How to search and find array in array?
显示 更早的评论
Hello,
I create an array below;
bigArray = rand(1,150);
bigArray(1,15:19) = [1 1 1 1 1]';
bigArray(1,25:29) = [1 1 1 1 1]';
bigArray(1,75:79) = [1 1 1 1 1]';
bigArray(1,105:109) = [1 1 1 1 1]';
bigArray(1,65) = 1;
bigArray(1,5:6) = [1 1]';
I want to find [1 1 1 1 1]' array indexes. But I run the code;
idx = find(ismember(bigArray,[1 1 1 1 1]'))
idx = 1×23
5 6 15 16 17 18 19 25 26 27 28 29 65 75 76 77 78 79 105 106 107 108 109
<mw-icon class=""></mw-icon>
<mw-icon class=""></mw-icon>
I want to see as an output; [15 16 17 18 19 25 26 27 28 29 75 76 77 78 79 105 106 107 108 109]
采纳的回答
The ismember function is doing exactly what it should. You need to examine ‘bigArray’ tto understand its output.
Try this —
bigArray = rand(1,150);
bigArray(1,15:19) = [1 1 1 1 1]';
bigArray(1,25:29) = [1 1 1 1 1]';
bigArray(1,75:79) = [1 1 1 1 1]';
bigArray(1,105:109) = [1 1 1 1 1]';
bigArray(1,65) = 1;
bigArray(1,5:6) = [1 1]';
disp(bigArray)
Columns 1 through 18
0.5068 0.9505 0.0516 0.0882 1.0000 1.0000 0.0318 0.3127 0.8880 0.0266 0.9131 0.8241 0.3292 0.0973 1.0000 1.0000 1.0000 1.0000
Columns 19 through 36
1.0000 0.4800 0.7703 0.2106 0.1942 0.6979 1.0000 1.0000 1.0000 1.0000 1.0000 0.4912 0.4754 0.2845 0.7561 0.9840 0.1853 0.1938
Columns 37 through 54
0.4194 0.0772 0.1116 0.3382 0.2339 0.0543 0.3256 0.9436 0.2975 0.6174 0.2227 0.7096 0.5371 0.9033 0.7377 0.5522 0.5055 0.3581
Columns 55 through 72
0.8638 0.2271 0.3195 0.0197 0.4622 0.0299 0.3055 0.4705 0.3989 0.4399 1.0000 0.6735 0.8151 0.1478 0.8166 0.2860 0.6436 0.8295
Columns 73 through 90
0.4801 0.7913 1.0000 1.0000 1.0000 1.0000 1.0000 0.8977 0.8132 0.6884 0.4697 0.2341 0.1666 0.4997 0.6061 0.5467 0.4436 0.5024
Columns 91 through 108
0.8998 0.2705 0.0215 0.1483 0.4976 0.9649 0.5099 0.9422 0.8145 0.3519 0.6390 0.0093 0.5664 0.9736 1.0000 1.0000 1.0000 1.0000
Columns 109 through 126
1.0000 0.7624 0.4387 0.6305 0.1326 0.6353 0.2669 0.3236 0.1274 0.4486 0.2744 0.5949 0.0264 0.7638 0.9727 0.1876 0.1710 0.6894
Columns 127 through 144
0.3853 0.9315 0.9158 0.8965 0.2594 0.0620 0.3116 0.6300 0.4641 0.4008 0.7085 0.1320 0.1949 0.4087 0.7125 0.2770 0.0934 0.5674
Columns 145 through 150
0.4633 0.3660 0.7867 0.9442 0.3935 0.3620
Lv = ismember(bigArray,[1 1 1 1 1]')
Lv = 1x150 logical array
Columns 1 through 45
0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Columns 46 through 90
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0
Columns 91 through 135
0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Columns 136 through 150
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
idx = find(Lv)
idx = 1×23
5 6 15 16 17 18 19 25 26 27 28 29 65 75 76 77 78 79 105 106 107 108 109
<mw-icon class=""></mw-icon>
<mw-icon class=""></mw-icon>
.
4 个评论
Thank you but I do not want to see 5 6 and 65 indexes. I just need 5 times repeated value indexes as an output. Also it should be faster than for loop.
To search for a specific pattern, use the strfind functiion. (It seems counterintuitive to use that here, however it works, after a fashion).
bigArray = rand(1,150);
bigArray(1,15:19) = [1 1 1 1 1]';
bigArray(1,25:29) = [1 1 1 1 1]';
bigArray(1,75:79) = [1 1 1 1 1]';
bigArray(1,105:109) = [1 1 1 1 1]';
bigArray(1,65) = 1;
bigArray(1,5:6) = [1 1]';
disp(bigArray)
Columns 1 through 18
0.2743 0.2407 0.8275 0.0096 1.0000 1.0000 0.8060 0.9308 0.5000 0.8108 0.5384 0.8746 0.4255 0.4682 1.0000 1.0000 1.0000 1.0000
Columns 19 through 36
1.0000 0.8638 0.2319 0.3997 0.2898 0.1223 1.0000 1.0000 1.0000 1.0000 1.0000 0.1297 0.7206 0.1497 0.6789 0.5847 0.5892 0.7993
Columns 37 through 54
0.2800 0.9085 0.7488 0.2514 0.1398 0.6313 0.5854 0.5353 0.4922 0.5730 0.1827 0.1280 0.9387 0.7217 0.2021 0.0497 0.3776 0.1897
Columns 55 through 72
0.2701 0.6038 0.7225 0.8177 0.6791 0.4523 0.2733 0.2405 0.7571 0.2166 1.0000 0.7963 0.6565 0.4824 0.2540 0.5467 0.0953 0.0753
Columns 73 through 90
0.4202 0.5929 1.0000 1.0000 1.0000 1.0000 1.0000 0.4567 0.3059 0.8577 0.0012 0.2719 0.9140 0.5484 0.2816 0.5734 0.8117 0.6676
Columns 91 through 108
0.5733 0.8570 0.0506 0.4219 0.2212 0.6640 0.7136 0.5508 0.5685 0.0510 0.7710 0.4171 0.2690 0.0529 1.0000 1.0000 1.0000 1.0000
Columns 109 through 126
1.0000 0.4731 0.3191 0.4956 0.4133 0.3051 0.1663 0.6878 0.8143 0.6790 0.9396 0.2974 0.8329 0.4112 0.1258 0.7320 0.4892 0.6529
Columns 127 through 144
0.9542 0.6012 0.7828 0.5601 0.3783 0.7228 0.5640 0.1776 0.6977 0.3227 0.6249 0.0279 0.2191 0.9455 0.1715 0.1862 0.7512 0.2973
Columns 145 through 150
0.7227 0.7914 0.1098 0.6703 0.2003 0.1265
pattern = [1 1 1 1 1];
Idx = strfind(bigArray,pattern);
Idx = 1×4
15 25 75 105
<mw-icon class=""></mw-icon>
<mw-icon class=""></mw-icon>
Idx = reshape([Idx(:) Idx(:) + repmat((1:numel(pattern)-1), numel(Idx), 1)].', 1, []);
disp(Idx)
15 16 17 18 19 25 26 27 28 29 75 76 77 78 79 105 106 107 108 109
The strfind function returns only the first index of the pattern, so it is necesary to add:
(1:numel(pattern)-1)
to each element. I did that here by transposing the original ‘Idx’ vector (the Idx(:) step), and summing it with that vector to create a matrix, then contatenating the transposed ‘Idx’ vector to the matrix I created in the first step, and then reshaped that to a row vector to get the desired result. It is not straightforward to get the result you want, however iit is definitely possiible.
The ismember function is quite useful at finding individual instances of the elements of its second argument, however it does not find patterns. There are other functions that operate similarly to strfind (the See Also section of the strfind function documentation page has a list of them), however strfind is a function everyone has.
.
Thank you very much!
As always, my pleasure!
更多回答(0 个)
类别
在 帮助中心 和 File Exchange 中查找有关 Creating and Concatenating Matrices 的更多信息
另请参阅
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!选择网站
选择网站以获取翻译的可用内容,以及查看当地活动和优惠。根据您的位置,我们建议您选择:。
您也可以从以下列表中选择网站:
如何获得最佳网站性能
选择中国网站(中文或英文)以获得最佳网站性能。其他 MathWorks 国家/地区网站并未针对您所在位置的访问进行优化。
美洲
- América Latina (Español)
- Canada (English)
- United States (English)
欧洲
- Belgium (English)
- Denmark (English)
- Deutschland (Deutsch)
- España (Español)
- Finland (English)
- France (Français)
- Ireland (English)
- Italia (Italiano)
- Luxembourg (English)
- Netherlands (English)
- Norway (English)
- Österreich (Deutsch)
- Portugal (English)
- Sweden (English)
- Switzerland
- United Kingdom (English)
