



'''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__":
							__lowerCAmelCase						      =       pd.read_csv('''sample_data.csv''', header=None)
							__lowerCAmelCase						      =       df.shape[:1][0]
							# If you're using some other dataset input the target column
							__lowerCAmelCase						      =       df.iloc[:, 1:2]
							__lowerCAmelCase						      =       actual_data.values.reshape(len_data, 1)
							__lowerCAmelCase						      =       MinMaxScaler().fit_transform(actual_data)
							__lowerCAmelCase						      =       10
							__lowerCAmelCase						      =       5
							__lowerCAmelCase						      =       20
							__lowerCAmelCase						      =       len_data - periods * look_back
							__lowerCAmelCase						      =       actual_data[:division]
							__lowerCAmelCase						      =       actual_data[division - look_back :]
							__lowerCAmelCase      , __lowerCAmelCase						      =       [], []
							__lowerCAmelCase      , __lowerCAmelCase						      =       [], []

							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])
							__lowerCAmelCase						      =       np.array(train_x)
							__lowerCAmelCase						      =       np.array(test_x)
							__lowerCAmelCase						      =       np.array([list(i.ravel()) for i in train_y])
							__lowerCAmelCase						      =       np.array([list(i.ravel()) for i in test_y])

							__lowerCAmelCase						      =       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''')
							__lowerCAmelCase						      =       model.fit(
							    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
							)
							__lowerCAmelCase						      =       model.predict(x_test)









'''simple docstring'''

from __future__ import annotations

import collections
import pprint
from pathlib import Path



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				str:
		return "".join(sorted(lowerCAmelCase_  )  )



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				list[str]:
		return word_by_signature[signature(lowerCAmelCase_  )]


__lowerCAmelCase						      =       Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
__lowerCAmelCase						      =       sorted({word.strip().lower() for word in data.splitlines()})

__lowerCAmelCase						      =       collections.defaultdict(list)
for word in word_list:
							word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
							__lowerCAmelCase						      =       {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

							with open('''anagrams.txt''', '''w''') as file:
														file.write('''all_anagrams = \n ''')
														file.write(pprint.pformat(all_anagrams))




