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@ -43,7 +43,7 @@ print('Размерность данных:')
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print(data.shape)
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print(data.shape)
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```
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```
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(картинка)
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<table>
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<table>
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<thead>
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<thead>
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@ -99,7 +99,8 @@ ae1_trained, IRE1, IREth1 = lib.create_fit_save_ae(data,'out/AE1.h5','out/AE1_ir
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lib.ire_plot('training', IRE1, IREth1, 'AE1')
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lib.ire_plot('training', IRE1, IREth1, 'AE1')
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```
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```
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(картинка)
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Порог ошибки реконструкции = 0.81
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Порог ошибки реконструкции = 0.81
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@ -118,7 +119,7 @@ ae2_trained, IRE2, IREth2 = lib.create_fit_save_ae(data,'out/AE2.h5','out/AE2_ir
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lib.ire_plot('training', IRE2, IREth2, 'AE2')
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lib.ire_plot('training', IRE2, IREth2, 'AE2')
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```
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```
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(картинка)
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Порог ошибки реконструкции = 0.38
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Порог ошибки реконструкции = 0.38
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@ -130,12 +131,15 @@ numb_square = 20
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xx, yy, Z1 = lib.square_calc(numb_square, data, ae1_trained, IREth1, '1', True)
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xx, yy, Z1 = lib.square_calc(numb_square, data, ae1_trained, IREth1, '1', True)
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```
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```
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(картинки картинки)
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amount: 19
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amount: 19
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amount_ae: 104
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amount_ae: 104
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**Оценка качества AE1**
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**Оценка качества AE1**
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@ -155,11 +159,17 @@ amount_ae: 104
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numb_square = 20
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numb_square = 20
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xx, yy, Z2 = lib.square_calc(numb_square, data, ae2_trained, IREth2, '2', True)
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xx, yy, Z2 = lib.square_calc(numb_square, data, ae2_trained, IREth2, '2', True)
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```
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```
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(картинки)
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amount: 19
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amount: 19
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amount_ae: 31
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amount_ae: 31
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**Оценка качества АЕ2**
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**Оценка качества АЕ2**
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* IDEAL = 0. Excess: 0.631578947368421
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* IDEAL = 0. Excess: 0.631578947368421
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* IDEAL = 0. Deficit: 0.0
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* IDEAL = 0. Deficit: 0.0
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@ -172,7 +182,8 @@ amount_ae: 31
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```python
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```python
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lib.plot2in1(data, xx, yy, Z1, Z2)
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lib.plot2in1(data, xx, yy, Z1, Z2)
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```
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```
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(картинки)
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---
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---
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### 8. Редактирование автокодировщика АЕ2
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### 8. Редактирование автокодировщика АЕ2
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@ -200,7 +211,9 @@ lib.anomaly_detection_ae(predicted_labels1, ire1, IREth1)
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lib.ire_plot('test', ire1, IREth1, 'AE1')
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lib.ire_plot('test', ire1, IREth1, 'AE1')
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```
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```
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Аномалий не обнаружено (картинка)
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Аномалий не обнаружено
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**АЕ2**
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**АЕ2**
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@ -252,13 +265,15 @@ lib.ire_plot('test', ire2, IREth2, 'AE2')
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Обнаружено 4.0 аномалий
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Обнаружено 4.0 аномалий
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(картинка)
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### 10. Визуализация элементов обучающей и тестовой выборки в областях пространства признаков
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### 10. Визуализация элементов обучающей и тестовой выборки в областях пространства признаков
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```python
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```python
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lib.plot2in1_anomaly(data, xx, yy, Z1, Z2, data_test)
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lib.plot2in1_anomaly(data, xx, yy, Z1, Z2, data_test)
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```
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```
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(картинка)
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### 11. Результаты
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### 11. Результаты
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<table>
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<table>
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@ -418,7 +433,9 @@ predicted_labels3_v1, ire3_v1 = lib.predict_ae(ae3_v1_trained, test, IREth3_v1)
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lib.ire_plot('test', ire3_v1, IREth3_v1, 'AE3_v1')
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lib.ire_plot('test', ire3_v1, IREth3_v1, 'AE3_v1')
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```
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```
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Картинка
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```python
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```python
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lib.anomaly_detection_ae(predicted_labels3_v1, IRE3_v1, IREth3_v1)
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lib.anomaly_detection_ae(predicted_labels3_v1, IRE3_v1, IREth3_v1)
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```
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```
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@ -580,6 +597,10 @@ predicted_labels3_v2, ire3_v2 = lib.predict_ae(ae3_v2_trained, test, IREth3_v2)
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```python
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```python
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lib.ire_plot('test', ire3_v2, IREth3_v2, 'AE3_v2')
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lib.ire_plot('test', ire3_v2, IREth3_v2, 'AE3_v2')
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```
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```
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```python
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```python
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lib.anomaly_detection_ae(predicted_labels3_v2, IRE3_v2, IREth3_v2)
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lib.anomaly_detection_ae(predicted_labels3_v2, IRE3_v2, IREth3_v2)
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```
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```
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@ -744,6 +765,8 @@ predicted_labels3_v3, ire3_v3 = lib.predict_ae(ae3_v3_trained, test, IREth3_v3)
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lib.ire_plot('test', ire3_v3, IREth3_v3, 'AE3_v3')
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lib.ire_plot('test', ire3_v3, IREth3_v3, 'AE3_v3')
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```
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```
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```python
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```python
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lib.anomaly_detection_ae(predicted_labels3_v3, IRE3_v3, IREth3_v3)
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lib.anomaly_detection_ae(predicted_labels3_v3, IRE3_v3, IREth3_v3)
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```
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```
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