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'] })