plateform stringclasses 1
value | repo_name stringlengths 13 113 | name stringlengths 3 74 | ext stringclasses 1
value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | eardi/sm-fpca-master | Compute_Dihedral_Angles.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Static_Codes/Isosurface_Meshing/@Mesher3Dmex/private/Compute_Dihedral_Angles.m | 1,222 | utf_8 | 9214fea100152a6a74051c2560aad1de | function A = Compute_Dihedral_Angles(p,t)
%Compute_Dihedral_Angles
%
% 3-D Mesh: A = Tx6 matrix, where each row contains the 6 interior dihedral
% angles of a single tetrahedron in the mesh. The angles are
% ordered with respect to the (local) edge index.
% Angles are given in... |
github | eardi/sm-fpca-master | Plot_Tree.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Static_Codes/Search_Trees/@mexQuadtree/Plot_Tree.m | 1,651 | utf_8 | 736f1afd7f8ab4736d8c1e78fd9813c5 | function FH = Plot_Tree(obj,Desired_Level,Plot_Points)
%Plot_Tree
%
% This plots the tree graphically. It plots all nodes at or above the given level.
%
% FH = obj.Plot_Tree(Desired_Level,Plot_Points);
%
% Desired_Level = node level to plot down to.
% Plot_Points = true/false: true = plot points in qua... |
github | eardi/sm-fpca-master | Plot_Tree.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Static_Codes/Search_Trees/@mexOctree/Plot_Tree.m | 2,193 | utf_8 | 791b459ba04e6f35cacb6ceca6d1ec73 | function FH = Plot_Tree(obj,Desired_Level,Plot_Points)
%Plot_Tree
%
% This plots the tree graphically. It plots all nodes at or above the given level.
%
% FH = obj.Plot_Tree(Desired_Level,Plot_Points);
%
% Desired_Level = node level to plot down to.
% Plot_Points = true/false: true = plot points in oct... |
github | eardi/sm-fpca-master | test_Quadtree_Random_Points.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Static_Codes/Search_Trees/Unit_Test/test_Quadtree_Random_Points.m | 2,198 | utf_8 | f3f8348133d4df922b17414a6c396fbb | function status = test_Quadtree_Random_Points()
%test_Quadtree_Random_Points
%
% Test code for FELICITY class.
% Copyright (c) 01-14-2014, Shawn W. Walker
status = 0; % init
% current_file = mfilename('fullpath');
% Current_Dir = fileparts(current_file);
NUM = 1000000;
points = rand(NUM,2);
disp... |
github | eardi/sm-fpca-master | test_Bitree_Random_Points.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Static_Codes/Search_Trees/Unit_Test/test_Bitree_Random_Points.m | 2,106 | utf_8 | 30b763e45893b9277b9c7b27bb543c4c | function status = test_Bitree_Random_Points()
%test_Bitree_Random_Points
%
% Test code for FELICITY class.
% Copyright (c) 01-14-2014, Shawn W. Walker
status = 0; % init
% current_file = mfilename('fullpath');
% Current_Dir = fileparts(current_file);
NUM = 1000000;
points = rand(NUM,1);
disp('cr... |
github | eardi/sm-fpca-master | test_Octree_Moving_Points.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Static_Codes/Search_Trees/Unit_Test/test_Octree_Moving_Points.m | 3,242 | utf_8 | 9c9ca16c5dacba831879ca9b37b8911a | function status = test_Octree_Moving_Points()
%test_Octree_Moving_Points
%
% Test code for FELICITY class.
% Copyright (c) 01-15-2014, Shawn W. Walker
status = 0; % init
current_file = mfilename('fullpath');
Current_Dir = fileparts(current_file);
% create a structured set of points
NUM = 500;
z_poin... |
github | eardi/sm-fpca-master | test_Octree_Random_Points.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Static_Codes/Search_Trees/Unit_Test/test_Octree_Random_Points.m | 2,266 | utf_8 | 998c28e9f9ee17b0e719522b59ff6914 | function status = test_Octree_Random_Points()
%test_Octree_Random_Points
%
% Test code for FELICITY class.
% Copyright (c) 01-14-2014, Shawn W. Walker
status = 0; % init
% current_file = mfilename('fullpath');
% Current_Dir = fileparts(current_file);
NUM = 1000000;
points = rand(NUM,3);
disp('cr... |
github | eardi/sm-fpca-master | Check_Tree.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Static_Codes/Search_Trees/@mexAbstracttree/Check_Tree.m | 1,009 | utf_8 | 55738c815dbdc0655d435f7ad1913f3c | function Valid = Check_Tree(obj)
%Check_Tree
%
% This checks that points in the tree actually belong to their enclosing leaf cell.
%
% Valid = obj.Check_Tree();
%
% Valid = true/false: true = the tree is properly formed; false = it is not!
% Copyright (c) 01-15-2014, Shawn W. Walker
Valid = true; % ... |
github | eardi/sm-fpca-master | test_LEPP_Bisection_2D.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Static_Codes/Lepp_Bisection_2D/Unit_Test/test_LEPP_Bisection_2D.m | 2,316 | utf_8 | 5ee68cf78e07faaf0417f0d848ac4e25 | function status = test_LEPP_Bisection_2D()
%test_LEPP_Bisection_2D
%
% Test code for FELICITY class.
% Copyright (c) 09-12-2011, Shawn W. Walker
% define single triangle mesh
Vtx = [0 0; 1 0; 0 1];
Tri = uint32([1 2 3]);
Neighbor = uint32([0 0 0]);
% init
New_Vtx = Vtx;
New_Tri = Tri;
New_Neighbor ... |
github | eardi/sm-fpca-master | Read_MeshGen_File_new.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Misc_Routines/Read_MeshGen_File_new.m | 2,230 | utf_8 | 83abc70597e5f09ab94ef00794b64b88 | function [Vtx, Tri_Elem, Bdy_Edge] = Read_MeshGen_File_new(varargin)
%Read_MeshGen_File_3x7
%
% This routine reads a data file in the MeshGen 2-D format for an unstructured triangle
% grid. See their documentation for more information. Note: the MeshGen file can
% contain very specific data on the types of... |
github | eardi/sm-fpca-master | inpolyhedron.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Misc_Routines/inpolyhedron.m | 15,529 | utf_8 | c5ece99ffeec5c11d58d41ed451f3f75 | function IN = inpolyhedron(varargin)
%INPOLYHEDRON Tests if points are inside a 3D triangulated (faces/vertices) surface
%
% IN = INPOLYHEDRON(FV,QPTS) tests if the query points (QPTS) are inside the
% patch/surface/polyhedron defined by FV (a structure with fields 'vertices' and
% 'faces'). QPTS is an N-by... |
github | eardi/sm-fpca-master | triangle_mesh_of_disk.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Misc_Routines/triangle_mesh_of_disk.m | 1,790 | utf_8 | 0fdecd9dc80be1d1383c254c8bed5569 | function [TRI, VTX] = triangle_mesh_of_disk(Center,Radius,Refine_Level)
%triangle_mesh_of_disk
%
% This generates a 2-D mesh of a disk (circle).
%
% [TRI, VTX] = triangle_mesh_of_disk(Center,Radius,Refine_Level)
%
% Center = (length 2 vector) containing the coordinates of the center of the disk.
% Radiu... |
github | eardi/sm-fpca-master | Refine_Entire_Mesh.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Misc_Routines/Refine_Entire_Mesh.m | 11,575 | utf_8 | bd7e00a8ef0c71cb88b27b5eb1e6825f | function [New_Mesh_Vertex_Coordinates, New_Triangle_Elements, New_Boundary_Elements] =...
Refine_Entire_Mesh(Vtx_Coord,Triangles,Bdy_Seg,Marked_Triangles)
%Refine_Entire_Mesh
%
% This routine takes a given mesh structure and refines the mesh wherever there is
% a "marked" triangle.
%
% [New_Mesh_... |
github | eardi/sm-fpca-master | Get_BDM1_Interpolant_of_Function.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Misc_Routines/Get_BDM1_Interpolant_of_Function.m | 3,293 | utf_8 | c7b415af6dd1eb5bb241a5e710dfa996 | function uu_VEC = Get_BDM1_Interpolant_of_Function(Vtx,Tri,BDM1_DoFmap,Orient,FUNC)
Num_Tri = size(Tri,1);
Num_BDM1_DoFs = max(BDM1_DoFmap(:));
if (Num_Tri~=size(BDM1_DoFmap,1))
error('Number of Tri''s must equal number of rows of BDM1_DoFmap!');
end
if (Num_Tri~=size(Orient,1))
error('Number of Tri'... |
github | eardi/sm-fpca-master | Refine_Entire_Mesh_3D.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Misc_Routines/Refine_Entire_Mesh_3D.m | 4,969 | utf_8 | aed16fe45b7f1d5c44781d3052c413c0 | function [New_Vtx, New_Tet] = Refine_Entire_Mesh_3D(Vtx,Tet)
%Refine_Entire_Mesh_3D
%
% This routine takes a given 3-D mesh structure and refines the entire
% mesh with uniform refinement.
%
% [New_Vtx, New_Tet] = Refine_Entire_Mesh_3D(Vtx,Tet);
%
% OUTPUTS
% -------
% New_Vtx, New_Tet:
% N... |
github | eardi/sm-fpca-master | mesh_classes_unit_test_dirs.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Classes/Mesh/mesh_classes_unit_test_dirs.m | 1,768 | utf_8 | 712d5625a9d2cf43020a3175e12c389c | function Unit_Test_Dirs = mesh_classes_unit_test_dirs()
%mesh_classes_unit_test_dirs
%
% This outputs a list of directories containing the unit tests.
% Copyright (c) 05-07-2019, Shawn W. Walker
% get the main directory that this function is in!
MFN = mfilename('fullpath');
Main_Dir = fileparts(MFN);
%... |
github | eardi/sm-fpca-master | Create_Embedding_Data.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Classes/Mesh/@MeshTriangle/Create_Embedding_Data.m | 5,332 | utf_8 | a391c50619896a59feef77ce2fa953f8 | function Embed = Create_Embedding_Data(obj,Sub,DoI)
%Create_Embedding_Data
%
% This fills in a struct containing the embedding information for the given
% individual embeddings Sub and DoI.
%
% Embed = obj.Create_Embedding_Data(Sub,DoI);
%
% Sub = struct of the form:
% Sub.Name
% Sub.D... |
github | eardi/sm-fpca-master | Get_Subdomain_1D.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Classes/Mesh/@MeshTriangle/private/Get_Subdomain_1D.m | 3,179 | utf_8 | 891f0215076eaa897a7fe87c97aeeda3 | function Data = Get_Subdomain_1D(obj,Oriented_Edges,STRICT)
%Get_Subdomain_1D
%
% This sets up a data structure for representing a sub-domain in a triangle
% mesh. Dimension of the subdomain is 1-D (i.e. oriented edges in the mesh).
%
% Data = obj.Get_Subdomain_1D(Oriented_Edges,STRICT);
%
% Oriented_E... |
github | eardi/sm-fpca-master | Get_Subdomain_0D.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Classes/Mesh/@MeshTriangle/private/Get_Subdomain_0D.m | 2,913 | utf_8 | 11211fe01eb67d2c62faba3ff814f09b | function Data = Get_Subdomain_0D(obj,Vtx_Indices,STRICT)
%Get_Subdomain_0D
%
% This sets up a data structure for representing a sub-domain in a triangle
% mesh. Dimension of the subdomain is 0-D (i.e. just individual vertices).
%
% Data = obj.Get_Subdomain_0D(Vtx_Indices,STRICT);
%
% Vtx_Indices = colu... |
github | eardi/sm-fpca-master | Generate_Subdomain_Embedding_Data.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Classes/Mesh/@AbstractMesh/Generate_Subdomain_Embedding_Data.m | 2,578 | utf_8 | 88ddfdfcca33379edf10e3e8ca50e129 | function Embed = Generate_Subdomain_Embedding_Data(obj,Domains_of_Integration)
%Generate_Subdomain_Embedding_Data
%
% This returns a struct containing the embedding information for all
% subdomains relative to the given Domains_of_Integration (DoI).
%
% Embed = obj.Generate_Subdomain_Embedding_Data(Domains_... |
github | eardi/sm-fpca-master | Compute_Simplex_Angles.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Classes/Mesh/@AbstractMesh/private/Compute_Simplex_Angles.m | 2,589 | utf_8 | da1b5734e8e2b07f0724bbbf62f3612e | function A = Compute_Simplex_Angles(p,t)
%Compute_Simplex_Angles
%
% This computes the internal element angles.
%
% A = Compute_Simplex_Angles(p,t);
%
% p = vertices of triangulation.
% t = triangulation connectivity.
%
% 1-D Mesh: A = [] (case not valid).
% 2-D Mesh: A = Tx3 matrix, where each r... |
github | eardi/sm-fpca-master | Create_Embedding_Data.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Classes/Mesh/@MeshInterval/Create_Embedding_Data.m | 3,356 | utf_8 | 14b80f984980a35901572f3431937672 | function Embed = Create_Embedding_Data(obj,Sub,DoI)
%Create_Embedding_Data
%
% This fills in a struct containing the embedding information for the given
% individual embeddings Sub and DoI.
%
% Embed = obj.Create_Embedding_Data(Sub,DoI);
%
% Sub = struct of the form:
% Sub.Name
% Sub.D... |
github | eardi/sm-fpca-master | Order_Cell_Vertices_For_Hcurl.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Classes/Mesh/@MeshTetrahedron/Order_Cell_Vertices_For_Hcurl.m | 1,342 | utf_8 | b819edaaaaff04a50d64fc0794d3a83b | function obj = Order_Cell_Vertices_For_Hcurl(obj)
%Order_Cell_Vertices_For_Hcurl
%
% This reorders the vertices of each tetrahedron, i.e.,
% Let [V_1, V_2, V_3, V_4] be the global vertex indices of the
% current mesh element. This routine reorders each tetrahedron so
% that they satisfy on of... |
github | eardi/sm-fpca-master | Create_Embedding_Data.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Classes/Mesh/@MeshTetrahedron/Create_Embedding_Data.m | 6,620 | utf_8 | 2db11e03294ba7e2a2175345a89bc49d | function Embed = Create_Embedding_Data(obj,Sub,DoI)
%Create_Embedding_Data
%
% This fills in a struct containing the embedding information for the given
% individual embeddings Sub and DoI.
%
% Embed = obj.Create_Embedding_Data(Sub,DoI);
%
% Sub = struct of the form:
% Sub.Name
% Sub.D... |
github | eardi/sm-fpca-master | Get_Subdomain_1D.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Classes/Mesh/@MeshTetrahedron/private/Get_Subdomain_1D.m | 3,742 | utf_8 | 7c5b7c966624ffdd4b6448ae3d0a4cac | function Data = Get_Subdomain_1D(obj,Oriented_Edges,STRICT)
%Get_Subdomain_1D
%
% This sets up a data structure for representing a sub-domain in a tetra
% mesh. Dimension of the subdomain is 1-D (i.e. oriented edges in the mesh).
%
% Data = obj.Get_Subdomain_1D(Oriented_Edges,STRICT);
%
% Oriented_Edge... |
github | eardi/sm-fpca-master | Get_Subdomain_0D.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Classes/Mesh/@MeshTetrahedron/private/Get_Subdomain_0D.m | 2,978 | utf_8 | 8ef484e454fbf7f2b871dddbb0464203 | function Data = Get_Subdomain_0D(obj,Vtx_Indices,STRICT)
%Get_Subdomain_0D
%
% This sets up a data structure for representing a sub-domain in a tetra
% mesh. Dimension of the subdomain is 0-D (i.e. just individual vertices).
%
% Data = obj.Get_Subdomain_0D(Vtx_Indices,STRICT);
%
% Vtx_Indices = column ... |
github | eardi/sm-fpca-master | managesim_classes_unit_test_dirs.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Classes/ManageSim/managesim_classes_unit_test_dirs.m | 815 | utf_8 | 1774e9847ea69ba61b7cf1869c5ab041 | function Unit_Test_Dirs = managesim_classes_unit_test_dirs()
%managesim_classes_unit_test_dirs
%
% This outputs a list of directories containing the unit tests.
% Copyright (c) 05-07-2019, Shawn W. Walker
% get the main directory that this function is in!
MFN = mfilename('fullpath');
Main_Dir = fileparts(... |
github | eardi/sm-fpca-master | test_FEL_Visualize_1.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Classes/ManageSim/Unit_Test/Test_Visualize/test_FEL_Visualize_1.m | 1,879 | utf_8 | c316f7610a6f5cabe6f069f3b39e2471 | function status = test_FEL_Visualize_1()
%test_FEL_Visualize_1
%
% Test code for FELICITY class.
% Copyright (c) 01-31-2017, Shawn W. Walker
% SWW: this test needs to be run manually by a user b/c MATLAB is lame!
current_file = mfilename('fullpath');
Current_Dir = fileparts(current_file);
% make a d... |
github | eardi/sm-fpca-master | Make_Movie_2.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Classes/ManageSim/@FEL_Visualize/Make_Movie_2.m | 3,720 | utf_8 | f1b9287904a16f9b491e5fc360564108 | function [Frames, Full_FN, FigHandle] = Make_Movie_2(obj, FileName, SS_cell, SL_dyn_cell,...
Plot_Func, Start_Index, End_Index, Step, FigHandle)
%Make_Movie_2
%
% Make a movie. Note: this does *not* save the movie. You still need to
% do that afterward, say, by us... |
github | eardi/sm-fpca-master | fem_classes_unit_test_dirs.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Classes/FEM/fem_classes_unit_test_dirs.m | 951 | utf_8 | 1cee035d76af95bbc41d179ed247c8cc | function Unit_Test_Dirs = fem_classes_unit_test_dirs()
%fem_classes_unit_test_dirs
%
% This outputs a list of directories containing the unit tests.
% Copyright (c) 05-07-2019, Shawn W. Walker
% get the main directory that this function is in!
MFN = mfilename('fullpath');
Main_Dir = fileparts(MFN);
% d... |
github | eardi/sm-fpca-master | Get_Nodes_On_Topological_Entity.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Classes/FEM/@ReferenceFiniteElement/Get_Nodes_On_Topological_Entity.m | 6,331 | utf_8 | fe5019fcd8526d91abd05798fe0ba599 | function DoFs_on_Entity = Get_Nodes_On_Topological_Entity(obj,Top_Entity_Dim)
%Get_Nodes_On_Topological_Entity
%
% This returns a rectangular array that specifies the local DoF indices
% attached to each topological entity of dimension 'Top_Entity_Dim'.
% Note: the functionality here is a little different fr... |
github | eardi/sm-fpca-master | Read_In_Element_Struct.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Classes/FEM/@ReferenceFiniteElement/private/Read_In_Element_Struct.m | 4,578 | utf_8 | fbc85a9ad7666476cc602234e7fcbe97 | function obj = Read_In_Element_Struct(obj,Elem)
%Read_In_Element_Struct
%
% This converts the Elem struct to something that the class/object can
% use.
% Copyright (c) 07-01-2019, Shawn W. Walker
% this is a simple element!
if strcmp(Elem.Type,'constant_one')
obj.Top_Dim = 0;
obj.Simplex_... |
github | eardi/sm-fpca-master | Set_mex_Dir.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Classes/FEM/@GeoElementSpace/Set_mex_Dir.m | 1,331 | utf_8 | 04407421ac2458ef0942dc4dea9dcb2a | function obj = Set_mex_Dir(obj,Input_Dir,mex_Name)
%Set_mex_Dir
%
% This sets the directory to hold (potential) mex files for performing
% interpolations.
%
% obj = obj.Set_mex_Dir(Input_Dir, mex_Name (optional) );
%
% Input_Dir = (string) containing the desired directory.
% mex_Name = (string) sets ... |
github | eardi/sm-fpca-master | Get_Fixed_DoFs.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Classes/FEM/@FiniteElementSpace/Get_Fixed_DoFs.m | 2,339 | utf_8 | 39e96cdaafccf6f4bdeb96054b1b3a30 | function DoFs = Get_Fixed_DoFs(obj,Mesh,ARG)
%Get_Fixed_DoFs
%
% Similar to 'Get_DoFs', except this only returns the DoFs that are fixed
% (i.e. fixed by some Dirichlet condition). Note: the functionality is a
% little different from 'Get_DoFs'.
%
% DoFs = obj.Get_Fixed_DoFs(Mesh);
%
% Mesh = (FELIC... |
github | eardi/sm-fpca-master | Lift_Barycentric_Coord_to_Enclosing_Cell.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Classes/FEM/@FiniteElementSpace/private/Lift_Barycentric_Coord_to_Enclosing_Cell.m | 3,765 | utf_8 | e9b4e0f05cbb2dc91e47a531163db4f0 | function Cell_BC = Lift_Barycentric_Coord_to_Enclosing_Cell(obj,Mesh,Sub_BC,Local_Sub_Indices)
%Lift_Barycentric_Coord_to_Enclosing_Cell
%
% This translates barycentric coordinates from some lower dimensional mesh
% entities to barycentric coordinates for the higher dimensional enclosing
% cell.
%
% Cell... |
github | eardi/sm-fpca-master | Uniform_Refine.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Classes/FEM/@ParametricMesh_2D/Uniform_Refine.m | 8,566 | utf_8 | 787871ba6faa427cfb251d0f0f779658 | function [TRI_new, VTX_new, BdyEDGE_new, BdyChart_Ind_new, BdyChart_Var_new, BdyTRI_Ind_new] =...
Uniform_Refine(obj,TRI,VTX,BdyEDGE,BdyChart_Ind,BdyChart_Var,BdyTRI_Ind)
%Uniform_Refine
%
% This refines a given mesh so that it conforms to the curved boundary.
%
% FH = obj.Uniform_Refine(XXXX);... |
github | eardi/sm-fpca-master | Generate_Triangle_Quad_Rules.m | .m | sm-fpca-master/FELICITY_Ver1.3.1/FELICITY/Quadrature/Generate_Quad_Rules/Generate_Triangle_Quad_Rules.m | 2,370 | utf_8 | a053c4e39af47c217b9b5a2a7e218ae7 | function Quad = Generate_Triangle_Quad_Rules(Deg_Of_Precision)
%Generate_Triangle_Quad_Rules
%
% This generates quadrature rules for the unit reference triangle.
%
% Note: This was adapted from MFEM version 3.0.1.
% Copyright (c) 10-05-2015, Shawn W. Walker
DIG = 25; % set num digits to use
% init!... |
github | mackenbaron/SimpleSipServer-master | echo_diagnostic.m | .m | SimpleSipServer-master/pjlibs/third_party/speex/libspeex/echo_diagnostic.m | 2,076 | utf_8 | 8d5e7563976fbd9bd2eda26711f7d8dc | % Attempts to diagnose AEC problems from recorded samples
%
% out = echo_diagnostic(rec_file, play_file, out_file, tail_length)
%
% Computes the full matrix inversion to cancel echo from the
% recording 'rec_file' using the far end signal 'play_file' using
% a filter length of 'tail_length'. The output is saved to 'o... |
github | CPFLAME/LOCO-master | voc_eval.m | .m | LOCO-master/lib/datasets/VOCdevkit-matlab-wrapper/voc_eval.m | 1,332 | utf_8 | 3ee1d5373b091ae4ab79d26ab657c962 | function res = voc_eval(path, comp_id, test_set, output_dir)
VOCopts = get_voc_opts(path);
VOCopts.testset = test_set;
for i = 1:length(VOCopts.classes)
cls = VOCopts.classes{i};
res(i) = voc_eval_cls(cls, VOCopts, comp_id, output_dir);
end
fprintf('\n~~~~~~~~~~~~~~~~~~~~\n');
fprintf('Results:\n');
aps = [res(:... |
github | roboticslibrary/bullet3-master | compareBulletMATLAB.m | .m | bullet3-master/examples/TwoJoint/compareBulletMATLAB.m | 822 | utf_8 | 857b4ddd17d5ff0c68c8c19157e9e8d1 | % License: Bullet3 license
% Author: Avik De <avikde@gmail.com>
robot = importrobot('../../data/TwoJointRobot_wo_fixedJoints.urdf');
show(robot)
robot.DataFormat = 'column';
X0 = zeros(4,1);
options = odeset('MaxStep',5e-3);
[t,X] = ode45(@(t, X) myDyn(t, X, robot), [0,5], X0, options);
subplot(211)
hold all
plot... |
github | LyricYang/Machine_Learning_Stanford-master | submit.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex2/ex2/submit.m | 1,605 | utf_8 | 9b63d386e9bd7bcca66b1a3d2fa37579 | function submit()
addpath('./lib');
conf.assignmentSlug = 'logistic-regression';
conf.itemName = 'Logistic Regression';
conf.partArrays = { ...
{ ...
'1', ...
{ 'sigmoid.m' }, ...
'Sigmoid Function', ...
}, ...
{ ...
'2', ...
{ 'costFunction.m' }, ...
'Logistic R... |
github | LyricYang/Machine_Learning_Stanford-master | submitWithConfiguration.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex2/ex2/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | LyricYang/Machine_Learning_Stanford-master | savejson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex2/ex2/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | LyricYang/Machine_Learning_Stanford-master | loadjson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex2/ex2/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | LyricYang/Machine_Learning_Stanford-master | loadubjson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex2/ex2/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | LyricYang/Machine_Learning_Stanford-master | saveubjson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex2/ex2/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | LyricYang/Machine_Learning_Stanford-master | submit.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex4/ex4/submit.m | 1,635 | utf_8 | ae9c236c78f9b5b09db8fbc2052990fc | function submit()
addpath('./lib');
conf.assignmentSlug = 'neural-network-learning';
conf.itemName = 'Neural Networks Learning';
conf.partArrays = { ...
{ ...
'1', ...
{ 'nnCostFunction.m' }, ...
'Feedforward and Cost Function', ...
}, ...
{ ...
'2', ...
{ 'nnCostFunct... |
github | LyricYang/Machine_Learning_Stanford-master | submitWithConfiguration.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex4/ex4/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | LyricYang/Machine_Learning_Stanford-master | savejson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex4/ex4/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | LyricYang/Machine_Learning_Stanford-master | loadjson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex4/ex4/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | LyricYang/Machine_Learning_Stanford-master | loadubjson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex4/ex4/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | LyricYang/Machine_Learning_Stanford-master | saveubjson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex4/ex4/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | LyricYang/Machine_Learning_Stanford-master | submit.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex6/ex6/submit.m | 1,318 | utf_8 | bfa0b4ffb8a7854d8e84276e91818107 | function submit()
addpath('./lib');
conf.assignmentSlug = 'support-vector-machines';
conf.itemName = 'Support Vector Machines';
conf.partArrays = { ...
{ ...
'1', ...
{ 'gaussianKernel.m' }, ...
'Gaussian Kernel', ...
}, ...
{ ...
'2', ...
{ 'dataset3Params.m' }, ...
... |
github | LyricYang/Machine_Learning_Stanford-master | porterStemmer.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex6/ex6/porterStemmer.m | 9,902 | utf_8 | 7ed5acd925808fde342fc72bd62ebc4d | function stem = porterStemmer(inString)
% Applies the Porter Stemming algorithm as presented in the following
% paper:
% Porter, 1980, An algorithm for suffix stripping, Program, Vol. 14,
% no. 3, pp 130-137
% Original code modeled after the C version provided at:
% http://www.tartarus.org/~martin/PorterStemmer/c.tx... |
github | LyricYang/Machine_Learning_Stanford-master | submitWithConfiguration.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex6/ex6/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | LyricYang/Machine_Learning_Stanford-master | savejson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex6/ex6/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | LyricYang/Machine_Learning_Stanford-master | loadjson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex6/ex6/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | LyricYang/Machine_Learning_Stanford-master | loadubjson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex6/ex6/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | LyricYang/Machine_Learning_Stanford-master | saveubjson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex6/ex6/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | LyricYang/Machine_Learning_Stanford-master | submit.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex7/ex7/submit.m | 1,438 | utf_8 | 665ea5906aad3ccfd94e33a40c58e2ce | function submit()
addpath('./lib');
conf.assignmentSlug = 'k-means-clustering-and-pca';
conf.itemName = 'K-Means Clustering and PCA';
conf.partArrays = { ...
{ ...
'1', ...
{ 'findClosestCentroids.m' }, ...
'Find Closest Centroids (k-Means)', ...
}, ...
{ ...
'2', ...
... |
github | LyricYang/Machine_Learning_Stanford-master | submitWithConfiguration.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex7/ex7/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | LyricYang/Machine_Learning_Stanford-master | savejson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex7/ex7/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | LyricYang/Machine_Learning_Stanford-master | loadjson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex7/ex7/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | LyricYang/Machine_Learning_Stanford-master | loadubjson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex7/ex7/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | LyricYang/Machine_Learning_Stanford-master | saveubjson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex7/ex7/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | LyricYang/Machine_Learning_Stanford-master | submit.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex5/ex5/submit.m | 1,765 | utf_8 | b1804fe5854d9744dca981d250eda251 | function submit()
addpath('./lib');
conf.assignmentSlug = 'regularized-linear-regression-and-bias-variance';
conf.itemName = 'Regularized Linear Regression and Bias/Variance';
conf.partArrays = { ...
{ ...
'1', ...
{ 'linearRegCostFunction.m' }, ...
'Regularized Linear Regression Cost Fun... |
github | LyricYang/Machine_Learning_Stanford-master | submitWithConfiguration.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex5/ex5/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | LyricYang/Machine_Learning_Stanford-master | savejson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex5/ex5/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | LyricYang/Machine_Learning_Stanford-master | loadjson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex5/ex5/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | LyricYang/Machine_Learning_Stanford-master | loadubjson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex5/ex5/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | LyricYang/Machine_Learning_Stanford-master | saveubjson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex5/ex5/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | LyricYang/Machine_Learning_Stanford-master | submit.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex3/ex3/submit.m | 1,567 | utf_8 | 1dba733a05282b2db9f2284548483b81 | function submit()
addpath('./lib');
conf.assignmentSlug = 'multi-class-classification-and-neural-networks';
conf.itemName = 'Multi-class Classification and Neural Networks';
conf.partArrays = { ...
{ ...
'1', ...
{ 'lrCostFunction.m' }, ...
'Regularized Logistic Regression', ...
}, ..... |
github | LyricYang/Machine_Learning_Stanford-master | submitWithConfiguration.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex3/ex3/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | LyricYang/Machine_Learning_Stanford-master | savejson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex3/ex3/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | LyricYang/Machine_Learning_Stanford-master | loadjson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex3/ex3/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | LyricYang/Machine_Learning_Stanford-master | loadubjson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex3/ex3/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | LyricYang/Machine_Learning_Stanford-master | saveubjson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex3/ex3/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | LyricYang/Machine_Learning_Stanford-master | submit.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex8/ex8/submit.m | 2,135 | utf_8 | eebb8c0a1db5a4df20b4c858603efad6 | function submit()
addpath('./lib');
conf.assignmentSlug = 'anomaly-detection-and-recommender-systems';
conf.itemName = 'Anomaly Detection and Recommender Systems';
conf.partArrays = { ...
{ ...
'1', ...
{ 'estimateGaussian.m' }, ...
'Estimate Gaussian Parameters', ...
}, ...
{ ...... |
github | LyricYang/Machine_Learning_Stanford-master | submitWithConfiguration.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex8/ex8/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | LyricYang/Machine_Learning_Stanford-master | savejson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex8/ex8/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | LyricYang/Machine_Learning_Stanford-master | loadjson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex8/ex8/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | LyricYang/Machine_Learning_Stanford-master | loadubjson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex8/ex8/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | LyricYang/Machine_Learning_Stanford-master | saveubjson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex8/ex8/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | LyricYang/Machine_Learning_Stanford-master | submit.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex1/ex1/submit.m | 1,876 | utf_8 | 8d1c467b830a89c187c05b121cb8fbfd | function submit()
addpath('./lib');
conf.assignmentSlug = 'linear-regression';
conf.itemName = 'Linear Regression with Multiple Variables';
conf.partArrays = { ...
{ ...
'1', ...
{ 'warmUpExercise.m' }, ...
'Warm-up Exercise', ...
}, ...
{ ...
'2', ...
{ 'computeCost.m... |
github | LyricYang/Machine_Learning_Stanford-master | submitWithConfiguration.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex1/ex1/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | LyricYang/Machine_Learning_Stanford-master | savejson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex1/ex1/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | LyricYang/Machine_Learning_Stanford-master | loadjson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex1/ex1/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | LyricYang/Machine_Learning_Stanford-master | loadubjson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex1/ex1/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | LyricYang/Machine_Learning_Stanford-master | saveubjson.m | .m | Machine_Learning_Stanford-master/ex/machine-learning-ex1/ex1/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | AggieChallenge/Cerebro-master | PSDs.m | .m | Cerebro-master/PSDs.m | 1,116 | utf_8 | 87706e0691d7c8a436dc5952941d0046 | % THIS FILE IS TO EXTRACT Snowball PSD (the first 30 seconds are removed)
function [PSD] = PSDs(no_channel,record)
% no_channel = 20; % Input the position of channel T7-FT19
data = record(no_channel,:);
PSD = [];
for loop = 1:length(data)/256
x = data(256*(loop-1)+1:256*loop);
fs = 256;
m = ... |
github | uwrobotics/MarsRover2018-master | ControlsTestv6(latest).m | .m | MarsRover2018-master/IK_Matlab/ControlsTestv6(latest).m | 9,462 | utf_8 | 82fe08bf38be6c956c480c96e846ae06 | % Controls Test v2
clear all;
close all;
writerObj = VideoWriter('IK_Sim.avi');
writerObj.FrameRate = 1;
%% Parameters
%Robot Parameters
%Distance between dof and previous dof along axis being rotated about
d2 = 0.03;
lee = 0.1;
d = [0,0,0];
%Distance between dof and previous dof along axis that is rotating
l1 = ... |
github | mz24cn/clnet-master | compare_tensors.m | .m | clnet-master/examples/compare_tensors.m | 804 | utf_8 | 5164dc8e569dc8171c501f76b3bdbec6 | %used for compare two tensor data difference
function [delta max_e most l r] = compare_tensors(dir_l, dir_r, name, transpose, threshold)
l = csvread([dir_l, name, '.csv'], 1, 0);
r = csvread([dir_r, name, '.csv'], 1, 0);
if exist('transpose', 'var') && transpose
r = r';
end
if ~exist(... |
github | cantuu/Synchronization-Techniques-master | gardner.m | .m | Synchronization-Techniques-master/src/gardner.m | 375 | utf_8 | e3f98f465161e2926109c14b1d4b1f82 |
function instants2 = gardner(y, sps_, mi_tau, mi_sps)
instants2 = [];
tau_hat = 0;
k = 0;
i = sps_+1;
tam = length(y);
while k <= length(y) - (2*sps_)
k = round(i + tau_hat);
k1 = round(i + tau_hat - sps_);
k_half = ((k1 + k)/2);
e = (y(k1) - y(k)) * y(floor(k_half));
tau_hat += mi_tau * e;
sps_ += mi_s... |
github | cantuu/Synchronization-Techniques-master | interpsinc.m | .m | Synchronization-Techniques-master/src/interpsinc.m | 805 | utf_8 | 2d090b444f47df33cb360c3d0167f18e | % RETIRADO DE: TELECOMMUNICATIONS BREAKDOWN; C.RICHARD JOHNSON JR e WILLIAM A. SETHARES
function y=interpsinc(x, t, l, beta)
% y=interpsinc(x, t, l, beta)
% interpolate to find a single point using the direct method
% x = sampled data
% t = place at which value desired
% l = one sided length of da... |
github | cantuu/Synchronization-Techniques-master | squarerootrcosfilter.m | .m | Synchronization-Techniques-master/src/squarerootrcosfilter.m | 732 | utf_8 | d6f46fcd6dd790875e74ffc9f3e4279b | % Adapted from Mathuranathan Viswanathan. The original code can be found in:
% <https://www.gaussianwaves.com/2011/04/square-root-raised-cosine-filter-matchedsplit-filter-implementation-2/>
function response=squarerootrcosfilter(roll_off, span, sps)
a=roll_off;
t=-span:1/sps:span;
p=zeros(1,length(t));
fo... |
github | cantuu/Synchronization-Techniques-master | srrc.m | .m | Synchronization-Techniques-master/src/srrc.m | 827 | utf_8 | 7b440e45b8465210f2521ad39603e5a8 | % RETIRADO DE: TELECOMMUNICATIONS BREAKDOWN; C.RICHARD JOHNSON JR e WILLIAM A. SETHARES
function s=srrc(syms, beta, P, t_off);
% s=srrc(syms, beta, P, t_off);
% Generate a Square-Root Raised Cosine Pulse
% 'syms' is 1/2 the length of srrc pulse in symbol durations
% 'beta' is the rolloff factor: beta=0 give... |
github | taj4din/MPsee-toolbox-master | MPsee.m | .m | MPsee-toolbox-master/MPsee.m | 6,935 | utf_8 | 7d7ff926dbbe3b2d6c6888902c67bb88 | function varargout = MPsee(varargin)
% This is the MPsee GUI function of MPsee toolbox by S. Tajeddin,
% This code comes with no guarantee or warranty of any kind
%
%MPSEE M-file for MPsee.fig
% MPSEE, by itself, creates a new MPSEE or raises the existing
% singleton*.
%
% H = MPSEE returns the handle to... |
github | taj4din/MPsee-toolbox-master | MPsee.m | .m | MPsee-toolbox-master/examples/quadrotor/MPsee.m | 6,935 | utf_8 | 7d7ff926dbbe3b2d6c6888902c67bb88 | function varargout = MPsee(varargin)
% This is the MPsee GUI function of MPsee toolbox by S. Tajeddin,
% This code comes with no guarantee or warranty of any kind
%
%MPSEE M-file for MPsee.fig
% MPSEE, by itself, creates a new MPSEE or raises the existing
% singleton*.
%
% H = MPSEE returns the handle to... |
github | sd007/SimpleSipClient-master | echo_diagnostic.m | .m | SimpleSipClient-master/pjlibs/third_party/speex/libspeex/echo_diagnostic.m | 2,076 | utf_8 | 8d5e7563976fbd9bd2eda26711f7d8dc | % Attempts to diagnose AEC problems from recorded samples
%
% out = echo_diagnostic(rec_file, play_file, out_file, tail_length)
%
% Computes the full matrix inversion to cancel echo from the
% recording 'rec_file' using the far end signal 'play_file' using
% a filter length of 'tail_length'. The output is saved to 'o... |
github | Sbte/RAILS-master | RAILSschur.m | .m | RAILS-master/matlab/RAILSschur.m | 2,849 | utf_8 | 08e30b23fbcec1297dc336e51259a4e3 | function [S, MS, BS, Sinv, Vtrans] = RAILSschur(A, M, B, factorize)
% [S, MS, BS, Sinv, Vtrans] = RAILSschur(A, M, B, factorize)
%
% Used for problems with a singular M. After this, RAILSsolver can
% be used as
%
% [...] = RAILSsolver(S, MS, BS, ...)
%
% and for the inverse (opts.projection_method > 1), one can use
%
%... |
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