






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)










import colorsys

from PIL import Image  # type: ignore






def 	UpperCamelCase	(							snake_case__  : float						,snake_case__  : float						,snake_case__  : int	):




     '''simple docstring'''






     __snake_case      :List[Any] 							=    x
     __snake_case      :str 							=    y
     for step in range(snake_case__	):  # noqa: B007
          __snake_case      :Tuple 							=    a * a - b * b + x
          __snake_case      :Dict 							=    2 * a * b + y
          __snake_case      :str 							=    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 	UpperCamelCase	(							snake_case__  : float	):




     '''simple docstring'''






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






def 	UpperCamelCase	(							snake_case__  : float	):




     '''simple docstring'''






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






def 	UpperCamelCase	(							snake_case__  : int = 800						,snake_case__  : int = 600						,snake_case__  : float = -0.6						,snake_case__  : float = 0						,snake_case__  : float = 3.2						,snake_case__  : int = 50						,snake_case__  : bool = True						,):




     '''simple docstring'''






     __snake_case      :Tuple 							=    Image.new("""RGB"""						,(image_width, image_height)	)
     __snake_case      :Dict 							=    img.load()

     # loop through the image-coordinates
     for image_x in range(snake_case__	):
          for image_y in range(snake_case__	):
               # determine the figure-coordinates based on the image-coordinates
               __snake_case      :Dict 							=    figure_width / image_width * image_height
               __snake_case      :Any 							=    figure_center_x + (image_x / image_width - 0.5) * figure_width
               __snake_case      :List[str] 							=    figure_center_y + (image_y / image_height - 0.5) * figure_height

               __snake_case      :Tuple 							=    get_distance(snake_case__						,snake_case__						,snake_case__	)

               # color the corresponding pixel based on the selected coloring-function
               if use_distance_color_coding:
                    __snake_case      :Optional[int] 							=    get_color_coded_rgb(snake_case__	)
               else:
                    __snake_case      :Optional[int] 							=    get_black_and_white_rgb(snake_case__	)

     return img


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # colored version, full figure
     lowerCamelCase__       		=  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()


