modeling

问题 I would like to make California's map by the contour plot. However, I do not know how to get the necessary x, y and z -coordinates. 回答1: Have you consulted the California's Geospatial Information Office? There's also geodata.gov, which links to a bunch of elevation datasets, including the USGS National Elevation Dataset. 回答2: The GDAL library (www.gdal.org) has many useful commands for processing these types of data. Most elevation data formats, such as those available through the national

问题 I followed what is outlined here. Here is my code: from google.appengine.api import users from google.appengine.ext import db class Book(db.Model): title = db.StringProperty() class Author(db.Model): name = db.StringProperty() class BookAuthor(db.Model): book = db.ReferenceProperty(Book, required=True, collection_name='books') author = db.ReferenceProperty(Author, required=True, collection_name='authors') b = Book(title="My Book") a = Author(name="Author of My Book") db.put([b, a]) ba =

问题 Got an interesting issue with the rugarch package. I noticed that when I changed the order of the external regressors, there are different values for the robust coefficient matrix. The values should be the same (respective to the ordering of the variables). However, I am getting drastically different results. At that time the model was arma(2,2) + garch(1,0). Is this considered a normal behavior of the rugarch package? I assume that when you change the ordering of the external regressors the

问题 I am trying to fit a Negative binomial mixture with PyMC. It seems I do something wrong, because the predictive doesn't look at all similar to the input data. The problem is probably in the prior of the Negative binomial parameters. Any suggestions? from sklearn.cluster import KMeans import pymc as mc n = 3 #Number of components of the mixture ndata = len(data) dd = mc.Dirichlet('dd', theta=(1,)*n) category = mc.Categorical('category', p=dd, size=ndata) kme = KMeans(n) # This is not needed

问题 I have got class Car, and each class has list of extras (leather, abs, Folding mirrors etc...) public Extra { public bool Paid {get;set;} public string Name {get;set;} public string Code {get;set;} } And now, better is: class Car { public Extra Leather {get;set;} public Extra FoldingMirrors {get;set;} public Extra Abs {get;set;} } or better: class Car { private List<Extra> _listOfExtras=new List<Extra> public List<Extra> ListOfExtras { ... } } And the worst part: My winforms application works

问题 I want to estimate the heritability of animal traits using an animal model. However, I can't figure out how to properly format my data so that MCMCglmm can create a model. After much trial and error and scouring the internet for advice, I'm stuck. To my knowledge, I've formatted the data as suggested by all available resources that I know of, yet I get the following error message:: Error in MCMCglmm(BWT ~ 1, random = ~animal, pedigree = Ped, data = Data, : some levels of animal do not have a

问题 This question was migrated from Cross Validated because it can be answered on Stack Overflow. Migrated 3 years ago . I am using dnbinom() for writing the log-likelihood function and then estimate parameters using mle2() {bbmle} in R. The problem is that I got 16 warnings for my negative binomial model, all of them NaNs produced like this one: 1: In dnbinom(y, mu = mu, size = k, log = TRUE) : NaNs produced My code: # data x <- c(0.35,0.45,0.90,0.05,1.00,0.50,0.45,0.25,0.15,0.40,0.26,0.37,0.43

问题 I am working at modelling a software system using Sparx Enterprise Architect 13. This system contains different versions of software components. We typically add services and/or APIs when we release a new version of a software component. Currently, to reflect the fact that component ServiceV1 exposes an interface A and ServiceV2 exposes interfaces A (the same as ServiceV1) and B, I make ServiceV2 extend ServiceV1. But that is not straightforward: the generalization link is not available in

问题 I have an algorithm that extracts company names from text. It generally does a good job, however, it also sometimes extracts strings that look like company names, but obviously aren't. For example, "Contact Us", "Colorado Springs CO", "Cosmetic Dentist" are obviously not company names. There are too many of such false positives to blacklist, so I want to introduce an algorithmic way of ranking the extracted strings, so that the lowest-ranking ones can be discarded. Currently, I'm thinking of

问题 I am currently trying to implement a model to simplify graphical chart creation. However, one of the attributes must be an array of attributes. For example: "Chart_Series" has a "name" which would be a string and a data field which would be separated by dates which is an array of arrays [[Date1, Value1],[Date2,Value2],[Date3,Value3]. The purpose is to create an array of "Chart_Series" so as to call upon something like: for series in @chart_series series.name for data in series.data data.[Date