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 | shangjingbo1226/DPPred-master | mrelnet.m | .m | DPPred-master/glmnet_matlab/mrelnet.m | 2,585 | utf_8 | 2f946c92aba76c5b8ef746baed5e437c | function fit = mrelnet(x,is_sparse,irs,pcs,y,weights,offset,parm,nobs,nvars,...
jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,jsd,intr,maxit,family)
nr = size(y, 2);
wym = wtmean(y, weights);
nulldev = sum(wtmean(bsxfun(@minus,y,wym).^2,weights)*sum(weights));
if isempty(offset)
offset = y * 0;
is_offset = f... |
github | shangjingbo1226/DPPred-master | coxnet.m | .m | DPPred-master/glmnet_matlab/coxnet.m | 1,563 | utf_8 | 895747785766daa2c82975f3d019dd18 | function fit = coxnet(x,is_sparse,irs,pcs,y,weights,offset,parm,nobs,nvars,...
jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,maxit,family)
% Internal glmnet function. See also glmnet, cvglmnet.
%time --- column 1
%status --- column 2
ty = y(:,1);
tevent = y(:,2);
if (any(ty <= 0))
error('negative event times enc... |
github | shangjingbo1226/DPPred-master | err.m | .m | DPPred-master/glmnet_matlab/err.m | 3,764 | utf_8 | eeab9274fcf914b604c55eb4ec2a867a | function output = err(n,maxit,pmax,family)
if n==0
output.n=0;
output.fatal=false;
output.msg='';
else
switch family
case 'gaussian'
output = err_elnet(n,maxit,pmax);
case 'binomial'
output = err_lognet(n,maxit,pmax);
case 'multinomial'
ou... |
github | shangjingbo1226/DPPred-master | glmnetPlot.m | .m | DPPred-master/glmnet_matlab/glmnetPlot.m | 7,609 | utf_8 | 12fcbd8fb5cafce7d419403fcfab5649 | function glmnetPlot( x, xvar, label, type, varargin )
%--------------------------------------------------------------------------
% glmnetPlot.m: plot coefficients from a "glmnet" object
%--------------------------------------------------------------------------
%
% DESCRIPTION:
% Produces a coefficient profile plo... |
github | shangjingbo1226/DPPred-master | glmnetPredict.m | .m | DPPred-master/glmnet_matlab/glmnetPredict.m | 16,486 | utf_8 | 58bccedbf1d9b31e6c7b0e05d2058f3d | function result = glmnetPredict(object, newx, s, type, exact, offset)
%--------------------------------------------------------------------------
% glmnetPredict.m: make predictions from a "glmnet" object.
%--------------------------------------------------------------------------
%
% DESCRIPTION:
% Similar to othe... |
github | shangjingbo1226/DPPred-master | fishnet.m | .m | DPPred-master/glmnet_matlab/fishnet.m | 1,567 | utf_8 | a38133450377adad1e403b48017b5d9e | function fit = fishnet(x,is_sparse,irs,pcs,y,weights,offset,parm,nobs,nvars,...
jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,intr,maxit,family)
if any(y < 0)
error('negative responses encountered; not permitted for Poisson family');
end
if isempty(offset)
offset = y * 0;
is_offset = false;
else
is_o... |
github | shangjingbo1226/DPPred-master | cvmrelnet.m | .m | DPPred-master/glmnet_matlab/cvmrelnet.m | 2,101 | utf_8 | d369fd63346be95c8ff0d36501321ea1 | function result = cvmrelnet(object, lambda, x, y, weights, offset, foldid, ...
type, grouped, keep)
if nargin < 10 || isempty(keep)
keep = false;
end
typenames = struct('deviance','Mean-Squared Error','mse','Mean-Squared Error',...
'mae','Mean Absolute Error');
if strcmp(type,'default')
type = 'mse';... |
github | shangjingbo1226/DPPred-master | cvmultnet.m | .m | DPPred-master/glmnet_matlab/cvmultnet.m | 2,980 | utf_8 | 245d373006161331bd149a8cc0756fcd | function result = cvmultnet(object, lambda, x, y, weights, offset, foldid, ...
type, grouped, keep)
if nargin < 10 || isempty(keep)
keep = false;
end
typenames = struct('mse','Mean-Squared Error','mae','Mean Absolute Error',...
'deviance','Multinomial Deviance','class','Misclassification Error');
if strc... |
github | shangjingbo1226/DPPred-master | lognet.m | .m | DPPred-master/glmnet_matlab/lognet.m | 4,177 | utf_8 | c749bb59e945862ae7b1962afc11fbfc | function fit = lognet(x,is_sparse,irs,pcs,y,weights,offset,parm,nobs,nvars,...
jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,intr,maxit,kopt,family)
[noo,nc] = size(y);
if noo ~= nobs
error('x and y have different number of rows in call to glmnet');
end
if nc == 1
[classes,~,sy] = unique(y);
nc = length(cl... |
github | shangjingbo1226/DPPred-master | elnet.m | .m | DPPred-master/glmnet_matlab/elnet.m | 1,671 | utf_8 | 5e93be0d78726b154d27eed1f02e36be | function fit = elnet(x, is_sparse, irs, pcs, y, weights, offset, gtype, ...
parm, lempty, nvars, jd, vp, cl, ne, nx, nlam, flmin, ulam, thresh, ...
isd, intr, maxit, family)
ybar = y' * weights/ sum(weights);
nulldev = (y' - ybar).^2 * weights;
ka = find(strncmp(gtype,{'covariance','naive'},length(gtype)),1);
... |
github | shangjingbo1226/DPPred-master | cvcoxnet.m | .m | DPPred-master/glmnet_matlab/cvcoxnet.m | 2,338 | utf_8 | eed15cafcfb188d8f0abde603f2b3ee9 | function result = cvcoxnet(object, lambda, x, y, weights, offset, foldid, ...
type, grouped, keep)
% Internal glmnet function. See also cvglmnet.
if nargin < 10 || isempty(keep)
keep = false;
end
typenames = struct('deviance','Partial Likelihood Deviance');
if strcmp(type, 'default')
type = 'deviance';
e... |
github | shangjingbo1226/DPPred-master | cvfishnet.m | .m | DPPred-master/glmnet_matlab/cvfishnet.m | 1,872 | utf_8 | d7cb4820ff676ff3db1adcb0418e402f | function result = cvfishnet(object,lambda,x,y,weights,offset,foldid,type,grouped,keep)
% Internal glmnet function. See also cvglmnet.
if nargin < 10 || isempty(keep)
keep = false;
end
typenames = struct('mse','Mean-Squared Error','mae','Mean Absolute Error','deviance','Poisson Deviance');
if strcmp(type, 'defaul... |
github | shangjingbo1226/DPPred-master | cvlognet.m | .m | DPPred-master/glmnet_matlab/cvlognet.m | 4,184 | utf_8 | 265205120a633a060736651a5eee583d | function result = cvlognet(object, lambda, x, y, weights, offset, foldid, ...
type, grouped, keep)
if nargin < 10 || isempty(keep)
keep = false;
end
typenames = struct('mse','Mean-Squared Error','mae','Mean Absolute Error',...
'deviance','Binomial Deviance','auc','AUC','class','Misclassification Error');
... |
github | FelixWinterstein/FPGA-shared-mem-master | generate_data_points.m | .m | FPGA-shared-mem-master/examples/filtering_algorithm/host/data/generate_data_points.m | 2,172 | utf_8 | deb2eed5cac280eaf68b63d34bd507ca | %**********************************************************************
% Felix Winterstein, Imperial College London, 2016
%
% File: generate_data_points
%
% Revision 1.01
% Additional Comments: distributed under an Apache-2.0 license, see LICENSE
%
%*********************************************************************... |
github | FelixWinterstein/FPGA-shared-mem-master | generate_data_points.m | .m | FPGA-shared-mem-master/examples/filtering_algorithm_no_svm/host/data/generate_data_points.m | 2,172 | utf_8 | 68f8ffaf55695f8a76b472d1df424125 | %**********************************************************************
% Felix Winterstein, Imperial College London, 2016
%
% File: generate_data_points
%
% Revision 1.01
% Additional Comments: distributed under an Apache-2.0 license, see LICENSE
%
%*********************************************************************... |
github | ewine-project/Flexible-GFDM-PHY-master | read_ini_file.m | .m | Flexible-GFDM-PHY-master/Host/Reader/read_ini_file.m | 2,463 | utf_8 | e3cf1e3ecd343ec9b56afbd0fa088639 | function [ini_struct] = read_ini_file(filename)
ini_struct = struct();
if (~exist('filename', 'var'))
filename = '';
end
if isempty(filename)
[filename, pathname] = uigetfile('*.ini', 'Select a ini file');
if isequal(filename,0)
error('User selected Cancel!'... |
github | ewine-project/Flexible-GFDM-PHY-master | write_ini_file.m | .m | Flexible-GFDM-PHY-master/Host/Reader/write_ini_file.m | 2,907 | utf_8 | 1db51ea5f88734a1e7ea4742f0afe1f1 | function [file_string] = write_ini_file(filename, ini_struct)
if (~exist('filename', 'var'))
filename = '';
end
if (~exist('ini_struct', 'var'))
error('No data to be saved!')
end
if isempty(filename)
[filename, pathname] = uiputfile('*.ini', 'Select a ... |
github | tangzhenyu/Scene-Text-Understanding-master | predict_depth.m | .m | Scene-Text-Understanding-master/SynthText_Chinese/prep_scripts/predict_depth.m | 3,416 | utf_8 | f03754d767f958f0bea597bd0c3564cc | % MATLAB script to regress a depth mask for an image.
% uses: (1) https://bitbucket.org/fayao/dcnf-fcsp/
% (2) vlfeat
% (3) matconvnet
% Author: Ankush Gupta
function predict_depth()
% setup vlfeat
%run( '../libs/vlfeat-0.9.18/toolbox/vl_setup');
run( '/home/yuz/lijiahui/fayao-dcnf-fcsp/l... |
github | tangzhenyu/Scene-Text-Understanding-master | classification_demo.m | .m | Scene-Text-Understanding-master/ctpn_crnn_ocr/CTPN/caffe/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 | karthik-kk/Autoware-master | velCapture.m | .m | Autoware-master/ros/src/system/gazebo/catvehicle/matlab files/velCapture.m | 1,047 | utf_8 | 994f8bb886b3e3ebef61b89d3c9c00a9 | % Function to capture catvehicle velocity and plotting live graph
function velCapture(ROS_IP, roboname)
%If number of argument is not two, flag message and exit.
if nargin < 2
disp('Uage: velocityProfiler(192.168.0.32, catvehicle)');
return;
end
close all;
%rosshutdown;
modelname = strcat('/',roboname);
%Connec... |
github | karthik-kk/Autoware-master | profileByMatrix.m | .m | Autoware-master/ros/src/system/gazebo/catvehicle/matlab files/profileByMatrix.m | 1,840 | utf_8 | f81181e46cb60adc3466c4779083ce0d | %Implementation of follower algorithm
%Developed by Rahul Kumar Bhadani <rahulbhadani@email.arizona.edu>
%ROS_IP = IP Address of ROS Master
%lead = name of the model of leader AV Car
%follower = name of the model of follower car
function profileByMatrix(ROS_IP, roboname, vel_input, time_input, tire_angle)
%If nu... |
github | karthik-kk/Autoware-master | velocityProfiler.m | .m | Autoware-master/ros/src/system/gazebo/catvehicle/matlab files/velocityProfiler.m | 1,758 | utf_8 | d12bb47043d421e21fc4fd4fa8ce4b02 | %Matlab scripto to publish velocity on /catvehicle/cmd_vel topic and
%subscribe to catvehicle/speed topic
%Developed by Rahul Kumar Bhadani <rahulbhadani@email.arizona.edu>
%ROS_IP = IP Address of ROS Master
%roboname = name of the model
function velocityProfiler(ROS_IP, roboname, tire_angle)
%If number of argument... |
github | karthik-kk/Autoware-master | follower_profile.m | .m | Autoware-master/ros/src/system/gazebo/catvehicle/matlab files/follower_profile.m | 2,381 | utf_8 | ea7d00e67f5e7709a2cd7287216af4af | %Implementation of follower algorithm
%Developed by Rahul Kumar Bhadani <rahulbhadani@email.arizona.edu>
%ROS_IP = IP Address of ROS Master
%lead = name of the model of leader AV Car
%follower = name of the model of follower car
function follower_profile(ROS_IP, lead, follower)
%If number of argument is not two, f... |
github | karthik-kk/Autoware-master | plotDisvout.m | .m | Autoware-master/ros/src/system/gazebo/catvehicle/simulink/plotDisvout.m | 241 | utf_8 | 241fa676f6444e00a60b8c69a5e19efd | % Author: Jonathan Sprinkle
% plots the distance outputs from a data file
function plotData( timeseries )
% this timeseries is what we have
figure
hold on
plot(timeseries.Data);
plot(timeseries.uVelOut);
legend({'Distance','VelOut'});
end |
github | karthik-kk/Autoware-master | plotData.m | .m | Autoware-master/ros/src/system/gazebo/catvehicle/simulink/plotData.m | 350 | utf_8 | 17951edcd31fa9c02deeb1c49a2e0d7b | % Author: Jonathan Sprinkle
% plots the distance outputs from a data file
function plotData( timeseries )
% this timeseries is what we have
figure
hold on
plot(timeseries.dist);
plot(timeseries.velConverted);
plot(timeseries.vdot);
plot(timeseries.vout);
plot(timeseries.uTireAngle);
legend({'dist','velConverted','vdo... |
github | karthik-kk/Autoware-master | plotDistances.m | .m | Autoware-master/ros/src/system/gazebo/catvehicle/simulink/plotDistances.m | 288 | utf_8 | c0779b1561faff69c733c5ec8a3a9ac7 | % Author: Jonathan Sprinkle
% plots the distance outputs from a data file
function plotDistances
load distances.mat
% this timeseries is what we have
figure
hold on
plot(DistanceEstimator.Data__signal_1_);
plot(DistanceEstimator.Data__signal_2_);
legend({'Distance','Angle (rad)'});
end |
github | rohinkumarreddy/Iris-Detection-master | hamdist.m | .m | Iris-Detection-master/hamdist.m | 710 | utf_8 | bc94aff9c7917be49e3bb10e78a5e3e4 |
function hd = hamdist(template1, template2, scales)
template1 = logical(template1);
template2 = logical(template2);
hd = NaN;
% shift template left and right, use the lowest Hamming distance
for shifts=-8:8
template1s = shiftbits(template1, shifts,scales);
totalbits = (size(template... |
github | YasaraPeiris/MNIST_Clusters-master | predict.m | .m | MNIST_Clusters-master/TestMNIST/MatlabCodes/predict.m | 1,720 | utf_8 | b06e9988934fa4b78b42512d26371e90 | function predict(layerset, dataSize)
global weights numLayers layers;
layers = [784, layerset, 10];
[~, numLayers] = size(layers);
images = loadTrainImages();
labels = loadTrainLabels();
selected = find(labels == 5 | labels == 1);
labels = labels(selected);
images = images(:, selected);
[~, c] = size(images);
data... |
github | YasaraPeiris/MNIST_Clusters-master | trainModel_yas.m | .m | MNIST_Clusters-master/TestMNIST/MatlabCodes/trainModel_yas.m | 1,924 | utf_8 | 6c4950f15dc3468e591c1a2c5aab6d28 |
function trainModel_yas(layerset, dataSize) % Using Oja's rule
trainingRatio = 0.8;
p = 0;
images = loadTrainImages();
labels = loadTrainLabels();
selected = find(labels == 0 | labels == 1 );
labels = labels(selected);
images = images(:, selected');
[~, c] = size(images);
dataSize = min(c, dataSize);
iterations ... |
github | danielemarinazzo/GC_SS_PNAS-master | InstModelfilter.m | .m | GC_SS_PNAS-master/InstModelfilter.m | 2,343 | utf_8 | 9ec58f552064a96d14d59ba96f833baf | %% realization of the instantaneous model : U = L*W
%%% OUTPUT
% U: N*M matrix of filtered noises
% INPUT
% N data length
% C: input covariance matrix (may be interpreted as Su or Sw, see above)
% B0: M*M matrix of instantaneous effects (when relevant)
% when flag='StrictlyCausal':
% given Su, applies Chole... |
github | danielemarinazzo/GC_SS_PNAS-master | MVARfilter.m | .m | GC_SS_PNAS-master/MVARfilter.m | 614 | utf_8 | 0290e5ff7f6435096eed5fecafbf75e9 | %% FILTER A VECTOR NOISE WITH A SPECIFIED STRICTLY CAUSAL MVAR MODEL: Y(n)=A(1)Y(n-1)+...+A(p)Y(n-p)+U(n)
%%% INPUT
% A=[A(1)...A(p)]: M*pM matrix of the MVAR model coefficients (strictly causal model)
% U: M*N matrix of innovations
%%% OUTPUT
% Y: M*N matrix of simulated time series
function [Y]=MVARfilter... |
github | danielemarinazzo/GC_SS_PNAS-master | varma2iss.m | .m | GC_SS_PNAS-master/varma2iss.m | 1,206 | utf_8 | 200c8d913b3e3fe6f30cf6228c4453c9 | %% VARMA with B0 term to (Innovations form) State Space parameters
% computes innovations form parameters for a state space model from VARMA
% parameters using Aoki's method - this version allows for zero-lag MA coefficients
function [A,C,K,R,lambda0] = varma2iss(Am,Bm,V,B0)
% INPUT: VARMA parameters Am, Bm, ... |
github | danielemarinazzo/GC_SS_PNAS-master | idMVAR.m | .m | GC_SS_PNAS-master/idMVAR.m | 1,446 | utf_8 | 02db6f116c8164266a38371b50da5231 | %% IDENTIFICATION OF STRICTLY CAUSAL MVAR MODEL: Y(n)=A(1)Y(n-1)+...+A(p)Y(n-p)+U(n)
% makes use of autocovariance method (vector least squares)
%%% input:
% Y, M*N matrix of time series (each time series is in a row)
% p, model order
% Mode, determines estimation algorithm (0:builtin least squares, else other m... |
github | danielemarinazzo/GC_SS_PNAS-master | block_fdMVAR.m | .m | GC_SS_PNAS-master/block_fdMVAR.m | 5,321 | utf_8 | c0626e923c90c31b6156e254a5e1785f | %% FREQUENCY DOMAIN BLOCK MVAR ANALYSIS
% References:
% L.Faes and G. Nollo, "Measuring Frequency Domain Granger Causality for Multiple Blocks of Interacting Time Series", Biological Cybernetics 2013. DOI: 10.1007/s00422-013-0547-5
% L.Faes, S. Erla and G. Nollo, "Block Partial Directed Coherence: a New Tool for the... |
github | bill-codes/netalign-master | bipartite_matching.m | .m | netalign-master/experiments/misc/gaimc/bipartite_matching.m | 6,580 | utf_8 | bd3212ac06f51f9037ca7a7d80b45981 | function [val m1 m2 mi]=bipartite_matching(varargin)
% BIPARTITE_MATCHING Solve a maximum weight bipartite matching problem
%
% [val m1 m2]=bipartite_matching(A) for a rectangular matrix A
% [val m1 m2 mi]=bipartite_matching(x,ei,ej,n,m) for a matrix stored
% in triplet format. This call also returns a matching indic... |
github | bill-codes/netalign-master | graph_draw.m | .m | netalign-master/experiments/misc/gaimc/graph_draw.m | 23,504 | utf_8 | 83adf66de4bc94aea62f934d2e1e3da0 | function h = graph_draw(adj, xy, varargin)
% GRAPH_DRAW Draw a picture of a graph when the coordinates are known
%
% graph_draw(A, xy) draws a picture of graph A where node i is placed
% at x = xy(i,1), y = xy(i,2). In the drawing, shaded nodes have
% self loops.
%
% Some of the parameters of the drawing are controll... |
github | bill-codes/netalign-master | netalignmr2.m | .m | netalign-master/experiments/crystalize_mr/netalignmr2.m | 6,168 | utf_8 | 4973769c6f25dc607067664b4d9444ae | function [xbest,status,hist] = netalignmr(S,w,a,b,li,lj,gamma,stepm,rtype,maxiter,verbose)
% NETALIGNMR Compute the matching relaxation heuristic for network alignment
%
% Given a network alignment problem, the matching heuristic solves a
% sequence of matching problems to generate good upper and lower bounds on
% the ... |
github | bill-codes/netalign-master | netalignbp_y.m | .m | netalign-master/experiments/old/rounding/netalignbp_y.m | 6,447 | utf_8 | 60b9cf0f094a4f2449195b5ec82a82d9 | function [mbest hista histb] = netalignbp(S,w,a,b,li,lj,gamma,maxiter,verbose)
% NETALIGNBP Solve the network alignment problem with Belief Propagation
%
%
% David F. Gleich, Ying Wang, and Mohsen Bayati
% Copyright, Stanford University, 2007-2008
% Computational Approaches to Digital Stewardship
if ~exist('a','var'... |
github | bill-codes/netalign-master | netalignbp_yz.m | .m | netalign-master/experiments/old/rounding/netalignbp_yz.m | 6,465 | utf_8 | 3bd7d5f146af758410916577c9f26650 | function [mbest hista histb] = netalignbp(S,w,a,b,li,lj,gamma,maxiter,verbose)
% NETALIGNBP Solve the network alignment problem with Belief Propagation
%
%
% David F. Gleich, Ying Wang, and Mohsen Bayati
% Copyright, Stanford University, 2007-2008
% Computational Approaches to Digital Stewardship
if ~exist('a','var'... |
github | bill-codes/netalign-master | netalignmr.m | .m | netalign-master/matlab/netalignmr.m | 5,472 | utf_8 | 3bba17d17e4d620f961d10dc0048c844 | function [xbest,status,hist] = netalignmr(S,w,a,b,li,lj,gamma,stepm,rtype,maxiter,verbose)
% NETALIGNMR Compute the matching relaxation heuristic for network alignment
%
% Given a network alignment problem, the matching heuristic solves a
% sequence of matching problems to generate good upper and lower bounds on
% the ... |
github | bill-codes/netalign-master | netalignbp.m | .m | netalign-master/matlab/netalignbp.m | 7,067 | utf_8 | 366425d10c4e61e2ec0d1782c9ac8c30 | function [mbest hista histb] = netalignbp(S,w,a,b,li,lj,gamma,dtype,maxiter,verbose)
% NETALIGNBP Solve the network alignment problem with Belief Propagation
%
%
% David F. Gleich, Ying Wang, and Mohsen Bayati
% Copyright, Stanford University, 2007-2009
% Computational Approaches to Digital Stewardship
% History
% ... |
github | bill-codes/netalign-master | evaluate_alignment.m | .m | netalign-master/matlab/evaluate_alignment.m | 4,797 | utf_8 | 11f4dfb2cdec144b885fe302325fedc0 | function [s Mcc]=evaluate_alignment(A,B,mi,li,lj)
% EVALUATE_ALIGNMENT Report properties of the graph alignment
%
% evaluate_alignment(A,B,mi,li,lj) returns properties of an alignment
% between graphs A and B.
% evaluate_alignment(A,B,ma,mb) returns properties of an alignment
% between graphs A and B.
%
% Edges - num... |
github | bill-codes/netalign-master | netalign_lagrange.m | .m | netalign-master/matlab/netalign_lagrange.m | 3,194 | utf_8 | c002a2f87068662095111cbc700b4541 | function [xbest fupper hist] = netalign_lagrange(S,w,a,b,li,lj,stepm,rtype,maxiter,verbose)
% NETALIGN_LAGRANGE Solve the network alignment problem
% with Lagrangean relaxation
%
% David F. Gleich, Ying Wang, and Mohsen Bayati
% Copyright, Stanford University, 2008-2009
% Computational Approaches to Digital Stewardsh... |
github | bill-codes/netalign-master | netalignbpmr.m | .m | netalign-master/matlab/netalignbpmr.m | 4,762 | utf_8 | 7286539d79a120310bfc2d322d442ccc | function [mbest hista histb] = netalignbpmr(S,w,a,b,li,lj,gamma,dtype,maxiter,verbose)
% NETALIGNMBP Solve the network alignment problem with Belief Propagation
%
% This version of network alignment uses the matrix formulation of the
% algorithm.
% David F. Gleich, Ying Wang, and Mohsen Bayati
% Copyright, Stanford U... |
github | bill-codes/netalign-master | netalignscbp.m | .m | netalign-master/matlab/netalignscbp.m | 7,240 | utf_8 | 4d86fc6727c6040b65240c5e0079a468 | function [mbest hista histb Sk] = netalignscbp(S,w,a,b,li,lj,gamma,dtype,maxiter,verbose)
% NETALIGNMPSC Solve the network alignment problem with message passing
%
% This version of network alignment uses the matrix formulation of the
% algorithm.
% David F. Gleich, Ying Wang, and Mohsen Bayati
% Copyright, Stanford ... |
github | bill-codes/netalign-master | expand_match.m | .m | netalign-master/matlab/expand_match.m | 2,500 | utf_8 | f69e78b513c268f81a604be27667dd91 | function L = expand_match(A,B,L)
% EXPAND_MATCH Expand a set of possible matchings with breadth first search
%
% L2 = expand_match(A,B,L) returns a new sparse match matrix L2 for the
% network alignment problem where L2 includes the neighbors of all existing
% matchings.
%
%
% David F. Gleich and Ying Wang
% Copyrigh... |
github | bill-codes/netalign-master | netalignmbp.m | .m | netalign-master/matlab/netalignmbp.m | 4,716 | utf_8 | e294c5801bc3cd3fa0ee4deed6698ebf | function [mbest hista histb] = netalignmbp(S,w,a,b,li,lj,gamma,dtype,maxiter,verbose)
% NETALIGNMBP Solve the network alignment problem with Belief Propagation
%
% This version of network alignment uses the matrix formulation of the
% algorithm.
% David F. Gleich, Ying Wang, and Mohsen Bayati
% Copyright, Stanford Un... |
github | snipsco/kaldi-master | Generate_mcTrainData_cut.m | .m | kaldi-master/egs/reverb/s5/local/Generate_mcTrainData_cut.m | 7,311 | utf_8 | f59dd892f0f8da04a515a2c58ff50a69 | function Generate_mcTrainData_cut(WSJ_dir_name, save_dir)
%
% Input variables:
% WSJ_dir_name: string name of user's clean wsjcam0 corpus directory
% (*Directory structure for wsjcam0 corpushas to be kept as it is after obtaining it from LDC.
% Otherwise this script does not wor... |
github | h-delgado/binary-key-diarizer-master | pdist22.m | .m | binary-key-diarizer-master/matlab/external/pdist22.m | 5,461 | utf_8 | 173103b09eefbe457c081a3d41cddd3d | % This function belongs to Piotr Dollar's Toolbox
% http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html
% Please refer to the above web page for definitions and clarifications
%
% Calculates the distance between sets of vectors.
%
% Let X be an m-by-p matrix representing m points in p-dimensional space
% and Y be an... |
github | KGuo26/WADG_Matlab-master | bern_sem_basis.m | .m | WADG_Matlab-master/bern_sem_basis.m | 1,476 | utf_8 | a00141b6425a6b137bfc093d5ed345aa | % wedge basis
% function V = bern_sem_basis(N,r,s,t)
% Vtri = bern_tri(N,r(:),s(:));
% % [r2D s2D] = Nodes2D(N); [r2D s2D] = xytors(r2D,s2D);
% % VWB = Vandermonde2D(N,r2D,s2D); Vtri = Vandermonde2D(N,r(:),s(:))/VWB;
% r1D = JacobiGL(0,0,N);
% VSEM = Vandermonde1D(N,r1D(:));
% V1D = Vandermonde1D(N,t(:))/V... |
github | KGuo26/WADG_Matlab-master | bern_basis_1D.m | .m | WADG_Matlab-master/bern_basis_1D.m | 424 | utf_8 | d9d840acea7aa008762de1bb0bfe1d93 | function [V Vr] = bern_basis_1D(N,r)
r = (1+r)/2; % convert to unit
for i = 0:N
V(:,i+1) = bern_1D(N,i,r);
Vr(:,i+1) = d_bern(N,i,r)*.5; % change of vars
end
function bi = bern_1D(N,i,r)
bi = nchoosek(N,i)*(r.^i).*(1-r).^(N-i);
function dbi = d_bern(N,i,r)
if (i==0)
dbi = -N*(1-r).^(N-1);
elseif (i==N)... |
github | KGuo26/WADG_Matlab-master | bern_quad.m | .m | WADG_Matlab-master/bern_quad.m | 689 | utf_8 | 355d83ac78285e23030c96d0fadf8fcc | function [V Vr Vs] = bern_quad(N,r,s)
r = (1+r)/2; % convert to unit
s = (1+s)/2; % convert to unit
V = zeros(length(r),(N+1)^2);
Vr = zeros(length(r),(N+1)^2);
Vs = zeros(length(r),(N+1)^2);
sk = 1;
for j = 0:N
for i = 0:N
V(:,sk) = bern_1D(N,i,r).*bern_1D(N,j,s);
Vr(:,sk) = .5*d_bern_1D(N,i,... |
github | KGuo26/WADG_Matlab-master | bern_basis_tet.m | .m | WADG_Matlab-master/bern_basis_tet.m | 1,786 | utf_8 | 402f402665ab18ed94d19eb13672619c | % V = VDM. Vrst = deriv matrices. V1-4 barycentric derivs. ids =
% permutation to match equispaced node ordering on tet.
function [V, Vr, Vs, Vt, V1, V2, V3, V4, id] = bern_basis_tet(N,r,s,t)
%[L1 L2 L3 L4] = rsttobary(r,s,t);
L1 = -(1+r+s+t)/2;
L2 = (1+r)/2;
L3 = (1+s)/2;
L4 = (1+t)/2;
dL1r = -.5; dL2r = .5; dL... |
github | KGuo26/WADG_Matlab-master | Wave3D.m | .m | WADG_Matlab-master/Wave3D.m | 4,005 | utf_8 | 61a2d10ebd5610a1443301f0f99b2ba0 | clear
Globals3D;
N = 4;
FinalTime = 1;
cfun = @(x,y,z) ones(size(x));
%cfun = @(x,y,z) 1 + 0.5*sin(pi*x).*sin(pi*y).*sin(pi*z); % smooth velocity
%cfun = @(x,y,z) (1 + .5*sin(2*pi*x).*sin(2*pi*y) + (y > 0)); % piecewise smooth velocity
%pfun = @(x,y,z,t) cos(pi*x).*cos(pi*y).*cos(pi*z).*cos(sqrt(3)*pi*t);
pfun = @... |
github | KGuo26/WADG_Matlab-master | bern_hex.m | .m | WADG_Matlab-master/bern_hex.m | 930 | utf_8 | c1ba4aade744a74da97433f578142f21 | function [V Vr Vs Vt] = bern_hex(N,r,s,t)
r = (1+r)/2; % convert to unit
s = (1+s)/2; % convert to unit
t = (1+t)/2; % convert to unit
Np = (N+1)^3;
V = zeros(length(r),Np);
Vr = zeros(length(r),Np);
Vs = zeros(length(r),Np);
Vt = zeros(length(r),Np);
sk = 1;
for k = 0:N
for j = 0:N
for i = 0:N
... |
github | KGuo26/WADG_Matlab-master | bern_tet.m | .m | WADG_Matlab-master/bern_tet.m | 2,132 | utf_8 | 4dc141800a89b218f1b7dff33be86eef | % V = VDM. Vrst = deriv matrices. V1-4 barycentric derivs. ids =
% permutation to match equispaced node ordering on tet.
% function [V Vr Vs Vt V1 V2 V3 V4 id] = bern_basis_tet(N,r,s,t)
%
% % use equivalence between W&B and equispaced nodes - get ordering
% [re se te] = EquiNodes3D(N);
% Ve = bern_tet(N,re,se,te)... |
github | KGuo26/WADG_Matlab-master | Wave2D_mesh_fullquadrature.m | .m | WADG_Matlab-master/Wave2D_mesh_fullquadrature.m | 4,216 | utf_8 | 8353d0375b964df4092a4909937506a5 | clear
Globals2D
kd=[4 8 16 32];
h=2./kd;
N = 4;
M = 1;
for i = 1:length(kd)
K1D = kd(i);
FinalTime = 1.0;
[Nv, VX, VY, K, EToV] = unif_tri_mesh(K1D);
StartUp2D;
%% Set up wavespeed function
%cfun = @(x,y) ones(size(x));
cfun = @(x,y) 1+ sin(pi*x).*sin(pi*y); % smooth velocity
%cfun = @(x,y) (1 + .5*sin(2*pi*x).*sin... |
github | KGuo26/WADG_Matlab-master | Wave2D_k_manufactured.m | .m | WADG_Matlab-master/Wave2D_k_manufactured.m | 3,325 | utf_8 | 0034b8526d028e8da590042ba197b35b | clear
Globals2D
k=[1 4 8 16]
N = 4;
M = 1
for i = 1:length(k)
K1D = 32;
FinalTime = 1.0;
[Nv, VX, VY, K, EToV] = unif_tri_mesh(K1D);
StartUp2D;
%% Set up wavespeed function
%cfun = @(x,y) ones(size(x));
cfun = @(x,y) 1+ 0.5*sin(pi*x).*sin(pi*y); % smooth velocity
%cfun = @(x,y) (1 + .5*sin(2*pi*x).*sin(2*pi*y) + (y... |
github | KGuo26/WADG_Matlab-master | Wave2D_mesh_manufactured.m | .m | WADG_Matlab-master/Wave2D_mesh_manufactured.m | 3,326 | utf_8 | 322817f72f150c1acafea4eaefbd319b | clear
Globals2D
kd=[64]
h=2./kd
N = 4;
M = 1;
k = 16
for i = 1:length(kd)
K1D = kd(i);
FinalTime = 1.5;
[Nv, VX, VY, K, EToV] = unif_tri_mesh(K1D);
StartUp2D;
%% Set up wavespeed function
%cfun = @(x,y) ones(size(x));
cfun = @(x,y) 1+ 0.5*sin(pi*x).*sin(pi*y); % smooth velocity
%cfun = @(x,y) (1 + .5*sin(2*pi*x).*si... |
github | KGuo26/WADG_Matlab-master | bern_tri.m | .m | WADG_Matlab-master/bern_tri.m | 1,444 | utf_8 | 3a04a72548c550e48525a3a60987827c | % function [V Vr Vs V1 V2 V3 id] = bern_tri(N,r,s)
%
% % use equivalence between W&B and equispaced nodes - get ordering
% [re se] = EquiNodes2D(N); [re se] = xytors(re,se);
% Ve = bern_tri_b(N,re,se);
% for i = 1:size(Ve,2)
% [val iid] = max(Ve(:,i));
% id(i) = iid;
% % id(i) = i;
% end
%
% [V Vr Vs V1 V2 ... |
github | KGuo26/WADG_Matlab-master | Wave2D_modified.m | .m | WADG_Matlab-master/Wave2D_modified.m | 2,725 | utf_8 | 16165584c54009cc2f3f03cf162a8f73 | Globals2D
N = 4;
K1D = 16;
c_flag = 0;
FinalTime = 0.5;
%cfun = @(x,y) ones(size(x));
cfun = @(x,y) 1 + sin(pi*x).*sin(pi*y); % smooth velocity
%cfun = @(x,y) (1 + .5*sin(2*pi*x).*sin(2*pi*y) + (y > 0)); % piecewise smooth velocity
[Nv, VX, VY, K, EToV] = unif_tri_mesh(K1D);
StartUp2D;
[rp sp] = EquiNodes2D(50); [r... |
github | KGuo26/WADG_Matlab-master | Wave2D_simple.m | .m | WADG_Matlab-master/Wave2D_simple.m | 2,633 | utf_8 | 977017e5edb69468778850473fbb3602 | function Wave2D
Globals2D
N = 4;
K1D = 16;
c_flag = 0;
FinalTime = 3;
%cfun = @(x,y) ones(size(x));
cfun = @(x,y) .5*sin(pi*x).*sin(pi*y); % smooth velocity
%cfun = @(x,y) (1 + .5*sin(2*pi*x).*sin(2*pi*y) + (y > 0)); % piecewise smooth velocity
[Nv, VX, VY, K, EToV] = unif_tri_mesh(K1D);
StartUp2D;
% plotting n... |
github | KGuo26/WADG_Matlab-master | bern_pyr.m | .m | WADG_Matlab-master/bern_pyr.m | 1,162 | utf_8 | e05be17e730cbb437109d06c7b108ec5 | function [V Vr Vs Vt Va Vb Vc] = bern_pyr(N,r,s,t)
a = 2*(r+1)./(1-t)-1;
b = 2*(s+1)./(1-t)-1;
c = t;
ids = abs(t-1)<1e-8;
a(ids) = -1;
b(ids) = -1;
dadr = 2./(1-t);
dbds = 2./(1-t);
dadt = (1+a)./(1-t);
dbdt = (1+b)./(1-t);
sk = 1;
for k = 0:N
for i = 0:N-k
for j = 0:N-k
... |
github | KGuo26/WADG_Matlab-master | bern_basis_tri.m | .m | WADG_Matlab-master/bern_basis_tri.m | 2,146 | utf_8 | b5c5ae2fee4e7f3bac98387d91b0b6d1 | function [V Vr Vs V1 V2 V3 id] = bern_basis_tri(N,r,s)
[V Vr Vs V1 V2 V3] = bern_tri(N,r,s);
return
%
% % use equivalence between W&B and equispaced nodes - get ordering
% [re se] = EquiNodes2D(N); [re se] = xytors(re,se);
% Ve = bern_tri(N,re,se);
% for i = 1:size(Ve,2)
% [val iid] = max(Ve(:,i));
% id(i) =... |
github | KGuo26/WADG_Matlab-master | Sample2D.m | .m | WADG_Matlab-master/Sample2D.m | 717 | utf_8 | be53a9f67b172f80cb774efda6b67d4b |
function [sampleweights,sampletri] = Sample2D(xout, yout)
% function [sampleweights,sampletri] = Sample2D(xout, yout)
% purpose: input = coordinates of output data point
% output = number of containing tri and interpolation weights
% [ only works for straight sided triangles ]
Globals2D;
% find... |
github | KGuo26/WADG_Matlab-master | tet_cubature.m | .m | WADG_Matlab-master/tet_cubature.m | 103,616 | utf_8 | 8f12d404868158cf03a4dc3cae2976bb | function [r s t w] = tet_cubature(N)
% CubatureData3D_GX.cpp
% database of precalculated cubature data
% 2012/05/02
%---------------------------------------------------------
%
% N [1 1 1] [2 2] [3 3] [4
% cubN 1 2 3 4 5 6 7 8
% Ncub 1 4 6 1... |
github | KGuo26/WADG_Matlab-master | ElasticityAcoustic2D_unified.m | .m | WADG_Matlab-master/ElasticityAcoustic2D_unified.m | 12,686 | utf_8 | a6984b83845491ed80d1ac4cd5a5ea33 | % function ElasticAcoustic2D_unified
clear all, clear
% clear -global *
Globals2D
global tau1 tau2
global ue3 ue4 ue5 ua3
global fa1 fa2 fe1 fe2
K1D = 16;
N = 4;
c_flag = 0;
FinalTime = 2.0;
tau1 = 1;
tau2 = 1;
[Nv, VX, VY, K, EToV] = unif_tri_mesh(K1D);
StartUp2D;
[rp sp] = EquiNodes2D(15); [rp sp] = xytors(rp... |
github | KGuo26/WADG_Matlab-master | Wave2D_M_manufactured.m | .m | WADG_Matlab-master/Wave2D_M_manufactured.m | 3,244 | utf_8 | 7f7a6ac983321b0bffeaf09bbd71fa7f | clear
Globals2D
N = 8;
k=1
for M = 0:N
K1D = 32;
FinalTime = 1.0;
[Nv, VX, VY, K, EToV] = unif_tri_mesh(K1D);
StartUp2D;
%% Set up wavespeed function
%cfun = @(x,y) ones(size(x));
cfun = @(x,y) 1+ 0.5*sin(k*pi*x).*sin(k*pi*y); % smooth velocity
%cfun = @(x,y) (1 + .5*sin(2*pi*x).*sin(2*pi*y) + (y > 0)); % piecewise... |
github | caghangir/MATLAB-Grid-Search-for-Neural-Networks-master | gridSearchNN.m | .m | MATLAB-Grid-Search-for-Neural-Networks-master/gridSearchNN.m | 2,393 | utf_8 | 40eb11e58ad1cf4dfde388d97884b636 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Grid Search for Matlab %
% %
% Copyright (C) 2017 Cagatay Demirel. All rights reserved. %
% demirelc16@itu.edu.tr %
% ... |
github | mrmushfiq/qalma-master | qalma.m | .m | qalma-master/qalma.m | 5,230 | utf_8 | 0e9226a0f71ab8eb22b7073be7ef050e | function varargout = qalma(varargin)
% QALMA MATLAB code for qalma.fig
% QALMA, by itself, creates a new QALMA or raises the existing
% singleton*.
%
% H = QALMA returns the handle to a new QALMA or the handle to
% the existing singleton*.
%
% QALMA('CALLBACK',hObject,eventData,handles,...) cal... |
github | mrmushfiq/qalma-master | star_shot.m | .m | qalma-master/star_shot/star_shot.m | 26,383 | utf_8 | 2b44cdb1fdf5db0023e49a8971a36020 | % M. Mushfiqur Rahman
% Florida Atlantic University
% August, 2017
function varargout = star_shot(varargin)
% STAR_SHOT MATLAB code for star_shot.fig
% STAR_SHOT, by itself, creates a new STAR_SHOT or raises the existing
% singleton*.
%
% H = STAR_SHOT returns the handle to a new STAR_SHOT or the handle... |
github | mrmushfiq/qalma-master | picket_panda.m | .m | qalma-master/picket_fence/picket_panda.m | 26,770 | utf_8 | d8e6b4b5b54d088f497dc1f0bef488f7 | % M. Mushfiqur Rahman
% Florida Atlantic University
% August, 2017
function varargout = picket_panda(varargin)
% PICKET_PANDA MATLAB code for picket_panda.fig
% PICKET_PANDA, by itself, creates a new PICKET_PANDA or raises the existing
% singleton*.
%
% H = PICKET_PANDA returns the handle to a new PICKE... |
github | mrmushfiq/qalma-master | dynalog.m | .m | qalma-master/dynalog/dynalog.m | 7,639 | utf_8 | 5c135192ef465a4baa5b642159850bd1 | % M. Mushfiqur Rahman
% Florida Atlantic University
% August, 2017
function varargout = dynalog(varargin)
% DYNALOG MATLAB code for dynalog.fig
% DYNALOG, by itself, creates a new DYNALOG or raises the existing
% singleton*.
%
% H = DYNALOG returns the handle to a new DYNALOG or the handle to
% th... |
github | mrmushfiq/qalma-master | wl.m | .m | qalma-master/winston_lutz/wl.m | 21,479 | utf_8 | 5c9533af6cf6ffa9e93af23d9a8cf14a | % M. Mushfiqur Rahman
% Florida Atlantic University
% August, 2017
function varargout = wl(varargin)
% WL MATLAB code for wl.fig
% WL, by itself, creates a new WL or raises the existing
% singleton*.
%
% H = WL returns the handle to a new WL or the handle to
% the existing singleton*.
%
% WL('... |
github | mrmushfiq/qalma-master | ci.m | .m | qalma-master/ci/ci.m | 25,849 | utf_8 | bda1d740da1c89b97e876fc2f9ffe66e | % M. Mushfiqur Rahman
% Florida Atlantic University
% August, 2017
function varargout = ci(varargin)
% CI MATLAB code for ci.fig
% CI, by itself, creates a new CI or raises the existing
% singleton*.
%
% H = CI returns the handle to a new CI or the handle to
% the existing singleton*.
%
% CI('... |
github | andyzeng/arc-robot-vision-master | fill_depth_cross_bf.m | .m | arc-robot-vision-master/suction-based-grasping/external/bxf/fill_depth_cross_bf.m | 1,990 | utf_8 | b7e5bbcb1bedcb7426978f7df1777af9 | % In-paints the depth image using a cross-bilateral filter. The operation
% is implemented via several filterings at various scales. The number of
% scales is determined by the number of spacial and range sigmas provided.
% 3 spacial/range sigmas translated into filtering at 3 scales.
%
% Args:
% imgRgb - the RGB im... |
github | andyzeng/arc-robot-vision-master | sub2ind2d.m | .m | arc-robot-vision-master/parallel-jaw-grasping/baseline/sub2ind2d.m | 135 | utf_8 | 4970286e0c7d89b91364ca0d54668cff | % A faster version of sub2ind for 2D case
function linIndex = sub2ind2d(sz, rowSub, colSub)
linIndex = (colSub-1) * sz(1) + rowSub;
|
github | drbenvincent/darc-experiments-matlab-master | addSubFoldersToPath.m | .m | darc-experiments-matlab-master/darc-toolbox/addSubFoldersToPath.m | 792 | utf_8 | 1e9a817e1ab0ef5e56a1b36ec9f9397c | function addSubFoldersToPath()
pathOfThisFunction = mfilename('fullpath');
[currentpath, ~, ~]= fileparts(pathOfThisFunction);
allSubpaths = strsplit( genpath(currentpath) ,':');
blacklist={'.git','.ignore','.graffle','.'}; % '.' is any hidden folder
pathsToAdd={};
for n=1:numel(allSubpaths)
if shouldAddThisPath(all... |
github | drbenvincent/darc-experiments-matlab-master | checkGitHubDependencies.m | .m | darc-experiments-matlab-master/darc-toolbox/checkGitHubDependencies.m | 2,973 | utf_8 | 30b69fd5ee94882628374c7dbfc419ea | function checkGitHubDependencies(dependencies)
% This function takes a cell array of url's to hithub repositories, loop through
% them and ensure they exist on the path, or clone them to your local machine.
%
% Example input:
%
% dependencies={...
% 'https://github.com/drbenvincent/mcmc-utils-matlab',...
% 'https://g... |
github | drbenvincent/darc-experiments-matlab-master | pdftops.m | .m | darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/pdftops.m | 6,161 | utf_8 | 5edac4bbbdae30223cb246a4ec7313d6 | function varargout = pdftops(cmd)
%PDFTOPS Calls a local pdftops executable with the input command
%
% Example:
% [status result] = pdftops(cmd)
%
% Attempts to locate a pdftops executable, finally asking the user to
% specify the directory pdftops was installed into. The resulting path is
% stored for futur... |
github | drbenvincent/darc-experiments-matlab-master | crop_borders.m | .m | darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/crop_borders.m | 5,133 | utf_8 | b744bf935914cfa6d9ff82140b48291e | function [A, vA, vB, bb_rel] = crop_borders(A, bcol, padding, crop_amounts)
%CROP_BORDERS Crop the borders of an image or stack of images
%
% [B, vA, vB, bb_rel] = crop_borders(A, bcol, [padding])
%
%IN:
% A - HxWxCxN stack of images.
% bcol - Cx1 background colour vector.
% padding - scalar indicating ... |
github | drbenvincent/darc-experiments-matlab-master | isolate_axes.m | .m | darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/isolate_axes.m | 4,851 | utf_8 | 611d9727e84ad6ba76dcb3543434d0ce | function fh = isolate_axes(ah, vis)
%ISOLATE_AXES Isolate the specified axes in a figure on their own
%
% Examples:
% fh = isolate_axes(ah)
% fh = isolate_axes(ah, vis)
%
% This function will create a new figure containing the axes/uipanels
% specified, and also their associated legends and colorbars. The o... |
github | drbenvincent/darc-experiments-matlab-master | im2gif.m | .m | darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/im2gif.m | 6,234 | utf_8 | 8ee74d7d94e524410788276aa41dd5f1 | %IM2GIF Convert a multiframe image to an animated GIF file
%
% Examples:
% im2gif infile
% im2gif infile outfile
% im2gif(A, outfile)
% im2gif(..., '-nocrop')
% im2gif(..., '-nodither')
% im2gif(..., '-ncolors', n)
% im2gif(..., '-loops', n)
% im2gif(..., '-delay', n)
%
% This function c... |
github | drbenvincent/darc-experiments-matlab-master | read_write_entire_textfile.m | .m | darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/read_write_entire_textfile.m | 961 | utf_8 | 775aa1f538c76516c7fb406a4f129320 | %READ_WRITE_ENTIRE_TEXTFILE Read or write a whole text file to/from memory
%
% Read or write an entire text file to/from memory, without leaving the
% file open if an error occurs.
%
% Reading:
% fstrm = read_write_entire_textfile(fname)
% Writing:
% read_write_entire_textfile(fname, fstrm)
%
%IN:
% fn... |
github | drbenvincent/darc-experiments-matlab-master | pdf2eps.m | .m | darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/pdf2eps.m | 1,522 | utf_8 | 4c8f0603619234278ed413670d24bdb6 | %PDF2EPS Convert a pdf file to eps format using pdftops
%
% Examples:
% pdf2eps source dest
%
% This function converts a pdf file to eps format.
%
% This function requires that you have pdftops, from the Xpdf suite of
% functions, installed on your system. This can be downloaded from:
% http://www.foolabs.c... |
github | drbenvincent/darc-experiments-matlab-master | print2array.m | .m | darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/print2array.m | 10,376 | utf_8 | a2022c32ae3efa6007a326692227bd39 | function [A, bcol] = print2array(fig, res, renderer, gs_options)
%PRINT2ARRAY Exports a figure to an image array
%
% Examples:
% A = print2array
% A = print2array(figure_handle)
% A = print2array(figure_handle, resolution)
% A = print2array(figure_handle, resolution, renderer)
% A = print2array(figur... |
github | drbenvincent/darc-experiments-matlab-master | append_pdfs.m | .m | darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/append_pdfs.m | 2,759 | utf_8 | 9b52be41aff48bea6f27992396900640 | %APPEND_PDFS Appends/concatenates multiple PDF files
%
% Example:
% append_pdfs(output, input1, input2, ...)
% append_pdfs(output, input_list{:})
% append_pdfs test.pdf temp1.pdf temp2.pdf
%
% This function appends multiple PDF files to an existing PDF file, or
% concatenates them into a PDF file if the o... |
github | drbenvincent/darc-experiments-matlab-master | using_hg2.m | .m | darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/using_hg2.m | 1,100 | utf_8 | 47ca10d86740c27b9f6b397373ae16cd | %USING_HG2 Determine if the HG2 graphics engine is used
%
% tf = using_hg2(fig)
%
%IN:
% fig - handle to the figure in question.
%
%OUT:
% tf - boolean indicating whether the HG2 graphics engine is being used
% (true) or not (false).
% 19/06/2015 - Suppress warning in R2015b; cache result for i... |
github | drbenvincent/darc-experiments-matlab-master | eps2pdf.m | .m | darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/eps2pdf.m | 8,793 | utf_8 | 474e976cf6454d5d7850baf14494fedf | function eps2pdf(source, dest, crop, append, gray, quality, gs_options)
%EPS2PDF Convert an eps file to pdf format using ghostscript
%
% Examples:
% eps2pdf source dest
% eps2pdf(source, dest, crop)
% eps2pdf(source, dest, crop, append)
% eps2pdf(source, dest, crop, append, gray)
% eps2pdf(source, de... |
github | drbenvincent/darc-experiments-matlab-master | export_fig.m | .m | darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/export_fig.m | 64,681 | utf_8 | 6eb52ba116cd2632d3e98cfafa45bca1 | function [imageData, alpha] = export_fig(varargin)
%EXPORT_FIG Exports figures in a publication-quality format
%
% Examples:
% imageData = export_fig
% [imageData, alpha] = export_fig
% export_fig filename
% export_fig filename -format1 -format2
% export_fig ... -nocrop
% export_fig ... -c[<val>,<... |
github | drbenvincent/darc-experiments-matlab-master | ghostscript.m | .m | darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/ghostscript.m | 7,902 | utf_8 | ff62a40d651197dbea5d3c39998b3bad | function varargout = ghostscript(cmd)
%GHOSTSCRIPT Calls a local GhostScript executable with the input command
%
% Example:
% [status result] = ghostscript(cmd)
%
% Attempts to locate a ghostscript executable, finally asking the user to
% specify the directory ghostcript was installed into. The resulting path... |
github | drbenvincent/darc-experiments-matlab-master | fix_lines.m | .m | darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/fix_lines.m | 6,441 | utf_8 | ffda929ebad8144b1e72d528fa5d9460 | %FIX_LINES Improves the line style of eps files generated by print
%
% Examples:
% fix_lines fname
% fix_lines fname fname2
% fstrm_out = fixlines(fstrm_in)
%
% This function improves the style of lines in eps files generated by
% MATLAB's print function, making them more similar to those seen on
% scre... |
github | drbenvincent/darc-experiments-matlab-master | PosteriorPrediction1D.m | .m | darc-experiments-matlab-master/darc-toolbox/dependencies/mcmc-utils-matlab/+mcmc/PosteriorPrediction1D.m | 7,063 | utf_8 | 3b31fa324f33b1e3af91b9ef1a04d68e | classdef PosteriorPrediction1D < handle
%% PosteriorPrediction1D
properties
variableNames
fh
xInterp
%samples
Y
nSamples
samples
ciType
nExamples
pointEstimateType
pointEstimate
shouldPlotData
xData, yData
ciWidth
h % a structure containing handles to figure and plot objects
end
prop... |
github | drbenvincent/darc-experiments-matlab-master | kde.m | .m | darc-experiments-matlab-master/darc-toolbox/dependencies/mcmc-utils-matlab/+mcmc/+kde/kde.m | 5,689 | utf_8 | 910728965b89850c5c417eef4698a074 | function [bandwidth,density,xmesh,cdf]=kde(data,n,tstar_max,MIN,MAX)
% Tom Edit: added a tstar_max to limit the bandwidth if desired. It is a
% scaled value, default = inf;
%
% Reliable and extremely fast kernel density estimator for one-dimensional data;
% Gaussian kernel is assumed and the bandwidth is chose... |
github | drbenvincent/darc-experiments-matlab-master | kde2d.m | .m | darc-experiments-matlab-master/darc-toolbox/dependencies/mcmc-utils-matlab/+mcmc/+kde2d/kde2d.m | 7,506 | utf_8 | 6d82435d2728e267990a5041d6b289b2 | function [bandwidth,density,X,Y]=kde2d(data,n,MIN_XY,MAX_XY)
% fast and accurate state-of-the-art
% bivariate kernel density estimator
% with diagonal bandwidth matrix.
% The kernel is assumed to be Gaussian.
% The two bandwidth parameters are
% chosen optimally without ever
% using/assuming a parametric model f... |
github | drbenvincent/darc-experiments-matlab-master | my_shaded_errorbar_zone_UL.m | .m | darc-experiments-matlab-master/darc-toolbox/utils-plotting/my_shaded_errorbar_zone_UL.m | 815 | utf_8 | 5455a1384ba84136387029c983c8a775 | function [h]=my_shaded_errorbar_zone_UL(x,upper,lower,col)
% Plots a shaded region of error
%
% my_shaded_errorbar_zone_UL([-10:0.1:10],[-10:0.1:10]+1,[-10:0.1:10]-1,[0.7 0.7 0.7])
%
% eg, my_shaded_errorbar_zone([-10:0.1:10],x,abs(randn(size(x)))+2,[0 0 1])
%
%handle=patch([x max(x)-x+min(x)],[y+e flipud(y'-e')... |
github | drbenvincent/darc-experiments-matlab-master | plotUtilityFunction.m | .m | darc-experiments-matlab-master/darc-experiments/@Model/plotUtilityFunction.m | 2,211 | utf_8 | e70f242efd3d46c6ec678cceec49d126 | function plotUtilityFunction(obj, thetaStruct, varargin)
p = inputParser;
p.FunctionName = mfilename;
p.addRequired('thetaStruct',@isstruct_or_table);
% p.addParameter('xScale','linear',@(x)any(strcmp(x,{'linear','log'})));
p.addParameter('data',[],@istable);
p.addParameter('pointEstimateType','mean',@isstr);
% p.addP... |
github | drbenvincent/darc-experiments-matlab-master | plotProbFunction.m | .m | darc-experiments-matlab-master/darc-experiments/@Model/plotProbFunction.m | 4,833 | utf_8 | 2fd2b28515f7226b9bda9d45b14e68a6 | function plotProbFunction(obj, thetaStruct, varargin)
p = inputParser;
p.FunctionName = mfilename;
p.addRequired('thetaStruct',@isstruct_or_table);
p.addParameter('xScale','linear',@(x)any(strcmp(x,{'linear','log'})));
p.addParameter('data',[],@istable);
p.addParameter('pointEstimateType','mean',@isstr);
p.addParamete... |
github | drbenvincent/darc-experiments-matlab-master | plotDiscountFunction.m | .m | darc-experiments-matlab-master/darc-experiments/@Model/plotDiscountFunction.m | 4,074 | UNKNOWN | cd3e7d94e8978e564eef501175a3a9c6 | function plotDiscountFunction(obj, thetaStruct, varargin)
p = inputParser;
p.FunctionName = mfilename;
p.addRequired('thetaStruct',@isstruct_or_table);
p.addParameter('xScale','linear',@(x)any(strcmp(x,{'linear','log'})));
p.addParameter('data',[],@istable);
p.addParameter('pointEstimateType','mean',@isstr);
p.addPara... |
github | drbenvincent/darc-experiments-matlab-master | plotDiscountSurface.m | .m | darc-experiments-matlab-master/darc-experiments/@Model_hyperbolic1_time_and_prob/plotDiscountSurface.m | 4,143 | UNKNOWN | 09dea0179f9b5215e875502ae0b37f08 | function plotDiscountSurface(obj, thetaStruct, varargin)
% plots prob and time discount surface
p = inputParser;
p.FunctionName = mfilename;
p.addRequired('thetaStruct',@isstruct);
p.addParameter('xScale','linear',@(x)any(strcmp(x,{'linear','log'})));
p.addParameter('data',[],@isstruct_or_table)
p.addParameter('pointE... |
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