





'''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__":
		_A				:    Union[str, Any]     						=pd.read_csv('''sample_data.csv''', header=None)
		_A				:    Tuple     						=df.shape[:1][0]
		# If you're using some other dataset input the target column
		_A				:    Optional[int]     						=df.iloc[:, 1:2]
		_A				:    List[Any]     						=actual_data.values.reshape(len_data, 1)
		_A				:    str     						=MinMaxScaler().fit_transform(actual_data)
		_A				:    List[Any]     						=1_0
		_A				:    Union[str, Any]     						=5
		_A				:    List[str]     						=2_0
		_A				:    Dict     						=len_data - periods * look_back
		_A				:    List[str]     						=actual_data[:division]
		_A				:    Tuple     						=actual_data[division - look_back :]
		_A						,			_A				:    Dict     						=[], []
		_A						,			_A				:    Optional[int]     						=[], []

		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])
		_A				:    List[str]     						=np.array(train_x)
		_A				:    Optional[Any]     						=np.array(test_x)
		_A				:    int     						=np.array([list(i.ravel()) for i in train_y])
		_A				:    List[str]     						=np.array([list(i.ravel()) for i in test_y])

		_A				:    Union[str, Any]     						=Sequential()
		model.add(LSTM(1_2_8, input_shape=(look_back, 1), return_sequences=True))
		model.add(LSTM(6_4, input_shape=(1_2_8, 1)))
		model.add(Dense(forward_days))
		model.compile(loss='''mean_squared_error''', optimizer='''adam''')
		_A				:    List[Any]     						=model.fit(
		    x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
		)
		_A				:    Optional[Any]     						=model.predict(x_test)





'''simple docstring'''

from __future__ import annotations

import requests







def 						__UpperCamelCase				(							_lowercase					) ->			dict:
	_lowercase					:       Optional[int]       =		f'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
	return requests.get(_lowercase					).json()







def 						__UpperCamelCase				(							_lowercase = 10					) ->			list[dict]:
	_lowercase					:       Union[str, Any]       =		'https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'
	_lowercase					:       Optional[Any]       =		requests.get(_lowercase					).json()[:max_stories]
	return [get_hackernews_story(_lowercase					) for story_id in story_ids]







def 						__UpperCamelCase				(							_lowercase = 10					) ->			str:
	_lowercase					:       Tuple       =		hackernews_top_stories(_lowercase					)
	return "\n".join('* [{title}]({url})'.format(**_lowercase					) for story in stories					)


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