keygen

http://pincopall.altervista.org/cratut/ThunderPwr-CDtoMP3115-tutorial7.htm

load dll asm

.file "loaddllex.cpp" .intel_syntax noprefix .def ___main; .scl 2; .type 32; .endef .section .rdata,"dr" LC0: .ascii "C:\\work\\dummy.dll\0" .text .globl _main .def _main; .scl 2; .type 32; .endef _main: LFB6: lea ecx, [esp+4] LCFI0: and esp, -16 push DWORD PTR [ecx-4] push ebp LCFI1: mov ebp, esp push ecx LCFI2: sub esp, 36 call ___main mov DWORD PTR [esp], OFFSET FLAT:LC0 call _LoadLibraryA@4 sub esp, 4 mov DWORD PTR [ebp-12], eax mov eax, 0 mov ecx, DWORD PTR [ebp-4] LCFI3: leave LCFI4: lea esp, [ecx-4] LCFI5: ret LFE6: .section .eh_frame,"w" Lframe1: .long LECIE1-LSCIE1 LSCIE1: .long 0 .byte 0x3 .ascii "\0" .uleb128 0x1 .sleb128 -4 .uleb128 0x8 .byte 0xc .uleb128 0x4 .uleb128 0x4 .byte 0x88 .uleb128 0x1 .align 4 LECIE1: LSFDE1: .long LEFDE1-LASFDE1 LASFDE1: .long LASFDE1-Lframe1 .long LFB6 .long LFE6-LFB6 .byte 0x4 .long LCFI0-LFB6 .byte 0xc .uleb128 0x1 .uleb128 0 .byte 0x4 .long LCFI1-LCFI0 .byte 0x10 .byte 0x5 .uleb128 0x2 .byte 0x75 .sleb128 0 .byte 0x4 .long LCFI2-LCFI1 .byte 0xf .uleb128 0x3 .byte 0x75 .sleb128 -4 .byte 0x6 .byte 0x4 .long LCFI3-LCFI2 .byte 0xc .uleb128 0x1 .uleb128 0 .byte 0x4 .long LCFI4-LCFI3 .byte 0xc5 .byte 0x4 .long LCFI5-LCFI4 .byte 0xc .uleb128 0x4 .uleb128 0x4 .align 4 LEFDE1: .ident "GCC: (GNU) 4.8.1" .def _LoadLibraryA@4; .scl 2; .type 32; .endef

k-mean clustering

require(graphics) beta1<-c(-0.130811881,-0.124113184,-0.111084035,-0.346505179,0.146765992,-0.203682819) beta2<-c(0.19744896,0.158675025,0.098311959,0.644843941,-0.197478252,0.278922585) beta1<-c(-0.130811881, -0.124113184, -0.111084035, -0.346505179, 0.146765992, -0.201818799, 0.109229194, -0.203682819, -0.124113184, -0.065281704, -0.089614531) beta2<-c(0.19744896, 0.158675025, 0.098311959, 0.644843941, -0.197478252, 0.100779252, -0.204082744, 0.278922585, 0.158675025, 0.099221018, 0.136914875) x <- cbind(beta1, beta2) colnames(x) <- c("x", "y") (cl <- kmeans(x, 2)) plot(x, col = cl$cluster) points(cl$centers, col = 1:2, pch = 8, cex = 2) # sum of squares ss <- function(x) sum(scale(x, scale = FALSE)^2) ## cluster centers "fitted" to each obs.: fitted.x <- fitted(cl); head(fitted.x) resid.x <- x - fitted(cl) ## Equalities : ---------------------------------- cbind(cl[c("betweenss", "tot.withinss", "totss")], # the same two columns c(ss(fitted.x), ss(resid.x), ss(x))) stopifnot(all.equal(cl$ totss, ss(x)), all.equal(cl$ tot.withinss, ss(resid.x)), ## these three are the same: all.equal(cl$ betweenss, ss(fitted.x)), all.equal(cl$ betweenss, cl$totss - cl$tot.withinss), ## and hence also all.equal(ss(x), ss(fitted.x) + ss(resid.x)) ) kmeans(x,1)$withinss # trivial one-cluster, (its W.SS == ss(x)) ## random starts do help here with too many clusters ## (and are often recommended anyway!): (cl <- kmeans(x, 5, nstart = 25)) plot(x, col = cl$cluster) points(cl$centers, col = 1:5, pch = 8)