def __init__(self, _OD, _paramsets={'C':100,'gamma':0.1}, grid=[0.1,1,10], parallel = True, train=True, verbose=True, log=None):
g={}
if type(_OD) is types.ListType:
ExpectedDistribution.__init__(self, _OD[0], _paramsets, parallel, train=False)
g['OD'] = _OD
else:
ExpectedDistribution.__init__(self, _OD, _paramsets, parallel, train=False)
self.verbose = verbose
self.log = log
if type(grid) is dict:
for param in self.params[0.5]: #gridsearch currently only supports EDs with paramsets uniform across contours
g[param] = np.atleast_1d(grid[param])*self.params[0.5][param]
else:
grid = np.atleast_1d(grid)
for param in self.params[0.5]: #gridsearch currently only supports EDs with paramsets uniform across contours
g[param] = grid*self.params[0.5][param]
self.grid = ParameterGrid(g)
if train:
self.train()
