
'''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   lowerCAmelCase(  a__    :  float      , a__    :  float      , a__    :  int      ):







       '''simple docstring'''






       seed(1      )
       return [gauss(lowerCamelCase_      , lowerCamelCase_      ) for _ in range(lowerCamelCase_      )]







def   lowerCAmelCase(  a__    :  int      , a__    :  list      ):







       '''simple docstring'''






       return [k for k in range(lowerCamelCase_      ) for _ in range(instance_count[k]      )]







def   lowerCAmelCase(  a__    :  int      , a__    :  list      ):







       '''simple docstring'''






       return sum(lowerCamelCase_      ) / instance_count







def   lowerCAmelCase(  a__    :  int      , a__    :  int      ):







       '''simple docstring'''






       return instance_count / total_count







def   lowerCAmelCase(  a__    :  list      , a__    :  list      , a__    :  int      ):







       '''simple docstring'''






       lowerCamelCase__				   =      []  # An empty list to store all squared differences
       # iterate over number of elements in items
       for i in range(len(lowerCamelCase_      )      ):
              # 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(lowerCamelCase_      )  # Number of classes in dataset
       return 1 / (total_count - n_classes) * sum(lowerCamelCase_      )







def   lowerCAmelCase(  a__    :  list      , a__    :  list      , a__    :  float      , a__    :  list      ):







       '''simple docstring'''






       lowerCamelCase__				   =      []
       # for loop iterates over number of elements in list
       for i in range(len(lowerCamelCase_      )      ):
              # 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(lowerCamelCase_      )      ):
                            # 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(lowerCamelCase_      )

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







def   lowerCAmelCase(  a__    :  list      , a__    :  list      ):







       '''simple docstring'''






       lowerCamelCase__				   =      sum(1 for i, j in zip(lowerCamelCase_      , lowerCamelCase_      ) 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(lowerCamelCase_      )) * 100


lowerCAmelCase_      =   TypeVar("num")







def   lowerCAmelCase(  a__    :  Callable[[object], num]      , a__    :  str      , a__    :  str      , a__    :  Callable[[num], bool] = lambda a__      : True      , a__    :  str | None = None      , ):







       '''simple docstring'''






       while True:
              try:
                     lowerCamelCase__				   =      input_type(input(lowerCamelCase_      ).strip() or default      )
                     if condition(lowerCamelCase_      ):
                            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   lowerCAmelCase(  ):







       '''simple docstring'''






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

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

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

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

              # Generating Ys to detecting corresponding classes
              lowerCamelCase__				   =      y_generator(lowerCamelCase_      , lowerCamelCase_      )
              print("Generated Corresponding Ys: \n"      , lowerCamelCase_      )
              print("-" * 100      )

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

              # Calculating the value of probabilities for each class
              lowerCamelCase__				   =      [
                  calculate_probabilities(counts[i]      , sum(lowerCamelCase_      )      ) for i in range(lowerCamelCase_      )
              ]

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

              # Calculating the values of variance for each class
              lowerCamelCase__				   =      calculate_variance(lowerCamelCase_      , lowerCamelCase_      , sum(lowerCamelCase_      )      )
              print("Variance: "      , lowerCamelCase_      )
              print("-" * 100      )

              # Predicting Y values
              # storing predicted Y values in 'pre_indexes' variable
              lowerCamelCase__				   =      predict_y_values(lowerCamelCase_      , lowerCamelCase_      , lowerCamelCase_      , lowerCamelCase_      )
              print("-" * 100      )

              # Calculating Accuracy of the model
              print(f"""Accuracy: {accuracy(lowerCamelCase_      , lowerCamelCase_      )}"""      )
              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"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCamelCase  =	{
    'configuration_open_llama': ['OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenLlamaConfig'],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase  =	['LlamaTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase  =	['LlamaTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase  =	[
	    'OpenLlamaForCausalLM',
	    'OpenLlamaModel',
	    'OpenLlamaPreTrainedModel',
	    'OpenLlamaForSequenceClassification',
	]


if TYPE_CHECKING:
	from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from transformers import LlamaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from transformers import LlamaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_open_llama import (
		    OpenLlamaForCausalLM,
		    OpenLlamaForSequenceClassification,
		    OpenLlamaModel,
		    OpenLlamaPreTrainedModel,
		)


else:
	import sys

	_lowerCamelCase  =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
