code: 
  
  
  
  
  
  
  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     UpperCAmelCase_		(      lowerCamelCase_				):
  
  
  
        """simple docstring"""
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_=False				)       ->   Optional[Any]:
            UpperCamelCase :List[Any]										=     (
                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(SCREAMING_SNAKE_CASE_				)					,       '''wb'''				).close()
            UpperCamelCase :Any										=     pa.Table.from_pydict({'''filename''': [Path(SCREAMING_SNAKE_CASE_				).name] * 100}				)
            if take == -1:
                UpperCamelCase :Dict										=     len(SCREAMING_SNAKE_CASE_				) - skip
            if skip is not None and take is not None:
                UpperCamelCase :Dict										=     pa_table.slice(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
            return pa_table
  
  
  
  
  
  
  
  class     UpperCAmelCase_		(      lowerCamelCase_				):
  
  
  
        """simple docstring"""
        def 					UpperCAmelCase		(       self				)       ->   Any:
            UpperCamelCase :int										=     "my_name"
            UpperCamelCase :Union[str, Any]										=     SplitInfo(name='''train'''					,       num_examples=100				)
            UpperCamelCase :Tuple										=     SplitInfo(name='''test'''					,       num_examples=100				)
            UpperCamelCase :int										=     [train_info, test_info]
            UpperCamelCase :Dict										=     SplitDict()
            split_dict.add(SCREAMING_SNAKE_CASE_				)
            split_dict.add(SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :Union[str, Any]										=     DatasetInfo(splits=SCREAMING_SNAKE_CASE_				)
  
            with tempfile.TemporaryDirectory() as tmp_dir:
                UpperCamelCase :List[Any]										=     ReaderTest(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
  
                UpperCamelCase :List[str]										=     "test[:33%]"
                UpperCamelCase :Tuple										=     Dataset(**reader.read(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)				)
                self.assertEqual(dset['''filename'''][0]					,       F'''{name}-test'''				)
                self.assertEqual(dset.num_rows					,       33				)
                self.assertEqual(dset.num_columns					,       1				)
  
                UpperCamelCase :Any										=     ["train", "test[:33%]"]
                UpperCamelCase :int										=     [Split.TRAIN, ReadInstruction.from_spec('''test[:33%]'''				)]
                for instructions in [instructionsa, instructionsa]:
                    UpperCamelCase :Any										=     [reader.read(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				) for instr in instructions]
                    UpperCamelCase :List[str]										=     (Dataset(**SCREAMING_SNAKE_CASE_				) for dataset_kwargs in datasets_kwargs)
                    self.assertEqual(train_dset['''filename'''][0]					,       F'''{name}-train'''				)
                    self.assertEqual(train_dset.num_rows					,       100				)
                    self.assertEqual(train_dset.num_columns					,       1				)
                    self.assertIsInstance(train_dset.split					,       SCREAMING_SNAKE_CASE_				)
                    self.assertEqual(str(train_dset.split				)					,       '''train'''				)
                    self.assertEqual(test_dset['''filename'''][0]					,       F'''{name}-test'''				)
                    self.assertEqual(test_dset.num_rows					,       33				)
                    self.assertEqual(test_dset.num_columns					,       1				)
                    self.assertIsInstance(test_dset.split					,       SCREAMING_SNAKE_CASE_				)
                    self.assertEqual(str(test_dset.split				)					,       '''test[:33%]'''				)
                    del train_dset, test_dset
        def 					UpperCAmelCase		(       self				)       ->   Any:
            UpperCamelCase :Optional[int]										=     "my_name"
            UpperCamelCase :List[str]										=     SplitInfo(name='''train'''					,       num_examples=1000					,       shard_lengths=[100] * 10				)
            UpperCamelCase :Union[str, Any]										=     [train_info]
            UpperCamelCase :List[str]										=     SplitDict()
            split_dict.add(SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :Optional[int]										=     DatasetInfo(splits=SCREAMING_SNAKE_CASE_				)
  
            with tempfile.TemporaryDirectory() as tmp_dir:
                UpperCamelCase :Tuple										=     ReaderTest(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
  
                UpperCamelCase :int										=     "train[:33%]"
                UpperCamelCase :int										=     Dataset(**reader.read(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_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					,       330				)
                self.assertEqual(dset.num_columns					,       1				)
  
  
  
  
        def 					UpperCAmelCase		(       self				)       ->   Optional[Any]:
            UpperCamelCase :Optional[Any]										=     SplitInfo(name='''train'''					,       num_examples=100				)
            UpperCamelCase :Dict										=     SplitInfo(name='''test'''					,       num_examples=100				)
            UpperCamelCase :str										=     SplitDict()
            split_dict.add(SCREAMING_SNAKE_CASE_				)
            split_dict.add(SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :Dict										=     DatasetInfo(splits=SCREAMING_SNAKE_CASE_				)
  
            with tempfile.TemporaryDirectory() as tmp_dir:
                UpperCamelCase :str										=     ReaderTest(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
  
                UpperCamelCase :int										=     [
                    {"filename": os.path.join(SCREAMING_SNAKE_CASE_					,       '''train'''				)},
                    {"filename": os.path.join(SCREAMING_SNAKE_CASE_					,       '''test'''				), "skip": 10, "take": 10},
                ]
                UpperCamelCase :Union[str, Any]										=     Dataset(**reader.read_files(SCREAMING_SNAKE_CASE_					,       original_instructions='''train+test[10:20]'''				)				)
                self.assertEqual(dset.num_rows					,       110				)
                self.assertEqual(dset.num_columns					,       1				)
                del dset
  
  
  
  
  
  
  
  @pytest.mark.parametrize('''in_memory'''  ,						[False, True] )
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			Optional[Any]  ,						SCREAMING_SNAKE_CASE__						:			List[Any]  ,						SCREAMING_SNAKE_CASE__						:			Optional[int] ):
      UpperCamelCase :Dict										=     arrow_file
      with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
          UpperCamelCase :Any										=     ArrowReader.read_table(lowercase__  ,						in_memory=lowercase__ )
      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 						_A     (       SCREAMING_SNAKE_CASE__						:			List[str]  ,						SCREAMING_SNAKE_CASE__						:			str  ,						SCREAMING_SNAKE_CASE__						:			str ):
      UpperCamelCase :str										=     arrow_file
      UpperCamelCase :Any										=     ArrowReader(''''''  ,						lowercase__ )
      with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
          UpperCamelCase :List[Any]										=     reader.read_files([{'''filename''': filename}]  ,						in_memory=lowercase__ )
      assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
      UpperCamelCase :List[str]										=     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 						_A     (       ):
      assert ReadInstruction('''train'''  ,						to=10  ,						unit='''abs''' ).to_spec() == "train[:10]"
      assert ReadInstruction('''train'''  ,						from_=-80  ,						to=10  ,						unit='''%''' ).to_spec() == "train[-80%:10%]"
  
      UpperCamelCase :Tuple										=     "train+test"
      assert ReadInstruction.from_spec(lowercase__ ).to_spec() == spec_train_test
  
      UpperCamelCase :str										=     "train[2:10]"
      assert ReadInstruction.from_spec(lowercase__ ).to_spec() == spec_train_abs
  
      UpperCamelCase :Optional[Any]										=     "train[15%:-20%]"
      assert ReadInstruction.from_spec(lowercase__ ).to_spec() == spec_train_pct
  
      UpperCamelCase :Union[str, Any]										=     "train[:10%](closest)"
      assert ReadInstruction.from_spec(lowercase__ ).to_spec() == "train[:10%]"
  
      UpperCamelCase :Dict										=     "train[:10%](pct1_dropremainder)"
      assert ReadInstruction.from_spec(lowercase__ ).to_spec() == spec_train_pct_rounding
  
      UpperCamelCase :Tuple										=     "train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"
      assert ReadInstruction.from_spec(lowercase__ ).to_spec() == spec_train_test_pct_rounding
  
  
  def 						_A     (       ):
      UpperCamelCase :List[str]										=     "dummy"
      UpperCamelCase :Optional[Any]										=     [SplitInfo(name='''train'''  ,						num_examples=100 )]
      UpperCamelCase :List[str]										=     "train[:33%]"
      UpperCamelCase :Optional[int]										=     "arrow"
      UpperCamelCase :Union[str, Any]										=     "prefix"
  
      UpperCamelCase :Tuple										=     make_file_instructions(lowercase__  ,						lowercase__  ,						lowercase__  ,						lowercase__  ,						lowercase__ )
      assert isinstance(lowercase__  ,						lowercase__ )
      assert file_instructions.num_examples == 33
      assert file_instructions.file_instructions == [
          {"filename": os.path.join(lowercase__  ,						F'''{name}-train.arrow''' ), "skip": 0, "take": 33}
      ]
  
      UpperCamelCase :Tuple										=     [SplitInfo(name='''train'''  ,						num_examples=100  ,						shard_lengths=[10] * 10 )]
      UpperCamelCase :List[str]										=     make_file_instructions(lowercase__  ,						lowercase__  ,						lowercase__  ,						lowercase__  ,						lowercase__ )
      assert isinstance(lowercase__  ,						lowercase__ )
      assert file_instructions.num_examples == 33
      assert file_instructions.file_instructions == [
          {"filename": os.path.join(lowercase__  ,						F'''{name}-train-00000-of-00010.arrow''' ), "skip": 0, "take": -1},
          {"filename": os.path.join(lowercase__  ,						F'''{name}-train-00001-of-00010.arrow''' ), "skip": 0, "take": -1},
          {"filename": os.path.join(lowercase__  ,						F'''{name}-train-00002-of-00010.arrow''' ), "skip": 0, "take": -1},
          {"filename": os.path.join(lowercase__  ,						F'''{name}-train-00003-of-00010.arrow''' ), "skip": 0, "take": 3},
      ]
  
  
  
  @pytest.mark.parametrize('''name, expected_exception'''  ,						[(None, TypeError), ('''''', ValueError)] )
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			Any  ,						SCREAMING_SNAKE_CASE__						:			List[Any] ):
      UpperCamelCase :Dict										=     [SplitInfo(name='''train'''  ,						num_examples=100 )]
      UpperCamelCase :Any										=     "train"
      UpperCamelCase :List[str]										=     "arrow"
      UpperCamelCase :Any										=     "prefix_path"
      with pytest.raises(lowercase__ ):
          UpperCamelCase :Optional[int]										=     make_file_instructions(lowercase__  ,						lowercase__  ,						lowercase__  ,						lowercase__  ,						lowercase__ )
  
  
  
  
style_context: 
  
  
  
  
  
  
  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     (       SCREAMING_SNAKE_CASE__						:			float  ,						SCREAMING_SNAKE_CASE__						:			float  ,						SCREAMING_SNAKE_CASE__						:			int ):
      seed(1 )
      return [gauss(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ ) for _ in range(SCREAMING_SNAKE_CASE__ )]
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			int  ,						SCREAMING_SNAKE_CASE__						:			list ):
      return [k for k in range(SCREAMING_SNAKE_CASE__ ) for _ in range(instance_count[k] )]
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			int  ,						SCREAMING_SNAKE_CASE__						:			list ):
      return sum(SCREAMING_SNAKE_CASE__ ) / instance_count
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			int  ,						SCREAMING_SNAKE_CASE__						:			int ):
      return instance_count / total_count
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			list  ,						SCREAMING_SNAKE_CASE__						:			list  ,						SCREAMING_SNAKE_CASE__						:			int ):
      UpperCamelCase :Optional[Any]										=     []  # An empty list to store all squared differences
      # iterate over number of elements in items
      for i in range(len(SCREAMING_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
      UpperCamelCase :Tuple										=     len(SCREAMING_SNAKE_CASE__ )  # Number of classes in dataset
      return 1 / (total_count - n_classes) * sum(SCREAMING_SNAKE_CASE__ )
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			list  ,						SCREAMING_SNAKE_CASE__						:			list  ,						SCREAMING_SNAKE_CASE__						:			float  ,						SCREAMING_SNAKE_CASE__						:			list ):
      UpperCamelCase :Dict										=     []
      # for loop iterates over number of elements in list
      for i in range(len(SCREAMING_SNAKE_CASE__ ) ):
          # for loop iterates over number of inner items of each element
          for j in range(len(x_items[i] ) ):
              UpperCamelCase :List[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(SCREAMING_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(SCREAMING_SNAKE_CASE__ )
  
      return [result.index(max(SCREAMING_SNAKE_CASE__ ) ) for result in results]
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			list  ,						SCREAMING_SNAKE_CASE__						:			list ):
      UpperCamelCase :List[Any]										=     sum(1 for i, j in zip(SCREAMING_SNAKE_CASE__  ,						SCREAMING_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(SCREAMING_SNAKE_CASE__ )) * 100
  
  
  __snake_case							      =						TypeVar("""num""")
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			Callable[[object], num]  ,						SCREAMING_SNAKE_CASE__						:			str  ,						SCREAMING_SNAKE_CASE__						:			str  ,						SCREAMING_SNAKE_CASE__						:			Callable[[num], bool] = lambda SCREAMING_SNAKE_CASE__ : True  ,						SCREAMING_SNAKE_CASE__						:			str | None = None  ,						):
      while True:
          try:
              UpperCamelCase :List[Any]										=     input_type(input(SCREAMING_SNAKE_CASE__ ).strip() or default )
              if condition(SCREAMING_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
          UpperCamelCase :List[Any]										=     valid_input(
              input_type=SCREAMING_SNAKE_CASE__  ,						condition=lambda SCREAMING_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
          UpperCamelCase :Any										=     valid_input(
              input_type=SCREAMING_SNAKE_CASE__  ,						condition=lambda SCREAMING_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
          UpperCamelCase :Tuple										=     []  # An empty list to store instance counts of classes in dataset
          for i in range(SCREAMING_SNAKE_CASE__ ):
              UpperCamelCase :List[Any]										=     valid_input(
                  input_type=SCREAMING_SNAKE_CASE__  ,						condition=lambda SCREAMING_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(SCREAMING_SNAKE_CASE__ )
          print('''-''' * 100 )
  
          # An empty list to store values of user-entered means of classes
          UpperCamelCase :Union[str, Any]										=     []
          for a in range(SCREAMING_SNAKE_CASE__ ):
              UpperCamelCase :str										=     valid_input(
                  input_type=SCREAMING_SNAKE_CASE__  ,						input_msg=(F'''Enter the value of mean for class_{a+1}: ''')  ,						err_msg='''This is an invalid value.'''  ,						)
              user_means.append(SCREAMING_SNAKE_CASE__ )
          print('''-''' * 100 )
  
          print('''Standard deviation: '''  ,						SCREAMING_SNAKE_CASE__ )
          # print out the number of instances in classes in separated line
          for i, count in enumerate(SCREAMING_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(SCREAMING_SNAKE_CASE__  ,						1 ):
              print(F'''Mean of class_{i} is: {user_mean}''' )
          print('''-''' * 100 )
  
          # Generating training dataset drawn from gaussian distribution
          UpperCamelCase :Tuple										=     [
              gaussian_distribution(user_means[j]  ,						SCREAMING_SNAKE_CASE__  ,						counts[j] )
              for j in range(SCREAMING_SNAKE_CASE__ )
          ]
          print('''Generated Normal Distribution: \n'''  ,						SCREAMING_SNAKE_CASE__ )
          print('''-''' * 100 )
  
          # Generating Ys to detecting corresponding classes
          UpperCamelCase :int										=     y_generator(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ )
          print('''Generated Corresponding Ys: \n'''  ,						SCREAMING_SNAKE_CASE__ )
          print('''-''' * 100 )
  
          # Calculating the value of actual mean for each class
          UpperCamelCase :Tuple										=     [calculate_mean(counts[k]  ,						x[k] ) for k in range(SCREAMING_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(SCREAMING_SNAKE_CASE__  ,						1 ):
              print(F'''Actual(Real) mean of class_{i} is: {actual_mean}''' )
          print('''-''' * 100 )
  
          # Calculating the value of probabilities for each class
          UpperCamelCase :Dict										=     [
              calculate_probabilities(counts[i]  ,						sum(SCREAMING_SNAKE_CASE__ ) ) for i in range(SCREAMING_SNAKE_CASE__ )
          ]
  
          # for loop iterates over number of elements in 'probabilities' list and print
          # out them in separated line
          for i, probability in enumerate(SCREAMING_SNAKE_CASE__  ,						1 ):
              print(F'''Probability of class_{i} is: {probability}''' )
          print('''-''' * 100 )
  
          # Calculating the values of variance for each class
          UpperCamelCase :int										=     calculate_variance(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						sum(SCREAMING_SNAKE_CASE__ ) )
          print('''Variance: '''  ,						SCREAMING_SNAKE_CASE__ )
          print('''-''' * 100 )
  
          # Predicting Y values
          # storing predicted Y values in 'pre_indexes' variable
          UpperCamelCase :Any										=     predict_y_values(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ )
          print('''-''' * 100 )
  
          # Calculating Accuracy of the model
          print(F'''Accuracy: {accuracy(SCREAMING_SNAKE_CASE__  ,						SCREAMING_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()
  
  
  
  
  
  