{"provider_url":"https://hatena.blog","version":"1.0","published":"2018-09-24 13:13:41","width":"100%","categories":["\u30ce\u30f3\u30d1\u30e9\u30e1\u30c8\u30ea\u30c3\u30af\u30fb\u30d9\u30a4\u30ba","\u30d9\u30a4\u30ba","\u56de\u5e30","R","python"],"author_url":"https://blog.hatena.ne.jp/ryamada22/","blog_url":"https://ryamada22.hatenablog.jp/","url":"https://ryamada22.hatenablog.jp/entry/20180924/1537762421","description":"\u307e\u305a\u3001\u5358\u7d14\u306b\u7dda\u5f62\u56de\u5e30 s <- rnorm(n,0,1) head(s) m <- matrix(s,ncol=2) head(m) lm_res <- lm(m[,2] ~ m[,1]) lm_res plot(m) abline(lm_res) python\u3067\u884c\u3053\u3046 Scikit learn \u306e gaussian process regression \u305d\u306eExamples\u306e\u4e00\u3064 \u30ab\u30fc\u30cd\u30eb\u95a2\u6570\u304b\u3089\u3001\u5024\u306esimilarity\u306e\u304a\u7d75\u304b\u304d x1 <- x2 <- seq(from=-2,to=2,length=100) x12 <- expand.grid(x1,x2) k <- function\u2026","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fryamada22.hatenablog.jp%2Fentry%2F20180924%2F1537762421\" title=\"\u30ce\u30f3\u30d1\u30e9\u30e1\u30c8\u30ea\u30c3\u30af\u30fb\u30d9\u30a4\u30ba\u5b9f\u8df5\u7de8\uff12\u3000\u56de\u5e30 - ryamada\u306e\u907a\u4f1d\u5b66\u30fb\u907a\u4f1d\u7d71\u8a08\u5b66\u30e1\u30e2\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","type":"rich","height":"190","provider_name":"Hatena Blog","title":"\u30ce\u30f3\u30d1\u30e9\u30e1\u30c8\u30ea\u30c3\u30af\u30fb\u30d9\u30a4\u30ba\u5b9f\u8df5\u7de8\uff12\u3000\u56de\u5e30","image_url":null,"blog_title":"ryamada\u306e\u907a\u4f1d\u5b66\u30fb\u907a\u4f1d\u7d71\u8a08\u5b66\u30e1\u30e2","author_name":"ryamada22"}