POLARIS

World time

time

obj=new Object;obj.clockfile=”world001-blue.swf”;obj.TimeZone=”GMT-0800″;obj.width=480;obj.height=250;obj.wmode=”transparent”;showClock(obj);

A Simple Example

mangosteen.cs.ttu.edu$ csp.out -f 3 3 3 3 1 3 1 The parameters of the random instances (n=3, d=3, e=3 , f=3 , t=1, # of instances=1, seed=3) Instance 0:The cn address is 0x960f0c8nt=9 ft=3——-all possible constraints——–0 10 21 2——–C/e constraints————–1 20 10 2——–2*C constraints———–1 22 10 11 00 22 0———F ...

adjGF

adjGF:0 : 8 1 : 2 : 5 3 : 4 4 : 3 6 5 : 7 8 6 : 7 : 8 : 3 funcross_link_list:0 : 8 1 : 6 2 : 4 5 3 : 4 8 4 : 2 3 6 5 : 2 7 8 ...

segmentation debug

————Newcomposed CSP begin:——— v:8 c_{3,4}[8]-1 cjk[v]-1 cjk[cij[v]]41 c_ik[v]-1 c_{3,6}: [ 0 8] c_{3,6}: [ 1 1] c_{3,6}: [ 2 3] c_{3,6}: [ 3 -1] c_{3,6}: [ 4 -1] c_{3,6}: [ 5 3] c_{3,6}: [ 6 4] c_{3,6}: [ 7 -1] c_{3,6}: [ 8 -1] ++++++++++++++++++ ++++++++++++++++++++++++++++++++++++++++++++ typecons=:2 removed v ...

Neural network -matlab implement

function sigmoid = sigmoid(var) sigmoid = 2/(1+exp(-var)) - 1; endfunction dsigmoid = dsigmoid(var) dsigmoid = 0.5 * (1 + sigmoid(var)) * (1 - sigmoid(var)); end [x,y,t] = textread(’hw3data.txt’,'%f %f %f’);m=1;n=1;for i =1 : length(t) if t(i) == 1 x1(m) = x(i); y1(m) = y(i); m = m + 1; else x2(n) ...

P.R.

load HW2pos.dat load HW2neg.dat x1=HW2pos(:1); x1=HW2pos(:,1); y1=HW2pos(:,2); x2=HW2neg(:,1); y2=HW2neg(:,2); scatter(x1,y1,’+r’,x2,y2,’+b’) scatters(x1,y1,’+r’,x2,y2,’+b’) scatter(x1,y1,’+r’,x2,y2,’+b’) scatter(x1,y1,’+r’) hold on scatter(x2,y2,’+g’) title(’Homework2′) legend(’+r, data +1′,’+g, data -1′) legend(’data +1′,’ data -1′) x1minusu=x1-4.0034; y1minusu=y1+0.0637; class1minusu1=[x1minusu y1minusu]; m=class1minusu1*class1minusu1′; sumofm=sum(m); hold off %——————— –% sumA=0 for i=1:300 A=[x1minusu(i) y1minusu(i)]’*[x1minusu(i) y1minusu(i)] sumA=sumA+A end sumA sumA./299 sigma1=sumA./299 inv(sigma1) inv6=inv(sigma1) W1=inv6.*(-0.5) x2minusu=x2.-4.0025 x2minusu=x2-4.0025 y2minusu=y2-2.0047 sumB=0 for i=1:300 B=[x2minusu(i) y2minusu(i)]’*[x2minusu(i) y2minusu(i)] sumB=sumB+B end sigma2=sumB./299 sumB inv62=inv(sigma2) W2=inv62*.(-0.5) W2=inv62.*(-0.5) W1-W2 norm(sigma1) (-0.5)*lnnorm(sigma1) (-0.5)*ln(norm(sigma1)) (-0.5)*log(norm(sigma1)) u1=[4.0034 -0.0637] u2=[4.0025 2.0047] W100=(-0.5)* u1 * inv6 * u1′ W100+0.0414+0.5 W200=(-0.5)* u2 inv62 * u2′ W200=(-0.5)* u2 * inv62 * u2′ W200+(-0.5) * log(norm(sigma2)) +0.5 (W1-W2)’ w1=inv6*u1 w1=inv6*u1′ w2= inv62* u2′ w1-w2 -377.0874-(-25.2259) f=-0.8412 g=f h=0.4546 w22=2.6536; w11=173.5067; w0=-351.8615; e=-21.1271; for i=1 :300 a=h b=(f+g)* x1(i)+ w22 for i=1 :300 a=h b=(f+g)* x1(i)+ w22;

Errata of Dr. Gelfond’ Notes

Here only list some important one. Then–>Than Lower Bound Algorithm condition 3. Condition should be stronged as the one and the only one to fuarantee the conclusion. Example2 for the Lower Bound Algorithm. It should stop in the previous step. There is a contradiction in the final step. For other errors please contact ...

Example of LE

SCC1:(A,B) (B,C)(C,D)(D,A)SCC2:(G,E)(E,F)(F,G)(E,H)(H,E)NonScc:(C,E)Suppose A is the base point(choose randomly), the time of composition is at least 3.However, if we choose C as the base point , we only need 2 compositions around SCC1. The similar situation happen to SCC2. we delete B(composition (A,C)), and then C(composition (A,D);composition (A,E)): —> we ...

Linear Elimination Algorithm

Linear Elimination(inout(N,D,C)) // What does inout mean?{ Found_Strongly_connected(GF); // the function is try to find all the SCC of GF of // (N,D,C) for(m=1;m