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


lowerCAmelCase_    ,							lowerCAmelCase_    ,							lowerCAmelCase_    ,							lowerCAmelCase_:					Dict     =					train_test_split(X, y, test_size=0.2, random_state=0)


lowerCAmelCase_:					Any     =					PolynomialFeatures(degree=4)
lowerCAmelCase_:					Tuple     =					poly_reg.fit_transform(X)
lowerCAmelCase_:					str     =					LinearRegression()
pol_reg.fit(X_poly, y)







def 					__a   (		):






     '''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 ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
 if not (is_transformers_available() and is_torch_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
 from .notes_encoder import SpectrogramNotesEncoder
 from .continous_encoder import SpectrogramContEncoder
 from .pipeline_spectrogram_diffusion import (
     SpectrogramContEncoder,
     SpectrogramDiffusionPipeline,
     TaFilmDecoder,
 )

try:
 if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
 from .midi_utils import MidiProcessor




