Complex or sparse input following 'like' is not supported

randn(100, 'like', 1j);

Error:
Error using randn
Complex or sparse input following 'like' is not supported.

Could anyone suggest alternative or solutions to solve the same ?.

Matlab version: 'Matlab 2020a'

[Dyuman Joshi]

Try "1i" -

y = randn(100, "like", 1i)

y = 
  Columns 1 through 10

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   0.0387 + 1.3706i   0.7453 - 0.9252i  -0.3012 - 0.3148i   0.2989 - 0.9656i  -0.6648 + 0.3998i  -0.0896 - 0.6618i   0.1577 + 0.1245i   0.2397 - 0.3601i   0.3217 + 0.0002i  -0.2953 + 0.0268i
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   0.9228 - 0.7263i  -0.8519 - 0.8016i   0.1799 + 0.2439i   0.6496 + 0.6544i  -0.7057 - 0.0338i   1.0717 - 0.6014i  -0.0954 + 0.5363i  -0.5563 + 0.3161i  -0.3469 + 0.1093i  -0.6668 - 0.0062i
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  Columns 11 through 20

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   1.0128 - 0.4452i   0.1002 + 0.5286i   0.7684 + 0.2987i  -0.0098 + 0.6440i   1.0941 - 0.9268i   1.1173 - 0.1916i   0.2956 + 0.5103i   0.1829 + 0.1720i   0.3682 + 0.0319i  -0.4647 + 1.7669i
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   0.7376 + 0.0652i   0.2169 - 0.0195i   0.5588 + 0.8214i   0.2468 - 0.9756i  -0.7823 + 0.5299i   0.7622 - 0.2629i   0.8498 + 0.2067i  -0.0420 + 0.5031i   0.0424 - 0.1598i   0.1039 - 0.5134i
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   0.2377 - 0.0467i  -0.7551 + 0.2894i  -0.3171 + 0.0848i  -0.5796 - 0.3953i  -0.4370 + 0.1361i  -0.4109 - 0.3771i  -1.5240 + 0.5291i   0.4959 - 0.0281i  -0.1554 + 0.2013i  -0.7526 - 1.0154i
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   0.2703 - 0.4950i   0.3279 - 0.7079i  -0.0004 + 0.0614i   0.1424 + 0.5994i   0.7598 + 0.8530i   0.8994 + 0.3181i  -0.3793 - 0.1498i  -0.9650 - 0.1676i   1.0765 - 0.7599i  -0.2175 + 0.2724i
   0.0813 + 0.3134i  -0.7073 + 0.8634i   0.4843 - 0.6999i   0.0882 + 1.2097i  -0.4451 + 0.4777i   0.6586 + 0.9989i   0.3763 + 0.2848i   0.1183 + 0.0860i  -0.4492 + 0.6600i   0.3426 - 0.2582i
  -0.7971 - 0.8869i   0.0458 + 0.6768i  -0.5073 + 0.1612i   0.0241 - 1.0621i  -0.7416 + 1.3916i   0.4796 + 0.7465i  -0.4908 - 0.5166i  -1.3960 - 0.2600i   0.4539 + 0.4934i   0.9661 - 0.6784i
   1.0334 - 0.2783i  -0.6743 + 0.5328i   0.6208 - 0.1789i   0.1043 - 1.5264i  -0.0206 - 0.4927i  -0.5599 + 0.2646i  -0.0656 - 0.4490i   0.8074 + 0.3608i   0.5206 - 0.5918i  -0.0324 - 0.1838i
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   1.7220 + 0.6827i   0.2919 + 0.7889i  -0.2444 + 0.3715i   1.1257 - 0.1764i   1.3051 + 1.3180i   0.3180 + 0.3736i  -1.2703 + 0.1640i  -0.3497 + 0.0829i   0.4463 - 1.8302i  -1.0571 + 0.4367i
   0.2736 - 1.3105i  -0.2952 - 0.5443i   0.3550 - 0.1781i   0.3763 + 1.0412i   0.3128 - 0.1507i  -0.2339 - 1.0875i  -0.0093 + 0.0471i  -1.5931 - 0.2252i   0.4715 - 0.7208i  -0.4639 + 0.8029i
  -0.2064 - 0.0766i   0.1685 + 0.1276i  -1.6441 - 0.7055i  -1.3797 - 0.1373i  -1.2427 - 1.3594i   0.6275 - 0.2084i  -0.1822 - 0.1503i  -0.3636 + 0.7530i   0.1523 + 0.2668i  -0.1885 + 0.7821i
  -0.6538 + 0.5030i   1.1518 + 0.9742i  -0.3995 - 0.1135i  -0.5776 + 0.1006i  -0.2279 + 0.3079i  -0.1874 - 0.4194i  -1.4549 + 0.3260i  -0.2671 + 1.3973i   0.9303 + 0.5381i  -0.8639 - 0.6609i
   0.9766 - 0.8829i  -0.5132 + 0.8813i   0.8297 + 0.3994i   1.3753 + 0.8596i  -0.0423 + 0.6489i   0.6909 + 0.1762i   0.1818 + 0.3974i   0.7207 - 0.2232i  -1.1358 - 0.1935i  -0.7448 - 0.4154i
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  -0.8996 + 0.3307i   0.3555 + 0.3677i  -0.8902 - 0.8553i  -0.0259 - 0.9576i  -0.6656 - 0.6342i   0.3708 - 1.1386i   0.7929 - 0.2475i   0.0022 + 0.0031i   0.9118 - 0.9945i   0.9511 - 0.5001i
   0.6057 - 0.2167i  -0.2024 - 0.6146i   0.6007 + 2.1421i  -0.7461 + 0.7106i   0.1657 - 1.5495i  -0.1605 + 0.7776i  -0.0307 + 0.3449i  -1.0755 + 0.1209i  -0.1942 - 0.0389i   0.1485 - 0.0339i
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  -0.0785 - 0.0416i   1.1062 - 0.3013i  -0.4747 - 0.0126i   0.7736 + 0.2781i   0.5708 + 0.0723i   0.5507 - 0.5278i  -0.6853 + 0.3545i  -0.2444 - 0.3359i  -0.8311 + 1.6075i  -1.4518 + 0.7192i
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   0.1155 + 0.6230i   0.3195 - 0.0928i  -0.2612 + 0.7795i  -0.1816 - 0.8920i  -0.3000 - 0.2671i  -1.1414 + 0.4074i   0.3800 - 1.1462i   0.6026 - 0.5023i  -0.1180 + 0.1123i   0.8302 + 0.5127i
  -1.3594 + 0.3043i   0.9505 + 1.1217i   0.4269 - 0.3596i   0.9112 + 0.3622i  -0.7823 + 0.3315i   0.2517 + 0.0472i  -0.9010 + 0.6315i   0.9882 + 0.0856i  -0.4274 - 0.5171i  -0.1396 + 0.0452i
   0.0457 + 0.5153i   0.1514 - 0.4443i  -0.8212 + 0.1504i  -0.0370 - 0.0512i   0.0545 - 0.2731i   0.5624 + 0.3428i   0.2759 - 0.4200i   1.1199 - 0.2288i  -0.3274 - 0.8579i   0.3107 - 0.9218i
   0.2875 - 0.7299i   0.4537 - 0.2022i  -0.2033 - 0.3097i   1.3685 - 0.5174i  -1.3268 + 0.1869i   0.0648 + 0.1138i  -0.2177 - 0.5967i   0.5572 + 0.0948i  -0.0179 - 1.0257i  -0.7182 + 0.1327i
   0.9665 - 0.1557i   0.1547 + 0.0761i  -0.3472 + 0.3525i   1.1640 + 0.5994i   0.0043 + 0.2642i  -1.2600 + 0.7685i   0.1352 + 0.2592i  -0.8211 + 1.3620i   0.0536 + 0.7052i   0.0691 + 0.1328i
  -0.0646 + 0.9129i   0.6020 + 0.6907i   0.1525 + 1.8224i  -0.4834 - 1.2453i   1.2835 + 0.7051i  -0.6998 - 0.2915i  -0.5278 + 0.2944i   0.0230 + 1.4618i   0.0970 + 1.5418i  -0.0532 + 1.3652i
  -1.0203 + 0.5131i   1.1667 - 0.3923i   0.3905 + 0.6092i  -0.8353 + 0.6719i   0.8851 + 0.0213i  -0.2889 - 0.4420i   0.8562 - 0.3790i  -0.0366 + 1.1579i   1.1057 - 0.2986i  -0.4082 - 1.9464i
   0.0518 + 0.4757i   1.0167 + 0.5230i  -0.2022 - 0.3851i  -0.8540 - 0.2280i   0.3303 - 0.2961i  -0.8756 + 0.2679i  -0.6245 + 0.5040i   0.0019 + 0.4327i  -0.2036 - 0.7025i   0.0520 - 0.4674i
   0.2799 + 0.5541i   0.1385 + 0.3246i   0.5215 - 0.5819i  -0.0606 + 0.1310i  -0.8808 - 0.1552i   0.1747 - 0.1993i   0.4274 - 0.2880i   0.4676 + 0.2706i  -0.1357 - 0.0864i   1.1535 - 0.3281i
   1.0626 - 0.4105i  -0.2605 - 0.0795i  -0.4570 + 0.2730i   1.1078 - 1.3975i  -0.9289 - 0.5971i  -0.3050 + 0.1477i  -0.8005 + 0.6422i  -0.4241 - 0.7340i   0.4367 + 1.0911i  -0.0532 + 0.3827i
   2.2611 - 0.0351i  -0.3083 + 0.1514i  -0.8781 + 0.8159i  -0.2782 + 0.5974i  -0.6508 - 0.3212i  -0.1074 + 0.4357i  -0.7792 + 1.2029i  -1.1057 - 1.2944i  -1.3159 + 0.5965i   1.4641 - 0.4457i
  -0.5478 - 0.6749i   0.5078 - 0.1893i   0.9736 - 0.2684i  -0.5507 + 1.0510i  -0.5330 + 0.6611i  -0.0047 + 0.7789i   0.2116 - 0.4008i  -0.0079 + 0.0461i  -0.8769 - 0.7066i   1.2777 + 1.5270i
   0.8388 + 0.3229i   0.0213 + 1.8003i   0.3711 + 0.3542i  -0.6871 + 0.2604i  -0.3353 - 0.9514i  -0.4545 - 1.1756i   0.2149 + 0.9104i   0.0334 - 0.0379i  -1.5713 - 0.0103i  -0.2965 + 1.0213i
  -0.0474 + 0.2199i  -0.4027 + 1.3355i  -0.5600 - 0.8734i  -1.1691 + 0.5520i  -0.2876 + 0.1425i  -0.6894 - 0.2205i  -0.1321 + 0.5916i   0.3465 - 0.0321i   0.2742 + 0.1034i   0.1356 - 0.1542i
   0.1811 - 0.1142i   0.4675 + 0.4429i   0.1748 - 0.1677i   1.3401 + 1.5152i   0.3374 + 0.2770i  -1.4068 + 0.0368i  -0.0837 + 0.4534i   0.0542 - 0.5598i  -0.0058 + 0.8843i  -0.1151 + 0.1822i
   0.6414 + 0.6371i   0.4982 - 0.5406i  -0.6702 + 0.5024i  -0.0254 + 0.3959i  -0.5251 - 0.1552i   1.0410 + 0.3127i   0.9356 + 0.7075i   0.0249 - 0.6891i   0.5438 + 0.2433i  -0.5435 - 0.2001i
   0.4082 + 1.2914i  -0.0434 - 0.4774i   1.6683 + 0.3923i   0.9784 + 0.6473i   1.1904 + 0.6816i   0.3107 - 0.0720i  -0.7648 - 0.3683i  -0.1268 + 0.2045i   0.4057 - 0.6061i   1.9678 + 0.7648i
  -0.0588 - 0.5509i  -0.8294 + 1.5079i  -0.0036 + 0.0561i   0.2054 - 0.1078i  -0.4440 - 1.0900i   0.5325 + 0.1513i  -0.0325 + 0.5957i   1.8350 - 0.8006i  -0.2104 + 0.5840i   0.6232 - 0.2489i
   0.2676 - 0.7645i  -1.1975 - 0.3516i   0.3108 + 0.0719i   1.0027 - 0.1131i  -0.5012 + 1.6068i  -0.3773 - 0.5107i   0.2305 + 0.0641i   1.7902 - 0.5020i   0.7650 + 0.3024i   0.2103 - 0.1018i
  -0.1521 - 0.0514i   0.7214 - 1.0755i  -0.5196 + 0.5642i   0.0755 - 0.8624i   1.2223 - 0.0496i  -0.0059 - 0.2350i  -0.1481 - 0.3940i   0.9851 + 1.2581i   0.3796 + 0.1452i   0.1353 + 0.3492i
  -0.6244 + 1.7968i   0.6072 + 0.4924i  -0.1412 + 0.8705i  -0.7359 + 0.7257i   0.5843 + 0.1247i   1.1712 + 0.3923i  -1.6642 - 0.4093i   0.3204 + 0.7054i  -0.2653 + 0.2120i  -0.6755 - 0.3173i
   0.0415 - 0.1624i   0.0857 + 0.6339i   0.1794 + 1.5405i   1.6630 + 0.2026i  -0.1752 + 0.4383i  -0.2708 - 0.9864i   0.0708 - 0.2771i  -0.2098 + 0.7111i  -0.5266 + 0.1898i  -0.2016 - 0.0099i
   1.2380 + 0.9774i  -0.2901 - 1.5446i   0.4374 + 0.4162i   1.1794 - 0.8179i  -0.4132 - 0.2307i   0.2167 - 0.0744i  -0.8288 + 0.7901i  -1.4094 + 0.1769i   0.4178 - 0.7731i   0.3439 - 0.7206i
   0.0927 + 0.4757i   0.0381 + 0.4113i  -1.0464 + 0.3193i   0.3396 + 0.8744i  -0.0797 + 1.8405i  -0.0072 - 0.3723i   0.1700 + 0.6865i  -0.3113 - 1.0333i   0.8857 + 0.3077i   1.1027 + 0.6156i
  -1.0988 + 0.7038i  -0.3995 + 0.6674i  -0.0647 - 0.0862i  -0.0895 - 0.2205i   0.4645 - 0.2093i  -0.5253 - 0.2331i   0.0413 - 0.3930i  -0.6434 - 0.8972i   0.8269 + 0.5220i   0.2779 - 0.8245i

  Columns 21 through 30

  -0.0036 + 0.6241i   0.9964 + 0.8353i   0.1242 - 0.4835i   0.6428 - 0.1549i   0.0580 - 0.7698i  -1.3600 + 1.1528i   0.4543 + 0.7295i   0.5212 - 1.0833i  -0.8591 - 0.0384i  -0.5869 + 0.0872i
  -0.0724 - 0.1278i  -0.1849 - 2.4178i  -1.1474 - 0.7652i  -0.3377 - 0.6312i   0.5976 + 0.3303i  -0.3224 + 1.2947i  -1.5515 - 0.1814i   0.8857 - 0.1613i   0.9133 + 0.1096i  -1.0821 - 0.9436i
   0.1003 - 0.5833i   0.1414 - 1.1632i  -0.2892 - 1.1543i  -1.1516 - 0.8324i  -1.0141 - 0.1529i   0.1033 + 0.5654i   0.2964 + 0.2237i  -0.6036 + 0.1559i   0.4020 - 0.4421i  -0.2084 - 0.3686i
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  Columns 31 through 40

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[Matlab111]

Thanks, but Still I face the same error, I'm using 2020a, FYI

randn(100, "like", 1i)
Error using randn
Complex or sparse input following 'like' is not supported.

[DGM]

I don't know why it wouldn't work with 1j just the same. Maybe something else is going on.

y = randn(100, 'like', 1j)

y = 
  Columns 1 through 10

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  Columns 11 through 20

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   0.0672 + 0.0927i  -0.8298 - 0.3572i  -0.6443 - 1.1015i   0.6845 - 0.7281i  -1.4672 + 0.1729i   0.9764 + 0.5609i  -0.7934 + 0.7816i   1.4053 - 0.8241i   0.9076 - 1.2481i  -0.2409 + 0.5403i
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  Columns 21 through 30

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  -1.0809 + 1.0963i  -0.8147 + 0.0324i   0.5958 - 0.9275i  -0.5090 + 1.9009i  -0.6963 - 0.8120i   0.3601 - 0.5180i  -0.6921 + 0.1842i   1.5577 - 1.1423i   1.2400 + 0.9081i  -0.0361 + 0.3984i
  -0.0278 + 0.0523i  -1.4659 + 1.0595i   0.5018 - 0.8785i  -0.2861 - 0.1909i  -0.3220 - 0.4358i  -0.9476 + 0.8761i   1.0633 + 0.2339i  -0.2957 - 0.9484i  -0.1338 - 0.8648i  -0.2998 + 0.6535i
  -0.3161 + 0.7778i   0.5158 - 0.2119i   1.0186 - 0.0437i   1.3138 + 0.3408i   0.2668 - 0.3904i  -0.0374 - 0.7889i  -1.3215 - 0.8450i  -0.1327 - 0.6299i   0.8306 + 0.3400i  -0.2525 + 0.3731i

  Columns 31 through 40

  -0.6310 + 0.4694i  -0.0897 + 0.0052i   0.5171 - 0.3427i   0.6491 + 1.6645i   0.6856 + 0.5106i  -1.1080 - 0.6078i   0.2911 - 0.6114i  -0.5189 + 0.6611i   0.2240 + 0.4795i  -0.2638 + 0.8049i
  -0.6343 + 0.9028i   0.1485 - 1.1966i   0.4064 - 0.0468i   1.0221 + 0.6625i  -0.4520 - 0.0520i  -0.9812 - 0.2396i  -0.2837 + 0.0527i  -0.1570 - 0.5609i   0.9788 - 1.0397i  -0.7093 - 0.6084i
   0.6474 + 0.2629i  -0.3210 + 0.4979i  -1.0460 - 0.3958i  -0.1447 - 0.4032i   1.0227 - 0.8797i   0.0741 - 0.3231i   0.8306 - 1.1031i  -0.5345 + 0.5810i  -0.0740 - 0.5835i   0.4419 - 0.3798i
   0.3099 - 0.8676i   0.8770 - 0.0880i  -0.3228 - 0.4210i   0.2478 - 0.3237i   0.3610 - 0.4958i   0.1820 - 0.3797i   1.1862 + 0.3808i   0.1348 - 0.2861i   1.0322 - 0.3798i  -1.1739 - 0.6308i
   0.2230 + 0.3362i   0.4113 - 0.0804i   0.5073 + 1.1262i  -0.4874 + 0.3671i   1.1551 - 0.1349i   1.0385 + 0.0001i   1.0234 - 0.0687i   0.8129 + 0.1876i   1.0328 - 0.4566i   0.3792 - 0.1949i
  -0.6264 - 0.1147i  -0.7396 - 0.1913i   0.7842 - 1.3262i  -0.6315 + 0.3442i   0.4877 + 1.1221i   0.7072 + 0.5846i  -1.1168 - 1.3138i   0.7580 - 0.8607i  -0.5374 + 1.3125i  -1.3256 - 0.5129i
   0.6412 - 0.2120i  -0.2032 + 0.7962i   1.1409 - 0.3196i  -0.1636 - 0.7405i   0.1487 - 1.4489i  -0.3914 - 0.2291i  -0.1606 + 1.0308i   1.1046 + 0.7886i   0.5844 + 0.4271i   0.4542 - 0.5850i
  -0.1820 - 0.5157i   0.8912 - 0.0850i  -0.0566 + 0.4118i   0.3652 - 0.2013i   0.0707 + 0.6389i   0.6101 - 0.4509i  -0.2414 + 0.2483i   0.3194 + 0.6681i  -1.0957 - 0.7148i  -0.7828 - 1.8272i
   0.7246 - 1.5120i  -0.0045 + 1.2522i   0.0638 - 0.8781i   1.0314 + 0.9336i  -0.0416 - 1.2142i  -0.2020 - 2.0726i   0.4335 + 0.4784i  -0.2704 + 0.1629i  -0.9818 - 0.2403i   0.8953 + 0.9828i
   0.6890 - 0.8246i  -0.1579 - 0.5823i   0.5581 + 0.0327i  -0.1288 + 1.1370i  -0.0048 + 0.2035i  -1.7801 - 0.3013i   0.0156 - 0.2836i  -0.6570 + 0.9222i   0.2520 + 0.4526i   0.1800 + 1.7233i
  -0.2288 + 0.4838i   0.3191 - 0.9270i  -0.8486 + 0.5925i  -0.7058 - 0.9156i   0.1919 - 0.5775i   1.2133 - 0.0026i   0.3019 - 0.1425i  -0.5470 + 0.2443i   0.0015 + 1.0428i   0.6147 - 0.3987i
  -0.5357 - 0.3703i   1.0828 + 0.6031i  -0.2239 + 0.2569i  -0.0596 - 0.1534i   0.7760 - 0.9372i   0.0259 - 1.3821i   1.1494 + 1.2156i  -0.1646 + 0.7756i  -0.5954 - 0.0930i  -0.7942 + 0.4625i
   0.3262 + 0.7903i  -0.3081 - 0.3339i  -0.3874 - 1.0234i   0.6940 - 0.5279i  -1.0002 + 0.5178i  -0.9295 + 0.8375i  -0.3463 + 0.1852i  -0.2863 - 0.5635i   1.6534 - 0.6325i   1.6729 + 0.2714i

[Matlab111]

Hi all, I'm using 2020a version. FYIP

Answer 1

[Dyuman Joshi]

This particular syntax of generating normally distributed complex number was added in R2022a - https://in.mathworks.com/help/releases/R2022a/matlab/ref/randn.html#mw_ed86de37-4aa2-4eff-b955-109aa0791624

You can try this method instead -

y = rand(5,1) + 1i*rand(5,1)

y = 
   0.6128 + 0.1289i
   0.8724 + 0.4457i
   0.3249 + 0.0643i
   0.9266 + 0.5437i
   0.9903 + 0.1540i

[Stephen23]

Also:

y = complex(rand(5,1),rand(5,1))

y = 
   0.8953 + 0.9832i
   0.2603 + 0.9094i
   0.3151 + 0.3252i
   0.3453 + 0.1122i
   0.8109 + 0.2145i