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 | NCAR/ncl-master | c_csa2xs.m | .m | ncl-master/ngmath/man/csagrid/c_csa2xs.m | 4,303 | utf_8 | 8509f8dc6e88bb0c88310fc3feadb43e | .\"
.\" $Id: c_csa2xs.m,v 1.4 2008-07-27 03:35:33 haley Exp $
.\"
.TH c_csa2xs 3NCARG "January 1999" UNIX "NCAR GRAPHICS"
.SH NAME
c_csa2xs - cubic spline approximation, expanded entry for two-dimensional input, gridded output
.SH FUNCTION PROTOTYPE
.nf
.cs R 24
float *c_csa2xs(int, float [], float [], float [], float ... |
github | NCAR/ncl-master | csa3xs.m | .m | ncl-master/ngmath/man/csagrid/csa3xs.m | 4,096 | utf_8 | faffa93dab0c5b17b2d23bbb5797e54e | .\"
.\" $Id: csa3xs.m,v 1.4 2008-07-27 03:35:34 haley Exp $
.\"
.TH CSA3XS 3NCARG "January 1999" UNIX "NCAR GRAPHICS"
.SH NAME
CSA3XS - cubic spline approximation, expanded entry for three-dimensional input
.SH SYNOPSIS
CALL CSA3XS (NI, XI, UI, WTS, KNOTS, SMTH, NDERIV,
.br
NXO, ,NYO, NZO, XO, YO, ZO, UO,... |
github | NCAR/ncl-master | csa3lxs.m | .m | ncl-master/ngmath/man/csagrid/csa3lxs.m | 3,779 | utf_8 | 35941eb8f2226f43353384426bd1188e | .\"
.\" $Id: csa3lxs.m,v 1.4 2008-07-27 03:35:34 haley Exp $
.\"
.TH CSA3LXS 3NCARG "January 1999" UNIX "NCAR GRAPHICS"
.SH NAME
CSA3LXS - cubic spline approximation, expanded
entry for three-dimensional input, list output
.SH SYNOPSIS
CALL CSA3LXS (NI, XI, UI, WTS, KNOTS, SMTH, NDERIV,
.br
N... |
github | NCAR/ncl-master | dspnt2d.m | .m | ncl-master/ngmath/man/dsgrid/dspnt2d.m | 1,962 | utf_8 | fd2b557c73adb119b538020fded53813 | .\"
.\" $Id: dspnt2d.m,v 1.6 2008-07-27 03:35:37 haley Exp $
.\"
.TH DSPNT2D 3NCARG "September 1997-1998" UNIX "NCAR GRAPHICS"
.SH NAME
DSPNT2D- Interpolate at a single point (or points) in 2D in double precision
.SH SYNOPSIS
CALL DSPNT2D (N, X, Y, Z, M, XO, YO, ZO, IER)
.SH DESCRIPTION
.IP N 12
(Integer, Input) - ... |
github | NCAR/ncl-master | dspnt2s.m | .m | ncl-master/ngmath/man/dsgrid/dspnt2s.m | 1,889 | utf_8 | b777c13d682d6f57eb0ea1487b6be1d1 | .\"
.\" $Id: dspnt2s.m,v 1.6 2008-07-27 03:35:37 haley Exp $
.\"
.TH DSPNT2S 3NCARG "September 1997-1998" UNIX "NCAR GRAPHICS"
.SH NAME
DSPNT2S- Interpolate at a single point (or points) in 2D in single precision
.SH SYNOPSIS
CALL DSPNT2S (N, X, Y, Z, M, XO, YO, ZO, IER)
.SH DESCRIPTION
.IP N 12
(Integer, Input) - ... |
github | NCAR/ncl-master | c_dspnt2s.m | .m | ncl-master/ngmath/man/dsgrid/c_dspnt2s.m | 2,035 | utf_8 | 7588e9670adc2d7c669c89ffafd679e8 | .\"
.\" $Id: c_dspnt2s.m,v 1.5 2008-07-27 03:35:36 haley Exp $
.\"
.TH c_dspnt2s 3NCARG "September 1997-1998" UNIX "NCAR GRAPHICS"
.na
.nh
.SH NAME
c_dspnt2s - Interpolate at a single point (or points) in 2D in single precision
.SH FUNCTION PROTOTYPE
.nf
.cs R 24
void c_dspnt2s(int, float [], float [], float [],
.b... |
github | NCAR/ncl-master | dspnt3s.m | .m | ncl-master/ngmath/man/dsgrid/dspnt3s.m | 2,133 | utf_8 | c6f2252d503a011217b2e07ea9c06532 | .\"
.\" $Id: dspnt3s.m,v 1.6 2008-07-27 03:35:37 haley Exp $
.\"
.TH DSPNT3S 3NCARG "September 1997-1998" UNIX "NCAR GRAPHICS"
.SH NAME
DSPNT3S- Interpolate at a single point (or points) in 3D in single precision
.SH SYNOPSIS
CALL DSPNT3S (N, X, Y, Z, U, M, XO, YO, ZO, UO, IER)
.SH DESCRIPTION
.IP N 12
(Integer, In... |
github | NCAR/ncl-master | c_dspnt2d.m | .m | ncl-master/ngmath/man/dsgrid/c_dspnt2d.m | 2,049 | utf_8 | d9866e06a2715a7fad8f75a4ec18d985 | .\"
.\" $Id: c_dspnt2d.m,v 1.5 2008-07-27 03:35:36 haley Exp $
.\"
.TH c_dspnt2d 3NCARG "September 1997-1998" UNIX "NCAR GRAPHICS"
.na
.nh
.SH NAME
c_dspnt2d - Interpolate at a single point (or points) in 2D in single precision
.SH FUNCTION PROTOTYPE
.nf
.cs R 24
void c_dspnt2d(int, double [], double [], double [],... |
github | NCAR/ncl-master | dspnt3d.m | .m | ncl-master/ngmath/man/dsgrid/dspnt3d.m | 2,229 | utf_8 | cc9fb321d6962c5385e22401ccb34aa5 | .\"
.\" $Id: dspnt3d.m,v 1.6 2008-07-27 03:35:37 haley Exp $
.\"
.TH DSPNT3D 3NCARG "September 1997-1998" UNIX "NCAR GRAPHICS"
.SH NAME
DSPNT3D- Interpolate at a single point (or points) in 3D in double precision
.SH SYNOPSIS
CALL DSPNT3D (N, X, Y, Z, U, M, XO, YO, ZO, UO, IER)
.SH DESCRIPTION
.IP N 12
(Integer, In... |
github | NCAR/ncl-master | c_ftcurvi.m | .m | ncl-master/ngmath/man/fitgrid/c_ftcurvi.m | 1,539 | utf_8 | 4e44f18fd9d45dd3e8f6aab0f45322ad | .\"
.\" $Id: c_ftcurvi.m,v 1.4 2008-07-27 03:35:38 haley Exp $
.\"
.TH c_ftcurvi 3NCARG "March 1998" UNIX "NCAR GRAPHICS"
.SH NAME
c_ftcurvi - calculate integrals
.SH FUNCTION PROTOTYPE
int c_ftcurvi (float, float, int, float [], float [], float *);
.SH SYNOPSIS
int c_ftcurvi (xl, xr, n, xi, yi, integral);
.SH DESCRIPT... |
github | NCAR/ncl-master | c_ftcurv.m | .m | ncl-master/ngmath/man/fitgrid/c_ftcurv.m | 1,621 | utf_8 | b8e2833fea4ede94d855c159aeaa2987 | .\"
.\" $Id: c_ftcurv.m,v 1.4 2008-07-27 03:35:37 haley Exp $
.\"
.TH c_ftcurv 3NCARG "March 1998" UNIX "NCAR GRAPHICS"
.SH NAME
c_ftcurv - 1D interpolation for non-periodic functions
.SH FUNCTION PROTOTYPE
int c_ftcurv (int, float [], float [], int, float [], float []);
.SH SYNOPSIS
int c_ftcurv (n, xi, yi, m, xo, yo)... |
github | NCAR/ncl-master | curvp2.m | .m | ncl-master/ngmath/man/fitgrid/curvp2.m | 1,679 | utf_8 | 68438eed0c7dcbeb0b23136d41391a03 | .\"
.\" $Id: curvp2.m,v 1.4 2008-07-27 03:35:39 haley Exp $
.\"
.TH CURVP2 3NCARG "March 1998" UNIX "NCAR GRAPHICS"
.SH NAME
CURVP2 - interpolate a periodic function at a specified point
.SH SYNOPSIS
FUNCTION CURVP2 (T, N, X, Y, P, YP, SIGMA)
.sp
This function interpolates a value at a specified point using a spline
u... |
github | NCAR/ncl-master | c_ftcurvs.m | .m | ncl-master/ngmath/man/fitgrid/c_ftcurvs.m | 3,457 | utf_8 | bd02fcae8e49b960caae3ecbf323f20b | .\"
.\" $Id: c_ftcurvs.m,v 1.4 2008-07-27 03:35:38 haley Exp $
.\"
.TH c_ftcurvs 3NCARG "March 1998" UNIX "NCAR GRAPHICS"
.SH NAME
c_ftcurvs - compute a smoothing spline
.SH FUNCTION PROTOTYPE
int c_ftcurvs (int, float [], float [], int, float [], int, float [], float []);
.SH SYNOPSIS
int c_ftcurvs (n, xi, yi, dflg, d... |
github | NCAR/ncl-master | c_ftcurvs1.m | .m | ncl-master/ngmath/man/fitgrid/c_ftcurvs1.m | 4,074 | utf_8 | c16b2624997e2ca1517edc60a0c40ab3 | .\"
.\" $Id: c_ftcurvs1.m,v 1.2 2008-07-27 03:35:38 haley Exp $
.\"
.TH c_ftcurvs1 3NCARG "August 2002" UNIX "NCAR GRAPHICS"
.SH NAME
c_ftcurvs1 - calculate values for a smoothing spline for data in the plane.
.SH FUNCTION PROTOTYPE
int c_ftcurvs1(int, float [], float [], int, float [],
int, float, float... |
github | NCAR/ncl-master | curv2.m | .m | ncl-master/ngmath/man/fitgrid/curv2.m | 1,605 | utf_8 | b48212b3af68fc2f7991a13472355630 | .\"
.\" $Id: curv2.m,v 1.4 2008-07-27 03:35:38 haley Exp $
.\"
.TH CURV2 3NCARG "March 1998" UNIX "NCAR GRAPHICS"
.SH NAME
CURV2 - 1D interpolation for non-periodic functions.
.SH SYNOPSIS
FUNCTION CURV2 (T, N, X, Y, YP, SIGMA)
.sp
This function interpolates a value at a specified point using a spline
under tension. ... |
github | NCAR/ncl-master | c_ftcurvps.m | .m | ncl-master/ngmath/man/fitgrid/c_ftcurvps.m | 3,629 | utf_8 | 69c68887d7f6109c9705a28335e1ee17 | .\"
.\" $Id: c_ftcurvps.m,v 1.4 2008-07-27 03:35:38 haley Exp $
.\"
.TH c_ftcurvps 3NCARG "March 1998" UNIX "NCAR GRAPHICS"
.SH NAME
c_ftcurvps - compute a smoothing spline for periodic functions.
.SH FUNCTION PROTOTYPE
int c_ftcurvps (int, float [], float [], float, int, float [], int,
float [], flo... |
github | NCAR/ncl-master | curvps.m | .m | ncl-master/ngmath/man/fitgrid/curvps.m | 3,693 | utf_8 | ace9e24ac0a92d230245e62097c9e835 | .\"
.\" $Id: curvps.m,v 1.4 2008-07-27 03:35:39 haley Exp $
.\"
.TH CURVPS 3NCARG "March 1998" UNIX "NCAR GRAPHICS"
.SH NAME
CURVPS - calculate values for a smoothing spline for a periodic function.
.SH SYNOPSIS
CALL CURVPS (N, X, Y, P, D, ISW, S, EPS, YS, YSP, SIGMA, TEMP, IER)
.sp
This subroutine calculates certain ... |
github | NCAR/ncl-master | curvs.m | .m | ncl-master/ngmath/man/fitgrid/curvs.m | 3,592 | utf_8 | bd20b8033246936070ba31f57b775315 | .\"
.\" $Id: curvs.m,v 1.5 2008-07-27 03:35:39 haley Exp $
.\"
.TH CURVS 3NCARG "March 1998" UNIX "NCAR GRAPHICS"
.SH NAME
CURVS - calculate values for a smoothing spline
.SH SYNOPSIS
CALL CURVS (N, X, Y, D, ISW, S, EPS, YS, YSP, SIGMA, TEMP, IER)
.sp
This subroutine calculates certain values that are used by CURV2 in... |
github | NCAR/ncl-master | c_ftcurvpi.m | .m | ncl-master/ngmath/man/fitgrid/c_ftcurvpi.m | 1,668 | utf_8 | a9e3a3e96fef009e82c34259ab50c79b | .\"
.\" $Id: c_ftcurvpi.m,v 1.4 2008-07-27 03:35:38 haley Exp $
.\"
.TH c_ftcurvpi 3NCARG "March 1998" UNIX "NCAR GRAPHICS"
.SH NAME
c_ftcurvpi - calculate integrals for periodic functions
.SH FUNCTION PROTOTYPE
int c_ftcurvpi (float, float, float, int, float [], float [], float *);
.SH SYNOPSIS
int c_ftcurvpi (xl, xr,... |
github | NCAR/ncl-master | c_ftcurvd.m | .m | ncl-master/ngmath/man/fitgrid/c_ftcurvd.m | 1,596 | utf_8 | d8a3089208ea1c381ebee942c9cdf25c | .\"
.\" $Id: c_ftcurvd.m,v 1.4 2008-07-27 03:35:37 haley Exp $
.\"
.TH c_ftcurvd 3NCARG "March 1998" UNIX "NCAR GRAPHICS"
.SH NAME
c_ftcurvd - calculate derivatives
.SH FUNCTION PROTOTYPE
int c_ftcurvd (int, float [], float [], int, float [], float []);
.SH SYNOPSIS
int c_ftcurvd (n, xi, yi, m, xo, yo);
.SH DESCRIPTION... |
github | NCAR/ncl-master | c_ftsurf.m | .m | ncl-master/ngmath/man/fitgrid/c_ftsurf.m | 4,101 | utf_8 | 4afb1103266eded1e849cbd7dbfc28f0 | .\"
.\" $Id: c_ftsurf.m,v 1.4 2008-07-27 03:35:38 haley Exp $
.\"
.TH c_ftsurf 3NCARG "March 1998" UNIX "NCAR GRAPHICS"
.SH NAME
c_ftsurf - 2D tension spline interpolation of rectangular data
.SH FUNCTION PROTOTYPE
float *c_ftsurf (int, int, float *, float *, float *,
int, int, float *, float *, int *... |
github | tolgagolbasi/Finance-master | buyOrSellfints.m | .m | Finance-master/buyOrSellfints.m | 2,840 | utf_8 | aa993628c307f9020539949fcc2c1ecc | function [] = buyOrSellfints()
filepath = strcat('C:\Users\Tolga\AppData\Roaming\MetaQuotes\Terminal\23E8DE8DA57B90C86B70E182D3461C60\MQL4\Files\','EURUSD`');
generatefile();
while(true)
c = clock;
c = fix(c);
if (mod(c(4),4) == 0 &&c(5) == 0 &&(c(6) == 0))
delete('C... |
github | tolgagolbasi/Finance-master | hurst_exponent.m | .m | Finance-master/hurst_exponent.m | 1,820 | utf_8 | 553b5158998b1c0392a91436b3647995 | % The Hurst exponent
%--------------------------------------------------------------------------
% The first 20 lines of code are a small test driver.
% You can delete or comment out this part when you are done validating the
% function to your satisfaction.
%
% Bill Davidson, quellen@yahoo.com
% 13 Nov 2005
function... |
github | lawlite19/MachineLearningEx-master | submit.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | submitWithConfiguration.m | .m | MachineLearningEx-master/machine-learning-ex2/ex2/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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 | lawlite19/MachineLearningEx-master | savejson.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | loadjson.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | loadubjson.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | saveubjson.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | submit.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | submitWithConfiguration.m | .m | MachineLearningEx-master/machine-learning-ex4/ex4/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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 | lawlite19/MachineLearningEx-master | savejson.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | loadjson.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | loadubjson.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | saveubjson.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | submit.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | porterStemmer.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | submitWithConfiguration.m | .m | MachineLearningEx-master/machine-learning-ex6/ex6/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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 | lawlite19/MachineLearningEx-master | savejson.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | loadjson.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | loadubjson.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | saveubjson.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | submit.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | submitWithConfiguration.m | .m | MachineLearningEx-master/machine-learning-ex7/ex7/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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 | lawlite19/MachineLearningEx-master | savejson.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | loadjson.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | loadubjson.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | saveubjson.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | submit.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | submitWithConfiguration.m | .m | MachineLearningEx-master/machine-learning-ex5/ex5/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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 | lawlite19/MachineLearningEx-master | savejson.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | loadjson.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | loadubjson.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | saveubjson.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | submit.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | submitWithConfiguration.m | .m | MachineLearningEx-master/machine-learning-ex3/ex3/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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 | lawlite19/MachineLearningEx-master | savejson.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | loadjson.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | loadubjson.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | saveubjson.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | submit.m | .m | MachineLearningEx-master/machine-learning-ex8/ex8/submit.m | 2,064 | utf_8 | 7c4fcf60df3a7e09d05a74f7772fed3b | 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 | lawlite19/MachineLearningEx-master | submitWithConfiguration.m | .m | MachineLearningEx-master/machine-learning-ex8/ex8/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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 | lawlite19/MachineLearningEx-master | savejson.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | loadjson.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | loadubjson.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | saveubjson.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | submit.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | submitWithConfiguration.m | .m | MachineLearningEx-master/machine-learning-ex1/ex1/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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 | lawlite19/MachineLearningEx-master | savejson.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | loadjson.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | loadubjson.m | .m | MachineLearningEx-master/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 | lawlite19/MachineLearningEx-master | saveubjson.m | .m | MachineLearningEx-master/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 | twtygqyy/caffe-augmentation-master | classification_demo.m | .m | caffe-augmentation-master/matlab/demo/classification_demo.m | 5,466 | utf_8 | 45745fb7cfe37ef723c307dfa06f1b97 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | ElsevierSoftwareX/SOFTX-D-16-00055-master | BL_auto.m | .m | SOFTX-D-16-00055-master/baseliner4-master_MATLAB/BL_auto.m | 3,880 | utf_8 | 2c1885885c7ec6bc740dfa18a0b16324 | % THIS SOFTWARE WAS DEVELOPED AT THE US FOREST SERVICE, SOUTHERN RESEARCH STATION (SRS), COWEETA HYDROLOGIC LABORATORY BY EMPLOYEES OF THE FEDERAL GOVERNMENT IN THE COURSE OF THEIR OFFICIAL DUTIES.
% PURSUANT TO TITLE 17 SECTION 105 OF THE UNITED STATES CODE, THIS SOFTWARE IS NOT SUBJECT TO COPYRIGHT PROTECTION AND IS... |
github | ElsevierSoftwareX/SOFTX-D-16-00055-master | cleanRawFluxData.m | .m | SOFTX-D-16-00055-master/baseliner4-master_MATLAB/cleanRawFluxData.m | 2,429 | utf_8 | 260a8f6582df013085b35ec782ca9a7a | % THIS SOFTWARE WAS DEVELOPED AT THE US FOREST SERVICE, SOUTHERN RESEARCH STATION (SRS), COWEETA HYDROLOGIC LABORATORY BY EMPLOYEES OF THE FEDERAL GOVERNMENT IN THE COURSE OF THEIR OFFICIAL DUTIES.
% PURSUANT TO TITLE 17 SECTION 105 OF THE UNITED STATES CODE, THIS SOFTWARE IS NOT SUBJECT TO COPYRIGHT PROTECTION AND IS... |
github | ElsevierSoftwareX/SOFTX-D-16-00055-master | loadSapflowConfig.m | .m | SOFTX-D-16-00055-master/baseliner4-master_MATLAB/loadSapflowConfig.m | 6,788 | utf_8 | 990cea9771e9953fc1effd8e2258485f | % THIS SOFTWARE WAS DEVELOPED AT THE US FOREST SERVICE, SOUTHERN RESEARCH STATION (SRS), COWEETA HYDROLOGIC LABORATORY BY EMPLOYEES OF THE FEDERAL GOVERNMENT IN THE COURSE OF THEIR OFFICIAL DUTIES.
% PURSUANT TO TITLE 17 SECTION 105 OF THE UNITED STATES CODE, THIS SOFTWARE IS NOT SUBJECT TO COPYRIGHT PROTECTION AND IS... |
github | ElsevierSoftwareX/SOFTX-D-16-00055-master | projectDialog.m | .m | SOFTX-D-16-00055-master/baseliner4-master_MATLAB/projectDialog.m | 6,347 | utf_8 | a8b912ddc8366f617c2d86f7513f0ff7 | % THIS SOFTWARE WAS DEVELOPED AT THE US FOREST SERVICE, SOUTHERN RESEARCH STATION (SRS), COWEETA HYDROLOGIC LABORATORY BY EMPLOYEES OF THE FEDERAL GOVERNMENT IN THE COURSE OF THEIR OFFICIAL DUTIES.
% PURSUANT TO TITLE 17 SECTION 105 OF THE UNITED STATES CODE, THIS SOFTWARE IS NOT SUBJECT TO COPYRIGHT PROTECTION AND IS... |
github | ElsevierSoftwareX/SOFTX-D-16-00055-master | BL_rand.m | .m | SOFTX-D-16-00055-master/baseliner4-master_MATLAB/BL_rand.m | 2,769 | utf_8 | 0eddea7c839b304e139bf8005b9484ef | % THIS SOFTWARE WAS DEVELOPED AT THE US FOREST SERVICE, SOUTHERN RESEARCH STATION (SRS), COWEETA HYDROLOGIC LABORATORY BY EMPLOYEES OF THE FEDERAL GOVERNMENT IN THE COURSE OF THEIR OFFICIAL DUTIES.
% PURSUANT TO TITLE 17 SECTION 105 OF THE UNITED STATES CODE, THIS SOFTWARE IS NOT SUBJECT TO COPYRIGHT PROTECTION AND IS... |
github | ElsevierSoftwareX/SOFTX-D-16-00055-master | processPar.m | .m | SOFTX-D-16-00055-master/baseliner4-master_MATLAB/processPar.m | 2,207 | utf_8 | 75ef10b61983795060a621f6833577b5 | % THIS SOFTWARE WAS DEVELOPED AT THE US FOREST SERVICE, SOUTHERN RESEARCH STATION (SRS), COWEETA HYDROLOGIC LABORATORY BY EMPLOYEES OF THE FEDERAL GOVERNMENT IN THE COURSE OF THEIR OFFICIAL DUTIES.
% PURSUANT TO TITLE 17 SECTION 105 OF THE UNITED STATES CODE, THIS SOFTWARE IS NOT SUBJECT TO COPYRIGHT PROTECTION AND IS... |
github | ElsevierSoftwareX/SOFTX-D-16-00055-master | cutShortRuns.m | .m | SOFTX-D-16-00055-master/baseliner4-master_MATLAB/cutShortRuns.m | 2,368 | utf_8 | 249748ab329e4c541634915e4d11a792 | % THIS SOFTWARE WAS DEVELOPED AT THE US FOREST SERVICE, SOUTHERN RESEARCH STATION (SRS), COWEETA HYDROLOGIC LABORATORY BY EMPLOYEES OF THE FEDERAL GOVERNMENT IN THE COURSE OF THEIR OFFICIAL DUTIES.
% PURSUANT TO TITLE 17 SECTION 105 OF THE UNITED STATES CODE, THIS SOFTWARE IS NOT SUBJECT TO COPYRIGHT PROTECTION AND IS... |
github | ElsevierSoftwareX/SOFTX-D-16-00055-master | getRanges.m | .m | SOFTX-D-16-00055-master/baseliner4-master_MATLAB/getRanges.m | 2,248 | utf_8 | 1cf67e44674bdbd3f19ea1c341fcb8cd | % THIS SOFTWARE WAS DEVELOPED AT THE US FOREST SERVICE, SOUTHERN RESEARCH STATION (SRS), COWEETA HYDROLOGIC LABORATORY BY EMPLOYEES OF THE FEDERAL GOVERNMENT IN THE COURSE OF THEIR OFFICIAL DUTIES.
% PURSUANT TO TITLE 17 SECTION 105 OF THE UNITED STATES CODE, THIS SOFTWARE IS NOT SUBJECT TO COPYRIGHT PROTECTION AND IS... |
github | ElsevierSoftwareX/SOFTX-D-16-00055-master | defaultConfig.m | .m | SOFTX-D-16-00055-master/baseliner4-master_MATLAB/defaultConfig.m | 2,151 | utf_8 | 0427db01a2e5d6164995b079902b29cc | % THIS SOFTWARE WAS DEVELOPED AT THE US FOREST SERVICE, SOUTHERN RESEARCH STATION (SRS), COWEETA HYDROLOGIC LABORATORY BY EMPLOYEES OF THE FEDERAL GOVERNMENT IN THE COURSE OF THEIR OFFICIAL DUTIES.
% PURSUANT TO TITLE 17 SECTION 105 OF THE UNITED STATES CODE, THIS SOFTWARE IS NOT SUBJECT TO COPYRIGHT PROTECTION AND IS... |
github | ElsevierSoftwareX/SOFTX-D-16-00055-master | loadRawSapflowData.m | .m | SOFTX-D-16-00055-master/baseliner4-master_MATLAB/loadRawSapflowData.m | 3,256 | utf_8 | 268ac9cafc942a39cf2e475e3ceba387 | % THIS SOFTWARE WAS DEVELOPED AT THE US FOREST SERVICE, SOUTHERN RESEARCH STATION (SRS), COWEETA HYDROLOGIC LABORATORY BY EMPLOYEES OF THE FEDERAL GOVERNMENT IN THE COURSE OF THEIR OFFICIAL DUTIES.
% PURSUANT TO TITLE 17 SECTION 105 OF THE UNITED STATES CODE, THIS SOFTWARE IS NOT SUBJECT TO COPYRIGHT PROTECTION AND IS... |
github | ElsevierSoftwareX/SOFTX-D-16-00055-master | BL_nightly.m | .m | SOFTX-D-16-00055-master/baseliner4-master_MATLAB/BL_nightly.m | 2,126 | utf_8 | 3d490a62ea1af3bc2741de82639684ec | % THIS SOFTWARE WAS DEVELOPED AT THE US FOREST SERVICE, SOUTHERN RESEARCH STATION (SRS), COWEETA HYDROLOGIC LABORATORY BY EMPLOYEES OF THE FEDERAL GOVERNMENT IN THE COURSE OF THEIR OFFICIAL DUTIES.
% PURSUANT TO TITLE 17 SECTION 105 OF THE UNITED STATES CODE, THIS SOFTWARE IS NOT SUBJECT TO COPYRIGHT PROTECTION AND IS... |
github | ElsevierSoftwareX/SOFTX-D-16-00055-master | oneByN.m | .m | SOFTX-D-16-00055-master/baseliner4-master_MATLAB/oneByN.m | 1,763 | utf_8 | 81d08a9edd6b2f9fb02e9d784f422cb8 | % THIS SOFTWARE WAS DEVELOPED AT THE US FOREST SERVICE, SOUTHERN RESEARCH STATION (SRS), COWEETA HYDROLOGIC LABORATORY BY EMPLOYEES OF THE FEDERAL GOVERNMENT IN THE COURSE OF THEIR OFFICIAL DUTIES.
% PURSUANT TO TITLE 17 SECTION 105 OF THE UNITED STATES CODE, THIS SOFTWARE IS NOT SUBJECT TO COPYRIGHT PROTECTION AND IS... |
github | tomMoral/AdaptiveOptim-master | prodbp.m | .m | AdaptiveOptim-master/factorisation/prodbp.m | 4,285 | utf_8 | 1f332303b6141a113bbda423587d02bd | function [Afin,Sfin, trt,tat,tet,B,tatref] = prodbp
N = 64;
M = 128;
L = 2000;
rho = 0.1;
lambda = 0.5;
D = randn(N, M).^3;
D = D./(repmat(sqrt(sum(D.^2)),[N 1]));
B = D'*D;
noise = 0.001;
Z = randn(M,L).*( rand(M,L) < rho );
X = D * Z;
lambda = lambda * mean((sum(X.^2)));
[A, S, ~]= svd(B,0);
A = A';
Sinv = (di... |
github | tomMoral/AdaptiveOptim-master | prodbp2.m | .m | AdaptiveOptim-master/factorisation/prodbp2.m | 3,663 | utf_8 | 69670074ae6d44622028dc01728868bb | function [Afin,Sfin, trt,tat,B,tatref] = prodb2
N = 64;
M = 128;
L = 2000;
rho = 0.1;
lambda = 0.1;
beta = 5000;
D = randn(N, M);
D = D./(repmat(sqrt(sum(D.^2)),[N 1]));
B = D'*D;
noise = 0.001;
Z = randn(M,L).*( rand(M,L) < rho );
X = D * Z;
lambda = lambda * mean((sum(X.^2)));
[A, S, ~]= svd(B,0);
A = A';
K = m... |
github | xioTechnologies/NGIMU-Software-Public-master | clickableLegend.m | .m | NGIMU-Software-Public-master/NgimuSynchronisedNetworkManager/MATLAB/clickableLegend.m | 7,280 | utf_8 | c76e8a2214087ff671f83e02aaf9645b | function varargout = clickableLegend(varargin)
% clickableLegend Interactive legend for toggling or highlighting graphics
%
% clickableLegend is a wrapper around the LEGEND function that provides
% interactive display toggling or highlighting of lines or patches in a MATLAB
% plot. It enables you to,
% * Toggle (hide/... |
github | xioTechnologies/NGIMU-Software-Public-master | importSession.m | .m | NGIMU-Software-Public-master/NgimuSynchronisedNetworkManager/MATLAB/importSession.m | 9,103 | utf_8 | c52cf61ef24c8886a482638a61a7cb4a | function sessionData = importSession(sessionDirectory, varargin)
% IMPORTSESSION Imports logged NGIMU data.
%
% sessionData = importSession(sessionDirectory), Imports a session
% directory containing data from one or more NGIMU.
%
% sessionData = importSession(sessionDirectory, 'FileNames', fileNames),
% Im... |
github | zheng-yuwei/Stacked_Autoencoder-master | WolfeLineSearch.m | .m | Stacked_Autoencoder-master/minFunc/WolfeLineSearch.m | 11,106 | utf_8 | f97d9ca0bf8aab87df9aa65e74f98589 | 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 starting lo... |
github | zheng-yuwei/Stacked_Autoencoder-master | minFunc_processInputOptions.m | .m | Stacked_Autoencoder-master/minFunc/minFunc_processInputOptions.m | 3,551 | utf_8 | ea7fbcf303b9cafeca4045921adad934 |
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,useNegCu... |
github | fotlogo/amb_ps_bmvc2016-master | calculate_b_s_fields.m | .m | amb_ps_bmvc2016-master/calculate_b_s_fields.m | 8,072 | utf_8 | f08a0b96bbb188ae1bb841c66c8db9dd | function [b,s]=calculate_b_s_fields(I,mask_indx,x,y,Z,f,C,A,H,thresholds, ambient)
%calculate_b_s_fields Under perspective projection with or without ambient
%chose one of the 2 versions
if ambient
[b,s]=calculate_b_s_fields_amb(I,mask_indx,x,y,Z,f,C,A,H,thresholds);
else
[b,s]=calculate_b_s_fields_dark(I,mask... |
github | fotlogo/amb_ps_bmvc2016-master | export_ply.m | .m | amb_ps_bmvc2016-master/export_ply.m | 2,518 | utf_8 | 8ae04bb83d4fe6914e34926576ac8bc6 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Description..... : Save the reconstruction PLY format, in
% mesh.ply, mesh.mtl and mesh.png
% INPUT : XYZ, mask
%
% Author ......... : Fotios Logothetis (adapted from Yvain Queau)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | fotlogo/amb_ps_bmvc2016-master | re_estimate_c.m | .m | amb_ps_bmvc2016-master/re_estimate_c.m | 7,052 | utf_8 | 6a007df3370a03607ff0862be716c8d7 | function [C_refined]=re_estimate_c(I,C,N,H,A,mask,shadow_threshold,ambient)
%Reestimate c using current estimates of geometry. ambient version quite
%different from dark on
if ambient
[C_refined]=re_estimate_c_amb(I,C,N,H,A,mask,shadow_threshold);
else
uniform_c=0; %get more accuracy for ob... |
github | StepMan91/MsCV-UE4-Robotics_Project-master | plot_camera_poses.m | .m | MsCV-UE4-Robotics_Project-master/Packages folder/pcl/gpu/kinfu/tools/plot_camera_poses.m | 3,407 | utf_8 | d210c150da98c3f4667f2c1e8d4eb6d2 | % 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 | nasa-jpl-memex/TattDL-master | voc_eval.m | .m | TattDL-master/lib/datasets/VOCdevkit-matlab-wrapper/voc_eval.m | 1,389 | utf_8 | fd77d0da53b2585aa65e0da5edc5fe33 | function res = voc_eval(path, comp_id, test_set, output_dir, rm_res)
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, rm_res);
end
fprintf('\n~~~~~~~~~~~~~~~~~~~~\n');
fprintf('Results:\n... |
github | nicolasavru/ppf-registration-spatial-hashing-master | compute_normals.m | .m | ppf-registration-spatial-hashing-master/compute_normals.m | 762 | utf_8 | 2a093bd434042339b800098c8ddfe76f | % Compute Normals for a PLY file.
function compute_normals(input_file, output_file, trans_adj)
[tri, pts, data, ~] = ply_read(input_file, 'tri');
TR = triangulation(tri.', pts.');
vn = vertexNormal(TR);
data.vertex.nx = vn(:,1);
data.vertex.ny = vn(:,2);
data.vertex.nz = vn(:,3);
data = rmfi... |
github | monzie9000/rory-master | dialog.m | .m | rory-master/sw/logalizer/dialog.m | 34,826 | utf_8 | 5407ab492113a3d0358e62c19dc1feab | %--------------------------------------------------------------------
%A simple MATLAB GUI for paparazzi autopilot log-file plotting
%Paparazzi Project [http://www.nongnu.org/paparazzi/]
%by Roman Krashhanitsa 28/10/2005
%adjustable parabeters:
% maxnum - increase if dialog window hangs up or doesnt refresh
% Nres - nu... |
github | monzie9000/rory-master | dialog.m | .m | rory-master/sw/logalizer/matlab_log/dialog.m | 41,730 | utf_8 | 658608b346efe2ebfced666d595a4060 | %--------------------------------------------------------------------
%A simple MATLAB GUI for paparazzi autopilot log-file plotting
%Paparazzi Project [http://www.nongnu.org/paparazzi/]
%by Roman Krashhanitsa 28/10/2005
%adjustable parabeters:
% maxnum - increase if dialog window hangs up or doesnt refresh
% Nres - nu... |
github | monzie9000/rory-master | tilt.m | .m | rory-master/sw/logalizer/matlab/tilt.m | 3,005 | utf_8 | 28f19a8ce44283009a8f4ba0410e4c0b | %
% this is a 2 states kalman filter used to fuse the readings of a
% two axis accelerometer and one axis gyro.
% The filter estimates the angle and the gyro bias.
%
%
function [angle, bias, rate, cov] = tilt(status, gyro, accel)
TILT_UNINIT = 0;
TILT_PREDICT = 1;
TILT_UPDATE = 2;
persistent tilt_angle; %... |
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