print('f cdf for 100:', stats.f.cdf(100,1,10))
print('sqt stats:', squaretg.stats(10, moments='mvsk'))
print('f stats:', stats.f.stats(1,10, moments='mvsk'))
#Note the results differ for skew and kurtosis. I think the 3rd and 4th moment
# in the scipy.stats.f distribution is incorrect.
# I corrected it now in stats.distributions.py in bzr branch
v1=1
v2=10
g1 = 2*(v2+2*v1-2.)/(v2-6.)*np.sqrt(2*(v2-4.)/(v1*(v2+v1-2.)))
g2 = 3/(2.*v2-16)*(8+g1*g1*(v2-6.))
print('corrected skew, kurtosis of f(1,10) is', g1, g2)
print(squarenormalg.rvs())
print(squarenormalg.rvs(size=(2,4)))
print('sqt random variables')
print(stats.f.rvs(1,10,size=4))
print(squaretg.rvs(10,size=4))
#a large number check:
np.random.seed(464239857)
rvstsq = squaretg.rvs(10,size=100000)
squaretg.moment(4,10)
(rvstsq**4).mean()
squaretg.moment(3,10)
(rvstsq**3).mean()
squaretg.stats(10, moments='mvsk')
stats.describe(rvstsq)
'''
>>> np.random.seed(464239857)
>>> rvstsq = squaretg.rvs(10,size=100000)
>>> squaretg.moment(4,10)
print('sqt stats:', squaretg.stats(10, moments='mvsk'))
print('f stats:', stats.f.stats(1, 10, moments='mvsk'))
#Note the results differ for skew and kurtosis. I think the 3rd and 4th moment
# in the scipy.stats.f distribution is incorrect.
# I corrected it now in stats.distributions.py in bzr branch
v1 = 1
v2 = 10
g1 = 2 * (v2 + 2 * v1 - 2.) / (v2 - 6.) * np.sqrt(2 * (v2 - 4.) /
(v1 * (v2 + v1 - 2.)))
g2 = 3 / (2. * v2 - 16) * (8 + g1 * g1 * (v2 - 6.))
print('corrected skew, kurtosis of f(1,10) is', g1, g2)
print(squarenormalg.rvs())
print(squarenormalg.rvs(size=(2, 4)))
print('sqt random variables')
print(stats.f.rvs(1, 10, size=4))
print(squaretg.rvs(10, size=4))
#a large number check:
np.random.seed(464239857)
rvstsq = squaretg.rvs(10, size=100000)
squaretg.moment(4, 10)
(rvstsq**4).mean()
squaretg.moment(3, 10)
(rvstsq**3).mean()
squaretg.stats(10, moments='mvsk')
stats.describe(rvstsq)
'''
>>> np.random.seed(464239857)
>>> rvstsq = squaretg.rvs(10,size=100000)
>>> squaretg.moment(4,10)
2734.3750000000009