{"published":"2018-01-24 11:15:43","author_name":"ryamada22","provider_name":"Hatena Blog","categories":["MMD","R","python","kernlab"],"author_url":"https://blog.hatena.ne.jp/ryamada22/","title":"Maximum Mean DIscrepancy \u305d\u306e3","blog_url":"https://ryamada22.hatenablog.jp/","description":"R\u306ekernlab library(kernlab) # create data x <- matrix(runif(300),100) y <- matrix(runif(300)+1,100) mmdo <- kmmd(x, y) mmdo > mmdo Kernel Maximum Mean Discrepancy object of class \"kmmd\" Gaussian Radial Basis kernel function. Hyperparameter : sigma = 1.18155987898832 H0 Hypothesis rejected : TRUE Rade\u2026","width":"100%","url":"https://ryamada22.hatenablog.jp/entry/20180124/1516414543","image_url":null,"height":"190","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fryamada22.hatenablog.jp%2Fentry%2F20180124%2F1516414543\" title=\"Maximum Mean DIscrepancy \u305d\u306e3 - 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>","blog_title":"ryamada\u306e\u907a\u4f1d\u5b66\u30fb\u907a\u4f1d\u7d71\u8a08\u5b66\u30e1\u30e2","type":"rich","version":"1.0","provider_url":"https://hatena.blog"}