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[½»Á÷] R °ü qkerntoolÒѾ­¸üе½ 1.19°æ±¾£¬»¶Ó­Ê¹ÓÃ

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ÕâÊÇÒ»¸ö¹ØÓÚÒԺ˷½·¨ÎªÖ÷µÄr°ü½éÉÜ£¬Õâ¸ö°ü°üº¬ÁËÐí¶àÓÃÓÚ·ÖÀ࣬¾ÛÀཱུάºÍ¿ÉÊÓ»¯µÄ·ÇÏßÐÔ»úÆ÷ѧϰ¹¤¾ß¡£¸Ã°üʹÓÃÁËÌõ¼þ¸º¶¨ºË(cnd kernel)£¬¹²¼¯³ÉÁË26¸öºË£¬°üº¬qºË°æ±¾ºÍcnd°æ±¾£¬ÆäÖаüº¬dbscan£¬Æ×¾ÛÀ࣬¹ãÒåÅбð·ÖÎö £¨gda£©£¬Ö÷³É·Ö·ÖÎö£¨pca£©£¬isomap£¬ÏßÐÔÅбð·ÖÎö£¨lle£©£¬sammon mappingºÍtsne¡£

1.ÔÚrÖÐÖ±½ÓÊäÈë´úÂëÔÚcranÉϲéÕÒ°²×°

install.packages("qkerntool" )


2 Ö±½ÓÏÂÔØºÃµÄqkerntool_1.19.tar.gz

install.packages("e:/qkerntool_1.18.tar.gz" )


3 rstudioÖÐÔڲ˵¥À¸tools-install packagesÖвéÕÒ°²×°
install fromÏÂÀ­²Ëµ¥¿ÉÒÔÑ¡ÔñÊÇ´Ócran»¹ÊDZ¾µØ°²×°°ü°²×°

cnd kernel
ÓÉÓÚÓÃÓÚÊý¾Ý·ÖÎöµÄ¾­µäºË·½·¨Í¨³£±»ÈÏΪ½öÏÞÓÚÕý¶¨ºË£¬Òò´Ë£¬ÔÚqkerntoolsÖÐÌá³öÁËеľàÀëËã·¨£¬Ðº˷½·¨¼ÆËã¸ßάͶӰ¿Õ¼äÖеľàÀ빫ʽÈçÏÂ
ÔÚÕâÀï²åÈëͼƬÃèÊö
qkernelʹÓÃÁ½¸öÏòÁ¿²ÎÊýÖ®¼äµÄÄں˺¯ÊýÖµ£¬¼´Í¨³£ÒÀÀµÓÚÓɶ¨ÒåµÄÄں˾ØÕóhÖ®ÀàµÄ±í´ïʽ
ÔÚÕâÀï²åÈëͼƬÃèÊö
¸ü¸ÄºËµÄ²ÎÊý»á¸ü¸ÄÊý¾ÝµÄͶӰ£¬´Ó¶ø¸ü¸ÄͶӰ¿Õ¼äÖеľàÀë¡£
ΪÁËͨ¹ýrʵÏÖ¸º¶¨ºË±í´ï£¬¹¹½¨ÁËÒ»¸ös4Ààr°ü£¬ÆäÖбíʾΪÀà¡°qkernel¡±»ò¡°cndkernel¡±µÄ¶ÔÏó¡£ÕâЩº¯Êý¿ÉÒÔ×÷Ϊ²ÎÊý´«µÝ¸ø·ºÐͺ¯Êý£¬ÕâЩº¯Êý¿ÉÒÔÆÀ¹À¸üÓÐÓõÄÄں˱í´ïʽ£¬ÈçºË¾ØÕó

qkernmatrix Óɺ˷½·¨¹¹³ÉµÄºË¾ØÕó

> library("qkerntool" )
> set.seed(20181110)
> x <- matrix(rnorm(300), ncol = 100)
> qkfunc <- rbfbase(sigma=10,q=0.8)
> qkfunc
qbase qkernel function.
hyperparameter : sigma =  10  q =  0.8
> qkfunc(x[1, ], x[2, ])
         [,1]
[1,] 4.976754

> qkernmatrix(qkfunc,x)
an object of class "qkernmatrix"
         [,1]     [,2]     [,3]
[1,] 0.000000 4.976754 4.969811
[2,] 4.976754 0.000000 4.960555
[3,] 4.969811 4.960555 0.000000

   
´´Ôì×Ô¼ºµÄºË
×Ô¼ºÊäÈëºËº¯Êý

> rbf1 <- function(x, y){
+   sdist <- crossprod(x) + crossprod(y) - 2*crossprod(x,y)
+   sdist <- (1 - exp(log(0.8)*sdist/10))/(1-0.8)
+   return(sdist)
>+ }
> class(rbf1) <- "qkernel"
> rbf1(x[1, ], x[2, ])

   
qkpca
²ÎÊýÃû         ÄÚÈÝ
x         n x d¾ØÕó£¨n¸öÑù±¾£¬d¸öÌØÕ÷£©»ò¼Ì³Ðcndkernmatrix £¬qkernmatrixµÄºË¾ØÕó
kernel         ºËÀàÐÍ
qpar         ºË²ÎÊý
features         Ñ¡ÔñµÄ³É·Ö¸öÊý
th         ºöÂÔÖ÷³É·ÖµÄÌØÕ÷ÖµµÄÖµ

ÒÔirisÊý¾Ý¼¯ÎªÀý£¬Ëæ»úÌáÈ¡¶þÊ®¸öÑù±¾Îª²âÊÔ¼¯£¬¶ÔÊ£ÓàÑù±¾Ê¹Óá°rbfbase¡±ºË½øÐнµÎ¬ÒÔ¼°¶ÔÓ¦µÄÔ¤²â£¬Àý×ÓʹÓÃÁËirisÊý¾Ý¼¯£¬´úÂë¼°½á¹ûÈçÏÂ

data(iris)
test <- sample(1:150,20)
qkpc <- qkpca(~.,data=iris[-test,-5],kernel="rbfbase",
              qpar=list(sigma=50,q=0.8),features=2)

# print the principal component vectors
pcv(qkpc)
#plot the data projection on the components
plot(rotated(qkpc),col=as.integer(iris[-test,5]),
     xlab="1st principal component",ylab="2nd principal component" )

# embed remaining points
emb <- predict(qkpc,iris[test,-5])
points(emb,col=as.integer(iris[test,5]))

[ last edited by jjdg on 2019-4-26 at 20:55 ]

[ Last edited by jjdg on 2019-4-26 at 20:55 ]
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