天体暦の力学的計算には長らく摂動論にもとづいた解析的な方法を用いることが主流であった。 現在でもフランスの VSOP (fr:Variations Séculaires des Orbites Planétaires) はこのような考えで作成されている。
一方で近年はコンピュータの発達により、数値積分による大規模な計算が可能となり、数値的な天体暦が主流となっている。 特に惑星探査機の運用に必要であったためもあり、このような数値的暦はアメリカとロシアにおいて精密なものが作られるようになった。 NASA ジェット推進研究所 (JPL) の DE (Development Ephemeris) とロシア科学アカデミー応用天文学研究所 (Институт прикладной астрономии, Institute of Applied Astoronomy) の EPM (Ephemerides of Planets and the Moon) がこのような数値的天体暦として代表的なものである[1]。 日本では、2009年まで海上保安庁海洋情報部から年刊の視天体暦『天体位置表』が刊行されていた[2]。
^荒木田英禎 (2007年). “天文単位は永年増加するか!? 太陽系天体の精密位置測定からの新たな問題”. 「高精度アストロメトリ観測の時代を迎えた21世紀の天文学」研究会. JASMINE ホームページ. 2010年11月10日閲覧。 Folkner, William M., James G. Williams, and Dale H. Boggs (2009年). “The Planetary and Lunar Ephemeris DE 421” (PDF). Interplanetary Network Progress Report42-178: C. http://tmo.jpl.nasa.gov/progress_report/42-178/178C.pdf. Pitjeva, E. V (2005年). “High-precision ephemerides of planets—EPM and determination of some astronomical constants” (PDF). Solar System Research39 (3): 176–186. http://iau-comm4.jpl.nasa.gov/EPM2004.pdf. (trans. from Астрономический вестник39 (3): 202–213).
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1 I having trouble getting my ResourceDictionary.MergedDictionaries to load from app.xaml. My WPF app has a static class with a Main defined and startup object set to it. Within Main I created an instance of App and run it. The override OnStartup fires and the mainwindow.cs InitializeComponent gives the error "Message "Cannot find resource named 'MaterialDesignFloatingActionMiniAccentButton'. If I put the resources in the mainwindow.xaml everything is fine, but I wanted them to load at the app level so I they are not in each page. Any help appreciated. public partial class App protected override void OnStartup(StartupEventArgs e) base.OnStartup(e); var app = new MainWindow(); var context = new MainWindowViewModel(); app.DataContext = context; app.Show(); from the Main.. var app = new App(); app.Run(); app.xaml.. <Application x:Class="GS.Server.App" xmlns="http://schemas.microsoft.com/winfx/2006/xaml/presentation" xmlns:...
up vote 2 down vote favorite There is a clear pattern that show for two separate subsets (set of columns); If one value is missing in a column, values of other columns in the same subset are missing for any row. Here is a visualization of missing data My tries up until now, I used ycimpute library to learn from other values, and applied Iterforest. I noted, score of Logistic regression is so weak (0.6) and thought Iterforest might not learn enough or anyway, except from outer subset which might not be enough? for example the subset with 11 columns might learn from the other columns but not from within it's members, and the same goes for the subset with four columns. This bar plot show better quantity of missings So of course, dealing with missings is better than dropping rows because It would affect my prediction which does contain the same missings quantity relatively. Any better way to deal with these ? [EDIT] The nullity pattern is confirmed: machine-learning cor...