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# Importing packages
import matplotlib.pyplot as plt

# Define data values
x1 = [50, 100, 150, 200, 250, 306]
y1 = [72.45, 75.26, 79.32, 84.25, 87.76, 94.77]

x2 = [
50,
100,
150,
200,
250,
295
]

y2 = [
71.29,
76.49,
80.23,
84.16,
89.87,
98.3
]

x3 = [
50,
100,
150,
200,
250,
300,
332
]

y3 = [
73.27,
76.49,
79.56,
83.28,
86.48,
89.11,
96.38
]

fig, ax = plt.subplots(figsize=[12,8])

ax.plot(x1,
         y1,
         marker='o', color='g', linewidth=2, label='Для задания 1', linestyle= 'dashed')
ax.plot(x2,
         y2,
         marker='x', color='r', linewidth=3, label='Для задания 2')

ax.plot(x3,
         y3,
         marker='+', color='b', linewidth=1.5, label='Для задания 3', linestyle= 'dotted')

ax.set_ylabel('Точность (%)', size=24)
ax.set_xlabel('Количество команд', size=24)

plt.xticks(fontsize=16)
plt.yticks(fontsize=16)


for index in range(len(x2)):
  ax.text(x2[index], y2[index], y2[index], size=21)

plt.legend(prop={'size': 21})

#plt.show()
plt.title('', size = 20)
plt.savefig('test.png')

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x = [i + 1 for i in range(20)]
print(x)
x= [1, 2,3, 4, 5, 6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]
y1 = []
y2 = []

print(type(x[0]))
import random
hs = 1
rev = True
for i in range(20):
    y1.append(hs * random.random())
    y2.append(hs * random.random())
y1.sort(reverse=rev)
y2.sort(reverse=rev)
print(y1)
type_txt = 'Потеря'

plt.figure(figsize = (10,7))
plt.plot(x, y1, 'g', label='Тренировка ' + type_txt)
plt.plot(x, y2, 'b', label='Тестирование ' + type_txt)
plt.title('Обучение и тестирование ' + type_txt, size = 24)
plt.xlabel('Эпохи', size=18)
plt.ylabel(type_txt, size=18)
plt.legend(prop={'size': 18})
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)

#plt.show()
plt.savefig('test1.png')

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import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.metrics import classification_report, confusion_matrix

## test data ###

t1 = 100
t2 = 200
t3 = 300
testy_org = []

for i in range(t1):
  testy_org.append(1)
for i in range(t2):
  testy_org.append(2)
for i in range(t3):
  testy_org.append(3)

test_y_pred = []
for i in range(80):
  test_y_pred.append(1)
for i in range(11):
  test_y_pred.append(2)
for i in range(9):
  test_y_pred.append(3)

for i in range(68):
    test_y_pred.append(1)

for i in range(200-68):
  test_y_pred.append(2)

for i in range(45):
  test_y_pred.append(1)
for i in range(55):
  test_y_pred.append(2)
for i in range(200):
  test_y_pred.append(3)
  
## end test data ####
  
    

cm = confusion_matrix(testy_org, test_y_pred)
cm_df = pd.DataFrame(cm,
                     index = ['задание 1','задание 2','задание 3'], 
                     columns = ['задание 1','задание 2','задание 3'])

plt.figure(figsize=(10,7))
sns.heatmap(cm_df, annot=True, fmt=".0f",cmap="Blues")
plt.title('Матрица путаницы', size = 24)
plt.ylabel('Фактические значения', size=21)
plt.xlabel('Прогнозируемые значения', size=21)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
sns.set(font_scale=1.4)
plt.show()

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fig, ax = plt.subplots(figsize=(10,7))
import pandas as pd
import numpy as np

data = {'type': ['CNN', 'LSTM', 'GRU'],
        'central': [388, 1226, 1422],
        'FL': [2628, 8307, 9687]
        }

df = pd.DataFrame(data)

x = np.arange(len(df))
width = 0.4

plt.bar(x-0.2, df['central'],
        width, color='g', label='Centralized data')
plt.bar(x+0.2, df['FL'],
        width, color='b', label='Decentralized data')

plt.title('Time training between centralized and decentralized data', fontsize=25)
plt.xlabel(None)
plt.xticks(df.index, df['type'], fontsize=17)
plt.ylabel('Training time (s)', fontsize=20)
plt.yticks(fontsize=17)
sns.despine(bottom=True)
ax.grid(False)
ax.tick_params(bottom=False, left=True)
plt.legend(frameon=False, fontsize=15)
plt.show()