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()