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 | wgshun/AndrewNG-Machinelearning-master | submit.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex1/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 | wgshun/AndrewNG-Machinelearning-master | submitWithConfiguration.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex1/machine-learning-ex1/ex1/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | wgshun/AndrewNG-Machinelearning-master | savejson.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex1/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 | wgshun/AndrewNG-Machinelearning-master | loadjson.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex1/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 | wgshun/AndrewNG-Machinelearning-master | loadubjson.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex1/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 | wgshun/AndrewNG-Machinelearning-master | saveubjson.m | .m | AndrewNG-Machinelearning-master/homework/machine-learning-ex1/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 | phcerdan/BLS-GSM_Denoising_Portilla-master | buildWpyr.m | .m | BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/buildWpyr.m | 2,705 | utf_8 | 1c4ff4ecab086742bb93ea70f1e9e015 | % [PYR, INDICES] = buildWpyr(IM, HEIGHT, FILT, EDGES)
%
% Construct a separable orthonormal QMF/wavelet pyramid on matrix (or vector) IM.
%
% HEIGHT (optional) specifies the number of pyramid levels to build. Default
% is maxPyrHt(IM,FILT). You can also specify 'auto' to use this value.
%
% FILT (optional) can ... |
github | phcerdan/BLS-GSM_Denoising_Portilla-master | pyrBand.m | .m | BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/pyrBand.m | 406 | utf_8 | 0cad3c43324c84276383d06f6f6a5b60 | % RES = pyrBand(PYR, INDICES, BAND_NUM)
%
% Access a subband from a pyramid (gaussian, laplacian, QMF/wavelet,
% or steerable). Subbands are numbered consecutively, from finest
% (highest spatial frequency) to coarsest (lowest spatial frequency).
% Eero Simoncelli, 6/96.
function res = pyrBand(pyr, pind, b... |
github | phcerdan/BLS-GSM_Denoising_Portilla-master | buildFullSFpyr2.m | .m | BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/buildFullSFpyr2.m | 3,031 | utf_8 | 4a1191e10f08e0d219040bdc7864a575 | % [PYR, INDICES, STEERMTX, HARMONICS] = buildFullSFpyr2(IM, HEIGHT, ORDER, TWIDTH)
%
% Construct a steerable pyramid on matrix IM, in the Fourier domain.
% Unlike the standard transform, subdivides the highpass band into
% orientations.
function [pyr,pind,steermtx,harmonics] = buildFullSFpyr2(im, ht, order, twidth)
%... |
github | phcerdan/BLS-GSM_Denoising_Portilla-master | var2.m | .m | BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/var2.m | 393 | utf_8 | 46f01727b1895ebb42fea033f942c562 | % V = VAR2(MTX,MEAN)
%
% Sample variance of a matrix.
% Passing MEAN (optional) makes the calculation faster.
function res = var2(mtx, mn)
if (exist('mn') ~= 1)
mn = mean2(mtx);
end
if (isreal(mtx))
res = sum(sum(abs(mtx-mn).^2)) / (prod(size(mtx)) - 1);
else
res = sum(sum(real(mtx-mn).^2)) + i... |
github | phcerdan/BLS-GSM_Denoising_Portilla-master | reconSFpyrLevs.m | .m | BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/reconSFpyrLevs.m | 2,013 | utf_8 | bff5aabf51101f5a06bf4a3a59d0de00 | % RESDFT = reconSFpyrLevs(PYR,INDICES,LOGRAD,XRCOS,YRCOS,ANGLE,NBANDS,LEVS,BANDS)
%
% Recursive function for reconstructing levels of a steerable pyramid
% representation. This is called by reconSFpyr, and is not usually
% called directly.
% Eero Simoncelli, 5/97.
function resdft = reconSFpyrLevs(pyr,pind,lo... |
github | phcerdan/BLS-GSM_Denoising_Portilla-master | rcosFn.m | .m | BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/rcosFn.m | 1,167 | utf_8 | e668c06ae18191dea3d63f2c3035cdcb | % [X, Y] = rcosFn(WIDTH, POSITION, VALUES)
%
% Return a lookup table (suitable for use by INTERP1)
% containing a "raised cosine" soft threshold function:
%
% Y = VALUES(1) + (VALUES(2)-VALUES(1)) *
% cos^2( PI/2 * (X - POSITION + WIDTH)/WIDTH )
%
% WIDTH is the width of the region over which... |
github | phcerdan/BLS-GSM_Denoising_Portilla-master | vector.m | .m | BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/vector.m | 240 | utf_8 | 99db83250fc29065ecdb3bff900669d3 | % [VEC] = vector(MTX)
%
% Pack elements of MTX into a column vector. Same as VEC = MTX(:)
% Previously named "vectorize" (changed to avoid overlap with Matlab's
% "vectorize" function).
function vec = vector(mtx)
vec = mtx(:);
|
github | phcerdan/BLS-GSM_Denoising_Portilla-master | showIm.m | .m | BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/showIm.m | 6,332 | utf_8 | fc722445cecdb4685413ce683c5c414f | % RANGE = showIm (MATRIX, RANGE, ZOOM, LABEL, NSHADES )
%
% Display a MatLab MATRIX as a grayscale image in the current figure,
% inside the current axes. If MATRIX is complex, the real and imaginary
% parts are shown side-by-side, with the same grayscale mapping.
%
% If MATRIX is a string, it should be the n... |
github | phcerdan/BLS-GSM_Denoising_Portilla-master | upConv.m | .m | BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/upConv.m | 2,750 | utf_8 | c396c00dd8d50ee2a930427fead849be | % RES = upConv(IM, FILT, EDGES, STEP, START, STOP, RES)
%
% Upsample matrix IM, followed by convolution with matrix FILT. These
% arguments should be 1D or 2D matrices, and IM must be larger (in
% both dimensions) than FILT. The origin of filt
% is assumed to be floor(size(filt)/2)+1.
%
% EDGES is a string det... |
github | phcerdan/BLS-GSM_Denoising_Portilla-master | range2.m | .m | BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/range2.m | 477 | utf_8 | 4b8ed3c6da04efbf2862f0b36b91d524 | % [MIN, MAX] = range2(MTX)
%
% Compute minimum and maximum values of MTX, returning them as a 2-vector.
% Eero Simoncelli, 3/97.
function [mn, mx] = range2(mtx)
%% NOTE: THIS CODE IS NOT ACTUALLY USED! (MEX FILE IS CALLED INSTEAD)
fprintf(1,'WARNING: You should compile the MEX code for "range2", found in ... |
github | phcerdan/BLS-GSM_Denoising_Portilla-master | reconFullSFpyr2.m | .m | BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/reconFullSFpyr2.m | 3,257 | utf_8 | 447f5204894b159fbb4ea4de2dccf877 | % RES = reconFullSFpyr2(PYR, INDICES, LEVS, BANDS, TWIDTH)
%
% Reconstruct image from its steerable pyramid representation, in the Fourier
% domain, as created by buildSFpyr.
% Unlike the standard transform, subdivides the highpass band into
% orientations.
function res = reconFullSFpyr2(pyr, pind, levs, bands, twidt... |
github | phcerdan/BLS-GSM_Denoising_Portilla-master | steer2HarmMtx.m | .m | BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/steer2HarmMtx.m | 1,874 | utf_8 | 8efc390a04b19bdec63526c7bbd1407e | % MTX = steer2HarmMtx(HARMONICS, ANGLES, REL_PHASES)
%
% Compute a steering matrix (maps a directional basis set onto the
% angular Fourier harmonics). HARMONICS is a vector specifying the
% angular harmonics contained in the steerable basis/filters. ANGLES
% (optional) is a vector specifying the angular positi... |
github | phcerdan/BLS-GSM_Denoising_Portilla-master | subMtx.m | .m | BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/subMtx.m | 441 | utf_8 | 18fe3fa6f65d3fbc8cd38682559c3619 | % MTX = subMtx(VEC, DIMENSIONS, START_INDEX)
%
% Reshape a portion of VEC starting from START_INDEX (optional,
% default=1) to the given dimensions.
% Eero Simoncelli, 6/96.
function mtx = subMtx(vec, sz, offset)
if (exist('offset') ~= 1)
offset = 1;
end
vec = vec(:);
sz = sz(:);
if (size(sz,1) ... |
github | phcerdan/BLS-GSM_Denoising_Portilla-master | spyrHt.m | .m | BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/spyrHt.m | 321 | utf_8 | acae1ee5a657f2e4d75b2e60e954a515 | % [HEIGHT] = spyrHt(INDICES)
%
% Compute height of steerable pyramid with given index matrix.
% Eero Simoncelli, 6/96.
function [ht] = spyrHt(pind)
nbands = spyrNumBands(pind);
% Don't count lowpass, or highpass residual bands
if (size(pind,1) > 2)
ht = (size(pind,1)-2)/nbands;
else
ht = 0;
end
... |
github | phcerdan/BLS-GSM_Denoising_Portilla-master | spyrBand.m | .m | BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/spyrBand.m | 853 | utf_8 | 24b93860a4de0346982a44edcb390ab5 | % [LEV,IND] = spyrBand(PYR,INDICES,LEVEL,BAND)
%
% Access a band from a steerable pyramid.
%
% LEVEL indicates the scale (finest = 1, coarsest = spyrHt(INDICES)).
%
% BAND (optional, default=1) indicates which subband
% (1 = vertical, rest proceeding anti-clockwise).
% Eero Simoncelli, 6/96.
fun... |
github | phcerdan/BLS-GSM_Denoising_Portilla-master | wpyrBand.m | .m | BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/wpyrBand.m | 912 | utf_8 | ec3f9e1a26cc9775110888b67417876a | % RES = wpyrBand(PYR, INDICES, LEVEL, BAND)
%
% Access a subband from a separable QMF/wavelet pyramid.
%
% LEVEL (optional, default=1) indicates the scale (finest = 1,
% coarsest = wpyrHt(INDICES)).
%
% BAND (optional, default=1) indicates which subband (1=horizontal,
% 2=vertical, 3=diagonal).
% Eero ... |
github | phcerdan/BLS-GSM_Denoising_Portilla-master | innerProd.m | .m | BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/innerProd.m | 415 | utf_8 | e759ef690a2e4eadf5f81e0b8282888f | % RES = innerProd(MTX)
%
% Compute (MTX' * MTX) efficiently (i.e., without copying the matrix)
function res = innerProd(mtx)
%% NOTE: THIS CODE SHOULD NOT BE USED! (MEX FILE IS CALLED INSTEAD)
fprintf(1,'WARNING: You should compile the MEX version of "innerProd.c",\n found in the MEX subdirectory of ... |
github | phcerdan/BLS-GSM_Denoising_Portilla-master | reconSFpyr.m | .m | BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/reconSFpyr.m | 3,141 | utf_8 | bb26483e3afdf1b4b46da4b35efaec7b | % RES = reconSFpyr(PYR, INDICES, LEVS, BANDS, TWIDTH)
%
% Reconstruct image from its steerable pyramid representation, in the Fourier
% domain, as created by buildSFpyr.
%
% PYR is a vector containing the N pyramid subbands, ordered from fine
% to coarse. INDICES is an Nx2 matrix containing the sizes of
% each ... |
github | phcerdan/BLS-GSM_Denoising_Portilla-master | corrDn.m | .m | BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/corrDn.m | 2,195 | utf_8 | 61533e2b3b4b039c523120d2ec1363aa | % RES = corrDn(IM, FILT, EDGES, STEP, START, STOP)
%
% Compute correlation of matrices IM with FILT, followed by
% downsampling. These arguments should be 1D or 2D matrices, and IM
% must be larger (in both dimensions) than FILT. The origin of filt
% is assumed to be floor(size(filt)/2)+1.
%
% EDGES is a stri... |
github | phcerdan/BLS-GSM_Denoising_Portilla-master | maxPyrHt.m | .m | BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/maxPyrHt.m | 628 | utf_8 | 00ee02d58475fcdf592ef59a15fa0af3 | % HEIGHT = maxPyrHt(IMSIZE, FILTSIZE)
%
% Compute maximum pyramid height for given image and filter sizes.
% Specifically: the number of corrDn operations that can be sequentially
% performed when subsampling by a factor of 2.
% Eero Simoncelli, 6/96.
function height = maxPyrHt(imsz, filtsz)
imsz = imsz(:)... |
github | phcerdan/BLS-GSM_Denoising_Portilla-master | buildSFpyrLevs.m | .m | BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/buildSFpyrLevs.m | 1,887 | utf_8 | e58fd43a3b9e8101ef5ada17c5116eed | % [PYR, INDICES] = buildSFpyrLevs(LODFT, LOGRAD, XRCOS, YRCOS, ANGLE, HEIGHT, NBANDS)
%
% Recursive function for constructing levels of a steerable pyramid. This
% is called by buildSFpyr, and is not usually called directly.
% Eero Simoncelli, 5/97.
function [pyr,pind] = buildSFpyrLevs(lodft,log_rad,Xrcos,Yrc... |
github | phcerdan/BLS-GSM_Denoising_Portilla-master | pixelAxes.m | .m | BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/pixelAxes.m | 2,053 | utf_8 | 177c4d9d58d2280676a45dd83ef3e50a | % [ZOOM] = pixelAxes(DIMS, ZOOM)
%
% Set the axes of the current plot to cover a multiple of DIMS pixels,
% thereby eliminating screen aliasing artifacts when displaying an
% image of size DIMS.
%
% ZOOM (optional, default='same') expresses the desired number of
% samples displayed per screen pixel. It shoul... |
github | phcerdan/BLS-GSM_Denoising_Portilla-master | pyrBandIndices.m | .m | BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/pyrBandIndices.m | 613 | utf_8 | a5387a946b44f72051b9a72faae6130c | % RES = pyrBandIndices(INDICES, BAND_NUM)
%
% Return indices for accessing a subband from a pyramid
% (gaussian, laplacian, QMF/wavelet, steerable).
% Eero Simoncelli, 6/96.
function indices = pyrBandIndices(pind,band)
if ((band > size(pind,1)) | (band < 1))
error(sprintf('BAND_NUM must be between 1 an... |
github | phcerdan/BLS-GSM_Denoising_Portilla-master | pointOp.m | .m | BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/pointOp.m | 1,159 | utf_8 | 040e1c3bc4afc4f9cfab4aa964e16082 | % RES = pointOp(IM, LUT, ORIGIN, INCREMENT, WARNINGS)
%
% Apply a point operation, specified by lookup table LUT, to image IM.
% LUT must be a row or column vector, and is assumed to contain
% (equi-spaced) samples of the function. ORIGIN specifies the
% abscissa associated with the first sample, and INCREMENT sp... |
github | phcerdan/BLS-GSM_Denoising_Portilla-master | reconWpyr.m | .m | BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/reconWpyr.m | 4,140 | utf_8 | de1ba8ead0f28186c92b026ec2d0631a | % RES = reconWpyr(PYR, INDICES, FILT, EDGES, LEVS, BANDS)
%
% Reconstruct image from its separable orthonormal QMF/wavelet pyramid
% representation, as created by buildWpyr.
%
% PYR is a vector containing the N pyramid subbands, ordered from fine
% to coarse. INDICES is an Nx2 matrix containing the sizes of
% e... |
github | phcerdan/BLS-GSM_Denoising_Portilla-master | shift.m | .m | BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/shift.m | 453 | utf_8 | 3e12f9ab9679c3cc88b56885c167121a | % [RES] = shift(MTX, OFFSET)
%
% Circular shift 2D matrix samples by OFFSET (a [Y,X] 2-vector),
% such that RES(POS) = MTX(POS-OFFSET).
function res = shift(mtx, offset)
dims = size(mtx);
offset = mod(-offset,dims);
res = [ mtx(offset(1)+1:dims(1), offset(2)+1:dims(2)), ...
mtx(offset(1)+1:... |
github | phcerdan/BLS-GSM_Denoising_Portilla-master | namedFilter.m | .m | BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/namedFilter.m | 3,278 | utf_8 | 837312a94d58a44dd9503ab3736cffe7 | % KERNEL = NAMED_FILTER(NAME)
%
% Some standard 1D filter kernels. These are scaled such that
% their L2-norm is 1.0.
%
% binomN - binomial coefficient filter of order N-1
% haar: - Haar wavelet.
% qmf8, qmf12, qmf16 - Symmetric Quadrature Mirror Filters [Johnston80]
% daub2,daub3,... |
github | phcerdan/BLS-GSM_Denoising_Portilla-master | wpyrHt.m | .m | BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/wpyrHt.m | 285 | utf_8 | 5b256ddf8ffadaa7888328a31159035e | % [HEIGHT] = wpyrHt(INDICES)
%
% Compute height of separable QMF/wavelet pyramid with given index matrix.
% Eero Simoncelli, 6/96.
function [ht] = wpyrHt(pind)
if ((pind(1,1) == 1) | (pind(1,2) ==1))
nbands = 1;
else
nbands = 3;
end
ht = (size(pind,1)-1)/nbands;
|
github | phcerdan/BLS-GSM_Denoising_Portilla-master | buildSFpyr.m | .m | BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/buildSFpyr.m | 3,362 | utf_8 | c23de2136a85b1bc58eff471a98d44aa | % [PYR, INDICES, STEERMTX, HARMONICS] = buildSFpyr(IM, HEIGHT, ORDER, TWIDTH)
%
% Construct a steerable pyramid on matrix IM, in the Fourier domain.
% This is similar to buildSpyr, except that:
%
% + Reconstruction is exact (within floating point errors)
% + It can produce any number of orientation bands.
... |
github | phcerdan/BLS-GSM_Denoising_Portilla-master | modulateFlip.m | .m | BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/modulateFlip.m | 480 | utf_8 | 9f2392c42cf1ad73accf1b25c3327ac7 | % [HFILT] = modulateFlipShift(LFILT)
%
% QMF/Wavelet highpass filter construction: modulate by (-1)^n,
% reverse order (and shift by one, which is handled by the convolution
% routines). This is an extension of the original definition of QMF's
% (e.g., see Simoncelli90).
% Eero Simoncelli, 7/96.
function [h... |
github | phcerdan/BLS-GSM_Denoising_Portilla-master | spyrNumBands.m | .m | BLS-GSM_Denoising_Portilla-master/Simoncelli_PyrTools/spyrNumBands.m | 500 | utf_8 | 2173f8841142e73d6f87cb5242a2f9a7 | % [NBANDS] = spyrNumBands(INDICES)
%
% Compute number of orientation bands in a steerable pyramid with
% given index matrix. If the pyramid contains only the highpass and
% lowpass bands (i.e., zero levels), returns 0.
% Eero Simoncelli, 2/97.
function [nbands] = spyrNumBands(pind)
if (size(pind,1) == 2)... |
github | biomedical-cybernetics/coalescent_embedding-master | lanbpro.m | .m | coalescent_embedding-master/coemb_svds_eigs/lanbpro.m | 19,514 | utf_8 | 897b157335c2a5c269845380328709c4 | function [U,B_k,V,p,ierr,work] = lanbpro(varargin)
%LANBPRO Lanczos bidiagonalization with partial reorthogonalization.
% LANBPRO computes the Lanczos bidiagonalization of a real
% matrix using the with partial reorthogonalization.
%
% [U_k,B_k,V_k,R,ierr,work] = LANBPRO(A,K,R0,OPTIONS,U_old,B_old,V_old)
% ... |
github | biomedical-cybernetics/coalescent_embedding-master | coalescent_embedding.m | .m | coalescent_embedding-master/coemb_svds_eigs/coalescent_embedding.m | 25,870 | utf_8 | 486c75e222bfe5b52daa60a6d09d78cb | function coords = coalescent_embedding(x, pre_weighting, dim_red, angular_adjustment, dims)
% Authors:
% - main code: Alessandro Muscoloni, 2017-09-21
% - support functions: indicated at the beginning of the function
% Released under MIT License
% Copyright (c) 2017 A. Muscoloni, J. M. Thomas, C. V. Cannistraci
% Re... |
github | biomedical-cybernetics/coalescent_embedding-master | coalescent_embedding.m | .m | coalescent_embedding-master/coemb_svd_eig/coalescent_embedding.m | 25,081 | utf_8 | 0f84d1345d19f28fe588f1bc8da8aeec | function coords = coalescent_embedding(x, pre_weighting, dim_red, angular_adjustment, dims)
% Authors:
% - main code: Alessandro Muscoloni, 2017-09-21
% - support functions: indicated at the beginning of the function
% Released under MIT License
% Copyright (c) 2017 A. Muscoloni, J. M. Thomas, C. V. Cannistraci
% Re... |
github | biomedical-cybernetics/coalescent_embedding-master | plot_embedding.m | .m | coalescent_embedding-master/usage_example/plot_embedding.m | 4,230 | utf_8 | 2f9d8f22d3ab6070f6eeaf9070e1ad04 | function plot_embedding(x, coords, coloring, labels)
% Authors:
% - main code: Alessandro Muscoloni, 2017-09-21
% - support functions: indicated at the beginning of the function
% Released under MIT License
% Copyright (c) 2017 A. Muscoloni, J. M. Thomas, C. V. Cannistraci
% Reference:
% A. Muscoloni, J. M. Thomas, ... |
github | biomedical-cybernetics/coalescent_embedding-master | coalescent_embedding.m | .m | coalescent_embedding-master/usage_example/coalescent_embedding.m | 25,081 | utf_8 | 0f84d1345d19f28fe588f1bc8da8aeec | function coords = coalescent_embedding(x, pre_weighting, dim_red, angular_adjustment, dims)
% Authors:
% - main code: Alessandro Muscoloni, 2017-09-21
% - support functions: indicated at the beginning of the function
% Released under MIT License
% Copyright (c) 2017 A. Muscoloni, J. M. Thomas, C. V. Cannistraci
% Re... |
github | biomedical-cybernetics/coalescent_embedding-master | plot_embedding.m | .m | coalescent_embedding-master/visualization_and_evaluation/plot_embedding.m | 4,230 | utf_8 | 2f9d8f22d3ab6070f6eeaf9070e1ad04 | function plot_embedding(x, coords, coloring, labels)
% Authors:
% - main code: Alessandro Muscoloni, 2017-09-21
% - support functions: indicated at the beginning of the function
% Released under MIT License
% Copyright (c) 2017 A. Muscoloni, J. M. Thomas, C. V. Cannistraci
% Reference:
% A. Muscoloni, J. M. Thomas, ... |
github | biomedical-cybernetics/coalescent_embedding-master | compute_angular_separation.m | .m | coalescent_embedding-master/visualization_and_evaluation/angular_separation_index/compute_angular_separation.m | 10,618 | utf_8 | ccbc95531e7b891f35adce0aed6455b5 | function [index, group_index, pvalue] = compute_angular_separation(coords, labels, show_plot, rand_reps, rand_seed, worst_comp)
% MATLAB implementation of the angular separation index (ASI):
% a quantitative measure to evaluate the separation of groups
% over the circle circumference (2D) or sphere surface (3D).
% Re... |
github | kareem1925/coursera-Neural-Networks-for-Machine-Learning-master | train.m | .m | coursera-Neural-Networks-for-Machine-Learning-master/week05/Assignment2/train.m | 8,724 | utf_8 | f1ced206e6c895129b06f256ffe18f88 | % This function trains a neural network language model.
function [model] = train(epochs)
% Inputs:
% epochs: Number of epochs to run.
% Output:
% model: A struct containing the learned weights and biases and vocabulary.
if size(ver('Octave'),1)
OctaveMode = 1;
warning('error', 'Octave:broadcast');
start_time... |
github | kareem1925/coursera-Neural-Networks-for-Machine-Learning-master | a4_main.m | .m | coursera-Neural-Networks-for-Machine-Learning-master/week13/Assignment4/a4_main.m | 4,551 | utf_8 | a36e706a0a625e7ca1eeadc45f05145f | % This file was published on Wed Nov 14 20:48:30 2012, UTC.
function a4_main(n_hid, lr_rbm, lr_classification, n_iterations)
% first, train the rbm
global report_calls_to_sample_bernoulli
report_calls_to_sample_bernoulli = false;
global data_sets
if prod(size(data_sets)) ~= 1,
error('You must r... |
github | kareem1925/coursera-Neural-Networks-for-Machine-Learning-master | a3.m | .m | coursera-Neural-Networks-for-Machine-Learning-master/week09/Assignment3/a3.m | 12,963 | utf_8 | cd34878083ef445c9f8930ac125fac6b | function a3(wd_coefficient, n_hid, n_iters, learning_rate, momentum_multiplier, do_early_stopping, mini_batch_size)
warning('error', 'Octave:broadcast');
if exist('page_output_immediately'), page_output_immediately(1); end
more off;
model = initial_model(n_hid);
from_data_file = load('data.mat');
datas = fr... |
github | kareem1925/coursera-Neural-Networks-for-Machine-Learning-master | learn_perceptron.m | .m | coursera-Neural-Networks-for-Machine-Learning-master/week03/Assignment1/learn_perceptron.m | 6,061 | utf_8 | 324d2562f581a7c4f740975df04da068 | %% Learns the weights of a perceptron and displays the results.
function [w] = learn_perceptron(neg_examples_nobias,pos_examples_nobias,w_init,w_gen_feas)
%%
% Learns the weights of a perceptron for a 2-dimensional dataset and plots
% the perceptron at each iteration where an iteration is defined as one
% full pass th... |
github | kareem1925/coursera-Neural-Networks-for-Machine-Learning-master | plot_perceptron.m | .m | coursera-Neural-Networks-for-Machine-Learning-master/week03/Assignment1/plot_perceptron.m | 3,409 | utf_8 | 808099ac46c6f636fa74de07abbcc8bb | %% Plots information about a perceptron classifier on a 2-dimensional dataset.
function plot_perceptron(neg_examples, pos_examples, mistakes0, mistakes1, num_err_history, w, w_dist_history)
%%
% The top-left plot shows the dataset and the classification boundary given by
% the weights of the perceptron. The negative ex... |
github | kwstat/nipals-main | empca_w.m | .m | nipals-main/old/mathworks/empca_w.m | 4,645 | utf_8 | 9a790ceff1d06189c3da4e99576ea16f | % use this file
function [u, s, v, a] = empca_w(a, w, ncomp, emtol, maxiters)
%EMPCA Expectation-Maximization Principal Component Analysis
% [U, S, V] = EMPCA(A,W,N) calculates N principal components of matrix A,
% using weight matrix W.
% Returns U, S, V that approximate the N-rank truncation of the singular
% ... |
github | hongzhenwang/RRPN-revise-master | classification_demo.m | .m | RRPN-revise-master/caffe-fast-rcnn/matlab/demo/classification_demo.m | 5,412 | utf_8 | 8f46deabe6cde287c4759f3bc8b7f819 | 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 | hongzhenwang/RRPN-revise-master | voc_eval.m | .m | RRPN-revise-master/lib/datasets/VOCdevkit-matlab-wrapper/voc_eval.m | 1,332 | utf_8 | 3ee1d5373b091ae4ab79d26ab657c962 | function res = voc_eval(path, comp_id, test_set, output_dir)
VOCopts = get_voc_opts(path);
VOCopts.testset = test_set;
for i = 1:length(VOCopts.classes)
cls = VOCopts.classes{i};
res(i) = voc_eval_cls(cls, VOCopts, comp_id, output_dir);
end
fprintf('\n~~~~~~~~~~~~~~~~~~~~\n');
fprintf('Results:\n');
aps = [res(:... |
github | he010103/CFWCR-master | CFWCR_VOT_CPU.m | .m | CFWCR-master/vot2017_trax/CFWCR_VOT_CPU.m | 43,818 | utf_8 | db95c9ee695c163e60cc186dafe33b36 | function CFWCR_VOT_CPU()
% *************************************************************
% VOT: Always call exit command at the end to terminate Matlab!
% *************************************************************
cleanup = onCleanup(@() exit() );
% ************************************************************... |
github | he010103/CFWCR-master | CFWCR_VOT.m | .m | CFWCR-master/vot2017_trax/CFWCR_VOT.m | 43,816 | utf_8 | 52fc356daac0e7d8a71d2b955e6a1313 | function CFWCR_VOT()
% *************************************************************
% VOT: Always call exit command at the end to terminate Matlab!
% *************************************************************
cleanup = onCleanup(@() exit() );
% *************************************************************
%... |
github | he010103/CFWCR-master | vot.m | .m | CFWCR-master/vot2017_trax/vot.m | 3,603 | utf_8 | 306282b396b7bee687e83a489af86142 | function [handle, image, region] = vot(format)
% vot Initialize communication and obtain communication structure
%
% This function is used to initialize communication with the toolkit.
%
% The resulting handle is a structure provides several functions for
% further interaction:
% - frame(handle): Get new frame from the... |
github | he010103/CFWCR-master | mtimesx_test_ssspeed.m | .m | CFWCR-master/external_libs/mtimesx/mtimesx_test_ssspeed.m | 415,311 | utf_8 | c663b5bc66edbfec752f88862a1805d1 | % Test routine for mtimesx, op(single) * op(single) speed vs MATLAB
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_test_ssspeed
% Filename: mtimesx_test_ssspeed.m
% Programmer: James Tursa
% ... |
github | he010103/CFWCR-master | mtimesx_build.m | .m | CFWCR-master/external_libs/mtimesx/mtimesx_build.m | 16,162 | utf_8 | 1133797528213727d31a9a075188a4d0 | % mtimesx_build compiles mtimesx.c with BLAS libraries
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_build
% Filename: mtimesx_build.m
% Programmer: James Tursa
% Version: 1.40
% Dat... |
github | he010103/CFWCR-master | mtimesx_test_nd.m | .m | CFWCR-master/external_libs/mtimesx/mtimesx_test_nd.m | 14,364 | utf_8 | 0d3b436cea001bccb9c6cccdaa21b34d | % Test routine for mtimesx, multi-dimensional speed and equality to MATLAB
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_test_nd
% Filename: mtimesx_test_nd.m
% Programmer: James Tursa
% ... |
github | he010103/CFWCR-master | mtimesx_test_sdequal.m | .m | CFWCR-master/external_libs/mtimesx/mtimesx_test_sdequal.m | 350,821 | utf_8 | 7e6a367b3ad6154ce1e4da70a91ba4cf | % Test routine for mtimesx, op(single) * op(double) equality vs MATLAB
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_test_sdequal
% Filename: mtimesx_test_sdequal.m
% Programmer: James Tursa... |
github | he010103/CFWCR-master | mtimesx_test_ddequal.m | .m | CFWCR-master/external_libs/mtimesx/mtimesx_test_ddequal.m | 94,229 | utf_8 | 219fa3623cf14a54da7d267a29e61151 | % Test routine for mtimesx, op(double) * op(double) equality vs MATLAB
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_test_ddequal
% Filename: mtimesx_test_ddequal.m
% Programmer: James Tur... |
github | he010103/CFWCR-master | mtimesx_test_dsequal.m | .m | CFWCR-master/external_libs/mtimesx/mtimesx_test_dsequal.m | 350,693 | utf_8 | 325490ae690791eb9f0e7d03408cc540 | % Test routine for mtimesx, op(double) * op(single) equality vs MATLAB
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_test_dsequal
% Filename: mtimesx_test_dsequal.m
% Programmer: James Tursa... |
github | he010103/CFWCR-master | mtimesx_test_sdspeed.m | .m | CFWCR-master/external_libs/mtimesx/mtimesx_test_sdspeed.m | 388,309 | utf_8 | 1ed55a613d5cbfe9a11579562f600c9a | % Test routine for mtimesx, op(single) * op(double) speed vs MATLAB
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_test_sdspeed
% Filename: mtimesx_test_sdspeed.m
% Programmer: James Tursa
% ... |
github | he010103/CFWCR-master | mtimesx_test_ddspeed.m | .m | CFWCR-master/external_libs/mtimesx/mtimesx_test_ddspeed.m | 121,611 | utf_8 | 32613fb321b2de56bd52cb4b4567187d | % Test routine for mtimesx, op(double) * op(double) speed vs MATLAB
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_test_ddspeed
% Filename: mtimesx_test_ddspeed.m
% Programmer: James Tursa
... |
github | he010103/CFWCR-master | mtimesx_sparse.m | .m | CFWCR-master/external_libs/mtimesx/mtimesx_sparse.m | 3,015 | utf_8 | eeb3eb2df4d70c69695b45188807e91c | % mtimesx_sparse does sparse matrix multiply of two inputs
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_sparse
% Filename: mtimesx_sparse.m
% Programmer: James Tursa
% Version: 1.00
... |
github | he010103/CFWCR-master | mtimesx_test_dsspeed.m | .m | CFWCR-master/external_libs/mtimesx/mtimesx_test_dsspeed.m | 388,140 | utf_8 | 53e3e8d0e86784747c58c68664ae0d85 | % Test routine for mtimesx, op(double) * op(single) speed vs MATLAB
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_test_dsspeed
% Filename: mtimesx_test_dsspeed.m
% Programmer: James Tursa
% ... |
github | he010103/CFWCR-master | mtimesx_test_ssequal.m | .m | CFWCR-master/external_libs/mtimesx/mtimesx_test_ssequal.m | 355,156 | utf_8 | 4c01cb508f7cf6adb1b848f98ee9ca41 | % Test routine for mtimesx, op(single) * op(single) equality vs MATLAB
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_test_ssequal
% Filename: mtimesx_test_ssequal.m
% Programmer: James Tursa... |
github | arun1993/mmWave-interference-mapping-master | getTH.m | .m | mmWave-interference-mapping-master/getTH.m | 1,611 | utf_8 | 2f3e49c97e988ca3fd3c3803f61f572e | function th = getTH(d, selSender)
th = [];
for ii = 1:length(selSender)
th(ii) = getTH_(d(ii, :), selSender(ii));
end
for ii = 1:length(selSender)
th(ii) = th(ii)/sum(selSender == selSender(ii));
end
end
function th = getTH_(d, activeSender)
global traces1 traces1N traces2 traces2N traces3 traces3N
persistent... |
github | arun1993/mmWave-interference-mapping-master | vectorplot.m | .m | mmWave-interference-mapping-master/vectorplot.m | 2,089 | utf_8 | ddca7ddc73c603be0bd73dbb2e39824a | % ########### ########### ########## ##########
% ############ ############ ############ ############
% ## ## ## ## ## ## ##
% ## ## ## ## ## ## ##
% ########### #### ###### ## ## ## ## ###... |
github | yinizhizhu/PKULessons-master | LMgist.m | .m | PKULessons-master/GITST/gistdescriptor/LMgist.m | 8,240 | utf_8 | bfdf40d00f3439f3864ce453bfce69d6 | function [gist, param] = LMgist(D, HOMEIMAGES, param, HOMEGIST)
%
% [gist, param] = LMgist(D, HOMEIMAGES, param);
% [gist, param] = LMgist(filename, HOMEIMAGES, param);
% [gist, param] = LMgist(filename, HOMEIMAGES, param, HOMEGIST);
%
% For a set of images:
% gist = LMgist(img, [], param);
%
% When calling LMgist with... |
github | yinizhizhu/PKULessons-master | showGist.m | .m | PKULessons-master/GITST/gistdescriptor/showGist.m | 1,954 | utf_8 | 926839f0ab3e7182c10a1b52d06e5e31 | function showGist(gist, param)
%
% Visualization of the gist descriptor
% showGist(gist, param)
%
% The plot is color coded, with one color per scale
%
% Example:
% img = zeros(256,256);
% img(64:128,64:128) = 255;
% gist = LMgist(img, '', param);
% showGist(gist, param)
[Nimages, Ndim] = size(gist);
nx = c... |
github | yinizhizhu/PKULessons-master | colorFilter.m | .m | PKULessons-master/Experimental_Statistics/SI_RGB/colorFilter.m | 684 | utf_8 | 413027f6fce44c81ddf288c35b9650ef | function [gColor, stdDeviration] = colorFilter(f)
hx=[-1 -2 -1;0 0 0 ;1 2 1];
hy=hx';
R = f(:,:,1);
G = f(:,:,2);
B = f(:,:,3);
Rxy = filterSobel(R, hx, hy);
Gxy = filterSobel(G, hx, hy);
Bxy = filterSobel(B, hx, hy);
rgbx = cat(3,Rxy,Gxy,Bxy);
gColor = rgb2gray(rgbx);
stdDeviration = std2(gColor);
show(Rxy, Gxy... |
github | yinizhizhu/PKULessons-master | LMgist.m | .m | PKULessons-master/Experimental_Statistics/Gist/LMgist.m | 8,279 | utf_8 | b710337dae3fc4dfdbfeca2f94fcaa63 | function [gist, param] = LMgist(D, HOMEIMAGES, param, HOMEGIST)
%
% [gist, param] = LMgist(D, HOMEIMAGES, param);
% [gist, param] = LMgist(filename, HOMEIMAGES, param);
% [gist, param] = LMgist(filename, HOMEIMAGES, param, HOMEGIST);
%
% For a set of images:
% gist = LMgist(img, [], param);
%
% When calling LMgist with... |
github | yinizhizhu/PKULessons-master | showGist.m | .m | PKULessons-master/Experimental_Statistics/Gist/showGist.m | 1,954 | utf_8 | 926839f0ab3e7182c10a1b52d06e5e31 | function showGist(gist, param)
%
% Visualization of the gist descriptor
% showGist(gist, param)
%
% The plot is color coded, with one color per scale
%
% Example:
% img = zeros(256,256);
% img(64:128,64:128) = 255;
% gist = LMgist(img, '', param);
% showGist(gist, param)
[Nimages, Ndim] = size(gist);
nx = c... |
github | nervehammer/asuswrt-master | echo_diagnostic.m | .m | asuswrt-master/release/src/router/asusnatnl/pjproject-1.12/third_party/speex/libspeex/echo_diagnostic.m | 2,076 | utf_8 | 8d5e7563976fbd9bd2eda26711f7d8dc | % Attempts to diagnose AEC problems from recorded samples
%
% out = echo_diagnostic(rec_file, play_file, out_file, tail_length)
%
% Computes the full matrix inversion to cancel echo from the
% recording 'rec_file' using the far end signal 'play_file' using
% a filter length of 'tail_length'. The output is saved to 'o... |
github | khanhnamle1994/machine-learning-master | submit.m | .m | machine-learning-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 | khanhnamle1994/machine-learning-master | submitWithConfiguration.m | .m | machine-learning-master/machine-learning-ex2/ex2/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | khanhnamle1994/machine-learning-master | savejson.m | .m | machine-learning-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 | khanhnamle1994/machine-learning-master | loadjson.m | .m | machine-learning-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 | khanhnamle1994/machine-learning-master | loadubjson.m | .m | machine-learning-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 | khanhnamle1994/machine-learning-master | saveubjson.m | .m | machine-learning-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 | khanhnamle1994/machine-learning-master | submit.m | .m | machine-learning-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 | khanhnamle1994/machine-learning-master | submitWithConfiguration.m | .m | machine-learning-master/machine-learning-ex4/ex4/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | khanhnamle1994/machine-learning-master | savejson.m | .m | machine-learning-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 | khanhnamle1994/machine-learning-master | loadjson.m | .m | machine-learning-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 | khanhnamle1994/machine-learning-master | loadubjson.m | .m | machine-learning-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 | khanhnamle1994/machine-learning-master | saveubjson.m | .m | machine-learning-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 | khanhnamle1994/machine-learning-master | submit.m | .m | machine-learning-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 | khanhnamle1994/machine-learning-master | porterStemmer.m | .m | machine-learning-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 | khanhnamle1994/machine-learning-master | submitWithConfiguration.m | .m | machine-learning-master/machine-learning-ex6/ex6/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | khanhnamle1994/machine-learning-master | savejson.m | .m | machine-learning-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 | khanhnamle1994/machine-learning-master | loadjson.m | .m | machine-learning-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 | khanhnamle1994/machine-learning-master | loadubjson.m | .m | machine-learning-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 | khanhnamle1994/machine-learning-master | saveubjson.m | .m | machine-learning-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 | khanhnamle1994/machine-learning-master | submit.m | .m | machine-learning-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 | khanhnamle1994/machine-learning-master | submitWithConfiguration.m | .m | machine-learning-master/machine-learning-ex7/ex7/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | khanhnamle1994/machine-learning-master | savejson.m | .m | machine-learning-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 | khanhnamle1994/machine-learning-master | loadjson.m | .m | machine-learning-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 | khanhnamle1994/machine-learning-master | loadubjson.m | .m | machine-learning-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 | khanhnamle1994/machine-learning-master | saveubjson.m | .m | machine-learning-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 | khanhnamle1994/machine-learning-master | submit.m | .m | machine-learning-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... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.