
"""simple docstring"""





import numpy

# List of input, output pairs
UpperCamelCase_					:						Tuple				= (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
UpperCamelCase_					:						Tuple				= (((515, 22, 13), 555), ((61, 35, 49), 150))
UpperCamelCase_					:						List[str]				= [2, 4, 1, 5]
UpperCamelCase_					:						Tuple				= len(train_data)
UpperCamelCase_					:						Optional[Any]				= 0.009
def 							A_     (__a				,   __a="train"				):

					'''simple docstring'''





					return calculate_hypothesis_value(_lowerCamelCase				,   _lowerCamelCase				) - output(
					    _lowerCamelCase				,   _lowerCamelCase				)
def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						0
					for i in range(len(_lowerCamelCase				) - 1				):
										hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
					hyp_val += parameter_vector[0]
					return hyp_val
def 							A_     (__a				,   __a				):

					'''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 							A_     (__a				,   __a				):

					'''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 							A_     (__a				,   __a=m				):

					'''simple docstring'''





					A_				 =						0
					for i in range(_lowerCamelCase				):
										if index == -1:
															summation_value += _error(_lowerCamelCase				)
										else:
															summation_value += _error(_lowerCamelCase				) * train_data[i][0][index]
					return summation_value
def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						summation_of_cost_derivative(_lowerCamelCase				,   _lowerCamelCase				) / m
					return cost_derivative_value
def 							A_     ():

					'''simple docstring'''





					global parameter_vector
					# Tune these values to set a tolerance value for predicted output
					A_				 =						0.000002
					A_				 =						0
					A_				 =						0
					while True:
										j += 1
										A_				 =						[0, 0, 0, 0]
										for i in range(0				,   len(_lowerCamelCase				)				):
															A_				 =						get_cost_derivative(i - 1				)
															A_				 =						(
															    parameter_vector[i] - LEARNING_RATE * cost_derivative
															)
										if numpy.allclose(
										    _lowerCamelCase				,   _lowerCamelCase				,   atol=_lowerCamelCase				,   rtol=_lowerCamelCase				,   ):
															break
										A_				 =						temp_parameter_vector
					print(("Number of iterations:", j)				)
def 							A_     ():

					'''simple docstring'''





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


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






"""simple docstring"""





# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase_					:						str				= {'''configuration_timm_backbone''': ['''TimmBackboneConfig''']}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_					:						Union[str, Any]				= ['''TimmBackbone''']


if TYPE_CHECKING:
		from .configuration_timm_backbone import TimmBackboneConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_timm_backbone import TimmBackbone

else:
		import sys

		UpperCamelCase_					:						List[Any]				= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




