dimension

Android ConstraintLayout @dimens replaced with hardcoded values

由 女生的网名这么多〃 提交于 2019-12-10 13:19:32
问题 Heyho mates, currently I am learning the new layout editor of Android Studio with the new ConstraintLayout . By the way, I hate it. But I got the issue, that if I want to specify a layout_height with @dimen , it gets replaced with a dp value instead. Someone else got this issue? Android Studio version 2.2.2 & 2.2.3 same issue. Newest gradle version. Thanks in advance guys! Edit : Code example : <?xml version="1.0" encoding="utf-8"?> <ScrollView xmlns:android="http://schemas.android.com/apk

Loading Rdata files from url

由 佐手、 提交于 2019-12-10 13:05:31
问题 I'm having difficulty loading a file so that it doesn't lose its dimensions. So here is where I'm at: > mood_data <- read.table("http://www.psychology.mcmaster.ca/bennett/psy710/datasets/mood_data.Rdata") If I do this it just gives me all the information disorganized in one line. I also tried: > mood_data <- url("http://www.psychology.mcmaster.ca/bennett/psy710/datasets/mood_data.Rdata") > load(mood_data) If I do this I get this weird stuff that doesn't make any sense to me. 回答1: You should

Activate a specific dimens.xml at runtime

由 时光怂恿深爱的人放手 提交于 2019-12-08 11:52:19
问题 I need to choose a specific dimens.xml at runtime after my app checks for screen capabilities. I DON'T WANT ANDROID CHOOSE IT FOR ME by selecting automatically by project folder (\values, \values-large, ...). I have to do it manually due to the bad screen configuration (density and dimension) of my low-cost china tablet. Can I do that? 回答1: Have you tried to force configuration change? DisplayMetrics metrics = new DisplayMetrics(); metrics.densityDpi = DisplayMetrics.DENSITY_MEDIUM; metrics

Efficiently `apply` on array and preserve structure

由 天涯浪子 提交于 2019-12-07 07:47:56
问题 I have an array of matrices. dims <- c(10000,5,5) mat_array <- array(rnorm(prod(dims)), dims) I would like to perform a matrix-based operation (e.g. inversion via the solve function) on each matrix, but preserve the full structure of the array. So far, I have come up with 3 options: Option 1 : A loop, which does exactly what I want, but is clunky and inefficient. mat_inv <- array(NA, dims) for(i in 1:dims[1]) mat_inv[i,,] <- solve(mat_array[i,,]) Option 2 : The apply function, which is faster

How to model process and status history in a data warehouse?

由 旧城冷巷雨未停 提交于 2019-12-07 07:32:51
问题 Let's say that we have D_PROCESS , D_WORKER and D_STATUS as dimensions, and the fact F_EVENT that links a process (what) with a worker (who's in charge) and the "current" status. The process status changes over time. Shoud we store in F_EVENT one line per process/status/worker, or one line per process/worker, and "somewhere else" one line per status change for a given process/worker? I'm new to Datawarehouse and it's hard to find best practices/tutorial related to data modelization. 回答1: Read

What is the best way to obtain the complete list of permission of a given user using AMO in C#

由 折月煮酒 提交于 2019-12-06 10:39:32
I am using Visual Studio 2013(C#) and SSAS 2014 (through AMO). I need to prepare a list of permissions of a given user in the SSAS database. For example, domainName\userName has permissions on the 2 dimensions out of 5 available in the database. I like to prepare a list like this. Dimension Name | Attributes | Dimension used in Cube | VisualTotal | Mdx Set (if any) | Role Name I can loop through Roles and members and get some information. But it seems that it is a long shot and will not be performance friendly in the production environment. Any advice is much appreciated! Try checking out

What are the types of dimension tables in star schema design? [closed]

由 谁都会走 提交于 2019-12-06 09:37:12
问题 As it currently stands, this question is not a good fit for our Q&A format. We expect answers to be supported by facts, references, or expertise, but this question will likely solicit debate, arguments, polling, or extended discussion. If you feel that this question can be improved and possibly reopened, visit the help center for guidance. Closed 7 years ago . When reading about star schema design I have seen that many people uses various names for different types of dimension tables. Please

ssas dimension processing key not found error

由 久未见 提交于 2019-12-06 04:08:47
I have this strange case where I am trying to process a dimension, the data source is MSSQL and when I try to process this dimension I am getting this error Errors in the OLAP storage engine: The attribute key cannot be found when processing: Table: 'application', Column: 'Full_Name', Value: 'Mr Peter McDonald'. The attribute is 'Applicant Full Name'. Since I think I know what the problem is, I have set the case sensitive property for this column and I tried processing again and it's giving me the same result. when I query for the column using like, I get two records Peter McDonald Peter

How to model process and status history in a data warehouse?

由 …衆ロ難τιáo~ 提交于 2019-12-05 15:56:10
Let's say that we have D_PROCESS , D_WORKER and D_STATUS as dimensions, and the fact F_EVENT that links a process (what) with a worker (who's in charge) and the "current" status. The process status changes over time. Shoud we store in F_EVENT one line per process/status/worker, or one line per process/worker, and "somewhere else" one line per status change for a given process/worker? I'm new to Datawarehouse and it's hard to find best practices/tutorial related to data modelization. Read The Data Warehouse Toolkit by Ralph Kimball for a good introduction to dimensional modeling. It sounds like

Efficiently `apply` on array and preserve structure

由 末鹿安然 提交于 2019-12-05 15:06:47
I have an array of matrices. dims <- c(10000,5,5) mat_array <- array(rnorm(prod(dims)), dims) I would like to perform a matrix-based operation (e.g. inversion via the solve function) on each matrix, but preserve the full structure of the array. So far, I have come up with 3 options: Option 1 : A loop, which does exactly what I want, but is clunky and inefficient. mat_inv <- array(NA, dims) for(i in 1:dims[1]) mat_inv[i,,] <- solve(mat_array[i,,]) Option 2 : The apply function, which is faster and cleaner, BUT squishes each matrix down to a vector. mat_inv <- apply(mat_array, 1, solve) dim(mat
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