"""simple docstring"""







def _a			(		):







      """simple docstring"""






      return [list(range(1000 - i				,	-1000 - i				,	-1							)							) for i in range(1000							)]


_UpperCamelCase					      =  generate_large_matrix()
_UpperCamelCase					      =  (
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)

def _a			(		_snake_case							):







      """simple docstring"""






      assert all(row == sorted(_snake_case				,	reverse=_snake_case							) for row in grid							)
      assert all(list(_snake_case							) == sorted(_snake_case				,	reverse=_snake_case							) for col in zip(*_snake_case							)							)

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	0
      UpperCAmelCase          =	len(_snake_case							) - 1

      # Edge cases such as no values or all numbers are negative.
      if not array or array[0] < 0:
            return 0

      while right + 1 > left:
            UpperCAmelCase          =	(left + right) // 2
            UpperCAmelCase          =	array[mid]

            # Num must be negative and the index must be greater than or equal to 0.
            if num < 0 and array[mid - 1] >= 0:
                  return mid

            if num >= 0:
                  UpperCAmelCase          =	mid + 1
            else:
                  UpperCAmelCase          =	mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
      return len(_snake_case							)

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	0
      UpperCAmelCase          =	len(grid[0]							)

      for i in range(len(_snake_case							)							):
            UpperCAmelCase          =	find_negative_index(grid[i][:bound]							)
            total += bound
      return (len(_snake_case							) * len(grid[0]							)) - total

def _a			(		_snake_case							):







      """simple docstring"""






      return len([number for row in grid for number in row if number < 0]							)

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	0
      for row in grid:
            for i, number in enumerate(_snake_case							):
                  if number < 0:
                        total += len(_snake_case							) - i
                        break
      return total

def _a			(		):







      """simple docstring"""






      from timeit import timeit

      print("""Running benchmarks"""							)
      UpperCAmelCase          =	(
          """from __main__ import count_negatives_binary_search, """
          """count_negatives_brute_force, count_negatives_brute_force_with_break, grid"""
      )
      for func in (
          "count_negatives_binary_search",  # took 0.7727 seconds
          "count_negatives_brute_force_with_break",  # took 4.6505 seconds
          "count_negatives_brute_force",  # took 12.8160 seconds
      ):
            UpperCAmelCase          =	timeit(F'''{func}(grid=grid)'''				,	setup=_snake_case				,	number=500							)
            print(F'''{func}() took {time:0.4f} seconds'''							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  benchmark()











"""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			(		):







      """simple docstring"""






      plt.scatter(_snake_case				,	_snake_case				,	color="""red"""							)
      plt.plot(_snake_case				,	pol_reg.predict(poly_reg.fit_transform(_snake_case							)							)				,	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



