def quot_chi_theor(n1, n2, x):
n1 = float(n1)
n2 = float(n2)
t1 = n1/2
t2 = n2/2
ly = lgamma(t1+t2) + t1*log(t1/4) + t2*log(t2/4) + (2*t1-1)*log(x)
ly -= lgamma(t1) + lgamma(t2) + (t1+t2)*log(t1/4*x*x+t2/4)
return 2*exp(ly)
def quot_chi_theor(n1, n2, x):
n1 = float(n1)
n2 = float(n2)
t1 = n1/2
t2 = n2/2
ly = lgamma(t1+t2) + t1*log(t1/4) + t2*log(t2/4) + (2*t1-1)*log(x)
ly -= lgamma(t1) + lgamma(t2) + (t1+t2)*log(t1/4*x*x+t2/4)
return 2*exp(ly)
def theor_quot_gamma(b1, b2, x):
lnorm = lgamma(b1 + b2) - lgamma(b1) - lgamma(b2)
return x**(b1 - 1) * (1 + x)**(-b1 - b2) * exp(lnorm)
def f_disrt_v2(df1, df2, x):
df1 = float(df1)
df2 = float(df2)
norm = exp(lgamma((df1 + df2) / 2) - lgamma(df1 / 2) - lgamma(df2 / 2))
y = norm * x**(df1 / 2 - 1) / (x + 1)**((df1 + df2) / 2)
return y
def theor_quot_gamma(b1, b2, x):
lnorm = lgamma(b1 + b2) - lgamma(b1) - lgamma(b2)
return x**(b1-1)*(1+x)**(-b1-b2)*exp(lnorm)
def f_disrt_v2(df1, df2, x):
df1 = float(df1)
df2 = float(df2)
norm = exp(lgamma((df1 + df2) / 2) - lgamma(df1 / 2) - lgamma(df2 / 2))
y = norm * x ** (df1 / 2 - 1) / (x + 1) ** ((df1 + df2) / 2)
return y
def gr1(m, p, p0, r, x):
ly = lgamma(float(p + r - 1) / m) - lgamma(float(p0 + r - 1) / m) - lgamma(
float(p - p0) / m) + (p0 + r - 1) * log(x)
y = m * exp(ly) * (1.0 - x**m)**(float(p - p0) / m - 1)
return y
def gen_f(p, m, a, h, x):
h = float(h)
logk = p / h * log(a) + lgamma(m) - lgamma(p / h) - lgamma(m - p / h)
k = h * exp(logk)
return k * x**(p - 1) / (1 + a * x**h)**m
def gr1(m, p, p0, r, x):
ly = lgamma(float(p+r-1)/m)-lgamma(float(p0+r-1)/m)-lgamma(float(p-p0)/m) + (p0+r-1)*log(x)
y = m * exp(ly)*(1.0-x**m) **(float(p-p0)/m - 1)
return y
def gen_f(p, m, a, h, x):
h = float(h)
logk = p/h*log(a)+lgamma(m)-lgamma(p/h)-lgamma(m-p/h)
k = h * exp(logk)
return k * x**(p-1) / (1 + a*x**h)**m
