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 | wschwanghart/topotoolbox-master | line2GRIDobj.m | .m | topotoolbox-master/@GRIDobj/line2GRIDobj.m | 2,452 | utf_8 | fdf709803da04db01e146056e52b242f | function L = line2GRIDobj(DEM,varargin)
%LINE2GRIDOBJ convert line to a grid
%
% Syntax
%
% L = line2GRIDobj(DEM,x,y)
% L = line2GRIDobj(DEM,MS)
%
% Description
%
% line2GRIDobj grids a polyline defined by a set of x and y
% coordinates. x and y can be nan-punctuated vectors. Alternatively,
% the p... |
github | wschwanghart/topotoolbox-master | GRIDobj.m | .m | topotoolbox-master/@GRIDobj/GRIDobj.m | 16,757 | utf_8 | 6ea7e6581d8a6516a18fa590e970ada9 | classdef GRIDobj
%GRIDobj Create instance of a GRIDobj
%
% Syntax
%
% DEM = GRIDobj(X,Y,dem)
% DEM = GRIDobj('ESRIasciiGrid.txt')
% DEM = GRIDobj('GeoTiff.tif')
% DEM = GRIDobj();
% DEM = GRIDobj([]);
% DEM = GRIDobj(FLOWobj or GRIDobj or STREAMobj,class)
%
%
% Description
%
% GRIDobj c... |
github | wschwanghart/topotoolbox-master | surf.m | .m | topotoolbox-master/@GRIDobj/surf.m | 6,852 | utf_8 | 45f9744f3f0139f3ecfa42461f63f7f0 | function ht = surf(DEM,varargin)
%SURF surface plot for GRIDobj
%
% Syntax
%
% surf(DEM)
% surf(DEM,A)
% surf(...,pn,pv,...)
% h = ...
%
% Description
%
% surf for GRIDobj overloads the surf command and thus provides fast
% access to 3D visualization of digital elevation models. Note that
% ... |
github | wschwanghart/topotoolbox-master | createmask.m | .m | topotoolbox-master/@GRIDobj/createmask.m | 1,576 | utf_8 | e6f1a75f902357fa601bca1fd25fc8a2 | function MASK = createmask(DEM,usehillshade)
%CREATEMASK create a binary mask using polygon mapping
%
% Syntax
%
% MASK = createmask(DEM)
% MASK = createmask(DEM,usehillshade)
%
% Description
%
% createmask is an interactive tool to create a mask based on an
% interactively mapped polygon.
%
% Input ar... |
github | wschwanghart/topotoolbox-master | inpaintnans.m | .m | topotoolbox-master/@GRIDobj/inpaintnans.m | 11,428 | utf_8 | 140920973e65c125e2266bcc64542930 | function DEM = inpaintnans(DEM,varargin)
%INPAINTNANS Interpolate or fill missing values in a grid (GRIDobj)
%
% Syntax
%
% DEMf = inpaintnans(DEM,type)
% DEMf = inpaintnans(DEM,type,k)
% DEMf = inpaintnans(DEM,type,k,conn)
% DEMf = inpaintnans(DEM,DEM2)
% DEMf = inpaintnans(DEM,DEM2,method)
% ... |
github | wschwanghart/topotoolbox-master | gradient8.m | .m | topotoolbox-master/@GRIDobj/gradient8.m | 3,766 | utf_8 | f749aa581aad377ab3f0fc89799f1c82 | function G = gradient8(DEM,unit,varargin)
%GRADIENT8 8-connected neighborhood gradient of a digital elevation model
%
% Syntax
%
% G = gradient8(DEM)
% G = gradient8(DEM,unit)
% G = gradient8(DEM,unit,pn,pv,...)
%
% Description
%
% gradient8 returns the numerical steepest downward gradient of a
% d... |
github | wschwanghart/topotoolbox-master | acv.m | .m | topotoolbox-master/@GRIDobj/acv.m | 2,639 | utf_8 | e7dab97e537450d261af006db317e472 | function DEM = acv(DEM)
%ACV Anisotropic coefficient of variation (ACV)
%
% Syntax
%
% C = acv(DEM)
%
% Description
%
% The anisotropic coefficient of variation describes the general
% geometry of the local land surface and can be used to distinguish
% elongated from oval landforms.
%
% Input
%
% ... |
github | wschwanghart/topotoolbox-master | curvature.m | .m | topotoolbox-master/@GRIDobj/curvature.m | 5,257 | utf_8 | 1ebd8990d90725b19fea8704f28e916c | function C = curvature(DEM,ctype,varargin)
%CURVATURE 8-connected neighborhood curvature of a digital elevation model
%
% Syntax
%
% C = curvature(DEM)
% C = curvature(DEM,type)
% C = curvature(DEM,type,pn,pv,...)
%
% Description
%
% curvature returns the second numerical derivative (curvature) o... |
github | wschwanghart/topotoolbox-master | dist2line.m | .m | topotoolbox-master/@GRIDobj/dist2line.m | 2,592 | utf_8 | 66b36b67a419b02a052eaa562c892eff | function [D] = dist2line(DEM,x0,y0,alpha)
%DIST2LINE labels pixels in a GRIDobj by their distance to a straight line
%
% Syntax
% D = dist2line(DEM,x0,y0,alpha)
%
% Description
%
% dist2line(DEM,x0,y0,alpha) computes the orthogonal distance of each
% pixel in a GRIDobj to a line that goes through the point... |
github | wschwanghart/topotoolbox-master | hillshade.m | .m | topotoolbox-master/@GRIDobj/hillshade.m | 4,823 | utf_8 | 717524829e593728e878154bb56ea185 | function OUT2 = hillshade(DEM,varargin)
%HILLSHADE create hillshading from a digital elevation model (GRIDobj)
%
% Syntax
%
% H = hillshade(DEM)
% H = hillshade(DEM,'pn','pv',...)
%
% Description
%
% Hillshading is a very powerful tool for relief depiction.
% hillshade calculates a shaded relief fo... |
github | wschwanghart/topotoolbox-master | GRIDobj2polygon.m | .m | topotoolbox-master/@GRIDobj/GRIDobj2polygon.m | 7,552 | utf_8 | 2ecdf8e4bbde4f680c75082f2320b19b | function [MS,x,y] = GRIDobj2polygon(DB,varargin)
%GRIDobj2polygon Conversion from drainage basin grid to polygon or polyline
%
% Syntax
%
% MS = GRIDobj2polygon(DB)
% MS = GRIDobj2polygon(DB,pn,pv,...)
% [MS,x,y] = ...
%
% Description
%
% GRIDobj2polygon converts a GRIDobj (label grid) to a mapstruct
%... |
github | wschwanghart/topotoolbox-master | GRIDobj2geotiff.m | .m | topotoolbox-master/@GRIDobj/GRIDobj2geotiff.m | 3,485 | utf_8 | 8ccb99afc25d3673f746f67381243357 | function GRIDobj2geotiff(A,file)
%GRIDobj2geotiff Exports an instance of GRIDobj to a geotiff file
%
% Syntax
%
% GRIDobj2geotiff(DEM)
% GRIDobj2geotiff(DEM,filename)
%
% Description
%
% GeoTIFF is a common image file format that stores coordinates and
% projection information to be read by most GI... |
github | wschwanghart/topotoolbox-master | reclassify.m | .m | topotoolbox-master/@GRIDobj/reclassify.m | 8,418 | utf_8 | 5c51382b064b1b7f66f23e7b2b8a582b | function DEM = reclassify(DEM,varargin)
%RECLASSIFY generate univariate class intervals for an instance of GRIDobj
%
% Syntax
%
% C = reclassify(DEM);
% C = reclassify(DEM,'method',value)
%
% Description
%
% reclassify bins continous values of an instance of GRIDobj by setting
% class intervals based o... |
github | wschwanghart/topotoolbox-master | polygon2GRIDobj.m | .m | topotoolbox-master/@GRIDobj/polygon2GRIDobj.m | 6,658 | utf_8 | 966dfbe54ab8836d023cd3326ee7daf2 | function P = polygon2GRIDobj(DEM,MS,varargin)
%POLYGON2GRIDobj convert polygon to a grid
%
% Syntax
%
% P = polygon2GRIDobj(DEM,MS)
% P = polygon2GRIDobj(DEM,MS,field)
% P = polygon2GRIDobj(DEM,MS,'pn',pv)
%
% Description
%
% polygon2GRIDobj maps polygons in the mapping structure MS to a
% GRIDobj... |
github | wschwanghart/topotoolbox-master | idw.m | .m | topotoolbox-master/@PPS/idw.m | 3,105 | utf_8 | 76959699a63701038e6ac2f02a7fe4c6 | function c = idw(P,marks,varargin)
%IDW Inverse distance weighted interpolation on stream networks
%
% Syntax
%
% c = idw(P,marks)
% c = idw(P,marks,pn,pv,...)
%
% Description
%
% idw computes an inverse distance weighted interpolation on a stream
% network. Distances are calculated as geodesic distanc... |
github | wschwanghart/topotoolbox-master | plotdz.m | .m | topotoolbox-master/@PPS/plotdz.m | 4,600 | utf_8 | 781e021368981a4095294e1dc6360978 | function varargout = plotdz(P,varargin)
%PLOTDZ plot upstream distance version elevation or covariate of a PPS
%
% Syntax
%
% plotdz(P)
%
% Description
%
% plot distance versus elevation of points and stream network in an
% instance of PPS.
%
% Input arguments
%
% P instance of PPS (needs z-prope... |
github | wschwanghart/topotoolbox-master | plotpoints.m | .m | topotoolbox-master/@PPS/plotpoints.m | 3,644 | utf_8 | a234be892ec75fab1559dc1bb596a2d8 | function h = plotpoints(P,varargin)
%PLOTPOINTS plot points of PPS
%
% Syntax
%
% plotpoints(P)
% plotpoints(P,'pn',pv,...)
% h = plotpoints(...)
%
% Description
%
% plotpoints plots the points of an instance of PPS. The function uses
% the build-in function scatter. Thus, it accepts all parameter
... |
github | wschwanghart/topotoolbox-master | aggregate.m | .m | topotoolbox-master/@PPS/aggregate.m | 5,971 | utf_8 | 9e0fbc15cca218f657f3f1eb53de4631 | function [P,locb] = aggregate(P,c,varargin)
%AGGREGATE Aggregate points in PPS to new point pattern
%
% Syntax
%
% P2 = aggregate(P,c)
% P2 = aggregate(P,c,pn,pv,...)
%
% Description
%
% aggregate merges points in a PPS object to a new PPS object based on
% the labels in the marks c. The labels can b... |
github | wschwanghart/topotoolbox-master | convhull.m | .m | topotoolbox-master/@PPS/convhull.m | 3,641 | utf_8 | 7dd0ac9cd40bcda35dd6d612617bb046 | function h = convhull(P,varargin)
%CONVHULL Convex hull around points in PPS
%
% Syntax
%
% psh = convhull(P)
% psh = convhull(P,'groups',c,'bufferwidth',bw);
% convhull(P,...)
%
% Description
%
% convhull returns the convex hull around all or groups of points in P
% as polyshape object. Without ou... |
github | wschwanghart/topotoolbox-master | bayesloglinear.m | .m | topotoolbox-master/@PPS/bayesloglinear.m | 3,435 | utf_8 | c7b83ddd36f61b9142262c276bc748c0 | function [mdl,int,intci,predstats,rank] = bayesloglinear(P,c,varargin)
%BAYESLOGLINEAR Bayesian analysis of a loglinear point process model
%
% Syntax
%
% [mdl,int,intci,predstats] = bayesloglinear(P,c,pn,pv,...)
%
% Description
%
% Loglinear models embrace numerous models that can be fitted to
% homogene... |
github | wschwanghart/topotoolbox-master | fitloglinear.m | .m | topotoolbox-master/@PPS/fitloglinear.m | 6,916 | utf_8 | 3bbc96894fc95c86395f330888cf3b04 | function [mdl,int,rts,rtssigma,ismx,sigmapred] = fitloglinear(P,c,varargin)
%FITLOGLINEAR fit loglinear model to point pattern
%
% Syntax
%
% [mdl,int] = fitloglinear(P,c)
% [mdl,int] = fitloglinear(P,c,pn,pv,...)
% [mdl,int,mx,sigmamx,ismx,sigmapred] = ...
% fitloglinear(P,c,'modelspec',... |
github | wschwanghart/topotoolbox-master | ploteffects.m | .m | topotoolbox-master/@PPS/ploteffects.m | 8,245 | utf_8 | eca174b26bf80832cf1bf27946b7502d | function h = ploteffects(P,mdl,varargin)
%PLOTEFFECTS Plot of slices through a loglinear point process model
%
% Syntax
%
% ploteffects(P,mdl)
% ploteffects(P,mdl,covariate)
% ploteffects(p,mdl,covariate,pn,pv,...)
% h = ...
%
% Description
%
% ploteffects plots the individual effects of a loglinea... |
github | wschwanghart/topotoolbox-master | dpsimplify.m | .m | topotoolbox-master/GIStools/dpsimplify.m | 6,439 | utf_8 | 520039e696aaacc7377a4ba3f7f12ebe | function [ps,ix] = dpsimplify(p,tol)
% Recursive Douglas-Peucker Polyline Simplification, Simplify
%
% [ps,ix] = dpsimplify(p,tol)
%
% dpsimplify uses the recursive Douglas-Peucker line simplification
% algorithm to reduce the number of vertices in a piecewise linear curve
% according to a specified tolerance. The a... |
github | wschwanghart/topotoolbox-master | zonalstats.m | .m | topotoolbox-master/GIStools/zonalstats.m | 8,482 | utf_8 | 53cb779cf9fd2ddfaa01d6a5fcbb1d18 | function MS = zonalstats(MS,attributes,varargin)
%ZONALSTATS Zonal statistics
%
% Syntax
%
% MS = zonalstats(MS)
% MS = zonalstats(MS,{varname1, vargrid1, varfun1, ...
% varname2, vargrid1, varfun2, ...})
% MS = zonalstats(MS,{varname1, vargrid1, varfun1, ...
% ... |
github | wschwanghart/topotoolbox-master | readopentopo.m | .m | topotoolbox-master/IOtools/readopentopo.m | 10,992 | utf_8 | d96ec589591c68696b47acb6acc3f01d | function DEM = readopentopo(varargin)
%READOPENTOPO Read DEM using the opentopography.org API
%
% Syntax
%
% DEM = readopentopo(pn,pv,...)
%
% Description
%
% readopentopo reads DEMs from opentopography.org using the API
% described on:
% http://www.opentopography.org/developers
% The DEM comes in ... |
github | wschwanghart/topotoolbox-master | FLOWobj2cell.m | .m | topotoolbox-master/@FLOWobj/FLOWobj2cell.m | 1,882 | utf_8 | 71c4541d170224c2ef6dc4d5e647e544 | function [CFD,D,A,cfix] = FLOWobj2cell(FD,IX)
%FLOWOBJ2CELL return cell array of FLOWobjs for individual drainage basins
%
% Syntax
%
% CF = FLOWobj2cell(FD)
% [CF,D,a] = FLOWobj2cell(FD)
% [CF,D,a,cfix] = FLOWobj2cell(FD,IX)
%
% Description
%
% FLOWobj2cell derives a cell array of FLOWobjs for each in... |
github | wschwanghart/topotoolbox-master | flow_matrix.m | .m | topotoolbox-master/@FLOWobj/private/flow_matrix.m | 10,451 | utf_8 | 7ad31c1c00660f3f2280acc9832bdce9 | function T = flow_matrix(E, R, d1, d2)
%flow_matrix System of linear equations representing pixel flow
%
% T = flow_matrix(E, R) computes a sparse linear system representing flow from
% pixel to pixel in the DEM represented by the matrix of height values, E. R
% is the matrix of pixel flow directions as computed... |
github | wschwanghart/topotoolbox-master | routeflats.m | .m | topotoolbox-master/@FLOWobj/private/routeflats.m | 5,974 | utf_8 | 411ad344f47b1cadb9c82e6c107a56eb | function [IXf,IXn] = routeflats(dem,type)
% route through flats of a digital elevation model
%
% Syntax
%
% [IXf,IXn] = routeflats(dem,type)
%
% Description
%
% routeflats is a subroutine used by some of the flowdirection
% algorithms in the toolbox. routeflats recursively creates flow paths
% through... |
github | wschwanghart/topotoolbox-master | jctcon.m | .m | topotoolbox-master/@DIVIDEobj/jctcon.m | 3,625 | utf_8 | 07d3aab34aad2e41e9ca2f7232a14b26 | function [CJ,varargout] = jctcon(D,varargin)
%JCTCON compute junction connectivity
%
% Syntax
%
% CJ = jctcon(D)
% CJ = jctcon(D,maxdist)
% [CJ,x,y] = jctcon(D)
%
%
% Description
%
% JCTCON computes the junction connectivity for junctions provided by
% the linear indices in ixj... |
github | wschwanghart/topotoolbox-master | DIVIDEobj2mapstruct.m | .m | topotoolbox-master/@DIVIDEobj/DIVIDEobj2mapstruct.m | 11,951 | utf_8 | d2d9ab5c45b3649f93c78ffacc502f96 | function MS = DIVIDEobj2mapstruct(D,DEM,seglen,varargin)
%DIVIDEPROPS obtain divide properties from GRIDobj
%
% Syntax
%
% D = DIVIDEobj2mapstruct(D,DEM,seglength)
% D = DIVIDEobj2mapstruct(D,DEM,seglength,...
% {'fieldname1' var1 aggfunction1},...
% {'fieldname2' var2 aggfunction2})
%
%... |
github | lampo808/Fit-master | ex_GFS_multipressure_Voigt.m | .m | Fit-master/examples/ex_GFS_multipressure_Voigt.m | 2,420 | utf_8 | bf622f2e9b4d0aa5e7620ab9f6a029f6 | % Example for the global fit class (GlobalFitSimple)
% Fit a set of Voigt profiles that represent the same absorption line
% measured at different pressures.
clear all
close all
addpath('./fadf')
rng(1) % Set a seed for the random number generation (for reproducibility)
% The model represents a pressure-boradened... |
github | lampo808/Fit-master | hessdiag.m | .m | Fit-master/DERIVESTsuite/hessdiag.m | 2,034 | utf_8 | ff31ada116a5b893f0b1b7ad4ef6336f | function [HD,err,finaldelta] = hessdiag(fun,x0)
% HESSDIAG: diagonal elements of the Hessian matrix (vector of second partials)
% usage: [HD,err,finaldelta] = hessdiag(fun,x0)
%
% When all that you want are the diagonal elements of the hessian
% matrix, it will be more efficient to call HESSDIAG than HESSIAN.
% HESSDIA... |
github | lampo808/Fit-master | hessian.m | .m | Fit-master/DERIVESTsuite/hessian.m | 5,157 | utf_8 | 8e0bddd9a2df4151adbee6e016f767cf | function [hess,err] = hessian(fun,x0)
% hessian: estimate elements of the Hessian matrix (array of 2nd partials)
% usage: [hess,err] = hessian(fun,x0)
%
% Hessian is NOT a tool for frequent use on an expensive
% to evaluate objective function, especially in a large
% number of dimensions. Its computation will use rough... |
github | lampo808/Fit-master | jacobianest.m | .m | Fit-master/DERIVESTsuite/jacobianest.m | 5,850 | utf_8 | eb3dd9ff0c56b1eb7316f8237dbee253 | function [jac,err] = jacobianest(fun,x0)
% gradest: estimate of the Jacobian matrix of a vector valued function of n variables
% usage: [jac,err] = jacobianest(fun,x0)
%
%
% arguments: (input)
% fun - (vector valued) analytical function to differentiate.
% fun must be a function of the vector or array x0.
%
%... |
github | lampo808/Fit-master | gradest.m | .m | Fit-master/DERIVESTsuite/gradest.m | 2,374 | utf_8 | 8164711b2f9bdaae657fae039afd34f0 | function [grad,err,finaldelta] = gradest(fun,x0)
% gradest: estimate of the gradient vector of an analytical function of n variables
% usage: [grad,err,finaldelta] = gradest(fun,x0)
%
% Uses derivest to provide both derivative estimates
% and error estimates. fun needs not be vectorized.
%
% arguments: (input)
% fun ... |
github | lampo808/Fit-master | derivest.m | .m | Fit-master/DERIVESTsuite/derivest.m | 23,018 | utf_8 | 3198e9636b2275d707eec59dbb9b8a2f | function [der,errest,finaldelta] = derivest(fun,x0,varargin)
% DERIVEST: estimate the n'th derivative of fun at x0, provide an error estimate
% usage: [der,errest] = DERIVEST(fun,x0) % first derivative
% usage: [der,errest] = DERIVEST(fun,x0,prop1,val1,prop2,val2,...)
%
% Derivest will perform numerical differentiatio... |
github | jacenfox/sun-moon-Positions-master | LunarCalendar.m | .m | sun-moon-Positions-master/3rd_party/LunarCalendar.m | 5,732 | utf_8 | 3d0ffddf382c0f40ca2ea0a47b249076 | function xx = LunarCalendar(y,m,d)
% function xx = LunarCalendar(y,m,d)
%
%
if nargin==0;
cccc=clock;
y=cccc(1);m=cccc(2);d=cccc(3);
else if ischar(y)
y = str2num(y); m = str2num(m); d = str2num(d);
end
end
% Animals={'鼠','牛','虎','兔','龙','蛇','马','羊','猴','鸡','狗','猪'};
% CnDayStr={'初一... |
github | jacenfox/sun-moon-Positions-master | LunarAzEl.m | .m | sun-moon-Positions-master/3rd_party/LunarAzEl.m | 8,768 | utf_8 | 304d78148bef528f612a5b76a8be0430 | function [Az h] = LunarAzEl(UTC,Lat,Lon,Alt)
% Programed by Darin C. Koblick 2/14/2009
%
% Updated on 03/04/2009 to clean up code and add quadrant check to Azimuth
% Thank you Doug W. for your help with the test code to find the quadrant check
% error.
%
% Updated on 04/13/2009 to add Lunar perturbation offse... |
github | revantkumar/Deep-Learning-master | checkNumericalGradient.m | .m | Deep-Learning-master/Assignments/Assignment 1/ex2/checkNumericalGradient.m | 1,982 | utf_8 | 689a352eb2927b0838af5dc508f6374d | function [] = checkNumericalGradient()
% This code can be used to check your numerical gradient implementation
% in computeNumericalGradient.m
% It analytically evaluates the gradient of a very simple function called
% simpleQuadraticFunction (see below) and compares the result with your numerical
% solution. Your num... |
github | revantkumar/Deep-Learning-master | sparseAutoencoderCost.m | .m | Deep-Learning-master/Assignments/Assignment 1/ex2/sparseAutoencoderCost.m | 4,010 | utf_8 | c24c49e3e21c0749cd4c5e1ab880bf38 | function [cost,grad] = sparseAutoencoderCost(theta, visibleSize, hiddenSize, ...
lambda, sparsityParam, beta, data)
% visibleSize: the number of input units (probably 64)
% hiddenSize: the number of hidden units (probably 25)
% lambda: weight decay parameter
% sparsityPar... |
github | revantkumar/Deep-Learning-master | sampleIMAGES.m | .m | Deep-Learning-master/Assignments/Assignment 1/ex2/sampleIMAGES.m | 2,165 | utf_8 | df015d16283096f395df05efdeb24b05 | function patches = sampleIMAGES()
% sampleIMAGES
% Returns 10000 patches for training
addpath ../data/
addpath ../mnist/
load IMAGES; % load images from disk
patchsize = 8; % we'll use 8x8 patches
numpatches = 10000;
% Initialize patches with zeros. Your code will fill in this matrix--one
% column per patch, 1... |
github | revantkumar/Deep-Learning-master | feedForwardAutoencoder.m | .m | Deep-Learning-master/Assignments/Assignment 1/ex4/feedForwardAutoencoder.m | 1,297 | utf_8 | 2c3b46b1ca573b264b8bc8392003e2d2 | function [activation] = feedForwardAutoencoder(theta, hiddenSize, visibleSize, data)
% theta: trained weights from the autoencoder
% visibleSize: the number of input units (probably 64)
% hiddenSize: the number of hidden units (probably 25)
% data: Our matrix containing the training data as columns. So, data(:,i) i... |
github | revantkumar/Deep-Learning-master | checkNumericalGradient.m | .m | Deep-Learning-master/Assignments/Assignment 1/ex1/checkNumericalGradient.m | 1,982 | utf_8 | 689a352eb2927b0838af5dc508f6374d | function [] = checkNumericalGradient()
% This code can be used to check your numerical gradient implementation
% in computeNumericalGradient.m
% It analytically evaluates the gradient of a very simple function called
% simpleQuadraticFunction (see below) and compares the result with your numerical
% solution. Your num... |
github | revantkumar/Deep-Learning-master | sparseAutoencoderCost.m | .m | Deep-Learning-master/Assignments/Assignment 1/ex1/sparseAutoencoderCost.m | 4,010 | utf_8 | c24c49e3e21c0749cd4c5e1ab880bf38 | function [cost,grad] = sparseAutoencoderCost(theta, visibleSize, hiddenSize, ...
lambda, sparsityParam, beta, data)
% visibleSize: the number of input units (probably 64)
% hiddenSize: the number of hidden units (probably 25)
% lambda: weight decay parameter
% sparsityPar... |
github | revantkumar/Deep-Learning-master | sampleIMAGES.m | .m | Deep-Learning-master/Assignments/Assignment 1/ex1/sampleIMAGES.m | 2,165 | utf_8 | df015d16283096f395df05efdeb24b05 | function patches = sampleIMAGES()
% sampleIMAGES
% Returns 10000 patches for training
addpath ../data/
addpath ../mnist/
load IMAGES; % load images from disk
patchsize = 8; % we'll use 8x8 patches
numpatches = 10000;
% Initialize patches with zeros. Your code will fill in this matrix--one
% column per patch, 1... |
github | revantkumar/Deep-Learning-master | WolfeLineSearch.m | .m | Deep-Learning-master/Assignments/Assignment 1/minFunc/WolfeLineSearch.m | 11,478 | utf_8 | d10187f2fedfa4143ebd6300537b6be4 | function [t,f_new,g_new,funEvals,H] = WolfeLineSearch(...
x,t,d,f,g,gtd,c1,c2,LS,maxLS,tolX,debug,doPlot,saveHessianComp,funObj,varargin)
%
% Bracketing Line Search to Satisfy Wolfe Conditions
%
% Inputs:
% x: starting location
% t: initial step size
% d: descent direction
% f: function value at st... |
github | revantkumar/Deep-Learning-master | minFunc_processInputOptions.m | .m | Deep-Learning-master/Assignments/Assignment 1/minFunc/minFunc_processInputOptions.m | 3,704 | utf_8 | dc74c67d849970de7f16c873fcf155bc |
function [verbose,verboseI,debug,doPlot,maxFunEvals,maxIter,tolFun,tolX,method,...
corrections,c1,c2,LS_init,LS,cgSolve,qnUpdate,cgUpdate,initialHessType,...
HessianModify,Fref,useComplex,numDiff,LS_saveHessianComp,...
DerivativeCheck,Damped,HvFunc,bbType,cycle,...
HessianIter,outputFcn,useMex,use... |
github | revantkumar/Deep-Learning-master | sparseAutoencoderCost.m | .m | Deep-Learning-master/Assignments/Assignment 2/revant_kumar/sparseAutoencoderCost.m | 4,010 | utf_8 | c24c49e3e21c0749cd4c5e1ab880bf38 | function [cost,grad] = sparseAutoencoderCost(theta, visibleSize, hiddenSize, ...
lambda, sparsityParam, beta, data)
% visibleSize: the number of input units (probably 64)
% hiddenSize: the number of hidden units (probably 25)
% lambda: weight decay parameter
% sparsityPar... |
github | revantkumar/Deep-Learning-master | stackedAEPredict.m | .m | Deep-Learning-master/Assignments/Assignment 2/revant_kumar/stackedAEPredict.m | 1,601 | utf_8 | 26a168f67dac4800fdf4ac363cb78db1 | function [pred] = stackedAEPredict(theta, inputSize, hiddenSize, numClasses, netconfig, data)
% stackedAEPredict: Takes a trained theta and a test data set,
% and returns the predicted labels for each example.
% theta: trained weights f... |
github | revantkumar/Deep-Learning-master | feedForwardAutoencoder.m | .m | Deep-Learning-master/Assignments/Assignment 2/revant_kumar/feedForwardAutoencoder.m | 1,297 | utf_8 | 2c3b46b1ca573b264b8bc8392003e2d2 | function [activation] = feedForwardAutoencoder(theta, hiddenSize, visibleSize, data)
% theta: trained weights from the autoencoder
% visibleSize: the number of input units (probably 64)
% hiddenSize: the number of hidden units (probably 25)
% data: Our matrix containing the training data as columns. So, data(:,i) i... |
github | revantkumar/Deep-Learning-master | stackedAECost.m | .m | Deep-Learning-master/Assignments/Assignment 2/revant_kumar/stackedAECost.m | 3,883 | utf_8 | 2b6f8b14e7d998bf8bc26b2d5c4c346c | function [ cost, grad ] = stackedAECost(theta, inputSize, hiddenSize, ...
numClasses, netconfig, ...
lambda, data, labels)
% stackedAECost: Takes a trained softmaxTheta and a trainin... |
github | revantkumar/Deep-Learning-master | WolfeLineSearch.m | .m | Deep-Learning-master/Assignments/Assignment 2/minFunc/WolfeLineSearch.m | 11,478 | utf_8 | d10187f2fedfa4143ebd6300537b6be4 | function [t,f_new,g_new,funEvals,H] = WolfeLineSearch(...
x,t,d,f,g,gtd,c1,c2,LS,maxLS,tolX,debug,doPlot,saveHessianComp,funObj,varargin)
%
% Bracketing Line Search to Satisfy Wolfe Conditions
%
% Inputs:
% x: starting location
% t: initial step size
% d: descent direction
% f: function value at st... |
github | revantkumar/Deep-Learning-master | minFunc_processInputOptions.m | .m | Deep-Learning-master/Assignments/Assignment 2/minFunc/minFunc_processInputOptions.m | 3,704 | utf_8 | dc74c67d849970de7f16c873fcf155bc |
function [verbose,verboseI,debug,doPlot,maxFunEvals,maxIter,tolFun,tolX,method,...
corrections,c1,c2,LS_init,LS,cgSolve,qnUpdate,cgUpdate,initialHessType,...
HessianModify,Fref,useComplex,numDiff,LS_saveHessianComp,...
DerivativeCheck,Damped,HvFunc,bbType,cycle,...
HessianIter,outputFcn,useMex,use... |
github | CognitiveRobotics/pcl-master | plot_camera_poses.m | .m | pcl-master/gpu/kinfu/tools/plot_camera_poses.m | 3,403 | utf_8 | 097aaeb35920a12acfe6320b3e4f498b | % Copyright (c) 2014-, Open Perception, Inc.
% All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions
% are met:
%
% * Redistributions of source code must retain the above copyright
% notice, this list of ... |
github | steflee/MCL_Caffe-master | prepare_batch.m | .m | MCL_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 | steflee/MCL_Caffe-master | matcaffe_demo_vgg.m | .m | MCL_Caffe-master/matlab/caffe/matcaffe_demo_vgg.m | 3,036 | utf_8 | f836eefad26027ac1be6e24421b59543 | function scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file)
% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file)
%
% Demo of the matlab wrapper using the networks described in the BMVC-2014 paper "Return of the Devil in the Details: Delving Deep into Convolutional... |
github | steflee/MCL_Caffe-master | matcaffe_demo.m | .m | MCL_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 | steflee/MCL_Caffe-master | matcaffe_demo_vgg_mean_pix.m | .m | MCL_Caffe-master/matlab/caffe/matcaffe_demo_vgg_mean_pix.m | 3,069 | utf_8 | 04b831d0f205ef0932c4f3cfa930d6f9 | function scores = matcaffe_demo_vgg_mean_pix(im, use_gpu, model_def_file, model_file)
% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file)
%
% Demo of the matlab wrapper based on the networks used for the "VGG" entry
% in the ILSVRC-2014 competition and described in the tech. report
% "Very Deep Convo... |
github | HosseinAbedi/FCM-master | euclidean.m | .m | FCM-master/fcm/euclidean.m | 296 | utf_8 | 957d8f1a16b24a66be0f58412f5867b7 | % A function for calculation of euclidean distance of a point x(e.g. [3, 2, 1, 1]) from...
% ...a set of points in matrix format Y(e.g. [3, 2, 1, 2; 4, 3, 2, 1])
function [d] = euclidean(x, Y)
S = size(Y);
d = sum((repmat(x, [S(1),1])-Y).^2, 2);
d = sqrt(d);
end |
github | HosseinAbedi/FCM-master | fcm.m | .m | FCM-master/fcm/fcm.m | 1,345 | utf_8 | ffc7c4736bf2a26bc8197dfe39a1b014 | %Fuzzy C-means Algorithm in GnuOctave (V.3.6.4)
%Fuzzy type 1 C-means algorithm
%Inputs:
%******c: Number of clusters
%******X: Data Matrix N_samples*N_features
%******U_up: U updating function
%******m: Fuzzifier as a real number
%******metric: Distance metric as a function (by default Euclidean)
%******Max: Maximum ... |
github | hailongfeng/huiyin-master | echo_diagnostic.m | .m | huiyin-master/第三方完整APP源码/mogutt/TTAndroidClient/mgandroid-teamtalk/jni/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 | hailongfeng/huiyin-master | echo_diagnostic.m | .m | huiyin-master/第三方完整APP源码/mogutt/TTWinClient/3rdParty/src/libspeex/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 | hailongfeng/huiyin-master | FMSearchTokenField.m | .m | huiyin-master/第三方完整APP源码/mogutt/TTMacClient/TeamTalk/interface/mainWindow/FMSearchTokenField.m | 4,519 | utf_8 | 2a89df28133e0c91280b5daf58944c94 | //
// FMSearchTokenField.m
// Duoduo
//
// Created by zuoye on 13-12-23.
// Copyright (c) 2013年 zuoye. All rights reserved.
//
#import "FMSearchTokenField.h"
#import "FMSearchTokenFieldCell.h"
@implementation FMSearchTokenField
@synthesize sendActionWhenEditing=_sendActionWhenEditing;
@synthesize alwaysSendAction... |
github | hailongfeng/huiyin-master | DDNinePartImage.m | .m | huiyin-master/第三方完整APP源码/mogutt/TTMacClient/TeamTalk/interface/mainWindow/searchField/DDNinePartImage.m | 6,722 | utf_8 | 6dac0c29b80d07b31ccfd0b48ec932de | //
// DDNinePartImage.m
// Duoduo
//
// Created by zuoye on 14-1-20.
// Copyright (c) 2014年 zuoye. All rights reserved.
//
#import "DDNinePartImage.h"
@implementation DDNinePartImage
-(id)initWithNSImage:(NSImage *)image leftPartWidth:(CGFloat)leftWidth rightPartWidth:(CGFloat)rightWidth topPartHeight:(CGFloat)t... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | publish_meta_analysis_report.m | .m | Canlab_MKDA_MetaAnalysis-master/publish_meta_analysis_report.m | 2,717 | utf_8 | 2e567ddc4ed722a5ad177fde06b32bc0 | % Runs batch analyses and publishes HTML report with figures and stats to
% results/published_output in local study-specific analysis directory.
% Run this from the main mediation results directory (basedir)
close all
warning off, clear all, warning on
resultsdir = pwd;
fprintf('Creating HTML report for ... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | Meta_Specificity.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/Meta_Specificity.m | 6,851 | utf_8 | 84d871e62043140571dbd0a5ad060018 | function OUT = whole_brain_ptask_givena(OUT,varargin)
% OUT = whole_brain_ptask_givena(OUT,verbose level, mask image or threshold value(for pa_overall) )
%
% OUT = whole_brain_ptask_givena(OUT,2,.001)
% OUT = whole_brain_ptask_givena(OUT,2,'Activation_thresholded.img')
% verbose flag
if length(varargin) > 0, vb = vara... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | Meta_Setup.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/Meta_Setup.m | 11,506 | utf_8 | a577bd786ca6fc8594713550d606edf3 | % DB = Meta_Setup(DB, [radius_mm], [con_dens_images])
% Set up Meta-analysis dataset
%
% use after read_database.m
% See the Manual for more complete information.
%
% Special Fields
%
% Subjects or N : sample size
% FixedRandom : fixed or random effects
% SubjectiveWeights : weighting vector based on FixedRan... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | Meta_Analysis_gui.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/Meta_Analysis_gui.m | 20,892 | utf_8 | 32cef756c1c6ebeb67d03a28a19a2ef7 | %
%
% Tor Wager & Brencho
%
% Thanks to Tom Nichols for the excellent GUI shell!
%-----------------------------functions-called------------------------
%
%-----------------------------functions-called------------------------
function varargout = Meta_Analysis_gui(Action,varargin)
% global variables we need for th... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | Meta_Select_Contrasts.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/Meta_Select_Contrasts.m | 5,138 | utf_8 | a9c2518ebf81a206895917a47667bf09 | function [DB] = Meta_Select_Contrasts(DB)
% [DB] = Meta_Select_Contrasts(DB)
%
% Set up logistic regression design matrix from DB
%
% needs to set up design:
%DB.(fields) % lists of fields containing task conditions for each coordinate point
%DB.pointind % indices of which coord points are in which unique
% ... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | Meta_Activation_FWE.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/Meta_Activation_FWE.m | 38,042 | utf_8 | 0399062ab3617ffc65421f8786e3b484 | % Meta_Activation_FWE(meth)
%
% This function sets up an MKDA analysis, starts or adds iterations, and
% retrieves and plots results. It has four modes, specified by the first input argument:
%
% 'setup' : Create activation map and save MC_SETUP file in current directory
% 'mc' : Add iterations and save in MC_I... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | Meta_cluster_tools.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/Meta_cluster_tools.m | 16,271 | utf_8 | e048c639895cb328e19867acf729fa67 | function varargout = Meta_cluster_tools(meth,varargin)
% varargout = Meta_cluster_tools(meth,varargin)
%
% This function contains multiple tools for working with clusters
% derived from Meta_Activation_FWE and Meta_SOM tools
%
%
% ------------------------------------------------------
% extract data and print a table f... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | Meta_Study_Table.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/Meta_Study_Table.m | 5,017 | utf_8 | 28b9f8f7d50425980d19eeaa29bd031f | function Meta_Study_Table(DB,varargin)
% function Meta_Study_Table(DB,['study'])
%
% Prints text table of all independent contrasts for export
%
% looks for Study or study field in DB (also takes clusters, cl)
% determines length, and looks for other fields of the same length
% uses specified fields in a particular ord... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | Meta_Logistic_Design.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/Meta_Logistic_Design.m | 10,343 | utf_8 | 2aa4e4681ab54e540c9b11f0e93cf967 | function [X,Xnms,DB,Xi,alltasknms,condf,testfield,conweights] = Meta_Logistic_Design(DB,varargin)
% [X,Xnms,DB,Xi,Xinms,condf,testfield,conweights] = Meta_Logistic_Design(DB,[control strings])
%
% Set up logistic regression design matrix from DB
%
% needs to set up design:
%DB.(fields) % lists of fields containing ta... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | Meta_Chisq.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/Meta_Chisq.m | 10,564 | utf_8 | 615773d81f72496057c6095494e7be4d | function DB = Meta_Chisq(DB,varargin)
% DB = Meta_Chisq(DB,[verbose],[mask image name],[control strings])
%
% NEEDS:
%
% to set up design:
%DB.(fields) % lists of fields containing task conditions for each coordinate point
%DB.pointind % indices of which coord points are in which unique
% contrast
%... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | Meta_Chisq_new.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/Meta_Chisq_new.m | 10,950 | utf_8 | a45864bb28584fb860ccb0f44481a6b7 | % Multi-mode function for performing voxel-wise chi-squared analysis on
% meta-analysis data (peak activations)
%
% R = Meta_Chisq_new('compute', MC_Setup, ['mask', maskimg])
% cl = Meta_Chisq_new('write', R);
%
% Note: Meta_Chisq works with list of image names for all study maps
% Meta_Chisq_new works with output of '... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | Meta_Logistic.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/Meta_Logistic.m | 11,335 | utf_8 | 21b6cd0028815afac8d86e99093ac0b1 | function DB = Meta_Logistic(DB,varargin)
% DB = Meta_Logistic(DB,[verbose],[mask image name],[control strings])
%
% NEEDS:
%
% to set up design:
%DB.(fields) % lists of fields containing task conditions for each coordinate point
%DB.pointind % indices of which coord points are in which unique
% cont... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | Meta_plot_points_on_slices.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/Meta_plot_points_on_slices.m | 4,404 | utf_8 | 33fce4f7291f828753137d7023ea7633 | function Meta_plot_points_on_slices(DB, MC_Setup)
% Meta_plot_points_on_slices(DB, MC_Setup)
%
% This function plots points on multiple slices. It can do it either on
% solid slices or "outline" contours. Right now, it's hard-coded for
% contours, but the main function it runs, plot_points_on_slice.m, has
% inp... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | Meta_Parcel.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/Meta_Parcel.m | 4,096 | utf_8 | 755684e4f35b451de4155bcc40ca8d47 | % [parcels, SVDinfo, parcel_stats] = Meta_Parcel(MC_Setup, varargin)
%
% documentation goes here.
%
%
% Example: use your own analysis mask:
% [parcels, SVDinfo, parcel_stats] = Meta_Parcel(MC_Setup, 'analysis_mask_name', 'acc_roi_mask.img');
function [parcels, SVDinfo, parcel_stats] = Meta_Parcel(MC_Setup, varargin)
... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | Meta_interactive_table_vox.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/Support_functions/Meta_interactive_table_vox.m | 11,061 | utf_8 | a5860b18f5d5d1ea2568e4b4e76cd5de | function Meta_interactive_table_vox(compareflag,varargin)
% Meta_interactive_table_vox(compareflag,[data matrix, volInfo struct])
% Make table output when you click on a voxel in orthviews.
%
% This version uses fields in DB.PP and computes voxel-based distances
% for consistency with Meta_Setup and other meta-analysis... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | get_contrast_indicator.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/Support_functions/get_contrast_indicator.m | 9,176 | utf_8 | 854063a26010ad065d235a26e52abff9 | function OUT = get_contrast_indicator(DB,testfield,varargin)
% OUT = get_contrast_indicator(DB,testfield,varargin)
% OUT = get_contrast_indicator(DB,'Method','create')
% OUT = get_contrast_indicator(DB,'valence','load',OUT)
% OUT = get_contrast_indicator(DB,'valence','create',[],{'pos' 'neg'})
% OUT = get_contrast_indi... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | meta_simulate_nonparamchi2.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/Support_functions/meta_simulate_nonparamchi2.m | 1,925 | utf_8 | ba275fef60ce0f11f1d5e0c20c502c42 | function [p_ste_avg,iterations] = meta_simulate_nonparamchi2(respfreq,numconds,N)
% [p_ste_avg,iterations] = meta_simulate_nonparamchi2(respfreq,numconds,N)
%
% Perform tests on made-up data to determine what the variability in
% nonparametric chi2 p-value estimates is as a function of the number
% of iterations in the... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | nonparam_specificity.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/Support_functions/nonparam_specificity.m | 1,480 | utf_8 | 211be41f3a4d0641c629e04c30701d2d | function [eff,p,sig,pt_given_a,pt,success,yp] = nonparam_specificity(y,X,iter,varargin)
% [eff,p,sig,pt_given_a,pt,success,yp] = nonparam_specificity(y,X,iter,[w])
%
% weights should be mean = 1
if length(varargin) > 0, w=varargin{1};, else, w=ones(size(y));, end
[n,k] = size(X); % number of obs. and tasks
% weig... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | meta_analyze_data.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/Support_functions/meta_analyze_data.m | 8,391 | utf_8 | 99febf8c4567c740dc1c69cc31e35a40 | function varargout = Meta_analyze_data(y,varargin)
% varargout = Meta_analyze_data(y,varargin)
%
% Inputs
% =========================================================================
% Weights:
%'w' % followed by weights
% Analysis types:
%'chi2' % weighted chi-square... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | mask2density.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/Support_functions/mask2density.m | 6,660 | utf_8 | a32e052314f9961dc1a7726bac60960a | function dm = mask2density(mask,radius,varargin)
% function dm = mask2density(mask,radius,[opt] searchmask, [opt] sphere_vol)
%
% mask is the mask with ones where activation points are
% radius is in voxels
%
% optional arguments:
% 1 searchmask
% searchmask is the whole brain search space [optional]
% Mas... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | Meta_Task_Indicator.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/Support_functions/Meta_Task_Indicator.m | 4,270 | utf_8 | 97f534f1e8359a2453f84bc20b886ea1 | function [Xi,alltasknms,condf,allti,testfield,prop_by_condition,num_by_condition] = Meta_Task_Indicator(DB,varargin)
% [Xi,alltasknms,condf,allti,testfield,prop_by_condition,num_by_condition] = Meta_Task_Indicator(DB,[data])
%
% Get task indicators and number / proportion of activating contrasts from DB
%
% needs to se... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | meta_count_contrasts.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/Support_functions/meta_count_contrasts.m | 1,072 | utf_8 | 13344e2c45dc1e1751b1778d6a67020a | % [num, wh, weighted_num, conindx] = meta_count_contrasts(DB, testfield, fieldvalue)
%
% e.g., testfield = 'Stimuli'
% fieldvalue = 'faces';
% [num, wh] = meta_count_contrasts(DB, testfield, fieldvalue)
function [num, wh, weighted_num, conindx] = meta_count_contrasts(DB, testfield, fieldvalue)
wh = strcmp(DB.(tes... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | xyz2density.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/Support_functions/xyz2density.m | 2,991 | utf_8 | 57225e0fc112e28248438a648c1cefa1 | function conmask = xyz2density(XYZmm,mask,V,str,radius,studyweight,varargin)
% conmask = xyz2density(XYZmm,mask,V,str,radius,studyweight,[enter vox xyz flag] AND [no write image])
%
% Take a list of xyz mm coordinates and turn it into a density mask
% with spherical convolution.
%
% XYZmm: n x 3, mask = zeros of corr... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | chi2test.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/Support_functions/chi2test.m | 5,080 | utf_8 | c1c3372e63d347f6744cbf34b5cc2908 | % [chi2,df,p,sig,warn,freq_table,expected_table,isnonparametric] = chi2test(counts,datatype,[obs. weights],[nonpar flag])
%
% Weighted or unweighted Chi-square test
% from frequency (contingency) table or rows of observations
% Optional nonparametric estimation for questionable results
%
%
% Takes either tabular (freq... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | Meta_Prune.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/Support_functions/Meta_Prune.m | 2,115 | utf_8 | aa7f1868008aadb6c3fae1f7a820fa25 | function DB = Meta_Prune(DB,include)
% DB = Meta_Prune(DB,include)
%
% Prunes database given an indicator vector of points (peaks) to include
% Includes only contrasts for which ALL peaks are included!
% Thus, if any peaks are excluded, the whole contrast is excluded.
%
% Called in Meta_Select_Contrasts and Meta_Logis... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | meta_chi2_matrix.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/Support_functions/meta_chi2_matrix.m | 2,102 | utf_8 | e89010ab138b590ac39d5a9644337c7c | function out = meta_chi2_matrix(dat,w)
w = w ./ mean(w);
myalpha = .05;
[N,npairs] = size(dat);
[rows,cols,ncorr] = corrcoef_indices(npairs);
str = sprintf('Computing differences among correlations %04d',0); fprintf(1,str);
chi2 = zeros(ncorr,1);
chi2p = zeros(ncorr,1);
diffr = zeros(ncor... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | chi2test_massive.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/Support_functions/chi2test_massive.m | 5,580 | utf_8 | a3bf795f3ded356e5d3ed47bc091c458 | % [chi2,df,p,sig,warn,isnonparametric] = chi2test_massive(seedcounts, counts,[obs. weights],[nonpar flag])
%
% Weighted or unweighted Chi-square test
% from observations
% Optional nonparametric estimation for questionable results
% SEE CHI2TEST.M
% THE PURPOSE OF THIS FUNCTION IS TO IMPLEMENT A CHI2 TEST EFFICIENTLY W... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | fishers_exact.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/Support_functions/fishers_exact.m | 1,475 | utf_8 | a5858286293f09391bfb7ceecb955ec6 | function [p,pobs] = fishers_exact(tab)
%
% m x n generalization of Fisher's exact test
%
% See Agresti, 1992. Exact inference for contingency tables. Statistical
% Science
%
% Eric W. Weisstein. "Fisher's Exact Test." From MathWorld--A Wolfram Web
% Resource. http://mathworld.wolfram.com/FishersExactTest.html
%
% Inp... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | meta_SOMclusters.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/Support_functions/meta_SOMclusters.m | 27,199 | utf_8 | d6babfd9f8dfa9ed8a3a58b56ae06e2c | function varargout = meta_SOMclusters(meth,SOMResults,varargin)
% varargout = meta_SOMclusters(meth,SOMResults,varargin)
%
% Multi-function toolbox for working with sets of clusters,
% particularly those extracted from SOM parcellation
%
% [cl,anyStudy,studyByCluster] = meta_SOMclusters(SOMResul... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | plot_points_on_slice.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/plotting_functions/plot_points_on_slice.m | 12,962 | utf_8 | 46d7cbf45163ac8a9f6d4c709c1889da | function [handles, wh_slice, my_z] = plot_points_on_slice(xyz, varargin)
% [handles, wh_slice, my_coords, texthandles] = plot_points_on_slice(xyz, varargin)
%
% Usage:
% handles = plot_points_on_slice(xyz, 'nodraw', 'color', [0 0 1], 'marker', 'o','close_enough',8);
%
% Optional inputs:
% {'noslice', 'nodraw'},... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | plot_points_on_brain.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/plotting_functions/plot_points_on_brain.m | 8,541 | utf_8 | 6dc643d8032261bb1f1db95be7894882 | function h = plot_points_on_brain(XYZ,varargin)
% function handles = plot_points_on_brain(XYZ,varargin)
%
% This function plots a 3-column vector of xyz points (coordinates from
% studies) or text labels for each point.
%
% - option to plot on a glass brain. Four different views are created in 2 figures.
% - same opti... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | plot_points_on_subcortex.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/plotting_functions/plot_points_on_subcortex.m | 5,636 | utf_8 | 47e5ed11cbddf191880c0b259d75d377 | function [cl,han,surfhan] = plot_points_on_subcortex(DB,name,colors,condf, varargin)
% [cl,han,surfhan] = plot_points_on_subcortex(DB,name,colors,condf, varargin)
%
% Extract and plot points for specific structure/structures.
%
% name input specifies structure to extract and plot on. Can be:
% - 'brainstem-thalamus', ... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | plot_points_on_medial_surface.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/plotting_functions/plot_points_on_medial_surface.m | 11,086 | utf_8 | 9374d5e4017b1d41d91d0f620c6970c0 | function plot_points_on_medial_surface(coords,colors,varargin)
% plot_points_on_medial_surface(coords,colors,varargin)
% plot_points_on_medial_surface(coords,colors,[factor variable],[factor levels],[newfig],[mytextlabels])
%
% examples:
% plot_points_on_medial_surface([EMDB.x EMDB.y EMDB.z],{'ro'})... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | Meta_interactive_point_slice_plot.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/plotting_functions/Meta_interactive_point_slice_plot.m | 3,451 | utf_8 | 7841a5b77d7ed22b39caa2d58c87b042 | function Meta_interactive_point_slice_plot(MC_Setup, DB)
% Meta_interactive_point_slice_plot(MC_Setup, DB)
%
% Set up interactive point plotting on slice
%
% tor wager, nov 2007
%
% Simple example for checking points manually, rather than setting up the interactive plotter:
% load SETUP
% V = DB.mas... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | bar_interactive.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/plotting_functions/bar_interactive.m | 2,717 | utf_8 | daa6fe813508338093f4e8a909f7622a | function bar_interactive(images, xnames)
% bar_interactive(images, xnames)
%
% create interactive bar-plot that pops up in spm_orthviews window
%
% tor wager
%
% 2006.05.02 - Modified by Matthew Davidson
% find the spm window, or make one from a p-image
spm_handle = findobj('Tag','Graphics');
if isempty(spm_handle) |... |
github | canlab/Canlab_MKDA_MetaAnalysis-master | plot_points_on_surface2.m | .m | Canlab_MKDA_MetaAnalysis-master/densityUtility3/plotting_functions/plot_points_on_surface2.m | 14,495 | utf_8 | 7e0bd5a23c6a200b529b7a01aeb79583 | function [h, pt, p] = plot_points_on_surface2(XYZ,varargin)
% function [axishan, pointhan, surfhan] = plot_points_on_surface2(XYZ,[{color(s)}, colorclasses, {textmarkers/contrastcodes}, varargin)
%
% This function plots a 3-column vector of xyz points (coordinates from
% studies) on a glass brain. Four different views... |
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