diff --git a/ТЕМА1/.gitkeep b/TEMA1/.gitkeep similarity index 100% rename from ТЕМА1/.gitkeep rename to TEMA1/.gitkeep diff --git a/TEMA1/1.png b/TEMA1/1.png new file mode 100644 index 0000000..e687146 Binary files /dev/null and b/TEMA1/1.png differ diff --git a/TEMA1/2.png b/TEMA1/2.png new file mode 100644 index 0000000..22e4572 Binary files /dev/null and b/TEMA1/2.png differ diff --git a/TEMA1/3.png b/TEMA1/3.png new file mode 100644 index 0000000..4019523 Binary files /dev/null and b/TEMA1/3.png differ diff --git a/TEMA1/4.png b/TEMA1/4.png new file mode 100644 index 0000000..e9e7a5d Binary files /dev/null and b/TEMA1/4.png differ diff --git a/TEMA1/5.png b/TEMA1/5.png new file mode 100644 index 0000000..84a43b5 Binary files /dev/null and b/TEMA1/5.png differ diff --git a/TEMA1/6.png b/TEMA1/6.png new file mode 100644 index 0000000..a7ece4f Binary files /dev/null and b/TEMA1/6.png differ diff --git a/TEMA1/7.png b/TEMA1/7.png new file mode 100644 index 0000000..ca51639 Binary files /dev/null and b/TEMA1/7.png differ diff --git a/TEMA1/Perem b/TEMA1/Perem new file mode 100644 index 0000000..bc19328 --- /dev/null +++ b/TEMA1/Perem @@ -0,0 +1,351 @@ +# Created by Octave 10.3.0, Sun Feb 22 19:22:33 2026 UTC +# name: A +# type: matrix +# rows: 4 +# columns: 6 + -0.15982161061842087 0.26846341615516361 0.60270611927804241 0.712672703978255 0.19204634944294763 0.19913155797960744 + 0.5643676089968257 0.064164160291927494 1.2690513832431134 0.62978389802746793 -0.2464804874499302 1.1841028927369184 + 1.4443758833884563 -0.86736261235335377 0.99873748711338073 0.45664782767794176 0.87605971248693149 0.22645936613104439 + 2.1487318577243668 2.2850753349852959 0.44033697967814373 0.50283268514264023 1.9515071924823917 -0.71998073354315639 + + +# name: B +# type: matrix +# rows: 4 +# columns: 7 + 0.44800575729054604 0.44449480329475854 0.89841568133174066 0.10612599244181442 0.31733160756937207 0.48856288506078005 0.94991924341112299 + 0.82932009442546706 0.20325055382820134 0.67060153182347737 0.29069865787967708 0.08668169130459813 0.70050638986256641 0.48650511617030578 + 0.56839773831590967 0.63621145328402962 0.58111676338220364 0.40383780372326072 0.22757913961278153 0.65675792010746226 0.97516297950272668 + 0.94106403564792362 0.86848676978462569 0.35065643505474398 0.82830765764549918 0.075934317820851138 0.83807893330600725 0.30281993872137847 + + +# name: B1 +# type: matrix +# rows: 4 +# columns: 7 + 0.6693323220124261 0.66670443473458207 0.94784792099352133 0.32576984581421042 0.56332193954201004 0.69897273556325501 0.97463800634446995 + 0.91067013480484083 0.45083317738183526 0.81890263879381742 0.53916477804069984 0.29441754585044372 0.83696259764852476 0.69749918721838366 + 0.75392157305379559 0.79762864372089193 0.76231014907464245 0.63548233942672294 0.47705255435096616 0.81040602176159959 0.98750340733727426 + 0.97008455077272704 0.93192637573180948 0.59216250730246678 0.91011409045542147 0.27556182213951763 0.91546651129683998 0.55029077651854064 + + +# name: B1D +# type: matrix +# rows: 4 +# columns: 1 + 0.6693323220124261 + 0.45083317738183526 + 0.76231014907464245 + 0.91011409045542147 + + +# name: B2 +# type: matrix +# rows: 4 +# columns: 7 + -0.80294919555475763 -0.81081691522192212 -0.10712242097766596 -2.2431282827635917 -1.1478079712054798 -0.71628708485139558 -0.051378304936504866 + -0.1871490772319353 -1.5933158056959302 -0.39958016069452451 -1.2354680882849405 -2.4455125904531352 -0.35595179138719829 -0.72050786113119669 + -0.56493386180315697 -0.45222429718212609 -0.54280357264515566 -0.90674195756654896 -1.4802572346528213 -0.4204397909422492 -0.025150663489374205 + -0.060744091076683875 -0.14100292682561297 -1.047948352378806 -0.18837062640616697 -2.5778865515651388 -0.17664299044661919 -1.1946169117618204 + + +# name: B3 +# type: matrix +# rows: 4 +# columns: 7 + 0.43316896030282748 0.4300018294545041 0.78234109866159751 0.10592689326100709 0.31203251111116942 0.46935738888356959 0.81336852739491616 + 0.73747234879157786 0.20185403477228037 0.62145736833513643 0.28662163989474743 0.08657318181528792 0.64460491295264988 0.46753936944909325 + 0.53828241095382889 0.59415238558856232 0.54895772571885659 0.39295031712674033 0.22561974739326354 0.61055241627335477 0.82779333606432581 + 0.80818519862281391 0.76335229127780801 0.34351437071082191 0.7367881940965183 0.07586136571577104 0.74335950610407342 0.29821301845291232 + + +# name: BS1 +# type: matrix +# rows: 4 +# columns: 7 + 0.44800575729054604 0.20325055382820134 0.35065643505474398 0.10612599244181442 0.075934317820851138 0.48856288506078005 0.30281993872137847 + 0.56839773831590967 0.44449480329475854 0.58111676338220364 0.29069865787967708 0.08668169130459813 0.65675792010746226 0.48650511617030578 + 0.82932009442546706 0.63621145328402962 0.67060153182347737 0.40383780372326072 0.22757913961278153 0.70050638986256641 0.94991924341112299 + 0.94106403564792362 0.86848676978462569 0.89841568133174066 0.82830765764549918 0.31733160756937207 0.83807893330600725 0.97516297950272668 + + +# name: BS2 +# type: matrix +# rows: 4 +# columns: 7 + 0.82932009442546706 0.20325055382820134 0.67060153182347737 0.29069865787967708 0.08668169130459813 0.70050638986256641 0.48650511617030578 + 0.44800575729054604 0.44449480329475854 0.89841568133174066 0.10612599244181442 0.31733160756937207 0.48856288506078005 0.94991924341112299 + 0.56839773831590967 0.63621145328402962 0.58111676338220364 0.40383780372326072 0.22757913961278153 0.65675792010746226 0.97516297950272668 + 0.94106403564792362 0.86848676978462569 0.35065643505474398 0.82830765764549918 0.075934317820851138 0.83807893330600725 0.30281993872137847 + + +# name: C +# type: double_range +# base, limit, increment +4 27 1 + + +# name: D +# type: matrix +# rows: 4 +# columns: 6 + 4 8 12 16 20 24 + 5 9 13 17 21 25 + 6 10 14 18 22 26 + 7 11 15 19 23 27 + + +# name: D1 +# type: scalar +22 + + +# name: D2 +# type: matrix +# rows: 1 +# columns: 3 + 18 22 26 + + +# name: D3 +# type: matrix +# rows: 2 +# columns: 3 + 13 17 21 + 14 18 22 + + +# name: D4 +# type: matrix +# rows: 1 +# columns: 5 + 19 20 21 22 23 + + +# name: D5 +# type: matrix +# rows: 2 +# columns: 3 + 6 14 26 + 7 15 27 + + +# name: DB +# type: diagonal matrix +# rows: 4 +# columns: 4 +0.6693323220124261 +0.45083317738183526 +0.76231014907464245 +0.91011409045542147 + + +# name: DDD +# type: matrix +# rows: 4 +# columns: 6 + 64 512 1728 4096 8000 13824 + 125 729 2197 4913 9261 15625 + 216 1000 2744 5832 10648 17576 + 343 1331 3375 6859 12167 19683 + + +# name: DL +# type: bool matrix +# rows: 4 +# columns: 6 + 0 0 0 0 1 1 + 0 0 0 0 1 1 + 0 0 0 0 1 1 + 0 0 0 0 1 1 + + +# name: DP1 +# type: matrix +# rows: 1 +# columns: 6 + 840 7920 32760 93024 212520 421200 + + +# name: DS1 +# type: matrix +# rows: 1 +# columns: 6 + 22 38 54 70 86 102 + + +# name: DS2 +# type: matrix +# rows: 4 +# columns: 1 + 84 + 90 + 96 + 102 + + +# name: Dstolb +# type: matrix +# rows: 24 +# columns: 1 + 4 + 5 + 6 + 7 + 8 + 9 + 10 + 11 + 12 + 13 + 14 + 15 + 16 + 17 + 18 + 19 + 20 + 21 + 22 + 23 + 24 + 25 + 26 + 27 + + +# name: Dsum +# type: scalar +22.547300573537278 + + +# name: Dsum2 +# type: scalar +-0.057010896737607175 + + +# name: E +# type: matrix +# rows: 7 +# columns: 6 + 3.2395146560388177 1.8308810528708013 2.3045310483602459 1.574329264620888 2.2160702424517482 0.52238342407961502 + 2.8287418238245432 1.5651036627373256 1.5436702036366816 1.1720113519821642 2.2874837093924723 -0.15203518771377095 + 1.8276972334897346 0.58145774394557659 2.1272985218560407 1.5042976040729812 1.2006487991830559 0.85209758459420337 + 2.5102043159239815 1.5896149676153528 1.2009367644521107 0.85962225132925518 1.9189639442388249 -0.13956254597296144 + 0.49007619885873338 0.066875785037017535 0.56198972815380022 0.42285008491497927 0.38713631427164968 0.16269696068816905 + 3.0666724134647922 1.521534899414881 2.2084043444977817 1.5106736350835748 2.1320417829916232 0.47208822821047935 + 2.1819308451900974 0.1323812209566885 2.297196772332073 1.5809484233586946 1.5077707926750763 0.76804128288437279 + + +# name: F +# type: matrix +# rows: 4 +# columns: 13 + -0.15982161061842087 0.26846341615516361 0.60270611927804241 0.712672703978255 0.19204634944294763 0.19913155797960744 0.44800575729054604 0.44449480329475854 0.89841568133174066 0.10612599244181442 0.31733160756937207 0.48856288506078005 0.94991924341112299 + 0.5643676089968257 0.064164160291927494 1.2690513832431134 0.62978389802746793 -0.2464804874499302 1.1841028927369184 0.82932009442546706 0.20325055382820134 0.67060153182347737 0.29069865787967708 0.08668169130459813 0.70050638986256641 0.48650511617030578 + 1.4443758833884563 -0.86736261235335377 0.99873748711338073 0.45664782767794176 0.87605971248693149 0.22645936613104439 0.56839773831590967 0.63621145328402962 0.58111676338220364 0.40383780372326072 0.22757913961278153 0.65675792010746226 0.97516297950272668 + 2.1487318577243668 2.2850753349852959 0.44033697967814373 0.50283268514264023 1.9515071924823917 -0.71998073354315639 0.94106403564792362 0.86848676978462569 0.35065643505474398 0.82830765764549918 0.075934317820851138 0.83807893330600725 0.30281993872137847 + + +# name: FF +# type: matrix +# rows: 2 +# columns: 4 + 1 1 1 1 + 1 1 1 1 + + +# name: G +# type: matrix +# rows: 4 +# columns: 6 + -0.63928644247368349 2.1477073292413089 7.2324734313365084 11.40276326365208 3.8409269888589526 4.7791573915105783 + 2.8218380449841285 0.57747744262734746 16.497667982160475 10.706326266466954 -5.1760902364485339 29.602572318422958 + 8.6662553003307377 -8.6736261235335377 13.98232481958733 8.2196608982029513 19.273313674712494 5.8879435194071545 + 15.041123004070567 25.135828684838255 6.6050546951721563 9.553821017710165 44.884665427095008 -19.439479805665222 + + +# name: GG +# type: matrix +# rows: 5 +# columns: 5 + 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 + + +# name: H +# type: sq_string +# elements: 1 +# length: 24 +This is a symbols vector + + +# name: L +# type: complex matrix +# rows: 1 +# columns: 2 + (-2,23.100000000000001) (3,-5.5999999999999996) + + +# name: M +# type: matrix +# rows: 4 +# columns: 6 + -0.14206365388304076 0.47726829538695753 1.6072163180747796 2.5339473919226845 0.85353933085754496 1.0620349758912395 + 0.62707512110758412 0.12832832058385499 3.6661484404801055 2.3791836147704344 -1.1502422747663408 6.5783494040939905 + 1.9258345111846085 -1.9274724718963416 3.1071832932416288 1.8265913107117671 4.2829585943805544 1.3084318932015899 + 3.3424717786823481 5.5857397077418343 1.4677899322604793 2.1230713372689256 9.974370094910002 -4.3198844012589381 + + +# name: NN +# type: matrix +# rows: 1 +# columns: 20 + 11.5 12.689473684210526 13.878947368421052 15.06842105263158 16.257894736842104 17.44736842105263 18.63684210526316 19.826315789473686 21.015789473684212 22.205263157894738 23.394736842105264 24.58421052631579 25.773684210526316 26.963157894736842 28.152631578947371 29.342105263157897 30.531578947368423 31.721052631578949 32.910526315789475 34.100000000000001 + + +# name: ans +# type: scalar +0 + + +# name: dinv +# type: matrix +# rows: 4 +# columns: 4 + 1.997544696857219 -0.7983350998512504 0.21607731557912829 -0.15393575877440541 + -0.7983350998512504 0.7039176138116332 -0.28914829532949571 0.084104520076889025 + 0.21607731557912829 -0.28914829532949571 0.37407452282192627 -0.084248003608083558 + -0.15393575877440541 0.084104520076889025 -0.084248003608083558 0.094346054472364549 + + +# name: dt +# type: scalar +77.947787455975757 + + +# name: elem +# type: scalar +28 + + +# name: i +# type: scalar +19 + + +# name: k +# type: scalar +7 + + +# name: nm +# type: matrix +# rows: 1 +# columns: 2 + 4 7 + + diff --git a/TEMA1/Prog1.m b/TEMA1/Prog1.m new file mode 100644 index 0000000..3089023 --- /dev/null +++ b/TEMA1/Prog1.m @@ -0,0 +1,5 @@ +D1=D(3,5) +D2=D(3,4:end) +D3=D(2:3,3:5) +D4=D(16:20) +D5=D(3:4,[1,3,6]) diff --git a/TEMA1/prog2.m b/TEMA1/prog2.m new file mode 100644 index 0000000..e050c5b --- /dev/null +++ b/TEMA1/prog2.m @@ -0,0 +1,10 @@ +MM = 10 + 8 * randn(5,7); +SR = mean(MM(:)); +MM(MM > SR + 8) = SR + 8; +MM(MM < SR - 8) = SR - 8; +MMC = MM(:); +MMC = sort(MMC); +index = round(length(MMC) / 2); +median_val = MMC(index) +MM1 = log(MM) + diff --git a/TEMA1/report.md b/TEMA1/report.md new file mode 100644 index 0000000..3aa95a2 --- /dev/null +++ b/TEMA1/report.md @@ -0,0 +1,490 @@ +# Отчет по теме 1 + +Долганов Всеволод, А-01-24 + +## 1 Знакомство с интерфейсом + +Запустил среду GNU Octave и визуально изучил расположение окон и главного меню. + +## 2 Настройка рабочей директории + +В окне «Текущая папка» установил путь к созданной директории TEMA1: +![Скриншот выбора текущей папки]([1.png]) + +## 3 Настройка отображаемых окон + +Через меню «Окно» включил отображение командного окна, журнала команд, диспетчера файлов, области переменных и редактора. + +## 4 Установка путей к рабочим папкам + +Через меню «Правка» -> «Установить путь» добавил пути к папкам TEMA1 и TEMA2. Убедился, что файлы отображаются в «Диспетчере файлов». +![Скриншот диспетчера файлов]([2.png]) + +## 5 Использование встроенной справки + +Изучил документацию через главное меню и проверил работу оперативной справки через командное окно: + +>> help randn +'randn' is a built-in function from the file libinterp/corefcn/rand.cc + + -- X = randn (N) + -- X = randn (M, N, ...) + -- X = randn ([M N ...]) + -- X = randn (..., "single") + -- X = randn (..., "double") + -- V = randn ("state") + -- randn ("state", V) + -- randn ("state", "reset") + -- V = randn ("seed") + -- randn ("seed", V) + -- randn ("seed", "reset") + Return a matrix with normally distributed random elements having + zero mean and variance one. + + The arguments are handled the same as the arguments for ‘rand’. + + By default, ‘randn’ uses the Marsaglia and Tsang "Ziggurat + technique" to transform from a uniform to a normal distribution. + + The class of the value returned can be controlled by a trailing + "double" or "single" argument. These are the only valid classes. + + Reference: G. Marsaglia and W.W. Tsang, ‘Ziggurat Method for + Generating Random Variables’, J. Statistical Software, vol 5, 2000, + + + See also: rand, rande, randg, randp. + +Additional help for built-in functions and operators is +available in the online version of the manual. Use the command +'doc ' to search the manual index. + +Help and information about Octave is also available on the WWW +at https://www.octave.org and https://octave.discourse.group/c/help/ + +## 6 Создание базовых матриц и векторов + +>> A = randn(4,6) +A = + + -0.159822 0.268463 0.602706 0.712673 0.192046 0.199132 + 0.564368 0.064164 1.269051 0.629784 -0.246480 1.184103 + 1.444376 -0.867363 0.998737 0.456648 0.876060 0.226459 + 2.148732 2.285075 0.440337 0.502833 1.951507 -0.719981 + +>> B = rand(4,7) +B = + + 0.448006 0.444495 0.898416 0.106126 0.317332 0.488563 0.949919 + 0.829320 0.203251 0.670602 0.290699 0.086682 0.700506 0.486505 + 0.568398 0.636211 0.581117 0.403838 0.227579 0.656758 0.975163 + 0.941064 0.868487 0.350656 0.828308 0.075934 0.838079 0.302820 + + +>> C = 4:27 +C = + + Columns 1 through 23: + + 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 + + Column 24: + + 27 + + +>> H = 'This is a symbols vector' +H = This is a symbols vector + +>> L = [-2+23.1j, 3-5.6j] +L = + + -2.0000 + 23.1000i 3.0000 - 5.6000i + + +## 7 Матричные вычисления и преобразования + +>> D = reshape(C,[],6) + +D = + + 4 8 12 16 20 24 + 5 9 13 17 21 25 + 6 10 14 18 22 26 + 7 11 15 19 23 27 + + +>> E = B'*A +E = + + 3.239515 1.830881 2.304531 1.574329 2.216070 0.522383 + 2.828742 1.565104 1.543670 1.172011 2.287484 -0.152035 + 1.827697 0.581458 2.127299 1.504298 1.200649 0.852098 + 2.510204 1.589615 1.200937 0.859622 1.918964 -0.139563 + 0.490076 0.066876 0.561990 0.422850 0.387136 0.162697 + 3.066672 1.521535 2.208404 1.510674 2.132042 0.472088 + 2.181931 0.132381 2.297197 1.580948 1.507771 0.768041 + + +>> F = [A,B] +F = + + Columns 1 through 10: + + -0.159822 0.268463 0.602706 0.712673 0.192046 0.199132 0.448006 0.444495 0.898416 0.106126 + 0.564368 0.064164 1.269051 0.629784 -0.246480 1.184103 0.829320 0.203251 0.670602 0.290699 + 1.444376 -0.867363 0.998737 0.456648 0.876060 0.226459 0.568398 0.636211 0.581117 0.403838 + 2.148732 2.285075 0.440337 0.502833 1.951507 -0.719981 0.941064 0.868487 0.350656 0.828308 + + Columns 11 through 13: + + 0.317332 0.488563 0.949919 + 0.086682 0.700506 0.486505 + 0.227579 0.656758 0.975163 + 0.075934 0.838079 0.302820 + + +>> G = A.*D +G = + + -0.6393 2.1477 7.2325 11.4028 3.8409 4.7792 + 2.8218 0.5775 16.4977 10.7063 -5.1761 29.6026 + 8.6663 -8.6736 13.9823 8.2197 19.2733 5.8879 + 15.0411 25.1358 6.6051 9.5538 44.8847 -19.4395 + + +>> M = G./4.5 +M = + + -0.1421 0.4773 1.6072 2.5339 0.8535 1.0620 + 0.6271 0.1283 3.6661 2.3792 -1.1502 6.5783 + 1.9258 -1.9275 3.1072 1.8266 4.2830 1.3084 + 3.3425 5.5857 1.4678 2.1231 9.9744 -4.3199 + +>> DDD = D.^3 +DDD = + + 64 512 1728 4096 8000 13824 + 125 729 2197 4913 9261 15625 + 216 1000 2744 5832 10648 17576 + 343 1331 3375 6859 12167 19683 + + +>> DL = D>=20 +DL = + + 0 0 0 0 1 1 + 0 0 0 0 1 1 + 0 0 0 0 1 1 + 0 0 0 0 1 1 + + +>> Dstolb = D(:) +Dstolb = + + 4 + 5 + 6 + 7 + 8 + 9 + 10 + 11 + 12 + 13 + 14 + 15 + 16 + 17 + 18 + 19 + 20 + 21 + 22 + 23 + 24 + 25 + 26 + 27 + + ## 8 Применение стандартных математических функций +>> B1 = sqrt(B); B2 = log(B); B3 = sin(B); +B1 = + + 0.6693 0.6667 0.9478 0.3258 0.5633 0.6990 0.9746 + 0.9107 0.4508 0.8189 0.5392 0.2944 0.8370 0.6975 + 0.7539 0.7976 0.7623 0.6355 0.4771 0.8104 0.9875 + 0.9701 0.9319 0.5922 0.9101 0.2756 0.9155 0.5503 + +B2 = + + -0.802949 -0.810817 -0.107122 -2.243128 -1.147808 -0.716287 -0.051378 + -0.187149 -1.593316 -0.399580 -1.235468 -2.445513 -0.355952 -0.720508 + -0.564934 -0.452224 -0.542804 -0.906742 -1.480257 -0.420440 -0.025151 + -0.060744 -0.141003 -1.047948 -0.188371 -2.577887 -0.176643 -1.194617 + +B3 = + + 0.433169 0.430002 0.782341 0.105927 0.312033 0.469357 0.813369 + 0.737472 0.201854 0.621457 0.286622 0.086573 0.644605 0.467539 + 0.538282 0.594152 0.548958 0.392950 0.225620 0.610552 0.827793 + 0.808185 0.763352 0.343514 0.736788 0.075861 0.743360 0.298213 + + +>> k = length(B1) +k = 7 + +>> nm = size(B1) +nm = + + 4 7 + + +>> elem = numel(B1) +elem = 28 + +>> NN=linspace(11.5,34.1,20) +NN = + + Columns 1 through 13: + + 11.500 12.689 13.879 15.068 16.258 17.447 18.637 19.826 21.016 22.205 23.395 24.584 25.774 + + Columns 14 through 20: + + 26.963 28.153 29.342 30.532 31.721 32.911 34.100 + +>> FF=ones(2,4) +FF = + + 1 1 1 1 + 1 1 1 1 + +>> GG=zeros(5) +GG = + + 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 + +>> B1D=diag(B1) +B1D = + + 0.6693 + 0.4508 + 0.7623 + 0.9101 + + +>> DB=diag(B1D) +DB = + +Diagonal Matrix + + 0.6693 0 0 0 + 0 0.4508 0 0 + 0 0 0.7623 0 + 0 0 0 0.9101 + +>> BS1=sort(B) +BS1 = + + 0.448006 0.203251 0.350656 0.106126 0.075934 0.488563 0.302820 + 0.568398 0.444495 0.581117 0.290699 0.086682 0.656758 0.486505 + 0.829320 0.636211 0.670602 0.403838 0.227579 0.700506 0.949919 + 0.941064 0.868487 0.898416 0.828308 0.317332 0.838079 0.975163 + +>> BS2=sortrows(B,2) +BS2 = + + 0.829320 0.203251 0.670602 0.290699 0.086682 0.700506 0.486505 + 0.448006 0.444495 0.898416 0.106126 0.317332 0.488563 0.949919 + 0.568398 0.636211 0.581117 0.403838 0.227579 0.656758 0.975163 + 0.941064 0.868487 0.350656 0.828308 0.075934 0.838079 0.302820 + +>> DS1=sum(D) +DS1 = + + 22 38 54 70 86 102 + +>> DS2=sum(D,2) +DS2 = + + 84 + 90 + 96 + 102 + +>> DP1=prod(D) +DP1 = + + 840 7920 32760 93024 212520 421200 + +>> dt=det(A*A') +dt = 77.948 +>> dinv=inv(A*A') +dinv = + + 1.997545 -0.798335 0.216077 -0.153936 + -0.798335 0.703918 -0.289148 0.084105 + 0.216077 -0.289148 0.374075 -0.084248 + -0.153936 0.084105 -0.084248 0.094346 + +>> + +## 9 Индексация элементов матриц + +>> D1=D(3,5) +D1 = 22 +>> D2=D(3,4:end) +D2 = + + 18 22 26 + +>> D3=D(2:3,3:5) +D3 = + + 13 17 21 + 14 18 22 + +>> D4=D(16:20) +D4 = + + 19 20 21 22 23 + +>> D5=D(3:4,[1,3,6]) +D5 = + + 6 14 26 + 7 15 27 + + +## 10 Управляющие конструкции: циклы и условия + +Цикл по перечислению: +>> Dsum=0 +Dsum = 0 +>> for i=1:6 +Dsum=Dsum+sqrt(D(2,i)) +endfor +Dsum = 2.2361 +Dsum = 5.2361 +Dsum = 8.8416 +Dsum = 12.965 +Dsum = 17.547 +Dsum = 22.547 + +Цикл пока выполняется условие: +>> Dsum2=0;i=1 +i = 1 +>> while (D(i)<22) +Dsum2=Dsum2+sin(D(i)) +i=i+1 +endwhile +Dsum2 = -0.7568 +i = 2 +Dsum2 = -1.7157 +i = 3 +Dsum2 = -1.9951 +i = 4 +Dsum2 = -1.3382 +i = 5 +Dsum2 = -0.3488 +i = 6 +Dsum2 = 0.063321 +i = 7 +Dsum2 = -0.4807 +i = 8 +Dsum2 = -1.4807 +i = 9 +Dsum2 = -2.0173 +i = 10 +Dsum2 = -1.5971 +i = 11 +Dsum2 = -0.6065 +i = 12 +Dsum2 = 0.043799 +i = 13 +Dsum2 = -0.2441 +i = 14 +Dsum2 = -1.2055 +i = 15 +Dsum2 = -1.9565 +i = 16 +Dsum2 = -1.8066 +i = 17 +Dsum2 = -0.8937 +i = 18 +Dsum2 = -0.057011 +i = 19 + +Условие if: + +>> if (D(3,5)>=20) +printf('D(3,5)>=20') +else +printf('D(3,5)<20') +endif +D(3,5)>=20>> + +## 11 Построение графиков, гистограмм, круговых и столбчатых диаграмм + +>> plot(D(1,:),B([2,4],1:6)) +([4.png]) +>> hist(A(:),6) +([5.png]) +>> pie(D(1,:)) +([6.png]) +>> bar(DS1) +([7.png]) +## 12 Изучение работы с текстовым редактором среды + +>> Prog1 + +D1 = 22 +D2 = + + 18 22 26 + +D3 = + + 13 17 21 + 14 18 22 + +D4 = + + 19 20 21 22 23 + +D5 = + + 6 14 26 + 7 15 27 + +>> Prog1 +D1 = 22 +D2 = + + 18 22 26 + +D3 = + + 13 17 21 + 14 18 22 + +D4 = + + 19 20 21 22 23 + +D5 = + + 6 14 26 + 7 15 27 + +## 13 Сохранение и загрузка рабочей области +Сохранил область переменных в файл Perem, перезапустил среду, установил рабочую директорию и успешно загрузил переменные обратно через главное меню + +## 14 Завершили сеанс работы со средой. + + diff --git a/TEMA1/task.md b/TEMA1/task.md new file mode 100644 index 0000000..4e13978 --- /dev/null +++ b/TEMA1/task.md @@ -0,0 +1,80 @@ +# Общее контрольное задание по теме 1 + +Долганов Всеволод, А-01-24 + +## Задание + +Создайте переменную ММ – матрицу 5х7 со случайными нормально рас-пределенными элементами с математическим ожиданием 10 и стандартным отклонением 8. + + +## Решение + +```matlab + +MM = 10 + 8 * randn(5,7); + +``` + +## Задание + +Рассчитайте среднее значение SR по всем элементам матрицы ММ. + + +## Решение + +```matlab + +SR = mean(MM(:)); + +``` + +## Задание + +Замените в ММ все значения, превышающие SR+8, на значение SR+8, а значения, меньшие, чем SR-8, - на SR-8. + + +## Решение + +```matlab + +MM(MM > SR + 8) = SR + 8; +MM(MM < SR - 8) = SR - 8; + +``` +## Задание + + Превратите ММ в вектор – столбец ММС. Упорядочьте его элементы по возрастанию. Определите значение медианы, в качестве которого возь-мите серединное по порядку индексов значение в упорядоченном векто-ре. + + +## Решение + +```matlab + +MMC = MM(:); +MMC = sort(MMC); +index = round(length(MMC) / 2); +median_val = MMC(index) + +``` +## Задание + +Рассчитайте матрицу ММ1 с элементами, равными натуральным лога-рифмам от значений соответствующих элементов из матрицы ММ. + + +## Решение + +```matlab + +MM1 = log(MM) + +``` + + +median_val = 12.380 +MM1 = + + 2.7678 2.0017 2.8529 2.4635 2.5311 2.9701 2.5270 + 2.7423 2.7336 1.2513 1.7647 2.6683 1.5513 1.2513 + 2.5130 2.8358 2.4100 1.2513 2.9200 1.2513 2.9701 + 2.1386 1.2513 2.4526 2.9701 1.2513 2.5482 2.1318 + 1.2513 2.9701 2.8475 2.9701 2.8416 1.2513 2.5161 diff --git a/ТЕМА2/ML1_01.txt b/TEMA2/ML1_01.txt similarity index 100% rename from ТЕМА2/ML1_01.txt rename to TEMA2/ML1_01.txt diff --git a/ТЕМА2/ML1_02.txt b/TEMA2/ML1_02.txt similarity index 100% rename from ТЕМА2/ML1_02.txt rename to TEMA2/ML1_02.txt diff --git a/ТЕМА2/ML1_03.txt b/TEMA2/ML1_03.txt similarity index 100% rename from ТЕМА2/ML1_03.txt rename to TEMA2/ML1_03.txt diff --git a/ТЕМА2/ML1_04.txt b/TEMA2/ML1_04.txt similarity index 100% rename from ТЕМА2/ML1_04.txt rename to TEMA2/ML1_04.txt diff --git a/ТЕМА2/ML1_05.txt b/TEMA2/ML1_05.txt similarity index 100% rename from ТЕМА2/ML1_05.txt rename to TEMA2/ML1_05.txt diff --git a/ТЕМА2/ML1_06.txt b/TEMA2/ML1_06.txt similarity index 100% rename from ТЕМА2/ML1_06.txt rename to TEMA2/ML1_06.txt diff --git a/ТЕМА2/ML1_07.txt b/TEMA2/ML1_07.txt similarity index 100% rename from ТЕМА2/ML1_07.txt rename to TEMA2/ML1_07.txt diff --git a/ТЕМА2/ML1_08.txt b/TEMA2/ML1_08.txt similarity index 100% rename from ТЕМА2/ML1_08.txt rename to TEMA2/ML1_08.txt diff --git a/ТЕМА2/ML1_09.txt b/TEMA2/ML1_09.txt similarity index 100% rename from ТЕМА2/ML1_09.txt rename to TEMA2/ML1_09.txt diff --git a/ТЕМА2/ML1_10.txt b/TEMA2/ML1_10.txt similarity index 100% rename from ТЕМА2/ML1_10.txt rename to TEMA2/ML1_10.txt diff --git a/ТЕМА2/ML1_11.TXT b/TEMA2/ML1_11.TXT similarity index 100% rename from ТЕМА2/ML1_11.TXT rename to TEMA2/ML1_11.TXT diff --git a/ТЕМА2/ML1_12.txt b/TEMA2/ML1_12.txt similarity index 100% rename from ТЕМА2/ML1_12.txt rename to TEMA2/ML1_12.txt diff --git a/ТЕМА2/ML1_13.txt b/TEMA2/ML1_13.txt similarity index 100% rename from ТЕМА2/ML1_13.txt rename to TEMA2/ML1_13.txt diff --git a/ТЕМА2/ML1_14.txt b/TEMA2/ML1_14.txt similarity index 100% rename from ТЕМА2/ML1_14.txt rename to TEMA2/ML1_14.txt diff --git a/ТЕМА2/ML1_15.txt b/TEMA2/ML1_15.txt similarity index 100% rename from ТЕМА2/ML1_15.txt rename to TEMA2/ML1_15.txt diff --git a/ТЕМА2/ML1_16.txt b/TEMA2/ML1_16.txt similarity index 100% rename from ТЕМА2/ML1_16.txt rename to TEMA2/ML1_16.txt diff --git a/ТЕМА2/ML1_17.txt b/TEMA2/ML1_17.txt similarity index 100% rename from ТЕМА2/ML1_17.txt rename to TEMA2/ML1_17.txt diff --git a/ТЕМА2/ML1_18.txt b/TEMA2/ML1_18.txt similarity index 100% rename from ТЕМА2/ML1_18.txt rename to TEMA2/ML1_18.txt diff --git a/ТЕМА2/ML1_19.txt b/TEMA2/ML1_19.txt similarity index 100% rename from ТЕМА2/ML1_19.txt rename to TEMA2/ML1_19.txt diff --git a/ТЕМА2/ML1_20.txt b/TEMA2/ML1_20.txt similarity index 100% rename from ТЕМА2/ML1_20.txt rename to TEMA2/ML1_20.txt diff --git a/ТЕМА2/ML1_21.txt b/TEMA2/ML1_21.txt similarity index 100% rename from ТЕМА2/ML1_21.txt rename to TEMA2/ML1_21.txt diff --git a/ТЕМА2/dan_vuz.txt b/TEMA2/dan_vuz.txt similarity index 100% rename from ТЕМА2/dan_vuz.txt rename to TEMA2/dan_vuz.txt