How to use the bigml.util.utf8 function in bigml

result = \"[%s, %s]\" % (values, prediction)
    print u\"%s\\t%s\" % (result, count)

for line in sys.stdin:
    values, prediction = line.strip().split('\\t')
    if previous is None:
        previous = (values, prediction)
    if values != previous[0]:
        print_result(previous[0], previous[1], count)
        previous = (values, prediction)
        count = 0
    count += 1
if count > 0:
    print_result(previous[0], previous[1], count)
"""
        out.write(utf8(output))
        out.flush()

out.write(utf8(TRIVIAL_MODEL))
        if any("," in name and name.split(",")[2] in ["A", "M"] for \
               name in model_names):
            out.write(utf8(SEASONAL_CODE))
        trends = [name.split(",")[1] for name in model_names if "," in name]
        trends.extend([name for name in model_names if "," not in name])
        trends = set(trends)
        models_function = []
        for trend in trends:
            models_function.append("\"%s\": _%s_forecast" % (trend, trend))
            out.write(utf8(SUBMODELS_CODE[trend]))
        out.write(utf8(u"\n\nMODELS = \\\n"))
        out.write(utf8("%s%s%s" % \
            (u"    {", u",\n     ".join(models_function), u"}")))

        out.write(utf8(FORECAST_FUNCTION))

"""
            if value is None:
                return ""
            impurity_literal = ""
            if impurity is not None and impurity > 0:
                impurity_literal = "; impurity: %.2f%%" % (round(impurity, 4))
            objective_type = self.fields[tree.objective_id]['optype']
            if objective_type == 'numeric':
                return u" [Error: %s]" % value
            else:
                return u" [Confidence: %.2f%%%s]" % ((round(value, 4) * 100),
                                                     impurity_literal)

        distribution = self.get_data_distribution()

        out.write(utf8(u"Data distribution:\n"))
        print_distribution(distribution, out=out)
        out.write(utf8(u"\n\n"))

        groups = self.group_prediction()
        predictions = self.get_prediction_distribution(groups)

        out.write(utf8(u"Predicted distribution:\n"))
        print_distribution(predictions, out=out)
        out.write(utf8(u"\n\n"))

        if self.field_importance:
            out.write(utf8(u"Field importance:\n"))
            print_importance(self, out=out)

        extract_common_path(groups)

self.fields[field_id]['name'],
                                              value)))
                connector = ", "
        out.write(u"\n\n")

        out.write(u"Distance distribution:\n\n")
        for centroid in centroids_list:
            centroid.print_statistics(out=out)
        out.write(u"\n")

        if len(self.centroids) > 1:
            out.write(u"Intercentroid distance:\n\n")
            centroids_list = (centroids_list[1:] if self.cluster_global else
                              centroids_list)
            for centroid in centroids_list:
                out.write(utf8(u"%sTo centroid: %s\n" % (INDENT,
                                                         centroid.name)))
                for measure, result in self.centroids_distance(centroid):
                    out.write(u"%s%s: %s\n" % (INDENT * 2, measure, result))
                out.write(u"\n")

details = groups[group]['details']
            path = Path(groups[group]['total'][0])
            data_per_group = groups[group]['total'][1] * 1.0 / tree.count
            pred_per_group = groups[group]['total'][2] * 1.0 / tree.count
            out.write(utf8(u"\n\n%s : (data %.2f%% / prediction %.2f%%) %s" %
                           (group,
                            round(data_per_group, 4) * 100,
                            round(pred_per_group, 4) * 100,
                            path.to_rules(self.fields, format=format))))

            if len(details) == 0:
                out.write(utf8(u"\n    The model will never predict this"
                               u" class\n"))
            elif len(details) == 1:
                subgroup = details[0]
                out.write(utf8(u"%s\n" % confidence_error(
                    subgroup[2], impurity=subgroup[3])))
            else:
                out.write(utf8(u"\n"))
                for j in range(0, len(details)):
                    subgroup = details[j]
                    pred_per_sgroup = subgroup[1] * 1.0 / \
                        groups[group]['total'][2]
                    path = Path(subgroup[0])
                    path_chain = path.to_rules(self.fields, format=format) if \
                        path.predicates else "(root node)"
                    out.write(utf8(u"    · %.2f%%: %s%s\n" %
                                   (round(pred_per_sgroup, 4) * 100,
                                    path_chain,
                                    confidence_error(subgroup[2],
                                                     impurity=subgroup[3]))))

def print_distribution(distribution, out=sys.stdout):
    """Prints distribution data

    """
    total = reduce(lambda x, y: x + y,
                   [group[1] for group in distribution])
    for group in distribution:
        out.write(utf8(
            u"    %s: %.2f%% (%d instance%s)\n" % (
                group[0],
                round(group[1] * 1.0 / total, 4) * 100,
                group[1],
                u"" if group[1] == 1 else u"s")))

if objective_type == 'numeric':
                return u" [Error: %s]" % value
            else:
                return u" [Confidence: %.2f%%%s]" % ((round(value, 4) * 100),
                                                     impurity_literal)

        distribution = self.get_data_distribution()

        out.write(utf8(u"Data distribution:\n"))
        print_distribution(distribution, out=out)
        out.write(utf8(u"\n\n"))

        groups = self.group_prediction()
        predictions = self.get_prediction_distribution(groups)

        out.write(utf8(u"Predicted distribution:\n"))
        print_distribution(predictions, out=out)
        out.write(utf8(u"\n\n"))

        if self.field_importance:
            out.write(utf8(u"Field importance:\n"))
            print_importance(self, out=out)

        extract_common_path(groups)

        out.write(utf8(u"\n\nRules summary:"))

        for group in [x[0] for x in predictions]:
            details = groups[group]['details']
            path = Path(groups[group]['total'][0])
            data_per_group = groups[group]['total'][1] * 1.0 / tree.count
            pred_per_group = groups[group]['total'][2] * 1.0 / tree.count

out.write(utf8(u"\n\n"))

        groups = self.group_prediction()
        predictions = self.get_prediction_distribution(groups)

        out.write(utf8(u"Predicted distribution:\n"))
        print_distribution(predictions, out=out)
        out.write(utf8(u"\n\n"))

        if self.field_importance:
            out.write(utf8(u"Field importance:\n"))
            print_importance(self, out=out)

        extract_common_path(groups)

        out.write(utf8(u"\n\nRules summary:"))

        for group in [x[0] for x in predictions]:
            details = groups[group]['details']
            path = Path(groups[group]['total'][0])
            data_per_group = groups[group]['total'][1] * 1.0 / tree.count
            pred_per_group = groups[group]['total'][2] * 1.0 / tree.count
            out.write(utf8(u"\n\n%s : (data %.2f%% / prediction %.2f%%) %s" %
                           (group,
                            round(data_per_group, 4) * 100,
                            round(pred_per_group, 4) * 100,
                            path.to_rules(self.fields, format=format))))

            if len(details) == 0:
                out.write(utf8(u"\n    The model will never predict this"
                               u" class\n"))
            elif len(details) == 1: