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values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | MingleiLI/fast-rcnn-master | fast_rcnn_load_net.m | .m | fast-rcnn-master/matlab/fast_rcnn_load_net.m | 687 | utf_8 | a32914abb31b109189f11729893e76a1 | % --------------------------------------------------------
% Fast R-CNN
% Copyright (c) 2015 Microsoft
% Licensed under The MIT License [see LICENSE for details]
% Written by Ross Girshick
% --------------------------------------------------------
function model = fast_rcnn_load_net(def, net, use_gpu)
% Load a Fast R-... |
github | MingleiLI/fast-rcnn-master | showboxes.m | .m | fast-rcnn-master/matlab/showboxes.m | 741 | utf_8 | 1429b3b8aebb962f3aefc255f17c204b | % --------------------------------------------------------
% Fast R-CNN
% Copyright (c) 2015 Microsoft
% Licensed under The MIT License [see LICENSE for details]
% Written by Ross Girshick
% --------------------------------------------------------
function showboxes(im, boxes)
image(im);
axis image;
axis off;
set(gcf... |
github | MingleiLI/fast-rcnn-master | fast_rcnn_demo.m | .m | fast-rcnn-master/matlab/fast_rcnn_demo.m | 1,815 | utf_8 | bf4f15d2215f13cd6cfa12f13d5b6aa8 | % --------------------------------------------------------
% Fast R-CNN
% Copyright (c) 2015 Microsoft
% Licensed under The MIT License [see LICENSE for details]
% Written by Ross Girshick
% --------------------------------------------------------
function fast_rcnn_demo()
% Fast R-CNN demo (in matlab).
[folder, name... |
github | MingleiLI/fast-rcnn-master | fast_rcnn_im_detect.m | .m | fast-rcnn-master/matlab/fast_rcnn_im_detect.m | 4,211 | utf_8 | 728920133ba2a640b1cb4e52f41c1977 | % --------------------------------------------------------
% Fast R-CNN
% Copyright (c) 2015 Microsoft
% Licensed under The MIT License [see LICENSE for details]
% Written by Ross Girshick
% --------------------------------------------------------
function dets = fast_rcnn_im_detect(model, im, boxes)
% Perform detecti... |
github | nijingchao/NoNCrossRank-master | BFS_Layer.m | .m | NoNCrossRank-master/CQ/BFS_Layer.m | 1,369 | utf_8 | d7ba0ae92072d8c339f1e6dedc98aba2 | %% One layer BFS search
function [BFS_Dis, BFS_Sq, BFS_Sqh, BFS_Sqt] = BFS_Layer(G, BFS_Dis, BFS_Sq, BFS_Sqh, BFS_Sqt, Layer)
% BFS Compute breadth first search distances, times, and tree for a graph
%
% [d dt pred] = bfs(A,u) returns the distance (d) and the discover time
% (dt) for each vertex in the graph in ... |
github | nijingchao/NoNCrossRank-master | RunCQ_Fast.m | .m | NoNCrossRank-master/CQ/RunCQ_Fast.m | 2,289 | utf_8 | 7f6ad8acde778cbba85b49f8bd3da9ce | %% CrossQuery-Fast evaluation on DBLP dataset
function [TopKAuthorNames, RelevantDomains] = RunCQ_Fast(alpha, c, epsilon, q, s, d, k)
%%% Input parameters
%
% If no input parameters are provided, the default values will be used.
%
% alpha: a regularization parameter for cross-network consistency
% c: a regular... |
github | nijingchao/NoNCrossRank-master | CQ_Fast.m | .m | NoNCrossRank-master/CQ/CQ_Fast.m | 1,829 | utf_8 | 931662a32f01f71d55c8efe494cfdd3f | %% CrossQuery-Fast
function [TopKResults, SubG_Idx] = CQ_Fast(Anorm, Y, G, q, s, d, k, alpha, c, epsilon, A_ID)
%%% Input parameters
%
% Anorm: the aggregated normalized adjacency matrix of domain-specific netowrks
% Y: the matrix encoding the cross-domain mapping information
% G: the adjacency matrix of the ma... |
github | nijingchao/NoNCrossRank-master | Precomputation.m | .m | NoNCrossRank-master/CQ/Precomputation.m | 1,348 | utf_8 | b7473eb02acb500147d603fb2c499bf0 | %% CR and CQ precomputation
function Precomputation(A, A_ID, G, PrecompFileName)
%%% Input parameters
%
% A: the domain-specific networks
% A_ID: the corresponding IDs of domain-specific networks in A
% G: the adjacency matrix of the main network
% PrecompFileName: the file name to store precomputation results... |
github | nijingchao/NoNCrossRank-master | RunCR_DBLP.m | .m | NoNCrossRank-master/CQ/RunCR_DBLP.m | 2,806 | utf_8 | a7eb3a6cc1a93909f7d2ebc8f907b61b | %% CrossRank evaluation on DBLP dataset
function TopKAuthorNames = RunCR_DBLP(alpha, c, MaxIter, epsilon, q, s, d, k)
%%% Input parameters
%
% If no input parameters are provided, the default values will be used.
%
% alpha: a regularization parameter for cross-network consistency
% c: a regularization paramete... |
github | nijingchao/NoNCrossRank-master | ExtractSubNet.m | .m | NoNCrossRank-master/CQ/ExtractSubNet.m | 3,698 | utf_8 | ab3b7dcaac5e1582bd1f3c98c6df5653 | %% Extract a relevant subnetwork from the main network w.r.t. source and target domains
function SubG_Idx = ExtractSubNet(G, s, d, epsilon)
%%% Input parameters
%
% G: the adjacency matrix of the main network
% s: the index of the source domain-specific network
% d: the index of the target domain-specific netwo... |
github | nijingchao/NoNCrossRank-master | CR.m | .m | NoNCrossRank-master/CQ/CR.m | 1,670 | utf_8 | d63b83343085ed5ad736c36640850a82 | %% CR
function [r, Objs, Deltas] = CR(Anorm, Ynorm, I_n, e, alpha, c, MaxIter, epsilon)
%%% Input parameters
%
% Anorm: the aggregated normalized adjacency matrix of domain-specific netowrks
% Ynorm: the normalized matrix encoding the cross-domain mapping information
% I_n: an identity matrix of size n
% e: th... |
github | nijingchao/NoNCrossRank-master | RunCQ_Basic.m | .m | NoNCrossRank-master/CQ/RunCQ_Basic.m | 2,129 | utf_8 | 29ce510784a4aa942fe9f6252bffb4f6 | %% CrossQuery-Basic evaluation on DBLP dataset
function TopKAuthorNames = RunCQ_Basic(alpha, c, q, s, d, k)
%%% Input parameters
%
% If no input parameters are provided, the default values will be used.
%
% alpha: a regularization parameter for cross-network consistency
% c: a regularization parameter for quer... |
github | nijingchao/NoNCrossRank-master | DijkstraExpansion.m | .m | NoNCrossRank-master/CQ/DijkstraExpansion.m | 2,421 | utf_8 | 27c1a397f1908382bd939c66b203cf42 | %% One step expansion of Dijkstra's algorithm
function [u, Len, H, P, Dis_s] = DijkstraExpansion(rp, ci, vi, H, P, Dis_s, Len)
%%% Input parameters
%
% rp, ci, ai: See sparse_to_csr.m
% H: The heap of node indices
% P: The heap positions of nodes
% Dis_s: The distance vector of each node to the source node
% ... |
github | nijingchao/NoNCrossRank-master | CQ_Basic.m | .m | NoNCrossRank-master/CQ/CQ_Basic.m | 2,313 | utf_8 | 0b136d853be47cc2ecc9b616f94b2d9e | %% CrossQuery-Basic
function TopKResults = CQ_Basic(W, q, s, d, k, tilde_c, A_ID)
%%% Input parameters
%
% W: the transition matrix
% q: the ID of the query node of interest
% s: the ID of the source domain-specific network
% d: the ID of the target domain-specific network
% k: the number of retrieved nodes
... |
github | nijingchao/NoNCrossRank-master | CR_Precomputation.m | .m | NoNCrossRank-master/CR/CR_Precomputation.m | 1,339 | utf_8 | d9e94380ac5d89f536d61408f724ba34 | %% CR precomputation
function CR_Precomputation(A, A_ID, G, PrecompFileName)
%%% Input parameters
%
% A: the domain-specific networks
% A_ID: the corresponding IDs of domain-specific networks in A
% G: the adjacency matrix of the main network
% PrecompFileName: the file name to store precomputation results
... |
github | nijingchao/NoNCrossRank-master | AUCValue.m | .m | NoNCrossRank-master/CR/AUCValue.m | 619 | utf_8 | 41d2ae67addd21294c37084d85d1ee4f | %% Compute AUC value with n false positives
function z = AUCValue(Rank, n)
%% Parameter initialization
loop = length(Rank);
numerator = 0;
TP = 0;
FP = 0;
AllTP = length(find(Rank == 1));
%% Calculation loop
for i = 1:loop
if Rank(i) == 1
TP = TP + 1;
else
FP = FP + 1;
... |
github | nijingchao/NoNCrossRank-master | CR_CrossValidation.m | .m | NoNCrossRank-master/CR/CR_CrossValidation.m | 4,151 | utf_8 | 8be6fca28f774edae8b7ca91706a7736 | %% CR leave-one-out cross validation on tissue-specific PPI networks
function CR_CrossValidation(alpha, c, MaxIter, epsilon)
%%% Input parameters
%
% If no input parameters are provided, the default values will be used.
%
% alpha: a regularization parameter for cross-network consistency
% c: a regularization p... |
github | nijingchao/NoNCrossRank-master | AUCEvaluation.m | .m | NoNCrossRank-master/CR/AUCEvaluation.m | 1,048 | utf_8 | 8b20ac87d57c9cf2602f8fe063856acc | %% AUC value evaluation
function AUCEvaluation(RankRecord, ExpandSeeds, AllGeneID)
ROCn = zeros(6, length(RankRecord));
topn = zeros(length(RankRecord{1}), length(RankRecord));
for j = 1:length(ExpandSeeds)
for k = 1:length(RankRecord{1})
real_row = AllGeneID(RankRecord{j}(k)); % I... |
github | nijingchao/NoNCrossRank-master | CR.m | .m | NoNCrossRank-master/CR/CR.m | 1,670 | utf_8 | d15390e9cccabc6118c7844ec0be2e8e | %% CR
function [r, Objs, Deltas] = CR(Anorm, Ynorm, I_n, e, alpha, c, MaxIter, epsilon)
%%% Input parameters
%
% Anorm: the aggregated normalized adjacency matrix of domain-specific netowrks
% Ynorm: the normalized matrix encoding the cross-domain mapping information
% I_n: an identity matrix of size n
% e: th... |
github | nijingchao/NoNCrossRank-master | J_CR.m | .m | NoNCrossRank-master/CR/J_CR.m | 193 | utf_8 | b5291be6a0f3108d0f6fba7dd5a73b3c | %% CrossRank objective function value
function Obj = J_CR(Anorm, Ynorm, I_n, r, e, alpha, c)
X = I_n - Ynorm;
Obj = r'*c*(I_n-Anorm)*r + (1-c)*norm(r-e, 'fro')^2 + 2*alpha*(r'*X*r);
end |
github | ominux/gpumcml-master | read_file_mco.m | .m | gpumcml-master/gpumcml/simple/viewoutput/read_file_mco.m | 6,141 | UNKNOWN | 71da1440997fbe337f73765fae52b03c | % [s] = read_file_mco;
%
% Reads an output file from MCML into a MatLab structure. The file is
% selected using a GUI.
%
% The structure contains 18 fields:
% step_size (Step sizes in z and r)
% step_num (Number of steps in z, r and a)
% spec_refl (Specular reflectance at the surface)
% diff_refl (Re... |
github | ominux/gpumcml-master | read_file_mco.m | .m | gpumcml-master/gpumcml/fast/viewoutput/read_file_mco.m | 6,141 | UNKNOWN | 71da1440997fbe337f73765fae52b03c | % [s] = read_file_mco;
%
% Reads an output file from MCML into a MatLab structure. The file is
% selected using a GUI.
%
% The structure contains 18 fields:
% step_size (Step sizes in z and r)
% step_num (Number of steps in z, r and a)
% spec_refl (Specular reflectance at the surface)
% diff_refl (Re... |
github | ominux/gpumcml-master | read_file_mco.m | .m | gpumcml-master/validation/read_file_mco.m | 6,141 | UNKNOWN | 71da1440997fbe337f73765fae52b03c | % [s] = read_file_mco;
%
% Reads an output file from MCML into a MatLab structure. The file is
% selected using a GUI.
%
% The structure contains 18 fields:
% step_size (Step sizes in z and r)
% step_num (Number of steps in z, r and a)
% spec_refl (Specular reflectance at the surface)
% diff_refl (Re... |
github | kapcom01/Curviliniar_Detector-master | curvilinear_gui.m | .m | Curviliniar_Detector-master/curvilinear_gui.m | 13,310 | utf_8 | 380c602f798236e4c39da4bb8c3b2279 | function varargout = curvilinear_gui(varargin)
% CURVILINEAR MATLAB code for CURVILINEAR.fig
% CURVILINEAR, by itself, creates a new CURVILINEAR or raises the existing
% singleton*.
% Edit the above text to modify the response to help CURVILINEAR
% Last Modified by GUIDE v2.5 13-Jul-2015 18:42:36
% Begin i... |
github | kapcom01/Curviliniar_Detector-master | gaussgradient.m | .m | Curviliniar_Detector-master/include/gaussgradient/gaussgradient.m | 1,190 | utf_8 | 9d8aa5a1274d37d5ea47c1774dab559b | function [gx,gy]=gaussgradient(IM,sigma)
%GAUSSGRADIENT Gradient using first order derivative of Gaussian.
% [gx,gy]=gaussgradient(IM,sigma) outputs the gradient image gx and gy of
% image IM using a 2-D Gaussian kernel. Sigma is the standard deviation of
% this kernel along both directions.
%
% Contributed b... |
github | cultpenguin/sippi-master | sippi_likelihood_obsolete.m | .m | sippi-master/sippi_likelihood_obsolete.m | 10,122 | utf_8 | c3f1973eb3f6ebfd293d2dddc5c17fb4 | % sippi_likelihood Compute likelihood given an observed dataset
%
% Call
% [logL,L,data]=sippi_likelihood(d,data);
%
%
% data{1}.d_obs [N_data,1] N_data data observations
% data{1}.d_std [N_data,1] N_data uncorrelated Gaussian STD
%
% data{1}.d_var [N_data,1] N_data uncorrelated Gaussian variances
%
%
% Gaussian m... |
github | cultpenguin/sippi-master | sippi_prior_gamma.m | .m | sippi-master/sippi_prior_gamma.m | 1,031 | utf_8 | 0a597e1d43d20554dbed8226ea382a75 | % sippi_prior_gamma : gamma prior for SIPPI
%
% See also: sippi_prior_init, sippi_prior
%
function [m_propose,prior]=sippi_prior_gamma(prior,m_current,ip);
if nargin<3;
ip=1;
end
if ~isfield(prior{ip},'init')
prior=sippi_prior_init(prior);
end
if ~isfield(prior{ip},'rprior')
% This can be ... |
github | cultpenguin/sippi-master | sippi_prior.m | .m | sippi-master/sippi_prior.m | 20,090 | utf_8 | 638951076a5e76d07aa92756b62e7083 | % sippi_prior: A priori models for SIPPI
%
% To generate a realization of the prior model defined by the prior structure use:
% [m_propose,prior]=sippi_prior(prior);
%
% To generate a realization of the prior model defined by the prior structure,
% in the vicinity of a current model (using sequential Gibbs samp... |
github | cultpenguin/sippi-master | sippi_forward.m | .m | sippi-master/sippi_forward.m | 3,958 | utf_8 | 901702bdc78be8af1d8dc2d7c4d283b0 | % sippi_forward Simple forward wrapper for SIPPI
%
% Assumes that the actual forward solver has been defined by
% forward.forward_function
%
% Call:
% [d,forward,prior,data]=sippi_forward(m,forward)
%
% Optional:
% [d,forward,prior,data]=sippi_forward(m,forward,prior)
% [d,forward,prior,data]=sippi_for... |
github | cultpenguin/sippi-master | sippi_adjust_step_size.m | .m | sippi-master/sippi_adjust_step_size.m | 503 | utf_8 | 2b734184749b7182450bbee7a1458a80 | % sippi_adjust_step_size Adjust step length length for Metropolis sampler in SIPPI
%
% Call :
% step=sippi_adjust_step_size(step,P_average,P_target);
%
% step : current step
% P_current : Current acceptance ratio
% P_target : preferred acceptance ratio (def=0.3);
%
% See also sippi_compute_acceptance_ra... |
github | cultpenguin/sippi-master | sippi_prior_birthdeath.m | .m | sippi-master/sippi_prior_birthdeath.m | 6,295 | utf_8 | 5ced0a363e153bcabfa7fced4b9260a6 | % sippi_prior_birthdeath
%
% Call:
% [m,prior]=sippi_prior_birthdeath(prior,m_current,im)
%
%
% prior{im}.type='birthdeath'
% prior{im}.N_layers_min % min number of layers
% prior{im}.N_layers_max % max number of layers
% prior{im}.v_min % min value in layer
% prior{im}.v_max % max value in... |
github | cultpenguin/sippi-master | sippi_prior_voronoi.m | .m | sippi-master/sippi_prior_voronoi.m | 6,480 | utf_8 | 16df4411d624b9d5609706b18c5e9a03 | % sippi_prior_voronoi:
%
% TMH/2014
%
% Example:
%
% cells_N_max=5;
% dx=0.5;
% ip=1;
% prior{ip}.type='voronoi';
% prior{ip}.x=1:dx:20;
% prior{ip}.y=1:dx:20;
% prior{ip}.cells_N=cells_N_max; % SET NUMBER OF CELLS
% prior{ip}.cells_N_min=3;
% prior{ip}.cells_N_max=cells... |
github | cultpenguin/sippi-master | sippi_prior_mixsim.m | .m | sippi-master/sippi_prior_mixsim.m | 2,035 | utf_8 | a7dcf2142b6460ee442b57744dd85dc3 | % sippi_prior_mixsim : MIXsim prior for SIPPI (only 2D)
%
% Example:
% ip=1;
% prior{ip}.type='mixsim'; % MIXSIM type
% prior{ip}.x=[1:1:20]; % X array
% prior{ip}.y=[1:1:40]; % Y array
% TI=channels;
% TI=TI(3:3:100,1:1:10)+1;
% prior{1}.TI=TI; % must be integer values starting with 1! (no zero va... |
github | cultpenguin/sippi-master | sippi_prior_cholesky.m | .m | sippi-master/sippi_prior_cholesky.m | 4,152 | utf_8 | f89dec221bbecc16e1790b510f826576 | % sippi_prior_cholesky : Cholesky type Gaussian prior for SIPPI
%
%% Example:
% ip=1;
% prior{ip}.type='cholesky';
% prior{ip}.m0=10;
% prior{ip}.Cm='.001 Nug(0) + 1 Gau(10)';
% prior{ip}.x=0:1:100;linspace(0,100,20);
% prior{ip}.y=0:1:50;linspace(0,33,30);
% [m,prior]=sippi_prior_cholesky(prior)... |
github | cultpenguin/sippi-master | sippi_anneal_temperature.m | .m | sippi-master/sippi_anneal_temperature.m | 3,210 | utf_8 | 42ea4d8f5528fc509c801a92e5f4c351 | % sippi_anneal_temperature : compute annealing temperature for
% annealing type sampling
%
% %% ANNEALING (TEMPERATURE AS A FUNTION OF ITERAITON NUMBER)
% i % iteration number
%
% mcmc.anneal.i_begin=1; % default, iteration number when annealing begins
% mcmc.anneal.i_end=100000; % iteration number w... |
github | cultpenguin/sippi-master | sippi_mcmc_init.m | .m | sippi-master/sippi_mcmc_init.m | 2,869 | utf_8 | 474f4c14fa77dcab08e7aad496de381f | % sippi_mcmc_init Initialize McMC options for Metropolis and rejection sampling in SIPPI
%
% Call:
% options=sippi_mcmc_init(options,prior);
%
function options=sippi_mcmc_init(options,prior);
options.mcmc.null='';
if ~isfield(options.mcmc,'nite');options.mcmc.nite=30000;end
if isfield(options.mcmc,'n_sampl... |
github | cultpenguin/sippi-master | sippi_prior_pdf2.m | .m | sippi-master/sippi_prior_pdf2.m | 2,940 | utf_8 | 9b04b4aa41484dbc6503f6aac88d6aa4 | % sippi_prior_pdf: sampled from 2D PDF
function [m_propose,prior]=sippi_prior_pdf2(prior,m_current,ip);
if nargin == 0;
mu = [6 12;-3 5];
sigma = cat(3,[4 15],[5 1]); % 1-by-2-by-2 array
mu = [-5 5;5 16];
sigma = cat(3,[3 4],[3 14]); % 1-by-2-by-2 array
gm = gmdistribution(mu,sigma);
... |
github | cultpenguin/sippi-master | sippi_prior_plurigaussian.m | .m | sippi-master/sippi_prior_plurigaussian.m | 3,907 | utf_8 | 03c24844a3440a33decb1a08cda6ef37 | % sippi_prior_plurigaussian: Plurigaussian type prior for SIPPI
%
%% Example:
% % PluriGaussian based on one Gaussian model / truncated Gaussian
% ip=1;
% prior{ip}.type='plurigaussian';
% prior{ip}.x=1:1:100;
% prior{ip}.y=1:1:100;
% prior{ip}.Cm='1 Gau(10)';
% prior{ip}.pg_map=[0 0 0 0 1 1 0 0 ... |
github | cultpenguin/sippi-master | sippi_prior_dummy.m | .m | sippi-master/sippi_prior_dummy.m | 313 | utf_8 | ec76686caf5a11473e7586130c0b6637 | % sippi_prior_dummy : 'Dummy' prior for SIPPI.
%
%% Example:
% ip=1;
% prior{ip}.type='dummy';
% sippi_prior(prior)
%
function [m_propose,prior]=sippi_prior_dummy(prior,m_current,ip);
if nargin<3;
ip=1;
end
if nargin>1
m_propose{ip}=m_current{ip};
else
m_propose{ip}=NaN;
end |
github | cultpenguin/sippi-master | sippi_prior_snesim_std.m | .m | sippi-master/sippi_prior_snesim_std.m | 4,487 | utf_8 | 005842196a9c5474748a4029278f14c4 | % sippi_prior_snesim_std : SNESIM_STD (SGeMS) type Gaussian prior for SIPPI
%
% Requires SGeMS version 2.1b, available from
% http://sgems.sourceforge.net/?q=node/77
%
%% Example:
% ip=1;
% prior{ip}.type='snesim_std';
% prior{ip}.x=1:1:80;
% ... |
github | cultpenguin/sippi-master | sippi_prior_visim.m | .m | sippi-master/sippi_prior_visim.m | 9,732 | utf_8 | 8ac865d4b3c0842f30dfbabf7ce5da5c | % sippi_prior_visim : VISIM type Gaussian prior for SIPPI
%
%% Example:
% ip=1;
% prior{ip}.type='visim';
% prior{ip}.x=1:1:80;
% prior{ip}.y=1:1:80;
% prior{ip}.Cm='1 Sph(60)';
% m=sippi_prior(prior);
% sippi_plot_prior(prior,m)
%
% % optionally a specific random can be set using
% ... |
github | cultpenguin/sippi-master | sippi_compute_modelization_forward_error.m | .m | sippi-master/sippi_compute_modelization_forward_error.m | 5,893 | utf_8 | 6024e0af13ddb33782c2864309094692 | % sippi_compute_modelization_forward_error Computes an estimate of the modelization erro
%
% Computes and estimate of the Gaussian modelization error, N(dt,Ct)
% caused by the use of an imperfect forward kernel
%
% If called with only one output '[Ct]=sippi..]' then the Gaussian model is
% assumed by centered aro... |
github | cultpenguin/sippi-master | sippi_prior_snesim.m | .m | sippi-master/sippi_prior_snesim.m | 8,194 | utf_8 | 791f0c4330ed477820c167e5b115298a | % sippi_prior_snesim : SNESIM type Gaussian prior for SIPPI
%
% Using SNESIM form
% https://github.com/SCRFpublic/snesim-standalone
% Please remember to recompile SNESIM to uou needs,
% before using it with SIPPI
%
%
%% Example... |
github | cultpenguin/sippi-master | sippi_metropolis_gibbs_random_iteration_2d.m | .m | sippi-master/sippi_metropolis_gibbs_random_iteration_2d.m | 9,249 | utf_8 | 709aad6c04fce7564f5838689a42323c | % sippi_metropolis_gibbs_random_iteration(C,mcmc,i);
% computes a 2D marginal (using Nm ranbdom voronois cells)
% and movies to a new realizations
%
% See also: sippi_metropolis_gibbs_random_iteration
%
function [C,mcmc]=sippi_metropolis_gibbs_random_iteration_2d(C,mcmc,i,doPlot);
mcmc.gibbs.null='';
if ~isfiel... |
github | cultpenguin/sippi-master | sippi_prior_layered.m | .m | sippi-master/sippi_prior_layered.m | 3,658 | utf_8 | bc5491ab31489978a4964dc044328a8b | % sippi_prior_layered
%
% Call:
% [m,prior]=sippi_prior_layered(prior,m_current,im)
%
%
% Example
% im=1;
% prior{im}.type = 'layered';
% prior{im}.name = 'GrevieLayer';
% prior{im}.x=z;
%
% j=0;
% j=j+1;
% prior{im}.p_rho{j}.name = 'PreCat';
... |
github | cultpenguin/sippi-master | sippi_prior_dsim.m | .m | sippi-master/sippi_prior_dsim.m | 2,075 | utf_8 | e1541d34afc71faf46f31157b0d5c1d5 | % sippi_prior_dsim : Direct simulation in SIPPI
%
% Example:
%
% prior{1}.type='dsim';
% prior{1}.x=1:1:40;;
% prior{1}.y=1:1:30;;
% prior{1}.ti=channels;;
%
% m=sippi_prior(prior);
% sippi_plot_prior(prior,m);
%
%
%
% % OPTIONAL OPTIONS
%
% prior{1}.options.n_cond [int]: number of conditional poin... |
github | cultpenguin/sippi-master | sippi_prior_set_steplength.m | .m | sippi-master/sippi_prior_set_steplength.m | 1,123 | utf_8 | ee21a4036f6f5d73997f80cd365706c6 | % sippi_prior_set_steplength Set step length for Metropolis sampler in SIPPI
%
% Call
% prior=sippi_prior_set_steplength(prior,mcmc,im);
%
function prior=sippi_prior_set_steplength(prior,mcmc,im);
if nargin<3
im=1;
end
if ~isfield(mcmc,'i')
mcmc.i=length(mcmc.acc(im,:));
end
i_perturb=find(mc... |
github | cultpenguin/sippi-master | sippi_get_resim_data.m | .m | sippi-master/sippi_get_resim_data.m | 668 | utf_8 | 629f3708ddcee5108465be91655d4721 | % sippi_get_resim_data: Get conditional data for resimulation
%
% d_cond=sippi_get_resim_data(m_current,prior,ip);
%
% c_cond [n_cond,4]: col1: x, col2: y, col4: z, col4: d
%
% See also sippi_prior, sippi_sequential_gibbs_resim
%
function d_cond=sippi_get_resim_data(m_current,prior,ip);
if nargin<3
ip=1;... |
github | cultpenguin/sippi-master | sippi_tikhonov.m | .m | sippi-master/sippi_tikhonov.m | 8,465 | UNKNOWN | 0346095f6092aba789f1f8b4dbbabb07 | % sippi_least_squares Least squares type inversion for SIPPI
%
% Call :
% [options,data,prior,forward,m_reals,m_est,Cm_est]=sippi_least_squares(data,prior,forward,options);
%
% options.lsq.type : LSQ type to use ('lsq' (classical linear leqast squares) is the default)
% options.lsq.n_reals : Number of r... |
github | cultpenguin/sippi-master | sippi_forward_linear.m | .m | sippi-master/sippi_forward_linear.m | 2,321 | utf_8 | 37934932a52b97670a2895b1174d5b97 | % sippi_forward_linear:
%
% % options:
% forward.G : Linear forward operator. such that d[
% d{id}=forward.G*m{im}(:)
% if not set, forward.G=eye(prod(size(m{im})));
%
% forward.force_sparse [0]: Use forward.G as is (default)
% [1]: force forward.G to be treated as a... |
github | cultpenguin/sippi-master | sippi_least_squares.m | .m | sippi-master/sippi_least_squares.m | 10,390 | utf_8 | 9db433844ca2804926f61e6c0c74f3e0 | % sippi_least_squares Least squares type inversion for SIPPI
%
% Call :
% [m_est,Cm_est,m_reals,options,data,prior,forward]=sippi_least_squares(data,prior,forward,options);
%
% options.lsq.type : LSQ type to use ('lsq' (classical linear leqast squares) is the default)
% options.lsq.n_reals : Number of re... |
github | cultpenguin/sippi-master | sippi_sequential_gibbs_resim.m | .m | sippi-master/sippi_sequential_gibbs_resim.m | 3,033 | utf_8 | b1cbf785f2474e5f1f4ee2adbc9a03a0 | % sippi_sequential_gibbs_resim: select model parameters for sequential
% gibbs resimulation
%
% Call
%
% i_resim=sippi_sequential_gibbs_resim(prior,ip,type,step);
%
function i_resim=sippi_sequential_gibbs_resim(prior,ip);
if ~isfield(prior{ip},'init');
prior=sippi_prior_init(prior);
end
if prior{ip}... |
github | cultpenguin/sippi-master | sippi_forward_jacobian.m | .m | sippi-master/sippi_forward_jacobian.m | 1,470 | utf_8 | 421313ca5e7f34f674fd9f09cfb71912 | % sippi_forward_jacobian: Compute jacobian / partial derivative
%
% Call:
% J=sippi_forward_jacobian(m,forward,prior);
% J=sippi_forward_jacobian(m,forward,prior,dm,);
% J=sippi_forward_jacobian(m,forward,prior,dm,used);
%
% In:
% m: SIPPI model, as in m=sippi_prior(prior(;
% forward: SIPPI forward ... |
github | cultpenguin/sippi-master | sippi_prior_sisim.m | .m | sippi-master/sippi_prior_sisim.m | 2,776 | utf_8 | d1d0242c155b8f106ec51fb383f53958 | % sippi_prior_sisim: SISIM (SGeMS) type prior for SIPPI
%
%% Example:
% ip=1;
% prior{ip}.type='sisim';
% prior{ip}.x=1:1:80;
% prior{ip}.y=1:1:80;
% prior{ip}.Cm='1 Sph(60)';
% prior{ip}.marginal_prob=[.1 .4 .5];
% m=sippi_prior(prior);
% sippi_plot_prior(prior,m)
%
% % optionall... |
github | cultpenguin/sippi-master | sippi_compute_acceptance_rate.m | .m | sippi-master/sippi_compute_acceptance_rate.m | 426 | utf_8 | a1fe861499bd3e3077d86c086a904751 | % sippi_compute_acceptance_rate Computes acceptance rate for the Metropolis sampler in SIPPI
%
% Call:
% P_acc=sippi_compute_acceptance_rate(acc,n_update_history);
%
function [P_acc,N_acc,N]=sippi_compute_acceptance_rate(acc,n_update_history);
if nargin<2
n_update_history=50;
end
i_max=length(acc);
i1... |
github | cultpenguin/sippi-master | sippi_set_path.m | .m | sippi-master/sippi_set_path.m | 2,405 | utf_8 | ca394c385d591e77a4c099baf9cd407a | % sippi_set_path Set paths for running sippi
function sippi_set_path();
%function [F,p]=sippi_set_path();
[p]=fileparts(which('sippi_set_path.m'));
if (isempty(p)|strcmp(p,'.'))
p=pwd;
end
% adding toolboxes shipped with SIPPI
i=0;
i=i+1;F{i}=p;
i=i+1;F{i}=[p,filesep,'data',filesep,'crosshole'];
% ... |
github | cultpenguin/sippi-master | sippi_prior_mps.m | .m | sippi-master/sippi_prior_mps.m | 7,740 | utf_8 | 906be8300d67da50aa6029e7d39265b7 | % sippi_prior_mps : prior based on MPS
%
% Using SNESIM/ENESIM FROM
% https://github.com/ergosimulation/mpslib
%
%% Example:
% ip=1;
% prior{ip}.type='mps';
% prior{ip}.method='mps_snesim';
% prior{ip}.x=1:1:80;
% prior{ip}.y=1:1:80;
% prior{ip}.t... |
github | cultpenguin/sippi-master | sippi_likelihood.m | .m | sippi-master/sippi_likelihood.m | 12,724 | utf_8 | 759a97a83fccd0ababc826a76241b0e0 | % sippi_likelihood Compute likelihood given an observed dataset
%
% Call
% [logL,LogL_all,data]=sippi_likelihood(d,data);
%
%
% data{1}.d_obs [N_data,1] N_data data observations
% data{1}.d_std [N_data,1] N_data uncorrelated Gaussian STD
%
% data{1}.d_var [N_data,1] N_data uncorrelated Gaussian variances
... |
github | cultpenguin/sippi-master | sippi_prior_uniform.m | .m | sippi-master/sippi_prior_uniform.m | 2,740 | utf_8 | 688f31b176c24a8c25636221cf844dbf | % sippi_prior_uniform : Uniform prior for SIPPI
%
%
%% Example 1D uniform
% ip=1;
% prior{ip}.type='uniform';
% prior{ip}.min=10;
% prior{ip}.max=25;
% [m,prior]=sippi_prior_uniform(prior);
% sippi_plot_prior_sample(prior);
%
%% Example 10D uniform
% ip=1;
% prior{ip}.type='uniform';
% p... |
github | cultpenguin/sippi-master | sippi_metropolis_mulrun.m | .m | sippi-master/sippi_metropolis_mulrun.m | 1,650 | utf_8 | cfa6eaece5d6100aadb53c1c3a29af11 | % sippi_metropolis_mulrun: multiple (independent) Metropolis chains in parallel
%
% Runs multiple independent Metropolis chains.
% If the Matlab parallel toolbox is available, each chain will be run
% on a different thread.
% This should provide close to linear speedup with the number of avilable
% threads.
%
% To s... |
github | cultpenguin/sippi-master | pg_transform.m | .m | sippi-master/misc/pg_transform.m | 2,943 | utf_8 | de35ac0444e9e73faefcb70b335ff50c | % pg_transform: plurigaussian transformation
%
% Call:
% [pg_d]=pg_transform(m,pg_map,pg_limits);
% m: realizations of gaussian distribtion(s)
% pg_map: Map defining pluri-Gaussian transformation
% pg_limits: Limit values for pg_map (def:[-3 3])
%
% pd_d: Pluri-Gaussian transformed data
%
% Se... |
github | cultpenguin/sippi-master | ESS.m | .m | sippi-master/misc/ESS.m | 1,983 | utf_8 | f7061e8afc3773eca6ead00383fd3d52 | % ESS: Effective Sample Size
%
% call:
% [ess,tau]=ESS(sample,n_use,doPlot,iLag)
%
% sample [nr,nm], nr:number of realizations, nm:number of model parameters
% n_use: The number of data point used to compute tau
% if n_use = 0, n_use os copmuted as 4 times the index of the
% first nega... |
github | cultpenguin/sippi-master | acf.m | .m | sippi-master/misc/acf.m | 2,517 | utf_8 | df7cf5b782009ddb8892a7bf9df989cc | function ta = acf(y,p,doPlot)
% ACF - Compute Autocorrelations Through p Lags
% >> myacf = acf(y,p)
%
% Inputs:
% y - series to compute acf for, nx1 column vector
% p - total number of lags, 1x1 integer
%
% Output:
% myacf - px1 vector containing autocorrelations
% (First lag computed is lag 1. Lag 0 ... |
github | cultpenguin/sippi-master | sippi_mcmc_cleanup.m | .m | sippi-master/misc/sippi_mcmc_cleanup.m | 755 | utf_8 | 38bfd762e3901c451e15eb60fe0b40a3 | % sippi_mcmc_cleanup: remove output dir of sippi_metropolis
%
% sippi_mcmc_cleanup(options.out);
% sippi_mcmc_cleanup(options.out);
%
%
function sippi_mcmc_cleanup(fname)
if isstruct(fname)
fname=fname.txt;
end
if ~exist(fname,'dir')
sippi_verbose(sprintf('%s: folder ''%s'' does not exist... |
github | cultpenguin/sippi-master | setup_Cm_corr.m | .m | sippi-master/misc/setup_Cm_corr.m | 1,011 | utf_8 | 9df57c656ea07fe6bf3c41bb221f2fb8 | % setup_Cm_corr: setup correlated covariance model structure according to
% Buland and Omre (2003)
%
%
% Example:
%
% mu_omre = log([3000 2250 3000]);
% var_omre = [0.0074 0.00240 .0074 ];
% cc=[1 0.8 0.7;0.8 1 0.5;0.7 0.5 1];
% Va=sprintf('0.001 Nug(0) + 0.999 Gau(20)');
% pos=[1:1:80]';... |
github | cultpenguin/sippi-master | autocorrelation.m | .m | sippi-master/misc/autocorrelation.m | 367 | utf_8 | be6d3faf5bdeb6a2ebce20a78d101ac8 | % autocorrelation: Computes the autocorrelation of a series
%
% Call
% [d_auto,lag]=autocorrelation(d)
%
% plot(lag,d_auto);
%
% See also: multiESS
function [d_autoc,lag]=autocorrelation(d)
if (size(d,2)==1)
d=d';
end
N=length(d);
dm=mean(d);
dc =conv(d-dm,fliplr(d-dm));
d_autoc=dc((N):end... |
github | cultpenguin/sippi-master | pg_plot.m | .m | sippi-master/misc/pg_plot.m | 968 | utf_8 | 0f3c24fa5db4cf16e3de9848fb8d0f57 | % pg_plot: plot plurigaussian transfer function
%
% Call:
% [M,x,y]=pg_plot(pg_map,pg_limits);
% See also: pg_transform
%
function [M,x,y]=pg_plot(pg_map,pg_limits,n);
if nargin<2
pg_limits=[-3 3];
end
if nargin<3
n=41;
end
[n2,n1]=size(pg_map);
if n2==1;
x=linspace(pg_limits(1),pg_limits(2),n);
... |
github | cultpenguin/sippi-master | multiESS.m | .m | sippi-master/misc/multiESS.m | 7,532 | utf_8 | adba9d43914b809e768d3dc07cb7cb90 | function [mESS,Sigma,b] = multiESS(X,Sigma,b,Noffsets,Nb)
%MULTIESS Compute multivariate effective sample size of Markov chain.
% MESS = MULTIESS(X) computes effective sample size MESS of single Markov
% chain X, using the multivariate dependence structure of the process.
% X is a n-by-p array, where each row i... |
github | cultpenguin/sippi-master | sippi_verbose.m | .m | sippi-master/misc/sippi_verbose.m | 2,076 | utf_8 | 04b0017c113e28adc9aa026bb3b3f4ef | % sippi_verbose : displays verbose information to the console
%
% Call:
% sippi_verbose(txt,verbose)
%
% txt [string] : text to be displayed
% verbose [integer] (def=0) : increase to see more information
%
% 'vlevel' must be set in the sippi_verbose.m m-file.
%
% All entries with vebose>vlevel are displayed
%
%
% entr... |
github | cultpenguin/sippi-master | prob_boundary_1d.m | .m | sippi-master/misc/prob_boundary_1d.m | 583 | utf_8 | b9d13b6ea16bfe6e17606d115e80fc2a | % prob_boundary_1d(D,boundary_width)
%
% IN:
% D [nx,n_reals] : matrix with n_reals realizations of length nx
% boundary_width [real]: minimum boundary change
% OUT:
% prob_boundary [nx-1,1]: probability of change>boundary_width
%
% Call :
% prob_boundary=prob_boundary_1d(D,boundary_width)
%
%
func... |
github | cultpenguin/sippi-master | sippi_anneal_adjust_noise.m | .m | sippi-master/obsolete/sippi_anneal_adjust_noise.m | 1,345 | utf_8 | ce289d0bece04747d2e8f098a682cdaf | % sippi_anneal_adjust_noise : Adjust noise level in annealing schedul
%
% Call:
% [data_adjust,mcmc]=sippi_anneal_adjust_noise(data,i,mcmc,prior);
%
% See also: sippi_metropolis, sippi_anneal_factor
%
function [data,mcmc]=sippi_anneal_adjust_noise(data_org,i,mcmc,prior);
data=data_org;
%% GET NOISE SCALI... |
github | cultpenguin/sippi-master | sippi_prior_voronoi_org.m | .m | sippi-master/obsolete/sippi_prior_voronoi_org.m | 1,306 | utf_8 | cd1bf571563ea0948f1254504264adc2 | % sippi_prior_voronoi:
%
% TMH/2014
%
% See also: sippi_prior_init, sippi_prior
%
function [m_propose,prior]=sippi_prior_voronoi(prior,m_current,ip);
if nargin<3;
ip=1;
end
if ~isfield(prior{ip},'init')
prior=sippi_prior_init(prior);
end
% number of voronoi cells
if ~isfield(prior{ip},'cells_N');
prior{i... |
github | cultpenguin/sippi-master | sippi_plot_current_model.m | .m | sippi-master/plotting/sippi_plot_current_model.m | 3,605 | utf_8 | a738b6dbebcd2e4fc45e3b47dcd75a32 | % sippi_plot_current_model Plots the current model during Metropolis sampling
%
% Call :
% sippi_plot_current_model(mcmc,data,d,m_current,prior,options);
%
function sippi_plot_current_model(mcmc,data,d,m_current,prior,options);
options.null='';
col=[
0 0 0
1 0 0
0 1 0
0 0 1
1 1 0
... |
github | cultpenguin/sippi-master | sippi_plot_posterior_data.m | .m | sippi-master/plotting/sippi_plot_posterior_data.m | 8,786 | utf_8 | 78a425b6bb52065bd2d9434f81d47751 | % sippi_plot_posterior_data: plots posterior data and noise relaizations
%
% Call
% [options]=sippi_plot_posterior_data(options,prior,data,forward);
%
% See also: sippi_plot_posterior
%
function [options]=sippi_plot_posterior_data(options,prior,data,forward);
%% LOAD THE CORRECT DATA
cwd=pwd;
i... |
github | cultpenguin/sippi-master | sippi_plot_posterior_loglikelihood.m | .m | sippi-master/plotting/sippi_plot_posterior_loglikelihood.m | 4,066 | utf_8 | cabe27aa836005b33420c9723fbc0bc0 | % sippi_plot_posterior_loglikelihod : plots log(L) and autorreation of log(L)
%
% Call:
% sippi_plot_posterior_loglikelihood; % when located in an output folder
% % generated by SIPPI
%
% sippi_plot_posterior_loglikelihood(foldername); % Where 'foldername'
% ... |
github | cultpenguin/sippi-master | sippi_plot_loglikelihood.m | .m | sippi-master/plotting/sippi_plot_loglikelihood.m | 1,920 | utf_8 | 0e4f9dfee103ac397b073e8cf1ba1415 | % sippi_plot_loglikelihood Plot loglikelihood time series
%
% Call :
% acc=sippi_plot_loglikelihood(logL,i_acc,N,itext)
%
function [acc,p,p2]=sippi_plot_loglikelihood(logL,i_acc,N,itext);
p2=[];
acc=NaN;
%cla;
nit=length(logL);
i=1:length(logL);
if nargin<2
i_acc=1:1:length(logL);
end
if nargin<3
N=1;
en... |
github | cultpenguin/sippi-master | sippi_colormap.m | .m | sippi-master/plotting/sippi_colormap.m | 696 | utf_8 | 3592687be777b6102bca079b3f002177 | % sippi_colormap Default colormap for sippi
%
% Call :
% sippi_colormap; % the same as sippi_colormap(3);
%
% or :
% sippi_colormap(1) - Red Green Black
% sippi_colormap(2) - Red Green Blue Black
% sippi_colormap(3) - Jet
% sippi_colormap(4) - Parula
% sippi_colormap(5) - Geosoft
%
function cmap... |
github | cultpenguin/sippi-master | sippi_plot_prior_movie.m | .m | sippi-master/plotting/sippi_plot_prior_movie.m | 1,824 | utf_8 | e8636ba003c0d4bf6d7d322d648033c1 | % sippi_plot_prior_movie: creates a movie file a random walk in the prior
%
% Call:
% sippi_plot_prior_movie(prior,n_frames,options,im_array);
%
% See also: sippi_plot_movie
%
function sippi_plot_prior_movie(prior,n_frames,options,im_array);
if nargin<2
n_frames=100;
end
if nargin<4
im_array=1... |
github | cultpenguin/sippi-master | sippi_plot_data.m | .m | sippi-master/plotting/sippi_plot_data.m | 2,492 | utf_8 | 366a6aa6aae310d22aff60df2adf79bb | % sippi_plot_data: Plot data response
%
% Call:
% sippi_plot_data(d,data);
%
% sippi_plot_data provides a very simple way to plot data.
% A more appropriate data plot can be implemented by implementing a new
% mfile called "sippi_plot_data" and add it the Matlab path before the
% main SIPPI folders
%
% A... |
github | cultpenguin/sippi-master | sippi_plot_set_axis.m | .m | sippi-master/plotting/sippi_plot_set_axis.m | 588 | utf_8 | 78c110158e98eecc4dbc9fba3edb32f9 | % sippi_plot_set_axis
% see also sippi_plot_defaults
function sippi_plot_set_axis(options)
if nargin==1
options=sippi_plot_defaults(options);
else
options=sippi_plot_defaults;
end
%ppp(options.plot.axis.width,options.plot.axis.height,options.plot.axis.fontsize,options.plot.axis.w0,options.plot.a... |
github | cultpenguin/sippi-master | sippi_plot_posterior_mixing.m | .m | sippi-master/plotting/sippi_plot_posterior_mixing.m | 4,970 | utf_8 | dea864871722c0570f397f0e35c7fdc4 | % sippi_plot_posterior_mixing: Plots to use for analysis if mxixing of seperate metropolis
% runs has takem place
%
% % Example:
% % 1. Run several different runs of sippi_metropolis using
% [o{1}]=sippi_metropolis(data,prior,forward);
% [o{2}]=sippi_metropolis(data,prior,forward);
% [o{3}]=sippi_metropolis(dat... |
github | cultpenguin/sippi-master | wiggle.m | .m | sippi-master/plotting/wiggle.m | 6,195 | utf_8 | cf40b43827f50d544d1b4c7fe6a131a9 | % wiggle : plot wiggle/VA/image plot
%
% Call
% wiggle(Data); % wiggle plot
% wiggle(Data,scale); % scaled wiggle plot
% wiggle(x,t,Data); % wiggle plt
% wiggle(x,t,Data,'VA') % variable Area (pos->black;neg->transp)
% wiggle(x,t,Data,'VA2') % variable Area (pos->black;neg->red)
% wiggle(x,t,D... |
github | cultpenguin/sippi-master | sippi_plot_posterior_2d_marg.m | .m | sippi-master/plotting/sippi_plot_posterior_2d_marg.m | 8,734 | utf_8 | 19dd207a1c91a1fc2882e36348b95815 | % sippi_plot_posterior_2d_marg: plots 2D posterior marginal distributions
%
% Call:
% [options,reals_all]=sippi_plot_posterior_2d_marg(options,prior,data,fname);
%
% See also: sippi_plot_posterior
%
function [options,reals_all]=sippi_plot_posterior_2d_marg(options,prior,data,fname);
%% LOAD THE CORRECT D... |
github | cultpenguin/sippi-master | sippi_plot_posterior_sample.m | .m | sippi-master/plotting/sippi_plot_posterior_sample.m | 23,339 | utf_8 | e5f5496dd7b8994c1e1b9b1da4884326 | % sippi_plot_posterior_sample: plots posterior sample statistics
%
% Call
% [options]=sippi_plot_posterior_sample(options,prior,data,forward);
%
% See also: sippi_plot_posterior
%
function [options]=sippi_plot_posterior_sample(options,prior,data,forward);
%% LOAD THE CORRECT DATA
cwd=pwd;
if nargin==0
... |
github | cultpenguin/sippi-master | sippi_plot_defaults.m | .m | sippi-master/plotting/sippi_plot_defaults.m | 3,009 | utf_8 | 10e7fd242c90cdc20e39f0968ec1a9ee | % sippi_plot_defaults: Sets default options for ploting (such as fontsize)
%
% Call :
% options==sippi_plot_defaults(options);
%
% % ALWAYS USE DEFULT SETTING (overrules options.axis)
% overrule=1; % {default overrule=0)
% options==sippi_plot_defaults(options,overrule);
%
% See also: sippi_plot_posteri... |
github | cultpenguin/sippi-master | sippi_get_posterior_data.m | .m | sippi-master/plotting/sippi_get_posterior_data.m | 657 | utf_8 | 9095c7dec174e95dea23c8610d608c6b | % sippi_get_posterior_data: load all data stored in mat-file
%
% Call:
%
% [data,prior,options,mcmc]=sippi_get_posterior_data(folder_name);
% [data,prior,options,mcmc]=sippi_get_posterior_data(output_stuct);
%
function [data,prior,options,mcmc]=sippi_get_posterior_data(folder_name);
%options,prior,data,forward
%% L... |
github | cultpenguin/sippi-master | sippi_forward_linefit.m | .m | sippi-master/examples/case_line_fit/sippi_forward_linefit.m | 439 | utf_8 | b5db45812ad91e26cafa479495da5c7b | % sippi_forward_linefit Line fit forward solver for SIPPI
%
% [d,forward,prior,data]=sippi_forward_linefit(m,forward,prior,data);
% [d,forward,prior,data]=sippi_forward_linefit(m,forward);
%
function [d,forward,prior,data]=sippi_forward_linefit(m,forward,prior,data);
if length(m)==1;
d{1}=forward.x.*0 + m... |
github | cultpenguin/sippi-master | sippi_forward_mynn.m | .m | sippi-master/examples/papers/hansen_and_cordua_2017_nn/sippi_forward_mynn.m | 9,392 | utf_8 | 16e8f66a6f450c6ffbb45595d90b06b6 | % sippi_forward_mynn: Neural Network forward
%
% A training data set consisting on N sets of model parameters and data
% needs to available as [ATTS,DATA]
%
%
% % required fields
% forward.ATTS [NM,N]
% forward.DATA [ND,N]
% where
% NM = length(m{1}(:))
% ND = length(data{1}.d_obs(:))
%
% Optional... |
github | cultpenguin/sippi-master | sippi_likelihood_bimodal_sambridge_2014.m | .m | sippi-master/examples/papers/sambridge_2014_parallel_tempering/sippi_likelihood_bimodal_sambridge_2014.m | 634 | utf_8 | bef96f472d707d0411f77acd2adc83ff | % sippi_likelihood_bimodal_sambridge_2013
%
% Likelihood accroding to Eqn. (13) in
% Sambridge, 2014. A Parallel Tempering algorithm for probabilistic
% sampling and multimodal optimizationexample from Sambridge (2013).
% doi: 10.1093/gji/ggt342
%
% See also: sippi_likelihood_bimodal_sambridge_2014
%
functi... |
github | cultpenguin/sippi-master | sippi_forward_covariance_inference.m | .m | sippi-master/toolboxes/covariance_inference/sippi_forward_covariance_inference.m | 8,232 | utf_8 | e522c7b72f5115b2ca6328aeaca61272 | % sippi_forward_covariance_inference : Probabilitsic covariance inference
%
% Call :
% [d,forward,prior,data]=sippi_forward_covariance_inference(m,forward,prior,data,id,im)
%
%
% forward.pos_known : [x' y' z'], [ndata,ndim] with position of observed data
% forward.G : Forward operator
%
% Prior covariance... |
github | cultpenguin/sippi-master | save1Dbin.m | .m | sippi-master/toolboxes/gpr_fd/save1Dbin.m | 806 | utf_8 | fa97729b319802637c0e050cc2422872 | % =========================================================================
% FUNCTION save2Dbin( fpath, fname, nx, nz, data);
% This function simply saves a 2D matrix to a binary file. It requires the
% following info: - fpath : location of the binary file
% - fname : name of the binary file
% ... |
github | cultpenguin/sippi-master | distribute_to_cores.m | .m | sippi-master/toolboxes/gpr_fd/distribute_to_cores.m | 6,926 | utf_8 | e76ec3cce5e537ee3bdcd602dba9b22e |
function [Ncores_applied error]=distribute_to_cores(ant_pos,Eps,Sig,addpar)
error=0;
Ncores=addpar.cores;
% Determine the number of transmitter positions
try
Ntrn=1;
pos_new_trn(Ntrn)=1;
for i=2:length(ant_pos(:,1))
if ant_pos(i,1)~=ant_pos(i-1,1) | ant_pos(i,2)~=ant_pos(i-1,2)
Ntrn=N... |
github | cultpenguin/sippi-master | load2Dbin.m | .m | sippi-master/toolboxes/gpr_fd/load2Dbin.m | 1,259 | utf_8 | 3b185966e5e7b3e6b604cb4614a84b9e | % =========================================================================
% FUNCTION [ nx, nz, data] = load2Dbin( fpath, fname);
% This function simply loads a 2D matrix from a binary file. It requires
% the following info: - fpath : location of the binary file
% - fname : name of the binary file
... |
github | cultpenguin/sippi-master | write_parameters_to_screen.m | .m | sippi-master/toolboxes/gpr_fd/write_parameters_to_screen.m | 11,881 | utf_8 | 7733eda3d9df3909b7e78e3de02b16a9 |
function [addpar error]=write_parameters_to_screen(ant_pos,Sig,Eps,addpar)
error=0;
try
%=============== Parameters used in the forward modelling ================%
disp('-----------------------------------------------')
% Name of forward controler #1:
try
disp(sprintf('%s %s','Name of forwar... |
github | cultpenguin/sippi-master | fwi_execute_parfor.m | .m | sippi-master/toolboxes/gpr_fd/fwi_execute_parfor.m | 14,070 | utf_8 | 70242628634578cd050970a1826d6d4b | function [dt nt error]=fwi_execute(Ncores_applied,ant_pos,sim_mode,addpar)
error=0;
tic
Positions=ant_pos;
Ncores=addpar.cores;
% Logicals which indicate if other then default values are applied in the
% simulation. Snapshot is always different from the default of the
% executable.
fr=0;
sn=1;
co=0; % 0=Default=Carte... |
github | cultpenguin/sippi-master | save2Dbin.m | .m | sippi-master/toolboxes/gpr_fd/save2Dbin.m | 832 | utf_8 | 8aa0ebe502dac03f93952ea0e2454536 | % =========================================================================
% FUNCTION save2Dbin( fpath, fname, nx, nz, data);
% This function simply saves a 2D matrix to a binary file. It requires the
% following info: - fpath : location of the binary file
% - fname : name of the binary file
% ... |
github | cultpenguin/sippi-master | save_wavelet.m | .m | sippi-master/toolboxes/gpr_fd/save_wavelet.m | 497 | utf_8 | c40965668bbc2dbe5d091ba0dba817ec | % save_wavelet: save wavelet for FDTD_fwi
%
% Call:
% save_wavelet(data,dt,fname)
% Input:
% - data : 1-D data stored in the file.
% - dt : sample interval
% - fname : name of the binary file (def='source.E');
%
% By Knud Cordua (2008)
function save_wave... |
github | cultpenguin/sippi-master | shift_signal.m | .m | sippi-master/toolboxes/gpr_fd/shift_signal.m | 328 | utf_8 | f5b5aca7db113036bc713bd12257801f |
function odata=shift_signal(idata,dt,nt,omega,eps_mean)
% FFT 2D -> 3D:
T=dt*nt;
Mu0=1.25663706143591730*1e-6; %Magnetic permeability of free space, Vs/Am (4*pi*10^-7 N/A^2)
fftdata=fft2(idata);
fftdata=fftshift(fftdata);
data2=fftdata./sqrt(2*pi*T/(-i*omega*eps_mean*Mu0));
data2=fftshift(data2);
odata=real(ifft2(... |
github | cultpenguin/sippi-master | sippi_plot_data_gpr.m | .m | sippi-master/toolboxes/gpr_fd/sippi_plot_data_gpr.m | 1,135 | utf_8 | ea555bf7bb82c39ded1260d8eb082844 | % sippi_plot_data_gpr: plot GPR data
% overwrites sippi_plot_data plot data in SIPPI
%
% Call.
% sippi_plot_data_gpr(d,data);
% sippi_plot_data_gpr(d,data,id_arr);
%
function sippi_plot_data_gpr(d,data,id_arr);
if nargin<3,
id_arr=1:length(d);
end
figure_focus(20+1);clf;set_paper('portrait... |
github | cultpenguin/sippi-master | FDTD_fwi.m | .m | sippi-master/toolboxes/gpr_fd/FDTD_fwi.m | 10,314 | utf_8 | f3c445d8d1ea5aa1dfdefbe651c59a7b |
function [dt nt addpar]=FDTD_fwi(Eps,Sig,dx_fwd,t,ant_pos,addpar)
%================ FDTD simulation of electromagnetic waves ================
%
% Call: [dt nt]=FDTD_fwi(Eps,Sig,dx_fwd,t,ant_pos,addpar);
%
% General: The first five input parameters have to be defined and several additional
% model parameters may be d... |
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