matlab - 检查单元格内的成员

Question

我有一个元胞数组(data)，如下所示(此处缩短):

'45.203885' '-90.600123'    '119-8001'  733144  NaN
'45.203885' '-90.600123'    '119-8001'  733147  NaN
'45.203885' '-90.600123'    '119-8001'  733150  NaN
'45.203885' '-90.600123'    '119-8001'  733153  NaN
'45.203885' '-90.600123'    '119-8001'  733156  NaN
'45.203885' '-90.600123'    '119-8001'  733159  NaN

我想根据第 4 列(已使用 datenum 转换的日期)是否匹配 来填充 NaN 的第 5 列B.

B(也是一个单元格)看起来像这样(为了使示例有意义，也缩短了很多):

'45.203885' '-90.600123'    '119-8001'  733144  '3.3'
'45.203885' '-90.600123'    '119-8001'  733150  '9.5'
'45.203885' '-90.600123'    '119-8001'  733156  '6.8'

如您所见，B 中的第 4 列日期并不一致。我正在尝试将 NaN 添加到第 5 列，其中 B(:,3) 和 B(:, 4) 不添加匹配 data(:,3) 和 data(:, 4)。

最终产品应该类似于:

'45.203885' '-90.600123'    '119-8001'  733144  '3.3'
'45.203885' '-90.600123'    '119-8001'  733147  NaN
'45.203885' '-90.600123'    '119-8001'  733150  '9.5'
'45.203885' '-90.600123'    '119-8001'  733153  NaN
'45.203885' '-90.600123'    '119-8001'  733156  '6.8'
'45.203885' '-90.600123'    '119-8001'  733159  NaN

如果data是一个矩阵，我只需执行以下操作:

data_ind = ismember(data(:,3:4),B(:,3:4),'rows');

但我不知道如何用单元格做到这一点。某种形式的 cellfun 可以解决这个问题吗？

Answer 1

sd = size(data,1); %// number of rows of data
sb = size(B,1); %// number of rows of B
[dd bb] = ndgrid(1:sd,1:sb); %// all combinations (row of data, row of B)
cond1 = strcmp(data(dd,3),B(bb,3)); %// test col 3 for all combinations
cond2 = [data{dd,4}].'==[B{bb,4}].'; %// test col 4 for all combinations
cond = reshape(cond1 & cond2, sd, sb); %// combine the two conditions
[ib, id] = max(cond); %// id contains the index of the first 1 (if any) ...
%// ... of each col in cond; and ib is a logical index of the row of that 1
id = id(ib); %// keep only id for which the maximum is 1
data(id,:) = B(ib,:); %// copy matching rows of B into data

data 和 B 都包含与另一个变量的任何行都不匹配的行的示例:

data = {
    '45.203885' '-90.600123'    '119-8001'  733144  NaN
    '45.203885' '-90.600123'    '119-8001'  733147  NaN
    '45.203885' '-90.600123'    '119-8001'  733150  NaN
    '45.203885' '-90.600123'    '119-8001'  733153  NaN
    '45.203885' '-90.600123'    '119-8001'  733156  NaN
    '45.203885' '-90.600123'    '119-8001'  733159  NaN};

B = {
    '45.203885'    '-90.600123'    '119-8001'    [733144]    '3.3'
    '45.203885'    '-90.600123'    '119-8001'    [733150]    '9.5'
    '45.203885'    '-90.600123'    '119-8001'    [733156]    '6.8'
    '45.203885'    '-90.600123'    '169-8001'    [833156]    '6.8'};

结果:

data = 

    '45.203885'    '-90.600123'    '119-8001'    [733144]    '3.3'
    '45.203885'    '-90.600123'    '119-8001'    [733147]    [NaN]
    '45.203885'    '-90.600123'    '119-8001'    [733150]    '9.5'
    '45.203885'    '-90.600123'    '119-8001'    [733153]    [NaN]
    '45.203885'    '-90.600123'    '119-8001'    [733156]    '6.8'
    '45.203885'    '-90.600123'    '119-8001'    [733159]    [NaN]