def test(self):
self.v_i = dict()
for i in xrange(0, self.m):
max_number = 2 ** (i+1) - 1
tmp = []
for j in xrange(0, max_number + 1):
b = "{0:b}".format(j)
if len(b) < self.m:
b = "0"*(i + 1 - len(b)) + b
tmp.append(len(Utils.get_all_indexes(self.seq[i+1], b)))
self.v_i[i+1] = tmp
xsi, xsi_2 = self.__calc_xsi()
pv1 = igamc(2**(self.m - 2), xsi)
pv2 = 0
if xsi_2 is not None:
pv2 = igamc(2**(self.m - 3), xsi_2)
is_random = False if pv1 < 0.01 and pv2 < 0.01 else True
return (pv1, pv2, is_random)
def __test(self):
chi = 0
consts = LongestRunOfOnes.CONSTS[self.M]
for i in range(0, consts['k'] + 1):
tmp = self.blocks_count * consts['pi'][i]
chi += (self.v_i.get(i, 0) - tmp) ** 2 / tmp
self.chi_2 = chi
self.p_value = igamc(consts['k'] / 2.0, self.chi_2 / 2.0)
self.is_random = False if self.p_value < 0.01 else True
def pdtr(k, m):
"""Returns sum of left tail of Poisson distribution, 0 through k.
See Cephes docs for details.
"""
if k < 0:
raise ValueError, "Poisson k must be >= 0."
if m < 0:
raise ValueError, "Poisson m must be >= 0."
return igamc(k+1, m)
def pdtr(k, m):
"""Returns sum of left tail of Poisson distribution, 0 through k.
See Cephes docs for details.
"""
if k < 0:
raise ValueError, "Poisson k must be >= 0."
if m < 0:
raise ValueError, "Poisson m must be >= 0."
return igamc(k + 1, m)
def chi_high(x, df):
"""Returns right-hand tail of chi-square distribution (x to infinity).
df, the degrees of freedom, ranges from 1 to infinity (assume integers).
Typically, df is (r-1)*(c-1) for a r by c table.
Result ranges from 0 to 1.
See Cephes docs for details.
"""
x = fix_rounding_error(x)
if x < 0:
raise ValueError, "chi_high: x must be >= 0 (got %s)." % x
if df < 1:
raise ValueError, "chi_high: df must be >= 1 (got %s)." % df
return igamc(df/2, x/2)
def chi_high(x, df):
"""Returns right-hand tail of chi-square distribution (x to infinity).
df, the degrees of freedom, ranges from 1 to infinity (assume integers).
Typically, df is (r-1)*(c-1) for a r by c table.
Result ranges from 0 to 1.
See Cephes docs for details.
"""
x = fix_rounding_error(x)
if x < 0:
raise ValueError, "chi_high: x must be >= 0 (got %s)." % x
if df < 1:
raise ValueError, "chi_high: df must be >= 1 (got %s)." % df
return igamc(df / 2, x / 2)
def gdtrc(a, b, x):
"""Returns integral from x to inf of Gamma distribution with params a and b.
"""
if x < 0.0:
raise ZeroDivisionError, "x must be at least 0."
return igamc(b, a * x)
def gdtrc(a, b, x):
"""Returns integral from x to inf of Gamma distribution with params a and b.
"""
if x < 0.0:
raise ZeroDivisionError, "x must be at least 0."
return igamc(b, a * x)
