





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


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


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






def 					SCREAMING_SNAKE_CASE__				(							):
		plt.scatter(UpperCamelCase__       , UpperCamelCase__       , color="""red"""     )
		plt.plot(UpperCamelCase__       , pol_reg.predict(poly_reg.fit_transform(UpperCamelCase__     )     )       , 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






from torch import nn






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str     ):
		if act_fn in ["swish", "silu"]:
				return nn.SiLU()
		elif act_fn == "mish":
				return nn.Mish()
		elif act_fn == "gelu":
				return nn.GELU()
		else:
				raise ValueError(f'''Unsupported activation function: {act_fn}'''     )