seytwo

def pulp_to_dual(lp):

  # A x <= b

  # 変数の数
  n = len(lp.variables())

  # 制約の数
  m = 0
  for constraint in lp.constraints.values():

seytwo

import numpy as np
from simplex_method import SimplexMethod

class ReverseSearch:
  def __init__(self, round_digit=5):
    self.round_digit = round_digit
    return

  def solve(self, A, b, basis):

seytwo

import numpy as np
from simplex_method import SimplexMethod

class PhaseOneSimplexMethod:

  def __init__(self, round_digit=5):
    self.round_digit = round_digit
    return
    
  def solve(self, A, b):

seytwo

import numpy as np

class SimplexMethod:

  OPTIMAL = 1
  UNBOUNDED = -1

  def __init__(self, round_digit=5):
    self.round_digit = round_digit
    return

seytwo

import numpy as np
from matplotlib import pyplot as plt

# タイプ一覧（名称）
types_name = [
  'normal',
  'fire',
  'water',
  'electric',
  'grass',

seytwo

from itertools import product, combinations
import numpy as np
from numpy import linalg as la

def get_M_list(n):
  M_list = [[]]
  for _ in range(n):
    M_list_ = []
    for M in M_list:
      cones = get_cones(n, M)

seytwo

import numpy as np
import pulp

# 行列ゲーム
def solve_matrix_game(A):
  n_type = len(A)
  lp = pulp.LpProblem(sense=pulp.LpMaximize)
  v = pulp.LpVariable("v") 
  x = [ pulp.LpVariable("x_"+str(i)) for i in range(n_type) ]
  for j in range(n_type):

seytwo

import numpy as np
import pulp

# タイプ一覧（名称）
types_name = [
  'ノーマル',
  '炎',
  '水',
  '電気',
  '草',

seytwo

import numpy as np
import pulp

# タイプ一覧（名称）
types_name = [
  'ノーマル',
  '炎',
  '水',
  '電気',
  '草',

seytwo

from itertools import product

import numpy as np
from numpy import linalg as la

def bruteforce(q, M):

  n = len(q)

  I = np.identity(n)