"""simple docstring"""


import numpy as np
import requests





def       lowerCamelCase_			( )							-> Any:




						'''simple docstring'''



						lowerCamelCase__ =requests.get(
						    "https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"
						    "master/Week1/ADRvsRating.csv"    )
						lowerCamelCase__ =response.text.splitlines()
						lowerCamelCase__ =[]
						for item in lines:
												lowerCamelCase__ =item.split(","    )
												data.append(__lowerCAmelCase    )
						data.pop(0    )  # This is for removing the labels from the list
						lowerCamelCase__ =np.matrix(__lowerCAmelCase    )
						return dataset





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> List[str]:




						'''simple docstring'''



						lowerCamelCase__ =len_data

						lowerCamelCase__ =np.dot(__lowerCAmelCase       ,   data_x.transpose()    )
						prod -= data_y.transpose()
						lowerCamelCase__ =np.dot(__lowerCAmelCase       ,   __lowerCAmelCase    )
						lowerCamelCase__ =theta - (alpha / n) * sum_grad
						return theta





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> List[str]:




						'''simple docstring'''



						lowerCamelCase__ =np.dot(__lowerCAmelCase       ,   data_x.transpose()    )
						prod -= data_y.transpose()
						lowerCamelCase__ =np.sum(np.square(__lowerCAmelCase    )    )
						lowerCamelCase__ =sum_elem / (2 * len_data)
						return error





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Union[str, Any]:




						'''simple docstring'''



						lowerCamelCase__ =10_0000
						lowerCamelCase__ =0.000_1550

						lowerCamelCase__ =data_x.shape[1]
						lowerCamelCase__ =data_x.shape[0] - 1

						lowerCamelCase__ =np.zeros((1, no_features)    )

						for i in range(0       ,   __lowerCAmelCase    ):
												lowerCamelCase__ =run_steep_gradient_descent(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )
												lowerCamelCase__ =sum_of_square_error(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )
												print(F'''At Iteration {i + 1} - Error is {error:.5f}'''    )

						return theta





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Optional[Any]:




						'''simple docstring'''



						lowerCamelCase__ =sum(abs(y - predicted_y[i]    ) for i, y in enumerate(__lowerCAmelCase    )    )
						return total / len(__lowerCAmelCase    )





def       lowerCamelCase_			( )							-> Tuple:




						'''simple docstring'''



						lowerCamelCase__ =collect_dataset()

						lowerCamelCase__ =data.shape[0]
						lowerCamelCase__ =np.c_[np.ones(__lowerCAmelCase    ), data[:, :-1]].astype(__lowerCAmelCase    )
						lowerCamelCase__ =data[:, -1].astype(__lowerCAmelCase    )

						lowerCamelCase__ =run_linear_regression(__lowerCAmelCase       ,   __lowerCAmelCase    )
						lowerCamelCase__ =theta.shape[1]
						print("Resultant Feature vector : "    )
						for i in range(0       ,   __lowerCAmelCase    ):
												print(F'''{theta[0, i]:.5f}'''    )


if __name__ == "__main__":
	main()



"""simple docstring"""


from typing import TYPE_CHECKING

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


a							={
    'configuration_openai': ['OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenAIGPTConfig'],
    'tokenization_openai': ['OpenAIGPTTokenizer'],
}

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							=[
	    'OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'OpenAIGPTDoubleHeadsModel',
	    'OpenAIGPTForSequenceClassification',
	    'OpenAIGPTLMHeadModel',
	    'OpenAIGPTModel',
	    'OpenAIGPTPreTrainedModel',
	    'load_tf_weights_in_openai_gpt',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							=[
	    'TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFOpenAIGPTDoubleHeadsModel',
	    'TFOpenAIGPTForSequenceClassification',
	    'TFOpenAIGPTLMHeadModel',
	    'TFOpenAIGPTMainLayer',
	    'TFOpenAIGPTModel',
	    'TFOpenAIGPTPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
	from .tokenization_openai import OpenAIGPTTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_openai_fast import OpenAIGPTTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_openai import (
		    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    OpenAIGPTDoubleHeadsModel,
		    OpenAIGPTForSequenceClassification,
		    OpenAIGPTLMHeadModel,
		    OpenAIGPTModel,
		    OpenAIGPTPreTrainedModel,
		    load_tf_weights_in_openai_gpt,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_openai import (
		    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFOpenAIGPTDoubleHeadsModel,
		    TFOpenAIGPTForSequenceClassification,
		    TFOpenAIGPTLMHeadModel,
		    TFOpenAIGPTMainLayer,
		    TFOpenAIGPTModel,
		    TFOpenAIGPTPreTrainedModel,
		)

else:
	import sys

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


