





'''simple docstring'''







from random import shuffle

import tensorflow as tf
from numpy import array





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			List[Any],			SCREAMING_SNAKE_CASE__      :			Optional[int]   ) ->	Tuple:
						UpperCAmelCase_			:      List[str]     						=  int(SCREAMING_SNAKE_CASE__   )
						assert noofclusters < len(SCREAMING_SNAKE_CASE__   )

						# Find out the dimensionality
						UpperCAmelCase_			:      Tuple     						=  len(vectors[0]   )

						# Will help select random centroids from among the available vectors
						UpperCAmelCase_			:      Optional[Any]     						=  list(range(len(SCREAMING_SNAKE_CASE__   )   )   )
						shuffle(SCREAMING_SNAKE_CASE__   )

						# GRAPH OF COMPUTATION
						# We initialize a new graph and set it as the default during each run
						# of this algorithm. This ensures that as this function is called
						# multiple times, the default graph doesn't keep getting crowded with
						# unused ops and Variables from previous function calls.

						UpperCAmelCase_			:      Union[str, Any]     						=  tf.Graph()

						with graph.as_default():
												# SESSION OF COMPUTATION

												UpperCAmelCase_			:      Union[str, Any]     						=  tf.Session()

												##CONSTRUCTING THE ELEMENTS OF COMPUTATION

												##First lets ensure we have a Variable vector for each centroid,
												##initialized to one of the vectors from the available data points
												UpperCAmelCase_			:      List[str]     						=  [
												    tf.Variable(vectors[vector_indices[i]]   ) for i in range(SCREAMING_SNAKE_CASE__   )
												]
												##These nodes will assign the centroid Variables the appropriate
												##values
												UpperCAmelCase_			:      Any     						=  tf.placeholder('''float64''',			[dim]   )
												UpperCAmelCase_			:      List[Any]     						=  []
												for centroid in centroids:
																		cent_assigns.append(tf.assign(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )   )

												##Variables for cluster assignments of individual vectors(initialized
												##to 0 at first)
												UpperCAmelCase_			:      Union[str, Any]     						=  [tf.Variable(0   ) for i in range(len(SCREAMING_SNAKE_CASE__   )   )]
												##These nodes will assign an assignment Variable the appropriate
												##value
												UpperCAmelCase_			:      Any     						=  tf.placeholder('''int32'''   )
												UpperCAmelCase_			:      Optional[Any]     						=  []
												for assignment in assignments:
																		cluster_assigns.append(tf.assign(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )   )

												##Now lets construct the node that will compute the mean
												# The placeholder for the input
												UpperCAmelCase_			:      Tuple     						=  tf.placeholder('''float''',			[None, dim]   )
												# The Node/op takes the input and computes a mean along the 0th
												# dimension, i.e. the list of input vectors
												UpperCAmelCase_			:      Dict     						=  tf.reduce_mean(SCREAMING_SNAKE_CASE__,			0   )

												##Node for computing Euclidean distances
												# Placeholders for input
												UpperCAmelCase_			:      int     						=  tf.placeholder('''float''',			[dim]   )
												UpperCAmelCase_			:      Optional[Any]     						=  tf.placeholder('''float''',			[dim]   )
												UpperCAmelCase_			:      Optional[int]     						=  tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),			2   )   )   )

												##This node will figure out which cluster to assign a vector to,
												##based on Euclidean distances of the vector from the centroids.
												# Placeholder for input
												UpperCAmelCase_			:      Dict     						=  tf.placeholder('''float''',			[noofclusters]   )
												UpperCAmelCase_			:      Dict     						=  tf.argmin(SCREAMING_SNAKE_CASE__,			0   )

												##INITIALIZING STATE VARIABLES

												##This will help initialization of all Variables defined with respect
												##to the graph. The Variable-initializer should be defined after
												##all the Variables have been constructed, so that each of them
												##will be included in the initialization.
												UpperCAmelCase_			:      str     						=  tf.initialize_all_variables()

												# Initialize all variables
												sess.run(SCREAMING_SNAKE_CASE__   )

												##CLUSTERING ITERATIONS

												# Now perform the Expectation-Maximization steps of K-Means clustering
												# iterations. To keep things simple, we will only do a set number of
												# iterations, instead of using a Stopping Criterion.
												UpperCAmelCase_			:      Optional[Any]     						=  100
												for _ in range(SCREAMING_SNAKE_CASE__   ):
																		##EXPECTATION STEP
																		##Based on the centroid locations till last iteration, compute
																		##the _expected_ centroid assignments.
																		# Iterate over each vector
																		for vector_n in range(len(SCREAMING_SNAKE_CASE__   )   ):
																								UpperCAmelCase_			:      Dict     						=  vectors[vector_n]
																								# Compute Euclidean distance between this vector and each
																								# centroid. Remember that this list cannot be named
																								#'centroid_distances', since that is the input to the
																								# cluster assignment node.
																								UpperCAmelCase_			:      List[Any]     						=  [
																								    sess.run(SCREAMING_SNAKE_CASE__,			feed_dict={va: vect, va: sess.run(SCREAMING_SNAKE_CASE__   )}   )
																								    for centroid in centroids
																								]
																								# Now use the cluster assignment node, with the distances
																								# as the input
																								UpperCAmelCase_			:      List[Any]     						=  sess.run(
																								    SCREAMING_SNAKE_CASE__,			feed_dict={centroid_distances: distances}   )
																								# Now assign the value to the appropriate state variable
																								sess.run(
																								    cluster_assigns[vector_n],			feed_dict={assignment_value: assignment}   )

																		##MAXIMIZATION STEP
																		# Based on the expected state computed from the Expectation Step,
																		# compute the locations of the centroids so as to maximize the
																		# overall objective of minimizing within-cluster Sum-of-Squares
																		for cluster_n in range(SCREAMING_SNAKE_CASE__   ):
																								# Collect all the vectors assigned to this cluster
																								UpperCAmelCase_			:      Dict     						=  [
																								    vectors[i]
																								    for i in range(len(SCREAMING_SNAKE_CASE__   )   )
																								    if sess.run(assignments[i]   ) == cluster_n
																								]
																								# Compute new centroid location
																								UpperCAmelCase_			:      Dict     						=  sess.run(
																								    SCREAMING_SNAKE_CASE__,			feed_dict={mean_input: array(SCREAMING_SNAKE_CASE__   )}   )
																								# Assign value to appropriate variable
																								sess.run(
																								    cent_assigns[cluster_n],			feed_dict={centroid_value: new_location}   )

        # Return centroids and assignments
												UpperCAmelCase_			:      Union[str, Any]     						=  sess.run(SCREAMING_SNAKE_CASE__   )
												UpperCAmelCase_			:      Optional[int]     						=  sess.run(SCREAMING_SNAKE_CASE__   )
												return centroids, assignments












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

					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])
					snake_case_     :       Any						=  np.array(train_x)
					snake_case_     :       Optional[Any]						=  np.array(test_x)
					snake_case_     :       Optional[Any]						=  np.array([list(i.ravel()) for i in train_y])
					snake_case_     :       List[str]						=  np.array([list(i.ravel()) for i in test_y])

					snake_case_     :       List[Any]						=  Sequential()
					model.add(LSTM(1_28, input_shape=(look_back, 1), return_sequences=True))
					model.add(LSTM(64, input_shape=(1_28, 1)))
					model.add(Dense(forward_days))
					model.compile(loss="mean_squared_error", optimizer="adam")
					snake_case_     :       Dict						=  model.fit(
					    x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
					)
					snake_case_     :       Optional[Any]						=  model.predict(x_test)





