mkingopng

import random


x = int(input("What's your input: "))
y = random.randint(5, 20)

x_payoff = []
y_payoff = []

if x > y:

mkingopng

from matplotlib import pyplot as plt
import seaborn as sns
import numpy as np
from matplotlib.pyplot import axvline
from scipy.stats import skewnorm, rv_histogram

data = skewnorm.rvs(5, loc=100, scale=200, size=1000).astype(int)  # create some random data

ax = sns.distplot(data, kde_kws={'label': 'kde of given data'}, label='histogram')  # draw histogram & kde of data
params = skewnorm.fit(data, 10, loc=80, scale=40)  # parameters to fit a skewnorm to the data

mkingopng

import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots()
x = np.linspace(0, 50, 100)

# plot the first function
plt.plot(x,  -0.5*x + 24, '-r', label="Firm 1's best response function")

# plot the second function

mkingopng

import nashpy as nash
import numpy as np

row = np.array([[10, 0], [0, 0]])
column = np.array([[10, 0], [0, 0]])
meeting_game = nash.Game(row, column)
print(meeting_game)
equilibria = meeting_game.support_enumeration()
for eq in equilibria:
    print(f"The unique Nash equilibrium is {eq}")

mkingopng

import nashpy as nash
import numpy as np

row = np.array([[90, 120, 120], [80, 120, 160], [100, 100, 150]])
column = np.array([[90, 80, 100], [120, 120, 100], [120, 160, 150]])
bars_v2 = nash.Game(row, column)
print(bars_v2)
equilibria = bars_v2.support_enumeration()
for eq in equilibria:
    print(f"The unique Nash equilibrium is {eq}")

mkingopng

import nashpy as nash
import numpy as np

row = np.array([[-3, 0], [-5, -1]])
column = np.array([[-3, -5], [0, -1]])
prisoners_dilemma = nash.Game(row, column)
print(prisoners_dilemma)
equilibria = prisoners_dilemma.support_enumeration()
for eq in equilibria:
    print(f"The unique Nash equilibrium is {eq}")

mkingopng

import numpy as np
from scipy.misc import derivative
import matplotlib.pyplot as plt

# define the parabola
# the quadratic function, which is y = -x^2 + 2.5x + 6 here
def function(x):
    return -x ** 2 + 2.5 * x + 6

# define the parabola derivative

mkingopng

import pandas as pd
import glob

full_month_file = open('result.txt')
directory = '../data/*.txt'


def read_mpc_txt_files():
    read_files = glob.glob(directory)
    with open('result.txt', 'wb') as outfile:

mkingopng

recency,history,used_discount,used_bogo,zip_code,is_referral,channel,offer,conversion
10,142.44,1,0,Surburban,0,Phone,Buy One Get One,0
6,329.08,1,1,Rural,1,Web,No Offer,0
7,180.65,0,1,Surburban,1,Web,Buy One Get One,0
9,675.83,1,0,Rural,1,Web,Discount,0
2,45.34,1,0,Urban,0,Web,Buy One Get One,0
6,134.83,0,1,Surburban,0,Phone,Buy One Get One,1
9,280.2,1,0,Surburban,1,Phone,Buy One Get One,0
9,46.42,0,1,Urban,0,Phone,Buy One Get One,0
9,675.07,1,1,Rural,1,Phone,Discount,0

mkingopng