def test_add(self):
binomial_one = Binomial(.4, 20)
binomial_two = Binomial(.4, 60)
binomial_sum = binomial_one + binomial_two
self.assertEqual(binomial_sum.p, .4)
self.assertEqual(binomial_sum.n, 80)
def estimate():
K = float(request.form['k'])
T = float(request.form['t'])
S0 = float(request.form['s0'])
sigma = float(request.form['sigma'])
r = float(request.form['r'])
q = float(request.form['q'])
N = int(request.form['n'])
Option = "P"
if len(request.form.getlist('option')) != 0:
Option = "C"
Exercise = "E"
if len(request.form.getlist('exercise')) != 0:
Exercise = "A"
price = round(Binomial(Option, K, T, S0, sigma, r, q, N, Exercise), 2)
return render_template("options.html", price=price)
def to_binomial(self):
return Binomial(n=1, p=self.p)
from binomial import Binomial
from exponential import Exponential
from geometric import Geometric
from poisson import Poisson
from discrete import Discrete
from continuous import Continuous
user = ''
while (user != 'q' and user != 'quit'):
problem = input("What type of random variable\n")
prompt = "Preform what operation: expected, variance"
if problem == 'q':
break
elif (problem == "binomial"):
probability = float(input("What is the probability?\n"))
problem = Binomial(probability)
prompt += ", probablityofsuccess(n,k)"
prompt += ", printTable"
elif problem == "exponential":
probability = float(input("What is the lambda?\n"))
problem = Exponential(1 / 2)
elif problem == "poisson":
probability = float(input("What is the lambda?\n"))
problem = Poisson(probability)
prompt += ", probablityofsuccess(n,k)"
prompt += ", printTable"
elif problem == "discrete":
probability = float(input("What is the probability?\n"))
problem = Discrete(probability)
elif problem == "geometric":
probability = float(input("What is the probability?\n"))
def setUp(self):
self.binomial = Binomial(0.4, 20)
self.binomial.read_data_file('numbers_binomial.txt')
self.short = "Joint"
self.inp = []
self.parameters = []
for i, pmf in enumerate(self.pmfs):
# TODO add indexes to repeats
self.inp += [inp + str(i + 1) for inp in pmf.inp]
self.parameters += [
parameter + str(i + 1) for parameter in pmf.parameters
]
def distribution(self, *args):
if len(args) != self.dim:
raise TypeError(f"expected {self.dim} arguments, got {len(args)}")
accum = 1
i = 0
for pmf in self.pmfs:
accum *= pmf.distribution(*(args[i:i + pmf.dim]))
i += pmf.dim
return accum
if __name__ == "__main__":
from binomial import Binomial
X = Binomial(10, 0.5)
Y = Binomial(10, 0.3)
#J = JointPMF(X, Y)
J = X * Y
J.plot()
