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github | ccdlcmu/shape_component_analysis_Matlab-master | extractSignals.m | .m | shape_component_analysis_Matlab-master/third_party_packages/SPHARM-MAT-v1-0-0/code/extractSignals.m | 4,509 | utf_8 | c697b3c48721ab453de794a0e1dac4ff | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Spherical Harmonic Modeling and Analysis Toolkit (SPHARM-MAT) is a 3D
% shape modeling and analysis toolkit.
% It is a software package developed at Shenlab in Center for Neuroimaging,
% Indiana University (SpharmMat@gmail.com, http://www.... |
github | ccdlcmu/shape_component_analysis_Matlab-master | match_param.m | .m | shape_component_analysis_Matlab-master/third_party_packages/SPHARM-MAT-v1-0-0/code/match_param.m | 2,844 | utf_8 | 4a0f8b57cee303b19260f061949c1cb9 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Spherical Harmonic Modeling and Analysis Toolkit (SPHARM-MAT) is a 3D
% shape modeling and analysis toolkit.
% It is a software package developed at Shenlab in Center for Neuroimaging,
% Indiana University (SpharmMat@gmail.com, http://www.... |
github | ccdlcmu/shape_component_analysis_Matlab-master | smootheCALD.m | .m | shape_component_analysis_Matlab-master/third_party_packages/SPHARM-MAT-v1-0-0/code/smootheCALD.m | 11,629 | utf_8 | a686fa8781e6cce77c699f83694ba626 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Spherical Harmonic Modeling and Analysis Toolkit (SPHARM-MAT) is a 3D
% shape modeling and analysis toolkit.
% It is a software package developed at Shenlab in Center for Neuroimaging,
% Indiana University (SpharmMat@gmail.com, http://www.... |
github | ccdlcmu/shape_component_analysis_Matlab-master | calculate_SPHARM_basis.m | .m | shape_component_analysis_Matlab-master/third_party_packages/SPHARM-MAT-v1-0-0/code/calculate_SPHARM_basis.m | 2,865 | utf_8 | 068a4b46eeea9266130a14e2658d028d | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Spherical Harmonic Modeling and Analysis Toolkit (SPHARM-MAT) is a 3D
% shape modeling and analysis toolkit.
% It is a software package developed at Shenlab in Center for Neuroimaging,
% Indiana University (SpharmMat@gmail.com, http://www.... |
github | ccdlcmu/shape_component_analysis_Matlab-master | SpharmMatAlignment.m | .m | shape_component_analysis_Matlab-master/third_party_packages/SPHARM-MAT-v1-0-0/code/SpharmMatAlignment.m | 2,892 | utf_8 | 08cd459bf8f304f15aea0cc3d9076b5f | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Spherical Harmonic Modeling and Analysis Toolkit (SPHARM-MAT) is a 3D
% shape modeling and analysis toolkit.
% It is a software package developed at Shenlab in Center for Neuroimaging,
% Indiana University (SpharmMat@gmail.com, http://www.... |
github | ccdlcmu/shape_component_analysis_Matlab-master | write_meta_tri.m | .m | shape_component_analysis_Matlab-master/third_party_packages/SPHARM-MAT-v1-0-0/code/write_meta_tri.m | 2,699 | utf_8 | db43ae7901d8802feb7b8ca730cedc63 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Spherical Harmonic Modeling and Analysis Toolkit (SPHARM-MAT) is a 3D
% shape modeling and analysis toolkit.
% It is a software package developed at Shenlab in Center for Neuroimaging,
% Indiana University (SpharmMat@gmail.com, http://www.... |
github | ccdlcmu/shape_component_analysis_Matlab-master | write_metaNcoef.m | .m | shape_component_analysis_Matlab-master/third_party_packages/SPHARM-MAT-v1-0-0/code/write_metaNcoef.m | 2,380 | utf_8 | f9a621c5758517913ad3d82c0321bbd0 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Spherical Harmonic Modeling and Analysis Toolkit (SPHARM-MAT) is a 3D
% shape modeling and analysis toolkit.
% It is a software package developed at Shenlab in Center for Neuroimaging,
% Indiana University (SpharmMat@gmail.com, http://www.... |
github | ccdlcmu/shape_component_analysis_Matlab-master | patch_light.m | .m | shape_component_analysis_Matlab-master/third_party_packages/SPHARM-MAT-v1-0-0/code/patch_light.m | 2,238 | utf_8 | ae8d8cd13dfbf3c3e9af26d7a1bb20ca | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Spherical Harmonic Modeling and Analysis Toolkit (SPHARM-MAT) is a 3D
% shape modeling and analysis toolkit.
% It is a software package developed at Shenlab in Center for Neuroimaging,
% Indiana University (SpharmMat@gmail.com, http://www.... |
github | ccdlcmu/shape_component_analysis_Matlab-master | SPHARM_rmsd.m | .m | shape_component_analysis_Matlab-master/third_party_packages/SPHARM-MAT-v1-0-0/code/SPHARM_rmsd.m | 1,782 | utf_8 | c725100a75b433c54065745fa850c1c0 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Spherical Harmonic Modeling and Analysis Toolkit (SPHARM-MAT) is a 3D
% shape modeling and analysis toolkit.
% It is a software package developed at Shenlab in Center for Neuroimaging,
% Indiana University (SpharmMat@gmail.com, http://www.... |
github | ccdlcmu/shape_component_analysis_Matlab-master | write_coef.m | .m | shape_component_analysis_Matlab-master/third_party_packages/SPHARM-MAT-v1-0-0/code/write_coef.m | 2,660 | utf_8 | c9255ca0517222d53c1970adcdc848cc | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Spherical Harmonic Modeling and Analysis Toolkit (SPHARM-MAT) is a 3D
% shape modeling and analysis toolkit.
% It is a software package developed at Shenlab in Center for Neuroimaging,
% Indiana University (SpharmMat@gmail.com, http://www.... |
github | ccdlcmu/shape_component_analysis_Matlab-master | smootheCALD2.m | .m | shape_component_analysis_Matlab-master/third_party_packages/SPHARM-MAT-v1-0-0/code/modified/smootheCALD2.m | 11,593 | utf_8 | 71913c109642bc5bbd185fce8f0e6192 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% parameters in the variable conf are as following:
% MeshGridSize: Used in global smoothing for creating regular meshes
% MaxSPHARMDegree: Used in global smoothing for interpolating the area scaling ratio functions
% Tolerance: Used in global ... |
github | ccdlcmu/shape_component_analysis_Matlab-master | initParamCALD2.m | .m | shape_component_analysis_Matlab-master/third_party_packages/SPHARM-MAT-v1-0-0/code/modified/initParamCALD2.m | 15,322 | utf_8 | c6bd425850cf4404e2b1f64f0128e5fd | function [sph_verts, poles, dateline] = initParamCALD2(vertices, faces)
if isempty(vertices) | isempty(faces)
disp('There is no useful information');
return;
end
maxfn = 10^7; % Maximum number of faces
switchcc = 0;
% adjust face numbers
fn = size(faces,1);
if fn>maxfn
disp(sprintf('Reduce face ... |
github | ccdlcmu/shape_component_analysis_Matlab-master | meshReduce.m | .m | shape_component_analysis_Matlab-master/third_party_packages/geom3d/meshes3d/meshReduce.m | 9,838 | utf_8 | cda72b8e992250f78636deaf35d499bd | function varargout = meshReduce(nodes, varargin)
%MESHREDUCE Merge coplanar faces of a polyhedral mesh
%
% Note: deprecated, should use "mergeCoplanarFaces" instead
%
% [NODES FACES] = meshReduce(NODES, FACES)
% [NODES EDGES FACES] = meshReduce(NODES, EDGES, FACES)
% NODES is a set of 3D points (as a Nn-by-3 ar... |
github | ccdlcmu/shape_component_analysis_Matlab-master | meshSurfaceArea.m | .m | shape_component_analysis_Matlab-master/third_party_packages/geom3d/meshes3d/meshSurfaceArea.m | 1,881 | utf_8 | cb41ab6d25b207c9d6858e95e7cb65b6 | function area = meshSurfaceArea(vertices, edges, faces)
%MESHSURFACEAREA Surface area of a polyhedral mesh
%
% S = meshSurfaceArea(V, F)
% S = meshSurfaceArea(V, E, F)
% Computes the surface area of the mesh specified by vertex array V and
% face array F. Vertex array is a NV-by-3 array of coordinates.
% Fac... |
github | ccdlcmu/shape_component_analysis_Matlab-master | mergeCoplanarFaces.m | .m | shape_component_analysis_Matlab-master/third_party_packages/geom3d/meshes3d/mergeCoplanarFaces.m | 9,906 | utf_8 | c2eece3ca2be23a866477123bbf34944 | function varargout = mergeCoplanarFaces(nodes, varargin)
%MERGECOPLANARFACES Merge coplanar faces of a polyhedral mesh
%
% [NODES FACES] = mergeCoplanarFaces(NODES, FACES)
% [NODES EDGES FACES] = mergeCoplanarFaces(NODES, EDGES, FACES)
% NODES is a set of 3D points (as a Nn-by-3 array),
% and FACES is one of:
... |
github | ccdlcmu/shape_component_analysis_Matlab-master | assignmentoptimal.m | .m | shape_component_analysis_Matlab-master/src/clustering_evaluation/assignmentoptimal.m | 7,787 | utf_8 | c9b870578bdec636e05ae1d4d711af41 | function [assignment, cost] = assignmentoptimal(distMatrix)
%ASSIGNMENTOPTIMAL Compute optimal assignment by Munkres algorithm
% ASSIGNMENTOPTIMAL(DISTMATRIX) computes the optimal assignment (minimum
% overall costs) for the given rectangular distance or cost matrix, for
% example the assignment of tracks (in row... |
github | gakarak/CHistHoG_Matlab-master | GUI_Matching_CHistHoG.m | .m | CHistHoG_Matlab-master/GUI_Matching_CHistHoG.m | 25,249 | utf_8 | b4b8ad580e5ff981ce67b83bf1bb1824 | function varargout = GUI_Matching_CHistHoG(varargin)
% GUI_MATCHING_CHISTHOG MATLAB code for GUI_Matching_CHistHoG.fig
% GUI_MATCHING_CHISTHOG, by itself, creates a new GUI_MATCHING_CHISTHOG or raises the existing
% singleton*.
%
% H = GUI_MATCHING_CHISTHOG returns the handle to a new GUI_MATCHING_CHISTH... |
github | ICOS-Carbon-Portal/data-master | triangleArea.m | .m | data-master/src/main/matlab/triangleArea.m | 379 | utf_8 | a88982f7c016ea9186991cc33e14a523 | % ************************************************************
%
% triangleArea.m
%
% Lars Harrie 20170507
%
% ************************************************************
%
function t_area=triangleArea(x1,y1,x2,y2,x3,y3)
%
t_area = 0.5 * abs( y1*(x2-x3) + y2*(x3-x1) + y3*(x1-x2) );
%
% *****... |
github | aroma123/caffe-master | prepare_batch.m | .m | caffe-master/matlab/caffe/prepare_batch.m | 1,298 | utf_8 | 68088231982895c248aef25b4886eab0 | % ------------------------------------------------------------------------
function images = prepare_batch(image_files,IMAGE_MEAN,batch_size)
% ------------------------------------------------------------------------
if nargin < 2
d = load('ilsvrc_2012_mean');
IMAGE_MEAN = d.image_mean;
end
num_images = length... |
github | aroma123/caffe-master | matcaffe_demo.m | .m | caffe-master/matlab/caffe/matcaffe_demo.m | 3,344 | utf_8 | 669622769508a684210d164ac749a614 | function [scores, maxlabel] = matcaffe_demo(im, use_gpu)
% scores = matcaffe_demo(im, use_gpu)
%
% Demo of the matlab wrapper using the ILSVRC network.
%
% input
% im color image as uint8 HxWx3
% use_gpu 1 to use the GPU, 0 to use the CPU
%
% output
% scores 1000-dimensional ILSVRC score vector
%
% You m... |
github | jack-h/aavs1-master | fir_core_config.m | .m | aavs1-master/fir_core_config.m | 3,909 | utf_8 | 31dae866b0b91ea20e01df03c96368d1 |
function fir_core_config(this_block)
% Revision History:
%
% 19-Dec-2011 (15:24 hours):
% Original code was machine generated by Xilinx's System Generator after parsing
% /home/rprimian/git/design_files/fir_core.vhd
%
%
this_block.setTopLevelLanguage('VHDL');
this_block.setEntityName('f... |
github | jack-h/aavs1-master | cmult_core_config.m | .m | aavs1-master/cmult_core_config.m | 8,760 | utf_8 | c7f31b8d22b856939b1c7282f38699ee |
function cmult_core_config(this_block)
% Revision History:
%
% 12-Nov-2013 (17:05 hours):
% Original code was machine generated by Xilinx's System Generator after parsing
% /media/data/jack/ami/ami_correlator/fft_core.vhd
%
%
this_block.setTopLevelLanguage('VHDL');
this_block.setEntityN... |
github | jack-h/aavs1-master | output_mux_core_config.m | .m | aavs1-master/output_mux_core_config.m | 9,840 | utf_8 | 678168f1cd137a5f98d3c6085bb247e1 |
function output_mux_core_config(this_block)
% Revision History:
%
% 12-Nov-2013 (17:05 hours):
% Original code was machine generated by Xilinx's System Generator after parsing
% /media/data/jack/ami/ami_correlator/fft_core.vhd
%
%
this_block.setTopLevelLanguage('VHDL');
this_block.setEn... |
github | jack-h/aavs1-master | fft_core_config.m | .m | aavs1-master/fft_core_config.m | 8,151 | utf_8 | 565a346e150189b0dc1985b5d56d1ada |
function fft_core_config(this_block)
% Revision History:
%
% 12-Nov-2013 (17:05 hours):
% Original code was machine generated by Xilinx's System Generator after parsing
% /media/data/jack/ami/ami_correlator/fft_core.vhd
%
%
this_block.setTopLevelLanguage('VHDL');
this_block.setEntityN... |
github | jethroFloyd/droplet-master | nrbloft.m | .m | droplet-master/nrbloft.m | 5,635 | utf_8 | 0895be28b252e7938d48139206d1116b | function [srf]=nrbloft(X,Y,Z,dir,mode)
% NRBLOFT Loft Univariate NURBS curves into a NURBS surface
%
% NRBLOFT constructs a surface by lofting NURBS curves. This is performed
% by an interpolation of the new control points from the ones specified in
% each curve.
%
% NRBLOFT(CURVES) where CURVES is either ... |
github | jethroFloyd/droplet-master | bspdegelev.m | .m | droplet-master/bspdegelev.m | 20,249 | utf_8 | 7a43bbd1e1aa05f66691430b4bcc9b1e | function [ic,ik] = bspdegelev(d,c,k,t)
% BSPDEGELEV Degree elevate a univariate B-Spline.
% -------------------------------------------------------------------------
% ADAPTATION of BSPDEGELEV from C Routine
% -------------------------------------------------------------------------
%
% Calling Sequence:
%
... |
github | jethroFloyd/droplet-master | AlphaHull.m | .m | droplet-master/AlphaHull.m | 5,564 | utf_8 | cb5d9c949f1cb0411ccd2d5813b8db0b | function [triHull, vbOutside, vbInside] = AlphaHull(mfPoints, fAlphaRadius, triDelaunay)
% AlphaHull - FUNCTION Find the alpha hull of a set of points
%
% Usage: [triHull, vbOutside, vbInside] = AlphaHull(mfPoints, fAlphaRadius <, triDelaunay>)
%
% This function computes the alpha shape / alpha hulls of a set of point... |
github | jethroFloyd/droplet-master | concavehull.m | .m | droplet-master/concavehull.m | 3,351 | utf_8 | 51ace97eb40258105e341bbb9bcff1bf | function k=concavehull(XYZ,thresh)
% This function tries to convert the convexhull generated by Qhull/conveshulln into a
% (more) concave hull. Equally, output is in format k [n x 3] which defines n
% triangles as rows in XYZ. If the function finds a triangle that has 2
% sides whose midpoints are further away than thr... |
github | jethroFloyd/droplet-master | alphavol.m | .m | droplet-master/alphavol.m | 6,537 | utf_8 | ef048ea526531f29fde44a729d4dd6a4 | function [V,S] = alphavol(X,R,fig)
%ALPHAVOL Alpha shape of 2D or 3D point set.
% V = ALPHAVOL(X,R) gives the area or volume V of the basic alpha shape
% for a 2D or 3D point set. X is a coordinate matrix of size Nx2 or Nx3.
%
% R is the probe radius with default value R = Inf. In the default case
% the b... |
github | jethroFloyd/droplet-master | curv_test.m | .m | droplet-master/curv_test.m | 10,487 | utf_8 | c3fc9799ad549e316248b0b8a82a3ff3 | function [Cmean,Cgaussian,Dir1,Dir2,Lambda1,Lambda2]=patchcurvature(FV,usethird)
% This function calculates the principal curvature directions and values
% of a triangulated mesh.
%
% The function first rotates the data so the normal of the current
% vertex becomes [-1 0 0], so we can describe the data by XY inst... |
github | jethroFloyd/droplet-master | patchcurvature.m | .m | droplet-master/patchcurvature.m | 10,312 | utf_8 | bfff9c1c97f7fa1029fa74d75a417cf7 | function [Cmean,Cgaussian,Dir1,Dir2,Lambda1,Lambda2]=patchcurvature(FV,usethird)
% This function calculates the principal curvature directions and values
% of a triangulated mesh.
%
% The function first rotates the data so the normal of the current
% vertex becomes [-1 0 0], so we can describe the data by XY inst... |
github | jethroFloyd/droplet-master | gnurbs .m | .m | droplet-master/gnurbs .m | 8,359 | utf_8 | 1e6c33e8b6ed0a49510a316926b0dc65 | function out = gnurbs(mode,res)
% GNURBS Interactive manipulation of NURBS surface/curve
%
% For use with the NURBS toolbox, GNURBS allows the user to have an
% intuitive manipulation of a NURBS surface/curve via control points.
%
% GNURBS with no inputs will generate a test surface for manipulation.
... |
github | jethroFloyd/droplet-master | bspdegelev.m | .m | droplet-master/nurbs_toolbox/bspdegelev.m | 20,521 | utf_8 | f4160356b672419a03664eead25cfea4 | function [ic,ik] = bspdegelev(d,c,k,t)
%
% Function Name:
%
% bspdegevel - Degree elevate a univariate B-Spline.
%
% Calling Sequence:
%
% [ic,ik] = bspdegelev(d,c,k,t)
%
% Parameters:
%
% d : Degree of the B-Spline.
%
% c : Control points, matrix of size (dim,nc).
%
% ... |
github | jethroFloyd/droplet-master | tricurv_v01.m | .m | droplet-master/tricurv/tricurv_v01.m | 6,238 | utf_8 | ed232ffe23e5004b71a5a3c0673df970 | function out=tricurv_v01(tri,p)
% Function to calculate the principal curvatures, and their respective
% directions on a triangular mesh. Approximations of curvature are based on
% local (N=1) neighborhood elements and vertices.
% Note that calculations at vertices with few adjacent triangles, and hence
% few adjacent... |
github | jethroFloyd/droplet-master | gridfit.m | .m | droplet-master/gridfitdir/gridfit.m | 34,995 | utf_8 | e58c0dba921cb156ee39a27dd18a4d1c | function [zgrid,xgrid,ygrid] = gridfit(x,y,z,xnodes,ynodes,varargin)
% gridfit: estimates a surface on a 2d grid, based on scattered data
% Replicates are allowed. All methods extrapolate to the grid
% boundaries. Gridfit uses a modified ridge estimator to
% generate the surface, where the bi... |
github | DIDSR/IQmodelo-master | create_images2.m | .m | IQmodelo-master/create_images2.m | 2,527 | utf_8 | b59d6d0abf20b354d20e4d19ac57271f | % create_images2.m
% Generate correlated random images for two imaging scenarios applied to the same
% case, where both scenarios have the same overall task-performance.
%
% Inputs:
% The input is a structure with the following fields:
% sp (boolean variable specifying if signal-present images are desired)
% Nx,Ny (num... |
github | DIDSR/IQmodelo-master | make_channels.m | .m | IQmodelo-master/make_channels.m | 3,833 | utf_8 | bd285c7d608441b619fe0feb33222fd1 | % make_channels.m
% Construct (normalized) channel matrix for a 2-D image.
% For details and references see
% A. Wunderlich and F. Noo, "Evaluation of the Impact of Tube Current
% Modulation on Lesion Detectability using Model Observers," Proc. Intl.
% Conf. IEEE Eng Med. Biol. Soc., pp. 2705-2708, Aug. 2008.
%
% Inp... |
github | DIDSR/IQmodelo-master | exactCI_CHO_km.m | .m | IQmodelo-master/exactCI_CHO_km.m | 3,048 | utf_8 | 5a7761945cfca087a754ee614e8676f4 | % exactCI_CHO_km.m
% For a CHO with known difference of class means, returns exact
% 1-(alpha1+alpha2) confidence intervals for SNR and AUC as described in
%
% A. Wunderlich and F. Noo, "New Theoretical Results On Channelized Hotelling
% Observer Performance Estimation with Known Difference of Class Means,"
% IEEE T... |
github | DIDSR/IQmodelo-master | npAEROC_CI.m | .m | IQmodelo-master/npAEROC_CI.m | 1,575 | utf_8 | 6b975241cf00bad79c052acab2d75b04 | % npAEROC_CI.m
% For an LROC/EROC assessment, returns an approximate 1-(alpha1+alpha2)
% confidence interval for a single AUC or for a difference of AUCs.
% This function requires the function EROCcov.m, and assumes that
% variabilty is due to cases only.
%
% Inputs: alpha1 (lower significance level), alph... |
github | DIDSR/IQmodelo-master | npAUC_CI.m | .m | IQmodelo-master/npAUC_CI.m | 2,348 | utf_8 | 7c236c4ecabdf5939bc090eb3f997e6d | % npAUC_CI.m
% For an ROC assessment, returns a 1-(alpha1+alpha2) confidence interval
% for a single AUC or for a difference of AUCs. This function requires the
% function fastDeLong.m, and assumes that variabilty is due to cases only.
%
% The confidence interval for a single AUC is computed using the logit
... |
github | DIDSR/IQmodelo-master | diffCI_kt.m | .m | IQmodelo-master/diffCI_kt.m | 1,842 | utf_8 | 0dd195d1faffd0ed27ff5a7a697e1842 | % diffCI_kt.m
% For a linear observer defined by a known (fixed) template, returns
% conservative 1-(alpha1+alpha2) confidence intervals for a difference of
% SNR or AUC values. This method uses exact intervals for each scenario
% obtained with exactCI_kt.m together with the Bonferroni inequality.
%
% Inputs: al... |
github | DIDSR/IQmodelo-master | diffCI_CHO.m | .m | IQmodelo-master/diffCI_CHO.m | 1,890 | utf_8 | 2343969828dfc99a4b1c3fd670f1dd33 | % diffCI_CHO.m
% For CHOs, returns conservative 1-(alpha1+alpha2) confidence intervals
% for a difference of SNR or AUC values. This method uses exact intervals
% for each imaging scenario obtained with exactCI_CHO.m together with the
% Bonferroni inequality.
%
% Inputs: alpha1 (lower significance level), alpha2... |
github | DIDSR/IQmodelo-master | exactCI_kt.m | .m | IQmodelo-master/exactCI_kt.m | 2,318 | utf_8 | efc4f885d9c1f68144e0aba0e3386c9f | % exactCI_kt.m
% For a linear observer defined by a known (fixed) template,
% returns exact 1-(alpha1+alpha2) confidence intervals for SNR and AUC as described in
%
% A. Wunderlich and F. Noo, "Confidence Intervals for Performance
% Assessment of Linear Observers," Medical Physics, vol. 38, no. S1,
% pp. S57-S68, Jul... |
github | DIDSR/IQmodelo-master | diffCI_ktkm.m | .m | IQmodelo-master/diffCI_ktkm.m | 4,355 | utf_8 | a19ecda582422f9bedd1223346768c45 | % diffCI_ktkm.m
% For a linear observer defined by a known (fixed) template, and known
% difference of class means, returns approximate 1-(alpha1+alpha2)
% confidence intervals for SNR and AUC differences as described in
%
% A. Wunderlich and F. Noo, "On Efficient Assessment of Image Quality Metrics Based on Linear M... |
github | DIDSR/IQmodelo-master | ORHmrmc.m | .m | IQmodelo-master/ORHmrmc.m | 5,167 | utf_8 | 8f48d1e1dbe49fefa5f434c00426e871 | % ORHmrmc.m
% Implements the Obuchowski-Rockette-Hillis method for random-reader MRMC
% analysis (i.e., the OR method with Hillis' degrees of freedom formula)
% as described in the following references:
%
% Obuchowski NA, Rockette HE, "Hypothesis testing of diagnostic accuracy
% for mutliple readers and multiple test... |
github | DIDSR/IQmodelo-master | diffCI_CHO_km.m | .m | IQmodelo-master/diffCI_CHO_km.m | 2,226 | utf_8 | 18e6b781899de59f61327ac9a7afce1e | % diffCI_CHO_km.m
% For CHOs with known difference of class means, returns conservative
% 1-(alpha1+alpha2) confidence intervals for a difference of SNR or AUC
% values. This method uses exact intervals for each imaging scenario
% obtained with exactCI_CHO_km.m together with the Bonferroni inequality.
%
% Inputs... |
github | DIDSR/IQmodelo-master | fastDeLong.m | .m | IQmodelo-master/fastDeLong.m | 1,439 | utf_8 | 214c9f786d5164129cd776507c761e5e | % fastDeLong.m
% For ROC analysis, implements a fast rank-based algorithm for the
% Mann-Whitney AUC estimator and for the DeLong covariance matrix estimator.
% References:
%
% E. R. DeLong, D. M. DeLong, and D. L. Clarke-Pearson, "Comparing the
% areas under two or more correlated receiver operating characteri... |
github | DIDSR/IQmodelo-master | binProp_CI.m | .m | IQmodelo-master/binProp_CI.m | 2,317 | utf_8 | 1fef7280481526febee22ef031c42a3a | % binProp_CI.m
% Returns a 1-(alpha1+alpha2) confidence interval for a single binomial
% proportion or for a difference of binomial proportions. This function
% requires the function binPropCov.m, and assumes that variabilty is due to
% cases only.
%
% The confidence interval for a single proportion is compu... |
github | DIDSR/IQmodelo-master | create_images1.m | .m | IQmodelo-master/create_images1.m | 2,200 | utf_8 | 9f4dfb6e1bd1ebd2df527c0ea6ebaadd | % create_images1.m
% Generate random images for one imaging senario.
%
% Inputs:
% The input is a structure with the following fields:
% sp (boolean variable specifying if signal-present images are desired)
% Nx,Ny (number of pixels in x and y dimensions, respectively)
% Rbg1, Rbg2 (range, i.e., twice amplitude, of ba... |
github | DIDSR/IQmodelo-master | exactCI_ktkm.m | .m | IQmodelo-master/exactCI_ktkm.m | 2,726 | utf_8 | afee5c8dcb4b4d2e4e5fa23c199d2d34 | % exactCI_ktkm.m
% For a linear observer defined by a known (fixed) template, and known difference of class means,
% returns exact 1-(alpha1+alpha2) confidence intervals for SNR and AUC as described in
%
% A. Wunderlich and F. Noo, "On Efficient Assessment of Image Quality Metrics Based on Linear Model
% Observers," I... |
github | joejoe978/WirelessNetwork_Lab3-master | decode.m | .m | WirelessNetwork_Lab3-master/code/decode.m | 6,699 | utf_8 | 604026d7ac8fb813d903001b833bac6e | function [] = decode()
evalin('caller','clear all');
close all;
global ANT_CNT LTS_LEN SYM_LEN NUM_SYM FFT_OFFSET LTS_CORR_THRESH
DO_CFO_CORRECTION = 1; % Enable CFO estimation/correction
DO_PHASE_TRACK = 1; % Enable phase tracking
LTS_LEN = 160;
NUM_LTS = 2;
NUM_SYM = 50;
NUM_AC = 0;
LTS_CORR_THRESH = 0.6;
FFT_OFFS... |
github | joejoe978/WirelessNetwork_Lab3-master | read_complex_binary.m | .m | WirelessNetwork_Lab3-master/code/read_complex_binary.m | 1,362 | utf_8 | 07292cc3372cd69d045a99fa16e0d1cd | %
% Copyright 2001 Free Software Foundation, Inc.
%
% This file is part of GNU Radio
%
% GNU Radio is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation; either version 3, or (at your option)
% any later version.
%... |
github | cloudbopper/vision-scene-classification-master | sp_dense_sift.m | .m | vision-scene-classification-master/SpatialPyramid/sp_dense_sift.m | 4,103 | utf_8 | c56a4af8fe7c5c5f975ca04a0cb6a974 | function [sift_arr, grid_x, grid_y] = sp_dense_sift(I, grid_spacing, patch_size)
% Original script by Svetlana Lazebnick
% Adapted by Antonio Torralba: modified using convolutions to speed up the computations.
% And brought back into Svetlana's library
if(~exist('grid_spacing','var'))
grid_spacing = 1;
end
... |
github | cloudbopper/vision-scene-classification-master | sp_find_sift_grid.m | .m | vision-scene-classification-master/SpatialPyramid/sp_find_sift_grid.m | 4,187 | utf_8 | 029daeb5a3d4d49bb26b0e1a64cc4b97 | function sift_arr = sp_find_sift_grid(I, grid_x, grid_y, patch_size, sigma_edge)
% parameters
num_angles = 8;
num_bins = 4;
num_samples = num_bins * num_bins;
alpha = 9;
if nargin < 5
sigma_edge = 1;
end
angle_step = 2 * pi / num_angles;
angles = 0:angle_step:2*pi;
angles(num_angles+1) = []; % bin ... |
github | qihongl/reinforcementLearning_practice-master | explore.m | .m | reinforcementLearning_practice-master/simpleStepper/explore.m | 793 | utf_8 | 481fc3d848e7a8a5c33e6515c8ed5835 | % reinforcement learning: 1-D walker on int [-5,5]
% written by Professor Jay McClelland
function [rundata] = explore(seed)
if nargin == 0
seed = randi([0 99],1);
end
% initialization
rng(seed);
global w a p h;
% w-world; a-agent; p-parameters; h-history
setupWorld;
h.stepsToReward = zeros(p.trials,1... |
github | qihongl/reinforcementLearning_practice-master | getNextState.m | .m | reinforcementLearning_practice-master/simpleStepper/getNextState.m | 313 | utf_8 | 404fb7000374a686ca9c820786138e64 | % written by Professor Jay McClelland
function [] = getNextState( )
%UNTITLED3 Summary of this function goes here
% Detailed explanation goes here
global w a p;
if w.cura == 1
w.nexts = w.curs - 1;
else
w.nexts = w.curs +1;
end
if w.nexts == 5
w.R = 1;
else
w.R = 0;
end
end
|
github | qihongl/reinforcementLearning_practice-master | setupState.m | .m | reinforcementLearning_practice-master/simpleStepper/setupState.m | 171 | utf_8 | 28e724730145f9a6aba605ce49d20105 | % written by Professor Jay McClelland
function [ ] = setupState( )
global w a p;
w.nexts = 0;
w.curs = 0;
w.cura = 0;
w.nexta = 0;
w.R = 0;
w.steps = 0;
end
|
github | qihongl/reinforcementLearning_practice-master | updateQandS.m | .m | reinforcementLearning_practice-master/simpleStepper/updateQandS.m | 565 | utf_8 | 7518037e525d4a742ed17115d5941187 | % written by Professor Jay McClelland
function [] = updateQandS()
global w a p
%we assume we have already chosen an a current action (cura) in a current
%state curs and that we have visited that state (nexts) and experienced
%the available reward
%we update the agent's representation of the value of cura in ... |
github | qihongl/reinforcementLearning_practice-master | chooseAction.m | .m | reinforcementLearning_practice-master/simpleStepper/chooseAction.m | 724 | utf_8 | bc0a0154af510cd55e133a28c73a3eb7 | % written by Professor Jay McClelland
function [] = chooseAction()
%UNTITLED2 Summary of this function goes here
% Detailed explanation goes here
global w a p;
prob = softmax(a.q(w.curs+p.range+1,:), p.qscale);
w.cura = sample(prob);
end
% softmax transformation
% expects the input x to be a vector a Q-v... |
github | qihongl/reinforcementLearning_practice-master | setupWorld.m | .m | reinforcementLearning_practice-master/simpleStepper/setupWorld.m | 375 | utf_8 | 2ae2fe6d3b658bb1bc265b5a966d6d55 | % written by Professor Jay McClelland
function [ ] = setupWorld()
%UNTITLED2 Summary of this function goes here
% Detailed explanation goes here
global w a p;
% w = struct([]);
% p = struct([]);
% a = struct([]);
p.range = 5;
p.gamma = .5;
p.alpha = .1;
p.qscale = 3;
p.nactions = 2;
p.trials = 100;... |
github | Guanghan/DeepLearnToolbox-master | myOctaveVersion.m | .m | DeepLearnToolbox-master/util/myOctaveVersion.m | 169 | utf_8 | d4603482a968c496b66a4ed4e7c72471 | % return OCTAVE_VERSION or 'undefined' as a string
function result = myOctaveVersion()
if isOctave()
result = OCTAVE_VERSION;
else
result = 'undefined';
end
|
github | Guanghan/DeepLearnToolbox-master | isOctave.m | .m | DeepLearnToolbox-master/util/isOctave.m | 108 | utf_8 | 4695e8d7c4478e1e67733cca9903f9ef | %detects if we're running Octave
function result = isOctave()
result = exist('OCTAVE_VERSION') ~= 0;
end |
github | Guanghan/DeepLearnToolbox-master | makeLMfilters.m | .m | DeepLearnToolbox-master/util/makeLMfilters.m | 1,895 | utf_8 | 21950924882d8a0c49ab03ef0681b618 | function F=makeLMfilters
% Returns the LML filter bank of size 49x49x48 in F. To convolve an
% image I with the filter bank you can either use the matlab function
% conv2, i.e. responses(:,:,i)=conv2(I,F(:,:,i),'valid'), or use the
% Fourier transform.
SUP=49; % Support of the largest filter (must be... |
github | Guanghan/DeepLearnToolbox-master | caenumgradcheck.m | .m | DeepLearnToolbox-master/CAE/caenumgradcheck.m | 3,618 | utf_8 | 6c481fc15ab7df32e0f476514100141a | function cae = caenumgradcheck(cae, x, y)
epsilon = 1e-4;
er = 1e-6;
disp('performing numerical gradient checking...')
for i = 1 : numel(cae.o)
p_cae = cae; p_cae.c{i} = p_cae.c{i} + epsilon;
m_cae = cae; m_cae.c{i} = m_cae.c{i} - epsilon;
[m_cae, p_cae] = caerun(m_cae, p_cae, x... |
github | caxenie/code-snippets-master | non-uniform-dist-gen.m | .m | code-snippets-master/non-uniform-dist-gen.m | 3,997 | utf_8 | 8837b6044971f1b1ebcfd8b5193b1bd3 | % Powerlaw distribution generator in MATLAB
function x=randht(n, varargin)
% Source: http://www.santafe.edu/~aaronc/powerlaws/
%
% case 'powerlaw'
% alpha = 2.5;
% sensory_data.x = randht(sensory_data.num_vals, nufrnd_type, alpha);
% sensory_data.y = sens... |
github | caxenie/code-snippets-master | fast_plot.m | .m | code-snippets-master/Matlab-fast-simultaneous-plot/fast_plot.m | 720 | utf_8 | 2b49141c9e3c9dc2102ec9cf1248f6fe | function fast_plot(varargin)
[ax,args] = axescheck(varargin{:});
[x,y] = deal(args{:});
ax = newplot(ax);
set(ax,'XMinorGrid','on');
set(ax,'YMinorGrid','on');
if ~ishold(ax)
[minx,maxx] = minmax(x);
[miny,maxy] = minmax(y);
axis(ax,[minx maxx miny maxy])
end
cur_sgt = line('linestyle','-','erase','xor','xd... |
github | caxenie/code-snippets-master | RadarEKF.m | .m | code-snippets-master/kalman_filtering/14.EKF/RadarEKF.m | 866 | utf_8 | ddc90ce66ed2d6e7c093b3889638b73e | function [pos vel alt] = RadarEKF(z, dt)
%
%
persistent A Q R
persistent x P
persistent firstRun
if isempty(firstRun)
A = eye(3) + dt*[ 0 1 0;
0 0 0;
0 0 0 ];
Q = [ 0 0 0;
0 0.001 0;
0 0 0.001 ];
R = 10;
x = [0 ... |
github | caxenie/code-snippets-master | EulerEKF.m | .m | code-snippets-master/kalman_filtering/14.EKF/EulerEKF/EulerEKF.m | 1,515 | utf_8 | 792faf55e2a4e023d561d30cd44f68e4 | function [phi theta psi] = EulerEKF(z, rates, dt)
%
%
persistent H Q R
persistent x P
persistent firstRun
if isempty(firstRun)
H = [ 1 0 0;
0 1 0 ];
Q = [ 0.0001 0 0;
0 0.0001 0;
0 0 0.1 ];
R = [ 6 0;
0 6 ];
x = [0 0 0]';
... |
github | caxenie/code-snippets-master | EulerUKF.m | .m | code-snippets-master/kalman_filtering/15.UKF/EulerUKF/EulerUKF.m | 1,310 | utf_8 | 835ccf39811af913a102f643ffccd109 | function [phi theta psi] = EulerUKF(z, rates, dt)
%
%
persistent Q R
persistent x P
persistent n m
persistent firstRun
if isempty(firstRun)
Q = [ 0.0001 0 0;
0 0.0001 0;
0 0 1 ];
R = [ 6 0;
0 12 ];
x = [0 0 0]';
P = 1*eye(3);
... |
github | caxenie/code-snippets-master | RadarUKF.m | .m | code-snippets-master/kalman_filtering/15.UKF/RadarUKF/RadarUKF.m | 1,085 | utf_8 | e7658d064a2034673ea236148052d4e7 | function [pos vel alt] = RadarUKF(z, dt)
%
%
persistent Q R
persistent x P
persistent n m
persistent firstRun
if isempty(firstRun)
Q = [ 0.01 0 0;
0 0.01 0;
0 0 0.01 ];
R = 100;
x = [0 90 1100]';
P = 100*eye(3);
n = 3;
m = 1;
firstRun = 1;... |
github | caxenie/code-snippets-master | CompFilterWithPI.m | .m | code-snippets-master/kalman_filtering/18.CompFilter/CompFilterWithPI.m | 978 | utf_8 | fab432ce28749cbe382f02e22cac2019 | function [phi theta psi] = CompFilterWithPI(p, q, r, ax, ay, dt)
%
%
persistent p_hat q_hat
persistent prevPhi prevTheta prevPsi
if isempty(p_hat)
p_hat = 0;
q_hat = 0;
prevPhi = 0;
prevTheta = 0;
prevPsi = 0;
end
[phi_a theta_a] = EulerAccel(ax, ay);
[dotPhi dotTheta dotPsi] = BodyT... |
github | gigascience/paper-zaritsky2015-master | speedKymograph.m | .m | paper-zaritsky2015-master/speedKymograph.m | 5,098 | utf_8 | cdcf85091a68fc69dbd8bd261b53a8ea | function [] = speedKymograph(dirname,distanceFromEdge)
% speedKymograph creates a kymograph visualizing spatiotemporal dynamics of
% cells speed.
% speedKymograph(dirname) - traverses over the .tif/.lsm files (each
% holding raw data of one time-lapse experiment) in the *main* directory
% "dirname" and generates a spa... |
github | tawsifkhan/MMathProject-master | disturbance2.m | .m | MMathProject-master/Generic Files/disturbance2.m | 2,494 | utf_8 | 11967917e1afdb952a2476c9451a99c2 |
function [G1, G2]=disturbance2(method)
% Function to create tanhyperbolic shaped disturbance.
% This is created by superposition of two tanh functions.
% IMPORTANT: The disturbance matrix is a dual concept of sensor. So this
% file contains the term sensor in most parts but is actually the disturbance.
% Returns tw... |
github | tawsifkhan/MMathProject-master | femShallowPoly.m | .m | MMathProject-master/FemShallowPoly/femShallowPoly.m | 2,164 | utf_8 | be0ffd5550e5150fff47b7ae4f34be27 | % FINITE ELEMENT CODE USING 6TH ORDER POLYNOMIAL BASIS
% Equation : u_tt=c^2*u_xx + beta*u_xxtt, c^2=g*H, beta=H^2/6
% Returns : coefficients at all times
function cMatrix=femShallowPoly
specs = getSpecs;
basis = getBasisPoly;
% Time Discretization
% 2nd order Leapfrog timestepping
% [M+beta*K][cs^(n+1)-2*cs^n+c^... |
github | tawsifkhan/MMathProject-master | femShallowLinear.m | .m | MMathProject-master/FemShallowLinear/femShallowLinear.m | 2,435 | utf_8 | 1d2e891de6927e09ce497e1d6d1fafdb | % FINITE ELEMENT CODE USING LINEAR BASIS
% Equation : u_tt=c^2*u_xx + beta*u_xxtt, c^2=g*H, beta=H^2/6
% Returns : coefficients at all times
function [c_matrix]=femShallowLinear
specs = getSpecs; % Physical Parameters of the problem
basis = getBasisLinear; % Spatial Discretization of ... |
github | tawsifkhan/MMathProject-master | getBasisSine.m | .m | MMathProject-master/FemShallowSine/getBasisSine.m | 1,536 | utf_8 | e706d58b63402accb9989e44b47cf160 | % Function defines the sine basis on the domain.
% NOTE: matchN allows the basis to match a given x-discretization by the
% number of elements. Each element has 6 gaussian points like the
% polynomial and linear basis.
% RETURNS: the shape functions, mass and stiffness matrices.
function basis = getBasisS... |
github | tawsifkhan/MMathProject-master | getBasisSineTrue.m | .m | MMathProject-master/FemShallowSineTrue/getBasisSineTrue.m | 1,496 | utf_8 | 20c8f07f3eb69ac05508c5707c428f0e | % Function defines the sine basis on the domain.
% NOTE: matchN allows the basis to match a given x-discretization by the
% number of elements. Each element has 6 gaussian points like the
% polynomial and linear basis.
% RETURNS: the shape functions, mass and stiffness matrices.
function basis = getBasisS... |
github | alphaliang/slam6d-master | rts_recordingtime_sync.m | .m | slam6d-master/bin/rts_recordingtime_sync.m | 1,539 | utf_8 | 1b43a53d2f2caabac4a73883def6b4a9 | % script for interpolating data of RTS Hannover
function output = recordingtime_sync(input, ref)
[input_m, input_n] = size(input);
[ref_m, ref_n] = size(ref);
output = zeros(ref_m, input_n);
for i = 1 : ref_m
upper_index = find(input(:,1) > ref(i,1));
upper = input(upper_index(1),:);
if(upper_inde... |
github | taoyilee/CEM_David-master | gaussder_norm.m | .m | CEM_David-master/Chapter 2/gaussder_norm.m | 237 | utf_8 | eb3564f7d6fd80a15cd0ce117fa989a1 | % A properly normalized Gaussian derivative pulse
function y=gaussder_norm(t,m,sigma)
%y= -1/sqrt(2*pi)*(t-m)/sigma^3*exp(-(t-m)^2/(2*sigma^2));
y= -exp(0.5)*(t-m)/sigma*exp(-(t-m)^2/(2*sigma^2)); % Better scaled version
end
|
github | taoyilee/CEM_David-master | cavity_modes3D.m | .m | CEM_David-master/Chapter 11/cavity_modes3D.m | 1,059 | utf_8 | f049912230df59b622631893375a79b2 | ## function incomplete ##
function [eigvalues_sorted,mode_sorted] = cavity_modes3D(a,b,d,max_index)
% CAVITY_MODES3D lists the first approximatly max_index^3 eigenmodes in a rectangular cavity
% See, for instance, Ramo, Whinnery and van Duzer, Chapter 10, "Fields and
% Waves in Communication Electronics",. 3rd ed... |
github | taoyilee/CEM_David-master | cyl_TM_echo_width.m | .m | CEM_David-master/Chapter 4/cyl_TM_echo_width.m | 1,515 | utf_8 | e5b91c438b85af93af026937fe0306be | function y = cyl_TM_echo_width(a,k,N,phi)
% Function to compute echo width of a PEC circular cylinder, TM_z
% incidence, using analytical result
% Inputs: a; radius of cylinder [m]
% k; wavenumber [rad/m] (May be a vector)
% phi; angle of scattering in radians, = pi for monostatic
% Returns the ec... |
github | taoyilee/CEM_David-master | MoM_TM_solver.m | .m | CEM_David-master/Chapter 4/MoM_TM_solver.m | 3,666 | utf_8 | 528e18016ee2f1c1a074d8ab052f9d03 | function [I_vec,phi_c,w,x_c,y_c,cond_num] = MoM_TM_solver(k,N,a,E_0,phi_inc,quad,eta,toeplitz_flag);
% MoM solution for TM incidence on PEC conductor.
% Author: DB Davidson, 22 Feb 2008.
% k: wavenumber
% N: number of segments
% a: radius of PEC cylinder
% E_0: amplitude of incident E field
% phi_inc: angle... |
github | taoyilee/CEM_David-master | FillZMatrixByFace.m | .m | CEM_David-master/Chapter 6/Mixed Potential EFIE RWG/FillZMatrixByFace.m | 4,012 | utf_8 | 2c23ec7d732fb00d3a2c4d820e22ca3c | function [Z] = FillZMatrixByFace(omega,eps_0,mu_0,k,r_c,rho_c_pls,rho_c_mns,quad_pts,sing,dof2edge,dof_RWG)
% FILLZMATRIXBYFACE Fill the impedance matrix by face pair.
% Code computes the integrals for one source triangle-field triangle
% interaction and then assembles by element, similar to a FEM code.
% Fillin... |
github | taoyilee/CEM_David-master | FillZMatrixByEdge.m | .m | CEM_David-master/Chapter 6/Mixed Potential EFIE RWG/FillZMatrixByEdge.m | 3,356 | utf_8 | 4039cb992b571b03712f124c04302c15 | function [Z] = FillZMatrixByEdge(omega,eps_0,mu_0,k,r_c,rho_c_pls,rho_c_mns,quad_pts,sing,dof2edge)
% FILLZMATRIXBYEDGE Fill the impedance matrix by edge pair.
% Code re-factored 24 Jan 2010, DBD.
global ELEMENTS NODE_COORD NUM_DOFS EDGECONXELEMS DOFLOCALNUM ELL
Z = zeros(NUM_DOFS,NUM_DOFS); % Generalized impe... |
github | taoyilee/CEM_David-master | gaussder_norm.m | .m | CEM_David-master/Chapter 3/FDTD_2D/gaussder_norm.m | 235 | utf_8 | 79abfe13828da653197adef770126745 | % A properly normalized Gaussian derivative pulse
function y=gaussder_norm(t,m,sigma)
%y= -1/sqrt(2*pi)*(t-m)/sigma^3*exp(-(t-m)^2/(2*sigma^2));
y= -exp(0.5)*(t-m)/sigma*exp(-(t-m)^2/(2*sigma^2)); % Better scaled version
end
|
github | speth/cantera-main | TestRunDisplay.m | .m | cantera-main/ext/matlab_xunit/TestRunDisplay.m | 9,772 | utf_8 | 5676f8f435e9c6ceeb16bd2b6bcdc6dd | classdef TestRunDisplay < TestRunMonitor
%TestRunDisplay Print test suite execution results.
% TestRunDisplay is a subclass of TestRunMonitor. If a TestRunDisplay
% object is passed to the run method of a TestComponent, such as a
% TestSuite or a TestCase, it will print information to the Command
% Window (or ... |
github | speth/cantera-main | TestSuite.m | .m | cantera-main/ext/matlab_xunit/TestSuite.m | 13,145 | utf_8 | a2f83a7c15ba0ad13fa330aa59d1451a | %TestSuite Collection of TestComponent objects
% The TestSuite class defines a collection of TestComponent objects.
%
% TestSuite methods:
% TestSuite - Constructor
% add - Add test component to test suite
% print - Display test suite summary to Comman... |
github | speth/cantera-main | runtests.m | .m | cantera-main/ext/matlab_xunit/runtests.m | 4,841 | utf_8 | a24f89548b654b7bf0fc9ec25be2e2f2 | function out = runtests(varargin)
%runtests Run unit tests
% runtests runs all the test cases that can be found in the current directory
% and summarizes the results in the Command Window.
%
% Test cases can be found in the following places in the current directory:
%
% * An M-file function whose name start... |
github | speth/cantera-main | isTestCaseSubclass.m | .m | cantera-main/ext/matlab_xunit/+xunit/+utils/isTestCaseSubclass.m | 894 | utf_8 | 5c7e3f1d9b1eb3e2148cad3cde93c5c7 | function tf = isTestCaseSubclass(name)
%isTestCaseSubclass True for name of a TestCase subclass
% tf = isTestCaseSubclass(name) returns true if the string name is the name of
% a TestCase subclass on the MATLAB path.
% Steven L. Eddins
% Copyright 2008-2009 The MathWorks, Inc.
tf = false;
class_meta = meta.c... |
github | speth/cantera-main | arrayToString.m | .m | cantera-main/ext/matlab_xunit/+xunit/+utils/arrayToString.m | 2,972 | utf_8 | 173fdeb3960985834f7a252c7916fa32 | function s = arrayToString(A)
%arrayToString Convert array to string for display.
% S = arrayToString(A) converts the array A into a string suitable for
% including in assertion messages. Small arrays are converted using disp(A).
% Large arrays are displayed similar to the way structure field values display
% ... |
github | speth/cantera-main | compareFloats.m | .m | cantera-main/ext/matlab_xunit/+xunit/+utils/compareFloats.m | 4,376 | utf_8 | f77141ccf2a6b351c309eb9be4211065 | function result = compareFloats(varargin)
%compareFloats Compare floating-point arrays using tolerance.
% result = compareFloats(A, B, compare_type, tol_type, tol, floor_tol)
% compares the floating-point arrays A and B using a tolerance. compare_type
% is either 'elementwise' or 'vector'. tol_type is either 'r... |
github | speth/cantera-main | CounterFlowDiffusionFlame.m | .m | cantera-main/interfaces/matlab/toolbox/1D/CounterFlowDiffusionFlame.m | 7,949 | utf_8 | 1365a758328135fbee7e6c9a58b5a0c0 | function flame = CounterFlowDiffusionFlame(left, flow, right, tp_f, tp_o, oxidizer)
% COUNTERFLOWDIFFUSIONFLAME Create a counter flow diffusion flame stack.
% flame = CounterFlowDiffusionFlame(left, flow, right, tp_f, tp_o, oxidizer)
% :param left:
% Object representing the left inlet, which must be
% created ... |
github | speth/cantera-main | lithium_ion_battery.m | .m | cantera-main/samples/matlab/lithium_ion_battery.m | 5,605 | utf_8 | 51e8a085356b328d59ef578aa34560a5 | % This example file calculates the cell voltage of a lithium-ion battery
% at given temperature, pressure, current, and range of state of charge (SOC).
%
% The thermodynamics are based on a graphite anode and a LiCoO2 cathode,
% modeled using the 'BinarySolutionTabulatedThermo' class.
% Further required cell parameters... |
github | speth/cantera-main | rankine.m | .m | cantera-main/samples/matlab/rankine.m | 1,515 | utf_8 | f4063b6735b208a636f05ae7bd2b3266 | function [work, efficiency] = rankine(t1, p2, eta_pump, eta_turbine)
%
% This example computes the efficiency of a simple vapor power cycle.
%
% Keywords: thermodynamics, thermodynamic cycle, non-ideal fluid
help rankine
% create an object representing water
w = Water;
% start with saturated liquid water at t1
set(w... |
github | speth/cantera-main | ignite.m | .m | cantera-main/samples/matlab/ignite.m | 2,661 | utf_8 | e601751a1d8eff006511d11e561c55fc | function plotdata = ignite(g)
% IGNITE Zero-dimensional kinetics: adiabatic, constant pressure.
%
% This example solves the same problem as 'reactor1,' but does
% it using one of MATLAB's ODE integrators, rather than using the
% Cantera Reactor class.
%
% Keywords: combustion, reactor network, ignition delay, ... |
github | ihsanarifr/TugasKuliah-master | Home.m | .m | TugasKuliah-master/CodeSistemPakar/project/Home.m | 4,152 | utf_8 | 42d855116f6607048f5621b5ce29aa56 | function varargout = Home(varargin)
% HOME MATLAB code for Home.fig
% HOME, by itself, creates a new HOME or raises the existing
% singleton*.
%
% H = HOME returns the handle to a new HOME or the handle to
% the existing singleton*.
%
% HOME('CALLBACK',hObject,eventData,handles,...) calls the l... |
github | ihsanarifr/TugasKuliah-master | hasil.m | .m | TugasKuliah-master/CodeSistemPakar/project/hasil.m | 13,694 | utf_8 | e7ba13f70a4280328d10c578604e64ca | function varargout = hasil(varargin)
% HASIL MATLAB code for hasil.fig
% HASIL, by itself, creates a new HASIL or raises the existing
% singleton*.
%
% H = HASIL returns the handle to a new HASIL or the handle to
% the existing singleton*.
%
% HASIL('CALLBACK',hObject,eventData,handles,...) cal... |
github | ihsanarifr/TugasKuliah-master | gejala.m | .m | TugasKuliah-master/CodeSistemPakar/project/gejala.m | 11,363 | utf_8 | fbbac8627e0917f1d21c7913dff34b2b | function varargout = gejala(varargin)
% GEJALA MATLAB code for gejala.fig
% GEJALA, by itself, creates a new GEJALA or raises the existing
% singleton*.
%
% H = GEJALA returns the handle to a new GEJALA or the handle to
% the existing singleton*.
%
% GEJALA('CALLBACK',hObject,eventData,handles,... |
github | ihsanarifr/TugasKuliah-master | informasi.m | .m | TugasKuliah-master/CodeSistemPakar/project/informasi.m | 23,848 | utf_8 | 0bf4e327c5efdb1849f19821d9228235 | function varargout = informasi(varargin)
% INFORMASI MATLAB code for informasi.fig
% INFORMASI, by itself, creates a new INFORMASI or raises the existing
% singleton*.
%
% H = INFORMASI returns the handle to a new INFORMASI or the handle to
% the existing singleton*.
%
% INFORMASI('CALLBACK',hO... |
github | ihsanarifr/TugasKuliah-master | produksi.m | .m | TugasKuliah-master/CodeSistemPakar/praktikum/praktikum-9/produksi.m | 6,747 | utf_8 | d556729da67277d22a1c4c04e88c0c62 |
function varargout = produksi(varargin)
% PRODUKSI MATLAB code for produksi.fig
% PRODUKSI, by itself, creates a new PRODUKSI or raises the existing
% singleton*.
%
% H = PRODUKSI returns the handle to a new PRODUKSI or the handle to
% the existing singleton*.
%
% PRODUKSI('CALLBACK',... |
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