逻辑回归matlab代码-PredictingPRNGs:使用机器学习技术预测伪随机数生成器

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逻辑回归matlab代码预测PRNG 使用机器学习技术预测伪随机数生成器 要运行一个学习者的单个实例,请使用exampleKNN.m脚本(例如,运行KNN)。 要重新运行实验,请运行deployConfig.m。 我们总共实施了五名学习者: 随机抽样-按比例随机抽取训练集中标签的比例 随机森林-传统的随机森林算法,以固定深度生长自举树-预测由树预测的标签的模式 KNN(k最近邻)-从训练集中预测k最近邻标签的模式 朴素贝叶斯-假设给定标签的每个特征在条件上均独立于所有其他特征-通过在训练集中计数来学习概率,并根据未归一化的贝叶斯规则预测具有最高概率的标签 Logistic回归-传统的logistic回归分类器使用Barzilai Borwein方程对更新进行了梯度下降训练-预测每个输出最可能的标签 我们还实现或硬编码了几个伪随机数生成器(PRNG)。 除非另有说明,否则每一项我们都支持k = 2、3和5个标签的值。 Mercenne Twister-我们在Matlab内置的Mercenne Twister算法的默认实现中包装了一个函数。 线性同余生成器-我们已使用Borland C /

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