






"""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							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







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



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







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



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  return sum(__UpperCamelCase					) / instance_count



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  return instance_count / total_count



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







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



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  __A		     =					[]
  # for loop iterates over number of elements in list
  for i in range(len(__UpperCamelCase					)					):
    # for loop iterates over number of inner items of each element
    for j in range(len(x_items[i]					)					):
      __A		     =					[]  # 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(__UpperCamelCase					)					):
        # 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(__UpperCamelCase					)

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



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  __A		     =					sum(1 for i, j in zip(__UpperCamelCase						,			__UpperCamelCase					) 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(__UpperCamelCase					)) * 1_0_0


lowercase_								=	TypeVar('num')



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase = lambda __UpperCamelCase					: True						,			__UpperCamelCase = None						,			):




  """simple docstring"""







  while True:
    try:
      __A		     =					input_type(input(__UpperCamelCase					).strip() or default					)
      if condition(__UpperCamelCase					):
        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(5_0						,			'''*'''					)					)
    print('''*''' * 5_0						,			'''\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
    __A		     =					valid_input(
        input_type=__UpperCamelCase						,			condition=lambda __UpperCamelCase					: x > 0						,			input_msg='''Enter the number of classes (Data Groupings): '''						,			err_msg='''Number of classes should be positive!'''						,			)

    print('''-''' * 1_0_0					)

    # Trying to get the value of standard deviation
    __A		     =					valid_input(
        input_type=__UpperCamelCase						,			condition=lambda __UpperCamelCase					: 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_0_0					)

    # Trying to get number of instances in classes and theirs means to generate
    # dataset
    __A		     =					[]  # An empty list to store instance counts of classes in dataset
    for i in range(__UpperCamelCase					):
      __A		     =					valid_input(
          input_type=__UpperCamelCase						,			condition=lambda __UpperCamelCase					: x > 0						,			input_msg=(f'Enter The number of instances for class_{i+1}: ')						,			err_msg='''Number of instances should be positive!'''						,			)
      counts.append(__UpperCamelCase					)
    print('''-''' * 1_0_0					)

    # An empty list to store values of user-entered means of classes
    __A		     =					[]
    for a in range(__UpperCamelCase					):
      __A		     =					valid_input(
          input_type=__UpperCamelCase						,			input_msg=(f'Enter the value of mean for class_{a+1}: ')						,			err_msg='''This is an invalid value.'''						,			)
      user_means.append(__UpperCamelCase					)
    print('''-''' * 1_0_0					)

    print('''Standard deviation: '''						,			__UpperCamelCase					)
    # print out the number of instances in classes in separated line
    for i, count in enumerate(__UpperCamelCase						,			1					):
      print(f'Number of instances in class_{i} is: {count}'					)
    print('''-''' * 1_0_0					)

    # print out mean values of classes separated line
    for i, user_mean in enumerate(__UpperCamelCase						,			1					):
      print(f'Mean of class_{i} is: {user_mean}'					)
    print('''-''' * 1_0_0					)

    # Generating training dataset drawn from gaussian distribution
    __A		     =					[
        gaussian_distribution(user_means[j]						,			__UpperCamelCase						,			counts[j]					)
        for j in range(__UpperCamelCase					)
    ]
    print('''Generated Normal Distribution: \n'''						,			__UpperCamelCase					)
    print('''-''' * 1_0_0					)

    # Generating Ys to detecting corresponding classes
    __A		     =					y_generator(__UpperCamelCase						,			__UpperCamelCase					)
    print('''Generated Corresponding Ys: \n'''						,			__UpperCamelCase					)
    print('''-''' * 1_0_0					)

    # Calculating the value of actual mean for each class
    __A		     =					[calculate_mean(counts[k]						,			x[k]					) for k in range(__UpperCamelCase					)]
    # for loop iterates over number of elements in 'actual_means' list and print
    # out them in separated line
    for i, actual_mean in enumerate(__UpperCamelCase						,			1					):
      print(f'Actual(Real) mean of class_{i} is: {actual_mean}'					)
    print('''-''' * 1_0_0					)

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

    # for loop iterates over number of elements in 'probabilities' list and print
    # out them in separated line
    for i, probability in enumerate(__UpperCamelCase						,			1					):
      print(f'Probability of class_{i} is: {probability}'					)
    print('''-''' * 1_0_0					)

    # Calculating the values of variance for each class
    __A		     =					calculate_variance(__UpperCamelCase						,			__UpperCamelCase						,			sum(__UpperCamelCase					)					)
    print('''Variance: '''						,			__UpperCamelCase					)
    print('''-''' * 1_0_0					)

    # Predicting Y values
    # storing predicted Y values in 'pre_indexes' variable
    __A		     =					predict_y_values(__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					)
    print('''-''' * 1_0_0					)

    # Calculating Accuracy of the model
    print(f'Accuracy: {accuracy(__UpperCamelCase						,			__UpperCamelCase					)}'					)
    print('''-''' * 1_0_0					)
    print(''' DONE '''.center(1_0_0						,			'''+'''					)					)

    if input('''Press any key to restart or \'q\' for quit: '''					).strip().lower() == "q":
      print('''\n''' + '''GoodBye!'''.center(1_0_0						,			'''-'''					) + '''\n'''					)
      break
    system('''cls''' if name == '''nt''' else '''clear'''					)  # noqa: S605


if __name__ == "__main__":
       main()






import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





class 		__lowerCamelCase     (   lowercase		):



							def     A__ (     self	,				__snake_case	,				__snake_case=False							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase:					Dict 						=     (
								    filename_skip_take["filename"],
								    filename_skip_take["skip"] if "skip" in filename_skip_take else None,
								    filename_skip_take["take"] if "take" in filename_skip_take else None,
								)
								open(os.path.join(__snake_case							)	,				"wb"							).close()
								UpperCAmelCase:					Any 						=     pa.Table.from_pydict({"filename": [Path(__snake_case							).name] * 1_0_0}							)
								if take == -1:
									UpperCAmelCase:					List[Any] 						=     len(__snake_case							) - skip
								if skip is not None and take is not None:
									UpperCAmelCase:					Optional[int] 						=     pa_table.slice(__snake_case	,				__snake_case							)
								return pa_table





class 		__lowerCamelCase     (   lowercase		):



							def     A__ (     self							)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     "my_name"
								UpperCAmelCase:					str 						=     SplitInfo(name="train"	,				num_examples=1_0_0							)
								UpperCAmelCase:					Optional[int] 						=     SplitInfo(name="test"	,				num_examples=1_0_0							)
								UpperCAmelCase:					Optional[int] 						=     [train_info, test_info]
								UpperCAmelCase:					Tuple 						=     SplitDict()
								split_dict.add(__snake_case							)
								split_dict.add(__snake_case							)
								UpperCAmelCase:					Optional[int] 						=     DatasetInfo(splits=__snake_case							)

								with tempfile.TemporaryDirectory() as tmp_dir:
									UpperCAmelCase:					int 						=     ReaderTest(__snake_case	,				__snake_case							)

									UpperCAmelCase:					int 						=     "test[:33%]"
									UpperCAmelCase:					Dict 						=     Dataset(**reader.read(__snake_case	,				__snake_case	,				__snake_case							)							)
									self.assertEqual(dset["filename"][0]	,				F'{name}-test'							)
									self.assertEqual(dset.num_rows	,				3_3							)
									self.assertEqual(dset.num_columns	,				1							)

									UpperCAmelCase:					int 						=     ["train", "test[:33%]"]
									UpperCAmelCase:					str 						=     [Split.TRAIN, ReadInstruction.from_spec("test[:33%]"							)]
									for instructions in [instructionsa, instructionsa]:
										UpperCAmelCase:					List[str] 						=     [reader.read(__snake_case	,				__snake_case	,				__snake_case							) for instr in instructions]
										UpperCAmelCase						,   UpperCAmelCase:					List[Any] 						=     (Dataset(**__snake_case							) for dataset_kwargs in datasets_kwargs)
										self.assertEqual(train_dset["filename"][0]	,				F'{name}-train'							)
										self.assertEqual(train_dset.num_rows	,				1_0_0							)
										self.assertEqual(train_dset.num_columns	,				1							)
										self.assertIsInstance(train_dset.split	,				__snake_case							)
										self.assertEqual(str(train_dset.split							)	,				"train"							)
										self.assertEqual(test_dset["filename"][0]	,				F'{name}-test'							)
										self.assertEqual(test_dset.num_rows	,				3_3							)
										self.assertEqual(test_dset.num_columns	,				1							)
										self.assertIsInstance(test_dset.split	,				__snake_case							)
										self.assertEqual(str(test_dset.split							)	,				"test[:33%]"							)
										del train_dset, test_dset



							def     A__ (     self							)						->     Optional[Any]:






								"""simple docstring"""



								UpperCAmelCase:					str 						=     "my_name"
								UpperCAmelCase:					List[Any] 						=     SplitInfo(name="train"	,				num_examples=1_0_0_0	,				shard_lengths=[1_0_0] * 1_0							)
								UpperCAmelCase:					List[Any] 						=     [train_info]
								UpperCAmelCase:					Dict 						=     SplitDict()
								split_dict.add(__snake_case							)
								UpperCAmelCase:					int 						=     DatasetInfo(splits=__snake_case							)

								with tempfile.TemporaryDirectory() as tmp_dir:
									UpperCAmelCase:					Optional[Any] 						=     ReaderTest(__snake_case	,				__snake_case							)

									UpperCAmelCase:					Optional[Any] 						=     "train[:33%]"
									UpperCAmelCase:					Tuple 						=     Dataset(**reader.read(__snake_case	,				__snake_case	,				__snake_case							)							)
									self.assertEqual(dset["filename"][0]	,				F'{name}-train-00000-of-00010'							)
									self.assertEqual(dset["filename"][-1]	,				F'{name}-train-00003-of-00010'							)
									self.assertEqual(dset.num_rows	,				3_3_0							)
									self.assertEqual(dset.num_columns	,				1							)




							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     SplitInfo(name="train"	,				num_examples=1_0_0							)
								UpperCAmelCase:					int 						=     SplitInfo(name="test"	,				num_examples=1_0_0							)
								UpperCAmelCase:					List[Any] 						=     SplitDict()
								split_dict.add(__snake_case							)
								split_dict.add(__snake_case							)
								UpperCAmelCase:					Optional[Any] 						=     DatasetInfo(splits=__snake_case							)

								with tempfile.TemporaryDirectory() as tmp_dir:
									UpperCAmelCase:					List[Any] 						=     ReaderTest(__snake_case	,				__snake_case							)

									UpperCAmelCase:					List[str] 						=     [
									    {"filename": os.path.join(__snake_case	,				"train"							)},
									    {"filename": os.path.join(__snake_case	,				"test"							), "skip": 1_0, "take": 1_0},
									]
									UpperCAmelCase:					Tuple 						=     Dataset(**reader.read_files(__snake_case	,				original_instructions="train+test[10:20]"							)							)
									self.assertEqual(dset.num_rows	,				1_1_0							)
									self.assertEqual(dset.num_columns	,				1							)
									del dset



@pytest.mark.parametrize("in_memory"				,					[False, True]				)
def      __UpperCAmelCase    (  snake_case_							:		str				,					snake_case_							:		str				,					snake_case_							:		Any				):



	'''simple docstring'''



	UpperCAmelCase:					Union[str, Any] 						=     arrow_file
	with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
		UpperCAmelCase:					int 						=     ArrowReader.read_table(snake_case_				,					in_memory=snake_case_				)
	assert table.shape == dataset.data.shape
	assert set(table.column_names				) == set(dataset.data.column_names				)
	assert dict(table.to_pydict()				) == dict(dataset.data.to_pydict()				)  # to_pydict returns OrderedDict



@pytest.mark.parametrize("in_memory"				,					[False, True]				)
def      __UpperCAmelCase    (  snake_case_							:		List[str]				,					snake_case_							:		Optional[int]				,					snake_case_							:		int				):



	'''simple docstring'''



	UpperCAmelCase:					List[Any] 						=     arrow_file
	UpperCAmelCase:					Tuple 						=     ArrowReader(""				,					snake_case_				)
	with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
		UpperCAmelCase:					Any 						=     reader.read_files([{"filename": filename}]				,					in_memory=snake_case_				)
	assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
	UpperCAmelCase:					Any 						=     dataset_kwargs["arrow_table"]
	assert table.shape == dataset.data.shape
	assert set(table.column_names				) == set(dataset.data.column_names				)
	assert dict(table.to_pydict()				) == dict(dataset.data.to_pydict()				)  # to_pydict returns OrderedDict



def      __UpperCAmelCase    (  ):



	'''simple docstring'''



	assert ReadInstruction("train"				,					to=1_0				,					unit="abs"				).to_spec() == "train[:10]"
	assert ReadInstruction("train"				,					from_=-8_0				,					to=1_0				,					unit="%"				).to_spec() == "train[-80%:10%]"

	UpperCAmelCase:					str 						=     "train+test"
	assert ReadInstruction.from_spec(snake_case_				).to_spec() == spec_train_test

	UpperCAmelCase:					List[Any] 						=     "train[2:10]"
	assert ReadInstruction.from_spec(snake_case_				).to_spec() == spec_train_abs

	UpperCAmelCase:					Tuple 						=     "train[15%:-20%]"
	assert ReadInstruction.from_spec(snake_case_				).to_spec() == spec_train_pct

	UpperCAmelCase:					Dict 						=     "train[:10%](closest)"
	assert ReadInstruction.from_spec(snake_case_				).to_spec() == "train[:10%]"

	UpperCAmelCase:					List[str] 						=     "train[:10%](pct1_dropremainder)"
	assert ReadInstruction.from_spec(snake_case_				).to_spec() == spec_train_pct_rounding

	UpperCAmelCase:					Any 						=     "train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"
	assert ReadInstruction.from_spec(snake_case_				).to_spec() == spec_train_test_pct_rounding



def      __UpperCAmelCase    (  ):



	'''simple docstring'''



	UpperCAmelCase:					Dict 						=     "dummy"
	UpperCAmelCase:					int 						=     [SplitInfo(name="train"				,					num_examples=1_0_0				)]
	UpperCAmelCase:					List[str] 						=     "train[:33%]"
	UpperCAmelCase:					Optional[int] 						=     "arrow"
	UpperCAmelCase:					List[Any] 						=     "prefix"

	UpperCAmelCase:					List[Any] 						=     make_file_instructions(snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				)
	assert isinstance(snake_case_				,					snake_case_				)
	assert file_instructions.num_examples == 3_3
	assert file_instructions.file_instructions == [
	    {"filename": os.path.join(snake_case_				,					F'{name}-train.arrow'				), "skip": 0, "take": 3_3}
	]

	UpperCAmelCase:					int 						=     [SplitInfo(name="train"				,					num_examples=1_0_0				,					shard_lengths=[1_0] * 1_0				)]
	UpperCAmelCase:					List[Any] 						=     make_file_instructions(snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				)
	assert isinstance(snake_case_				,					snake_case_				)
	assert file_instructions.num_examples == 3_3
	assert file_instructions.file_instructions == [
	    {"filename": os.path.join(snake_case_				,					F'{name}-train-00000-of-00010.arrow'				), "skip": 0, "take": -1},
	    {"filename": os.path.join(snake_case_				,					F'{name}-train-00001-of-00010.arrow'				), "skip": 0, "take": -1},
	    {"filename": os.path.join(snake_case_				,					F'{name}-train-00002-of-00010.arrow'				), "skip": 0, "take": -1},
	    {"filename": os.path.join(snake_case_				,					F'{name}-train-00003-of-00010.arrow'				), "skip": 0, "take": 3},
	]



@pytest.mark.parametrize("name, expected_exception"				,					[(None, TypeError), ("", ValueError)]				)
def      __UpperCAmelCase    (  snake_case_							:		Union[str, Any]				,					snake_case_							:		Dict				):



	'''simple docstring'''



	UpperCAmelCase:					Optional[int] 						=     [SplitInfo(name="train"				,					num_examples=1_0_0				)]
	UpperCAmelCase:					Union[str, Any] 						=     "train"
	UpperCAmelCase:					List[str] 						=     "arrow"
	UpperCAmelCase:					Any 						=     "prefix_path"
	with pytest.raises(snake_case_				):
		UpperCAmelCase:					List[Any] 						=     make_file_instructions(snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				)
