

from __future__ import annotations

from typing import Any

class   _a					:




      """simple docstring"""

      def __init__(   self							:				Optional[int]						,			UpperCAmelCase							:				int						,			UpperCAmelCase							:				int						,			UpperCAmelCase							:				float = 0	):
            A_  ,     A_						  =				row, column
            A_						  =				[[default_value for c in range(UpperCAmelCase	)] for r in range(UpperCAmelCase	)]





      def __str__(   self							:				Tuple	):
            A_						  =				f'''Matrix consist of {self.row} rows and {self.column} columns\n'''

            # Make string identifier
            A_						  =				0
            for row_vector in self.array:
                  for obj in row_vector:
                        A_						  =				max(UpperCAmelCase						,			len(str(UpperCAmelCase	)	)	)
            A_						  =				f'''%{max_element_length}s'''

            # Make string and return
            def single_line(UpperCAmelCase							:				list[float]	) -> str:
                  nonlocal string_format_identifier
                  A_						  =				"["
                  line += ", ".join(string_format_identifier % (obj,) for obj in row_vector	)
                  line += "]"
                  return line

            s += "\n".join(single_line(UpperCAmelCase	) for row_vector in self.array	)
            return s





      def __repr__(   self							:				Union[str, Any]	):
            return str(self	)





      def 		__A	(   self							:				List[Any]						,			UpperCAmelCase							:				tuple[int, int]	):
            if not (isinstance(UpperCAmelCase						,			(list, tuple)	) and len(UpperCAmelCase	) == 2):
                  return False
            elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
                  return False
            else:
                  return True





      def __getitem__(   self							:				Optional[int]						,			UpperCAmelCase							:				tuple[int, int]	):
            assert self.validate_indicies(UpperCAmelCase	)
            return self.array[loc[0]][loc[1]]





      def __setitem__(   self							:				str						,			UpperCAmelCase							:				tuple[int, int]						,			UpperCAmelCase							:				float	):
            assert self.validate_indicies(UpperCAmelCase	)
            A_						  =				value





      def __add__(   self							:				Tuple						,			UpperCAmelCase							:				Matrix	):
            assert isinstance(UpperCAmelCase						,			UpperCAmelCase	)
            assert self.row == another.row and self.column == another.column

            # Add
            A_						  =				Matrix(self.row						,			self.column	)
            for r in range(self.row	):
                  for c in range(self.column	):
                        A_						  =				self[r, c] + another[r, c]
            return result





      def __neg__(   self							:				int	):
            A_						  =				Matrix(self.row						,			self.column	)
            for r in range(self.row	):
                  for c in range(self.column	):
                        A_						  =				-self[r, c]
            return result





      def __sub__(   self							:				Union[str, Any]						,			UpperCAmelCase							:				Matrix	):
            return self + (-another)





      def __mul__(   self							:				Tuple						,			UpperCAmelCase							:				int | float | Matrix	):

            if isinstance(UpperCAmelCase						,			(int, float)	):  # Scalar multiplication
                  A_						  =				Matrix(self.row						,			self.column	)
                  for r in range(self.row	):
                        for c in range(self.column	):
                              A_						  =				self[r, c] * another
                  return result
            elif isinstance(UpperCAmelCase						,			UpperCAmelCase	):  # Matrix multiplication
                  assert self.column == another.row
                  A_						  =				Matrix(self.row						,			another.column	)
                  for r in range(self.row	):
                        for c in range(another.column	):
                              for i in range(self.column	):
                                    result[r, c] += self[r, i] * another[i, c]
                  return result
            else:
                  A_						  =				f'''Unsupported type given for another ({type(UpperCAmelCase	)})'''
                  raise TypeError(UpperCAmelCase	)





      def 		__A	(   self							:				Optional[int]	):
            A_						  =				Matrix(self.column						,			self.row	)
            for r in range(self.row	):
                  for c in range(self.column	):
                        A_						  =				self[r, c]
            return result







      def 		__A	(   self							:				List[Any]						,			UpperCAmelCase							:				Matrix						,			UpperCAmelCase							:				Matrix	):
            assert isinstance(UpperCAmelCase						,			UpperCAmelCase	) and isinstance(UpperCAmelCase						,			UpperCAmelCase	)
            assert self.row == self.column == u.row == v.row  # u, v should be column vector
            assert u.column == v.column == 1  # u, v should be column vector

            # Calculate
            A_						  =				v.transpose()
            A_						  =				(v_t * self * u)[0, 0] + 1
            if numerator_factor == 0:
                  return None  # It's not invertable
            return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":

      def       __snake_case						( ):



            """simple docstring"""

            A_						  =				Matrix(3				,3				,0    )
            for i in range(3    ):
                  A_						  =				1
            print(f'''a^(-1) is {ainv}'''    )
            # u, v
            A_						  =				Matrix(3				,1				,0    )
            A_  ,     A_  ,     A_						  =				1, 2, -3
            A_						  =				Matrix(3				,1				,0    )
            A_  ,     A_  ,     A_						  =				4, -2, 5
            print(f'''u is {u}'''    )
            print(f'''v is {v}'''    )
            print(f'''uv^T is {u * v.transpose()}'''    )
            # Sherman Morrison
            print(f'''(a + uv^T)^(-1) is {ainv.sherman_morrison(__UpperCamelCase				,__UpperCamelCase    )}'''    )

      def       __snake_case						( ):



            """simple docstring"""

            import doctest

            doctest.testmod()



      testa()

"""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)





