def ksmetric(self, **kwargs): """ Return the kolmogorov-smirnov metric for lognormal Input: **kwargs: points = [set of points to compute the cdf] -or- cdf = [Already computed cdf] Output: ks metric """ c = None if "cdf" in kwargs: c = kwargs["cdf"] else: p = kwargs["points"] p.sort() c = util.ecdf(p, issorted=True) y = self.cdf(c[:,0]) return util.kstest(c[:,1],c[:,2],y)
def ksmetric(self, **kwargs):
"""
Return the kolmogorov-smirnov metric for lognormal
Input:
**kwargs:
points = [set of points to compute the cdf]
-or-
cdf = [Already computed cdf]
Output:
ks metric
"""
c = None
if "cdf" in kwargs:
c = kwargs["cdf"]
else:
p = kwargs["points"]
p.sort()
c = util.ecdf(p, issorted=True)
y = self.cdf(c[:, 0])
return util.kstest(c[:, 1], c[:, 2], y)
def ksmetric(self, **kwargs): """ Return the ks metris for truncated pareto Input: **kwargs: points = [set of points] -or- cdf = [Precomputed cdf] Output: KS metric """ c = None if "cdf" in kwargs: c = kwargs["cdf"] else: p = kwargs["points"] p.sort() c = util.ecdf(p, issorted=True) y = self.cdf(c[:,0]) return util.kstest(c[:,1],c[:,2],y)
def ksmetric(self, **kwargs):
"""
Return the ks metris for truncated pareto
Input:
**kwargs:
points = [set of points]
-or-
cdf = [Precomputed cdf]
Output:
KS metric
"""
c = None
if "cdf" in kwargs:
c = kwargs["cdf"]
else:
p = kwargs["points"]
p.sort()
c = util.ecdf(p, issorted=True)
y = self.cdf(c[:, 0])
return util.kstest(c[:, 1], c[:, 2], y)