





'''simple docstring'''






import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
__UpperCamelCase					    =	pd.read_csv(
    "https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
    "position_salaries.csv"
)
__UpperCamelCase					    =	dataset.iloc[:, 1:2].values
__UpperCamelCase					    =	dataset.iloc[:, 2].values


__UpperCamelCase    ,		__UpperCamelCase    ,		__UpperCamelCase    ,		__UpperCamelCase					    =	train_test_split(X, y, test_size=0.2, random_state=0)


__UpperCamelCase					    =	PolynomialFeatures(degree=4)
__UpperCamelCase					    =	poly_reg.fit_transform(X)
__UpperCamelCase					    =	LinearRegression()
pol_reg.fit(X_poly, y)







def 					_a						(       ) ->				Tuple:



			"""simple docstring"""


			plt.scatter(_lowerCamelCase    ,	_lowerCamelCase    ,	color="""red""" )
			plt.plot(_lowerCamelCase    ,	pol_reg.predict(poly_reg.fit_transform(_lowerCamelCase ) )    ,	color="""blue""" )
			plt.title("""Truth or Bluff (Linear Regression)""" )
			plt.xlabel("""Position level""" )
			plt.ylabel("""Salary""" )
			plt.show()


if __name__ == "__main__":
	viz_polymonial()

	# Predicting a new result with Polymonial Regression
	pol_reg.predict(poly_reg.fit_transform([[5.5]]))
	# output should be 132148.43750003







"""simple docstring"""

from typing import TYPE_CHECKING

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


A_						:    str				=      {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    Optional[int]				=      [
							    'UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'UniSpeechForCTC',
							    'UniSpeechForPreTraining',
							    'UniSpeechForSequenceClassification',
							    'UniSpeechModel',
							    'UniSpeechPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_unispeech import (
														    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
														    UniSpeechForCTC,
														    UniSpeechForPreTraining,
														    UniSpeechForSequenceClassification,
														    UniSpeechModel,
														    UniSpeechPreTrainedModel,
														)

else:
							import sys

							A_						:    int				=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





