def _object_func(params, data, model_func, sel_dist, theta, lower_bound=None, upper_bound=None, verbose=0, multinom=False, flush_delay=0, func_args=[], func_kwargs={}, fixed_params=None, ll_scale=1, output_stream=sys.stdout, store_thetas=False): """ Objective function for optimization. """ global _counter _counter += 1 # Deal with fixed parameters params_up = Inference._project_params_up(params, fixed_params) # Check our parameter bounds if lower_bound is not None: for pval, bound in zip(params_up, lower_bound): if bound is not None and pval < bound: return -_out_of_bounds_val / ll_scale if upper_bound is not None: for pval, bound in zip(params_up, upper_bound): if bound is not None and pval > bound: return -_out_of_bounds_val / ll_scale ns = data.sample_sizes all_args = [params_up, ns, sel_dist, theta] + list(func_args) sfs = model_func(*all_args, **func_kwargs) if multinom: result = Inference.ll_multinom(sfs, data) else: result = Inference.ll(sfs, data) if store_thetas: global _theta_store _theta_store[tuple(params)] = optimal_sfs_scaling(sfs, data) # Bad result if numpy.isnan(result): result = _out_of_bounds_val if (verbose > 0) and (_counter % verbose == 0): param_str = 'array([%s])' % (', '.join( ['%- 12g' % v for v in params_up])) output_stream.write('%-8i, %-12g, %s%s' % (_counter, result, param_str, os.linesep)) Misc.delayed_flush(delay=flush_delay) return -result / ll_scale
grad_temp = get_grad(func, numpy.log(p0), eps) J_temp = numpy.outer(grad_temp, grad_temp) J = J + J_temp J = J/len(all_boot) J_inv = numpy.linalg.inv(J) # G = H*J^-1*H godambe = numpy.dot(numpy.dot(hess, J_inv), hess) return godambe, hess def uncert(func_ex, all_boot, p0, data, eps, log=True): godambe, hess = get_godambe(func_ex, all_boot, p0, data, eps, log) return numpy.sqrt(numpy.diag(numpy.linalg.inv((godambe)))) def LRT(func_ex, all_boot, p0, data, eps, diff=1) #p0 is the best fit parameters in the simple model with the complex model parameter(s) as the first diff number of parameters in p0 adjust = 0.0 func = lambda param: Inference.ll_multinom(func_ex([param[:diff]+p0[diff:]], ns, grid_pts), data) H = -get_hess_log(func, p0[:diff], eps) J_boot = numpy.zeros([diff, diff]) J_array = [] for i in range(0, len(all_boot)): boot = Spectrum(all_boot[i]) func = lambda param: Inference.ll_multinom(func_ex([param[:diff]+p0[diff:]], ns, grid_pts), boot) cU_theta = get_grad_log(func, p0[:diff], eps) J_theta = numpy.outer(cU_theta, cU_theta) J_boot = J_boot + J_theta J_array.append(J_theta) J = J_boot/len(all_boot) adjust = diff/numpy.trace(numpy.dot(J, numpy.linalg.inv(H))) return adjust
def _object_func(params, data1, data2, cache1, cache2, model_func, sel_dist, scal_fac1, scal_fac2, theta1, theta2, lower_bound=None, upper_bound=None, verbose=0, multinom=False, flush_delay=0, func_args=[], func_kwargs={}, fixed_params1=None, fixed_params2=None, ll_scale=1, output_stream=sys.stdout, store_thetas=False): """ Objective function for optimization. """ global _counter _counter += 1 # Scaling factors scales sel_dist differently for species 1 and species 2 sel_dist1 = copy_func( sel_dist, defaults=scal_fac1) # scal_fac1 should be 2*Nea of pop 1 sel_dist2 = copy_func( sel_dist, defaults=scal_fac2) # scal_fac2 should be 4*Nea of pop 2 # Deal with fixed parameters params_up1 = Inference._project_params_up(params, fixed_params1) params_up2 = Inference._project_params_up(params, fixed_params2) # Check our parameter bounds if lower_bound is not None: for pval, bound in zip(params_up1, lower_bound): if bound is not None and pval < bound: return -_out_of_bounds_val / ll_scale if upper_bound is not None: for pval, bound in zip(params_up1, upper_bound): if bound is not None and pval > bound: return -_out_of_bounds_val / ll_scale ns1 = data1.sample_sizes ns2 = data2.sample_sizes all_args1 = [params_up1, ns1, sel_dist1, theta1, cache1] + list(func_args) all_args2 = [params_up2, ns2, sel_dist2, theta2, cache2] + list(func_args) # Pass the pts argument via keyword, but don't alter the passed-in # func_kwargs #func_kwargs = func_kwargs.copy() #func_kwargs['pts'] = pts sfs1 = model_func(*all_args1, **func_kwargs) sfs2 = model_func(*all_args2, **func_kwargs) if multinom: result = Inference.ll_multinom(sfs1, data1) + Inference.ll_multinom( sfs2, data2) else: result = Inference.ll(sfs1, data1) + Inference.ll(sfs2, data2) # Bad result if numpy.isnan(result): result = _out_of_bounds_val if (verbose > 0) and (_counter % verbose == 0): param_str = 'array([%s])' % (', '.join( ['%- 12g' % v for v in params_up1])) output_stream.write('%-8i, %-12g, %s%s' % (_counter, result, param_str, os.linesep)) Misc.delayed_flush(delay=flush_delay) return -result / ll_scale