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


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


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







def  _UpperCamelCase (     ):




    """simple docstring"""



    plt.scatter(_a				,     _a				,     color='red'			)
    plt.plot(_a				,     pol_reg.predict(poly_reg.fit_transform(_a			)			)				,     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_torch_available


a=  {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  [
      '''VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''ViTMSNModel''',
      '''ViTMSNForImageClassification''',
      '''ViTMSNPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vit_msn import (
        VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
        ViTMSNForImageClassification,
        ViTMSNModel,
        ViTMSNPreTrainedModel,
    )

else:
  import sys

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



