def __init__(self, *args, **kwargs):
# parse parameters correctly
param = parseParameters(args, kwargs)
# set default parameters
self.name = 'NakaRushton Distribution'
self.param = {
'sigma': 1.0,
'kappa': 2.0,
's': 1.0,
'n': 2.0,
'p': 2.0,
'gamma': 2.0,
'delta': 0.0
}
if param != None:
for k in param.keys():
self.param[k] = float(param[k])
if param is None or 's' not in param.keys():
self.param['s'] = .5 * (gammafunc(1.0 / self.param['p']) /
gammafunc(3.0 / self.param['p']))**(
self.param['p'] / 2.0)
if self.param['delta'] == 0.0:
self.primary = [
'sigma'
] # gamma would also be possible here, but stays out for backwards compatibility reasons
else:
self.primary = ['sigma', 'kappa', 'gamma', 'delta']
def __init__(self, *args, **kwargs):
# parse parameters correctly
self.param = {'f': None, 'finv': None, 'dfinvdy': None, 'q': None}
param = parseParameters(args, kwargs)
for k, v in param.items():
self.param[k] = v
# set default parameters
self.name = 'Transformed %s Distribution' % (self.param['q'].name, )
self.primary = []
def __init__(self, *args,**kwargs):
# parse parameters correctly
self.param = {'f':None,'finv':None,'dfinvdy':None,'q':None}
param = parseParameters(args,kwargs)
for k,v in param.items():
self.param[k] = v
# set default parameters
self.name = 'Transformed %s Distribution' % (self.param['q'].name,)
self.primary = []
def __init__(self, *args,**kwargs):
# parse parameters correctly
param = parseParameters(args,kwargs)
# set default parameters
self.name = 'Truncated Exponential Power Distribution'
self.param = {'p':1.0,'s':1.0,'a':0.0,'b':1.0}
if param != None:
for k in param.keys():
self.param[k] = float(param[k])
self.primary = ['p','s']
def __init__(self, *args, **kwargs):
# parse parameters correctly
param = parseParameters(args, kwargs)
# set default parameters
self.name = 'TModelRadial Distribution'
self.param = {'beta': 1.0, 'alpha': 1.0, 'n': 2}
if param != None:
for k in param.keys():
self.param[k] = float(param[k])
self.primary = ['alpha', 'beta']
def __init__(self, *args,**kwargs):
# parse parameters correctly
param = parseParameters(args,kwargs)
# set default parameters
self.name = 'SkewedGaussian Distribution'
self.param = {'mu':1.0,'sigma':1.0,'alpha':1.0}
if param != None:
for k in param.keys():
self.param[k] = float(param[k])
self.primary = ['mu','sigma','alpha']
def __init__(self, *args, **kwargs):
# parse parameters correctly
param = parseParameters(args, kwargs)
# set default parameters
self.name = 'Kumaraswamy Distribution'
self.param = {'a': 1.0, 'b': 1.0, 'B': 1.0}
if param != None:
for k in param.keys():
self.param[k] = float(param[k])
self.primary = ['a', 'b']
def __init__(self, *args,**kwargs):
# parse parameters correctly
param = parseParameters(args,kwargs)
# set default parameters
self.name = 'TModelRadial Distribution'
self.param = {'beta':1.0,'alpha':1.0,'n':2}
if param != None:
for k in param.keys():
self.param[k] = float(param[k])
self.primary = ['alpha','beta']
def __init__(self, *args,**kwargs):
# parse parameters correctly
param = parseParameters(args,kwargs)
# set default parameters
self.name = 'Kumaraswamy Distribution'
self.param = {'a':1.0,'b':1.0,'B':1.0}
if param != None:
for k in param.keys():
self.param[k] = float(param[k])
self.primary = ['a','b']
def __init__(self, *args, **kwargs):
# parse parameters correctly
param = parseParameters(args, kwargs)
# set default parameters
self.name = 'Truncated Exponential Power Distribution'
self.param = {'p': 1.0, 's': 1.0, 'a': 0.0, 'b': 1.0}
if param != None:
for k in param.keys():
self.param[k] = float(param[k])
self.primary = ['p', 's']
def __init__(self, *args, **kwargs):
param = parseParameters(args,kwargs)
# set default parameters
self.name = 'Beta Distribution'
self.param = {'alpha':1.0,'beta':1.0}
if param is not None:
for k in param.keys():
self.param[k] = float(param[k])
self.primary = ['alpha','beta']
def __init__(self, *args,**kwargs):
# parse parameters correctly
# (special.gamma(1.0/p)/special.gamma(3.0/p))**(p/2.0)
param = parseParameters(args,kwargs)
# set default parameters
self.param = {'n':2, 'p':2.0}
self.name = 'PCauchy Distribution'
if param != None:
for k in param.keys():
self.param[k] = float(param[k])
self.primary = ['p']
def __init__(self, *args, **kwargs):
# parse parameters correctly
# (special.gamma(1.0/p)/special.gamma(3.0/p))**(p/2.0)
param = parseParameters(args, kwargs)
# set default parameters
self.param = {'n': 2, 'p': 2.0}
self.name = 'PCauchy Distribution'
if param != None:
for k in param.keys():
self.param[k] = float(param[k])
self.primary = ['p']
def __init__(self, *args,**kwargs):
self.numericalSigmaBoundary = 6.0
# parse parameters correctly
param = parseParameters(args,kwargs)
# set default parameters
self.name = 'TruncatedGaussian Distribution'
self.param = {'a':0.0,'b':1.0,'mu':1.0,'sigma':1.0}
if param != None:
for k,v in param.items():
self[k] = float(v)
self.primary = ['mu','sigma']
def __init__(self, *args, **kwargs):
# parse parameters correctly
param = parseParameters(args, kwargs)
# set default parameters
self.name = 'Histogram Distribution'
self.param = {'p': None, 'b': None}
if param != None:
for k in param.keys():
self.param[k] = param[k]
self.primary = ['p', 'b']
def __init__(self, *args,**kwargs):
# parse parameters correctly
param = parseParameters(args,kwargs)
# set default parameters
self.name = 'Histogram Distribution'
self.param = {'p':None,'b':None}
if param != None:
for k in param.keys():
self.param[k] = param[k]
self.primary = ['p','b']
def __init__(self, *args,**kwargs):
# parse parameters correctly
# (special.gamma(1.0/p)/special.gamma(3.0/p))**(p/2.0)
param = parseParameters(args,kwargs)
# set default parameters
self.param = {'n':2, 'p':2.0}
self.name = 'ChiP Distribution'
if param != None:
for k in param.keys():
self.param[k] = float(param[k])
if not self.param.has_key('s'):
self.param['s'] = (special.gamma(1.0/self.param['p'])/special.gamma(3.0/self.param['p']))**(self.param['p']/2.0)
self.primary = ['s']
self.baseDist = GammaP({'u':(self.param['n']/float(self.param['p'])),'s':self.param['s'],'p':self.param['p']})
def __init__(self, *args,**kwargs):
# parse parameters correctly
param = parseParameters(args,kwargs)
# set default parameters
self.name = 'NakaRushton Distribution'
self.param = {'sigma':1.0,'kappa':2.0,'s':1.0,'n':2.0,'p':2.0,'gamma':2.0,'delta':0.0}
if param != None:
for k in param.keys():
self.param[k] = float(param[k])
if param is None or 's' not in param.keys():
self.param['s'] = .5*(gammafunc(1.0/self.param['p'])/gammafunc(3.0/self.param['p']))**(self.param['p']/2.0)
if self.param['delta'] == 0.0:
self.primary = ['sigma'] # gamma would also be possible here, but stays out for backwards compatibility reasons
else:
self.primary = ['sigma','kappa','gamma','delta']
def __init__(self,*args, **kwargs):
# set initial parameters
param = parseParameters(args,kwargs)
self.param = {}
if param is not None:
for k,v in param.items():
self[k] = v
if not self.param.has_key('P'):
self.param['P'] = [Gaussian(n=1,mu=randn(1)*3.0) for dummy in xrange(3)]
K = len(self.param['P'])
if not self.param.has_key('alpha'):
self['alpha'] = ones(K)/K
self.name = "Finite mixture of %s distributions" % (", ".join(unique(array([p.name for p in self['P']],dtype=object))),)
self.primary = ['alpha','P']
def __init__(self, *args, **kwargs):
# parse parameters correctly
# (special.gamma(1.0/p)/special.gamma(3.0/p))**(p/2.0)
param = parseParameters(args, kwargs)
# set default parameters
self.param = {'n': 2, 'p': 2.0}
self.name = 'ChiP Distribution'
if param != None:
for k in param.keys():
self.param[k] = float(param[k])
if not self.param.has_key('s'):
self.param['s'] = (special.gamma(1.0 / self.param['p']) /
special.gamma(3.0 / self.param['p']))**(
self.param['p'] / 2.0)
self.primary = ['s']
self.baseDist = GammaP({
'u': (self.param['n'] / float(self.param['p'])),
's': self.param['s'],
'p': self.param['p']
})