ответвлено от main/it-labs
738 строки
15 KiB
Markdown
738 строки
15 KiB
Markdown
# Отчёт TEMA 1
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##1
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##2
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Считываем данные с файла
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```matlab
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>> XX=load("dan_vuz.txt")
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XX =
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Columns 1 through 3:
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1.9700e+02 1.3717e+06 8.0000e+00
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1.9800e+02 7.3820e+05 4.0000e+00
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1.9900e+02 2.4167e+05 1.0000e+00
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2.0000e+02 6.1990e+05 3.0000e+00
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2.0100e+02 1.7553e+06 7.0000e+00
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2.0200e+02 5.7215e+05 0
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2.0300e+02 1.4322e+06 0
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2.0400e+02 1.3277e+06 5.0000e+00
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2.0500e+02 4.9080e+05 7.0000e+00
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2.0600e+02 6.3472e+05 1.0000e+00
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2.0700e+02 7.4157e+05 8.0000e+00
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2.0800e+02 1.2874e+06 9.0000e+00
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2.0900e+02 8.2497e+05 5.0000e+00
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2.1000e+02 2.1060e+05 1.0000e+00
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2.1100e+02 3.1755e+06 2.0000e+00
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2.1200e+02 1.2958e+05 1.0000e+00
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2.1300e+02 5.2322e+06 2.0000e+00
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...
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```
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Проверяем размерность матрицы XX
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```matlab
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>> size(XX)
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ans =
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290 15
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```
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Выделяем в отдельну матрицу данные столбцов с 3 по 13
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```matlab
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>> X=XX(:,3:13)
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X =
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Columns 1 through 6:
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8 4 2 53 7 76
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4 5 6 71 5 36
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1 0 1 5 5 2
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3 1 1 28 0 24
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7 10 6 54 7 46
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0 2 2 22 7 17
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0 6 11 30 8 88
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5 2 0 78 3 40
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7 0 0 20 0 30
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1 1 1 12 3 13
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8 4 3 33 1 37
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9 5 6 24 8 36
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5 5 4 57 7 56
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1 4 0 7 0 1
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2 8 0 83 6 70
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1 0 0 0 0 6
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2 28 8 326 76 213
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1 1 1 42 2 0
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1 0 0 13 0 0
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3 2 2 76 8 92
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0 1 0 7 1 7
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7 2 1 70 0 23
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3 0 0 11 0 3
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...
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```
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Рассчитаkb матрицу корреляций между показателями результативности
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```matlab
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>> R=corr(X)
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R =
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Columns 1 through 3:
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1.0000e+00 4.4320e-01 4.5229e-01
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4.4320e-01 1.0000e+00 8.5319e-01
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4.5229e-01 8.5319e-01 1.0000e+00
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4.4779e-01 8.5331e-01 8.4660e-01
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3.8123e-01 8.6240e-01 8.8651e-01
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4.6516e-01 8.5436e-01 9.0335e-01
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3.1487e-01 5.5145e-01 5.5091e-01
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6.5579e-02 2.5082e-02 3.8840e-03
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2.9153e-01 4.2348e-01 4.4396e-01
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4.8811e-01 8.2170e-01 7.8358e-01
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3.9815e-01 2.6183e-01 2.6408e-01
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Columns 4 through 6:
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4.4779e-01 3.8123e-01 4.6516e-01
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8.5331e-01 8.6240e-01 8.5436e-01
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8.4660e-01 8.8651e-01 9.0335e-01
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1.0000e+00 8.7038e-01 9.3849e-01
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8.7038e-01 1.0000e+00 9.3605e-01
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9.3849e-01 9.3605e-01 1.0000e+00
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7.0924e-01 5.7668e-01 6.3033e-01
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4.9500e-02 3.7562e-02 4.7121e-02
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4.5873e-01 3.8322e-01 4.7592e-01
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8.5183e-01 7.7266e-01 8.3810e-01
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3.4420e-01 1.8751e-01 3.3118e-01
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Columns 7 through 9:
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3.1487e-01 6.5579e-02 2.9153e-01
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5.5145e-01 2.5082e-02 4.2348e-01
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5.5091e-01 3.8840e-03 4.4396e-01
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7.0924e-01 4.9500e-02 4.5873e-01
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5.7668e-01 3.7562e-02 3.8322e-01
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6.3033e-01 4.7121e-02 4.7592e-01
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1.0000e+00 7.9448e-02 4.1878e-01
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7.9448e-02 1.0000e+00 4.7985e-02
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4.1878e-01 4.7985e-02 1.0000e+00
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6.2936e-01 5.6462e-02 6.2616e-01
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2.8287e-01 1.3662e-01 4.5537e-01
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Columns 10 and 11:
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4.8811e-01 3.9815e-01
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8.2170e-01 2.6183e-01
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7.8358e-01 2.6408e-01
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8.5183e-01 3.4420e-01
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7.7266e-01 1.8751e-01
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8.3810e-01 3.3118e-01
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6.2936e-01 2.8287e-01
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5.6462e-02 1.3662e-01
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6.2616e-01 4.5537e-01
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1.0000e+00 3.8799e-01
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3.8799e-01 1.0000e+00
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```
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```matlab
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>> [vect,lambda]=eig(X'*X)
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vect =
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Columns 1 through 3:
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1.3928e-03 3.7187e-02 -6.5276e-02
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-8.0998e-04 6.0909e-01 3.8181e-01
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-7.5396e-03 -4.5900e-01 -5.2153e-01
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-1.5197e-04 -2.3868e-03 -3.9384e-02
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1.0985e-03 -2.1296e-02 -1.7772e-02
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4.6341e-05 2.4446e-02 3.4514e-02
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1.1860e-03 4.2558e-03 2.3496e-02
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-9.9994e-01 5.6340e-04 6.5198e-03
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-1.6282e-03 4.6826e-01 -6.5978e-01
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1.7002e-03 -4.2581e-01 3.3001e-01
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7.7010e-03 -1.2368e-01 1.7350e-01
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Columns 4 through 6:
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1.1402e-01 -5.7482e-02 -4.3369e-01
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-5.6588e-01 -2.6231e-01 2.2396e-01
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-6.7233e-01 -1.9752e-01 9.4377e-02
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2.0471e-02 2.9085e-02 -4.2879e-02
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1.0189e-01 1.5147e-01 2.9246e-02
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6.8266e-03 -2.8773e-02 -3.6347e-02
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-4.8185e-02 -1.5673e-02 -5.8142e-02
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4.0470e-03 7.5475e-03 1.2608e-03
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2.6872e-01 2.8869e-02 4.9535e-01
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2.8074e-01 -3.9004e-01 6.2610e-01
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-2.2481e-01 8.4320e-01 3.2876e-01
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Columns 7 through 9:
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-8.6174e-01 1.8078e-02 -2.0942e-01
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-1.8894e-01 -2.6535e-02 7.3599e-02
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-1.1098e-01 -3.7697e-03 2.9602e-02
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3.9810e-02 -2.5705e-01 1.7315e-01
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-2.2268e-01 9.3732e-02 8.4203e-01
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1.2360e-01 5.5571e-02 -4.0806e-01
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5.8108e-02 9.5701e-01 5.2365e-03
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-2.3724e-03 1.4645e-03 -1.0271e-03
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-1.2025e-01 5.8771e-02 -9.1452e-02
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-2.7487e-01 3.6416e-02 -5.9676e-02
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-2.2260e-01 1.8627e-02 -1.7997e-01
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Columns 10 and 11:
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4.4068e-02 3.5306e-02
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6.2111e-03 4.6772e-02
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-4.1502e-02 4.8953e-02
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7.2027e-01 6.1556e-01
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-3.7246e-01 2.4277e-01
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-5.1787e-01 7.3685e-01
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2.5496e-01 9.5893e-02
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5.5851e-04 1.6945e-04
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2.0459e-02 1.7911e-02
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5.3342e-02 5.9523e-02
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4.8313e-02 1.7425e-02
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lambda =
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Diagonal Matrix
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Columns 1 through 3:
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2.2947e+01 0 0
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0 1.9317e+03 0
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0 0 2.5940e+03
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0 0 0
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0 0 0
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0 0 0
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0 0 0
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0 0 0
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0 0 0
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0 0 0
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0 0 0
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Columns 4 through 6:
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0 0 0
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0 0 0
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0 0 0
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3.4573e+03 0 0
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0 5.6252e+03 0
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0 0 8.6721e+03
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0 0 0
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0 0 0
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0 0 0
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0 0 0
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0 0 0
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Columns 7 through 9:
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0 0 0
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0 0 0
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0 0 0
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0 0 0
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0 0 0
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0 0 0
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1.8915e+04 0 0
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0 4.7523e+04 0
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0 0 5.7484e+04
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0 0 0
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0 0 0
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Columns 10 and 11:
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0 0
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0 0
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0 0
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0 0
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0 0
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0 0
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0 0
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0 0
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0 0
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2.2565e+05 0
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0 7.4946e+06
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```
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```matlab
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>> Sobst=diag(lambda)
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Sobst =
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2.2947e+01
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1.9317e+03
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2.5940e+03
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3.4573e+03
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5.6252e+03
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8.6721e+03
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1.8915e+04
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4.7523e+04
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5.7484e+04
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2.2565e+05
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7.4946e+06
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```
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```matlab
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>> fprintf('Eigenvalues:\n %f \n',Sobst)
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Eigenvalues:
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22.946585
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Eigenvalues:
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1931.665464
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Eigenvalues:
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2593.979592
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Eigenvalues:
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3457.339562
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Eigenvalues:
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5625.151474
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Eigenvalues:
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8672.065947
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Eigenvalues:
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18914.627989
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Eigenvalues:
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47522.678185
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Eigenvalues:
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57483.681267
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Eigenvalues:
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225653.068540
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Eigenvalues:
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7494628.795394
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```
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```matlab
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>> SobMax=Sobst(end)
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SobMax = 7.4946e+06
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>> GlComp=vect(:,end)
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GlComp =
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3.5306e-02
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4.6772e-02
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4.8953e-02
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6.1556e-01
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2.4277e-01
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7.3685e-01
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9.5893e-02
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1.6945e-04
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1.7911e-02
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5.9523e-02
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1.7425e-02
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```
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```matlab
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>> Delt=100*SobMax/sum(Sobst)
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Delt = 95.273
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>> fprintf('Delta= %d \n ',round(Delt))
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Delta= 95
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```
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```matlab
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>> Res=X*GlComp
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Res =
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9.2542e+01
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7.3433e+01
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5.8855e+00
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3.5300e+01
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7.0208e+01
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2.8096e+01
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8.7136e+01
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7.9776e+01
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3.6243e+01
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1.8250e+01
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4.9667e+01
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4.5067e+01
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8.1785e+01
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5.5559e+00
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...
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>> fprintf(' Results \n ')
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Results
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>> fprintf('%d %f \n ',[XX(:,1),Res] ')
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197 92.541636
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198 73.432513
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199 5.885468
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200 35.300393
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201 70.208100
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202 28.096191
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203 87.136298
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204 79.776499
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205 36.243011
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206 18.249808
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207 49.666520
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208 45.067095
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209 81.785392
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210 5.555862
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211 105.361366
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212 4.575460
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213 381.204021
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214 26.712747
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216 8.037618
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217 119.627795
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218 10.061485
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...
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```
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```matlab
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>> save res.mat Res -mat
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```
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```matlab
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>> hist(Res,20)
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>> xlabel('Results ')
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>> ylabel('Number of Unis ')
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```
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```matlab
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>> saveas(gcf, 'Hist.jpg ', 'jpg ')
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```
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```
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>> CorFin=corr(Res,XX(:,2))
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CorFin = 0.8437
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>> fprintf('Correlation of Results and Money =
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%f \n',CorFin)
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Correlation of Results and Money = 0.843710
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```
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##3
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##4
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Поставив после каждой строчки ';' в окне текстового редактора мы предотвращаем вывод нежелательных команд(всех кроме команд fprintf)
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```
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>> Prog1
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Eigenvalues:
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22.946585
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Eigenvalues:
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1931.665464
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Eigenvalues:
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2593.979592
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Eigenvalues:
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3457.339562
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Eigenvalues:
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5625.151474
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Eigenvalues:
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8672.065947
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Eigenvalues:
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18914.627989
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Eigenvalues:
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47522.678185
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Eigenvalues:
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57483.681267
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Eigenvalues:
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225653.068540
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Eigenvalues:
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7494628.795394
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Delta= 95
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Results
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197 92.541636
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198 73.432513
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199 5.885468
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200 35.300393
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201 70.208100
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202 28.096191
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203 87.136298
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204 79.776499
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205 36.243011
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206 18.249808
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207 49.666520
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208 45.067095
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209 81.785392
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210 5.555862
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211 105.361366
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212 4.575460
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213 381.204021
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214 26.712747
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216 8.037618
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217 119.627795
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218 10.061485
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219 63.762947
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220 9.087658
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221 41.684105
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222 35.907417
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223 76.139589
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224 23.752550
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225 142.216169
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226 67.755801
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227 20.597788
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228 76.818771
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229 104.284923
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230 18.541601
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231 4.473983
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232 3.580878
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233 224.758597
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234 26.863645
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235 212.911324
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236 50.921549
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237 33.628254
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238 42.168327
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239 103.701129
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240 136.060809
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241 713.711764
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242 34.027235
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245 4.102289
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246 27.086730
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247 2.667541
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248 2.497556
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252 103.829221
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253 7.460715
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256 34.755449
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257 4.359736
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258 5.741187
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259 15.989432
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261 45.399798
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264 2.462250
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267 6.424390
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268 66.503024
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273 73.935542
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275 11.130530
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296 3.684256
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304 9.970486
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305 28.031419
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311 1.352414
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318 40.905330
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322 11.722703
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325 30.793455
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326 42.716264
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329 10.023429
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330 32.260491
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334 25.495269
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335 36.870098
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336 42.948416
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339 87.963238
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340 228.668981
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341 99.146097
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342 28.749899
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343 9.622160
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346 1.359974
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347 54.213640
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348 107.547742
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349 116.106427
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352 299.102890
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356 14.333164
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357 20.479493
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362 144.908794
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365 14.952535
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366 25.976099
|
|
371 268.149429
|
|
372 12.353605
|
|
373 4.526119
|
|
376 104.664655
|
|
377 91.264814
|
|
379 209.329940
|
|
381 196.452926
|
|
383 4.812125
|
|
387 1.387719
|
|
388 50.732954
|
|
389 118.633179
|
|
391 212.528679
|
|
392 37.744725
|
|
393 30.102473
|
|
394 320.669960
|
|
395 0.000000
|
|
399 41.450044
|
|
410 141.123026
|
|
412 405.347359
|
|
413 12.875247
|
|
414 117.612150
|
|
441 8.989886
|
|
446 74.655250
|
|
448 89.566395
|
|
451 37.992598
|
|
456 40.599701
|
|
465 37.339465
|
|
466 142.928780
|
|
467 9.122607
|
|
472 53.463843
|
|
476 93.289532
|
|
477 57.155319
|
|
484 19.139209
|
|
1001 5.491474
|
|
1002 85.213352
|
|
1004 170.642969
|
|
1017 90.453986
|
|
1030 57.810669
|
|
1034 7.372961
|
|
1035 9.614944
|
|
1037 1.846687
|
|
1038 17.646937
|
|
1039 153.910670
|
|
1041 119.835954
|
|
1044 5.010210
|
|
1 2.691959
|
|
2 379.300890
|
|
3 13.497203
|
|
4 2.462250
|
|
6 19.718182
|
|
7 200.667783
|
|
8 93.618235
|
|
9 25.650544
|
|
10 26.344823
|
|
11 65.285854
|
|
12 21.514761
|
|
13 176.510003
|
|
14 1898.884523
|
|
15 91.241365
|
|
16 304.552394
|
|
17 6.355976
|
|
18 3.439437
|
|
19 158.859588
|
|
20 187.802059
|
|
21 26.561371
|
|
22 8.657907
|
|
23 110.316703
|
|
26 21.286647
|
|
28 8.028500
|
|
29 76.976887
|
|
33 148.320170
|
|
34 97.178361
|
|
35 6.713123
|
|
36 88.631285
|
|
37 333.404629
|
|
38 27.838269
|
|
40 11.662869
|
|
41 0.000000
|
|
42 71.250154
|
|
43 0.369757
|
|
44 10.360736
|
|
45 3.855089
|
|
51 81.568715
|
|
52 54.636552
|
|
53 23.501931
|
|
54 90.446620
|
|
55 57.162095
|
|
56 162.850196
|
|
57 305.874565
|
|
58 6.323299
|
|
59 59.415851
|
|
60 46.966205
|
|
62 56.660855
|
|
63 815.675312
|
|
64 47.723188
|
|
65 277.021471
|
|
66 328.086215
|
|
67 113.590454
|
|
68 11.709047
|
|
69 33.969900
|
|
71 0.913379
|
|
72 122.671912
|
|
73 59.978744
|
|
74 7.491057
|
|
75 12.504248
|
|
76 24.208529
|
|
77 400.110964
|
|
78 263.576362
|
|
79 25.930889
|
|
80 36.895259
|
|
81 254.783774
|
|
82 6.870138
|
|
84 12.625935
|
|
85 23.783568
|
|
86 3.159890
|
|
87 5.151827
|
|
88 0.000000
|
|
89 106.765711
|
|
90 15.783445
|
|
91 30.701284
|
|
92 64.989933
|
|
93 43.087958
|
|
94 31.124285
|
|
95 5.171761
|
|
96 48.085148
|
|
97 5.347890
|
|
99 2.124571
|
|
100 0.082078
|
|
101 21.635603
|
|
102 52.057076
|
|
103 25.625192
|
|
104 9.750228
|
|
105 36.202865
|
|
107 4.626653
|
|
108 3.190574
|
|
109 9.578291
|
|
110 6.374896
|
|
111 3.684453
|
|
112 5.784829
|
|
113 8.236760
|
|
114 6.872598
|
|
115 41.304826
|
|
116 1.988474
|
|
118 7.571316
|
|
119 1.231125
|
|
120 5.081094
|
|
121 21.547440
|
|
122 30.299927
|
|
123 45.503740
|
|
124 9.093912
|
|
132 10.369658
|
|
133 7.125935
|
|
134 14.133228
|
|
135 26.102416
|
|
136 5.071976
|
|
137 12.085286
|
|
138 0.713469
|
|
139 0.675086
|
|
140 4.092547
|
|
141 7.454102
|
|
142 184.663991
|
|
143 10.455368
|
|
144 7.448129
|
|
146 6.242521
|
|
147 8.860180
|
|
148 39.991951
|
|
149 6.891026
|
|
150 5.160709
|
|
151 15.489599
|
|
152 5.590467
|
|
153 27.378151
|
|
154 10.801195
|
|
162 23.750968
|
|
166 11.426264
|
|
167 8.473236
|
|
168 6.898371
|
|
170 3.416283
|
|
171 208.746883
|
|
172 12.158394
|
|
173 3.563165
|
|
174 4.158866
|
|
175 0.000000
|
|
176 1.847873
|
|
177 8.478820
|
|
178 1.277897
|
|
179 0.035306
|
|
180 0.070611
|
|
181 0.035306
|
|
182 3.039279
|
|
183 0.000000
|
|
184 4.205715
|
|
185 30.094936
|
|
186 20.128055
|
|
187 5.773718
|
|
188 31.539990
|
|
189 147.387148
|
|
190 18.314164
|
|
191 32.846011
|
|
192 14.511067
|
|
193 19.235467
|
|
194 0.000000
|
|
Correlation of Results and Money = 0.843710
|
|
```
|
|
|
|
##5
|
|
|
|
|
|
|
|
|
|
|
|
##6
|
|
|
|
|
|
|
|
 |