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github | cs1471/Modelling-master | icwtband2.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/directfit_tutorial/strflab/preprocessing/wavelet/icwtband2.m | 4,543 | utf_8 | 839221419d4955e146d2b49f809f7b9c | function [cm,V] = icwtband2(ym,S,level,orientation,real_or_cplx);
% 2-D Dual-tree Complex Wavelet Transform:
% Function to determine where to insert a modified subimage ym in C
% and to convert from complex to real format if ym is complex.
% It is necessary to use 'C(V) = cm;' to then do the insertion.
% (For la... |
github | cs1471/Modelling-master | cwtband6.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/directfit_tutorial/strflab/preprocessing/wavelet/cwtband6.m | 2,631 | utf_8 | 607c99791f8114c2bd3b60f70f7a2248 | function Z = cwtband6(C,S,level)
% 2-D Dual-tree Complex Wavelet Transform:
% Function to retrieve the set of 6 complex bandpass subimages
% at level from the 2-D DT CWT vector C.
%
% output = cwtband6(C,S,level)
%
% C -> The column vector containing the Subbands
% S -> The "Bookkeeping" matrix
% ... |
github | cs1471/Modelling-master | cwtband2.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/directfit_tutorial/strflab/preprocessing/wavelet/kbcode/cwtband2.m | 4,589 | utf_8 | 327a4f44ce0ca7a9231e4a1fc4e761f0 | function Z = cwtband2(C,S,level,orientation,real_or_cplx)
% 2-D Dual-tree Complex Wavelet Transform:
% Function to retrieve the subimage required from the 2-D DT CWT vector C.
%
% output = cwtband2(C,S,level,orientation,real_or_cplx)
%
% C -> The column vector containing the Subbands
% S -> The "Bookk... |
github | cs1471/Modelling-master | icwtband6.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/directfit_tutorial/strflab/preprocessing/wavelet/kbcode/icwtband6.m | 2,871 | utf_8 | afe7d5b2e1c1041fc00289e23d399c9e | function [cm,V] = icwtband6(ym,S,level);
% 2-D Dual-tree Complex Wavelet Transform:
% Function to determine where to insert a modified set of 6 subimages ym
% into C and to convert from complex to real format.
% It is necessary to use 'C(V) = cm;' to then do the insertion.
% (For large arrays C, this is much mo... |
github | cs1471/Modelling-master | icwtband2.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/directfit_tutorial/strflab/preprocessing/wavelet/kbcode/icwtband2.m | 4,543 | utf_8 | 839221419d4955e146d2b49f809f7b9c | function [cm,V] = icwtband2(ym,S,level,orientation,real_or_cplx);
% 2-D Dual-tree Complex Wavelet Transform:
% Function to determine where to insert a modified subimage ym in C
% and to convert from complex to real format if ym is complex.
% It is necessary to use 'C(V) = cm;' to then do the insertion.
% (For la... |
github | cs1471/Modelling-master | cwtband6.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/directfit_tutorial/strflab/preprocessing/wavelet/kbcode/cwtband6.m | 2,631 | utf_8 | 607c99791f8114c2bd3b60f70f7a2248 | function Z = cwtband6(C,S,level)
% 2-D Dual-tree Complex Wavelet Transform:
% Function to retrieve the set of 6 complex bandpass subimages
% at level from the 2-D DT CWT vector C.
%
% output = cwtband6(C,S,level)
%
% C -> The column vector containing the Subbands
% S -> The "Bookkeeping" matrix
% ... |
github | cs1471/Modelling-master | wfb2rec.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/directfit_tutorial/strflab/preprocessing/contourlet/wfb2rec.m | 1,419 | utf_8 | a8eb98892d022925b472758e34d4640d | function x = wfb2rec(x_LL, x_LH, x_HL, x_HH, h, g)
% WFB2REC 2-D Wavelet Filter Bank Decomposition
%
% x = wfb2rec(x_LL, x_LH, x_HL, x_HH, h, g)
%
% Input:
% x_LL, x_LH, x_HL, x_HH: Four 2-D wavelet subbands
% h, g: lowpass analysis and synthesis wavelet filters
%
% Output:
% x: reconst... |
github | cs1471/Modelling-master | wfb2dec.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/directfit_tutorial/strflab/preprocessing/contourlet/wfb2dec.m | 1,359 | utf_8 | cf0a7abcc9abae631039550460b07a48 | function [x_LL, x_LH, x_HL, x_HH] = wfb2dec(x, h, g)
% WFB2DEC 2-D Wavelet Filter Bank Decomposition
%
% y = wfb2dec(x, h, g)
%
% Input:
% x: input image
% h, g: lowpass analysis and synthesis wavelet filters
%
% Output:
% x_LL, x_LH, x_HL, x_HH: Four 2-D wavelet subbands
% Make sure... |
github | cs1471/Modelling-master | extend2.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/directfit_tutorial/strflab/preprocessing/contourlet/extend2.m | 1,861 | utf_8 | 40bc6d67909280efd214bb2536a4a46f | function y = extend2(x, ru, rd, cl, cr, extmod)
% EXTEND2 2D extension
%
% y = extend2(x, ru, rd, cl, cr, extmod)
%
% Input:
% x: input image
% ru, rd: amount of extension, up and down, for rows
% cl, cr: amount of extension, left and rigth, for column
% extmod: extension mode. The valid modes are:
% 'per... |
github | cs1471/Modelling-master | trnEN.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/directfit_tutorial/strflab/layer_3optim/trnEN.m | 6,291 | utf_8 | fec66d3cc7aaed9fe281af346773c0b4 | function beta = larsen(X, y, options)
% LARSEN The LARSEN algorithm for elastic net regression.
% BETA = LARSEN(X, Y) performs elastic net regression on the variables
% in X to approximate the response Y. Variables X are assumed to be
% normalized (zero mean, unit length), the response Y is assumed to be... |
github | cs1471/Modelling-master | trnDirectFit.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/directfit_tutorial/strflab/layer_3optim/trnDirectFit.m | 9,092 | utf_8 | 71f4e915420f97acebbe8d67db9f486c | %% Direct Fit training routine
%
% Input:
% modelParams:
%
% datIdx:
%
% options:
% .sparsenesses: a vector of sparseness values to test against
% .tolerances: a vector of tolerance values to use
% .separable: 0=space-time separable, 1=non-separable (defaults to 1)
% ... |
github | cs1471/Modelling-master | trnGradDescLineSearch.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/directfit_tutorial/strflab/layer_3optim/trnGradDescLineSearch.m | 13,829 | utf_8 | 378fe173f3d849598537191c42bccc0c | function [strf,options]=trnGradDescAdaptive(strf,datIdx,options,varargin)
%function [strf,options]=trnGradDescAdaptive(strf,datIdx,options,varargin)
%
% Gradient descent / coordinate descent optimization of STRF.
%
% INPUT:
% [strf] = model structure obtained via upper level *Init functions
% ... |
github | cs1471/Modelling-master | get_function_dir.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/directfit_tutorial/strflab/auxFunc/get_function_dir.m | 210 | utf_8 | 3997eff604223e9deb52d1baa2b379b2 | %% Gets the directory in which a function resides
function dname = get_function_dir(funcName)
dname = '';
fpath = which(funcName);
if ~isempty(fpath)
dname = fileparts(fpath);
end
|
github | cs1471/Modelling-master | cv.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/directfit_tutorial/strflab/auxFunc/cv.m | 160 | utf_8 | 5f66257a915476d907f128db55f0655b | %column vector
function b = cv(a)
b = a;
sz = size(a);
isvect = (sz(1) == 1) || (sz(2) == 1);
if (isvect)
if (sz(2) == 1)
b = a';
end
end
|
github | cs1471/Modelling-master | rv.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/directfit_tutorial/strflab/auxFunc/rv.m | 156 | utf_8 | 2f21009ec2efedb59c7ef77232cb7673 | %row vector
function b = rv(a)
b = a;
sz = size(a);
isvect = (sz(1) == 1) || (sz(2) == 1);
if (isvect)
if (sz(1) == 1)
b = a';
end
end
|
github | cs1471/Modelling-master | get_filenames.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/directfit_tutorial/strflab/auxFunc/get_filenames.m | 1,015 | utf_8 | 9c16cf99e5ff7c2dc8fc73810b5df29b | %% Get a list of filenames in a directory that match a given regular expression
%
% Input:
% datadir: directory that contains data files
% regex: regular expression to match against file names
% preprendDataDir: whether to prepend datadir to file names in output
%
% Output:
% filenames: a ce... |
github | cs1471/Modelling-master | conv_strf.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/directfit_tutorial/strflab/auxFunc/conv_strf.m | 907 | utf_8 | 79ced193fba953e5498341eb6384dcb1 | %% Convolve a stimulus with a STRF
%
% Input:
% allstim:
%
% delays:
%
% strf:
%
% groupIndex:
%
% Output:
%
% modelResponse:
%
%
function modelResponse = conv_strf(allstim, delays, strf, groupIndex)
nDatasets = length(unique(groupIndex));
timeLen = size(allstim, 1);
a = z... |
github | cs1471/Modelling-master | getSubMatrix.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/directfit_tutorial/strflab/auxFunc/getSubMatrix.m | 2,494 | utf_8 | 087245d938e0c04c28716f81e1b5a9cb | function [A, idxr, idxc] = getSubMatrix(hsize, locality, sdimension);
%function A = getSubMatrix(hsize, sdimension)
%
% A function to extract the relevant parts of a covariance matrix
%
% INPUT:
% [hsize] = vector of sizes for each dimension of the stimulus
% [locality] = determines how far away to consider pixe... |
github | cs1471/Modelling-master | sr2df.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/directfit_tutorial/strflab/auxFunc/direct_fit/sr2df.m | 1,451 | utf_8 | 2438793dd336e70acbcaae97b4b3d94d | %% Convert stim/response data structure (from preprocess.sound.m) to the DS
%% structure that direct_fit uses
%
% Input:
% srData: a stim/response data structure produced by
% preprocess_sound.m
%
% outputPath: an arbitrary directory where intermediate files are
% written, used to load sti... |
github | cs1471/Modelling-master | sr2df.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/directfit_tutorial/direct_fit/sr2df.m | 1,451 | utf_8 | 2438793dd336e70acbcaae97b4b3d94d | %% Convert stim/response data structure (from preprocess.sound.m) to the DS
%% structure that direct_fit uses
%
% Input:
% srData: a stim/response data structure produced by
% preprocess_sound.m
%
% outputPath: an arbitrary directory where intermediate files are
% written, used to load sti... |
github | cs1471/Modelling-master | compute_coherence_mean.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/directfit_tutorial/validation/compute_coherence_mean.m | 3,951 | utf_8 | 795c723ea465e5a65ea164505bdd11d7 | %% Compute the jacknifed multi-tapered coherence and normal mutual information
%% between a model response and mean PSTH. From:
%%
%% Anne Hsu et al 2004 Network: Comput. Neural Syst. 15 91-109
%
% Input:
% modelResponse: time series model response
%
% psth: time series actual PSTH, should be same length... |
github | cs1471/Modelling-master | get_filenames.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/directfit_tutorial/validation/get_filenames.m | 1,074 | utf_8 | 3bde12774fc1a51842eb451203f5189c | %% Get a list of filenames in a directory that match a given regular expression
%
% Input:
% datadir: directory that contains data files
% regex: regular expression to match against file names
% preprendDataDir: whether to prepend datadir to file names in output
%
% Output:
% filenames: a ce... |
github | cs1471/Modelling-master | compute_coherence_bound.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/directfit_tutorial/validation/compute_coherence_bound.m | 1,431 | utf_8 | e9aaad8d12284c82b20a5163f980517c | %% compute the expected coherence of a single spike train and estimated PSTH
function cStruct = compute_coherence_bound(psthHalf1, psthHalf2, numSpikeTrials, sampleRate, freqCutoff, windowSize)
%% compute coherence between two halves of PSTH
cMeanHalves = compute_coherence_mean(psthHalf1, psthHalf2, sampleRate... |
github | cs1471/Modelling-master | find_datasets.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/directfit_tutorial/validation/find_datasets.m | 2,235 | utf_8 | f3dba9d78c1295c89731e32d6a35c672 | function datasets = find_datasets(rootdir, stimDir, expr)
if nargin < 3
expr = [];
end
%conspecific: zfsongs, conspecific
datasets = {};
dcnt = 1;
fnames = dir(rootdir);
for k = 1:length(fnames)
if ~strcmp('.', fnames(k).name) && ~strcmp('..', fnames(k).name)
... |
github | cs1471/Modelling-master | poisson_gen_spikes.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/cross-coherence/poisson_gen_spikes.m | 1,299 | utf_8 | c394c2afda9b8256ad44a3f71a544f54 |
function spiketimes = poisson_gen_spikes(psth, meanfr, numTrials)
% Generates an array of spike arrival times at a mean firing rate given by
% meanfr and a time varying rate given by psth in ms.
% Written by Alex Huth 11/12/2009
% modified by Mike Schachter
%generates spike times in units of seconds
inbinwidth = 1; %... |
github | cs1471/Modelling-master | preprocess_sound.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/preprocessing/preprocess_sound.m | 6,341 | utf_8 | ad622ee869178fab285bb02775b7c227 | %% Preprocess .wav files and spike times into time-frequency representations and PSTHs
%
% Input:
% rawStimFiles: a cell array of .wav file names
%
% rawRespFiles: a cell array of spike-time file names. Each file
% contains a space-separated list of file times, one line for each
% trial. ... |
github | cs1471/Modelling-master | split_psth.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/preprocessing/split_psth.m | 1,098 | utf_8 | ced7f94ed52f33644b35bbb26e10afbc | %% Takes a cell array of spike time vectors (one cell for each trial), and
% converts it to a PSTH. It also splits the trials in half, and creates a
% PSTH for each half.
% stimLengthMs: The length of the stimlulus in milliseconds
%
% Returns a struct psthdata, where:
% psthdata.psth: PSTH from all trials
% ... |
github | cs1471/Modelling-master | rv.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/preprocessing/rv.m | 156 | utf_8 | 2f21009ec2efedb59c7ef77232cb7673 | %row vector
function b = rv(a)
b = a;
sz = size(a);
isvect = (sz(1) == 1) || (sz(2) == 1);
if (isvect)
if (sz(1) == 1)
b = a';
end
end
|
github | cs1471/Modelling-master | timefreq.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/preprocessing/timefreq.m | 4,328 | utf_8 | 9082007afd83813b174a853cc71a5abe | %% General purpose time-frequency representation function
%
% Input:
% wavFileName: path to .wav file
%
% typeName: 'ft' for short-time fourier transforms
% 'wavelet' for wavelet transforms
% 'lyons' for lyons-model
%
% params: depends on typeName, default values used... |
github | cs1471/Modelling-master | make_tfrep.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/preprocessing/make_tfrep.m | 1,774 | utf_8 | a44e2f132640a5c289cdf4d5cc2af4a8 | %% Create a time-frequency representation structure
% Input:
% typeName: 'ft', 'wavelet', 'lyons'
%
% params: parameters to assign tfrep (optional, if not given then
% default values will be specified for type)
%
% Output:
% tfrep: the time-frequency structure, for use with display_tfrep... |
github | cs1471/Modelling-master | check_fields.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/preprocessing/check_fields.m | 1,505 | utf_8 | dbd1d425a24ecc49caa9c2b6760b3ea6 | %% Checks a structure to make sure it contains the proper fields
%
% Input:
%
% structInstance: a structure to check
%
% requiredFields: a cell array of field names to verify
%
% messageTemplate: a string error message with a %s to specify param
% name
%
% defaultValues: a cell ar... |
github | cs1471/Modelling-master | getSmoothnessPrior.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/preprocessing/getSmoothnessPrior.m | 2,580 | utf_8 | 45a2210c36f4f8eda447c5dc67c87fe2 | function A = getSmoothnessPrior(hsize, sdimension);
%function A = getSmoothnessPrior(hsize, sdimension)
%
% A function to make a smoothnessprior matrix for N-D matrix of size: hsize
% The matrix can be either 1D ~ 3D or 4D
%
% INPUT:
% [hsize] = vector of sizes for each dimension of matrix
% [sdimension] = determi... |
github | cs1471/Modelling-master | preprocWavelets_2007-07-25.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/preprocessing/preprocWavelets_2007-07-25.m | 11,761 | utf_8 | 1f1e200b20a024b7214830ecfd01b681 | function [PS, params] = preprocWavelets(S, params);
% function [PS, params] = preprocWavelets(S, params);
%
% A script for preprocessing of stimuli using a Gabor wavelet bais set
%
% PARAMS = preprocWavelets;
% returns the default set of parameters.
%
% [PS, PARAMS] = preprocWavelets(S, PARAMS)
% returns prepro... |
github | cs1471/Modelling-master | ndimages.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/preprocessing/ndimages.m | 4,733 | utf_8 | fa310b35265e783c1b2df62977387af2 | function out = ndimages(im, Params)
%function out = ndimages(im, Params)
%
% Allows for the display of n-dimensional (up to 6-d) images by tiling
% along higher dimensions
%
% INPUT:
% [im] = a matrix to be displayed (up to 6 dimensions)
% [Params] = structure that contains parameters
% .clim = The minimum a... |
github | cs1471/Modelling-master | preprocWavelets3d.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/preprocessing/preprocWavelets3d.m | 13,460 | utf_8 | aa654f7554386e10e8bd5589b475f585 | function [stim, params] = preprocWavelets3d(rawStim, params);
% function [stim, params] = preprocWavelets(rawStim, params);
%
% A script for preprocessing of stimuli using a Gabor wavelet bais set
%
% INPUT:
% [rawStim] = A X-by-Y-by-T matrix containing stimuli (movie)
% [params] = structure that c... |
github | cs1471/Modelling-master | preprocSpectraVis.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/preprocessing/preprocSpectraVis.m | 715 | utf_8 | 2d681ef925efced874883daa70a5be13 | function preprocSpectraVis(net);
%function preprocSpectraVis(net);
%
% A visualizer of glm net preprocessed by preprocSpectra
%
% INPUT:
% [net] = strf structure to be visualized
%
params = net.params;
fsize = params.fSize;
w = net.w1;
delays = net.delays;
k = reshape(w, [fsize length(delays)]);
maxk = max(abs(k(:... |
github | cs1471/Modelling-master | fdct_wrapping_dispcoef.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/preprocessing/curvelet/fdct_wrapping_dispcoef.m | 1,993 | utf_8 | e67846c8e34ac43ee9cc8b47a70a7976 | function img = fdct_wrapping_dispcoef(C)
% fdct_wrapping_dispcoef - returns an image containing all the curvelet coefficients
%
% Inputs
% C Curvelet coefficients
%
% Outputs
% img Image containing all the curvelet coefficients. The coefficents are rescaled so that
% the largest c... |
github | cs1471/Modelling-master | icwtband.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/preprocessing/wavelet/icwtband.m | 3,340 | utf_8 | f16dde8670ec29d13224d272f1c1c525 | function [cm,V] = icwtband(ym,L,level,passband,real_or_cplx);
% Function to determine where to insert a modified subband ym in C
% and to convert from complex to real format if ym is complex.
% It is necessary to use 'C(V) = cm;' to then do the insertion.
% (For large arrays C, this is much more efficient than co... |
github | cs1471/Modelling-master | icdwt2.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/preprocessing/wavelet/icdwt2.m | 969 | utf_8 | e66b61d78f490f361d00b82a5fc3ddfc | % icdwt2.m
%
% Wrapper function for NGK's 2D dual-tree complex wavelet code
% Inverse 2D transform (synthesis)
% The wavelet set (near_sym_a, qshift_a) is hardwired in right now.
% Usage : x = icdwt2(w1, w2, L)
%
% Written by : Justin Romberg
% Created : 1/30/2001
function x = icdwt2(w1, w2, L)
N = size(w... |
github | cs1471/Modelling-master | cwtband2.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/preprocessing/wavelet/cwtband2.m | 4,589 | utf_8 | 327a4f44ce0ca7a9231e4a1fc4e761f0 | function Z = cwtband2(C,S,level,orientation,real_or_cplx)
% 2-D Dual-tree Complex Wavelet Transform:
% Function to retrieve the subimage required from the 2-D DT CWT vector C.
%
% output = cwtband2(C,S,level,orientation,real_or_cplx)
%
% C -> The column vector containing the Subbands
% S -> The "Bookk... |
github | cs1471/Modelling-master | cdwt2.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/preprocessing/wavelet/cdwt2.m | 1,503 | utf_8 | bdf964b0a98da471e135259b23c41d1c | % cdwt2.m
%
% Wrapper function for NGK's 2D dual-tree complex wavelet code
% Forward 2D transform (analysis)
% The wavelet set (near_sym_a, qshift_a) is hardwired in right now.
% Usage : [w1, w2] = cdwt2(x, L)
% w1 - subbands with directions
% -----------------
% | | |
% | X |... |
github | cs1471/Modelling-master | icwtband6.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/preprocessing/wavelet/icwtband6.m | 2,871 | utf_8 | afe7d5b2e1c1041fc00289e23d399c9e | function [cm,V] = icwtband6(ym,S,level);
% 2-D Dual-tree Complex Wavelet Transform:
% Function to determine where to insert a modified set of 6 subimages ym
% into C and to convert from complex to real format.
% It is necessary to use 'C(V) = cm;' to then do the insertion.
% (For large arrays C, this is much mo... |
github | cs1471/Modelling-master | cdwt.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/preprocessing/wavelet/cdwt.m | 743 | utf_8 | 09074428fddca9c42995a2dfa15938a4 | % cdwt.m
%
% Wrapper function for NGK's 1D dual-tree complex wavelet code.
% The wavelet set (near_sym_a, qshift_a) is hardwired in right now.
% Usage : w = cdwt(x, L)
%
% Written by : Justin Romberg
% Created : 12/5/2000
function w = cdwt(x, L)
% make x a column vector
rw = 0;
if (size(x,1) == 1)
x =... |
github | cs1471/Modelling-master | icdwt.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/preprocessing/wavelet/icdwt.m | 711 | utf_8 | dee542ce3519fb5296ec228cb2d062a0 | % icdwt.m
%
% Wrapper function for NGK's 1D dual-tree complex wavelet code.
% The wavelet set (near_sym_a, qshift_a) is hardwired.
% Usage : x = icdwt(w, L)
%
% Written by : Justin Romberg
% Created : 12/5/2000
function x = icdwt(w, L)
rw = 0;
if (size(w,1) == 1)
rw = 1;
w = w.';
end
Lx = log2(l... |
github | cs1471/Modelling-master | icwtband2.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/preprocessing/wavelet/icwtband2.m | 4,543 | utf_8 | 839221419d4955e146d2b49f809f7b9c | function [cm,V] = icwtband2(ym,S,level,orientation,real_or_cplx);
% 2-D Dual-tree Complex Wavelet Transform:
% Function to determine where to insert a modified subimage ym in C
% and to convert from complex to real format if ym is complex.
% It is necessary to use 'C(V) = cm;' to then do the insertion.
% (For la... |
github | cs1471/Modelling-master | cwtband6.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/preprocessing/wavelet/cwtband6.m | 2,631 | utf_8 | 607c99791f8114c2bd3b60f70f7a2248 | function Z = cwtband6(C,S,level)
% 2-D Dual-tree Complex Wavelet Transform:
% Function to retrieve the set of 6 complex bandpass subimages
% at level from the 2-D DT CWT vector C.
%
% output = cwtband6(C,S,level)
%
% C -> The column vector containing the Subbands
% S -> The "Bookkeeping" matrix
% ... |
github | cs1471/Modelling-master | cwtband2.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/preprocessing/wavelet/kbcode/cwtband2.m | 4,589 | utf_8 | 327a4f44ce0ca7a9231e4a1fc4e761f0 | function Z = cwtband2(C,S,level,orientation,real_or_cplx)
% 2-D Dual-tree Complex Wavelet Transform:
% Function to retrieve the subimage required from the 2-D DT CWT vector C.
%
% output = cwtband2(C,S,level,orientation,real_or_cplx)
%
% C -> The column vector containing the Subbands
% S -> The "Bookk... |
github | cs1471/Modelling-master | icwtband6.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/preprocessing/wavelet/kbcode/icwtband6.m | 2,871 | utf_8 | afe7d5b2e1c1041fc00289e23d399c9e | function [cm,V] = icwtband6(ym,S,level);
% 2-D Dual-tree Complex Wavelet Transform:
% Function to determine where to insert a modified set of 6 subimages ym
% into C and to convert from complex to real format.
% It is necessary to use 'C(V) = cm;' to then do the insertion.
% (For large arrays C, this is much mo... |
github | cs1471/Modelling-master | icwtband2.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/preprocessing/wavelet/kbcode/icwtband2.m | 4,543 | utf_8 | 839221419d4955e146d2b49f809f7b9c | function [cm,V] = icwtband2(ym,S,level,orientation,real_or_cplx);
% 2-D Dual-tree Complex Wavelet Transform:
% Function to determine where to insert a modified subimage ym in C
% and to convert from complex to real format if ym is complex.
% It is necessary to use 'C(V) = cm;' to then do the insertion.
% (For la... |
github | cs1471/Modelling-master | cwtband6.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/preprocessing/wavelet/kbcode/cwtband6.m | 2,631 | utf_8 | 607c99791f8114c2bd3b60f70f7a2248 | function Z = cwtband6(C,S,level)
% 2-D Dual-tree Complex Wavelet Transform:
% Function to retrieve the set of 6 complex bandpass subimages
% at level from the 2-D DT CWT vector C.
%
% output = cwtband6(C,S,level)
%
% C -> The column vector containing the Subbands
% S -> The "Bookkeeping" matrix
% ... |
github | cs1471/Modelling-master | wfb2rec.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/preprocessing/contourlet/wfb2rec.m | 1,419 | utf_8 | a8eb98892d022925b472758e34d4640d | function x = wfb2rec(x_LL, x_LH, x_HL, x_HH, h, g)
% WFB2REC 2-D Wavelet Filter Bank Decomposition
%
% x = wfb2rec(x_LL, x_LH, x_HL, x_HH, h, g)
%
% Input:
% x_LL, x_LH, x_HL, x_HH: Four 2-D wavelet subbands
% h, g: lowpass analysis and synthesis wavelet filters
%
% Output:
% x: reconst... |
github | cs1471/Modelling-master | wfb2dec.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/preprocessing/contourlet/wfb2dec.m | 1,359 | utf_8 | cf0a7abcc9abae631039550460b07a48 | function [x_LL, x_LH, x_HL, x_HH] = wfb2dec(x, h, g)
% WFB2DEC 2-D Wavelet Filter Bank Decomposition
%
% y = wfb2dec(x, h, g)
%
% Input:
% x: input image
% h, g: lowpass analysis and synthesis wavelet filters
%
% Output:
% x_LL, x_LH, x_HL, x_HH: Four 2-D wavelet subbands
% Make sure... |
github | cs1471/Modelling-master | extend2.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/preprocessing/contourlet/extend2.m | 1,861 | utf_8 | 40bc6d67909280efd214bb2536a4a46f | function y = extend2(x, ru, rd, cl, cr, extmod)
% EXTEND2 2D extension
%
% y = extend2(x, ru, rd, cl, cr, extmod)
%
% Input:
% x: input image
% ru, rd: amount of extension, up and down, for rows
% cl, cr: amount of extension, left and rigth, for column
% extmod: extension mode. The valid modes are:
% 'per... |
github | cs1471/Modelling-master | trnEN.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/layer_3optim/trnEN.m | 6,291 | utf_8 | fec66d3cc7aaed9fe281af346773c0b4 | function beta = larsen(X, y, options)
% LARSEN The LARSEN algorithm for elastic net regression.
% BETA = LARSEN(X, Y) performs elastic net regression on the variables
% in X to approximate the response Y. Variables X are assumed to be
% normalized (zero mean, unit length), the response Y is assumed to be... |
github | cs1471/Modelling-master | trnDirectFit.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/layer_3optim/trnDirectFit.m | 9,092 | utf_8 | 71f4e915420f97acebbe8d67db9f486c | %% Direct Fit training routine
%
% Input:
% modelParams:
%
% datIdx:
%
% options:
% .sparsenesses: a vector of sparseness values to test against
% .tolerances: a vector of tolerance values to use
% .separable: 0=space-time separable, 1=non-separable (defaults to 1)
% ... |
github | cs1471/Modelling-master | trnGradDescLineSearch.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/layer_3optim/trnGradDescLineSearch.m | 13,829 | utf_8 | 378fe173f3d849598537191c42bccc0c | function [strf,options]=trnGradDescAdaptive(strf,datIdx,options,varargin)
%function [strf,options]=trnGradDescAdaptive(strf,datIdx,options,varargin)
%
% Gradient descent / coordinate descent optimization of STRF.
%
% INPUT:
% [strf] = model structure obtained via upper level *Init functions
% ... |
github | cs1471/Modelling-master | get_function_dir.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/auxFunc/get_function_dir.m | 210 | utf_8 | 3997eff604223e9deb52d1baa2b379b2 | %% Gets the directory in which a function resides
function dname = get_function_dir(funcName)
dname = '';
fpath = which(funcName);
if ~isempty(fpath)
dname = fileparts(fpath);
end
|
github | cs1471/Modelling-master | cv.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/auxFunc/cv.m | 160 | utf_8 | 5f66257a915476d907f128db55f0655b | %column vector
function b = cv(a)
b = a;
sz = size(a);
isvect = (sz(1) == 1) || (sz(2) == 1);
if (isvect)
if (sz(2) == 1)
b = a';
end
end
|
github | cs1471/Modelling-master | rv.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/auxFunc/rv.m | 156 | utf_8 | 2f21009ec2efedb59c7ef77232cb7673 | %row vector
function b = rv(a)
b = a;
sz = size(a);
isvect = (sz(1) == 1) || (sz(2) == 1);
if (isvect)
if (sz(1) == 1)
b = a';
end
end
|
github | cs1471/Modelling-master | get_filenames.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/auxFunc/get_filenames.m | 1,015 | utf_8 | 9c16cf99e5ff7c2dc8fc73810b5df29b | %% Get a list of filenames in a directory that match a given regular expression
%
% Input:
% datadir: directory that contains data files
% regex: regular expression to match against file names
% preprendDataDir: whether to prepend datadir to file names in output
%
% Output:
% filenames: a ce... |
github | cs1471/Modelling-master | conv_strf.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/auxFunc/conv_strf.m | 907 | utf_8 | 79ced193fba953e5498341eb6384dcb1 | %% Convolve a stimulus with a STRF
%
% Input:
% allstim:
%
% delays:
%
% strf:
%
% groupIndex:
%
% Output:
%
% modelResponse:
%
%
function modelResponse = conv_strf(allstim, delays, strf, groupIndex)
nDatasets = length(unique(groupIndex));
timeLen = size(allstim, 1);
a = z... |
github | cs1471/Modelling-master | getSubMatrix.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/auxFunc/getSubMatrix.m | 2,494 | utf_8 | 087245d938e0c04c28716f81e1b5a9cb | function [A, idxr, idxc] = getSubMatrix(hsize, locality, sdimension);
%function A = getSubMatrix(hsize, sdimension)
%
% A function to extract the relevant parts of a covariance matrix
%
% INPUT:
% [hsize] = vector of sizes for each dimension of the stimulus
% [locality] = determines how far away to consider pixe... |
github | cs1471/Modelling-master | sr2df.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/strflab/auxFunc/direct_fit/sr2df.m | 1,451 | utf_8 | 2438793dd336e70acbcaae97b4b3d94d | %% Convert stim/response data structure (from preprocess.sound.m) to the DS
%% structure that direct_fit uses
%
% Input:
% srData: a stim/response data structure produced by
% preprocess_sound.m
%
% outputPath: an arbitrary directory where intermediate files are
% written, used to load sti... |
github | cs1471/Modelling-master | compute_coherence_mean.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/validation/compute_coherence_mean.m | 3,951 | utf_8 | 795c723ea465e5a65ea164505bdd11d7 | %% Compute the jacknifed multi-tapered coherence and normal mutual information
%% between a model response and mean PSTH. From:
%%
%% Anne Hsu et al 2004 Network: Comput. Neural Syst. 15 91-109
%
% Input:
% modelResponse: time series model response
%
% psth: time series actual PSTH, should be same length... |
github | cs1471/Modelling-master | get_filenames.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/validation/get_filenames.m | 1,074 | utf_8 | 3bde12774fc1a51842eb451203f5189c | %% Get a list of filenames in a directory that match a given regular expression
%
% Input:
% datadir: directory that contains data files
% regex: regular expression to match against file names
% preprendDataDir: whether to prepend datadir to file names in output
%
% Output:
% filenames: a ce... |
github | cs1471/Modelling-master | compute_coherence_bound.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/validation/compute_coherence_bound.m | 1,431 | utf_8 | e9aaad8d12284c82b20a5163f980517c | %% compute the expected coherence of a single spike train and estimated PSTH
function cStruct = compute_coherence_bound(psthHalf1, psthHalf2, numSpikeTrials, sampleRate, freqCutoff, windowSize)
%% compute coherence between two halves of PSTH
cMeanHalves = compute_coherence_mean(psthHalf1, psthHalf2, sampleRate... |
github | cs1471/Modelling-master | find_datasets.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/strflab_graddesc/validation/find_datasets.m | 2,235 | utf_8 | f3dba9d78c1295c89731e32d6a35c672 | function datasets = find_datasets(rootdir, stimDir, expr)
if nargin < 3
expr = [];
end
%conspecific: zfsongs, conspecific
datasets = {};
dcnt = 1;
fnames = dir(rootdir);
for k = 1:length(fnames)
if ~strcmp('.', fnames(k).name) && ~strcmp('..', fnames(k).name)
... |
github | cs1471/Modelling-master | preprocess_sound.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/coherence_tutorial/preprocessing/preprocess_sound.m | 6,342 | utf_8 | e91d29d5b29f792a623454d0d567b9fe | %% Preprocess .wav files and spike times into time-frequency representations and PSTHs
%
% Input:
% rawStimFiles: a cell array of .wav file names
%
% rawRespFiles: a cell array of spike-time file names. Each file
% contains a space-separated list of file times, one line for each
% trial. ... |
github | cs1471/Modelling-master | rv.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/coherence_tutorial/preprocessing/rv.m | 156 | utf_8 | 2f21009ec2efedb59c7ef77232cb7673 | %row vector
function b = rv(a)
b = a;
sz = size(a);
isvect = (sz(1) == 1) || (sz(2) == 1);
if (isvect)
if (sz(1) == 1)
b = a';
end
end
|
github | cs1471/Modelling-master | timefreq.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/coherence_tutorial/preprocessing/timefreq.m | 4,328 | utf_8 | 9082007afd83813b174a853cc71a5abe | %% General purpose time-frequency representation function
%
% Input:
% wavFileName: path to .wav file
%
% typeName: 'ft' for short-time fourier transforms
% 'wavelet' for wavelet transforms
% 'lyons' for lyons-model
%
% params: depends on typeName, default values used... |
github | cs1471/Modelling-master | make_tfrep.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/coherence_tutorial/preprocessing/make_tfrep.m | 1,774 | utf_8 | a44e2f132640a5c289cdf4d5cc2af4a8 | %% Create a time-frequency representation structure
% Input:
% typeName: 'ft', 'wavelet', 'lyons'
%
% params: parameters to assign tfrep (optional, if not given then
% default values will be specified for type)
%
% Output:
% tfrep: the time-frequency structure, for use with display_tfrep... |
github | cs1471/Modelling-master | check_fields.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/coherence_tutorial/preprocessing/check_fields.m | 1,505 | utf_8 | dbd1d425a24ecc49caa9c2b6760b3ea6 | %% Checks a structure to make sure it contains the proper fields
%
% Input:
%
% structInstance: a structure to check
%
% requiredFields: a cell array of field names to verify
%
% messageTemplate: a string error message with a %s to specify param
% name
%
% defaultValues: a cell ar... |
github | cs1471/Modelling-master | compute_coherence_mean.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/coherence_tutorial/validation/compute_coherence_mean.m | 3,951 | utf_8 | 795c723ea465e5a65ea164505bdd11d7 | %% Compute the jacknifed multi-tapered coherence and normal mutual information
%% between a model response and mean PSTH. From:
%%
%% Anne Hsu et al 2004 Network: Comput. Neural Syst. 15 91-109
%
% Input:
% modelResponse: time series model response
%
% psth: time series actual PSTH, should be same length... |
github | cs1471/Modelling-master | get_filenames.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/coherence_tutorial/validation/get_filenames.m | 1,074 | utf_8 | 3bde12774fc1a51842eb451203f5189c | %% Get a list of filenames in a directory that match a given regular expression
%
% Input:
% datadir: directory that contains data files
% regex: regular expression to match against file names
% preprendDataDir: whether to prepend datadir to file names in output
%
% Output:
% filenames: a ce... |
github | cs1471/Modelling-master | compute_coherence_bound.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/coherence_tutorial/validation/compute_coherence_bound.m | 1,431 | utf_8 | e9aaad8d12284c82b20a5163f980517c | %% compute the expected coherence of a single spike train and estimated PSTH
function cStruct = compute_coherence_bound(psthHalf1, psthHalf2, numSpikeTrials, sampleRate, freqCutoff, windowSize)
%% compute coherence between two halves of PSTH
cMeanHalves = compute_coherence_mean(psthHalf1, psthHalf2, sampleRate... |
github | cs1471/Modelling-master | find_datasets.m | .m | Modelling-master/month_Frederic_Theunissen/CodingPractice/coherence_tutorial/validation/find_datasets.m | 2,235 | utf_8 | f3dba9d78c1295c89731e32d6a35c672 | function datasets = find_datasets(rootdir, stimDir, expr)
if nargin < 3
expr = [];
end
%conspecific: zfsongs, conspecific
datasets = {};
dcnt = 1;
fnames = dir(rootdir);
for k = 1:length(fnames)
if ~strcmp('.', fnames(k).name) && ~strcmp('..', fnames(k).name)
... |
github | cs1471/Modelling-master | estimate.m | .m | Modelling-master/month_OdeliaSchwartz/codingPractice/BerkeleyCourse2016/imageica/code/estimate.m | 9,216 | utf_8 | 4c1f3e22c37f089f81eb7eb952f821f4 | function estimate( whiteningMatrix, dewhiteningMatrix, fname, p )
% estimate - algorithms for topographic, subspace, and standard ICA
%
% SYNTAX:
% estimate( whiteningMatrix, dewhiteningMatrix, fname, dims );
%
% NOTE: X passed as global!
%
% X preprocessed (whitened) sample vectors in columns
% whiten... |
github | cs1471/Modelling-master | estimateModif.m | .m | Modelling-master/month_OdeliaSchwartz/codingPractice/BerkeleyCourse2016/imageica/code/estimateModif.m | 9,320 | utf_8 | 68690a2792bcf343c2c9a6a7fd7449b5 | function estimateModif( whiteningMatrix, dewhiteningMatrix, fname, p, numIters)
% estimate - algorithms for topographic, subspace, and standard ICA
%
% SYNTAX:
% estimate( whiteningMatrix, dewhiteningMatrix, fname, dims );
%
% NOTE: X passed as global!
%
% X preprocessed (whitened) sample vectors in co... |
github | cs1471/Modelling-master | gaussEstim.m | .m | Modelling-master/month_OdeliaSchwartz/codingPractice/BerkeleyCourse2016/LabExtraFiles/gaussEstim.m | 429 | utf_8 | fb08549dfb064b58948074d17c0ca2a7 |
function theres = gaussEstim(l, lself, n, a);
% function theres = gaussEstim(l, lself, n, a);
% estimate Gaussian component of the GSM
% a Rayleigh parameter
% n number of filters
% lself is filter response; l is gain pool response
thefac = (1./(a.*lself.^2).^.25) .* (l.^2./lself.^2).^.25;
thefac=1./thefac;
the... |
github | cs1471/Modelling-master | ica4.m | .m | Modelling-master/month_OdeliaSchwartz/codingPractice/BerkeleyCourse2016/LabExtraFiles/ica4.m | 902 | utf_8 | 5e8e0fe321e0c752c01d0359b7228d22 | % [NEWSIG, MAT, EVALS] = ica4(SIG)
%
% If SIG is a matrix of col vector signals, NEWSIG are the signals
% separated out into independent components through Cardoso's Fourth
% Order Blind Identification (FOBI) algorithm.
% MAT gives the transformation matrix:
% PSIG = MAT * SIG
%
% EVALS gives the eigenvalues of ... |
github | cs1471/Modelling-master | mypca.m | .m | Modelling-master/month_OdeliaSchwartz/codingPractice/BerkeleyCourse2016/LabExtraFiles/mypca.m | 1,168 | utf_8 | 02d5cd1f9b764aee66a519632e59cef3 | % [NEWSIG, EVECS, EVALS, MAT] = pca(SIG,WHITEN)
%
% If SIG is a matrix whose columns are sample vectors drawn from a
% probability density, NEWSIG is matrix whose columns have been
% projected onto the principal components of the data. If
% WHITEN is non-zero (default), the values will be scaled to unit
% variance ... |
github | cs1471/Modelling-master | sortedEig.m | .m | Modelling-master/month_OdeliaSchwartz/codingPractice/BerkeleyCourse2016/LabExtraFiles/sortedEig.m | 362 | utf_8 | 6c209c32b6332a5d3acbbfa0c4c65947 | % [E, D] = sortedEig(X)
%
% Calls matlab's "eig" function for computing
% eigenvalues/eigenvectors, but re-orders the solution to run from
% largest eigenvalue to smallest.
% Eero Simoncelli, 11/97.
function [E, D] = sortedEig(X)
[Eo, Do] = eig(X);
Do = diag(Do);
[junk,Ind] = sort(Do);
D = diag(Do(Ind(size(Ind,1):... |
github | cs1471/Modelling-master | innerProd.m | .m | Modelling-master/month_OdeliaSchwartz/codingPractice/BerkeleyCourse2016/LabExtraFiles/innerProd.m | 405 | utf_8 | e323c3c423813239c338506b982ca8a2 | % 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 matlabP... |
github | cs1471/Modelling-master | mybowtie.m | .m | Modelling-master/month_OdeliaSchwartz/codingPractice/BerkeleyCourse2016/LabExtraFiles/mybowtie.m | 277 | utf_8 | 6c9176393601db72a32855ed7fde668e |
% function bowtie(n1, n2)
% Plot a bowtie conditional dependency between n1 and n2
function bowtie(n1, n2)
binsz=51;
[H,Y,X] = jhisto(n1, n2, binsz);
colmax = max(1,max(H));
H = H ./ (ones(size(H,1),1)*colmax);
imagesc(X,Y,H); axis('xy'); axis('square');
colormap('gray');
|
github | cs1471/Modelling-master | jhisto.m | .m | Modelling-master/month_OdeliaSchwartz/codingPractice/BerkeleyCourse2016/LabExtraFiles/jhisto.m | 1,393 | utf_8 | 16dc756c1a6b85fb196dff1cb6821628 | % [N, Y, X] = JHISTO(YIMAGE, XIMAGE, NBINS_OR_BINSIZE, BIN_CENTER)
%
% Compute joint histogram of Yimage and Ximage.
%
% NBINS_OR_BINSIZE (optional, default = 101) specifies either
% the number of histogram bins, or the negative of the binsize.
% It can be a [Y,X] 2-vector or it can be a scalar.
%
% BIN_CENTER (optiona... |
github | cs1471/Modelling-master | histoMatchMod.m | .m | Modelling-master/month_OdeliaSchwartz/codingPractice/BerkeleyCourse2016/LabExtraFiles/histoMatchMod.m | 977 | utf_8 | c5fbabf600ece1dab7487bbfc0b5a861 | % RES = histoMatch(MTX, N, X)
%
% Modify elements of MTX so that normalized histogram matches that
% specified by vectors X and N, where N contains the histogram counts
% and X the histogram bin positions (see histo).
% Eero Simoncelli, 7/96.
% OS 2003 Change to 0 if nan in interpolation
function res = histoMatch(mtx... |
github | cs1471/Modelling-master | sp3Filters.m | .m | Modelling-master/month_OdeliaSchwartz/codingPractice/BerkeleyCourse2016/matlabPyrTools/sp3Filters.m | 17,188 | utf_8 | a3514d77e5a92b96d96197ebad343395 | % Steerable pyramid filters. Transform described in:
%
% @INPROCEEDINGS{Simoncelli95b,
% TITLE = "The Steerable Pyramid: A Flexible Architecture for
% Multi-Scale Derivative Computation",
% AUTHOR = "E P Simoncelli and W T Freeman",
% BOOKTITLE = "Second Int'l Conf on Image Processing",
% ADDRESS = "Washington, DC"... |
github | cs1471/Modelling-master | buildWpyr.m | .m | Modelling-master/month_OdeliaSchwartz/codingPractice/BerkeleyCourse2016/matlabPyrTools/buildWpyr.m | 2,644 | utf_8 | e741b2037d9a1358d4de94580ebcc4bf | % [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 be a st... |
github | cs1471/Modelling-master | reconLpyr.m | .m | Modelling-master/month_OdeliaSchwartz/codingPractice/BerkeleyCourse2016/matlabPyrTools/reconLpyr.m | 2,049 | utf_8 | 657b8012a1ed06b6573496855309b2ae | % RES = reconLpyr(PYR, INDICES, LEVS, FILT2, EDGES)
%
% Reconstruct image from Laplacian pyramid, as created by buildLpyr.
%
% PYR is a vector containing the N pyramid subbands, ordered from fine
% to coarse. INDICES is an Nx2 matrix containing the sizes of
% each subband. This is compatible with the MatLab Wavelet t... |
github | cs1471/Modelling-master | mkDisc.m | .m | Modelling-master/month_OdeliaSchwartz/codingPractice/BerkeleyCourse2016/matlabPyrTools/mkDisc.m | 1,428 | utf_8 | c6acae0ac1aa738b7ef094eb2ac60f52 | % IM = mkDisc(SIZE, RADIUS, ORIGIN, TWIDTH, VALS)
%
% Make a "disk" image. SIZE specifies the matrix size, as for
% zeros(). RADIUS (default = min(size)/4) specifies the radius of
% the disk. ORIGIN (default = (size+1)/2) specifies the
% location of the disk center. TWIDTH (in pixels, default = 2)
% specifies th... |
github | cs1471/Modelling-master | spyrHigh.m | .m | Modelling-master/month_OdeliaSchwartz/codingPractice/BerkeleyCourse2016/matlabPyrTools/spyrHigh.m | 189 | utf_8 | 3132a747e0c7bf1879bb32e4d5fda257 | % RES = spyrHigh(PYR, INDICES)
%
% Access the highpass residual band from a steerable pyramid.
% Eero Simoncelli, 6/96.
function res = spyrHigh(pyr,pind)
res = pyrBand(pyr, pind, 1);
|
github | cs1471/Modelling-master | mkImpulse.m | .m | Modelling-master/month_OdeliaSchwartz/codingPractice/BerkeleyCourse2016/matlabPyrTools/mkImpulse.m | 529 | utf_8 | cf6f3809cb123791501bb0e92845a0c6 | % IM = mkImpulse(SIZE, ORIGIN, AMPLITUDE)
%
% Compute a matrix of dimension SIZE (a [Y X] 2-vector, or a scalar)
% containing a single non-zero entry, at position ORIGIN (defaults to
% ceil(size/2)), of value AMPLITUDE (defaults to 1).
% Eero Simoncelli, 6/96.
function [res] = mkImpulse(sz, origin, amplitude)
sz = s... |
github | cs1471/Modelling-master | rconv2.m | .m | Modelling-master/month_OdeliaSchwartz/codingPractice/BerkeleyCourse2016/matlabPyrTools/rconv2.m | 1,435 | utf_8 | da3a54d6c7b1ac17c78eab0a4a7648d9 | % RES = RCONV2(MTX1, MTX2, CTR)
%
% Convolution of two matrices, with boundaries handled via reflection
% about the edge pixels. Result will be of size of LARGER matrix.
%
% The origin of the smaller matrix is assumed to be its center.
% For even dimensions, the origin is determined by the CTR (optional)
% argument:... |
github | cs1471/Modelling-master | mkR.m | .m | Modelling-master/month_OdeliaSchwartz/codingPractice/BerkeleyCourse2016/matlabPyrTools/mkR.m | 775 | utf_8 | 504bc90f67c72730edc4a9d86630bdf2 | % IM = mkR(SIZE, EXPT, ORIGIN)
%
% Compute a matrix of dimension SIZE (a [Y X] 2-vector, or a scalar)
% containing samples of a radial ramp function, raised to power EXPT
% (default = 1), with given ORIGIN (default = (size+1)/2, [1 1] =
% upper left). All but the first argument are optional.
% Eero Simoncelli, 6/96.... |
github | cs1471/Modelling-master | blur.m | .m | Modelling-master/month_OdeliaSchwartz/codingPractice/BerkeleyCourse2016/matlabPyrTools/blur.m | 1,731 | utf_8 | a2dc57fdfe85f98549b2f7ddf721c298 | % RES = blur(IM, LEVELS, FILT)
%
% Blur an image, by filtering and downsampling LEVELS times
% (default=1), followed by upsampling and filtering LEVELS times. The
% blurring is done with filter kernel specified by FILT (default =
% 'binom5'), which can be a string (to be passed to namedFilter), a
% vector (applied sep... |
github | cs1471/Modelling-master | pyrBand.m | .m | Modelling-master/month_OdeliaSchwartz/codingPractice/BerkeleyCourse2016/matlabPyrTools/pyrBand.m | 395 | utf_8 | 39c1e3772426a1362119d302d9767a24 | % 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, band)
re... |
github | cs1471/Modelling-master | kurt2.m | .m | Modelling-master/month_OdeliaSchwartz/codingPractice/BerkeleyCourse2016/matlabPyrTools/kurt2.m | 551 | utf_8 | cd20c1a5155cba8de454011552ec4f2a | % K = KURT2(MTX,MEAN,VAR)
%
% Sample kurtosis (fourth moment divided by squared variance)
% of a matrix. Kurtosis of a Gaussian distribution is 3.
% MEAN (optional) and VAR (optional) make the computation faster.
% Eero Simoncelli, 6/96.
function res = kurt2(mtx, mn, v)
if (exist('mn') ~= 1)
mn = mean(mean(mtx)... |
github | cs1471/Modelling-master | buildSpyr.m | .m | Modelling-master/month_OdeliaSchwartz/codingPractice/BerkeleyCourse2016/matlabPyrTools/buildSpyr.m | 2,064 | utf_8 | b0caf60e63a52aa734956dd74ea39f95 | % [PYR, INDICES, STEERMTX, HARMONICS] = buildSpyr(IM, HEIGHT, FILTFILE, EDGES)
%
% Construct a steerable pyramid on matrix IM. Convolutions are
% done with spatial filters.
%
% HEIGHT (optional) specifies the number of pyramid levels to build. Default
% is maxPyrHt(size(IM),size(FILT)).
% You can also specify 'auto' ... |
github | cs1471/Modelling-master | setPyrBand.m | .m | Modelling-master/month_OdeliaSchwartz/codingPractice/BerkeleyCourse2016/matlabPyrTools/setPyrBand.m | 1,049 | utf_8 | 76554eb735e969097a4723a508facd37 | % NEWPYR = setPyrBand(PYR, INDICES, NEWBAND, BAND_NUM)
%
% Insert an image (BAND) into a pyramid (gaussian, laplacian, QMF/wavelet,
% or steerable). Subbands are numbered consecutively, from finest
% (highest spatial frequency) to coarsest (lowest spatial frequency).
% Eero Simoncelli, 1/03.
function pyr = setPyrB... |
github | cs1471/Modelling-master | var2.m | .m | Modelling-master/month_OdeliaSchwartz/codingPractice/BerkeleyCourse2016/matlabPyrTools/var2.m | 390 | utf_8 | de26727e055081aa670024c273f0e885 | % 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)) / max((prod(size(mtx)) - 1),1);
else
res = sum(sum(real(mtx-mn).^2)) + i*sum(su... |
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