covariance

问题 In earlier versions of C# IEnumerable was defined like this: public interface IEnumerable<T> : IEnumerable Since C# 4 the definition is: public interface IEnumerable<out T> : IEnumerable Is it just to make the annoying casts in LINQ expressions go away? Won't this introduce the same problems like with string[] <: object[] (broken array variance) in C#? How was the addition of the covariance done from a compatibility point of view? Will earlier code still work on later versions of .NET or is

问题 try flow link I've been messing around with typing a "camel caser" function (one that consumes JSON and camel cases its keys). I've run into a few issues along the way, and I'm curious if y'all have any suggestions. A camel caser never changes the shape of its argument, so I would like to preserve the type of whatever I pass in; ideally, calling camelize on an array of numbers would return another array of numbers, etc. I've started with the following: type JSON = null | string | number |

问题 Question In R, I would like to create n variables of length L which relationship is given by a correlation matrix called cor_matrix . The important point is that the n variables may follow different distributions (including continuous vs discrete distributions). Related posts how-to-generate-sample-data-with-exact-moments generate-a-random-variable-with-a-defined-correlation-to-an-existing-variable r-constructing-correlated-variables Modified from the third post listed above, the following is

问题 This question already has answers here : Is List<Dog> a subclass of List<Animal>? Why are Java generics not implicitly polymorphic? (17 answers) Closed 5 years ago . I`m wondering why this conversion is not working: ArrayList<Song> arrayList =new ArrayList<MediaItem>(); I may have to add that Song extends MediaItem. I think this conversion should work because Song has the ability to store all the information form MediaItem. So no information is lost. Does anyone have an explanation for me?

问题 The time series model is expressed as y(t) = 0.5 + 0.3y(t-1) + n(t) where n(t) = 0.1*randn(500,1) for t=1,2,...,500 Slides contain the Correlation and covariance matrix. The formula for correlation is: E[y(t)*y(t)^T] which can be invoked by using xcorr . I would like to know how one can calculate the individual Correlation matrix for its lagged version E[y(t-1)*y(t-1)^T] without using the inbuilt commands so that I can finally implement the following expression trace([E[y(t-1)*y(t-1)']]^-1)

问题 This is the definition of a covariance matrix. http://en.wikipedia.org/wiki/Covariance_matrix#Definition Each element in the matrix, except in the principal diagonal, (if I am not wrong) simplifies to E(x_{i} * x_{j}) - mean(i)*mean(j) where i and j are the row number and column number of the covariance matrix. From the numpy documentation, x = np.array([[0, 2], [1, 1], [2, 0]]).T x array([[0, 1, 2], [2, 1, 0]]) np.cov(x) array([[ 1., -1.], [-1., 1.]]) The first row i.e [0, 1, 2] corresponds

问题 There are similar questions on the website, but I could not find an answer to this seemingly very simple problem. I fit a mixture of two gaussians on the Old Faithful Dataset: if(!require("mixtools")) { install.packages("mixtools"); require("mixtools") } data_f <- faithful plot(data_f$waiting, data_f$eruptions) data_f.k2 = mvnormalmixEM(as.matrix(data_f), k=2, maxit=100, epsilon=0.01) data_f.k2$mu # estimated mean coordinates for the 2 multivariate Gaussians data_f.k2$sigma # estimated

问题 I have multiple data-points and an associated data-processor for each. public interface IDataPointProcessor<T> where T : DataPointInputBase { DataPointOutputBase GetOutput(T input); } I load a list of data points from a file and wish to process them using its single associated processor. foreach (DataPointInputBase item in input.DataPoints) { //assuming item coming in is of type 'X' how do I get correct processor var type = typeof(IDataPointProcessor<X>); var types = AppDomain.CurrentDomain

问题 I'm trying to implement some C# code as F# for a p2p application.. I must admit I don't have full understanding of the p2p implementation and I hope it is irrelevant for fixing the type problem.. All of the p2p library is implemented in C#. The C# implementation: public class TestMessage : Message { public string Text { get; set; } } ... var p = new Peer(); p.Subscribe(new Subscription<TestMessage> { Callback = x => Console.WriteLine(x.Text), }); The basic idea is that the 'p' peer now

问题 According to covariance definition: Q[+B] means that Q can take any class, but if A is a subclass of B, then Q[A] is considered to be a subclass of Q[B]. Let's see the following example: trait SomeA trait SomeB extends SomeA trait SomeC extends SomeB case class List1[+B](elements: B*) val a = List1[SomeA](new SomeA{},new SomeB{}) val b = List1[SomeB](new SomeB{},new SomeC{}) Everything is fine, but I don't see why List1[SomeB] is a subclass of List1[SomeA] , or in other words why b is a