def analyze_results(params, stats, what_stats, aux_names=None, aux_stats=None): d = {} for idx, what_stat in enumerate(what_stats): for idx2, param in enumerate(params): d[(what_stat, get_p(param))] = stats[idx][idx2] headers = ['+'] + [what_stat.NAME for what_stat in what_stats] table = [] for param in params: row = [get_p(param).name] for what_stat in what_stats: row.append(str(d[(what_stat, get_p(param))])) table.append(row) print tabulate(table, headers, tablefmt='simple') ################################################## if aux_names is not None: print '' table2 = [] for idx, name in enumerate(AUX_NAMES): table2.append([name] + aux_stats[idx].tolist()) headers = ['+'] + aux_names print tabulate(table2, headers, tablefmt='simple')
def comp_one(self, param, info): return T.max(T.abs_(get_p(param)))
def comp_one(self, param, info): return T.mean(T.abs_(info.diff / get_p(param)))
def comp_one(self, param, info): return info.diff.norm(L=p) / get_p(param).norm(L=p)
def comp_one(self, param, grad=None, diff=None): return T.max(T.abs_(get_p(param)))