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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 |
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github | qxcv/comp2560-master | visualizeskeleton.m | .m | comp2560-master/thirdparty/yang-ramanan-2011/code-full/visualization/visualizeskeleton.m | 5,684 | utf_8 | 39e2b8729720405dba8a58449462fea2 | function visualizeskeleton(model)
bs = 4;
% assuming only one component
c = model.components{1};
numparts = length(c);
Nmix = zeros(1,numparts);
for k = 1:numparts
Nmix(k) = length(c(k).filterid);
end
ovec = [0 1 0 -1; 1 0 -1 0];
I = zeros(numparts,size(ovec,2));
for k = 2:numparts
part = c(k);
anchor = zer... |
github | qxcv/comp2560-master | visualise_skeleton.m | .m | comp2560-master/poster/figures/visualise_skeleton.m | 1,162 | utf_8 | ead0d788efede09109a42c2c6550eaae | % Displays skeletons from boxes (spooky)
function visualise_skeleton(img, boxes, max_to_save)
parent = [0 1 2 3 4 5 6 3 8 9 2 11 12 13 14 11 16 17];
if nargin < 3
max_to_save = 1;
end
for i = 1:length(parent)
x1(:,i) = boxes(:,1+(i-1)*4);
y1(:,i) = boxes(:,2+(i-1)*4);
x2(:,i) = boxes(:,3+(i-1)*4);
... |
github | kaldi-asr/kaldi-master | stoi_estoi_sdr.m | .m | kaldi-master/egs/chime4/s5_1ch/local/stoi_estoi_sdr.m | 2,387 | utf_8 | 6ccaf17b52e136af470f21a451e8a010 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Copyright 2017 Johns Hopkins University (Author: Aswin Shanmugam Subramanian)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function stoi_estoi_sdr(nj,enhancement_method,destination_directory,set)
... |
github | kaldi-asr/kaldi-master | Generate_mcTrainData_cut.m | .m | kaldi-master/egs/reverb/s5/local/Generate_mcTrainData_cut.m | 7,191 | utf_8 | 8249e376ded707283d0e27954204a774 | function Generate_mcTrainData_cut(WSJ_dir_name, save_dir)
%
% Input variables:
% WSJ_dir_name: string name of WAV file directory converted from original wsjcam0 SPHERE files
% (*Directory structure for wsjcam0 corpus to be kept as it is after obtaining it from LDC.
% Otherwise th... |
github | fau-fablab/docs-master | rpm.m | .m | docs-master/rpm.m | 251 | windows_1250 | f4b9e030b1d7a88e64a5c60ed03c9d18 | %% funktion zur Errechnung von Drehzahlen aus VC d und Zahnzahl
function n = rpm(vc, d, z)
% vc = pi * d * n * z (vc in Meter/min)
% mit den Variablen:
% d = Durchmesser (in mm)
% n = Drehzahl (in upm)
% z = Zähnezahl
n = vc / ( pi * d*10^-3 * z); |
github | anzezupanic/FC_analysis-master | function_mmdTestBoot.m | .m | FC_analysis-master/function_mmdTestBoot.m | 3,403 | utf_8 | c09e6db6773142e9e0a560cdb2294a00 | % function_mmdTestBoot.m
% Maximum mean discrepancy multivariate two sample test
% for testing whether two multivariate distributions are different. It
% gives good results for testing the difference between distribution with
% low number of samples and high dimensionality, but also
% for distributions with high sampl... |
github | danstowell/code_GLM-master | fitSumOfSplines.m | .m | code_GLM-master/tools_splines/fitSumOfSplines.m | 3,513 | utf_8 | aee8cdcb73d683d6ffdd02362ed2ad0a | function [ff,splfuns] = fitSumOfSplines(Y,X,splineStruct);
% [ff,splfuns] = fitSumOfSplines(Y,X,breaks,smoothness,extrapDeg);
%
% Fit parameters for spline functions f1, f2, f3, ....
% in order to fit: Y = f1(X(:,1)) + f2(X(:,2)) + f3(X(:,3) + ...
% via least-squares regression
%
% Inputs: Y - dependent variable (co... |
github | danstowell/code_GLM-master | fitSplinePos_LogErr.m | .m | code_GLM-master/tools_splines/fitSplinePos_LogErr.m | 1,020 | utf_8 | 3f1a4ead299f7773e794288fa464cd77 | function [fun,Mspline,splinePrs] = fitSplinePos_LogErr(knots, x, y, smoothness, extrapDeg);
% [fun,Mspline,splinePrs] = fitSplinePos(knots, x, y, smoothness, extrapDeg);
%
% Fit a function y = f(x) with a (strictly positive) spline defined using a set of knots
% (discontinuities of a piecewise-polynomial function... |
github | danstowell/code_GLM-master | fitSplineLNP2.m | .m | code_GLM-master/tools_splines/fitSplineLNP2.m | 1,794 | utf_8 | 63def03ff4674ae92fbd476a2296947c | function [fun,pp,Mspline,splinePrs,fval] = fitSplineLNP(knots, xx, spnds, smoothness, extrapDeg,prs0,minval);
% [fun,pp,Mspline,splinePrs] = fitSplineLNP(knots, x, y, smoothness, extrapDeg);
%
% Fit a nonlinear function in an LNP neuron with a (strictly
% positive) spline defined using a set of knots
% (discon... |
github | danstowell/code_GLM-master | fitSpline.m | .m | code_GLM-master/tools_splines/fitSpline.m | 1,356 | utf_8 | 85d2f2463db6b3f4de32367fa6c96e22 | function [fun,pp,Mspline,splinePrs]=fitSpline(knots,x,y,smoothness,extrapDeg);
% [fun,pp,Mspline,splinePrs]=fitSpline(knots,x,y,smoothness,extrapDeg);
%
% Fit a function y = f(x) with a spline defined using a set of knots
% (discontinuities of a piecewise-polynomial function), using MSE loss
%
% Inputs:
% kn... |
github | danstowell/code_GLM-master | fitSplinePos.m | .m | code_GLM-master/tools_splines/fitSplinePos.m | 1,578 | utf_8 | a752ec24b548f05b9be2a16c75071399 | function [fun,pp,Mspline,splinePrs]=fitSplinePos(knots,x,y,smoothness,extrapDeg,minval);
% [fun,pp,Mspline,splinePrs]=fitSplinePos(knots,x,y,smoothness,extrapDeg);
%
% Fit a function y = f(x) with a (strictly positive) spline
% defined using a set of knots
% (discontinuities of a piecewise-polynomial function)... |
github | danstowell/code_GLM-master | formSplineFunHandles.m | .m | code_GLM-master/tools_splines/formSplineFunHandles.m | 1,989 | utf_8 | 10842842a2158364c4234ff12f34fd64 | function [ff,splfuns] = formSplineFunHandles(prs,splineStruct);
% [ff,splfuns] = formSplineFunHandles(paramvec,splineStruct);
%
% Re-insert reduced parameters into a piecewise polynomial struct (e.g.,
% after fitting a sum of splines to data).
%
% Inputs: Y - dependent variable (column vector)
% X - indep vari... |
github | danstowell/code_GLM-master | fitSplineLNP.m | .m | code_GLM-master/tools_splines/fitSplineLNP.m | 1,794 | utf_8 | 63def03ff4674ae92fbd476a2296947c | function [fun,pp,Mspline,splinePrs,fval] = fitSplineLNP(knots, xx, spnds, smoothness, extrapDeg,prs0,minval);
% [fun,pp,Mspline,splinePrs] = fitSplineLNP(knots, x, y, smoothness, extrapDeg);
%
% Fit a nonlinear function in an LNP neuron with a (strictly
% positive) spline defined using a set of knots
% (discon... |
github | danstowell/code_GLM-master | simGLM.m | .m | code_GLM-master/GLMcode/simGLM.m | 4,175 | utf_8 | fdfb890810992d8cda5cd38008478fca | function [tsp,Vmem,Ispk] = simGLM(glmprs,Stim);
% [tsp, Vmem,Ispk] = simGLM(glmprs,Stim);
%
% Compute response of glm to stimulus Stim.
%
% Uses time rescaling instead of Bernouli approximation to conditionally
% Poisson process
%
% Dynamics: Filters the Stimulus with glmprs.k, passes this through a
% nonlin... |
github | txizzle/ReachabilityD3-master | pursuitEvasionDefense4D.m | .m | ReachabilityD3-master/pursuitEvasionDefense4D.m | 16,922 | utf_8 | 7bd4800501687c942ad913f8eebc45e6 | function [ g, g2D, time_trace, value_trace, target_trace, obstacle_trace compTime] = pursuitEvasionDefense4D(Nx, accuracy)
% air3D: demonstrate the 3D aircraft collision avoidance example
%
% [ data, g, data0 ] = air3D(accuracy)
%
% System coordinates
%
% The state is given by [p_x, p_y, d_x]
%
% where the defender a... |
github | txizzle/ReachabilityD3-master | xyEvader.m | .m | ReachabilityD3-master/xyEvader.m | 740 | utf_8 | 2c0d07a7864f697ec0e8f101030d349a | function xy = xyEvader(xs)
% This function computes the position of the evader in (x,y) space from the
% curve parametrization s of a rectangle defined as:
% Bottom side: 0<s<1.2
% Right side: 1.2<s<2.8
% Top side: 2.8<s<4.0
% Left side: 4.0<s<5.6(=0)
xy{1} = arrayfun(@xScalarEvader,xs);
xy{2} = arrayfun(@ySca... |
github | txizzle/ReachabilityD3-master | xEvader.m | .m | ReachabilityD3-master/xEvader.m | 503 | utf_8 | 0816f392595d991575665fd7bb40e533 | function xs = xEvader(ss)
% This function computes the horizontal position of the evader from the
% curve parametrization s of a rectangle defined as:
% Bottom side: 0<s<1.2
% Right side: 1.2<s<2.8
% Top side: 2.8<s<4.0
% Left side: 4.0<s<5.6(=0)
xs = arrayfun(@xScalarEvader,ss);
end
% Auxiliary functions:... |
github | txizzle/ReachabilityD3-master | pursuitEvasion3D.m | .m | ReachabilityD3-master/pursuitEvasion3D.m | 15,551 | utf_8 | 53744ec9b2ca80dc120dda20df5a7797 | function [ g, g2D, time_trace, value_trace, target_trace, obstacle_trace,compTime] = pursuitEvasion3D(Nx, accuracy)
% air3D: demonstrate the 3D aircraft collision avoidance example
%
% [ data, g, data0 ] = air3D(accuracy)
%
% System coordinates
%
% The state is given by [p_x, p_y, e_s]
%
% where e_s parametrizes the ... |
github | txizzle/ReachabilityD3-master | yEvader.m | .m | ReachabilityD3-master/yEvader.m | 508 | utf_8 | 9890bab99a24aea4ce0a1469acf5f5d5 | function ys = yEvader(ss)
% This function computes the vertical position of the evader from the
% curve parametrization s of a rectangle defined as:
% Bottom side: 0<s<1.2
% Right side: 1.2<s<2.8
% Top side: 2.8<s<4.0
% Left side: 4.0<s<5.6(=0)
ys = arrayfun(@yScalarEvader,ss);
end
% Auxiliary functions:
fu... |
github | txizzle/ReachabilityD3-master | compareTerms.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Examples/Vector/compareTerms.m | 15,862 | utf_8 | 01d69e6f9800584f42275a7d1f99b57f | function [ dataC, dataH, g, data0 ] = ...
compareTerms(flowType, initShape, accuracy, displayType)
% compareTerms: compare convective and general Hamiltonian approximations
%
% [ dataC, dataH, g, data0 ] = ...
% compareTerms(flowType, initShape, accuracy, displayType)
%
% T... |
github | txizzle/ReachabilityD3-master | smerekaSpirals.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Examples/Vector/smerekaSpirals.m | 16,878 | utf_8 | 6b5fb2171248837a6a3277522a0c9aed | function [ dataCurve, dataMask, g, tPlot ] = ...
smerekaSpirals(whichFig, exactCopy, accuracy, tMax)
% smerekaSpirals: example of dynamic open curves by vector level sets.
%
% [ dataCurve, dataMask, g, tPlot ] = ...
% smerekaSpirals(whichFig, exactCopy, accuracy, ... |
github | txizzle/ReachabilityD3-master | exerciseO169b.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Examples/SDE/exerciseO169b.m | 10,274 | utf_8 | ed8bc11b92418555718b48fff3af381f | function [ data, g, data0 ] = exerciseO169b
% exerciseO169b: Solve Exercise 8.6 from Oksendal, pp.169-170
%
% This script solves Exercise 8.6, pp.169-170 from
% Oksendal, "Stochastic Differential Equations", sixth edition.
%
% The initial value PDE for x \in \R is
%
% D_t u = -\rho u + \alpha x D_x u + 0.5 \be... |
github | txizzle/ReachabilityD3-master | normalStarDemo.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Examples/OsherFedkiw/normalStarDemo.m | 8,945 | utf_8 | aa138508b2b1815537c95cdda6969c18 | function [ data, g, data0 ] = normalStarDemo(accuracy, reverseFlow,displayType)
% normalStarDemo: demonstrate motion by surface normal on star interface.
%
% [ data, g, data0 ] = normalStarDemo(accuracy, reverseFlow, displayType)
%
% Recreates figure 6.1 from O&F chapter 6, showing motion by surface normal
% of a s... |
github | txizzle/ReachabilityD3-master | curvatureStarDemo.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Examples/OsherFedkiw/curvatureStarDemo.m | 8,676 | utf_8 | b558859f73b8de878c3b862142e6d86a | function [ data, g, data0 ] = curvatureStarDemo(accuracy,splitFlow,displayType)
% curvatureStarDemo: demonstrate motion by mean curvature on star interface.
%
% [ data, g, data0 ] = curvatureStarDemo(accuracy, splitFlow, displayType)
%
% Recreates figure 4.2 from O&F chapter 4, showing motion by mean curvature
% of a... |
github | txizzle/ReachabilityD3-master | animateAir3D.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Examples/Reachability/animateAir3D.m | 15,935 | utf_8 | fa600bdfc750a326b78667e11d02a758 | function [ data, g, data0 ] = animateAir3D(filename, accuracy, compress)
% animateAir3D: create an animation of the growth of the air3D reach set.
%
% [ data, g, data0 ] = animateAir3D(filename, accuracy, compress)
%
% This file generates an animation showing how the reachable set grows
% as time progresses. It is... |
github | txizzle/ReachabilityD3-master | air3D.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Examples/Reachability/air3D.m | 13,044 | utf_8 | 0127b8a2e7b4b1a73fa1d5b053811fc1 | function [ data, g, data0 ] = air3D(accuracy)
% air3D: demonstrate the 3D aircraft collision avoidance example
%
% [ data, g, data0 ] = air3D(accuracy)
%
% In this example, the target set is a circle at the origin (cylinder in 3D)
% that represents a collision in relative coordinates between the evader
% (player a,... |
github | txizzle/ReachabilityD3-master | acoustic.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Examples/Reachability/acoustic.m | 12,885 | utf_8 | edcc17ff30673311149e9bcc736471ee | function [ data, g, data0 ] = acoustic(accuracy)
% acoustic: demonstrate the acoustic capture reachable set.
%
% [ data, g, data0 ] = acoustic(accuracy)
%
% In this example the target set is a horizontal wide but shallow rectangle
% near the origin, which represents the pursuer's capture set.
% The computation ... |
github | txizzle/ReachabilityD3-master | airMode.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Examples/Reachability/airMode.m | 12,803 | utf_8 | 00d35f50786811551fa09393605a457b | function [ reach, g, avoid, data0 ] = airMode(accuracy)
% airMode: demonstrate the 3 mode collision avoidance scenario.
%
% [ reach, g, avoid, data0 ] = airMode(accuracy)
%
% In this example, the target set is a circle at the origin
% that represents a collision in relative coordinates between the evader
% (pla... |
github | txizzle/ReachabilityD3-master | animateAcoustic.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Examples/Reachability/animateAcoustic.m | 14,632 | utf_8 | a1deb8a16f37a778c14e760af7e9a153 | function [ data, g, data0 ] = acoustic(filename, accuracy, compress)
% animateAcoustic: create an animation of the growth of the acoustic reach set.
%
% [ data, g, data0 ] = animateAcoustic(filename, accuracy, compress)
%
% This file generates an animation showing how the reachable set grows
% as time progresses.... |
github | txizzle/ReachabilityD3-master | analyticSumSquareTTR.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Examples/TimeToReach/analyticSumSquareTTR.m | 2,832 | utf_8 | 6084ff0fa9459cc657037b93e7b05ca9 | function mttr = analyticSumSquareTTR(radius, grid)
% analyticSumSquareTTR: analytic solution special holonomic time to reach.
%
% mttr = analyticSumSquareTTR(radius, grid)
%
% Computes the analytic minimum time to reach each node in the grid
% for the holonomic 2D integrator under unit bounded input.
%
% This rou... |
github | txizzle/ReachabilityD3-master | doubleIntegratorTTR.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Examples/TimeToReach/doubleIntegratorTTR.m | 13,884 | utf_8 | e5c05a746ac60871c6cf5711c979a7fc | function [ mttr, attr, data, gridOut, data0 ] = ...
doubleIntegratorTTR(accuracy, gridIn)
% doubleIntegratorTTR: demonstrate the double integrator time to reach.
%
% [ mttr, attr, data, gridOut, data0 ] = doubleIntegratorTTR(accuracy,gridIn)
%
% In this example we calculate... |
github | txizzle/ReachabilityD3-master | holonomicTTR.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Examples/TimeToReach/holonomicTTR.m | 14,598 | utf_8 | 724089456fff38986326c1a1cb9e970d | function [ mttr, attr, data, gridOut, data0 ] = ...
holonomicTTR(whichNorm, accuracy,gridIn)
% holonomicTTR: demonstrate a holonomic time to reach function.
%
% [ mttr, attr, data, gridOut, data0 ] = ...
% holonomicTTR(whichNorm, accuracy, gri... |
github | txizzle/ReachabilityD3-master | dumbbell1.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Examples/Sethian/dumbbell1.m | 7,646 | utf_8 | ca95c9817bc62943fd2ec7279a7ab4cf | function [ data, g, data0 ] = dumbbell1(accuracy)
% dumbbell1: recreate figure 14.2 from Sethian
%
% [ data, g, data0 ] = dumbbell1(accuracy)
%
% Recreates figure 14.2 from Sethian chapter 14,
% showing motion by mean curvature of a 3D dumbbell shaped region.
% This example is interesting because it shows pinch ... |
github | txizzle/ReachabilityD3-master | convectionDemo.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Examples/Basic/convectionDemo.m | 9,360 | utf_8 | aa1a10b47823229f869e8e5b15d5ba75 | function [ data, g, data0 ] = convectionDemo(flowType, accuracy, displayType)
% convectionDemo: demonstrate a simple convective flow field.
%
% [ data, g, data0 ] = convectionDemo(flowType, accuracy, displayType)
%
% This function was originally designed as a script file, so most of the
% options can only be modi... |
github | txizzle/ReachabilityD3-master | laxFriedrichsDemo.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Examples/Basic/laxFriedrichsDemo.m | 12,603 | utf_8 | ac195e3d68139622b3b1c42e59d0be98 | function [ data, g, data0 ] = ...
laxFriedrichsDemo(flowType, initShape, accuracy, dissType, displayType)
% laxFriedrichsDemo: demonstrate Lax-Friedrichs on a convective flow field.
%
% [ data, g, data0 ] = ...
% laxFriedrichsDemo(flowType, initShape, accuracy, dissType, displayType)
%
% This function dem... |
github | txizzle/ReachabilityD3-master | maskDemo.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Examples/Basic/maskDemo.m | 11,281 | utf_8 | 671869fb336477d864b9e880a8f53760 | function [ data, g, data0 ] = maskDemo(accuracy, displayType)
% maskDemo: demonstrate the masking process on a convective flow.
%
% [ data, g, data0 ] = maskDemo(accuracy, displayType)
%
% This function was originally designed as a script file, so most of the
% options can only be modified in the file.
%
% For exam... |
github | txizzle/ReachabilityD3-master | burgersLF.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Examples/OsherShu/burgersLF.m | 11,664 | utf_8 | 74bc3436e1b170305aa14207aa22d33b | function [ data, g, data0 ] = ...
burgersLF(accuracy, dissType, gridDim, gridSize, tMax)
% burgersLF: demonstrate Lax-Friedrichs on Burgers' equation.
%
% [ data, g, data0 ] = burgersLF(accuracy, dissType, gridDim, gridSize, tMax)
%
% This function demonstrates how the Lax-Friedrichs HJ ter... |
github | txizzle/ReachabilityD3-master | nonconvexLF.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Examples/OsherShu/nonconvexLF.m | 11,518 | utf_8 | 260951da8404ed0b020ca9832353b745 | function [ data, g, data0 ] = ...
nonconvexLF(accuracy, dissType, gridDim, gridSize, tMax)
% nonconvexLF: demonstrate Lax-Friedrichs on a nonconvex Hamiltonian.
%
% [ data, g, data0 ] = nonconvexLF(accuracy, dissType, gridDim, gridSize, tMax)
%
% This function demonstrates how the Lax-Friedri... |
github | txizzle/ReachabilityD3-master | initialConditionsTest2D.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Examples/Test/initialConditionsTest2D.m | 5,845 | utf_8 | 8fc68a86d827ecda23cb13aeba360c13 | function initialConditionsTest2D()
% initialConditionsTest2D: test initial condition routines in 2 dimensions.
%
% initialConditionsTest2D (no arguments)
%
% This function (basically a script file) generates a sequence of
% shapes built by constructive solid geometry methods.
%
% We show both the 2D implicit surfa... |
github | txizzle/ReachabilityD3-master | reinitTest.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Examples/Test/reinitTest.m | 6,169 | utf_8 | e508d4adebe843b3a0a7ef19695fecb7 | function [ data, g, data0 ] = reinitTest(initialType, accuracy, displayType)
% reinitTest: test signedDistanceIterative.
%
% [ data, g, data0 ] = reinitTest(initialType, accuracy, displayType)
%
% Demonstrates how signedDistanceIterative can be used to turn one of
% several different dynamic surface functions into ... |
github | txizzle/ReachabilityD3-master | initialConditionsTest3D.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Examples/Test/initialConditionsTest3D.m | 5,863 | utf_8 | 08410a50dc664acf7c24732c7b601fa7 | function initialConditionsTest3D()
% initialConditionsTest3D: test initial condition routines in 3 dimensions.
%
% initialConditionsTest3D (no arguments)
%
% This function (basically a script file) generates a sequence of
% shapes built by constructive solid geometry methods
%
% In 3D, it is rather hard to visuali... |
github | txizzle/ReachabilityD3-master | firstDerivSpatialTest1.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Examples/Test/firstDerivSpatialTest1.m | 8,654 | utf_8 | 973f6c3a21d917d81675911d62018b6f | function [ errorL, errorR, time ] = ...
firstDerivSpatialTest1(scheme, dim, whichDim, dx)
% firstDerivSpatialTest1: test various approximations of first derivative.
%
% [ errorL, errorR, time ] = firstDerivSpatialTest1(scheme, dim, whichDim, dx)
%
% Function to test the various approximations of the first spatial
%... |
github | txizzle/ReachabilityD3-master | argumentSemanticsTest.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Examples/Test/argumentSemanticsTest.m | 3,992 | utf_8 | 2d46ecfb9c9ce30c1c7a8f29454f27cf | function argumentSemanticsTest(loops, matSize)
% argumentSemanticsTest: test Matlab's argument passing speed.
%
% argumentSemanticsTest(loops, matSize)
%
% Script file to test the effectiveness of Matlab's purported pass by value
% semantics with pass by reference speed.
%
% Specifically, Matlab uses pass by value ... |
github | txizzle/ReachabilityD3-master | initialConditionsTest1D.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Examples/Test/initialConditionsTest1D.m | 3,945 | utf_8 | b85d3eee079d02ec91045238eedcf2ad | function initialConditionsTest1D()
% initialConditionsTest1D: test initial condition routines in 1 dimension.
%
% initialConditionsTest1D (no arguments)
%
% This function (basically a script file) generates a sequence of
% shapes built by constructive solid geometry methods
%
% In 1D, basically all the implicit su... |
github | txizzle/ReachabilityD3-master | processGrid.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Kernel/Grids/processGrid.m | 13,026 | utf_8 | ce2cfee64d2d34f6a88464c406fad239 | function gridOut = processGrid(gridIn, data)
% processGrid: Construct a grid data structure, and check for consistency.
%
% gridOut = processGrid(gridIn, data)
%
% Processes all the various types of grid argument allowed.
%
% Input Parameters:
%
% gridIn: A scalar, a vector, or a structure.
%
% Scalar: It is as... |
github | txizzle/ReachabilityD3-master | termTraceHessian.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Kernel/ExplicitIntegration/Term/termTraceHessian.m | 6,674 | utf_8 | 0716268f53af9971bced54e80b462be3 | function [ ydot, stepBound, schemeData ] = termTraceHessian(t, y, schemeData)
% termTraceHessian: approximate update by the trace of the Hessian
%
% [ ydot, stepBound, schemeData ] = termTraceHessian(t, y, schemeData)
%
% Computes an approximation to
%
% - trace(L(x,t) D_x^2 \phi R(x,t))
%
% where L(x,t) and R(x,t) ... |
github | txizzle/ReachabilityD3-master | termReinit.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Kernel/ExplicitIntegration/Term/termReinit.m | 13,370 | utf_8 | 7703114d09172284582170cd8efe55c5 | function [ ydot, stepBound, schemeData ] = termReinit(t, y, schemeData)
% termReinit: a Godunov solver for the reinitialization HJ PDE.
%
% [ ydot, stepBound, schemeData ] = termReinit(t, y, schemeData)
%
% Computes a Godunov approximation to motion by the reinitialization
% equation. While the reinitialization equati... |
github | txizzle/ReachabilityD3-master | postTimestepReinit.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Kernel/Helper/PostTimestep/postTimestepReinit.m | 5,548 | utf_8 | 4f1d832ab9121ba1580c05690a26ee4f | function [ yOut, schemeDataOut ] = postTimestepReinit(t, yIn, schemeDataIn)
% postTimestepReinit: postTimestep routine to perform some reinitialization.
%
% [ yOut, schemeDataOut ] = postTimestepReinit(t, yIn, schemeDataIn)
%
% This routine implements the postTimestepFunc prototype for a common
% operation: reinitia... |
github | txizzle/ReachabilityD3-master | terminalEventConverge.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Kernel/Helper/TerminalEvent/terminalEventConverge.m | 4,228 | utf_8 | 0c21c929f93db7e3d2e1723d5c7c63b0 | function [ value, schemeDataOut ] = ...
terminalEventConverge(t, y, tOld, yOld, schemeDataIn)
% terminalEventConverge: Detects convergence of the integration.
%
% [ value, schemeDataOut ] = ...
% terminalEventConverge(t, y, tOld, yOld, schemeDataIn)
%
% This routine implements t... |
github | txizzle/ReachabilityD3-master | upwindFirstENO3bHelper.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Kernel/SpatialDerivative/UpwindFirst/upwindFirstENO3bHelper.m | 5,948 | utf_8 | 5e44bbe5fdde242b2adac27354e3868e | function [ varargout ] = upwindFirstENO3bHelper(grid, gdata, dim, direction)
% upwindFirstENO3bHelper: helper function for upwindFirstENO3b.
%
% [ deriv, smooth, epsilon] = ...
% upwindFirstENO3bHelper(grid, gdata, dim, direction)
%
% Helper function to compute the ENO and WENO directional ap... |
github | txizzle/ReachabilityD3-master | upwindFirstENO3b.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Kernel/SpatialDerivative/UpwindFirst/upwindFirstENO3b.m | 4,601 | utf_8 | 01b4821cea336410233f85a3ff774c9c | function [ derivL, derivR ] = upwindFirstENO3b(grid, data, dim, generateAll)
% upwindFirstENO3b: third order upwind approx of first deriv by direct calc.
%
% [ derivL, derivR ] = upwindFirstENO3b(grid, data, dim, generateAll)
%
% Computes a third order directional approximation to the first
% derivative, using an E... |
github | txizzle/ReachabilityD3-master | upwindFirstWENO5a.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Kernel/SpatialDerivative/UpwindFirst/upwindFirstWENO5a.m | 7,861 | utf_8 | 55a4310dfae6b87d4c56463a05e9be5a | function [ derivL, derivR ] = upwindFirstWENO5a(grid, data, dim, generateAll)
% upwindFirstWENO5a: fifth order upwind approx of first deriv by divided diffs.
%
% [ derivL, derivR ] = upwindFirstWENO5a(grid, data, dim, generateAll)
%
% Computes a fifth order directional approximation to the first derivative,
% usin... |
github | txizzle/ReachabilityD3-master | upwindFirstWENO5b.m | .m | ReachabilityD3-master/ian_mitchell-toolboxls-a09a844e6229/Kernel/SpatialDerivative/UpwindFirst/upwindFirstWENO5b.m | 3,985 | utf_8 | 2a379e0635c506d39184342a55324af3 | function [ derivL, derivR ] = upwindFirstWENO5b(grid, data, dim, generateAll)
% upwindFirstWENO5b: fifth order upwind approx of first deriv by direct calc.
%
% [ derivL, derivR ] = upwindFirstWENO5b(grid, data, dim, generateAll)
%
% Computes a fifth order directional approximation to the first derivative,
% using ... |
github | DukeFun/Load-Balanced-LSH-master | lpnorm.m | .m | Load-Balanced-LSH-master/lpnorm.m | 3,897 | utf_8 | 926949f9e58fb48a8c255fb8e2b03773 | function d = lpnorm(x1,x2,p,CHUNKSIZE)
% d = lpnorm(X1,X2,P)
%
% Computate distances between X1 and X2, using L_P norm (default P=1)
% Assumes that the data are in columns of x1 and x2, and
% d(i)=dist(x1(:,i),x2(:,i).
% If x1 or x2 is a vector, it is repmat'ed appropriately - i.e., if x1 is
% a single column, d(i)=di... |
github | DukeFun/Load-Balanced-LSH-master | lshlookup.m | .m | Load-Balanced-LSH-master/lshlookup.m | 3,972 | utf_8 | fd9e757a1da730ae6191489953b0c85d | function [iNN,cand] = lshlookup(x0,x,T,varargin)
% [iNN,cand] = lshlookup(x0,x,T)
%
% iNN contains indices of matches in T for a single query x0;
% x is the representation in the feature space; assumes to be a cell
% array with equal size cells (this is a hack around Matlab's problem
% with allocating large con... |
github | freesouls/caffe-master | classification_demo.m | .m | caffe-master/matlab/demo/classification_demo.m | 5,412 | utf_8 | 8f46deabe6cde287c4759f3bc8b7f819 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | jamoma/jamoma2-master | InterpolatorTargetOutput.m | .m | jamoma2-master/test/Interpolator/InterpolatorTargetOutput.m | 3,797 | utf_8 | cae8118e70692c05edbc383a270839af | % @file
% @ingroup jamoma2
%
% @brief Generates the Expected Target Output for Interpolators using Octave
%
% @author Nathan Wolek
% @copyright Copyright (c) 2005-2015 The Jamoma Group, http://jamoma.org.
% @license This project is released under the terms of the MIT License.
clear
% starting values
x0 = -1.0;
... |
github | jamoma/jamoma2-master | GeneratorTargetOutput.m | .m | jamoma2-master/test/Generator/GeneratorTargetOutput.m | 2,056 | utf_8 | b7885419b3fc27d6ee65079aeb6a0b5d | % @file
% @ingroup jamoma2
%
% @brief Generates the Expected Target Output for Generators using Octave
%
% @author Nathan Wolek
% @copyright Copyright (c) 2005-2015 The Jamoma Group, http://jamoma.org.
% @license This project is released under the terms of the MIT License.
clear
output_ramp = double (1 : 64);
o... |
github | stochasticHydroTools/RigidMultiblobsWall-master | Rod_Plot.m | .m | RigidMultiblobsWall-master/cRigid_cFibers/Rigid_Rods/Rod_Plot.m | 2,035 | utf_8 | e934d1a4b31eaec90abbd84b3307b5c9 |
cfg = dlmread('../../multi_bodies/Structures/Cylinder_N_86_Lg_1_9384_Rg_0_1484.vertex');
cfg(1,:) = [];
cfg(cfg>1e4) = 0;
A = dlmread(['./data/DP_run.config']);
n_bods = round(A(1,1));
rem = mod(length(A),n_bods+1);
A(end-rem+1:end,:) = [];
A(1:n_bods+1:end,:) = [];
zmax = 1.0
a = 0.07419999999999999
L = 16.1802159... |
github | stochasticHydroTools/RigidMultiblobsWall-master | Rod_Plot.m | .m | RigidMultiblobsWall-master/cRigid_cFibers/Rigid_Rods/data/Rod_Plot.m | 2,015 | utf_8 | 376a987e7cc48cff65b66faffb34a830 |
cfg = dlmread('../../Structures/Cylinder_N_86_Lg_1_9384_Rg_0_1484.vertex');
cfg(1,:) = [];
cfg(cfg>1e4) = 0;
A = dlmread(['DP_run.config']);
n_bods = round(A(1,1));
rem = mod(length(A),n_bods+1);
A(end-rem+1:end,:) = [];
A(1:n_bods+1:end,:) = [];
zmax = 1.0
a = 0.07419999999999999
L = 16.18021593796416 %40.10605239... |
github | moiseevigor/elliptic-master | arclength_ellipse.m | .m | elliptic-master/arclength_ellipse.m | 8,822 | utf_8 | c9f4fbd78eabd250101ffec6eb133174 | function [arclength] = arclength_ellipse(a, b, theta0, theta1)
%ARCLENGTH_ELLIPSE Calculates the arclength of ellipse.
%
% ARCLENGTH_ELLIPSE(A, B, THETA0, THETA1) Calculates the arclength of ellipse
% using the precise formulas based on the representation of
% the arclength by the Elliptic integral of the secon... |
github | moiseevigor/elliptic-master | elliptic3.m | .m | elliptic-master/elliptic3.m | 2,977 | utf_8 | 1cdf2938d9f4781bb8c673db63e821b1 | function Pi = elliptic3(u,m,c);
% ELLIPTIC3 evaluates incomplete elliptic integral of the third kind.
% Pi = ELLIPTIC3(U,M,C) where U is a phase in radians, 0<M<1 is
% the module and 0<C<1 is a parameter.
%
% ELLIPTIC3 uses Gauss-Legendre 10 points quadrature template
% described in [3] to determine the valu... |
github | moiseevigor/elliptic-master | elliptic123.m | .m | elliptic-master/elliptic123.m | 24,069 | utf_8 | b6565844f1b186ee555191d3b9eea08c | function [F,E,P]=elliptic123(a1,a2,a3)
%ELLIPTIC123 computes the first, second and third elliptic integrals for
% both the complete and incomplete cases and no restriction on the input
% arguments. (Modulo some bugs; see below.)
%
% [F,E]=elliptic123(b,m)
% Calculates incomplete elliptic integrals of the first and se... |
github | francocurotto/Markov-Reduction-master | getMinBCDB.m | .m | Markov-Reduction-master/Paper Algorithm/Aggregate/aggregateFun/getMinBCDB.m | 1,365 | utf_8 | 5e5a90a70428e6e4c427ed83518becd8 | % This program 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 License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% bu... |
github | francocurotto/Markov-Reduction-master | addWBinBDB.m | .m | Markov-Reduction-master/Paper Algorithm/Aggregate/aggregateFun/addWBinBDB.m | 1,146 | utf_8 | 349f605c6c7522427e47a53768b7e3f4 | % This program 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 License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% bu... |
github | francocurotto/Markov-Reduction-master | createBC.m | .m | Markov-Reduction-master/Paper Algorithm/Aggregate/aggregateFun/createBC.m | 1,401 | utf_8 | 0569525821f32cc4ecd6b501feb961b6 | % This program 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 License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% bu... |
github | francocurotto/Markov-Reduction-master | createNewBDB.m | .m | Markov-Reduction-master/Paper Algorithm/Aggregate/aggregateFun/createNewBDB.m | 972 | utf_8 | 0d0cf45a92cd0e73b6f533cb4fab04dd | % This program 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 License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% bu... |
github | francocurotto/Markov-Reduction-master | calculateQ.m | .m | Markov-Reduction-master/commonFunctions/calculateQ.m | 1,203 | utf_8 | 82bc18c1b0830bda1c37646b5f413644 | % This program 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 License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% bu... |
github | francocurotto/Markov-Reduction-master | getLargerLambdaIndex.m | .m | Markov-Reduction-master/commonFunctions/getLargerLambdaIndex.m | 1,304 | utf_8 | f94942ac84d59344a8127b6934859ca8 | % This program 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 License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% bu... |
github | francocurotto/Markov-Reduction-master | generatePlots.m | .m | Markov-Reduction-master/commonFunctions/generatePlots.m | 2,031 | utf_8 | e46e0b6338437a822fcbf3562efbc67a | % This program 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 License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% bu... |
github | francocurotto/Markov-Reduction-master | invariant.m | .m | Markov-Reduction-master/commonFunctions/invariant.m | 1,578 | utf_8 | fa7cc04cb35733ed9d1762b21e18336f | % This program 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 License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,... |
github | francocurotto/Markov-Reduction-master | submatrix.m | .m | Markov-Reduction-master/commonFunctions/submatrix.m | 1,118 | utf_8 | 8ea9e1a6c70bda3f84e6e62c2c76d76d | % This program 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 License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% bu... |
github | francocurotto/Markov-Reduction-master | solveEigProblem.m | .m | Markov-Reduction-master/commonFunctions/solveEigProblem.m | 1,374 | utf_8 | a0446c03d51e28fe3a2401a297d0fb0b | % This program 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 License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% bu... |
github | francocurotto/Markov-Reduction-master | extendsVectors.m | .m | Markov-Reduction-master/commonFunctions/extendsVectors.m | 984 | utf_8 | 1de421ba5ae8045a18686a733a4f93f6 | % This program 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 License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% bu... |
github | francocurotto/Markov-Reduction-master | generateNCDMC.m | .m | Markov-Reduction-master/commonFunctions/generateNCDMC.m | 1,109 | utf_8 | e602aba3faf1e680294b95af07e53454 | % This program 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 License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% bu... |
github | francocurotto/Markov-Reduction-master | calculateR.m | .m | Markov-Reduction-master/commonFunctions/calculateR.m | 1,281 | utf_8 | c3b8b5c68c709f917398df134e0be65d | % This program 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 License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% bu... |
github | francocurotto/Markov-Reduction-master | generateMarkov.m | .m | Markov-Reduction-master/commonFunctions/generateMarkov.m | 931 | utf_8 | b2373da2f1069b6a4b511e817372781e | % This program 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 License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% bu... |
github | francocurotto/Markov-Reduction-master | aggregate2.m | .m | Markov-Reduction-master/New Algorithm/Aggregate2/aggregate2.m | 1,776 | utf_8 | bcac06de61920045fee56a3578b8151b | % This program 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 License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% bu... |
github | francocurotto/Markov-Reduction-master | getWQ.m | .m | Markov-Reduction-master/New Algorithm/Aggregate2/aggregate2Fun/getWQ.m | 1,359 | utf_8 | 59e6f829c1c65dbb174af8d93a1ab214 | % This program 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 License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% bu... |
github | francocurotto/Markov-Reduction-master | calculateBCs.m | .m | Markov-Reduction-master/New Algorithm/Aggregate2/aggregate2Fun/calculateBCs.m | 2,047 | utf_8 | ad1528198dbcfb64aef5f6cff628b2b2 | % This program 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 License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% bu... |
github | francocurotto/Markov-Reduction-master | calculateNewQs.m | .m | Markov-Reduction-master/New Algorithm/Aggregate2/aggregate2Fun/calculateNewQs.m | 1,196 | utf_8 | 057b5f859b1a0fa31701cfb7074e74f9 | % This program 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 License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% bu... |
github | francocurotto/Markov-Reduction-master | aggregatePhi.m | .m | Markov-Reduction-master/New Algorithm/Aggregate2/aggregate2Fun/aggregatePhi.m | 996 | utf_8 | d9c2db90fa9da397bcfefb81ecdf3f23 | % This program 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 License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% bu... |
github | francocurotto/Markov-Reduction-master | aggregate2QStates.m | .m | Markov-Reduction-master/New Algorithm/Aggregate2/aggregate2Fun/aggregate2QStates.m | 1,606 | utf_8 | d60806bcedd22c6519f5c4ad0a1e4f5e | % This program 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 License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% bu... |
github | Jane333/Mustererkennung-master | f1.m | .m | Mustererkennung-master/ueb08/f1.m | 266 | utf_8 | 9ea5d16b9ed0e4238a9f7c541a93d80d | % f00*2^0 + f01*2^1 + f10*2^2 + f11*2^3 = 1
function [y] = f1(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 | f2.m | .m | Mustererkennung-master/ueb08/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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 | f1.m | .m | Mustererkennung-master/ueb08/abgegeben/f1.m | 266 | utf_8 | 9ea5d16b9ed0e4238a9f7c541a93d80d | % f00*2^0 + f01*2^1 + f10*2^2 + f11*2^3 = 1
function [y] = f1(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
|
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