Rで解く最適化問題 線型計画問題編
9. X n Rn f S aij , bi , cj (i = 1, · · · , m; j = 1, · · · , n) xj (j = 1, · · · , n) min. c 1 x1 + · · · + c n xn s. t. ai1 x1 + · · · + ain xn ≤ bi (i = 1, · · · , l) ai1 x1 + · · · + ain xn = bi (i = l + 1, · · · , m) 9 10. aij , bi , cj (i = 1, · · · , m; j = 1, · · · , n) min. c 1 x1 + · · · + c n xn s. t. ai1 x1 + · · · + ain xn ≥ bi (i = 1, · · · , m) xj ≥ 0(j = 1, · · · , n) min. cT x s.
Prophet: How Facebook operationalizes time series forecasting at scale
Revolutions Milestones in AI, Machine Learning, Data Science, and visualization with R and Python since 2008 Facebook is a famously data-driven organization, and an important goal in any data science activity is forecasting. Now, Facebook has released Prophet, an open-source package for R and Python that implements the time-series methodology that Facebook uses in production for forecasting at sca
The R Journal: Overview
The R Journal is the peer-reviewed, open-access scientific journal, published by The R Foundation. It features short to medium length articles (up to 20 pages) covering topics of interest to users and developers of R. For example, description and implementation details of R packages, surveys or reviews of R software related to a particular area, applications of R software or descriptions of teachi
Forest Floor Visualizations of Random Forests
We propose a novel methodology, forest floor, to visualize and interpret random forest (RF) models. RF is a popular and useful tool for non-linear multi-variate classification and regression, which yields a good trade-off between robustness (low variance) and adaptiveness (low bias). Direct interpretation of a RF model is difficult, as the explicit ensemble model of hundreds of deep trees is compl
Bayesian Optimization of Machine Learning Models
Revolutions Milestones in AI, Machine Learning, Data Science, and visualization with R and Python since 2008 Many predictive and machine learning models have structural or tuning parameters that cannot be directly estimated from the data. For example, when using K-nearest neighbor model, there is no analytical estimator for K (the number of neighbors). Typically, resampling is used to get good per
Gaussian Process Hyperparameter Estimation
Quick Way longer then expected post and some code for looking into the estimation of kernel hyperparameters using STAN HMC/MCMC and R. I wanted to drop this work here for safe keeping. Partially for the exercise of thinking it through and writing it down, but also because it my be useful to someone. I wrote a little about GP in a previous post, but my understanding is rather pedestrian, so these
二地点の緯度・経度からその距離を計算する（日本は山だらけ〜）
更新：コードのライセンスを MIT ライセンスに変更しました。（平成二十四年五月十日） 概要 各山のページに、 その山の近くにある別の山のリストを載せようと思い、 二地点の緯度と経度から距離を求める方法について調査しました。 ヒュベニ (Hubeny) の公式を用いると簡単な計算で精度よく距離が求まることがわかりました。 ヒュベニの公式ではいくつか定数が出てきますが、旧日本測地系、世界測地系、 GPS で利用されている測地系の 3 種類についての定数を調べ、算出しました。 Java および R のプログラムと合わせて研究成果を報告します。 緯度経度からの距離計算 国土地理院・測地部のウェブサイト [1] に測量に関する様々な情報が載っています。 特に [2] には様々な計算式が載っており、 緯度・経度から距離を計算する式 [3] もあります。 しかしここにあるのは緯度・経度を平面直角座標と
Cluster analysis in R: determine the optimal number of clusters
If your question is "how can I determine how many clusters are appropriate for a kmeans analysis of my data?", then here are some options. The wikipedia article on determining numbers of clusters has a good review of some of these methods. First, some reproducible data (the data in the Q are... unclear to me): n = 100 g = 6 set.seed(g) d <- data.frame(x = unlist(lapply(1:g, function(i) rnorm(n/g,
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Automatic Time Series Forecasting: The forecast Package for R | Journal of Statistical Software
GPfit: An R package for Gaussian Process Model Fitting using a New Optimization Algorithm