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 | Jane333/Mustererkennung-master | f2.m | .m | Mustererkennung-master/ueb08/abgegeben/f2.m | 266 | utf_8 | 8a45d3a85364e16a8f77f01deebc9f6a | % f00*2^0 + f01*2^1 + f10*2^2 + f11*2^3 = 2
function [y] = f2(v, x)
f00 = x*v(1) + x*v(2) + v(3) < 0;
f01 = x*v(1) + 1*v(2) + v(3) >= 0;
f10 = 1*v(1) + x*v(2) + v(3) < 0;
f11 = 1*v(1) + 1*v(2) + v(3) < 0;
if f00 && f01 && f10 && f11
y = 1;
else
y = 0;
end
|
github | Jane333/Mustererkennung-master | f14.m | .m | Mustererkennung-master/ueb08/abgegeben/f14.m | 270 | utf_8 | ca360bc5710192ba023a76cdbd2a8cb2 | % f00*2^0 + f01*2^1 + f10*2^2 + f11*2^3 = 14
function [y] = f14(v, x)
f00 = x*v(1) + x*v(2) + v(3) < 0;
f01 = x*v(1) + 1*v(2) + v(3) >= 0;
f10 = 1*v(1) + x*v(2) + v(3) >= 0;
f11 = 1*v(1) + 1*v(2) + v(3) >= 0;
if f00 && f01 && f10 && f11
y = 1;
else
y = 0;
end
|
github | Jane333/Mustererkennung-master | f8.m | .m | Mustererkennung-master/ueb08/abgegeben/f8.m | 271 | utf_8 | 962d6ad1f3f4ebb7ee09306d03b08020 | % f00*2^0 + f01*2^1 + f10*2^2 + f11*2^3 = 8
% AND
function [y] = f8(v, x)
f00 = x*v(1) + x*v(2) + v(3) < 0;
f01 = x*v(1) + 1*v(2) + v(3) < 0;
f10 = 1*v(1) + x*v(2) + v(3) < 0;
f11 = 1*v(1) + 1*v(2) + v(3) >= 0;
if f00 && f01 && f10 && f11
y = 1;
else
y = 0;
end
|
github | Jane333/Mustererkennung-master | f3.m | .m | Mustererkennung-master/ueb08/abgegeben/f3.m | 267 | utf_8 | 2d634f0c01d805aba4ae428c25154824 | % f00*2^0 + f01*2^1 + f10*2^2 + f11*2^3 = 3
function [y] = f3(v, x)
f00 = x*v(1) + x*v(2) + v(3) >= 0;
f01 = x*v(1) + 1*v(2) + v(3) >= 0;
f10 = 1*v(1) + x*v(2) + v(3) < 0;
f11 = 1*v(1) + 1*v(2) + v(3) < 0;
if f00 && f01 && f10 && f11
y = 1;
else
y = 0;
end
|
github | Jane333/Mustererkennung-master | f9.m | .m | Mustererkennung-master/ueb08/abgegeben/f9.m | 267 | utf_8 | 79364805321b54ba2f31353bc3afcb6c | % f00*2^0 + f01*2^1 + f10*2^2 + f11*2^3 = 9
function [y] = f9(v, x)
f00 = x*v(1) + x*v(2) + v(3) >= 0;
f01 = x*v(1) + 1*v(2) + v(3) < 0;
f10 = 1*v(1) + x*v(2) + v(3) < 0;
f11 = 1*v(1) + 1*v(2) + v(3) >= 0;
if f00 && f01 && f10 && f11
y = 1;
else
y = 0;
end
|
github | Jane333/Mustererkennung-master | f13.m | .m | Mustererkennung-master/ueb08/abgegeben/f13.m | 270 | utf_8 | 350e66548c14486c8584df2ea54c1df4 | % f00*2^0 + f01*2^1 + f10*2^2 + f11*2^3 = 13
function [y] = f13(v, x)
f00 = x*v(1) + x*v(2) + v(3) >= 0;
f01 = x*v(1) + 1*v(2) + v(3) < 0;
f10 = 1*v(1) + x*v(2) + v(3) >= 0;
f11 = 1*v(1) + 1*v(2) + v(3) >= 0;
if f00 && f01 && f10 && f11
y = 1;
else
y = 0;
end
|
github | Jane333/Mustererkennung-master | f7.m | .m | Mustererkennung-master/ueb08/abgegeben/f7.m | 268 | utf_8 | 7f776dc681cec1e9255d4b92cc7ebc14 | % f00*2^0 + f01*2^1 + f10*2^2 + f11*2^3 = 7
function [y] = f7(v, x)
f00 = x*v(1) + x*v(2) + v(3) >= 0;
f01 = x*v(1) + 1*v(2) + v(3) >= 0;
f10 = 1*v(1) + x*v(2) + v(3) >= 0;
f11 = 1*v(1) + 1*v(2) + v(3) < 0;
if f00 && f01 && f10 && f11
y = 1;
else
y = 0;
end
|
github | Jane333/Mustererkennung-master | f12.m | .m | Mustererkennung-master/ueb08/abgegeben/f12.m | 269 | utf_8 | 2ad5140421960ca6e2d06419b95927a8 | % f00*2^0 + f01*2^1 + f10*2^2 + f11*2^3 = 12
function [y] = f12(v, x)
f00 = x*v(1) + x*v(2) + v(3) < 0;
f01 = x*v(1) + 1*v(2) + v(3) < 0;
f10 = 1*v(1) + x*v(2) + v(3) >= 0;
f11 = 1*v(1) + 1*v(2) + v(3) >= 0;
if f00 && f01 && f10 && f11
y = 1;
else
y = 0;
end
|
github | Jane333/Mustererkennung-master | f10.m | .m | Mustererkennung-master/ueb08/abgegeben/f10.m | 269 | utf_8 | 635292686290c8cd5cc1171cc82dafb7 | % f00*2^0 + f01*2^1 + f10*2^2 + f11*2^3 = 10
function [y] = f10(v, x)
f00 = x*v(1) + x*v(2) + v(3) < 0;
f01 = x*v(1) + 1*v(2) + v(3) >= 0;
f10 = 1*v(1) + x*v(2) + v(3) < 0;
f11 = 1*v(1) + 1*v(2) + v(3) >= 0;
if f00 && f01 && f10 && f11
y = 1;
else
y = 0;
end
|
github | Jane333/Mustererkennung-master | f6.m | .m | Mustererkennung-master/ueb08/abgegeben/f6.m | 267 | utf_8 | ab8d5fd5ce7dba9af79d92f4e56df6c0 | % f00*2^0 + f01*2^1 + f10*2^2 + f11*2^3 = 6
function [y] = f6(v, x)
f00 = x*v(1) + x*v(2) + v(3) < 0;
f01 = x*v(1) + 1*v(2) + v(3) >= 0;
f10 = 1*v(1) + x*v(2) + v(3) >= 0;
f11 = 1*v(1) + 1*v(2) + v(3) < 0;
if f00 && f01 && f10 && f11
y = 1;
else
y = 0;
end
|
github | Jane333/Mustererkennung-master | f15.m | .m | Mustererkennung-master/ueb08/abgegeben/f15.m | 271 | utf_8 | d5f21bb60f6e2c885d2e2561ed80c8f0 | % f00*2^0 + f01*2^1 + f10*2^2 + f11*2^3 = 15
function [y] = f15(v, x)
f00 = x*v(1) + x*v(2) + v(3) >= 0;
f01 = x*v(1) + 1*v(2) + v(3) >= 0;
f10 = 1*v(1) + x*v(2) + v(3) >= 0;
f11 = 1*v(1) + 1*v(2) + v(3) >= 0;
if f00 && f01 && f10 && f11
y = 1;
else
y = 0;
end
|
github | Jane333/Mustererkennung-master | f11.m | .m | Mustererkennung-master/ueb08/abgegeben/f11.m | 270 | utf_8 | 0d57c313cefdcbd1129cbc5c677bc113 | % f00*2^0 + f01*2^1 + f10*2^2 + f11*2^3 = 11
function [y] = f11(v, x)
f00 = x*v(1) + x*v(2) + v(3) >= 0;
f01 = x*v(1) + 1*v(2) + v(3) >= 0;
f10 = 1*v(1) + x*v(2) + v(3) < 0;
f11 = 1*v(1) + 1*v(2) + v(3) >= 0;
if f00 && f01 && f10 && f11
y = 1;
else
y = 0;
end
|
github | Jane333/Mustererkennung-master | f5.m | .m | Mustererkennung-master/ueb08/abgegeben/f5.m | 267 | utf_8 | 47d763be66d558180a174b6f5e3d0bef | % f00*2^0 + f01*2^1 + f10*2^2 + f11*2^3 = 5
function [y] = f5(v, x)
f00 = x*v(1) + x*v(2) + v(3) >= 0;
f01 = x*v(1) + 1*v(2) + v(3) < 0;
f10 = 1*v(1) + x*v(2) + v(3) >= 0;
f11 = 1*v(1) + 1*v(2) + v(3) < 0;
if f00 && f01 && f10 && f11
y = 1;
else
y = 0;
end
|
github | Jane333/Mustererkennung-master | f4.m | .m | Mustererkennung-master/ueb08/abgegeben/f4.m | 266 | utf_8 | d3d5df84abd6554a4ac6f7b385cafeab | % f00*2^0 + f01*2^1 + f10*2^2 + f11*2^3 = 4
function [y] = f4(v, x)
f00 = x*v(1) + x*v(2) + v(3) < 0;
f01 = x*v(1) + 1*v(2) + v(3) < 0;
f10 = 1*v(1) + x*v(2) + v(3) >= 0;
f11 = 1*v(1) + 1*v(2) + v(3) < 0;
if f00 && f01 && f10 && f11
y = 1;
else
y = 0;
end
|
github | Jane333/Mustererkennung-master | f0.m | .m | Mustererkennung-master/ueb08/abgegeben/f0.m | 265 | utf_8 | 3bcde2d41cacbe213583029835ae8c44 | % f00*2^0 + f01*2^1 + f10*2^2 + f11*2^3 = 0
function [y] = f0(v, x)
f00 = x*v(1) + x*v(2) + v(3) < 0;
f01 = x*v(1) + 1*v(2) + v(3) < 0;
f10 = 1*v(1) + x*v(2) + v(3) < 0;
f11 = 1*v(1) + 1*v(2) + v(3) < 0;
if f00 && f01 && f10 && f11
y = 1;
else
y = 0;
end
|
github | Jane333/Mustererkennung-master | jana_ueb02.m | .m | Mustererkennung-master/ueb02/jana_ueb02.m | 4,312 | utf_8 | 945eaeb4d8aeb962f70910bbc09718dd | % AUFGABE 1 - Code. Loesung siehe unten.
function y = classifier() % h ist das zu klassifizierende Huhn
A = load('chickwts_training.csv'); % Spalte 1 = Huhn-ID, Spalte 2 = Gewicht, Spalte 3 = Futterklasse
A_Sorted = sortrows(A,2); % nach Gewichten sortieren
A_Training = A(:,2:3);
U = unique(A_Training,'rows');
... |
github | noureldien/TimeSeriesAnalysis-master | cond_indep_fisher_z.m | .m | TimeSeriesAnalysis-master/KalmanFilter/KPMstats/cond_indep_fisher_z.m | 3,647 | utf_8 | e3291330222ba7b37c56cc824decff44 | function [CI, r, p] = cond_indep_fisher_z(X, Y, S, C, N, alpha)
% COND_INDEP_FISHER_Z Test if X indep Y given Z using Fisher's Z test
% CI = cond_indep_fisher_z(X, Y, S, C, N, alpha)
%
% C is the covariance (or correlation) matrix
% N is the sample size
% alpha is the significance level (default: 0.05)
%
% See p133 of ... |
github | noureldien/TimeSeriesAnalysis-master | logistK.m | .m | TimeSeriesAnalysis-master/KalmanFilter/KPMstats/logistK.m | 7,253 | utf_8 | 9539c8105ebca14d632373f5f9f4b70d | function [beta,post,lli] = logistK(x,y,w,beta)
% [beta,post,lli] = logistK(x,y,beta,w)
%
% k-class logistic regression with optional sample weights
%
% k = number of classes
% n = number of samples
% d = dimensionality of samples
%
% INPUT
% x dxn matrix of n input column vectors
% y kxn vector of class assignment... |
github | noureldien/TimeSeriesAnalysis-master | multipdf.m | .m | TimeSeriesAnalysis-master/KalmanFilter/KPMstats/multipdf.m | 1,192 | utf_8 | 1fce56db4c9a59d35960bd25df11b1f9 | function p = multipdf(x,theta)
%MULTIPDF Multinomial probability density function.
% p = multipdf(x,theta) returns the probabilities of
% vector x, under the multinomial distribution
% with parameter vector theta.
%
% Author: David Ross
%--------------------------------------------------------
% Check the arg... |
github | noureldien/TimeSeriesAnalysis-master | subv2ind.m | .m | TimeSeriesAnalysis-master/KalmanFilter/KPMtools/subv2ind.m | 1,574 | utf_8 | e85d0bab88fc0d35b803436fa1dc0e15 | function ndx = subv2ind(siz, subv)
% SUBV2IND Like the built-in sub2ind, but the subscripts are given as row vectors.
% ind = subv2ind(siz,subv)
%
% siz can be a row or column vector of size d.
% subv should be a collection of N row vectors of size d.
% ind will be of size N * 1.
%
% Example:
% subv = [1 1 1;
% ... |
github | noureldien/TimeSeriesAnalysis-master | zipload.m | .m | TimeSeriesAnalysis-master/KalmanFilter/KPMtools/zipload.m | 1,611 | utf_8 | 67412c21b14bebb640784443e9e3bbd8 | %ZIPLOAD Load compressed data file created with ZIPSAVE
%
% [data] = zipload( filename )
% filename: string variable that contains the name of the
% compressed file (do not include '.zip' extension)
% Use only with files created with 'zipsave'
% pkzip25.exe has to be in the matlab path. This file is a compression ut... |
github | noureldien/TimeSeriesAnalysis-master | plot_ellipse.m | .m | TimeSeriesAnalysis-master/KalmanFilter/KPMtools/plot_ellipse.m | 507 | utf_8 | 3a27bbd5c1bdfe99983171e96789da6d | % PLOT_ELLIPSE
% h=plot_ellipse(x,y,theta,a,b)
%
% This routine plots an ellipse with centre (x,y), axis lengths a,b
% with major axis at an angle of theta radians from the horizontal.
%
% Author: P. Fieguth
% Jan. 98
%
%http://ocho.uwaterloo.ca/~pfieguth/Teaching/372/plot_ellipse.m
function h=plot_ellipse(x,... |
github | noureldien/TimeSeriesAnalysis-master | bipartiteMatchingHungarian.m | .m | TimeSeriesAnalysis-master/KalmanFilter/KPMtools/bipartiteMatchingHungarian.m | 2,593 | utf_8 | 983df7bc538a844b42427ae58d69c75b | % MATCH - Solves the weighted bipartite matching (or assignment)
% problem.
%
% Usage: a = match(C);
%
% Arguments:
% C - an m x n cost matrix; the sets are taken to be
% 1:m and 1:n; C(i, j) gives the cost of matching
% items i (of the first set) and j (of the se... |
github | noureldien/TimeSeriesAnalysis-master | conf2mahal.m | .m | TimeSeriesAnalysis-master/KalmanFilter/KPMtools/conf2mahal.m | 2,424 | utf_8 | 682226ca8c1183325f4204e0c22de0a7 | % CONF2MAHAL - Translates a confidence interval to a Mahalanobis
% distance. Consider a multivariate Gaussian
% distribution of the form
%
% p(x) = 1/sqrt((2 * pi)^d * det(C)) * exp((-1/2) * MD(x, m, inv(C)))
%
% where MD(x, m, P) is the Mahalanobis distance from x
% ... |
github | noureldien/TimeSeriesAnalysis-master | plotgauss2d.m | .m | TimeSeriesAnalysis-master/KalmanFilter/KPMtools/plotgauss2d.m | 4,119 | utf_8 | 1bf48827c7a74f224086a54411e4ac82 | function h=plotgauss2d(mu, Sigma)
% PLOTGAUSS2D Plot a 2D Gaussian as an ellipse with optional cross hairs
% h=plotgauss2(mu, Sigma)
%
h = plotcov2(mu, Sigma);
return;
%%%%%%%%%%%%%%%%%%%%%%%%
% PLOTCOV2 - Plots a covariance ellipse with major and minor axes
% for a bivariate Gaussian distribution.
%
% Us... |
github | noureldien/TimeSeriesAnalysis-master | zipsave.m | .m | TimeSeriesAnalysis-master/KalmanFilter/KPMtools/zipsave.m | 1,480 | utf_8 | cc543374345b9e369d147c452008bc36 | %ZIPSAVE Save data in compressed format
%
% zipsave( filename, data )
% filename: string variable that contains the name of the resulting
% compressed file (do not include '.zip' extension)
% pkzip25.exe has to be in the matlab path. This file is a compression utility
% made by Pkware, Inc. It can be dowloaded from... |
github | noureldien/TimeSeriesAnalysis-master | matprint.m | .m | TimeSeriesAnalysis-master/KalmanFilter/KPMtools/matprint.m | 1,020 | utf_8 | e92a96dad0e0b9f25d2fe56280ba6393 | % MATPRINT - prints a matrix with specified format string
%
% Usage: matprint(a, fmt, fid)
%
% a - Matrix to be printed.
% fmt - C style format string to use for each value.
% fid - Optional file id.
%
% Eg. matprint(a,'%3.1f') will print each entry to 1 decimal place
... |
github | noureldien/TimeSeriesAnalysis-master | plotcov3.m | .m | TimeSeriesAnalysis-master/KalmanFilter/KPMtools/plotcov3.m | 4,040 | utf_8 | 1f186acd56002148a3da006a9fc8b6a2 | % PLOTCOV3 - Plots a covariance ellipsoid with axes for a trivariate
% Gaussian distribution.
%
% Usage:
% [h, s] = plotcov3(mu, Sigma[, OPTIONS]);
%
% Inputs:
% mu - a 3 x 1 vector giving the mean of the distribution.
% Sigma - a 3 x 3 symmetric positive semi-definite matrix giving
% the... |
github | noureldien/TimeSeriesAnalysis-master | exportfig.m | .m | TimeSeriesAnalysis-master/KalmanFilter/KPMtools/exportfig.m | 30,663 | utf_8 | 838a8ee93ca6a9b6a85a90fa68976617 | function varargout = exportfig(varargin)
%EXPORTFIG Export a figure.
% EXPORTFIG(H, FILENAME) writes the figure H to FILENAME. H is
% a figure handle and FILENAME is a string that specifies the
% name of the output file.
%
% EXPORTFIG(H, FILENAME, OPTIONS) writes the figure H to FILENAME
% with options init... |
github | noureldien/TimeSeriesAnalysis-master | montageKPM.m | .m | TimeSeriesAnalysis-master/KalmanFilter/KPMtools/montageKPM.m | 2,917 | utf_8 | 3863707e80820a96eac8635dfccbbf11 | function h = montageKPM(arg)
% montageKPM is like the built-in montage, but assumes input is MxNxK or filenames
%
% Converts patches (y,x,i) into patches(y,x,1,i)
% Also, adds a black border aroudn them
if iscell(arg)
h= montageFilenames(arg);
else
nr = size(arg,1); nc = size(arg,2); Npatches = size(arg,3);
patc... |
github | noureldien/TimeSeriesAnalysis-master | optimalMatching.m | .m | TimeSeriesAnalysis-master/KalmanFilter/KPMtools/optimalMatching.m | 2,593 | utf_8 | 983df7bc538a844b42427ae58d69c75b | % MATCH - Solves the weighted bipartite matching (or assignment)
% problem.
%
% Usage: a = match(C);
%
% Arguments:
% C - an m x n cost matrix; the sets are taken to be
% 1:m and 1:n; C(i, j) gives the cost of matching
% items i (of the first set) and j (of the se... |
github | noureldien/TimeSeriesAnalysis-master | plotcov2.m | .m | TimeSeriesAnalysis-master/KalmanFilter/KPMtools/plotcov2.m | 3,013 | utf_8 | 4305f11ba0280ef8ebcad4c8a4c4013c | % PLOTCOV2 - Plots a covariance ellipse with major and minor axes
% for a bivariate Gaussian distribution.
%
% Usage:
% h = plotcov2(mu, Sigma[, OPTIONS]);
%
% Inputs:
% mu - a 2 x 1 vector giving the mean of the distribution.
% Sigma - a 2 x 2 symmetric positive semi-definite matrix giving
% ... |
github | noureldien/TimeSeriesAnalysis-master | ind2subv.m | .m | TimeSeriesAnalysis-master/KalmanFilter/KPMtools/ind2subv.m | 1,206 | utf_8 | 5c2e8689803ece8fca091e60c913809d | function sub = ind2subv(siz, ndx)
% IND2SUBV Like the built-in ind2sub, but returns the answer as a row vector.
% sub = ind2subv(siz, ndx)
%
% siz and ndx can be row or column vectors.
% sub will be of size length(ndx) * length(siz).
%
% Example
% ind2subv([2 2 2], 1:8) returns
% [1 1 1
% 2 1 1
% ...
% 2 2 2]
% ... |
github | noureldien/TimeSeriesAnalysis-master | process_options.m | .m | TimeSeriesAnalysis-master/KalmanFilter/KPMtools/process_options.m | 4,394 | utf_8 | 483b50d27e3bdb68fd2903a0cab9df44 | % PROCESS_OPTIONS - Processes options passed to a Matlab function.
% This function provides a simple means of
% parsing attribute-value options. Each option is
% named by a unique string and is given a default
% value.
%
% Usage: [var1, var2, ...... |
github | noureldien/TimeSeriesAnalysis-master | nonmaxsup.m | .m | TimeSeriesAnalysis-master/KalmanFilter/KPMtools/nonmaxsup.m | 1,708 | utf_8 | ad451680a9d414f907da2969e0809c22 | % NONMAXSUP - Non-maximal Suppression
%
% Usage: cim = nonmaxsup(im, radius)
%
% Arguments:
% im - image to be processed.
% radius - radius of region considered in non-maximal
% suppression (optional). Typical values to use might
% be 1-3. Default i... |
github | noureldien/TimeSeriesAnalysis-master | learn_kalman.m | .m | TimeSeriesAnalysis-master/KalmanFilter/Kalman/learn_kalman.m | 5,515 | utf_8 | d0a3eadd7f797f9383d3eaa4c716787b | function [A, C, Q, R, initx, initV, LL] = ...
learn_kalman(data, A, C, Q, R, initx, initV, max_iter, diagQ, diagR, ARmode, constr_fun, varargin)
% LEARN_KALMAN Find the ML parameters of a stochastic Linear Dynamical System using EM.
%
% [A, C, Q, R, INITX, INITV, LL] = LEARN_KALMAN(DATA, A0, C0, Q0, R0, INITX0, INI... |
github | noureldien/TimeSeriesAnalysis-master | dlmfit.m | .m | TimeSeriesAnalysis-master/dlmtbx/dlmfit.m | 9,293 | utf_8 | cbbf3715be73ddbf26e534bd928b26d4 | function out = dlmfit(y,s,wdiag,x0,C0, X, options)
%DLMFIT Fit DLM time series model
% Fits dlm time series model with local level, trend, seasonal, and proxies
% out = dlmfit(y,s,wdiag,x0,C0, X, options)
% Input:
% y time series, n*p
% s obs uncertainty, n*p or 1*1
% w sqrt of first diagonal entries of the model erro... |
github | noureldien/TimeSeriesAnalysis-master | meannan.m | .m | TimeSeriesAnalysis-master/dlmtbx/meannan.m | 659 | utf_8 | 39818015bad9cb2775051a8cecde31cd | function y=meannan(x,x2,w1)
%MEANNAN mean ignoring NaNs
% meannan(x) mean of columns of x
% meannan(oldmean, x, oldweight) updates oldmean with x
% $Revision: 1.2 $ $Date: 2011/06/22 13:28:01 $
if nargin == 1
[m,n] = size(x);
if m==1
x = x';
m = n;
n = 1;
end
y = zeros(1,n);
for i=1:n
y(... |
github | bewantbe/point-neuron-network-simulator-master | gen_neu.m | .m | point-neuron-network-simulator-master/mfile/gen_neu.m | 29,438 | utf_8 | 8312d0c4bd10d6fc6787db9fe4a06245 | % Neuron network simulator (interface to gen_neu).
% Can use cached data automatically.
%
% [X, isi, ras, pm, extra_data] = gen_neu(pm [, gen_cmd [, data_dir_prefix]])
%
% Usage example 1: % the items with default value are optional
% pm = [];
% %pm.prog_path = '../bin/gen_neu'; % path to the executable.
% p... |
github | bewantbe/point-neuron-network-simulator-master | save_network.m | .m | point-neuron-network-simulator-master/mfile/save_network.m | 3,181 | utf_8 | b95fc1e94c1a4d2c8a2975ca2274e69d | % Save the adjacency matrix `A' in path `path_prefix' with a hashed file name:
% matpath = save_network(A, path_prefix);
% To read the matrix from the file:
% A = load('-ascii', matpath);
function [matpath, matname] = save_network(A, path_prefix)
% sane test of the matrix A
if ~exist('A','var') || isempty(A) || (... |
github | bewantbe/point-neuron-network-simulator-master | get_neu_psp.m | .m | point-neuron-network-simulator-master/mfile/get_neu_psp.m | 3,976 | utf_8 | 2356f183b36b3a30a002d6f79500d316 | % Return an estimation of EPSP IPSP and the peak time.
% Usage:
% % Ex1:
% pm = [];
% pm.prog_path = '../bin/gen_neu';
% pm.neuron_model = 'HH-GH-cont-syn';
% PSP = get_neu_psp(pm)
%
% % Ex2:
% PSP = get_neu_psp('HH-GH-cont-syn')
function PSP = get_neu_psp(pm0)
[~, tmp_f_name] = fileparts(tempname(['.' filesep]... |
github | bewantbe/point-neuron-network-simulator-master | SpikeSlicing.m | .m | point-neuron-network-simulator-master/mfile/SpikeSlicing.m | 1,028 | utf_8 | 8efb14e33d7f724fb0e7442cf879c6fc | % Extract sections of sample points (of id_to) around spikes (of id_from)
% Usage example:
% [X_ref, ISI_ref, ras_ref] = gen_neu(pm, 'new,rm');
% t_range = 20; % -10 ~ 10 ms
% id_from = 1;
% id_to = 1;
% [Y, T] = SpikeSlicing(X_ref, ras_ref, pm.stv, id_to, id_from, t_range);
% plot(T', Y');
function [Y, T, id_s... |
github | bewantbe/point-neuron-network-simulator-master | get_network.m | .m | point-neuron-network-simulator-master/mfile/get_network.m | 1,470 | utf_8 | b6fa50eaaee6d19ce65eb81daba64b90 | % Get the adjacency matrix from its (file) name.
% Assume the matrix is stored in plain text file. Which essentially can be
% loaded by `network = load('-ascii', [netstr, '.txt'])'
% It will search current working dir (or path_prefix if specified) and
% dir of this function.
% It also possible to specifies the ful... |
github | bewantbe/point-neuron-network-simulator-master | SpikeTrains.m | .m | point-neuron-network-simulator-master/mfile/SpikeTrains.m | 590 | utf_8 | 2b158adba0cb4cefa77b33ac5049bc9b | % Output spike train from spike time data
function X = SpikeTrains(ras, p, len, stv, st_mode)
if any(ras(:,1)>p)
error('!! any(ras(:,1)>p)');
end
if ras(end, 2) >= stv*len || ras(1, 2) < 0
% Assume ras(:,2) is sorted.
error('!! ras(end, 2) >= stv*len || ras(1, 2) < 0');
end
if ~exist('st_mode', '... |
github | bewantbe/point-neuron-network-simulator-master | ras_pick.m | .m | point-neuron-network-simulator-master/mfile/ras_pick.m | 2,152 | utf_8 | e6fb0a9fed3bfcd6ae707185fda7bc28 | % Pick spike events in neuron set id_neu and time range t_range.
% id_neu list the neuron index wantted in a row.
% if id_neu has second row, further rewrite neuron index to it.
% t_range: length=1: select range [0 t_range]
% length=2: select range [t_range(1) t_range(2)] and subtract
% ... |
github | bewantbe/point-neuron-network-simulator-master | adj2dot.m | .m | point-neuron-network-simulator-master/mfile/adj2dot.m | 2,920 | utf_8 | 8021932945b833a07f0e0795dfda159f | % Use it like
% network_adjacency_matrix = rand(10)>0.4;
% file_name = 'aaaaa';
% adj2dot(network_adjacency_matrix, file_name);
%
% reference: http://graphviz.org
function adj2dot(network, basename, b_fix, node_name, node_color)
gheader0 = {
'digraph "G" {',
' rankdir = "LR"',
' node [',
' fontname = "A... |
github | bewantbe/point-neuron-network-simulator-master | ras_plot.m | .m | point-neuron-network-simulator-master/mfile/ras_plot.m | 671 | utf_8 | b8398254ffe17a28e6695ed0ccd2cf82 | % Plot raster (spiking events).
% Usage:
% hd = ras_plot(ras, true);
% set(hd, 'linewidth', 2);
% xlabel('time (ms)');
% ylabel('neuron id');
% To restore the old behaviour:
% hd = ras_plot(ras, t_bg, t_ed, id_neu, y_scale);
% Write this:
% hd = ras_plot( ras_pick(ras, id_neu, [t_bg, t_ed]) );
function hd ... |
github | bewantbe/point-neuron-network-simulator-master | gen_net_er.m | .m | point-neuron-network-simulator-master/mfile/gen_net_er.m | 408 | utf_8 | 1d1f1afb3a64e15e8b1851db63e10036 | % Generator for Erdős–Rényi random graphs
% https://en.wikipedia.org/wiki/Erd%C5%91s%E2%80%93R%C3%A9nyi_model
function net = gen_net_er(p, sparseness, seed)
if isstruct(p)
sparseness = p.sparseness;
seed = p.seed;
p = p.p;
end
s = randMT19937('state');
randMT19937('state', seed);
n... |
github | bewantbe/point-neuron-network-simulator-master | ReadDouble.m | .m | point-neuron-network-simulator-master/test/ReadDouble.m | 130 | utf_8 | 251fa0b9b6eb5a8b4e9eb36c94e1d631 | %
function X = ReadDouble(fname, p)
if p<=0
error('');
end
fid = fopen(fname);
X = fread(fid, [p Inf], 'double');
fclose(fid);
|
github | bewantbe/point-neuron-network-simulator-master | search_defect.m | .m | point-neuron-network-simulator-master/test/search_defect.m | 2,457 | utf_8 | d3a41a98cc4c2aa5861ae5790a46a09e | % Test consistency across versions
function search_defe(k_case)
addpath('../mfile');
maxabs = @(x) max(abs(x(:)));
path_tag_executable = '../bin/gen_neu';
session_random = char(randi(26, 1, 10) - 1 + 'a');
for seed = (1:2000)+2000*k_case
pm = [];
pm.prog_path = path_tag_executable;
pm.neuron_model = 'LI... |
github | bewantbe/point-neuron-network-simulator-master | read_compact_sparse_net.m | .m | point-neuron-network-simulator-master/test/test_if_jump/read_compact_sparse_net.m | 306 | utf_8 | 26e673da9717dda007ad7f127b6c9f6b | % Read compact sparse format
function w = read_compact_sparse_net(net_path)
nv=load(net_path);
p = find(nv - (length(nv)-1:-1:0)' == 0, 1) - 1;
cntj = nv(1:p+1);
wj = nv(p+2:end) + 1;
wi = zeros(size(wj));
for k = 1 : length(cntj)-1
wi(cntj(k)+1:cntj(k+1)) = k;
end
w = sparse(wj, wi, 1);
w(p,p) = 0;
|
github | bewantbe/point-neuron-network-simulator-master | matching_HH_cont.m | .m | point-neuron-network-simulator-master/test/HH_cont_match/matching_HH_cont.m | 796 | utf_8 | ec0faf712e235fdb876ca1c3f8c08894 | % s = 0.7145, 0.7278, 0.7261, 0.7369 pr=0.2*2:0.7545
% d = 0.34, 0.3371, 0.3343
function v = toMin1(d, s)
clear('pm');
pm.prog_path = '../bin/gen_neu';
pm.net = ones(15);
pm.nI = 0;
% 0.746/10;%delay=0.34 % 0.7145/10;% HH-GH simple % 1.3/10; % cont-syn
%pm.scee = 0.7145/10;
pm.scie = 0.00;
pm.scei = 0.00... |
github | asheshjain399/GraphicalModels-master | hmmFit.m | .m | GraphicalModels-master/HMM/hmmFit.m | 8,091 | utf_8 | d613aa07bf26ffd42230bf7090aec27a | %% Implementation of HMM for both discrete and continuous output
% Author: Ashesh Jain
% Email: ashesh@cs.cornell.edu
function [model,ll] = hmmFit( truemodel, data, model, numiter )
% Input descriptions:
% model
% type: 'discrete' or 'gauss'. Use 'discrete' for discrete observations and 'gauss' for continuous observa... |
github | asheshjain399/GraphicalModels-master | initializeHMMmodel.m | .m | GraphicalModels-master/HMM/initializeHMMmodel.m | 1,081 | utf_8 | ca9fa9c69046b5e0e8139b7d8cde8b97 | function model = initializeHMMmodel(type,nstates,ostates)
model.nstates = nstates;
model.type = type;
if strcmp(type,'discrete')
model.ostates = ostates;
model.pi = (1.0/nstates)*ones(nstates,1);
model.A = rand(nstates,nstates);
model.A = model.A./repmat(sum(model.A,2),1,nsta... |
github | asheshjain399/GraphicalModels-master | hmmDemo.m | .m | GraphicalModels-master/HMM/hmmDemo.m | 2,160 | utf_8 | 3736f854854fc1977b9cdf90d96218c4 | function model = hmmDemo( )
clc
model.type = 'gauss';
%% Demo for discrete case
if strcmp(model.type,'discrete')
model.nstates = 4;
model.ostates = 6;
model.B =[1/6 1/6 1/6 1/6 1/6 1/6 ;
1/10 1/10 1/10 1/10 1/10 5/10 ;
2/6... |
github | asheshjain399/GraphicalModels-master | IOhmmFit.m | .m | GraphicalModels-master/IOHMM/IOhmmFit.m | 15,072 | utf_8 | 5cefad0883c336d8aa5327bf959deba0 | %% Implementation of Input-Output HMM
% Author: Ashesh Jain
% Email: ashesh@cs.cornell.edu
function [model,ll] = IOhmmFit( truemodel, data, inputObs, model, numiter)
% Input descriptions:
% model
% type: 'discrete' or 'gauss'. Use 'discrete' for discrete observations and 'gauss' for continuous observations
% nstates: ... |
github | asheshjain399/GraphicalModels-master | AIOhmmFit.m | .m | GraphicalModels-master/AIOHMM/AIOhmmFit.m | 17,313 | utf_8 | 3e237a396f3fab70c169653172f25934 | %% Implementation of Autoregressive Input-Output HMM
% Author: Ashesh Jain
% Email: ashesh@cs.cornell.edu
function [model,ll] = AIOhmmFit( truemodel, data, inputObs, model, numiter )
% Input descriptions:
% model
% type: 'discrete' or 'gauss'. Use 'discrete' for discrete observations and 'gauss' for continuous observa... |
github | asheshjain399/GraphicalModels-master | minimizeFunc.m | .m | GraphicalModels-master/minFunc/minimizeFunc.m | 1,133 | utf_8 | de9a8350faf374958add4d0a578e5df2 | function [ minObj,parameters ] = minimizeFunc (funcObj,funcGrad,parameters,data )
%parameters = [0;0;-1];
%funcGrad = @gradient;
%funcObj = @objective;
THRESH = 0.4;
grad = funcGrad(parameters,data);
startObj = funcObj(parameters,data);
minObj = startObj;
numiter = 1;
while no... |
github | ShangruZhong/Firefly_Algorithm_WSN-master | ffa_wsn.m | .m | Firefly_Algorithm_WSN-master/ffa_wsn.m | 2,579 | utf_8 | e2ab0a4d825a53f2581eff5f22141ecd | function [nxbest,nybest,fbest,NumEval,maxzn]...
= ffa_wsn(u0, Lb, Ub, para,q)
%% Check input parameters (otherwise set as default values)
%if nargin<6,
%para=[20 150 0.25 0.20 1];
%end
%if nargin<5, Ub=[]; end
%if nargin<4, Lb=[]; end
%if nargin<3,
%disp('Usuage: FA_wsn(u0,Lb,Ub,para)');
%end
% n=number of ... |
github | ShangruZhong/Firefly_Algorithm_WSN-master | firefly_simple.m | .m | Firefly_Algorithm_WSN-master/source code/firefly_simple.m | 3,527 | utf_8 | e8afa47369b19c8355008e535c71e549 | % ======================================================== %
% Files of the Matlab programs included in the book: %
% Xin-She Yang, Nature-Inspired Metaheuristic Algorithms, %
% Second Edition, Luniver Press, (2010). www.luniver.com %
% ======================================================== %
% Firefly... |
github | ShangruZhong/Firefly_Algorithm_WSN-master | alpha_new.m | .m | Firefly_Algorithm_WSN-master/source code/alpha_new.m | 373 | utf_8 | 245ba35828ebbc20f5399bf645924afe | % This function is optional, as it is not in the original FA
% The idea to reduce randomness is to increase the convergence,
% however, if you reduce randomness too quickly, then premature
% convergence can occur. So use with care.
function alpha=alpha_new(alpha,NGen)
% alpha_n=alpha_0(1-delta)^NGen=0.005
% alpha_0=0.9... |
github | ShangruZhong/Firefly_Algorithm_WSN-master | fa_mincon.m | .m | Firefly_Algorithm_WSN-master/source code/fa_mincon.m | 7,748 | utf_8 | 00c13174781b1a7897baf2b12e6d3edc | % ======================================================== %
% Files of the Matlab programs included in the book: %
% Xin-She Yang, Nature-Inspired Metaheuristic Algorithms, %
% Second Edition, Luniver Press, (2010). www.luniver.com %
% ======================================================== %
% -------... |
github | autism-research-centre/matlabGeneral-master | savefiles.m | .m | matlabGeneral-master/savefiles.m | 996 | utf_8 | 6846d6d0dbdc6e3e6b5bff977c2f9753 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%
%% StoreFiles
%%
%% Load files with a specific suffix and store them in a mat-file
%%
%% © R.A.I. Bethlehem 2012
%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [] = rb_StoreFiles(Dir,Suffix)
% check... |
github | autism-research-centre/matlabGeneral-master | plotUnifiedCompass.m | .m | matlabGeneral-master/plotUnifiedCompass.m | 869 | utf_8 | 1af4b39a13f787a62421617bfcd0e653 | % Matlab seems to have some issues with drawing compass plot and then
% afterwards changing the axes limits, this is a 'simple' work-around.
%
% First we draw a fake compass-plot with the desired axes sizes, then we
% draw the real one, then remove the fake one
%
%% © R.A.I. Bethlehem 2013
function [] = drawUnifiedCo... |
github | autism-research-centre/matlabGeneral-master | signalDetection.m | .m | matlabGeneral-master/signalDetection.m | 763 | utf_8 | 03177f595b13645a1cceb68f628ced9c | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Matlab analyses function for calculating signal detection theorie values
% from both z-scores and regular scores
%
% Takes in two single vector arrays with equal dimensions, hits and false
% alarms
%
%% © R.A.I. Bethlehem 2012
%
%%%%%%%%%%%%%... |
github | autism-research-centre/matlabGeneral-master | circlePlot.m | .m | matlabGeneral-master/circlePlot.m | 1,299 | utf_8 | e5519e8e6aaf7f3aab75d70d059c8ca5 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Matlab function to plot 3 vectors as a circular plot using griddata
%
%% © R.A.I. Bethlehem 2012
%
% Version History:
%
% Richard Bethlehem
% 02-09-2012: Created basic structure & experimented with pcolor functionality for plotting
% 04-09-20... |
github | autism-research-centre/matlabGeneral-master | plotBatch.m | .m | matlabGeneral-master/plotBatch.m | 1,551 | UNKNOWN | 7da1d00c04bc04474b18253d3196df87 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Function to plot matrices with subject by data structure
% Plots all subjects data seperately
%
%% � R.A.I. Bethlehem 2012
%
% Version History:
%
% 01-08-2012: Basic structure and functionality
% 14-08-2012: Randomized line colors
% 15-08-201... |
github | DXI-Ltd/PjSIP-master | echo_diagnostic.m | .m | PjSIP-master/pjsip/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 | fry-/octave_unique-master | unique_stable.m | .m | octave_unique-master/unique_stable.m | 11,277 | utf_8 | 873096446361e6df54b684d05a930da5 | <<<<<<< HEAD
function [C, ia, ic] = unique_stable (A, varargin)
if (nargin < 1)
print_usage ();
elseif (!(isnumeric (A) || islogical (A) || ischar (A) || iscellstr (A)))
error ("unique: A must be an array or cell array of strings");
endif
if (nargin > 1)
## parse options
if (! iscell... |
github | fry-/octave_unique-master | unique_octave_orig.m | .m | octave_unique-master/unique_octave_orig.m | 7,256 | utf_8 | 730df144d5f84e0362c332c7f1ccd825 | ## Copyright (C) 2000-2015 Paul Kienzle
## Copyright (C) 2008-2009 Jaroslav Hajek
##
## This file is part of Octave.
##
## Octave is free software; you can redistribute it and/or modify it
## under the terms of the GNU General Public License as published by
## the Free Software Foundation; either version 3 of the Licen... |
github | tangxifan/tangxifan-eda-tools-master | cal_transfer_func.m | .m | tangxifan-eda-tools-master/trunk/DTT/SRC/cal_transfer_func.m | 2,658 | utf_8 | 250b4144f028f1c56bdac8831dc9cf4b |
%% Cal_transfer_func.m
% Author : Xifan TANG
% Organization: EPFL
% Description : The denominator of each sink node should be same.
% Actually, they share the common denominator of
% the source node.
% Then with the given working frequency,
% the coefficients ... |
github | tangxifan/tangxifan-eda-tools-master | check_tree.m | .m | tangxifan-eda-tools-master/trunk/DTT/SRC/check_tree.m | 2,737 | utf_8 | 02e72af4ee61916718d1f2fa9157cbb5 |
%% check_tree.m
% Check the tree :
% 1. Check all the sinks are connected to source
% 2. Check whether there is any loop in the path from sinks to source
%
% Author : Xifan TANG
% LSI, EPFL
%%
function [ stree ] = check_tree(stree,srcidx,debug)
disp(['Checking paths from sinks to source and Loop...'])... |
github | tangxifan/tangxifan-eda-tools-master | rm_inverter.m | .m | tangxifan-eda-tools-master/trunk/DTT/SRC/rm_inverter.m | 2,840 | utf_8 | dd77959364c69be19c35aa9bfbdd60bd |
%% ----------------------------%
% rm_inverter.m %
% remove inverter from node %
% Author : Xifan TANG %
% LSI, EPFL %
%------------------------------%
%% function rm_inverter
% With given node list, remove inverters from these nodes
function [ stree ] = rm_inverter(stre... |
github | tangxifan/tangxifan-eda-tools-master | add_lc_bank.m | .m | tangxifan-eda-tools-master/trunk/DTT/SRC/add_lc_bank.m | 3,368 | utf_8 | 109d21528c3757bd7dcb8e5c61837096 |
%% add_lc_bank.m
% Add LC Tanks
% Author: Xifan TANG
% LSI,EPFL
%
function [ stree ] = add_lc_bank( stree,n,nidxes,rls,lls,cls,debug )
% Add LC banks with given node list
disp(['Adding Dircect LC banks...'])
for i = 1:n
if (debug == 1)
disp(['Add Direct LC bank(NodeIndex=' num2str(nidxes(i... |
github | tangxifan/tangxifan-eda-tools-master | build_binary_tree.m | .m | tangxifan-eda-tools-master/trunk/DTT/SRC/build_binary_tree.m | 5,618 | utf_8 | 4ae92c3ed5cc1edcb40e866ae36e963f | %%
% Build Binary Tree based on Benchmark
%
% Author: Xifan TANG
% LSI,EPFL
%
%% A few new nodes are created in this function for binary tree generation
% All the new nodes are stored in structure stree.
function [ stree ] = build_binary_tree( stree,nodes,debug )
% print statistics for debugging
nu... |
github | tangxifan/tangxifan-eda-tools-master | rm_rinser_lc_bank.m | .m | tangxifan-eda-tools-master/trunk/DTT/SRC/rm_rinser_lc_bank.m | 3,616 | utf_8 | e4c24f346fea7214b8712359d12fdf82 |
%% rm_rinser_lc_bank.m
% Remove LC Banks
% Author: Xifan TANG
% LSI,EPFL
%
function [ stree ] = rm_rinser_lc_bank( stree,dirlst,indirlst,debug )
% Remove LC banks with given node list
[ stree ] = rm_lc_bank(stree,length(dirlst),dirlst,debug);
disp(['Removing Indirect LC banks...'])
for i = length(... |
github | tangxifan/tangxifan-eda-tools-master | new_build_tree.m | .m | tangxifan-eda-tools-master/trunk/DTT/SRC/new_build_tree.m | 4,697 | utf_8 | cfb6f330022150d98d87c365117bc811 |
%% new_build_tree.m
% Build the Tree based on Benchmark
% A NEW Version !
% Author: Xifan TANG
% LSI,EPFL
%
%%
function [ stree ] = new_build_tree(nodes,sinks,wire,wires,debug)
% unit of Capaciance and Resistance
% Please refer the the reference/explain_format.txt
% where define the unit of res... |
github | tangxifan/tangxifan-eda-tools-master | add_inverter.m | .m | tangxifan-eda-tools-master/trunk/DTT/SRC/add_inverter.m | 2,851 | utf_8 | 42d9f9458604774a365655204b01ee88 |
%% ------------------------%
% add_inverter.m %
% Add Inverter to nodes %
% Author : Xifan TANG %
% LSI, EPFL %
%--------------------------%
%% function add_inverter
% With given node list, add inverters to these specific nodes
function [ stree ] = add_inverter( stree,n,nidxes,lci,lr... |
github | tangxifan/tangxifan-eda-tools-master | cal_input_impedance.m | .m | tangxifan-eda-tools-master/trunk/DTT/SRC/cal_input_impedance.m | 2,037 | utf_8 | 7ae403e1d029213b3ed2825866ffca46 | %% Calculate the Input Impedance of Tree with given frequency
function [ stree ] = cal_input_impedance( stree,srcidx,freq,debug )
% With given frequency, the impedance of each branch of tree can be
% determined.
% Condition A:
% For each sink node composed of R,L and, C, the impedance is
% R+jwL+1/(jwC).... |
github | tangxifan/tangxifan-eda-tools-master | full_rinsert_lc_bank.m | .m | tangxifan-eda-tools-master/trunk/DTT/SRC/full_rinsert_lc_bank.m | 456 | utf_8 | d4755f4c079aadb04623108299d1c9a6 | %% Insert LC Banks to specific node whose resistances meet the given threshold
function [ stree ] = full_rinsert_lc_bank(stree,rthres,dircands,dirlen,dirr,dirl,dirc,indircands,indirlen,indirr,indirl,indirc,debug)
%% Insert the LC banks to candidates
%% Direct Adds
[ stree ] = add_lc_bank(stree,dirlen,dircands,dir... |
github | tangxifan/tangxifan-eda-tools-master | rm_rin.m | .m | tangxifan-eda-tools-master/trunk/DTT/SRC/rm_rin.m | 1,973 | utf_8 | 13f877a7591cd0b93d2c8dd6dc74c951 |
%% ----------------------------%
% rm_rin.m %
% remove input resistance %
% Author : Xifan TANG %
% LSI, EPFL %
%------------------------------%
%% function rm_rin
% With given node list, remove inverters from these nodes
function [ stree newsrc] = rm_rin(stree,sr... |
github | tangxifan/tangxifan-eda-tools-master | rinsert_lc_bank.m | .m | tangxifan-eda-tools-master/trunk/DTT/SRC/rinsert_lc_bank.m | 3,667 | utf_8 | 58d3e69d2805f65b894e1ecb92a36665 |
%% rinsert_lc_bank.m
% Add LC Tanks
% Author: Xifan TANG
% LSI,EPFL
%
function [ stree ] = rinsert_lc_bank(stree,indirlen,indircands,rthres,indirr,indirl,indirc,debug)
% Add LC banks with given node list
disp(['Adding Indirect LC banks...'])
for i = 1:indirlen
if (debug == 1)
disp(['Add In... |
github | tangxifan/tangxifan-eda-tools-master | truncate_zeros_array.m | .m | tangxifan-eda-tools-master/trunk/DTT/SRC/truncate_zeros_array.m | 275 | utf_8 | 38e96c3555951238e849dfc6c0fd6857 | %% Function Truncate the useless zeros at the end of array
function [ array_out ] = truncate_zeros_array(array_in)
zero_flag = (array_in ~= 0);
nonzero_idx = strfind(zero_flag,1);
final_index = max(nonzero_idx);
array_out(1:final_index) = array_in(1:final_index);
end |
github | tangxifan/tangxifan-eda-tools-master | add_rin.m | .m | tangxifan-eda-tools-master/trunk/DTT/SRC/add_rin.m | 1,809 | utf_8 | 88e19842a339c016dd8b336a37c36643 |
%% ------------------------%
% add_rin.m %
% Add Rin to source node %
% Author : Xifan TANG %
% LSI, EPFL %
%--------------------------%
%% function add_rin
% With given source node, add input resistance Rin
function [ stree newnode] = add_rin( stree,srcidx,rin,debug)
% Actually, ... |
github | tangxifan/tangxifan-eda-tools-master | dtt.m | .m | tangxifan-eda-tools-master/trunk/DTT/SRC/dtt.m | 4,804 | utf_8 | b818e899f1af2c8eaf229818c611c164 | %% dtt.m
% Direct Truncation of the Transfer Function(DTT)
% on Clock Tree Structure
%
% Author: Xifan TANG
% LSI,EPFL
%
function [ stree ] = dtt( srcidx,stree,debug )
disp(['DTT Start!(Source Index=' num2str(srcidx) ')'])
disp(['Clear all the d,m,n'])
[ stree ] = clear_dml(stree);
disp(['Calc... |
github | tangxifan/tangxifan-eda-tools-master | rm_lc_bank.m | .m | tangxifan-eda-tools-master/trunk/DTT/SRC/rm_lc_bank.m | 3,713 | utf_8 | 9afe97a6e9cb62c43507d5adedcb2299 |
%% rm_lc_bank.m
% Remove LC Banks
% Author: Xifan TANG
% LSI,EPFL
%
function [ stree ] = rm_lc_bank( stree,n,nidxes,debug )
% Remove LC banks with given node list
disp(['Removing Direct LC banks...'])
for i = n:-1:1
if (debug == 1)
disp(['Remove Direct LC bank(NodeIndex=' num2str(nidxes(i)... |
github | tangxifan/tangxifan-eda-tools-master | rdfs_binary_tree.m | .m | tangxifan-eda-tools-master/trunk/DTT/SRC/rdfs_binary_tree.m | 798 | utf_8 | a65a7ba5729b3e2f6b508e0bc5851c77 | %% Calculate the accumulated resistance for each node
function [ stree ] = rdfs_binary_tree( stree,srcidx,rsum,debug)
% Depth-First Search the Tree use recursive algorithm.
if (stree.bnode(srcidx).rtree.nidx == -1)
stree.bnode(srcidx).accumr = stree.bnode(srcidx).r+rsum;
else
rcuridx = stree.bnode(srcidx... |
github | tangxifan/tangxifan-eda-tools-master | get_candidate_nodes.m | .m | tangxifan-eda-tools-master/trunk/DTT/SRC/get_candidate_nodes.m | 1,184 | utf_8 | c84ed96c85cbca13ce7f8861ebe74f5f | %% Determine the candidates which LC banks would be inserted
function [ direct_lst indirect_lst ] = get_candidate_nodes(stree,nodes,rthres,debug)
numnode = size(nodes);
direct_num = 0;
indirect_num = 0;
for ind = 1:numnode(1)
nidx = nodes(ind,1);
if (stree.bnode(nidx).accumr == rthres)
direct_nu... |
github | tangxifan/tangxifan-eda-tools-master | full_dtt.m | .m | tangxifan-eda-tools-master/trunk/DTT/SRC/full_dtt.m | 6,574 | utf_8 | d528a54a744c4c5e2cec03bdee1e90eb | %% ------------------------%
% full_dtt.m %
% FLow of DTT Algorithm %
% Author : Xifan TANG %
% LSI, EPFL %
%--------------------------%
%% Function of full dtt
% Description : a flow of DTT, calculate DTT with each source,
% then check the results simply. Update the De... |
github | gkioxari/ActionTubes-master | rcnn_cache_fc7_features_jhmdb.m | .m | ActionTubes-master/extract_features/rcnn_cache_fc7_features_jhmdb.m | 5,172 | utf_8 | 44ee4d572f9491ab2d5b88062a89a7a3 | function rcnn_cache_fc7_features_jhmdb(varargin)
% PARAMETERS
% type : 'spatial' or 'motion'
% split : split for JHMDB
% target : 1 or 2 for train or test
% net_def_file : prototxt for feature extraction
% net_file : caffemodel
% output_dir : directory to store features
% img_dir : dire... |
github | gkioxari/ActionTubes-master | get_ROC_curve_JHMDB.m | .m | ActionTubes-master/evaluate/get_ROC_curve_JHMDB.m | 5,007 | utf_8 | 90b416f45d6e79728668d69dd8b0a64d | function output = get_ROC_curve_JHMDB(annot,tubes,actions,iou_thresh,draw)
% ---------------------------------------------------------
% Copyright (c) 2015, Georgia Gkioxari
%
% This file is part of the Action Tubes code and is available
% under the terms of the Simplified BSD License provided in
% LICENSE. Please r... |
github | gkioxari/ActionTubes-master | compute_tubes.m | .m | ActionTubes-master/train_svm/compute_tubes.m | 3,865 | utf_8 | 11141eae24003a946cf79201b0c9c4a7 | function tubes = compute_tubes(split, annot, rcnn_model, varargin)
% AUTORIGHTS
% ---------------------------------------------------------
% Copyright (c) 2015, Georgia Gkioxari
%
% This file is part of the Action Tubes code and is available
% under the terms of the Simplified BSD License provided in
% LICENSE. Ple... |
github | gkioxari/ActionTubes-master | zero_jump_link.m | .m | ActionTubes-master/train_svm/zero_jump_link.m | 3,252 | utf_8 | 49574c7dc7a3a31fd0893862c092867f | function paths = zero_jump_link(frames)
% AUTORIGHTS
% ---------------------------------------------------------
% Copyright (c) 2015, Georgia Gkioxari
%
% This file is part of the Action Tubes code and is available
% under the terms of the Simplified BSD License provided in
% LICENSE. Please retain this notice and ... |
github | gkioxari/ActionTubes-master | train_jhmdb.m | .m | ActionTubes-master/train_svm/train_jhmdb.m | 16,058 | utf_8 | 546bfd6bce3661dfead4a3a78486ce8b | function rcnn_model = train_jhmdb(split, annot, varargin)
% AUTORIGHTS
% ---------------------------------------------------------
% Copyright (c) 2014, Ross Girshick
%
% This file is part of the R-CNN code and is available
% under the terms of the Simplified BSD License provided in
% LICENSE. Please retain this no... |
github | gkioxari/ActionTubes-master | get_motion_salient_boxes.m | .m | ActionTubes-master/motion_saliency/get_motion_salient_boxes.m | 3,957 | utf_8 | 6054b1c47b23534a04cdc2ee5912af68 | function MotionSalBoxes = get_motion_salient_boxes(varargin)
% PARAMETERS
% annot : JHMDB annotations (jhmdb_annot.mat)
% split : split for JHMDB
% target : 1 or 2 or [] for train or test or all
% ss_dir : directory of selective search boxes
% flow_dir : directory containing optical flow
... |
github | densilcabrera/aarae-source-old---please-use-aarae-for-current-repository--master | aarae.m | .m | aarae-source-old---please-use-aarae-for-current-repository--master/aarae.m | 276,590 | utf_8 | ac8dd8ed7eb06d5871aabf1a21fec77a | % DO NOT EDIT THIS INITIALIZATION FUNCTION!!!!!!!!!!!!!!!!!!!!!!!!!!!
function varargout = aarae(varargin)
% AARAE MATLAB code for aarae.fig
% AARAE, by itself, creates a new AARAE or raises the existing
% singleton*.
%
% H = AARAE returns the handle to a new AARAE or the handle to
% the existing si... |
github | densilcabrera/aarae-source-old---please-use-aarae-for-current-repository--master | reduce_to_width.m | .m | aarae-source-old---please-use-aarae-for-current-repository--master/Utilities/reduce_to_width.m | 4,302 | utf_8 | cd2471e89886807280c9fe920b0b8a32 | function [x_reduced, y_reduced] = reduce_to_width(x, y, width, lims)
% [x_reduced, y_reduced] = reduce_to_width(x, y, width, lims)
%
% (This function is primarily used by LinePlotReducer, but has been
% provided as a stand-alone function outside of that class so that it can
% be used potentially in other proj... |
github | densilcabrera/aarae-source-old---please-use-aarae-for-current-repository--master | eartest.m | .m | aarae-source-old---please-use-aarae-for-current-repository--master/Utilities/eartest/eartest.m | 8,374 | utf_8 | 85e6b61dc76e33e10d49a65a91dac284 | % % % The ear has a non-flat frequency response. This means that tones played
% % % at the same volume with different frequencies can sound like they are
% % % being played at different volume levels. So you can hear some tones
% % % easier than others just based on the way the ear is made and its response
% % % to... |
github | densilcabrera/aarae-source-old---please-use-aarae-for-current-repository--master | notBoxPlot.m | .m | aarae-source-old---please-use-aarae-for-current-repository--master/Utilities/notBoxPlot/notBoxPlot.m | 7,288 | utf_8 | 440dfda581d52a19fe9ba4f225d6013a | function varargout=notBoxPlot(y,x,jitter,style,mainaxes)
% notBoxPlot - Doesn't plot box plots!
%
% function notBoxPlot(y,x,jitter,style)
%
%
% Purpose
% An alternative to a box plot, where the focus is on showing raw
% data. Plots columns of y as different groups located at points
% along the x axis defined by the opt... |
github | densilcabrera/aarae-source-old---please-use-aarae-for-current-repository--master | pulsegen_vis.m | .m | aarae-source-old---please-use-aarae-for-current-repository--master/Generators/Pulses/pulsegen/pulsegen_vis.m | 13,296 | utf_8 | c726358da9db3815b918c1d00f2da1da | function varargout = pulsegen_vis(varargin)
% PULSEGEN_VIS MATLAB code for pulsegen_vis.fig
% PULSEGEN_VIS, by itself, creates a new PULSEGEN_VIS or raises the existing
% singleton*.
%
% H = PULSEGEN_VIS returns the handle to a new PULSEGEN_VIS or the handle to
% the existing singleton*.
%
% PU... |
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