Selection.py 文件源码

def optimize(p0, data, model_func, sel_dist, theta, lower_bound=None,
             upper_bound=None, verbose=0, flush_delay=0.5, epsilon=1e-3, 
             gtol=1e-5, multinom=False, maxiter=None, full_output=False,
             func_args=[], func_kwargs={}, fixed_params=None, ll_scale=1,
             output_file=None):
    """
    optimizer for use with distributions where log transformations do not work,
    e.g. when gamma is positive and negative
    """
    if output_file:
        output_stream = file(output_file, 'w')
    else:
        output_stream = sys.stdout

    args = (data, model_func, sel_dist, theta, lower_bound, upper_bound, 
            verbose, multinom, flush_delay, func_args, func_kwargs,
            fixed_params, ll_scale, output_stream)

    p0 = _project_params_down(p0, fixed_params)
    outputs = scipy.optimize.fmin_bfgs(_object_func, p0, 
                                       epsilon=epsilon,
                                       args = args, gtol=gtol, 
                                       full_output=True,
                                       disp=False,
                                       maxiter=maxiter)
    xopt, fopt, gopt, Bopt, func_calls, grad_calls, warnflag = outputs
    xopt = Inference._project_params_up(xopt, fixed_params)

    if output_file:
        output_stream.close()

    if not full_output:
        return xopt
    else:
        return xopt, fopt, gopt, Bopt, func_calls, grad_calls, warnflag

##end of dadi.Inference code