





'''simple docstring'''



from __future__ import annotations

import requests




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				f'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
  return requests.get(SCREAMING_SNAKE_CASE__       ).json()




def 				snake_case_      (		SCREAMING_SNAKE_CASE__ = 10       ):







  '''simple docstring'''



  _snake_case     		=				"https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty"
  _snake_case     		=				requests.get(SCREAMING_SNAKE_CASE__       ).json()[:max_stories]
  return [get_hackernews_story(SCREAMING_SNAKE_CASE__       ) for story_id in story_ids]




def 				snake_case_      (		SCREAMING_SNAKE_CASE__ = 10       ):







  '''simple docstring'''



  _snake_case     		=				hackernews_top_stories(SCREAMING_SNAKE_CASE__       )
  return "\n".join("* [{title}]({url})".format(**SCREAMING_SNAKE_CASE__       ) for story in stories       )


if __name__ == "__main__":
 print(hackernews_top_stories_as_markdown())









'''simple docstring'''



import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential

if __name__ == "__main__":
 __magic_name__   :						Union[str, Any]    =			pd.read_csv("""sample_data.csv""", header=None)
 __magic_name__   :						Optional[Any]    =			df.shape[:1][0]
 # If you're using some other dataset input the target column
 __magic_name__   :						str    =			df.iloc[:, 1:2]
 __magic_name__   :						Dict    =			actual_data.values.reshape(len_data, 1)
 __magic_name__   :						Tuple    =			MinMaxScaler().fit_transform(actual_data)
 __magic_name__   :						Union[str, Any]    =			10
 __magic_name__   :						Optional[int]    =			5
 __magic_name__   :						Any    =			20
 __magic_name__   :						int    =			len_data - periods * look_back
 __magic_name__   :						Union[str, Any]    =			actual_data[:division]
 __magic_name__   :						int    =			actual_data[division - look_back :]
 __magic_name__   ,    __magic_name__   :						List[str]    =			[], []
 __magic_name__   ,    __magic_name__   :						Tuple    =			[], []

 for i in range(0, len(train_data) - forward_days - look_back + 1):
  train_x.append(train_data[i : i + look_back])
  train_y.append(train_data[i + look_back : i + look_back + forward_days])
 for i in range(0, len(test_data) - forward_days - look_back + 1):
  test_x.append(test_data[i : i + look_back])
  test_y.append(test_data[i + look_back : i + look_back + forward_days])
 __magic_name__   :						List[str]    =			np.array(train_x)
 __magic_name__   :						List[str]    =			np.array(test_x)
 __magic_name__   :						int    =			np.array([list(i.ravel()) for i in train_y])
 __magic_name__   :						int    =			np.array([list(i.ravel()) for i in test_y])

 __magic_name__   :						Tuple    =			Sequential()
 model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
 model.add(LSTM(64, input_shape=(128, 1)))
 model.add(Dense(forward_days))
 model.compile(loss="""mean_squared_error""", optimizer="""adam""")
 __magic_name__   :						int    =			model.fit(
     x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
 )
 __magic_name__   :						int    =			model.predict(x_test)


