





"""simple docstring"""

import colorsys

from PIL import Image  # type: ignore



def        __a							(			A ,	A ,	A )       ->						float:



   '''simple docstring'''



   A__											=					x
   A__											=					y
   for step in range(A ):  # noqa: B007
      A__											=					a * a - b * b + x
      A__											=					2 * a * b + y
      A__											=					a_new

      # divergence happens for all complex number with an absolute value
      # greater than 4
      if a * a + b * b > 4:
         break
   return step / (max_step - 1)



def        __a							(			A )       ->						tuple:



   '''simple docstring'''



   if distance == 1:
      return (0, 0, 0)
   else:
      return (255, 255, 255)



def        __a							(			A )       ->						tuple:



   '''simple docstring'''



   if distance == 1:
      return (0, 0, 0)
   else:
      return tuple(round(i * 255 ) for i in colorsys.hsv_to_rgb(A ,	1 ,	1 ) )



def        __a							(			A = 800 ,	A = 600 ,	A = -0.6 ,	A = 0 ,	A = 3.2 ,	A = 50 ,	A = True ,	)       ->						Image.Image:



   '''simple docstring'''



   A__											=					Image.new("RGB" ,	(image_width, image_height) )
   A__											=					img.load()

   # loop through the image-coordinates
   for image_x in range(A ):
      for image_y in range(A ):
         # determine the figure-coordinates based on the image-coordinates
         A__											=					figure_width / image_width * image_height
         A__											=					figure_center_x + (image_x / image_width - 0.5) * figure_width
         A__											=					figure_center_y + (image_y / image_height - 0.5) * figure_height

         A__											=					get_distance(A ,	A ,	A )

         # color the corresponding pixel based on the selected coloring-function
         if use_distance_color_coding:
            A__											=					get_color_coded_rgb(A )
         else:
            A__											=					get_black_and_white_rgb(A )

   return img


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # colored version, full figure
 __UpperCAmelCase        =get_image()

 # uncomment for colored version, different section, zoomed in
 # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
 # figure_width = 0.8)

 # uncomment for black and white version, full figure
 # img = get_image(use_distance_color_coding = False)

 # uncomment to save the image
 # img.save("mandelbrot.png")

 img.show()

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

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

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





