首页 \ 问答 \ WP7 ApplicationBarIcon可见性(WP7 ApplicationBarIcon Visibility)

WP7 ApplicationBarIcon可见性(WP7 ApplicationBarIcon Visibility)

感谢WP7 ApplicationBarIcon不是标准控件。

我需要能够以编程方式隐藏它(我需要隐藏而不是禁用)

1 /除了添加/删除图标之外,还有其他方法可以做到这一点

2 /假设我必须添加和删除它,如何将事件与我添加的控件相关联?

  • 谢谢

Appreciate that the WP7 ApplicationBarIcon is not standard control as such.

I need to be able to hide this programatically (I need to hide rather than disable)

1/ is there any other way I can do this other than adding/removing the icon

2/ assuming that I have to add and remove it, how do I associate an event to the control that I am adding?

  • thanks

原文:https://stackoverflow.com/questions/6702866
更新时间:2021-11-07 22:11

最满意答案

你可能需要这个:

% generate small dummy data
nxs = 2;
nls = 3;
nms = 4;
nks = 5;
MS = rand(nxs, nls, nms);
KS = rand(nxs, nls, nks);

R = sum(abs(bsxfun(@minus,MS,permute(KS,[1,2,4,3]))),4)

这将产生一个大小为[2,3,4]的矩阵,即[nxs,nls,nms] 。 每个元素[k1,k2,k3]将对应于

R(k1,k2,k3) == sum_k abs(MS(k1,k2,k3) - KS(k1,k2,k))

例如,在我的随机运行中

R(2,1,3)

ans =

   1.255765020150647

>> sum(abs(MS(2,1,3)-KS(2,1,:)))

ans =

   1.255765020150647

诀窍是引入具有permute单一维度: permute(KS,[1,2,4,3])的大小为[nxs,nls,1,nks] ,而大小为[nxs,nls,nms] MS是隐式的也是大小[nxs,nls,nms,1] :假设MATLAB中的每个数组都具有可数无限数量的尾随单例维度。 从这里可以很容易地看到如何将大小分别为[nxs,nls,nms,1][nxs,nls,1,nks]数组bsxfun组合在一起,以获得大小为[nxs,nls,nms,nks] 。 沿着维度4求和可以达成交易。


我在评论中指出,将求和指数置于首位可能会更快。 事实证明,这本身会使代码运行得更慢 。 但是,通过重新整形数组以减小尺寸大小,整体性能会提高(由于最佳内存访问)。 比较一下:

% generate larger dummy data
nxs = 20;
nls = 30;
nms = 40;
nks = 500;
MS = rand(nxs, nls, nms);
KS = rand(nxs, nls, nks);

MS2 = permute(MS,[4 3 2 1]);
KS2 = permute(KS,[3 4 2 1]);
R3 = permute(squeeze(sum(abs(bsxfun(@minus,MS2,KS2)),1)),[3 2 1]);

我所做的是将求和nks维度放在第一位,然后按降序排列其余维度。 这可以自动完成,我只是不想让这个例子过于复杂。 在您的使用案例中,您可能无论如何都知道尺寸的大小。

具有以上两个代码的运行时:原始的0.07028秒,重新排序的一个的0.051162秒(最好的5个)。 不幸的是,现在更大的例子不适合我。


You might need this:

% generate small dummy data
nxs = 2;
nls = 3;
nms = 4;
nks = 5;
MS = rand(nxs, nls, nms);
KS = rand(nxs, nls, nks);

R = sum(abs(bsxfun(@minus,MS,permute(KS,[1,2,4,3]))),4)

This will produce a matrix of size [2,3,4], i.e. [nxs,nls,nms]. Each element [k1,k2,k3] will correspond to

R(k1,k2,k3) == sum_k abs(MS(k1,k2,k3) - KS(k1,k2,k))

For instance, in my random run

R(2,1,3)

ans =

   1.255765020150647

>> sum(abs(MS(2,1,3)-KS(2,1,:)))

ans =

   1.255765020150647

The trick is to introduce singleton dimensions with permute: permute(KS,[1,2,4,3]) is of size [nxs,nls,1,nks], while MS of size [nxs,nls,nms] is implicitly also of size [nxs,nls,nms,1]: every array in MATLAB is assumed to possess a countably infinite number of trailing singleton dimensions. From here it's easy to see how you can bsxfun together arrays of size [nxs,nls,nms,1] and [nxs,nls,1,nks], respectively, to obtain one with size [nxs,nls,nms,nks]. Summing along dimension 4 seals the deal.


I noted in a comment, that it might be faster to permute the summing index to be in the first place. Turns out that this by itself makes the code run slower. However, by reshaping the arrays to have decreasing dimension sizes, the overall performance increases (due to optimal memory access). Compare this:

% generate larger dummy data
nxs = 20;
nls = 30;
nms = 40;
nks = 500;
MS = rand(nxs, nls, nms);
KS = rand(nxs, nls, nks);

MS2 = permute(MS,[4 3 2 1]);
KS2 = permute(KS,[3 4 2 1]);
R3 = permute(squeeze(sum(abs(bsxfun(@minus,MS2,KS2)),1)),[3 2 1]);

What I did was put the summing nks dimension into first place, and order the rest of the dimensions in decreasing order. This could be done automatically, I just didn't want to overcomplicate the example. In your use case you'll probably know the magnitude of the dimensions anyway.

Runtimes with the above two codes: 0.07028 s for the original, 0.051162 s for the reordered one (best out of 5). Larger examples don't fit into memory for me now, unfortunately.

相关问答

更多

相关文章

更多

最新问答

更多
  • 您如何使用git diff文件,并将其应用于同一存储库的副本的本地分支?(How do you take a git diff file, and apply it to a local branch that is a copy of the same repository?)
  • 将长浮点值剪切为2个小数点并复制到字符数组(Cut Long Float Value to 2 decimal points and copy to Character Array)
  • OctoberCMS侧边栏不呈现(OctoberCMS Sidebar not rendering)
  • 页面加载后对象是否有资格进行垃圾回收?(Are objects eligible for garbage collection after the page loads?)
  • codeigniter中的语言不能按预期工作(language in codeigniter doesn' t work as expected)
  • 在计算机拍照在哪里进入
  • 使用cin.get()从c ++中的输入流中丢弃不需要的字符(Using cin.get() to discard unwanted characters from the input stream in c++)
  • No for循环将在for循环中运行。(No for loop will run inside for loop. Testing for primes)
  • 单页应用程序:页面重新加载(Single Page Application: page reload)
  • 在循环中选择具有相似模式的列名称(Selecting Column Name With Similar Pattern in a Loop)
  • System.StackOverflow错误(System.StackOverflow error)
  • KnockoutJS未在嵌套模板上应用beforeRemove和afterAdd(KnockoutJS not applying beforeRemove and afterAdd on nested templates)
  • 散列包括方法和/或嵌套属性(Hash include methods and/or nested attributes)
  • android - 如何避免使用Samsung RFS文件系统延迟/冻结?(android - how to avoid lag/freezes with Samsung RFS filesystem?)
  • TensorFlow:基于索引列表创建新张量(TensorFlow: Create a new tensor based on list of indices)
  • 企业安全培训的各项内容
  • 错误:RPC失败;(error: RPC failed; curl transfer closed with outstanding read data remaining)
  • C#类名中允许哪些字符?(What characters are allowed in C# class name?)
  • NumPy:将int64值存储在np.array中并使用dtype float64并将其转换回整数是否安全?(NumPy: Is it safe to store an int64 value in an np.array with dtype float64 and later convert it back to integer?)
  • 注销后如何隐藏导航portlet?(How to hide navigation portlet after logout?)
  • 将多个行和可变行移动到列(moving multiple and variable rows to columns)
  • 提交表单时忽略基础href,而不使用Javascript(ignore base href when submitting form, without using Javascript)
  • 对setOnInfoWindowClickListener的意图(Intent on setOnInfoWindowClickListener)
  • Angular $资源不会改变方法(Angular $resource doesn't change method)
  • 在Angular 5中不是一个函数(is not a function in Angular 5)
  • 如何配置Composite C1以将.m和桌面作为同一站点提供服务(How to configure Composite C1 to serve .m and desktop as the same site)
  • 不适用:悬停在悬停时:在元素之前[复制](Don't apply :hover when hovering on :before element [duplicate])
  • 常见的python rpc和cli接口(Common python rpc and cli interface)
  • Mysql DB单个字段匹配多个其他字段(Mysql DB single field matching to multiple other fields)
  • 产品页面上的Magento Up出售对齐问题(Magento Up sell alignment issue on the products page)