To split class in a cell array

Hi,

I am having cell array (A) of 3072*30 cells. I need to mention in my code that first 15 columns (1 to 15) belongs to class_1 and rest of the 15 columns (16 to 30) belongs to class_2. How can I do this? Kindly help me with this. Thanks in advance.

[Dyuman Joshi]

Mention how exactly? Do you want classify them as variables? Or In a table? struct?

[SP]

I need to classify them as variables.

Answer 1

[Dyuman Joshi]

Looks like a simple variable assignment

%random data
y=num2cell(rand(10,30))

y = 10×30 cell array
    {[0.0429]}    {[0.6573]}    {[0.4491]}    {[0.8743]}    {[0.8910]}    {[0.6037]}    {[0.4992]}    {[0.3161]}    {[0.5085]}    {[0.8149]}    {[0.4411]}    {[0.9675]}    {[0.9415]}    {[0.5472]}    {[0.2184]}    {[0.0718]}    {[0.4556]}    {[0.8457]}    {[0.3144]}    {[0.1920]}    {[0.0047]}    {[0.3226]}    {[0.9917]}    {[0.9409]}    {[0.0397]}    {[0.8016]}    {[0.3305]}    {[0.8369]}    {[0.1377]}    {[0.2737]}
    {[0.2022]}    {[0.4322]}    {[0.9603]}    {[0.1885]}    {[0.7555]}    {[0.2301]}    {[0.3248]}    {[0.3585]}    {[0.1594]}    {[0.2277]}    {[0.0316]}    {[0.4856]}    {[0.7924]}    {[0.3109]}    {[0.1601]}    {[0.6248]}    {[0.8429]}    {[0.9777]}    {[0.6154]}    {[0.9903]}    {[0.1442]}    {[0.6801]}    {[0.5512]}    {[0.1111]}    {[0.1293]}    {[0.6780]}    {[0.3227]}    {[0.7576]}    {[0.9418]}    {[0.3936]}
    {[0.6480]}    {[0.1052]}    {[0.7991]}    {[0.3137]}    {[0.2548]}    {[0.5887]}    {[0.8145]}    {[0.9278]}    {[0.3293]}    {[0.0333]}    {[0.9802]}    {[0.2802]}    {[0.1858]}    {[0.4807]}    {[0.8810]}    {[0.5598]}    {[0.7994]}    {[0.8112]}    {[0.1728]}    {[0.4948]}    {[0.8629]}    {[0.8291]}    {[0.2189]}    {[0.3770]}    {[0.4297]}    {[0.2367]}    {[0.6692]}    {[0.1430]}    {[0.9057]}    {[0.0910]}
    {[0.9861]}    {[0.6404]}    {[0.0969]}    {[0.6612]}    {[0.8557]}    {[0.9868]}    {[0.3536]}    {[0.1109]}    {[0.0372]}    {[0.8165]}    {[0.6213]}    {[0.5194]}    {[0.8011]}    {[0.9668]}    {[0.7000]}    {[0.4871]}    {[0.2498]}    {[0.8631]}    {[0.3664]}    {[0.8057]}    {[0.0339]}    {[0.5204]}    {[0.8456]}    {[0.4612]}    {[0.9991]}    {[0.2468]}    {[0.2655]}    {[0.2095]}    {[0.4647]}    {[0.8753]}
    {[0.5260]}    {[0.8953]}    {[0.6656]}    {[0.6436]}    {[0.4224]}    {[0.7272]}    {[0.5952]}    {[0.4938]}    {[0.9162]}    {[0.7548]}    {[0.0474]}    {[0.3580]}    {[0.1411]}    {[0.6557]}    {[0.5780]}    {[0.5417]}    {[0.1439]}    {[0.1115]}    {[0.9631]}    {[0.3361]}    {[0.7031]}    {[0.2404]}    {[0.9601]}    {[0.9841]}    {[0.1836]}    {[0.0819]}    {[0.2653]}    {[0.4867]}    {[0.2826]}    {[0.9345]}
    {[0.6508]}    {[0.3016]}    {[0.4134]}    {[0.7558]}    {[0.8422]}    {[0.7709]}    {[0.9673]}    {[0.0363]}    {[0.1084]}    {[0.3923]}    {[0.5217]}    {[0.4439]}    {[0.3219]}    {[0.5695]}    {[0.1429]}    {[0.6350]}    {[0.5820]}    {[0.6552]}    {[0.9112]}    {[0.8541]}    {[0.7789]}    {[0.2666]}    {[0.1783]}    {[0.8171]}    {[0.3276]}    {[0.9600]}    {[0.8336]}    {[0.2152]}    {[0.9361]}    {[0.1189]}
    {[0.5196]}    {[0.6815]}    {[0.1660]}    {[0.3689]}    {[0.1190]}    {[0.6952]}    {[0.9645]}    {[0.9824]}    {[0.1223]}    {[0.2963]}    {[0.2798]}    {[0.8806]}    {[0.6004]}    {[0.3141]}    {[0.5818]}    {[0.5378]}    {[0.2794]}    {[0.1674]}    {[0.5274]}    {[0.5888]}    {[0.9491]}    {[0.7921]}    {[0.3445]}    {[0.5471]}    {[0.3866]}    {[0.8285]}    {[0.6762]}    {[0.1552]}    {[0.1990]}    {[0.3763]}
    {[0.3030]}    {[0.7435]}    {[0.2165]}    {[0.0967]}    {[0.7550]}    {[0.2750]}    {[0.8788]}    {[0.9565]}    {[0.6438]}    {[0.8846]}    {[0.5238]}    {[0.4508]}    {[0.6687]}    {[0.8660]}    {[0.1495]}    {[0.8615]}    {[0.9213]}    {[0.9919]}    {[0.6902]}    {[0.5372]}    {[0.0036]}    {[0.5433]}    {[0.4141]}    {[0.1074]}    {[0.8653]}    {[0.2755]}    {[0.7174]}    {[0.9411]}    {[0.6437]}    {[0.8933]}
    {[0.3792]}    {[0.0201]}    {[0.8181]}    {[0.1260]}    {[0.6229]}    {[0.0427]}    {[0.2552]}    {[0.8650]}    {[0.7686]}    {[0.4479]}    {[0.2435]}    {[0.1535]}    {[0.0433]}    {[0.7223]}    {[0.1383]}    {[0.7527]}    {[0.0435]}    {[0.4947]}    {[0.0367]}    {[0.4833]}    {[0.1654]}    {[0.0025]}    {[0.0632]}    {[0.3833]}    {[0.9368]}    {[0.0685]}    {[0.8810]}    {[0.2998]}    {[0.6078]}    {[0.2701]}
    {[0.2163]}    {[0.1600]}    {[0.3561]}    {[0.5374]}    {[0.5097]}    {[0.9155]}    {[0.2198]}    {[0.3409]}    {[0.8855]}    {[0.6301]}    {[0.5137]}    {[0.3076]}    {[0.9558]}    {[0.4299]}    {[0.5303]}    {[0.6680]}    {[0.8918]}    {[0.5571]}    {[0.7782]}    {[0.2087]}    {[0.8924]}    {[0.0161]}    {[0.6197]}    {[0.9198]}    {[0.7194]}    {[0.2045]}    {[0.0928]}    {[0.0480]}    {[0.7515]}    {[0.0187]}

class_1 = y(:,1:15)

class_1 = 10×15 cell array
    {[0.0429]}    {[0.6573]}    {[0.4491]}    {[0.8743]}    {[0.8910]}    {[0.6037]}    {[0.4992]}    {[0.3161]}    {[0.5085]}    {[0.8149]}    {[0.4411]}    {[0.9675]}    {[0.9415]}    {[0.5472]}    {[0.2184]}
    {[0.2022]}    {[0.4322]}    {[0.9603]}    {[0.1885]}    {[0.7555]}    {[0.2301]}    {[0.3248]}    {[0.3585]}    {[0.1594]}    {[0.2277]}    {[0.0316]}    {[0.4856]}    {[0.7924]}    {[0.3109]}    {[0.1601]}
    {[0.6480]}    {[0.1052]}    {[0.7991]}    {[0.3137]}    {[0.2548]}    {[0.5887]}    {[0.8145]}    {[0.9278]}    {[0.3293]}    {[0.0333]}    {[0.9802]}    {[0.2802]}    {[0.1858]}    {[0.4807]}    {[0.8810]}
    {[0.9861]}    {[0.6404]}    {[0.0969]}    {[0.6612]}    {[0.8557]}    {[0.9868]}    {[0.3536]}    {[0.1109]}    {[0.0372]}    {[0.8165]}    {[0.6213]}    {[0.5194]}    {[0.8011]}    {[0.9668]}    {[0.7000]}
    {[0.5260]}    {[0.8953]}    {[0.6656]}    {[0.6436]}    {[0.4224]}    {[0.7272]}    {[0.5952]}    {[0.4938]}    {[0.9162]}    {[0.7548]}    {[0.0474]}    {[0.3580]}    {[0.1411]}    {[0.6557]}    {[0.5780]}
    {[0.6508]}    {[0.3016]}    {[0.4134]}    {[0.7558]}    {[0.8422]}    {[0.7709]}    {[0.9673]}    {[0.0363]}    {[0.1084]}    {[0.3923]}    {[0.5217]}    {[0.4439]}    {[0.3219]}    {[0.5695]}    {[0.1429]}
    {[0.5196]}    {[0.6815]}    {[0.1660]}    {[0.3689]}    {[0.1190]}    {[0.6952]}    {[0.9645]}    {[0.9824]}    {[0.1223]}    {[0.2963]}    {[0.2798]}    {[0.8806]}    {[0.6004]}    {[0.3141]}    {[0.5818]}
    {[0.3030]}    {[0.7435]}    {[0.2165]}    {[0.0967]}    {[0.7550]}    {[0.2750]}    {[0.8788]}    {[0.9565]}    {[0.6438]}    {[0.8846]}    {[0.5238]}    {[0.4508]}    {[0.6687]}    {[0.8660]}    {[0.1495]}
    {[0.3792]}    {[0.0201]}    {[0.8181]}    {[0.1260]}    {[0.6229]}    {[0.0427]}    {[0.2552]}    {[0.8650]}    {[0.7686]}    {[0.4479]}    {[0.2435]}    {[0.1535]}    {[0.0433]}    {[0.7223]}    {[0.1383]}
    {[0.2163]}    {[0.1600]}    {[0.3561]}    {[0.5374]}    {[0.5097]}    {[0.9155]}    {[0.2198]}    {[0.3409]}    {[0.8855]}    {[0.6301]}    {[0.5137]}    {[0.3076]}    {[0.9558]}    {[0.4299]}    {[0.5303]}

class_2 = y(:,16:30)

class_2 = 10×15 cell array
    {[0.0718]}    {[0.4556]}    {[0.8457]}    {[0.3144]}    {[0.1920]}    {[0.0047]}    {[0.3226]}    {[0.9917]}    {[0.9409]}    {[0.0397]}    {[0.8016]}    {[0.3305]}    {[0.8369]}    {[0.1377]}    {[0.2737]}
    {[0.6248]}    {[0.8429]}    {[0.9777]}    {[0.6154]}    {[0.9903]}    {[0.1442]}    {[0.6801]}    {[0.5512]}    {[0.1111]}    {[0.1293]}    {[0.6780]}    {[0.3227]}    {[0.7576]}    {[0.9418]}    {[0.3936]}
    {[0.5598]}    {[0.7994]}    {[0.8112]}    {[0.1728]}    {[0.4948]}    {[0.8629]}    {[0.8291]}    {[0.2189]}    {[0.3770]}    {[0.4297]}    {[0.2367]}    {[0.6692]}    {[0.1430]}    {[0.9057]}    {[0.0910]}
    {[0.4871]}    {[0.2498]}    {[0.8631]}    {[0.3664]}    {[0.8057]}    {[0.0339]}    {[0.5204]}    {[0.8456]}    {[0.4612]}    {[0.9991]}    {[0.2468]}    {[0.2655]}    {[0.2095]}    {[0.4647]}    {[0.8753]}
    {[0.5417]}    {[0.1439]}    {[0.1115]}    {[0.9631]}    {[0.3361]}    {[0.7031]}    {[0.2404]}    {[0.9601]}    {[0.9841]}    {[0.1836]}    {[0.0819]}    {[0.2653]}    {[0.4867]}    {[0.2826]}    {[0.9345]}
    {[0.6350]}    {[0.5820]}    {[0.6552]}    {[0.9112]}    {[0.8541]}    {[0.7789]}    {[0.2666]}    {[0.1783]}    {[0.8171]}    {[0.3276]}    {[0.9600]}    {[0.8336]}    {[0.2152]}    {[0.9361]}    {[0.1189]}
    {[0.5378]}    {[0.2794]}    {[0.1674]}    {[0.5274]}    {[0.5888]}    {[0.9491]}    {[0.7921]}    {[0.3445]}    {[0.5471]}    {[0.3866]}    {[0.8285]}    {[0.6762]}    {[0.1552]}    {[0.1990]}    {[0.3763]}
    {[0.8615]}    {[0.9213]}    {[0.9919]}    {[0.6902]}    {[0.5372]}    {[0.0036]}    {[0.5433]}    {[0.4141]}    {[0.1074]}    {[0.8653]}    {[0.2755]}    {[0.7174]}    {[0.9411]}    {[0.6437]}    {[0.8933]}
    {[0.7527]}    {[0.0435]}    {[0.4947]}    {[0.0367]}    {[0.4833]}    {[0.1654]}    {[0.0025]}    {[0.0632]}    {[0.3833]}    {[0.9368]}    {[0.0685]}    {[0.8810]}    {[0.2998]}    {[0.6078]}    {[0.2701]}
    {[0.6680]}    {[0.8918]}    {[0.5571]}    {[0.7782]}    {[0.2087]}    {[0.8924]}    {[0.0161]}    {[0.6197]}    {[0.9198]}    {[0.7194]}    {[0.2045]}    {[0.0928]}    {[0.0480]}    {[0.7515]}    {[0.0187]}

%if you need matrix instead cell
class_1 = cell2mat(y(:,1:15))

class_1 = 10×15

0.0429 0.6573 0.4491 0.8743 0.8910 0.6037 0.4992 0.3161 0.5085 0.8149 0.4411 0.9675 0.9415 0.5472 0.2184 0.2022 0.4322 0.9603 0.1885 0.7555 0.2301 0.3248 0.3585 0.1594 0.2277 0.0316 0.4856 0.7924 0.3109 0.1601 0.6480 0.1052 0.7991 0.3137 0.2548 0.5887 0.8145 0.9278 0.3293 0.0333 0.9802 0.2802 0.1858 0.4807 0.8810 0.9861 0.6404 0.0969 0.6612 0.8557 0.9868 0.3536 0.1109 0.0372 0.8165 0.6213 0.5194 0.8011 0.9668 0.7000 0.5260 0.8953 0.6656 0.6436 0.4224 0.7272 0.5952 0.4938 0.9162 0.7548 0.0474 0.3580 0.1411 0.6557 0.5780 0.6508 0.3016 0.4134 0.7558 0.8422 0.7709 0.9673 0.0363 0.1084 0.3923 0.5217 0.4439 0.3219 0.5695 0.1429 0.5196 0.6815 0.1660 0.3689 0.1190 0.6952 0.9645 0.9824 0.1223 0.2963 0.2798 0.8806 0.6004 0.3141 0.5818 0.3030 0.7435 0.2165 0.0967 0.7550 0.2750 0.8788 0.9565 0.6438 0.8846 0.5238 0.4508 0.6687 0.8660 0.1495 0.3792 0.0201 0.8181 0.1260 0.6229 0.0427 0.2552 0.8650 0.7686 0.4479 0.2435 0.1535 0.0433 0.7223 0.1383 0.2163 0.1600 0.3561 0.5374 0.5097 0.9155 0.2198 0.3409 0.8855 0.6301 0.5137 0.3076 0.9558 0.4299 0.5303