






import numpy

# List of input, output pairs
__UpperCAmelCase							:   Tuple							=  (
    ((5, 2, 3), 1_5),
    ((6, 5, 9), 2_5),
    ((1_1, 1_2, 1_3), 4_1),
    ((1, 1, 1), 8),
    ((1_1, 1_2, 1_3), 4_1),
)
__UpperCAmelCase							:   Optional[int]							=  (((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
__UpperCAmelCase							:   Any							=  [2, 4, 1, 5]
__UpperCAmelCase							:   int							=  len(train_data)
__UpperCAmelCase							:   Optional[int]							=  0.009





def 					lowercase_ (    __snake_case     :							Optional[Any]	,	__snake_case     :							Dict="train"      )     -> int:





				'''simple docstring'''


				return calculate_hypothesis_value(__A	,	__A      ) - output(
				    __A	,	__A      )





def 					lowercase_ (    __snake_case     :							Dict      )     -> List[str]:





				'''simple docstring'''


				snake_case__      :Any         =   0
				for i in range(len(__A      ) - 1      ):
								hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
				hyp_val += parameter_vector[0]
				return hyp_val





def 					lowercase_ (    __snake_case     :							Optional[int]	,	__snake_case     :							Union[str, Any]      )     -> Union[str, Any]:





				'''simple docstring'''


				if data_set == "train":
								return train_data[example_no][1]
				elif data_set == "test":
								return test_data[example_no][1]
				return None





def 					lowercase_ (    __snake_case     :							List[Any]	,	__snake_case     :							List[Any]      )     -> str:





				'''simple docstring'''


				if data_set == "train":
								return _hypothesis_value(train_data[example_no][0]      )
				elif data_set == "test":
								return _hypothesis_value(test_data[example_no][0]      )
				return None





def 					lowercase_ (    __snake_case     :							List[str]	,	__snake_case     :							Optional[int]=m      )     -> Optional[int]:





				'''simple docstring'''


				snake_case__      :Dict         =   0
				for i in range(__A      ):
								if index == -1:
												summation_value += _error(__A      )
								else:
												summation_value += _error(__A      ) * train_data[i][0][index]
				return summation_value





def 					lowercase_ (    __snake_case     :							Union[str, Any]      )     -> Dict:





				'''simple docstring'''


				snake_case__      :Optional[Any]         =   summation_of_cost_derivative(__A	,	__A      ) / m
				return cost_derivative_value





def 					lowercase_ (    )     -> Dict:





				'''simple docstring'''


				global parameter_vector
				# Tune these values to set a tolerance value for predicted output
				snake_case__      :str         =   0.0_0_0_0_0_2
				snake_case__      :Union[str, Any]         =   0
				snake_case__      :Dict         =   0
				while True:
								j += 1
								snake_case__      :Dict         =   [0, 0, 0, 0]
								for i in range(0	,	len(__A      )      ):
												snake_case__      :Tuple         =   get_cost_derivative(i - 1      )
												snake_case__      :int         =   (
												    parameter_vector[i] - LEARNING_RATE * cost_derivative
												)
								if numpy.allclose(
								    __A	,	__A	,	atol=__A	,	rtol=__A	,	):
												break
								snake_case__      :Optional[Any]         =   temp_parameter_vector
				print(("Number of iterations:", j)      )





def 					lowercase_ (    )     -> Optional[int]:





				'''simple docstring'''


				for i in range(len(__A      )      ):
								print(("Actual output value:", output(__A	,	"test"      ))      )
								print(("Hypothesis output:", calculate_hypothesis_value(__A	,	"test"      ))      )


if __name__ == "__main__":
	run_gradient_descent()
	print("\nTesting gradient descent for a linear hypothesis function.\n")
	test_gradient_descent()









import pytest


__UpperCAmelCase							:   int							=  "__dummy_dataset1__"

__UpperCAmelCase							:   int							=  "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n    def _info(self):\n        features = datasets.Features(\n            {\n                \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\n                \"ner_tags\": datasets.Sequence(\n                    datasets.features.ClassLabel(\n                        names=[\n                            \"O\",\n                            \"B-PER\",\n                            \"I-PER\",\n                            \"B-ORG\",\n                            \"I-ORG\",\n                            \"B-LOC\",\n                            \"I-LOC\",\n                        ]\n                    )\n                ),\n                \"langs\": datasets.Sequence(datasets.Value(\"string\")),\n                \"spans\": datasets.Sequence(datasets.Value(\"string\")),\n            }\n        )\n        return datasets.DatasetInfo(features=features)\n\n    def _split_generators(self, dl_manager):\n        dl_path = dl_manager.download(URLS)\n        return [\n            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),\n            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),\n        ]\n\n    def _generate_examples(self, filepath):\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\n            for i, line in enumerate(f):\n                yield i, json.loads(line)\n"





@pytest.fixture
def 					lowercase_ (    )     -> Optional[Any]:





				'''simple docstring'''


				return DATASET_LOADING_SCRIPT_NAME





@pytest.fixture
def 					lowercase_ (    )     -> Optional[int]:





				'''simple docstring'''


				return DATASET_LOADING_SCRIPT_CODE





@pytest.fixture
def 					lowercase_ (    __snake_case     :							Optional[int]	,	__snake_case     :							List[Any]	,	__snake_case     :							Any      )     -> Dict:





				'''simple docstring'''


				snake_case__      :Optional[Any]         =   dataset_loading_script_name
				snake_case__      :Optional[Any]         =   tmp_path / "datasets" / script_name
				script_dir.mkdir(parents=__snake_case      )
				snake_case__      :List[Any]         =   script_dir / F'{script_name}.py'
				with open(__snake_case	,	"w"      ) as f:
								f.write(__snake_case      )
				return str(__snake_case      )