






import random
import sys

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

__A					=	"Usage of script: script_name <size_of_canvas:int>"

__A					=	[0] * 1_00 + [1] * 10
random.shuffle(choice)







def       lowercase__	(					A_:						int		)	->				list[list[bool]]:







       """simple docstring"""






       __UpperCAmelCase     				=[[False for i in range(A_		)] for j in range(A_		)]
       return canvas







def       lowercase__	(					A_:						list[list[bool]]		)	->				None:







       """simple docstring"""






       for i, row in enumerate(A_		):
              for j, _ in enumerate(A_		):
                     __UpperCAmelCase     				=bool(random.getrandbits(1		)		)







def       lowercase__	(					A_:						list[list[bool]]		)	->				list[list[bool]]:







       """simple docstring"""






       __UpperCAmelCase     				=np.array(A_		)
       __UpperCAmelCase     				=np.array(create_canvas(current_canvas.shape[0]		)		)
       for r, row in enumerate(A_		):
              for c, pt in enumerate(A_		):
                     __UpperCAmelCase     				=__judge_point(
                         A_  ,      current_canvas[r - 1 : r + 2, c - 1 : c + 2]		)

       __UpperCAmelCase     				=next_gen_canvas
       del next_gen_canvas  # cleaning memory as we move on.
       __UpperCAmelCase     				=current_canvas.tolist()
       return return_canvas







def       lowercase__	(					A_:						bool  ,      A_:						list[list[bool]]		)	->				bool:







       """simple docstring"""






       __UpperCAmelCase     				=0
       __UpperCAmelCase     				=0
       # finding dead or alive neighbours count.
       for i in neighbours:
              for status in i:
                     if status:
                            alive += 1
                     else:
                            dead += 1

    # handling duplicate entry for focus pt.
       if pt:
              alive -= 1
       else:
              dead -= 1

       # running the rules of game here.
       __UpperCAmelCase     				=pt
       if pt:
              if alive < 2:
                     __UpperCAmelCase     				=False
              elif alive == 2 or alive == 3:
                     __UpperCAmelCase     				=True
              elif alive > 3:
                     __UpperCAmelCase     				=False
       else:
              if alive == 3:
                     __UpperCAmelCase     				=True

       return state


if __name__ == "__main__":
   if len(sys.argv) != 2:
      raise Exception(usage_doc)

   __A					=	int(sys.argv[1])
   # main working structure of this module.
   __A					=	create_canvas(canvas_size)
   seed(c)
   __A				,     __A					=	plt.subplots()
   fig.show()
   __A					=	ListedColormap(["w", "k"])
   try:
      while True:
         __A					=	run(c)
         ax.matshow(c, cmap=cmap)
         fig.canvas.draw()
         ax.cla()
   except KeyboardInterrupt:
      # do nothing.
      pass










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

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

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






