




from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar
def 		a_       (   __lowerCAmelCase      ,   __lowerCAmelCase      ,   __lowerCAmelCase							):
  seed(1							)
  return [gauss(snake_case_      ,   snake_case_							) for _ in range(snake_case_							)]
def 		a_       (   __lowerCAmelCase      ,   __lowerCAmelCase							):
  return [k for k in range(snake_case_							) for _ in range(instance_count[k]							)]
def 		a_       (   __lowerCAmelCase      ,   __lowerCAmelCase							):
  return sum(snake_case_							) / instance_count
def 		a_       (   __lowerCAmelCase      ,   __lowerCAmelCase							):
  return instance_count / total_count
def 		a_       (   __lowerCAmelCase      ,   __lowerCAmelCase      ,   __lowerCAmelCase							):
  lowerCAmelCase__								=			[]  # 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
  lowerCAmelCase__								=			len(snake_case_							)  # Number of classes in dataset
  return 1 / (total_count - n_classes) * sum(snake_case_							)
def 		a_       (   __lowerCAmelCase      ,   __lowerCAmelCase      ,   __lowerCAmelCase      ,   __lowerCAmelCase							):
  lowerCAmelCase__								=			[]
  # 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]							)							):
      lowerCAmelCase__								=			[]  # 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 		a_       (   __lowerCAmelCase      ,   __lowerCAmelCase							):
  lowerCAmelCase__								=			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_							)) * 1_00


__magic_name__     :     List[Any]  							=					TypeVar("""num""")
def 		a_       (   __lowerCAmelCase      ,   __lowerCAmelCase      ,   __lowerCAmelCase      ,   __lowerCAmelCase = lambda __lowerCAmelCase							: True      ,   __lowerCAmelCase = None      ,   ):
  while True:
    try:
      lowerCAmelCase__								=			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 		a_       (   ):
  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
    lowerCAmelCase__								=			valid_input(
        input_type=snake_case_      ,   condition=lambda __lowerCAmelCase							: x > 0      ,   input_msg='''Enter the number of classes (Data Groupings): '''      ,   err_msg='''Number of classes should be positive!'''      ,   )

    print('''-''' * 1_00							)

    # Trying to get the value of standard deviation
    lowerCAmelCase__								=			valid_input(
        input_type=snake_case_      ,   condition=lambda __lowerCAmelCase							: 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('''-''' * 1_00							)

    # Trying to get number of instances in classes and theirs means to generate
    # dataset
    lowerCAmelCase__								=			[]  # An empty list to store instance counts of classes in dataset
    for i in range(snake_case_							):
      lowerCAmelCase__								=			valid_input(
          input_type=snake_case_      ,   condition=lambda __lowerCAmelCase							: 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('''-''' * 1_00							)

    # An empty list to store values of user-entered means of classes
    lowerCAmelCase__								=			[]
    for a in range(snake_case_							):
      lowerCAmelCase__								=			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('''-''' * 1_00							)

    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('''-''' * 1_00							)

    # 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('''-''' * 1_00							)

    # Generating training dataset drawn from gaussian distribution
    lowerCAmelCase__								=			[
        gaussian_distribution(user_means[j]      ,   snake_case_      ,   counts[j]							)
        for j in range(snake_case_							)
    ]
    print('''Generated Normal Distribution: \n'''      ,   snake_case_							)
    print('''-''' * 1_00							)

    # Generating Ys to detecting corresponding classes
    lowerCAmelCase__								=			y_generator(snake_case_      ,   snake_case_							)
    print('''Generated Corresponding Ys: \n'''      ,   snake_case_							)
    print('''-''' * 1_00							)

    # Calculating the value of actual mean for each class
    lowerCAmelCase__								=			[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('''-''' * 1_00							)

    # Calculating the value of probabilities for each class
    lowerCAmelCase__								=			[
        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('''-''' * 1_00							)

    # Calculating the values of variance for each class
    lowerCAmelCase__								=			calculate_variance(snake_case_      ,   snake_case_      ,   sum(snake_case_							)							)
    print('''Variance: '''      ,   snake_case_							)
    print('''-''' * 1_00							)

    # Predicting Y values
    # storing predicted Y values in 'pre_indexes' variable
    lowerCAmelCase__								=			predict_y_values(snake_case_      ,   snake_case_      ,   snake_case_      ,   snake_case_							)
    print('''-''' * 1_00							)

    # Calculating Accuracy of the model
    print(F"""Accuracy: {accuracy(snake_case_      ,   snake_case_							)}"""							)
    print('''-''' * 1_00							)
    print(''' DONE '''.center(1_00      ,   '''+'''							)							)

    if input('''Press any key to restart or \'q\' for quit: '''							).strip().lower() == "q":
      print('''\n''' + '''GoodBye!'''.center(1_00      ,   '''-'''							) + '''\n'''							)
      break
    system('''cls''' if name == '''nt''' else '''clear'''							)  # noqa: S605


if __name__ == "__main__":
      main()







import os

from ...utils import logging
from .utils import DataProcessor, InputExample


__magic_name__     :     str  							=					logging.get_logger(__name__)





class 						SCREAMING_SNAKE_CASE__		(_a ):
     def __init__(	self  :		Any					,	__lowerCamelCase  :		Any					,	__lowerCamelCase  :		Optional[int]=None	):



       """simple docstring"""
       lowerCAmelCase__								=			language
       lowerCAmelCase__								=			train_language
     def 					A__			(	self  :		str					,	__lowerCamelCase  :		List[str]	):



       """simple docstring"""
       lowerCAmelCase__								=			self.language if self.train_language is None else self.train_language
       lowerCAmelCase__								=			self._read_tsv(os.path.join(__lowerCamelCase					,	F"""XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv"""	)	)
       lowerCAmelCase__								=			[]
       for i, line in enumerate(__lowerCamelCase	):
         if i == 0:
           continue
         lowerCAmelCase__								=			F"""train-{i}"""
         lowerCAmelCase__								=			line[0]
         lowerCAmelCase__								=			line[1]
         lowerCAmelCase__								=			'''contradiction''' if line[2] == '''contradictory''' else line[2]
         if not isinstance(__lowerCamelCase					,	__lowerCamelCase	):
           raise ValueError(F"""Training input {text_a} is not a string"""	)
         if not isinstance(__lowerCamelCase					,	__lowerCamelCase	):
           raise ValueError(F"""Training input {text_b} is not a string"""	)
         if not isinstance(__lowerCamelCase					,	__lowerCamelCase	):
           raise ValueError(F"""Training label {label} is not a string"""	)
         examples.append(InputExample(guid=__lowerCamelCase					,	text_a=__lowerCamelCase					,	text_b=__lowerCamelCase					,	label=__lowerCamelCase	)	)
       return examples
     def 					A__			(	self  :		Dict					,	__lowerCamelCase  :		Union[str, Any]	):



       """simple docstring"""
       lowerCAmelCase__								=			self._read_tsv(os.path.join(__lowerCamelCase					,	'''XNLI-1.0/xnli.test.tsv'''	)	)
       lowerCAmelCase__								=			[]
       for i, line in enumerate(__lowerCamelCase	):
         if i == 0:
           continue
         lowerCAmelCase__								=			line[0]
         if language != self.language:
           continue
         lowerCAmelCase__								=			F"""test-{i}"""
         lowerCAmelCase__								=			line[6]
         lowerCAmelCase__								=			line[7]
         lowerCAmelCase__								=			line[1]
         if not isinstance(__lowerCamelCase					,	__lowerCamelCase	):
           raise ValueError(F"""Training input {text_a} is not a string"""	)
         if not isinstance(__lowerCamelCase					,	__lowerCamelCase	):
           raise ValueError(F"""Training input {text_b} is not a string"""	)
         if not isinstance(__lowerCamelCase					,	__lowerCamelCase	):
           raise ValueError(F"""Training label {label} is not a string"""	)
         examples.append(InputExample(guid=__lowerCamelCase					,	text_a=__lowerCamelCase					,	text_b=__lowerCamelCase					,	label=__lowerCamelCase	)	)
       return examples



     def 					A__			(	self  :		List[str]	):



       """simple docstring"""
       return ["contradiction", "entailment", "neutral"]



__magic_name__     :     Tuple  							=					{
    """xnli""": XnliProcessor,
}

__magic_name__     :     List[Any]  							=					{
    """xnli""": """classification""",
}

__magic_name__     :     str  							=					{
    """xnli""": 3,
}

