Алан Гибизов

import tkinter as tk
from tkinter import ttk
import asyncio


class App:
    async def exec(self):
        self.window = Window(asyncio.get_event_loop())
        await self.window.show();


class Window(tk.Tk):
    def __init__(self, loop):
        self.loop = loop
        self.root = tk.Tk()
        self.animation = "░▒▒▒▒▒"
        self.label = tk.Label(text="")
        self.label.grid(row=0, columnspan=2, padx=(8, 8), pady=(16, 0))
        self.progressbar = ttk.Progressbar(length=280)
        self.progressbar.grid(row=1, columnspan=2, padx=(8, 8), pady=(16, 0))
        button_block = tk.Button(text="Calculate Sync", width=10, command=self.calculate_sync)
        button_block.grid(row=2, column=0, sticky=tk.W, padx=8, pady=8)
        button_non_block = tk.Button(text="Calculate Async", width=10, command=lambda: self.loop.create_task(self.calculate_async()))
        button_non_block.grid(row=2, column=1, sticky=tk.W, padx=8, pady=8)

    async def show(self):
        while True:
            self.label["text"] = self.animation
            self.animation = self.animation[1:] + self.animation[0]
            self.root.update()
            await asyncio.sleep(.1)

    def calculate_sync(self):
        max = 3000000
        for i in range(1, max):
            self.progressbar["value"] = i / max * 100

    async def calculate_async(self):
        max = 3000000
        for i in range(1, max):
            self.progressbar["value"] = i / max * 100
            if i % 1000 == 0:
                await asyncio.sleep(0)


asyncio.run(App().exec())

print((f'accuracy:{accuracy_score(y_perd,y_test) :.3}'))

sns.scatterplot(x='petal_width', y='petal_lenght',data=iris,hue = 'species', s=70)

from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import seaborn as sns
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
from sklearn import datasets

iris= sns.load_dataset('iris')
#iris
x_train, x_test, y_train, y_test= train_test_split(
    iris.iloc[:,:-1],
    iris.iloc[:,-1],
    test_size= 0.15
)

def init_model(k, x_train, y_train, x_test):
  model = KNeighborsClassifier(n_neighbors=k)
  # print(model)
  model.fit(x_train,y_train)

  y_pred = model.predict(x_test)
  return y_pred

def grath(y_test,x_test,y_pred):
  plt.figure(figsize=(10, 7))
  sns.scatterplot(x='petal_width', y='petal_length',data=iris,hue = 'species', s=70)
  plt.xlabel('Длина лепестка,см')
  plt.ylabel('Ширина лепестка,см')
  plt.legend(loc=2)
  plt.grid()
  for i in range(len(y_test)):
      if np.array(y_test)[i] != y_pred[i]:
          plt.scatter(x_test.iloc[i,3],x_test.iloc[i,2],color = 'red', s = 150)



# x_train.shape, x_test.shape, y_train.shape, y_test.shape

# x_train.head()
# y_train.head()

k = int(input('Чему равно k? '))

y_pred = init_model(k,x_train,y_train,x_test)
print((f'accuracy:{accuracy_score(y_pred,y_test) :.3}'))
grath(y_test,x_test,y_pred)

Ошибка при установки discord.py?

error: Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-buil...

cursor.execute(f'''INSERT INTO {table_name}(value)
                    VALUES(?);''',(text,))
add_text('value')

cursor.execute(f'''INSERT INTO {table_name}(value1, value2, value3)
                    VALUES(?);''', text)
add_text(('value', 'second', third'))

coroutine 'ApplicationContext.respond' was never awaited

ctx.respond(f'Вы уже использовали {us} из {kyp}',ephemeral=True)