_BXE.m | 2,434 | utf_8 | 4fb4ed77b580dc09d69346bc07a2cd16 | function model = SGME_train_BXE(R,labels,nu,zdim,niters,timeout,test)
if nargin==0
test_this();
return;
end
[rdim,n] = size(R);
spoi = sparse(labels);
tar = bsxfun(@eq,spoi,spoi.');
ntar = 0;
nnon = 0;
for k=1:n-1
jj = k+1:n;
tari = full(tar(k,jj));
... |
github | bsxfan/meta-embeddings-master | SGME_extract.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/SGME_extract.m | 1,065 | utf_8 | b9106e80e9a78235222680c566b510fd | function [A,b,back] = SGME_extract(P,H,nu,R)
if nargin==0
test_this();
return;
end
[zdim,rdim] = size(P);
nuprime = nu + rdim - zdim;
HR = H*R;
q = sum(HR.^2,1);
den = nu + q;
b = nuprime./den;
M = P*R;
A = bsxfun(@times,b,M);
back = @back_this;
... |
github | bsxfan/meta-embeddings-master | sumlogsumexp.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/sumlogsumexp.m | 455 | utf_8 | cccd5f3ae0b7894b95682910eba4a060 | function [y,back] = sumlogsumexp(X)
if nargin==0
test_this();
return;
end
mx = max(real(X),[],1);
yy = mx + log(sum(exp(bsxfun(@minus,X,mx)),1));
y = sum(yy,2);
back = @back_this;
function dX = back_this(dy)
dX = dy*exp(bsxfun(@minus,X,yy));
... |
github | bsxfan/meta-embeddings-master | SGME_logexpectation.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/SGME_logexpectation.m | 1,796 | utf_8 | 46f79c08a985ae0a1833cad86fb74983 | function [y,back] = SGME_logexpectation(A,b,d)
% log expected values (w.r.t. standard normal) of diagonalized SGMEs
% Inputs:
% A: dim-by-n, natural parameters (precision *mean) for n SGMEs
% b: 1-by-n, precision scale factors for these SGMEs
% d: dim-by-1, common diagonal precision
%
% Note:
% bsxfun(... |
github | bsxfan/meta-embeddings-master | SGME_train_MXE.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/SGME_train_MXE.m | 2,514 | utf_8 | 939eef34cb61a4493dfe9c98a11d633c | function model = SGME_train_MXE(R,labels,nu,zdim,niters,timeout,test)
if nargin==0
test_this();
return;
end
[rdim,n] = size(R);
m = max(labels);
blocks = sparse(labels,1:n,true,m,n);
counts = sum(blocks,2);
logPrior = [log(counts);-inf];
delta = rdim... |
github | bsxfan/meta-embeddings-master | SGME_BXE.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/SGME_BXE.m | 1,927 | utf_8 | 43f8a07c46e1df00ef02abdfbbc38dde | function [y,back] = SGME_BXE(A,B,D,plo,wt,wn,tar)
if nargin==0
test_this();
return;
end
n = size(A,2);
[LEc,back1] = SGME_logexpectation(A,B,D);
y = 0;
dA = zeros(size(A));
dB = zeros(size(B));
dLEc = zeros(size(LEc));
dD = zeros(size(D));
for i=1:n-1
... |
github | bsxfan/meta-embeddings-master | plotGaussian.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/plotGaussian.m | 1,323 | utf_8 | 16ea9cd804af31a79f3ccd3cf5687a49 | function tikz = plotGaussian(mu,C,colr,c)
if nargin==0
test_this();
return;
end
if isempty(C) %assume mu is a GME
[mu,C] = mu.get_mu_cov();
end
[V,D] = eig(C);
v1 = V(:,1);
v2 = V(:,2);
if all(v1>=0)
r1 = sqrt(D(1,1));
r2 = sqrt(D(... |
github | bsxfan/meta-embeddings-master | create_HTPLDA_extractor.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/create_HTPLDA_extractor.m | 5,955 | utf_8 | 1304b09dbdcd66e16a53851e8e270761 | function HTPLDA = create_HTPLDA_extractor(F,nu,W)
if nargin==0
test_PsL();
%test_this();
return;
end
[rdim,zdim] = size(F);
assert(rdim>zdim);
nu_prime = nu + rdim - zdim;
if ~exist('W','var') || isempty(W)
W = speye(rdim);
end
E = F.'*W*F;
... |
github | bsxfan/meta-embeddings-master | SGME_MXE2.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/SGME_MXE2.m | 1,787 | utf_8 | 353320c477be13a9cd785ec811fdd210 | function [y,back] = SGME_MXE2(A,B,D,As,Bs,labels,logPrior)
if nargin==0
test_this();
return;
end
dA = zeros(size(A));
dB = zeros(size(B));
dD = zeros(size(D));
dAs = zeros(size(As));
dBs = zeros(size(Bs));
[LEs,back2] = SGME_logexpectation(As,Bs,D);
dLE... |
github | bsxfan/meta-embeddings-master | SGME_train_MXE2.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/SGME_train_MXE2.m | 2,510 | utf_8 | b71a75273c325f1e45edf8af7e971f30 | function model = SGME_train_MXE2(R,labels,nu,zdim,niters,timeout,test)
if nargin==0
test_this();
return;
end
[rdim,n] = size(R);
m = max(labels);
blocks = sparse(labels,1:n,true,m,n);
counts = sum(blocks,2);
logPrior = log(counts);
delta = rdim - zdi... |
github | bsxfan/meta-embeddings-master | asChol.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/asChol.m | 2,365 | utf_8 | ea86b12ae1d2edfe698ac2881861b35f | function CA = asChol(A)
if nargin==0
test_this();
return;
end
if isreal(A)
C = chol(A); %C'C = A
r = true;
else
[L,U] = lu(A); % LU = A
r = false;
end
dim = size(A,1);
CA.logdet = @logdet;
CA.solve = @solve;
func... |
github | bsxfan/meta-embeddings-master | SGME_logPsL.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/SGME_logPsL.m | 3,902 | utf_8 | 2459f9858e466eb1e4b939681dce8f05 | function [y,back] = SGME_logPsL(A,B,d,blocks,poi,num,logPrior)
if nargin==0
test_this();
return;
end
if isempty(blocks)
m = max(poi);
n = length(poi);
blocks = sparse(poi,1:n,true,m+1,n);
num = find(blocks(:));
else
m = size(blocks,1... |
github | bsxfan/meta-embeddings-master | sumlogsoftmax.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/sumlogsoftmax.m | 517 | utf_8 | 5591b4f9a440f97900ac26aefd1faf62 | function [y,back] = sumlogsoftmax(X,num)
if nargin==0
test_this();
return;
end
[den,back1] = sumlogsumexp(X);
y = sum(X(num)) - den;
back = @back_this;
function dX = back_this(dy)
dX = back1(-dy);
dX(num) = dX(num) + dy;
... |
github | bsxfan/meta-embeddings-master | create_SGME_calculator.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/create_SGME_calculator.m | 3,098 | utf_8 | 22c43d447699e600cb1e2c8a1f4c4a2d | function [SGME,LEfun] = create_SGME_calculator(E)
if nargin==0
test_this();
return;
end
[V,D] = eig(E); % E = VDV'
d = diag(D); % eigenvalues
dd = zeros(size(d)); %gradient w.r.t. d backpropagated from log_expectations
zdim = length(d);
ii = reshape(logical(eye(zd... |
github | bsxfan/meta-embeddings-master | logsumexp.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/logsumexp.m | 456 | utf_8 | ba0f6dd080d4fa7a7cd270a5055c5980 | function [y,back] = logsumexp(X)
if nargin==0
test_this();
return;
end
mx = max(X,[],1);
y = bsxfun(@plus,log(sum(exp(bsxfun(@minus,X,mx)),1)),mx);
back = @back_this;
function dX = back_this(dy)
dX = bsxfun(@times,dy,exp(bsxfun(@minus,X,y)));
... |
github | bsxfan/meta-embeddings-master | sample_speaker.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/synthdata/sample_speaker.m | 1,520 | utf_8 | f0f62cb9af06dc368f90cf9c9d6c92d3 | function [X,precisions] = sample_speaker(z,F,k,n,chi_sq)
% Sample n heavy-tailed observations of speaker with identity variable z.
% Inputs:
% z: d-by-1 speaker identity variable
% F: D-by-d factor loading matrix
% k: integer, k>=1, where nu=2k is degrees of freedom of resulting
% t-distribution
% n: numbe... |
github | bsxfan/meta-embeddings-master | sample_HTnoise.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/synthdata/sample_HTnoise.m | 695 | utf_8 | 9ffb422905007acca5d9b5c71ee828a9 | function [X,precisions] = sample_HTnoise(nu,dim,n)
% Sample n heavy-tailed observations of speaker with identity variable z.
% Inputs:
% nu: integer nu >=1, degrees of freedom of resulting t-distribution
% n: number of samples
%
% Output:
% X: dim-by-n samples
% precisions: 1-by-n, the hidden precisions
if... |
github | bsxfan/meta-embeddings-master | qfuser_linear.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/bosaris_toolkit/fusion/funcs/qfuser_linear.m | 2,337 | utf_8 | 0fe31df563db3c6f4f08ea791e83c340 | function [fusion,w0] = qfuser_linear(w,scores,scrQ,ndx,w_init)
% This function does the actual quality fusion (and is passed to
% the training function when training the quality fusion weights).
% The scores from the linear fusion are added to the combined
% quality measure for each trial to produce the final score.
% ... |
github | bsxfan/meta-embeddings-master | AWB_sparse.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/bosaris_toolkit/fusion/funcs/AWB_sparse.m | 2,062 | utf_8 | dcb6e85fdcca1dfb1b5cdee3eb6ab112 | function fh = AWB_sparse(qual,ndx,w)
% Produces trial quality measures from segment quality measures
% using the weighting matrix 'w'.
% This is almost an MV2DF, but it does not return derivatives on numeric
% input, w.
%
% Algorithm: Y = A*reshape(w,..)*B
% Inputs:
% qual: A Quality object containing quality measure... |
github | bsxfan/meta-embeddings-master | dcfplot.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/bosaris_toolkit/plotting/dcfplot.m | 1,889 | utf_8 | 9fbbba6b08ba70f285386536481e29d5 | function dcfplot(devkeyname,evalkeyname,devscrfilename,evalscrfilename,outfilename,plot_title,xmin,xmax,ymin,ymax,prior)
% Makes a Norm_DCF plot of the dev and eval scores for a system.
% Inputs:
% devkeyname: The name of the file containing the Key for
% the dev scores.
% evalkeyname: The name of the file co... |
github | bsxfan/meta-embeddings-master | fast_actDCF.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/bosaris_toolkit/stats/fast_actDCF.m | 3,032 | utf_8 | 5e62c5e1058f0ba3f5a59149249da2a9 | function [dcf,Pmiss,Pfa] = fast_actDCF(tar,non,plo,normalize)
% Computes the actual average cost of making Bayes decisions with scores
% calibrated to act as log-likelihood-ratios. The average cost (DCF) is
% computed for a given range of target priors and for unity cost of error.
% If un-normalized, DCF is just the B... |
github | bsxfan/meta-embeddings-master | fast_minDCF.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/bosaris_toolkit/stats/fast_minDCF.m | 2,585 | utf_8 | 6a709a2b121037d7919f57c87d835531 | function [minDCF,Pmiss,Pfa,prbep,eer] = fast_minDCF(tar,non,plo,normalize)
% Inputs:
%
% tar: vector of target scores
% non: vector of non-target scores
% plo: vector of prior-log-odds: plo = logit(Ptar)
% = log(Ptar) - log(1-Ptar)
%
% normalize: if true, return normalized ... |
github | bsxfan/meta-embeddings-master | rocch.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/bosaris_toolkit/utility_funcs/det/rocch.m | 2,725 | utf_8 | 68aaac9f8a1f40d0d5eac901abc533d5 | function [pmiss,pfa] = rocch(tar_scores,nontar_scores)
% ROCCH: ROC Convex Hull.
% Usage: [pmiss,pfa] = rocch(tar_scores,nontar_scores)
% (This function has the same interface as compute_roc.)
%
% Note: pmiss and pfa contain the coordinates of the vertices of the
% ROC Convex Hull.
%
% For a demonstration that pl... |
github | bsxfan/meta-embeddings-master | compute_roc.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/bosaris_toolkit/utility_funcs/det/compute_roc.m | 1,956 | utf_8 | 16907ef9816ee330ac64b4eeb708366b | function [Pmiss, Pfa] = compute_roc(true_scores, false_scores)
% compute_roc computes the (observed) miss/false_alarm probabilities
% for a set of detection output scores.
%
% true_scores (false_scores) are detection output scores for a set of
% detection trials, given that the target hypothesis is true (false).
% ... |
github | bsxfan/meta-embeddings-master | rocchdet.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/bosaris_toolkit/utility_funcs/det/rocchdet.m | 5,471 | utf_8 | 2452dd1f98aad313c79879d410214cb2 | function [x,y,eer,mindcf] = rocchdet(tar,non,dcfweights,pfa_min,pfa_max,pmiss_min,pmiss_max,dps)
% ROCCHDET: Computes ROC Convex Hull and then maps that to the DET axes.
%
% (For demo, type 'rocchdet' on command line.)
%
% Inputs:
%
% tar: vector of target scores
% non: vector of non-target scores
%
% dcfw... |
github | bsxfan/meta-embeddings-master | map_mod_names.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/bosaris_toolkit/utility_funcs/manip/map_mod_names.m | 3,127 | utf_8 | 6aa97cdf9b5df6095e803bd14f612e52 | function ndx = map_mod_names(ndx,src_map,dst_map)
% Changes the model names in an index using two maps. The one map
% lists the training segment for each model name and the other map
% lists the new model name for each training segment. Existing
% model names are replaced by new model names that are mapped to
% the s... |
github | bsxfan/meta-embeddings-master | maplookup.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/bosaris_toolkit/utility_funcs/manip/maplookup.m | 3,084 | utf_8 | 9e8a55e6a2201b6a0e975469dfe9c299 | function [values,is_present] = maplookup(map,keys)
% Does a map lookup, to map mutliple keys to multiple values in one call.
% The parameter 'map' represents a function, where each key maps to a
% unique value. Each value may be mapped to by one or more keys.
%
% Inputs:
% map.keySet: a one-dimensional cell array;
%... |
github | bsxfan/meta-embeddings-master | test_binary_classifier.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/test_binary_classifier.m | 1,332 | utf_8 | 9683ce2757d7eb67c8a8ec37954cbab4 | function obj_val = test_binary_classifier(objective_function,classf, ...
prior,system,input_data)
% Returns the result of the objective function evaluated on the
% scores.
%
% Inputs:
% objective_function: a function handle to the objective function
% to feed the scores into
% classf: le... |
github | bsxfan/meta-embeddings-master | evaluate_objective.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/evaluate_objective.m | 1,417 | utf_8 | 70262971965caac5629612bd125dd0a2 | function obj_val = evaluate_objective(objective_function,scores,classf, ...
prior)
% Returns the result of the objective function evaluated on the
% scores.
%
% Inputs:
% objective_function: a function handle to the objective function
% to feed the scores into
% scores: length T vector o... |
github | bsxfan/meta-embeddings-master | train_binary_classifier.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/train_binary_classifier.m | 3,938 | utf_8 | de96b98d88aa8e3d0c36785a2f9a3a94 | function [w,cxe,w_pen,optimizerState,converged] = ...
train_binary_classifier(classifier,classf,w0,objective_function,prior,...
penalizer,lambda,maxiters,maxCG,optimizerState,...
quiet,cstepHessian)
%
% Supervised training of a regularized fusion.
%
%
% Inp... |
github | bsxfan/meta-embeddings-master | qfuser_v5.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/qfuser_v5.m | 921 | utf_8 | f82cbe0c178dae2a667496466b612770 | function [fusion,w0] = qfuser_v5(w,scores,wfuse)
if nargin==0
test_this();
return;
end
% block 1
f1 = linear_fuser([],scores.scores);
w1 = wfuse;
[whead,wtail] = splitvec_fh(length(w1));
f1 = f1(whead);
% block 2
modelQ = scores.modelQ;
[q,n1] = size(modelQ);
modelQ = [modelQ;ones(1,n1)];
segQ = scores.segQ... |
github | bsxfan/meta-embeddings-master | qfuser_v2.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/qfuser_v2.m | 1,166 | utf_8 | e10bf159cbd2dacaf85be8d4a90554f6 | function [fusion,params] = qfuser_v2(w,scores)
%
% Inputs:
%
% scores: the primary detection scores, for training
% D-by-T matrix of T scores for D input systems
%
% quality_input: K-by-T matrix of quality measures
%
% Output:
% fusion: is numeric if w is numeric, or a handle to an MV2DF, represe... |
github | bsxfan/meta-embeddings-master | linear_fuser.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/linear_fuser.m | 2,654 | utf_8 | 627fab3e121d1d87d9fad2a3234d26f8 | function [fusion,params] = linear_fuser(w,scores)
%
% Does affine fusion of scores: It does a weighted sum of scores and adds
% an offset.
%
% Inputs:
% scores: M-by-N matrix of N scores for each of M input systems.
% w: Optional:
% - when supplied, the output 'fusion' i... |
github | bsxfan/meta-embeddings-master | qfuser_v3.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/qfuser_v3.m | 1,290 | utf_8 | a2245f6284afa9f203096fc932e8cf07 | function [fusion,params] = qfuser_v3(w,scores)
%
% Inputs:
%
% scores: the primary detection scores, for training
% D-by-T matrix of T scores for D input systems
%
% quality_input: K-by-T matrix of quality measures
%
% Output:
% fusion: is numeric if w is numeric, or a handle to an MV2DF, represe... |
github | bsxfan/meta-embeddings-master | qfuser_v6.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/qfuser_v6.m | 1,013 | utf_8 | 0bcb6e5fbd79494afd1c1c36eff1e95c | function [fusion,w0] = qfuser_v6(w,scores,wfuse)
if nargin==0
test_this();
return;
end
% block 1
f1 = linear_fuser([],scores.scores);
w1 = wfuse;
[whead,wtail] = splitvec_fh(length(w1));
f1 = f1(whead);
% block 2
modelQ = scores.modelQ;
[q,n1] = size(modelQ);
modelQ = [modelQ;ones(1,n1)];
segQ = scores.segQ... |
github | bsxfan/meta-embeddings-master | qfuser_v1.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/qfuser_v1.m | 1,137 | utf_8 | 8dcda09e63d0f7e6a3f1fc2298b84d7e | function [fusion,params] = qfuser_v1(w,scores)
%
% Inputs:
%
% scores: the primary detection scores, for training
% D-by-T matrix of T scores for D input systems
%
% quality_input: K-by-T matrix of quality measures
%
% Output:
% fusion: is numeric if w is numeric, or a handle to an MV2DF, represe... |
github | bsxfan/meta-embeddings-master | qfuser_v7.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/qfuser_v7.m | 1,107 | utf_8 | 8d156ad2d97a7aa1b90d702cb2f0a195 | function [fusion,w0] = qfuser_v7(w,scores,wfuse)
if nargin==0
test_this();
return;
end
% block 1
f1 = linear_fuser([],scores.scores);
w1 = wfuse;
[whead,wtail] = splitvec_fh(length(w1));
f1 = f1(whead);
% block 2
modelQ = scores.modelQ;
[q,n1] = size(modelQ);
modelQ = [modelQ;ones(1,n1)];
segQ = scores.segQ... |
github | bsxfan/meta-embeddings-master | qfuser_v4.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/qfuser_v4.m | 1,388 | utf_8 | cd65aea99057c92c142fc7e024dc1d53 | function [fusion,w0] = qfuser_v4(w,scores,wfuse)
% qindx: index set for rows of scores.scores which are per-trial quality
% measures.
%
% sindx: index set for rows of scores.scores which are normal discriminative
% scores.
if nargin==0
test_this();
return;
end
sindx = scores.sindx;
qindx = sco... |
github | bsxfan/meta-embeddings-master | scal_fuser.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/scalibration/scal_fuser.m | 2,918 | utf_8 | 7e49185b74a064be721d9c243a08c07f | function [fusion,params] = scal_fuser(w,scores)
%
% Does scal calibration
%
% Inputs:
% scores: M-by-N matrix of N scores for each of M input systems.
% w: Optional:
% - when supplied, the output 'fusion' is the vector of fused scores.
% - when w=[], the output 'fusion' is a function handle, t... |
github | bsxfan/meta-embeddings-master | scal_fuser_slow.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/scalibration/scal_fuser_slow.m | 2,972 | utf_8 | abc2a78dc2b6cf08cfdd508f4dabdb71 | function [fusion,params] = scal_fuser_slow(w,scores)
%
% Does scal calibration
%
% Inputs:
% scores: M-by-N matrix of N scores for each of M input systems.
% w: Optional:
% - when supplied, the output 'fusion' is the vector of fused scores.
% - when w=[], the output 'fusion' is a function hand... |
github | bsxfan/meta-embeddings-master | logsumexp_special.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/scalibration/logsumexp_special.m | 1,102 | utf_8 | a15ffa60b181fdc8b0a1e3fb4bcfd403 | function [y,deriv] = logsumexp_special(w)
% This is a MV2DF. See MV2DF_API_DEFINITION.readme.
%
% If w = [x;r], where r is scalar and x vector, then
% y = log(exp(x)+exp(r))
if nargin==0
test_this();
return;
end
if isempty(w)
y = @(w)logsumexp_special(w);
return;
end
if isa(w,'function_handle')
... |
github | bsxfan/meta-embeddings-master | scalibration_fh.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/scalibration/scalibration_fh.m | 1,735 | utf_8 | b9918a8e2a9fa07dfcef33933013931b | function f = scalibration_fh(w)
% This is a factory for a function handle to an MV2DF, which represents
% the vectorization of the s-calibration function. The whole mapping works like
% this, in MATLAB-style pseudocode:
%
% If y = f([x;r;s]), where x,r,s are column vectors of size m, then y
% is a column vector of ... |
github | bsxfan/meta-embeddings-master | scalibration_fragile_fh.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/scalibration/scalibration_fragile_fh.m | 2,389 | utf_8 | 8eec3ccf6bcd5f130a3d399194acd676 | function f = scalibration_fragile_fh(direction,w)
%
% Don't use this function, it is just for reference. It will break for
% large argument values.
%
% This is a factory for a function handle to an MV2DF, which represents
% the vectorization of the logsumexp function. The whole mapping works like
% this, in MATLAB-styl... |
github | bsxfan/meta-embeddings-master | scal_simple_fh.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/scalibration/scal_simple_fh.m | 1,903 | utf_8 | b6e3992c13b4424d2129302a3c51424c | function f = scal_simple_fh(w)
% This is a factory for a function handle to an MV2DF, which represents
% the vectorization of the s-calibration function. The whole mapping works like
% this, in MATLAB-style pseudocode:
%
% If y = f([x;r;s]), where r,s are scalar, x is column vector of size m,
% then y is a column ... |
github | bsxfan/meta-embeddings-master | quality_fuser_v3.m | .m | meta-embeddings-master/code/snapshot_for_anya/matlab/bosaris_toolkit/utility_funcs/Optimization_Toolkit/applications/fusion2class/systems/aside/quality_fuser_v3.m | 1,843 | utf_8 | 1be42594eb854e9b0b4d89daa27c0759 | function [fusion,params] = quality_fuser_v3(w,scores,train_vecs,test_vecs,train_ndx,test_ndx,ddim)
%
% Inputs:
%
% scores: the primary detection scores, for training
% D-by-T matrix of T scores for D input systems
%
% train_vecs: K1-by-M matrix, one column-vector for each of M training
% ... |
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