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values | md5 stringlengths 32 32 | text stringlengths 23 843k |
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github | yanweifu/embedding_zero-shot-learning-master | UGM_Sample_Exact.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/UGM_2011/sample/UGM_Sample_Exact.m | 2,224 | utf_8 | e5830d750168ece6ffdf65930058cbb5 | function [samples] = UGM_Sample_Exact(nodePot,edgePot,edgeStruct)
% Exact sampling
UGM_assert(prod(double(edgeStruct.nStates)) < 50000000,'Brute Force Exact Sampling not recommended for models with > 50 000 000 states');
[nNodes,maxState] = size(nodePot);
nEdges = size(edgePot,3);
edgeEnds = edgeStruct.edgeEnds;
nSta... |
github | yanweifu/embedding_zero-shot-learning-master | UGM_Infer_Junction.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/UGM_2011/infer/UGM_Infer_Junction.m | 12,409 | utf_8 | 6c6101251220de098e8703b970216454 | function [nodeBel, edgeBel, logZ] = UGM_Infer_Junction(nodePot,edgePot,edgeStruct,ordering)
debug = 0;
[nNodes,maxState] = size(nodePot);
nEdges = size(edgePot,3);
edgeEnds = edgeStruct.edgeEnds;
V = edgeStruct.V;
E = edgeStruct.E;
nStates = edgeStruct.nStates;
if nargin < 4
ordering = 1:nNodes;
end
%% Triangul... |
github | yanweifu/embedding_zero-shot-learning-master | UGM_Infer_Exact.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/UGM_2011/infer/UGM_Infer_Exact.m | 2,095 | utf_8 | face628c54a4f9094f2b95b6863da608 | function [nodeBel, edgeBel, logZ] = UGM_Infer_Exact(nodePot, edgePot, edgeStruct)
% INPUT
% nodePot(node,class)
% edgePot(class,class,edge) where e is referenced by V,E (must be the same
% between feature engine and inference engine)
%
% OUTPUT
% nodeBel(node,class) - marginal beliefs
% edgeBel(class,class,e) ... |
github | yanweifu/embedding_zero-shot-learning-master | UGM_Infer_TRBP.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/UGM_2011/infer/UGM_Infer_TRBP.m | 4,712 | utf_8 | e0b193923d055f385f4ffe36e9d2c89f | function [nodeBel, edgeBel, logZ] = UGM_Infer_TRBP(nodePot,edgePot,edgeStruct,mu)
if nargin < 4
mu = 1;
end
[nNodes,maxStates] = size(nodePot);
nEdges = size(edgePot,3);
if isscalar(mu) % Weights not provided, construct them using one of the methods below
% Compute Edge Appearance Probabilities
if m... |
github | yanweifu/embedding_zero-shot-learning-master | UGM_Infer_LBP.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/UGM_2011/infer/UGM_Infer_LBP.m | 2,797 | utf_8 | e81d55d1b47194fcec918db398aa06dc | function [nodeBel, edgeBel, logZ] = UGM_Infer_LBP(nodePot,edgePot,edgeStruct)
if edgeStruct.useMex
[nodeBel,edgeBel,logZ] = UGM_Infer_LBPC(nodePot,edgePot,int32(edgeStruct.edgeEnds),int32(edgeStruct.nStates),int32(edgeStruct.V),int32(edgeStruct.E),edgeStruct.maxIter);
else
[nodeBel, edgeBel, logZ] = Infer... |
github | yanweifu/embedding_zero-shot-learning-master | UGM_Infer_MeanField.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/UGM_2011/infer/UGM_Infer_MeanField.m | 1,935 | utf_8 | a7d836d74a2666c384348bce3b33ab0f | function [nodeBel, edgeBel, logZ] = UGM_Infer_MF(nodePot,edgePot,edgeStruct)
if edgeStruct.useMex
[nodeBel,edgeBel,logZ] = UGM_Infer_MFC(nodePot,edgePot,edgeStruct.edgeEnds,edgeStruct.nStates,edgeStruct.V,edgeStruct.E,int32(edgeStruct.maxIter));
else
[nodeBel,edgeBel,logZ] = Infer_MF(nodePot,edgePot,edgeStruct... |
github | yanweifu/embedding_zero-shot-learning-master | UGM_MFGibbsFreeEnergy.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/UGM_2011/sub/UGM_MFGibbsFreeEnergy.m | 862 | utf_8 | 1b19c7d476859b346a5f98ca9dd1e9f0 |
function [F] = MFGibbsFreeEnergy(nodePot,edgePot,nodeBel,nStates,edgeEnds,V,E)
[nNodes,maxState] = size(nodePot);
nEdges = size(edgeEnds,1);
threshold = 1e-10;
U1 = 0;
U2 = 0;
S1 = 0;
for n = 1:nNodes
% Local Mean-Field Average Energy Term
b = nodeBel(n,1:nStates(n));
U1 = U1 + sum(b .* ... |
github | yanweifu/embedding_zero-shot-learning-master | UGM_TreeBP.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/UGM_2011/sub/UGM_TreeBP.m | 3,036 | utf_8 | b1e462d283b246a4787e60c0e2801206 | function [messages] = UGM_TreeBP(nodePot,edgePot,edgeStruct,maximize)
[nNodes,maxState] = size(nodePot);
nEdges = size(edgePot,3);
edgeEnds = edgeStruct.edgeEnds;
nStates = edgeStruct.nStates;
V = double(edgeStruct.V);
E = edgeStruct.E;
% Count number of neighbors
nNeighbors = zeros(nNodes,1);
for n = 1:nNodes
n... |
github | yanweifu/embedding_zero-shot-learning-master | UGM_Decode_ICM.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/UGM_2011/decode/UGM_Decode_ICM.m | 1,587 | utf_8 | 790763424978baca5450de6df6cec222 | function [y] = UGM_Decode_ICM(nodePot, edgePot, edgeStruct,y)
% INPUT
% nodePot(node,class)
% edgePot(class,class,edge) where e is referenced by V,E (must be the same
% between feature engine and inference engine)
%
% OUTPUT
% nodeLabel(node)
if nargin < 4
[junk y] = max(nodePot,[],2);
end
if edgeSt... |
github | yanweifu/embedding_zero-shot-learning-master | UGM_Decode_Junction.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/UGM_2011/decode/UGM_Decode_Junction.m | 10,178 | utf_8 | a45e0c766be3f279e47d366ea5a7fe46 | function [yMap] = UGM_Decode_Junction(nodePot, edgePot, edgeStruct, ordering)
% INPUT
% nodePot(node,class)
% edgePot(class,class,edge) where e is referenced by V,E (must be the same
% between feature engine and inference engine)
%
% OUTPUT
% nodeLabel(node)
debug = 0;
[nNodes,maxState] = size(nodePot);
... |
github | yanweifu/embedding_zero-shot-learning-master | UGM_Decode_AlphaExpansionBetaShrink.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/UGM_2011/decode/UGM_Decode_AlphaExpansionBetaShrink.m | 4,141 | utf_8 | 34ccfcddae983ec8e5d10336d7434458 | function [y] = UGM_Decode_AlphaExpansionBetaShrink(nodePot, edgePot, edgeStruct, decodeFunc, betaSelect, y)
% INPUT
% nodePot(node,class)
% edgePot(class,class,edge) where e is referenced by V,E (must be the same
% between feature engine and inference engine)
%
% OUTPUT
% nodeLabel(node)
[nNodes,maxStates] =... |
github | yanweifu/embedding_zero-shot-learning-master | UGM_Decode_Exact.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/UGM_2011/decode/UGM_Decode_Exact.m | 1,353 | utf_8 | 7377c6644da0e32d4bff47256d89f430 | function [nodeLabels] = UGM_Decode_Exact(nodePot, edgePot, edgeStruct)
% INPUT
% nodePot(node,class)
% edgePot(class,class,edge) where e is referenced by V,E (must be the same
% between feature engine and inference engine)
%
% OUTPUT
% nodeLabel(node)
UGM_assert(prod(double(edgeStruct.nStates)) < 50000000,'B... |
github | yanweifu/embedding_zero-shot-learning-master | WolfeLineSearch.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/minFunc_2012/WolfeLineSearch.m | 10,590 | utf_8 | f962bc5ae0a1e9f80202a9aaab106dab | function [t,f_new,g_new,funEvals,H] = WolfeLineSearch(...
x,t,d,f,g,gtd,c1,c2,LS_interp,LS_multi,maxLS,progTol,debug,doPlot,saveHessianComp,funObj,varargin)
%
% Bracketing Line Search to Satisfy Wolfe Conditions
%
% Inputs:
% x: starting location
% t: initial step size
% d: descent direction
% f: function v... |
github | yanweifu/embedding_zero-shot-learning-master | minFunc_processInputOptions.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/minFunc_2012/minFunc_processInputOptions.m | 4,103 | utf_8 | 8822581c3541eabe5ce7c7927a57c9ab |
function [verbose,verboseI,debug,doPlot,maxFunEvals,maxIter,optTol,progTol,method,...
corrections,c1,c2,LS_init,cgSolve,qnUpdate,cgUpdate,initialHessType,...
HessianModify,Fref,useComplex,numDiff,LS_saveHessianComp,...
Damped,HvFunc,bbType,cycle,...
HessianIter,outputFcn,useMex,useNegCurv,precFunc... |
github | yanweifu/embedding_zero-shot-learning-master | drawGraph.m | .m | embedding_zero-shot-learning-master/internal/L1GeneralExamples/KPM/drawGraph.m | 46,847 | utf_8 | d2429b94526ebcbca9649f90cd0a6b9d | function drawGraph(adj, varargin)
% drawGraph Automatic graph layout: interface to Neato (see http://www.graphviz.org/)
%
% drawGraph(adjMat, ...) draws a graph in a matlab figure
%
% Optional arguments (string/value pair) [default in brackets]
%
% labels - labels{i} is a *string* for node i [1:n]
% removeSelf... |
github | schurterb/convnet-master | evaluate_predictions.m | .m | convnet-master/evaluate_predictions.m | 9,751 | utf_8 | 481150b11d6050ce19752c048b47cf0b |
function evaluate_predictions(target_file, prediction_file, report_file, description)
addpath(genpath('matlab/'));
addpath(genpath('matlab/seunglab/'));
addpath(genpath('matlab/seunglab/segmentation/'));
% initial_thresholds = [0.0:0.2:0.8 0.9:0.005:0.99 0.99:0.001:0.999 0.999:0.0001:0.9999 0.9999:0.0... |
github | drdv/bmsd-master | trajC.m | .m | bmsd-master/general_purpose/trajC.m | 4,701 | utf_8 | d481271bffe45849114566bc1370df40 | function [P1,dP1] = trajC(x,y,z,t_f,d_time,disp_flag)
%
% ------------------------------------------------------
% | Basic Multibody Simulator Derived (Matlab toolbox) |
% ------------------------------------------------------
% | General purpose |
% -------------------
%
% trajC
%
% Generation of Cartesian t... |
github | drdv/bmsd-master | Draw_System.m | .m | bmsd-master/general_purpose/Draw_System.m | 5,486 | utf_8 | 97699bf0366e3caca6ea0456755f2a6e | function Draw_System(SP, SV, bN, bP, frame, draw_flag)
%
% ------------------------------------------------------
% | Basic Multibody Simulator Derived (Matlab toolbox) |
% ------------------------------------------------------
% | General purpose |
% -------------------
%
% Draw_System
%
% Plots the manipula... |
github | dingliumath/VFIToolkit-matlab-master | getFredData.m | .m | VFIToolkit-matlab-master/DataEtc/FRED/getFredData.m | 7,595 | utf_8 | 5d2f2670a7f65759667e94d92b91b537 | function [output] = getFredData(series_id, observation_start, observation_end, units, frequency, aggregation_method, ondate, realtime_end)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Connects to FRED database and retrieves the data series identified by series_id.
%
% Examples b... |
github | jjmorgan/error-prone-master | passfailStats.m | .m | error-prone-master/ContextTree/passfailStats.m | 2,371 | utf_8 | 2b83335e557da39ae70a453bb30443e5 | %% Author: Pedro Borges
%%%
%%%Receives a vector of structure of the type stacks_stats with previous
%%% stats, a vector of stacks and if the test was a pass or fail. Returns
%%% the updated stacks_stats vector with the correct number of pass and
%%% fail tests.
%%%Input:
%%% stacks_stats: structure containing {on... |
github | jjmorgan/error-prone-master | findStack.m | .m | error-prone-master/ContextTree/findStack.m | 1,024 | utf_8 | ea02600c37e0864c0fc733a3ae082b0a | %% Author: Pedro Borges
%%%% Searches for the stack in the array of stacks_stats
%%%% Retuns the index in the stacks_stats array where it found the stack
%%%% Returns 0 if didn't find the stack
function [index] = findStack(stack, stacks_stats)
index = 0;
for i = 1:length(stacks_stats)
isequal = cellComp(stack{... |
github | jjmorgan/error-prone-master | trimWhites.m | .m | error-prone-master/ContextTree/trimWhites.m | 318 | utf_8 | 2b67708be40237bafbf50c17948273c4 | %% Author: Pedro Borges
%%% Trim any white spaces from a stack_stats array
function [stack_stats] = trimWhites(stack_stats)
num_stacks = length(stack_stats);
for i = 1:num_stacks
for j = 1:length(stack_stats(i).stack{1})
stack_stats(i).stack{1}(j) =strtrim( stack_stats(i).stack{1}(j));
end
end
end |
github | jjmorgan/error-prone-master | CumulaCompl.m | .m | error-prone-master/ContextTree/CumulaCompl.m | 1,304 | utf_8 | d8f1fe8ae5bec8676f70ace36813c1c7 | %% Author: Pedro Borges
function [ cumul_complexity ] = CumulaCompl( stacks_stats, comple_file )
tline = filetoarray(comple_file);
[methods_list, remain] = strtok(tline(1:end), ',');
complexity_list = strtok(remain, ',');
complexity = 0;
num_stacks = length(stacks_stats);
cumul_complexity = zeros(1, num_stacks) ;
len... |
github | jjmorgan/error-prone-master | mergeStats.m | .m | error-prone-master/ContextTree/mergeStats.m | 876 | utf_8 | 73e73cc6dd85a2650efeac5b8aaa1ca8 | %% Author: Pedro Borges
%%%% funciton used to merge two stack_stats
%%%% Reminder: Pass the smallest stack_stats as the A!
function [stack_statsA] = mergeStats(stack_statsA,stack_statsB )
stack_statsA = trimWhites(stack_statsA);
stack_statsB = trimWhites(stack_statsB);
num_stacksA = length(stack_statsA);
num_stacksB ... |
github | jjmorgan/error-prone-master | filetoarray.m | .m | error-prone-master/ContextTree/filetoarray.m | 139 | utf_8 | a4b7af5119fd1bd837b8d63c5c879c60 |
function [tline] = filetoarray(File)
fid = fopen(File);
tline = textscan(fid,'%s','Delimiter','\n');
tline = tline{1};
fclose(fid);
end |
github | jjmorgan/error-prone-master | contextTree.m | .m | error-prone-master/ContextTree/contextTree.m | 4,241 | utf_8 | e5e88a3c80b383528ba941cb124d9797 | %% Author: Pedro Borges
%%%%% Reads Log from file and creates a context tree
%%%%% Input:
%%%%% File: File containing lines of CALL name and RETURN name
%%%%% Ouput:
%%%%% tree: Calling Context Tree resulting from the prossecing of the
%%%%% file
function [ tree_sctr ] = contextTree( File )
tic
%%%%% Used whe... |
github | jjmorgan/error-prone-master | writeResults.m | .m | error-prone-master/ContextTree/writeResults.m | 1,149 | utf_8 | 81557c077dd823d4dc12f07bb845be3f | %% Author: Pedro Borges
%%Function Receives the stacks and their respective stats and write them to
%%two csv filed. One file contain the stacks. The other file contain their
%%stats. The stats are number of passed tests and number of failed tests
function [ ] = writeResults(stacks_stats, cumulative_complexity, Tota... |
github | jjmorgan/error-prone-master | parse.m | .m | error-prone-master/ContextTree/parse.m | 311 | utf_8 | 63672b9036dfe926e746ee3aa59f8ed1 | %% Author: Pedro Borges
%%% function to parse the text file to take the path off the method.
function [CallOrRetunr, method] = parseFile(file)
[tline] = filetoarray(file);
[CallOrRetunr, remain] = strtok(tline(1:end));
[token1, remain1] = strtok(remain(1:end), '.');
method = strtok(remain1(1:end), '.');
end |
github | jjmorgan/error-prone-master | statsMultipleCalls.m | .m | error-prone-master/ContextTree/statsMultipleCalls.m | 1,975 | utf_8 | e7c471c7329697e174d90b363e6b0908 | %% Author: Pedro Borges
% Receives the directories containing files with output logs for passed
% tests and failed tests. Returns each stack annotated with the number of
% times it was encounter on a pass and on a fail test. Also returns the
% total number of passed tests and te total number of failed tests
function [ ... |
github | jjmorgan/error-prone-master | stackCall.m | .m | error-prone-master/ContextTree/stackCall.m | 1,262 | utf_8 | dec8993ec39a61f4b6080b491dfaa821 | %% Author: Pedro Borges
%%%% Uses a tree and forms the stacks of calls from it.
%%%% receives a stack, a node and a tree of method calls. Returns all the
%%%% resulting stacks. In order to call this function the first time, you
%%%% should pass an empty stack element.
function [stacks] = stackCall(stack, node, Calltr... |
github | xiuxiazhang/cnn_stanford_exercise-master | cnnCost.m | .m | cnn_stanford_exercise-master/cnnCost.m | 7,626 | utf_8 | 97b073dbf36d7bab6e68f5f8bfb269a6 | function [cost, grad, preds] = cnnCost(theta,images,labels,numClasses,...
filterDim,numFilters,poolDim,pred)
% Calcualte cost and gradient for a single layer convolutional
% neural network followed by a softmax layer with cross entropy
% objective.
%
% Paramet... |
github | xiuxiazhang/cnn_stanford_exercise-master | cnnConvolve.m | .m | cnn_stanford_exercise-master/cnnConvolve.m | 2,614 | utf_8 | 60618d3f794d913f78f419cb347a8d62 | function convolvedFeatures = cnnConvolve(filterDim, numFilters, images, W, b)
%cnnConvolve Returns the convolution of the features given by W and b with
%the given images
%
% Parameters:
% filterDim - filter (feature) dimension
% numFilters - number of feature maps
% images - large images to convolve with, matrix in... |
github | xiuxiazhang/cnn_stanford_exercise-master | WolfeLineSearch.m | .m | cnn_stanford_exercise-master/common/minFunc_2012/minFunc/WolfeLineSearch.m | 10,590 | utf_8 | f962bc5ae0a1e9f80202a9aaab106dab | function [t,f_new,g_new,funEvals,H] = WolfeLineSearch(...
x,t,d,f,g,gtd,c1,c2,LS_interp,LS_multi,maxLS,progTol,debug,doPlot,saveHessianComp,funObj,varargin)
%
% Bracketing Line Search to Satisfy Wolfe Conditions
%
% Inputs:
% x: starting location
% t: initial step size
% d: descent direction
% f: function v... |
github | xiuxiazhang/cnn_stanford_exercise-master | minFunc_processInputOptions.m | .m | cnn_stanford_exercise-master/common/minFunc_2012/minFunc/minFunc_processInputOptions.m | 4,103 | utf_8 | 8822581c3541eabe5ce7c7927a57c9ab |
function [verbose,verboseI,debug,doPlot,maxFunEvals,maxIter,optTol,progTol,method,...
corrections,c1,c2,LS_init,cgSolve,qnUpdate,cgUpdate,initialHessType,...
HessianModify,Fref,useComplex,numDiff,LS_saveHessianComp,...
Damped,HvFunc,bbType,cycle,...
HessianIter,outputFcn,useMex,useNegCurv,precFunc... |
github | CCampJr/CRIkit-master | KKHilbert.m | .m | CRIkit-master/MATLAB/KKHilbert.m | 4,272 | utf_8 | a59a5e53e802eccdca05a8680c4799a7 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%
%%% KKHilbert - Retrieve real and imaginary components of raw CARS
%%% spectrum utilizing a Kramers-Kronig relation.
%%%
%%% This is a re-implementation of the "modified time-domain
%%% Kramers-Kronig transform" (see Refere... |
github | CCampJr/CRIkit-master | Hilbert.m | .m | CRIkit-master/MATLAB/Hilbert.m | 2,811 | utf_8 | 9f3f730c2f373d8dd02600cb3ff58d62 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%
%%% Hilbert - FFT implementation of the Hilbert transform that takes
%%% in a signal (or multiple signals in parallel) and outputs an
%%% analytic signal(s) based on the Hilbert transform.
%%%
%%% If you use this software, please ... |
github | CCampJr/CRIkit-master | arPLS_baseline_v0.m | .m | CRIkit-master/MATLAB/arPLS_baseline_v0.m | 3,048 | utf_8 | 386caaf8eb1d7211b1a97a180655d461 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%
%%% arPLS - Asymmetric reweighted penalized least square (arPLS)
%%% baseline removal.
%%%
%%% Compute the baseline_current of signal_input using an asymmetric
%%% reweighted penalized least square methods (arPLS) algorith... |
github | CCampJr/CRIkit-master | asLS_baseline_v1.m | .m | CRIkit-master/MATLAB/asLS_baseline_v1.m | 3,340 | utf_8 | 8b8b740ea8ac62addaae29229467843d | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%
%%% asLS - Asymmetric least square (asLS) baseline removal.
%%%
%%% Compute the baseline_current of signal_input using an asymmetric
%%% least square methods (asLS, AsLS, ALS, etc) algorithm
%%% designed by P.H. Eilers and... |
github | muellerj/mat2dcm-master | make.m | .m | mat2dcm-master/make.m | 718 | utf_8 | fcec0020ddc246631ba8a53040e489b1 | function make(option, varargin)
%MAKE
%
% Project specific Makefile for ASD application. Executes common tasks
% depending on the context and the `option` passed by parameter:
%
% make [option]
% Add library paths
addpath(genpath([rootpath '/lib']));
addpath(genpath([rootpath '/spec']));
if nargin < 1
m... |
github | muellerj/mat2dcm-master | mat2dcm.m | .m | mat2dcm-master/lib/mat2dcm.m | 9,366 | utf_8 | 8e68a166313e97128f89c669df1098eb | function mat2dcm(matfilename, dcmfilename, varargin)
%FUNCTION MAT2DCM
% Write a DCM of all variables saved in file MATFILENAME to DCMFILENAME.
% Parameters can be adapted to the INCA format, whereby matrices are reshaped
% into their transposed dimensions. Usage:
%
% MAT2DCM(MATFILENAME, DCMFILENAME[, KEY1, VAL1, ...... |
github | muellerj/mat2dcm-master | run_specs.m | .m | mat2dcm-master/lib/spec/run_specs.m | 2,811 | utf_8 | 8f7be41f762bd7e8eb00f127d7f6efc0 | function run_specs(varargin)
%RUN_SPECS [SEARCHSTR]
%
% Run all available specs matching SEARCHSTR inside
% [rootpath]/spec/*
global ASSERTIONS;
ASSERTIONS = {};
EXCEPTIONS = {};
if nargin > 0
searchstr = varargin{1};
else
searchstr = '.';
end
specfiles = collectfiles({}, fullfile(rootpath, '... |
github | samstern/Honours-Project-master | rough_work.m | .m | Honours-Project-master/MATLAB/rough_work.m | 4,158 | utf_8 | 8fe065a7453d8502fe99fba0d05a32f6 | load('children.mat')
load('x_data_4_weeks.mat')
%% Removing outliers
[numhouse_data,numpts]=size(x_data); % number of houses and number of readings for the 28 days
x_filtered=zeros(numhouse_data-2,numpts);
children_filtered=rand(length(children)-2,1);
[numhouse,numpts]=size(x_filtered);
j=1;
for i=1:numhouse_data
... |
github | samstern/Honours-Project-master | sgEvaluate.m | .m | Honours-Project-master/MATLAB/sgEvaluate.m | 8,824 | utf_8 | 96c2037f75854b1bf91c135441731136 | function sgEvaluate(ord,nom,rf,knn,ordMan,nomMan,rfMan,knnMan,baseline,y_test)
%--Accuracy
figure;
subplot(3,1,1)
accuracies=[ord.accuracy,ordMan.accuracy;nom.accuracy,nomMan.accuracy;rf.accuracy,rfMan.accuracy;knn.accuracy,knnMan.accuracy];
labels={'Ordinal LR','Nominal LR','Random Forest','K Nearest Neighbor'};
p1=ba... |
github | samstern/Honours-Project-master | cEvaluate.m | .m | Honours-Project-master/MATLAB/cEvaluate.m | 2,447 | utf_8 | 1b145a3f7ddc9549fa1113da5b92a71a | function cEvaluate(log_reg,rf,knn,lrMan,rfMan,knnMan,baseline)
%--Accuracy
accuracies=[log_reg.accuracy,lrMan.accuracy;rf.accuracy,rfMan.accuracy;knn.accuracy,knnMan.accuracy].*100
labels={'Logistic Regression','Random Forest','K Nearest Neighbor'};
p1=bar(accuracies);
ylim([0 100])
title('Classifier Accuracy')
ylabel... |
github | samstern/Honours-Project-master | matlab2tikz.m | .m | Honours-Project-master/MATLAB/matlab2tikz-matlab2tikz-722609f/src/matlab2tikz.m | 228,343 | utf_8 | f02dd0c10aca3bebe50920de57998d36 | function matlab2tikz(varargin)
%MATLAB2TIKZ Save figure in native LaTeX (TikZ/Pgfplots).
% MATLAB2TIKZ() saves the current figure as LaTeX file.
% MATLAB2TIKZ comes with several options that can be combined at will.
%
% MATLAB2TIKZ(FILENAME,...) or MATLAB2TIKZ('filename',FILENAME,...)
% stores the LaTeX code... |
github | samstern/Honours-Project-master | figure2dot.m | .m | Honours-Project-master/MATLAB/matlab2tikz-matlab2tikz-722609f/src/figure2dot.m | 5,034 | windows_1250 | eb9eb8e933bf48ddec4adb6c9a9d21ba | function figure2dot(filename)
%FIGURE2DOT Save figure in Graphviz (.dot) file.
% FIGURE2DOT() saves the current figure as dot-file.
%
% Copyright (c) 2008--2014, Nico Schlömer <nico.schloemer@gmail.com>
% All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modific... |
github | samstern/Honours-Project-master | m2tInputParser.m | .m | Honours-Project-master/MATLAB/matlab2tikz-matlab2tikz-722609f/src/m2tInputParser.m | 9,321 | windows_1250 | cb0bfe25e4baa11d5c8b65d4d6c2c5ca | function parser = m2tInputParser()
%MATLAB2TIKZINPUTPARSER Input parsing for matlab2tikz..
% This implementation exists because Octave is lacking one.
% Copyright (c) 2008--2014 Nico Schlömer
% All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modification, are p... |
github | samstern/Honours-Project-master | cleanfigure.m | .m | Honours-Project-master/MATLAB/matlab2tikz-matlab2tikz-722609f/src/cleanfigure.m | 18,172 | windows_1250 | efaf9f664d0ee99054078152a52a7d16 | function cleanfigure(varargin)
% CLEANFIGURE() removes the unnecessary objects from your MATLAB plot
% to give you a better experience with matlab2tikz.
% CLEANFIGURE comes with several options that can be combined at will.
%
% CLEANFIGURE('handle',HANDLE,...) explicitly specifies the
% handle of the figure t... |
github | samstern/Honours-Project-master | m2tUpdater.m | .m | Honours-Project-master/MATLAB/matlab2tikz-matlab2tikz-722609f/src/private/m2tUpdater.m | 5,723 | windows_1250 | 8820599196c3d2783f54113d40689aa4 | function updater(name, fileExchangeUrl, version, verbose, env)
%UPDATER Auto-update matlab2tikz.
% Only for internal usage.
% Copyright (c) 2012--2014, Nico Schlömer <nico.schloemer@gmail.com>
% All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modification, are ... |
github | samstern/Honours-Project-master | matlab2tikz_acidtest.m | .m | Honours-Project-master/MATLAB/matlab2tikz-matlab2tikz-722609f/test/matlab2tikz_acidtest.m | 23,835 | windows_1250 | b99e57686e7f0c7401938fd1d279cd55 | function matlab2tikz_acidtest(varargin)
%MATLAB2TIKZ_ACIDTEST unit test driver for matlab2tikz
%
% MATLAB2TIKZ_ACIDTEST('testFunctionIndices', INDICES, ...) or
% MATLAB2TIKZ_ACIDTEST(INDICES, ...) runs the test only for the specified
% indices. When empty, all tests are run. (Default: []).
%
% MATLAB2TIKZ_ACIDTE... |
github | samstern/Honours-Project-master | pointReductionTest.m | .m | Honours-Project-master/MATLAB/matlab2tikz-matlab2tikz-722609f/test/pointReductionTest.m | 831 | utf_8 | a7decc0c4e146f9b8c35bd07b8e422d6 | % ==============================================================================
function pointReductionTest()
breakTime = 5.0;
testPlots = {@testPlot1, ...
};
%@testPlot2};
for testPlot = testPlots
testPlot();
'a'
%pause(breakTime);
%pointReduction2d(0.1);
... |
github | samstern/Honours-Project-master | issues.m | .m | Honours-Project-master/MATLAB/matlab2tikz-matlab2tikz-722609f/test/issues.m | 1,177 | utf_8 | 1397f8c26f390cec77480b1be9b15ada | function [ status ] = issues( k )
%ISSUES M2T Test cases related to issues
%
% Issue-related test cases for matlab2tikz
%
% See also: ACID, matlab2tikz_acidtest
testfunction_handles = {
@scatter3Plot3
};
numFunctions = length( testfunction_handles );
if (k<=0)... |
github | samstern/Honours-Project-master | ACID.m | .m | Honours-Project-master/MATLAB/matlab2tikz-matlab2tikz-722609f/test/ACID.m | 80,044 | utf_8 | db4705908aefb3969b97d26d311b03c4 | % =========================================================================
% *** FUNCTION ACID
% ***
% *** MATLAB2TikZ ACID test functions
% ***
% =========================================================================
% ***
% *** Copyright (c) 2008--2014, Nico Schlömer <nico.schloemer@gmail.com>
% *** All rights re... |
github | samstern/Honours-Project-master | codeReport.m | .m | Honours-Project-master/MATLAB/matlab2tikz-matlab2tikz-722609f/test/codeReport.m | 9,142 | utf_8 | ca595b1a0866219fef85a62d65801a55 | function [ report ] = codeReport( varargin )
%CODEREPORT Builds a report of the code health
%
% This function generates a Markdown report on the code health. At the moment
% this is limited to the McCabe (cyclomatic) complexity of a function and its
% subfunctions.
%
% This makes use of |checkcode| in MATLAB.
%
% Usage... |
github | samstern/Honours-Project-master | testPatches.m | .m | Honours-Project-master/MATLAB/matlab2tikz-matlab2tikz-722609f/test/testPatches.m | 3,833 | utf_8 | 8ef0aa89ea707e858dd91e8842aaeb6c | function status = testPatches(k)
% TESTPATCHES Test suite for patches
%
% See also: ACID, matlab2tikz_acidtest
testfunction_handles = {
@patch01;
@patch02;
@patch03;
@patch04;
@patch05;
@patch06;
@patch07;
@patch08;
};
numFunctions = length( testfunction_handles );
if nargin < 1 |... |
github | samstern/Honours-Project-master | testSurfshader.m | .m | Honours-Project-master/MATLAB/matlab2tikz-matlab2tikz-722609f/test/testSurfshader.m | 3,299 | utf_8 | e54bba251638b2acfc2051b1f073984a | function status = testSurfshader(k)
% TESTSURFSHADER Test suite for Surf/mesh shaders (coloring)
%
% See also: ACID, matlab2tikz_acidtest
testfunction_handles = {
@surfShader1;
@surfShader2;
@surfShader3;
@surfShader4;
@surfShader5;
@surfNoShader;
@surfNoPlot;
@surfMes... |
github | samstern/Honours-Project-master | ATDOW.m | .m | Honours-Project-master/MATLAB/Create Features/ATDOW.m | 1,715 | utf_8 | c1e85877d55f2addefed6f0bc81d71a8 | %% Average total usage for each day of the week for each household
function [dayAverages] = ATDOW(numhouse,daylength,numpts,x_filtered,children_filtered,social_grade)
dayAverages.all = zeros(numhouse,7);
for i = 1:numhouse
k=1;
for j=1:daylength:numpts
dayAverages.all(i,k)=dayAverages.a... |
github | samstern/Honours-Project-master | APOD.m | .m | Honours-Project-master/MATLAB/Create Features/APOD.m | 1,872 | utf_8 | d424ca7c3dc235f842cdab3fe68ba1b5 | function x=APOD(numhouse,x_POD,children_filtered,social_grade)
vec_len=28;
x.all=zeros(numhouse,vec_len);
for i=1:numhouse
x.all(i,:)= sumPOD(x_POD(i,:),vec_len);
end
means=mean(x.all);
three_std=3*std(x.all);
for i=1:numhouse
for j=1:vec_len
if x.all(i,... |
github | samstern/Honours-Project-master | ADF.m | .m | Honours-Project-master/MATLAB/Create Features/ADF.m | 1,177 | utf_8 | 42092cfc44b44efbd39f05d176819788 | % Take the fourier transform of each day seperately, then average values
% for each day of the week
function [fourier_features,fisher_scores] = ADF(numhouse,x,children_filtered)
x_ADF=zeros(numhouse,1008);
size(x,1);
for i=1:numhouse
x_temp=zeros(7,144);
x_day=fft_day(x(i,:));
day=1;... |
github | samstern/Honours-Project-master | ADV.m | .m | Honours-Project-master/MATLAB/Create Features/ADV.m | 1,529 | utf_8 | 5257f3fca1980d0b67fed729724f0496 | %% Average total varianceeach day of the week for each household
function dayStd = ADV(numhouse,daylength,numpts,x_filtered,children_filtered,social_grade)
dayStd.all = zeros(numhouse,7);
for i = 1:numhouse
k=1;
for j=1:daylength:numpts
dayStd.all(i,k)=dayStd.all(i,k)+var(x_filtered(i,j... |
github | samstern/Honours-Project-master | POW_rat.m | .m | Honours-Project-master/MATLAB/Create Features/POW_rat.m | 1,552 | utf_8 | 3ee245bff79e17b6b4697564f57ce937 | %part of week ratio
function x=POW_rat(numhouse,monthSum,dayAverages,children_filtered,social_grade)
weeklyAve=monthSum.all/4;
for i=1:numhouse
pow(i,:)=[sum(dayAverages.all(i,2:5)),dayAverages.all(i,6),dayAverages.all(i,1)];
%x.all(i,:)=[pow(i,:)/weeklyAve(i),pow(i,2)/pow(i,1),pow(i,3)/pow(i,1)... |
github | samstern/Honours-Project-master | WC.m | .m | Honours-Project-master/MATLAB/Create Features/WC.m | 2,062 | utf_8 | 7aeb5aaa6cf809c9dcef79cb7ab1954f | %Correlation between weekdays
function x=WC(numhouse,x_filtered,children_filtered,social_grade)
xH=toHours(x_filtered);
xW=weeksplits(xH);
x.all=calcCor(xW);
[x.child,x.noChild]=split_children(numhouse,x,children_filtered);
[x.a,x.b,x.c1,x.c2,x.d,x.e]=split_se(numhouse,x,social_grade);
end
funct... |
github | samstern/Honours-Project-master | fourierFeatures.m | .m | Honours-Project-master/MATLAB/Create Features/fourierFeatures.m | 1,653 | utf_8 | ae61622f74d2100fd679aa3b228e9144 | function [fourier_features]=fourierFeatures(x,children_filtered,social_grade)
x_ft = fft(x')';
numhouse= size(x,1);
fisher_scores = fsFisher(x_ft',children_filtered.all);
numFeatures=10;
fList=fisher_scores.fList(1:numFeatures);
fisher_scores.W;
fourier_features.all=bestEnergy(x_ft);
[fourier_features.child,fourier_fe... |
github | samstern/Honours-Project-master | compositeFeatures.m | .m | Honours-Project-master/MATLAB/Create Features/compositeFeatures.m | 951 | utf_8 | f27e9e994ec7e6179957ac0a13ce6488 |
function x=compositeFeatures(varargin)
x.all=[];
x.child=[];
x.noChild=[];
x.e=[];
x.d=[];
x.c2=[];
x.c1=[];
x.b=[];
x.a=[];
for i=1:length(varargin)
x.all=[x.all varargin{i}.all];
x.child=[x.child varargin{i}.child];
x.noChild=[x.noChild varargi... |
github | samstern/Honours-Project-master | POD.m | .m | Honours-Project-master/MATLAB/Create Features/POD.m | 489 | utf_8 | 8ab613c18f958783b7553340871799ea | %% Create part-of-day features
function [x_POD,numPOD]=POD(numhouse,x_filtered)
i=1;
numPOD=111;
x=zeros(1,numPOD);
x(1)=37;
while i < length(x)
x(i+1)=x(i)+18;
x(i+2)=x(i+1)+36;
x(i+3)=x(i+2)+42;
x(i+4)=x(i+3)+48;
i=i+4;
end
x_POD=zeros(numhouse,numP... |
github | samstern/Honours-Project-master | total_energy.m | .m | Honours-Project-master/MATLAB/Create Features/total_energy.m | 1,313 | utf_8 | c35cd14a8632214147fc8c270d227db0 | %% Total energy used each household in 4 week period
function [monthSum]=total_energy(numhouse,x_filtered,children_filtered,social_grade)
monthSum.all=zeros(numhouse,1);
j=1;
k=1;
monthSum.a=[];
monthSum.b=[];
monthSum.c1=[];
monthSum.c2=[];
monthSum.d=[];
monthSum.e=[];
... |
github | samstern/Honours-Project-master | POD_ATDOW_ratio.m | .m | Honours-Project-master/MATLAB/Create Features/POD_ATDOW_ratio.m | 532 | utf_8 | b9b339a1d29de29b748e4e190d29dc13 | %% Part_of_Day/average total daily usage ratio
function pod_atd_ratio=POD_ATDOW_ratio(numhouse,x_POD,numPOD,dayAverages)
pod_atd_ratio = zeros(size(x_POD));
for i=1:numhouse
divNum=1;
pod_atd_ratio(i,1:3)=x_POD(i,1:3)/dayAverages(i,divNum);
divNum=divNum+1;
for j=4:4:numPOD
... |
github | samstern/Honours-Project-master | plotTS.m | .m | Honours-Project-master/MATLAB/Plotting/plotTS.m | 1,261 | utf_8 | 69f8339722481240f7e48a2b80d13827 | function plotTS(x_filtered)
load('/Users/samstern/Uni/Honours_Project/MATLAB/data/ts.mat');
ts1.TimeInfo.StartDate = '00-Jan-0000';
ts1.TimeInfo.Units='days';
ts1.Name = 'Energy Used (Wats)';
yMax=30;
yMin=0;
yRange=[yMin,yMax];
%plot(ts1.getsamples(1:4032));
%clear ts
count=1;
ax(1)=gca;
for j=1:length(ts)
... |
github | samstern/Honours-Project-master | plotChildTS.m | .m | Honours-Project-master/MATLAB/Plotting/plotChildTS.m | 1,389 | utf_8 | a3fd1a2a61d18e49771523de647b1193 | function plotChildTS(x_filtered,children,arg1,outliers)
load('/Users/samstern/Uni/Honours_Project/MATLAB/data/ts.mat');
ts(outliers)=[];
ts1.TimeInfo.StartDate = '00-Jan-0000';
ts1.TimeInfo.Units='days';
ts1.Name = 'Energy Used (Wats)';
yMax=20000;
yMin=0;
yRange=[yMin,yMax];
figure;
%plot(ts1.getsamples(1:4032));
%cle... |
github | samstern/Honours-Project-master | plots.m | .m | Honours-Project-master/MATLAB/Plotting/plots.m | 945 | utf_8 | 9d3f75f7b0335ab7ae309ec169619605 | function out = aveDayBoxplot(dayAveChild,dayAveNoChild)
figure;
ax1=subplot(1,2,1);
boxplot(dayAveChild,'whisker',5)
title('children')
xlabel('day')
ylabel('mean energy use')
ax2=subplot(1,2,2);
boxplot(dayAveNoChild,'whisker',5)
xlabel('day')
ylabel('mean energy use')
ti... |
github | samstern/Honours-Project-master | scatterPlots.m | .m | Honours-Project-master/MATLAB/Plotting/scatterPlots.m | 913 | utf_8 | dac5fbcc557438527c409185041e7458 | function scatterPlots(cx,ncx)
numpts=size(cx,2);
count=1;
ax(1)=gca;
for i=1:numpts
for j=i:numpts
ax(count) = axes('position',get(ax(1),'position'));
count=count+1;
scatter(cx(:,i),cx(:,j));
hold on
scatter(ncx(:,i),ncx(:,j));
f = gcf;
set(findobj(ax(count-... |
github | samstern/Honours-Project-master | importMonthlyData.m | .m | Honours-Project-master/MATLAB/Monthly/importMonthlyData.m | 1,115 | utf_8 | 512df49286df07384931074706d8e9fc | function [data] = importMonthlyData(dbConn)
selectHouseholds ='show tables';
%get names of each instance
householdNames = char(fetch(dbConn,selectHouseholds));
for i =1:length(householdNames)
data{i}=importTableData(householdNames(i,:));
end
function [data] = importTableData(table)
%{
connects to the databa... |
github | samstern/Honours-Project-master | socialGradeToInts.m | .m | Honours-Project-master/MATLAB/Socio-Economic/socialGradeToInts.m | 581 | utf_8 | 4c703ccca5fb1bba9e5cac484392183b |
%Converts Social Grade to integers in order to be used for higherarchial
%classification
function ints=socialGradeToInts(sg)
sg=char(sg);
ints = zeros(length(sg),1);
for i=1:length(sg)
if sg(i)=='E'
ints(i,:)=1;
elseif sg(i)=='D'
ints(i,:)=2;
elseif strcmp(sg... |
github | samstern/Honours-Project-master | selectFeatures.m | .m | Honours-Project-master/MATLAB/Classification/selectFeatures.m | 2,128 | utf_8 | 0376b980dff5c52ff2219f45ef33f03d | %Use sequential feature selection to find the best features to perform
%classification
function x_opt = selectFeatures(x,y,task,numfeatures,classifier)
ys=y.all;
xs=x.all;
c = cvpartition(y.all,'k',5);
opts = statset('display','iter','TolTypeFun','abs');
fun = @(XT,yT,Xt,yt)(sum(~strcmp(yt,classify(... |
github | samstern/Honours-Project-master | crossval_run_knn.m | .m | Honours-Project-master/MATLAB/Classification/crossval_run_knn.m | 2,247 | utf_8 | b18ad40250463a09dd48fc5c119ff921 | function acc=crossval_run_knn(x_train,y_train,task,k)
%cross validation
shuffled=shuffle(x_train,y_train);
x_shuffled=shuffled.x;
y_shuffled=shuffled.y;
numFolds=5;%5 fold cross validation
splitt=split(x_shuffled,y_shuffled,numFolds);
x_split=splitt.x;
y_split=splitt.y;
accurac... |
github | samstern/Honours-Project-master | crossval_run_log_reg.m | .m | Honours-Project-master/MATLAB/Classification/crossval_run_log_reg.m | 2,426 | utf_8 | 54fb54e6e669cd6ebf45126871e6f890 | function output=crossval_run_log_reg(x_train,y_train,task)
%cross validation
shuffled=shuffle(x_train,y_train);
x_shuffled=shuffled.x;
y_shuffled=shuffled.y;
k=5;%5 fold cross validation
splitt=split(x_shuffled,y_shuffled,k);
x_split=splitt.x;
y_split=splitt.y;
accuracy=zeros(k... |
github | samstern/Honours-Project-master | runLogReg.m | .m | Honours-Project-master/MATLAB/Classification/runLogReg.m | 2,062 | utf_8 | 2517af94632895b48f6c457d37326422 | function log_reg=runLogReg(x_train,x_test,y_train,y_test,task)
%k=10;
mdl = fitLogReg(x_train,x_test,y_train,task);
evaluated = evalLogRed(mdl,x_test,task);
log_reg.b =mdl.Coefficients.Estimate;
log_reg.yhat =evaluated.yhat;
log_reg.score=evaluated.predProb;
%log_reg.loss=loss(mdl,x_test,y_test);
log_reg.confusion = c... |
github | samstern/Honours-Project-master | importMonthlyData.m | .m | Honours-Project-master/MATLAB/Loading from Database/importMonthlyData.m | 1,114 | utf_8 | 68f259c0d6ceff7124c54e8c9d65a09d | function [data] = importMonthlyData(dbConn)
selectHouseholds ='show tables';
%get names of each instance
householdNames = char(fetch(dbConn,selectHouseholds));
for i =1:length(householdNames)
data{i}=importTableData(householdNames(i,:));
end
function [data] = importTableData(table)
%{
connects to the databas... |
github | samstern/Honours-Project-master | import4WeekData.m | .m | Honours-Project-master/MATLAB/Loading from Database/import4WeekData.m | 1,187 | utf_8 | 1bcd549e1d476e6bd25483ce6565bef4 | function [data] = import4WeekData(dbConn)
selectHouseholds ='show tables';
%get names of each instance
householdNames = char(fetch(dbConn,selectHouseholds));
for i =1:length(householdNames)
data{i}=importTableData(householdNames(i,:));
end
function [data] = importTableData(table)
%{
connects to the database ... |
github | samstern/Honours-Project-master | plotMyClassFeatures.m | .m | Honours-Project-master/Sarah's Stuff/PROJECT_SUBMISSION_FOLDER/MATLAB/UG4PROJECT/plotMyClassFeatures.m | 1,414 | utf_8 | 54fe1025b04025d0fe4094fab2080c38 | %%% Wanting to Plot the class vs features %%%%
function plotMyClassFeatures(X,Y,NameOfFeatures)
% assuming that I'm using this for 6 classes
if length(unique(Y)) == 6
One = find(Y==1);
Twos = find(Y==2);
Threes = find(Y==3);
Fours = find(Y==4);
Fives = find(Y... |
github | samstern/Honours-Project-master | plotMyClassFeatures.m | .m | Honours-Project-master/Sarah's Stuff/PROJECT_SUBMISSION_FOLDER/MATLAB/UG4PROJECT/SingleRunScripts/plotMyClassFeatures.m | 1,414 | utf_8 | 54fe1025b04025d0fe4094fab2080c38 | %%% Wanting to Plot the class vs features %%%%
function plotMyClassFeatures(X,Y,NameOfFeatures)
% assuming that I'm using this for 6 classes
if length(unique(Y)) == 6
One = find(Y==1);
Twos = find(Y==2);
Threes = find(Y==3);
Fours = find(Y==4);
Fives = find(Y... |
github | wangyida/caffe-master | classification_demo.m | .m | caffe-master/matlab/demo/classification_demo.m | 5,412 | utf_8 | 8f46deabe6cde287c4759f3bc8b7f819 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | jwyang/lfw_face_verification_experiment-master | face_db_align.m | .m | lfw_face_verification_experiment-master/code/face_db_align.m | 4,509 | utf_8 | 4426c0a166ad354a4842788735db7a86 | function res = face_db_align(face_dir, ffp_dir, ec_mc_y, ec_y, img_size, save_dir)
% center of eyes (ec), center of l&r mouth(mc), rotate and resize
% ec_mc_y: y_mc-y_ec, diff of height of ec & mc, to scale the image.
% ec_y: top of ec, to crop the face.
clck = clock();
log_fn = sprintf('fa2_%4d%02d%02d%02d%02d%02d.l... |
github | jwyang/lfw_face_verification_experiment-master | evaluate.m | .m | lfw_face_verification_experiment-master/code/+evaluation/evaluate.m | 794 | utf_8 | 314cb210d3873a7ed213132bfdb21993 | % Copyright (c) 2014, Karen Simonyan
% All rights reserved.
% This code is made available under the terms of the BSD license (see COPYING file).
function result = evaluate(config, scores, gt)
scores = reshape(scores, 1, []);
switch config
case 'ap'
[res, extra] = evaluation.ap.ev... |
github | jwyang/lfw_face_verification_experiment-master | eval_best.m | .m | lfw_face_verification_experiment-master/code/+evaluation/+accuracy/eval_best.m | 760 | utf_8 | d6b2827fa8c6d71da9202777323361a7 | % Copyright (c) 2014, Karen Simonyan
% All rights reserved.
% This code is made available under the terms of the BSD license (see COPYING file).
function [res, extra] = eval_best(config, scores, gt)
% finds an optimal threshold - the threshold which maximises the accuracy
% threshold scores and get th... |
github | jwyang/lfw_face_verification_experiment-master | eval.m | .m | lfw_face_verification_experiment-master/code/+evaluation/+accuracy/eval.m | 364 | utf_8 | 4c83bb71f43434ef3a659bc96f38cd50 | % Copyright (c) 2014, Karen Simonyan
% All rights reserved.
% This code is made available under the terms of the BSD license (see COPYING file).
function [res, extra] = eval(config, scores, gt)
% predicted labels
class = 2 * (scores >= config.threshold) - 1;
% class-n accuracy
res = mean(c... |
github | jwyang/lfw_face_verification_experiment-master | eval.m | .m | lfw_face_verification_experiment-master/code/+evaluation/+ap/eval.m | 288 | utf_8 | 857d1a2eadc52f2dc2c02c62e5272211 | % Copyright (c) 2014, Karen Simonyan
% All rights reserved.
% This code is made available under the terms of the BSD license (see COPYING file).
function [res, extra] = eval(config, scores, gt)
[~,~,info] = vl_pr(gt, scores);
res = info.auc * 100;
extra = info;
end
|
github | jwyang/lfw_face_verification_experiment-master | eval.m | .m | lfw_face_verification_experiment-master/code/+evaluation/+roc/eval.m | 415 | utf_8 | 26bc17027058b476501887c08907e512 | % Copyright (c) 2014, Karen Simonyan
% All rights reserved.
% This code is made available under the terms of the BSD license (see COPYING file).
function [res, extra] = eval(config, scores, gt)
[~,~,info] = vl_roc(gt, scores);
% the accuracy at the ROC operating point where the error rates are equ... |
github | giannisdoukas/ScientificComputation-master | dif_A.m | .m | ScientificComputation-master/ex3/dif_A.m | 379 | utf_8 | f44903e8179f9c145d9d2ad3c50df1d2 | function A = dif_A(n, x_max, x_min)
h = (x_max - x_min) / (n+1);
a = 2/(h^2) + 1;
g = -1/(h^2);
b = -1/(h^2) ;
A = trid(g, a, b, n);
end
function A = trid(g, a, b, n)
A = zeros(n,n);
A(1,1) = a;
A(1,2) = b;
for j=2:n-1
A(j, j-1) = g;
A(j, j) = a;
A(j, j... |
github | aodn/imos-toolbox-master | batchTesting.m | .m | imos-toolbox-master/batchTesting.m | 5,645 | utf_8 | 5dab349c1aeb3306aa2be0fc01932662 | function batchTesting(parallel,print_stats)
% function batchTesting(parallel)
%
% Execute all the xunit Test functions
% and docstring tests
%
% Inputs:
%
% parallel[bool] - true for parallel execution.
% print_stats[bool] = true for printing statistics.
%
% Example:
%
% % trigger all tests hiding the output
% % and ru... |
github | aodn/imos-toolbox-master | magneticDeclinationPP.m | .m | imos-toolbox-master/Preprocessing/magneticDeclinationPP.m | 12,864 | utf_8 | 33a1b683790c2913c13daf474c1ded38 | function sample_data = magneticDeclinationPP( sample_data, qcLevel, auto )
%MAGNETICDECLINATIONPP computes and applies the relevant magnetic
% declination correction to the datasets.
%
% Makes use of the NOAA Geomag software to compute the magnetic declination
% at a specific location and time (centre of data time cov... |
github | aodn/imos-toolbox-master | sbe43OxygenTransform.m | .m | imos-toolbox-master/Preprocessing/Transform/sbe43OxygenTransform.m | 6,333 | utf_8 | 36ffa820f166bb86cc9bbc9cf1814e84 | function [data, name, comment, history] = sbe43OxygenTransform( sam, varIdx )
%SBE43OXYGENTRANSFORM Implementation of SBE43 voltage to oxygen concentration
%data.
%
% This function provides an implementation of the oxygen concentration
% formula, specified in Seabird Application Note 64:
%
% http://www.seabird.com/ap... |
github | aodn/imos-toolbox-master | startDialog.m | .m | imos-toolbox-master/GUI/startDialog.m | 13,508 | utf_8 | 1bc05910faeb089b58242eede981895d | function [fieldTrip dataDir] = startDialog(mode)
%STARTDIALOG Displays a dialog prompting the user to select a Field Trip
% and a directory which contains raw data files.
%
% The user is able to choose from a list of field trip IDs, limited by a
% date range; the field trips are retrieved from the deployment database... |
github | aodn/imos-toolbox-master | executeDDBQuery.m | .m | imos-toolbox-master/DDB/executeDDBQuery.m | 5,674 | utf_8 | 97b996b1336a54f9d383119709e181ed | function result = executeDDBQuery( table, field, value)
%EXECUTEDDBQUERY Wrapper around Java DDB interface, allowing queries to the
%DDB.
%
% Executes a query against the DDB, of the form:
%
% select * from table where field = value
%
% See Java/org/imos/ddb/DDB.java for more information.
%
% Inputs:
% table - The... |
github | aodn/imos-toolbox-master | executeCSVQuery.m | .m | imos-toolbox-master/DDB/executeCSVQuery.m | 5,004 | utf_8 | f4e50458398b2acd9f1c27ad1266ddd5 | function result = executeCSVQuery( file, field, value)
%EXECUTECSVQUERY Alternative to executeDDBQuery, uses CSV files.
%
% Uses multiple csv files to obtain information equivalent to
% executeDDBQuery.
%
% Inputs:
% file - The csv file to query.
%
% field - Name of field to search for value. If passed in as an ... |
github | aodn/imos-toolbox-master | StarmonMiniParse.m | .m | imos-toolbox-master/Parser/StarmonMiniParse.m | 16,658 | utf_8 | 6a5e88b399800927d3da2572cd40c072 | function sample_data = StarmonMiniParse( filename, mode )
%STARMONMINIPARSE Parses an ASCII file from Starmon Mini .DAT file format
% as described in http://imos-toolbox.googlecode.com/svn/wiki/documents/Instruments/Star_ODDI/StarmonT.pdf
%
% The files consist of two sections:
%
% - file headerContent - headerContent... |
github | aodn/imos-toolbox-master | VemcoParse.m | .m | imos-toolbox-master/Parser/VemcoParse.m | 9,377 | UNKNOWN | 7ac183badb818418736829ca5cfb2deb | function sample_data = VemcoParse( filename, mode )
%VemcoParse Parses a .csv data file from a Vemco Minilog-II-T logger.
%
% This function is able to read in a .csv data file produced via an export
% option of the Vemco Logger Vue software. It reads specific instrument header
% format and makes use of a lower level f... |
github | aodn/imos-toolbox-master | aquatecParse.m | .m | imos-toolbox-master/Parser/aquatecParse.m | 12,807 | utf_8 | 45c177063a212f791fdd4ba551dff6fb | function sample_data = aquatecParse( filename, mode )
%AQUATECPARSE Parses a raw data file retrieved from an Aquatec AQUAlogger.
%
% Parses a raw data file retrieved from an Aquatec AQUAlogger 520. The
% AQUAlogger 520 range of sensors provide logging capability for temperature
% and pressure.
% (http://www.aquatecgrou... |
github | aodn/imos-toolbox-master | echoviewParse.m | .m | imos-toolbox-master/Parser/echoviewParse.m | 25,282 | utf_8 | 19acbff9bb8654d0aec9cd5244b30666 | function sample_data = echoviewParse( filename, platform, config )
%ECHOVIEWPARSE Parses EchoView results CSV file.
%
% This is an early draft attempt to parse an echoview results
% comma separated variable (CSV) file into a sample_data struct.
%
% This function is almost a generic CSV Parser.
% The list of fields to d... |
github | aodn/imos-toolbox-master | readParadoppBinary.m | .m | imos-toolbox-master/Parser/readParadoppBinary.m | 97,114 | utf_8 | 4a083c70b3de047a73924530fbe5496f | function structures = readParadoppBinary( filename )
%READPARADOPPBINARY Reads a binary file retrieved from a 'Paradopp'
% instrument. Does not support AWAC wave data.
%
% This function is able to parse raw binary data from any Nortek instrument
% which is defined in the Firmware Data Structures section of the Nortek
%... |
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