decomposition

问题 Just curious why I can't do this: let myFn (data : obj) = match data with | :? (string * string) as (s1, s2) -> sprintf "(%s, %s)" s1 s2 |> Some | :? (string * string * int) as (s1, s2, i) -> sprintf "(%s, %s, %d)" s1 s2 i |> Some | _ -> None How come? 回答1: See F# spec, section 7.3 "As patterns" An as pattern is of the form pat as ident Which means you need to use an identifier after as : let myFn (data : obj) = match data with | :? (string * string) as s1s2 -> let (s1, s2) = s1s2 in sprintf

问题 Decomposing rotation matrix (x,y',z'') - Cartesian angles Im currently working with rotation matrices and I have the following problem: Given three coordinate systems ( O0,x0,y0,z0; O1,x1,y1,z1; O2,x2,y2,z2 ) which coincide. We rotate first the frame #1 with the respect to frame #0, then the frame #2 with respect to frame #1. The order of the rotations: R = Rx_alpha * Ry_beta * Rz_gamma , so first about x , then y' , then z'' , which are also known as the Cartesian angles . If R1 stands for

问题 I'm a first year computer science student. We are currently programming in java and I often try decompose my program into well named methods so that my main method logic can read as close to pseudo-code as possible. The problem I find is that often I'm ending up writing so many small private methods that I feel I might be overdoing it. Are there any good rules of thumb or stylistic considerations to take into account when deciding whether to decompose a problem even further? 回答1: Most new

问题 I'm a first year computer science student. We are currently programming in java and I often try decompose my program into well named methods so that my main method logic can read as close to pseudo-code as possible. The problem I find is that often I'm ending up writing so many small private methods that I feel I might be overdoing it. Are there any good rules of thumb or stylistic considerations to take into account when deciding whether to decompose a problem even further? 回答1: Most new

问题 The bfast() function in package bfast is supposed to be able to detect both breakpoints in long-term trends and changes in the seasonal component. One example is this graph (source): In this graph, subplot no. 2 shows a detected change in seasonality, while no. 3 shows a breakpoint in the trend. However, I don't understand how to tell bfast() to look for changes/breakpoints in seasonality. All I get is breakpoints in the long-term trend. Here is a reproducible example, simulating a 50-year

问题 The bfast() function in package bfast is supposed to be able to detect both breakpoints in long-term trends and changes in the seasonal component. One example is this graph (source): In this graph, subplot no. 2 shows a detected change in seasonality, while no. 3 shows a breakpoint in the trend. However, I don't understand how to tell bfast() to look for changes/breakpoints in seasonality. All I get is breakpoints in the long-term trend. Here is a reproducible example, simulating a 50-year

问题 I have an xts timeseries called hourplot in R with a period of 24 (hourly data) over two weeks, indexed by timestamp objects of POSIXlt class, like the following: > dput(hourplot) structure(c(1, 1, 1, 1, 1, 1, 1.11221374045802, 1.3368, 1.18, 1.0032, 1, 1, 1, 1, 1, 1, 1.0736, 1.2536, 1, 1.0032, 1.1856, 1.0048, 1, 1, 1, 1, 1, 1, 1, 1, 1.04045801526718, 1.20229007633588, 1.00229007633588, 1, 1, 1, 1, 1, 1, 1, 1.1152, 1.008, 1, 1, 1.2648, 1.1832, 1, 1, 1, 1, 1, 1, 1, 1.0424, 1.2952, 1.6496, 1

问题 I'm currently trying to run a historical decomposition on my data series in R. I've read a ton of papers and they all provide the following explanation of how to do a historical decomposition: Where the sum on the right hand side is a "dynamic forecast" or "base projection" of Yt+k conditional on info available at time t. The sum on the left hand side is the difference between the actual series and the base projection due to innovation in variables in periods t+1 to t+k I get very confused

问题 Really confused why the QR output using RcppArmadillo is different than QR output from R; Armadillo documentation doesnt give a clear answer either. Essentially when I give R a matrix Y that is n * q (say 1000 X 20 ) , I get back Q which is 1000 X 20 and R 20 X 1000. This is what I need. But when I use the QR solver in Armadillo, it throws me back Q 1000 X 1000 and R 1000 X 20. Can I call R's qr function instead? I need Q to have dimension n x q, not q x q. Code below is what I am using(its a

问题 I'm attempting to reduce a wide dataset (51 features, ~1300 individuals) using PCA through the ml.linalg method as follows: 1) Named my columns as one list: features = indi_prep_df.select([c for c in indi_prep_df.columns if c not in{'indi_nbr','label'}]).columns 2) Imported the necessary libraries from pyspark.ml.feature import PCA as PCAML from pyspark.ml.linalg import Vector from pyspark.ml.feature import VectorAssembler from pyspark.ml.linalg import DenseVector 3) Collapsed the features to