def test_pdf(self):
# Calculate standard normal pdf for x=1
a = pdf(1)
self.assertAlmostEqual(a, 0.24197072451914337)
# Calculate standard normal pdf for x=(-1)
a = pdf(-1)
self.assertAlmostEqual(a, 0.24197072451914337)
# Calculate standard normal pdf for x=13, mean=10, std_dev=1
a = pdf(x=13, mean=10, std_dev=1)
self.assertAlmostEqual(a, 0.004431848411938008)
def test_pdf(self):
# Calculate standard normal pdf for x=1
a = pdf(1)
self.assertAlmostEqual(a, 0.24197072451914337)
# Calculate standard normal pdf for x=(-1)
a = pdf(-1)
self.assertAlmostEqual(a, 0.24197072451914337)
# Calculate standard normal pdf for x=13, mean=10, std_dev=1
a = pdf(x=13, mean=10, std_dev=1)
self.assertAlmostEqual(a, 0.004431848411938008)
def cdf(x, iterations=300):
product = 1.0
taylor_exp = [x]
for i in range(3, iterations, 2):
product *= i
taylor_exp.append(float(x**i) / product)
taylor_fact = sum(taylor_exp)
return (0.5 + (taylor_fact * std_normal_pdf.pdf(x, mean=0, std_dev=1)))
def cdf(x, iterations=300):
product = 1.0
taylor_exp = [x]
for i in range(3, iterations, 2):
product *= i
taylor_exp.append(float(x**i)/product)
taylor_fact = sum(taylor_exp)
return (0.5 + (taylor_fact * std_normal_pdf.pdf(x, mean=0, std_dev=1)))
def mth_operations_1_input(f_code, v):
if f_code == 1:
res = approx_cdf.cdf(v)
return res
elif f_code == 4:
res = primality_test.is_prime(v)
return res
elif f_code == 5:
res = sieve_atkin.atkin(v)
return res
elif f_code == 6:
res = sieve_eratosthenes.eratosthenes(v)
return res
elif f_code == 7:
res = std_normal_pdf.pdf(v)
return res
def cdf(x, iterations=300):
"""
Calculates the cumulative distribution function of the normal distribution.
Uses a taylor exponent to calculate this.
:param x: An integer that represents the taylor exponent.
:param iterations: An integer representing the number of iterations.
:rtype: The normal distribution
"""
product = 1.0
taylor_exp = [x]
for i in range(3, iterations, 2):
product *= i
taylor_exp.append(float(x**i)/product)
taylor_fact = sum(taylor_exp)
return 0.5 + (taylor_fact * std_normal_pdf.pdf(x, mean=0, std_dev=1))
def cdf(x, iterations=300):
"""
Calculates the cumulative distribution function of the normal distribution.
Uses a taylor exponent to calculate this.
:param x: An integer that represents the taylor exponent.
:param iterations: An integer representing the number of iterations.
:rtype: The normal distribution
"""
product = 1.0
taylor_exp = [x]
for i in range(3, iterations, 2):
product *= i
taylor_exp.append(float(x**i)/product)
taylor_fact = sum(taylor_exp)
return (0.5 + (taylor_fact * std_normal_pdf.pdf(x, mean=0, std_dev=1)))
