





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)









class     lowercase__  :






  def __init__(					self      :					List[str]		,						_lowercase      :					list ):




         """simple docstring"""







         UpperCAmelCase__										=     set_counts
         UpperCAmelCase__										=     max(_lowercase )
         UpperCAmelCase__										=     len(_lowercase )
         UpperCAmelCase__										=     [1] * num_sets
         UpperCAmelCase__										=     list(range(_lowercase ) )






  def 			_UpperCAmelCase  (					self      :					Dict		,						_lowercase      :					int		,						_lowercase      :					int ):




         """simple docstring"""







         UpperCAmelCase__										=     self.get_parent(_lowercase )
         UpperCAmelCase__										=     self.get_parent(_lowercase )

         if src_parent == dst_parent:
                return False

         if self.ranks[dst_parent] >= self.ranks[src_parent]:
                self.set_counts[dst_parent] += self.set_counts[src_parent]
                UpperCAmelCase__										=     0
                UpperCAmelCase__										=     dst_parent
                if self.ranks[dst_parent] == self.ranks[src_parent]:
                       self.ranks[dst_parent] += 1
                UpperCAmelCase__										=     self.set_counts[dst_parent]
         else:
                self.set_counts[src_parent] += self.set_counts[dst_parent]
                UpperCAmelCase__										=     0
                UpperCAmelCase__										=     src_parent
                UpperCAmelCase__										=     self.set_counts[src_parent]

         UpperCAmelCase__										=     max(self.max_set		,						_lowercase )
         return True






  def 			_UpperCAmelCase  (					self      :					Optional[Any]		,						_lowercase      :					int ):




         """simple docstring"""







         if self.parents[disj_set] == disj_set:
                return disj_set
         UpperCAmelCase__										=     self.get_parent(self.parents[disj_set] )
         return self.parents[disj_set]





