小编som*_*one的帖子

我正在尝试使用pyspark来计算出现次数.

假设我有这样的数据:

data = sc.parallelize([(1,[u'a',u'b',u'd']),
                       (2,[u'a',u'c',u'd']),
                       (3,[u'a']) ])

count = sc.parallelize([(u'a',0),(u'b',0),(u'c',0),(u'd',0)])

是否可以计算出现次数data并更新count？

结果应该是这样的[(u'a',3),(u'b',1),(u'c',1),(u'd',2)].

问题是关于特征检测的概念。找到图像的角点后卡住了，我想知道如何在计算的角点内找到特征点。

假设我有像这样的数据的灰度图像

A = [ 1 1 1 1 1 1 1 1;
      1 3 3 3 1 1 4 1;
      1 3 5 3 1 4 4 4;
      1 3 3 3 1 4 4 4; 
      1 1 1 1 1 4 6 4;
      1 1 1 1 1 4 4 4]

如果我用

B = imregionalmax(A);

结果将是这样

B = [ 0 0 0 0 0 0 0 0;
      0 1 1 1 0 0 1 0;
      0 1 1 …

I am stuck on the on how to manipulate the data structure.

I have header file that declare like this

struct item{
 int    i;  
 char   str[88];
};

and I have a C file that I want to make 9 structure items (I declare as global variable and I already include the header file):

struct item a[9];

but when I want to put the data that I want into

foo()

    {
    ...
      // let's say I have data int in index …