def draw_parameter(self, random): return self.Parameter( p_hour=dist.uniform_float(random, 0, 1), p_minute=dist.uniform_float(random, 0, 1), p_second=dist.uniform_float(random, 0, 1), month=dist.non_empty_subset(random, list(range(1, 13))), naive_chance=dist.uniform_float(random, 0, 0.5), timezones=self.timezones and dist.non_empty_subset(random, self.timezones), )
def produce_parameter(self, random): return self.Parameter( p_hour=dist.uniform_float(random, 0, 1), p_minute=dist.uniform_float(random, 0, 1), p_second=dist.uniform_float(random, 0, 1), month=dist.non_empty_subset(random, list(range(1, 13))), naive_chance=dist.uniform_float(random, 0, 0.5), utc_chance=dist.uniform_float(random, 0, 1), timezones=dist.non_empty_subset( random, list(map(pytz.timezone, pytz.all_timezones))), naive_options=dist.non_empty_subset(random, self.naive_options))
def produce_parameter(self, random): return self.Parameter( p_hour=dist.uniform_float(random, 0, 1), p_minute=dist.uniform_float(random, 0, 1), p_second=dist.uniform_float(random, 0, 1), month=dist.non_empty_subset(random, list(range(1, 13))), naive_chance=dist.uniform_float(random, 0, 0.5), utc_chance=dist.uniform_float(random, 0, 1), timezones=dist.non_empty_subset( random, list( map(pytz.timezone, pytz.all_timezones)) ), naive_options=dist.non_empty_subset(random, self.naive_options ) )
def draw_parameter(self, random): return self.Parameter( leaf_parameter=self.leaf_strategy.draw_parameter(random), branch_key_parameter=self.branch_key_strategy.draw_parameter( random), branch_label_parameter=self.branch_label_strategy.draw_parameter( random), branch_factor=uniform_float(random, 0.75, 0.99), )
def draw_parameter(self, random): return self.Parameter( negative_probability=dist.uniform_float(random, 0, 1), subnormal_probability=dist.uniform_float(random, 0, 0.5), )
def produce_parameter(self, random): return self.Parameter( negative_probability=dist.uniform_float(random, 0, 1), subnormal_probability=dist.uniform_float(random, 0, 0.5), )
def produce_parameter(self, random): return self.Parameter( negative_probability=dist.uniform_float(random, 0, 1), min_exponent=random.randint(0, self.max_exponent) )