
"""simple docstring"""




from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar

def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case						):
							seed(1						)
							return [gauss(_snake_case       ,_snake_case						) for _ in range(_snake_case						)]

def lowercase_  (       _snake_case       ,_snake_case						):
							return [k for k in range(_snake_case						) for _ in range(instance_count[k]						)]

def lowercase_  (       _snake_case       ,_snake_case						):
							return sum(_snake_case						) / instance_count

def lowercase_  (       _snake_case       ,_snake_case						):
							return instance_count / total_count

def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:	str									=       []  # An empty list to store all squared differences
							# iterate over number of elements in items
							for i in range(len(_snake_case						)						):
														# for loop iterates over number of elements in inner layer of items
														for j in range(len(items[i]						)						):
																					# appending squared differences to 'squared_diff' list
																					squared_diff.append((items[i][j] - means[i]) ** 2						)

    # one divided by (the number of all instances - number of classes) multiplied by
    # sum of all squared differences
							SCREAMING_SNAKE_CASE__						:	Dict									=       len(_snake_case						)  # Number of classes in dataset
							return 1 / (total_count - n_classes) * sum(_snake_case						)

def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case       ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:	Any									=       []
							# for loop iterates over number of elements in list
							for i in range(len(_snake_case						)						):
														# for loop iterates over number of inner items of each element
														for j in range(len(x_items[i]						)						):
																					SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       []  # to store all discriminant values of each item as a list
																					# for loop iterates over number of classes we have in our dataset
																					for k in range(len(_snake_case						)						):
																												# appending values of discriminants for each class to 'temp' list
																												temp.append(
																												    x_items[i][j] * (means[k] / variance)
																												    - (means[k] ** 2 / (2 * variance))
																												    + log(probabilities[k]						)						)
																					# appending discriminant values of each item to 'results' list
																					results.append(_snake_case						)

							return [result.index(max(_snake_case						)						) for result in results]

def lowercase_  (       _snake_case       ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:	List[str]									=       sum(1 for i, j in zip(_snake_case       ,_snake_case						) if i == j						)
							# percentage of accuracy equals to number of correct predictions divided by number
							# of all data and multiplied by 100
							return (correct / len(_snake_case						)) * 100


UpperCAmelCase__				:   List[str]   							=			TypeVar('num')

def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case       ,_snake_case = lambda _snake_case						: True       ,_snake_case = None       ,):
							while True:
														try:
																					SCREAMING_SNAKE_CASE__						:	List[Any]									=       input_type(input(_snake_case						).strip() or default						)
																					if condition(_snake_case						):
																												return user_input
																					else:
																												print(f'''{user_input}: {err_msg}'''						)
																												continue
														except ValueError:
																					print(
																					    f'''{user_input}: Incorrect input type, expected {input_type.__name__!r}'''						)

def lowercase_  (       ):
							while True:
														print(""" Linear Discriminant Analysis """.center(50       ,"""*"""						)						)
														print("""*""" * 50       ,"""\n"""						)
														print("""First of all we should specify the number of classes that"""						)
														print("""we want to generate as training dataset"""						)
														# Trying to get number of classes
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       valid_input(
														    input_type=_snake_case       ,condition=lambda _snake_case						: x > 0       ,input_msg="""Enter the number of classes (Data Groupings): """       ,err_msg="""Number of classes should be positive!"""       ,)

														print("""-""" * 100						)

														# Trying to get the value of standard deviation
														SCREAMING_SNAKE_CASE__						:	Any									=       valid_input(
														    input_type=_snake_case       ,condition=lambda _snake_case						: x >= 0       ,input_msg=(
														        """Enter the value of standard deviation"""
														        """(Default value is 1.0 for all classes): """
														    )       ,err_msg="""Standard deviation should not be negative!"""       ,default="""1.0"""       ,)

														print("""-""" * 100						)

														# Trying to get number of instances in classes and theirs means to generate
														# dataset
														SCREAMING_SNAKE_CASE__						:	Dict									=       []  # An empty list to store instance counts of classes in dataset
														for i in range(_snake_case						):
																					SCREAMING_SNAKE_CASE__						:	Dict									=       valid_input(
																					    input_type=_snake_case       ,condition=lambda _snake_case						: x > 0       ,input_msg=(f'''Enter The number of instances for class_{i+1}: ''')       ,err_msg="""Number of instances should be positive!"""       ,)
																					counts.append(_snake_case						)
														print("""-""" * 100						)

														# An empty list to store values of user-entered means of classes
														SCREAMING_SNAKE_CASE__						:	int									=       []
														for a in range(_snake_case						):
																					SCREAMING_SNAKE_CASE__						:	str									=       valid_input(
																					    input_type=_snake_case       ,input_msg=(f'''Enter the value of mean for class_{a+1}: ''')       ,err_msg="""This is an invalid value."""       ,)
																					user_means.append(_snake_case						)
														print("""-""" * 100						)

														print("""Standard deviation: """       ,_snake_case						)
														# print out the number of instances in classes in separated line
														for i, count in enumerate(_snake_case       ,1						):
																					print(f'''Number of instances in class_{i} is: {count}'''						)
														print("""-""" * 100						)

														# print out mean values of classes separated line
														for i, user_mean in enumerate(_snake_case       ,1						):
																					print(f'''Mean of class_{i} is: {user_mean}'''						)
														print("""-""" * 100						)

														# Generating training dataset drawn from gaussian distribution
														SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       [
														    gaussian_distribution(user_means[j]       ,_snake_case       ,counts[j]						)
														    for j in range(_snake_case						)
														]
														print("""Generated Normal Distribution: \n"""       ,_snake_case						)
														print("""-""" * 100						)

														# Generating Ys to detecting corresponding classes
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       y_generator(_snake_case       ,_snake_case						)
														print("""Generated Corresponding Ys: \n"""       ,_snake_case						)
														print("""-""" * 100						)

														# Calculating the value of actual mean for each class
														SCREAMING_SNAKE_CASE__						:	str									=       [calculate_mean(counts[k]       ,x[k]						) for k in range(_snake_case						)]
														# for loop iterates over number of elements in 'actual_means' list and print
														# out them in separated line
														for i, actual_mean in enumerate(_snake_case       ,1						):
																					print(f'''Actual(Real) mean of class_{i} is: {actual_mean}'''						)
														print("""-""" * 100						)

														# Calculating the value of probabilities for each class
														SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       [
														    calculate_probabilities(counts[i]       ,sum(_snake_case						)						) for i in range(_snake_case						)
														]

														# for loop iterates over number of elements in 'probabilities' list and print
														# out them in separated line
														for i, probability in enumerate(_snake_case       ,1						):
																					print(f'''Probability of class_{i} is: {probability}'''						)
														print("""-""" * 100						)

														# Calculating the values of variance for each class
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       calculate_variance(_snake_case       ,_snake_case       ,sum(_snake_case						)						)
														print("""Variance: """       ,_snake_case						)
														print("""-""" * 100						)

														# Predicting Y values
														# storing predicted Y values in 'pre_indexes' variable
														SCREAMING_SNAKE_CASE__						:	str									=       predict_y_values(_snake_case       ,_snake_case       ,_snake_case       ,_snake_case						)
														print("""-""" * 100						)

														# Calculating Accuracy of the model
														print(f'''Accuracy: {accuracy(_snake_case       ,_snake_case						)}'''						)
														print("""-""" * 100						)
														print(""" DONE """.center(100       ,"""+"""						)						)

														if input("""Press any key to restart or 'q' for quit: """						).strip().lower() == "q":
																					print("""\n""" + """GoodBye!""".center(100       ,"""-"""						) + """\n"""						)
																					break
														system("""cls""" if name == """nt""" else """clear"""						)  # noqa: S605


if __name__ == "__main__":
					main()






"""simple docstring"""




import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


if TYPE_CHECKING:
					import sqlitea

					import sqlalchemy


UpperCAmelCase__				:   Union[str, Any]   							=			datasets.utils.logging.get_logger(__name__)



@dataclass
class      lowerCAmelCase_						(datasets.BuilderConfig  ):


				"""simple docstring"""



				__UpperCamelCase				:						Union[str, "sqlalchemy.sql.Selectable"] 	=       None
				__UpperCamelCase				:						Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"] 	=       None
				__UpperCamelCase				:						Optional[Union[str, List[str]]] 	=       None
				__UpperCamelCase				:						bool 	=       True
				__UpperCamelCase				:						Optional[Union[List, Tuple, Dict]] 	=       None
				__UpperCamelCase				:						Optional[Union[List, Dict]] 	=       None
				__UpperCamelCase				:						Optional[List[str]] 	=       None
				__UpperCamelCase				:						Optional[int] 	=       10000
				__UpperCamelCase				:						Optional[datasets.Features] 	=       None



				def      __magic_name__   (self   ) ->		Dict:



											"""simple docstring"""




											if self.sql is None:
																		raise ValueError("""sql must be specified"""   )
											if self.con is None:
																		raise ValueError("""con must be specified"""   )



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__ = None       ,	) ->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	List[Any]									=       config_kwargs.copy()
											# We need to stringify the Selectable object to make its hash deterministic

											# The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
											SCREAMING_SNAKE_CASE__						:	Dict									=       config_kwargs["""sql"""]
											if not isinstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ):
																		if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
																									import sqlalchemy

																									if isinstance(SCREAMING_SNAKE_CASE__       ,	sqlalchemy.sql.Selectable   ):
																																SCREAMING_SNAKE_CASE__						:	str									=       sqlalchemy.create_engine(config_kwargs["""con"""].split("""://"""   )[0] + """://"""   )
																																SCREAMING_SNAKE_CASE__						:	Tuple									=       str(sql.compile(dialect=engine.dialect   )   )
																																SCREAMING_SNAKE_CASE__						:	Any									=       sql_str
																									else:
																																raise TypeError(
																																    F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(SCREAMING_SNAKE_CASE__   )}: {sql}'''   )
																		else:
																									raise TypeError(
																									    F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(SCREAMING_SNAKE_CASE__   )}: {sql}'''   )
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       config_kwargs["""con"""]
											if not isinstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ):
																		SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       id(SCREAMING_SNAKE_CASE__   )
																		logger.info(
																		    F'''SQL connection \'con\' of type {type(SCREAMING_SNAKE_CASE__   )} couldn\'t be hashed properly. To enable hashing, specify \'con\' as URI string instead.'''   )

											return super().create_config_id(SCREAMING_SNAKE_CASE__       ,	custom_features=SCREAMING_SNAKE_CASE__   )



				@property
				def      __magic_name__   (self   ) ->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       {
											    """index_col""": self.index_col,
											    """columns""": self.columns,
											    """params""": self.params,
											    """coerce_float""": self.coerce_float,
											    """parse_dates""": self.parse_dates,
											}
											return pd_read_sql_kwargs






class      lowerCAmelCase_						(datasets.ArrowBasedBuilder  ):


				"""simple docstring"""



				__UpperCamelCase				:						List[str] 	=       SqlConfig



				def      __magic_name__   (self   ) ->		List[Any]:



											"""simple docstring"""




											return datasets.DatasetInfo(features=self.config.features   )



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__   ) ->		List[str]:



											"""simple docstring"""




											return [datasets.SplitGenerator(name=datasets.Split.TRAIN       ,	gen_kwargs={}   )]



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__   ) ->		pa.Table:



											"""simple docstring"""




											if self.config.features is not None:
																		SCREAMING_SNAKE_CASE__						:	List[str]									=       self.config.features.arrow_schema
																		if all(not require_storage_cast(SCREAMING_SNAKE_CASE__   ) for feature in self.config.features.values()   ):
																									# cheaper cast
																									SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       pa.Table.from_arrays([pa_table[field.name] for field in schema]       ,	schema=SCREAMING_SNAKE_CASE__   )
																		else:
																									# more expensive cast; allows str <-> int/float or str to Audio for example
																									SCREAMING_SNAKE_CASE__						:	Optional[int]									=       table_cast(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   )
											return pa_table



				def      __magic_name__   (self   ) ->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	List[Any]									=       self.config.chunksize
											SCREAMING_SNAKE_CASE__						:	Any									=       pd.read_sql(
											    self.config.sql       ,	self.config.con       ,	chunksize=SCREAMING_SNAKE_CASE__       ,	**self.config.pd_read_sql_kwargs   )
											SCREAMING_SNAKE_CASE__						:	str									=       [sql_reader] if chunksize is None else sql_reader
											for chunk_idx, df in enumerate(SCREAMING_SNAKE_CASE__   ):
																		SCREAMING_SNAKE_CASE__						:	int									=       pa.Table.from_pandas(SCREAMING_SNAKE_CASE__   )
																		yield chunk_idx, self._cast_table(SCREAMING_SNAKE_CASE__   )




