b7d464b5c7536b05e2794a83c1b2b8d8da9c3041,optuna/samplers/random.py,RandomSampler,sample_independent,#RandomSampler#Any#Any#Any#Any#,62

Before Change

            return float(min(max(v, param_distribution.low), param_distribution.high))
        elif isinstance(param_distribution, distributions.IntUniformDistribution):
            // numpy.random.randint includes low but excludes high.
            return self._rng.randint(param_distribution.low, param_distribution.high + 1)
        elif isinstance(param_distribution, distributions.CategoricalDistribution):
            choices = param_distribution.choices
            index = self._rng.randint(0, len(choices))
            return choices[index]

After Change

            return float(min(max(v, param_distribution.low), param_distribution.high))
        elif isinstance(param_distribution, distributions.IntUniformDistribution):
            // numpy.random.randint includes low but excludes high.
            low = int(param_distribution.low / param_distribution.q)
            high = int(param_distribution.high / param_distribution.q)
            return int(self._rng.randint(low, high + 1) * param_distribution.q)
        elif isinstance(param_distribution, distributions.CategoricalDistribution):
            choices = param_distribution.choices
            index = self._rng.randint(0, len(choices))
            return choices[index]

Instances