百度了半天yusugomori，也不知道他是谁。不过这位老兄写了deep learning的代码，包括RBM、逻辑回归、DBN、autoencoder等，实现语言包括c、c++、java、python等。是学习的好材料。代码下载地址：https://github.com/yusugomori/DeepLearning。不过这位老兄不喜欢写注释，而且这些模型的原理、公式什么的，不了解的话就看不懂代码。我从给他写注释开始，边看资料、边理解它的代码、边给他写上注释。

工具包中RBM的实现包含了两个文件，RBM.h和RBM.cpp。RBM.h添加注释后，如下：

class RBM

{

public:

	// the number of training sample

	int N;

	// the number of visiable node

	int n_visible;

	// the number of hidden node

	int n_hidden;

	// the weight connecting the visiable node and the hidden node

	double **W;

	// the bias of hidden node

	double *hbias;

	// the bias of visiable node

	double *vbias;

public:

	// construct the RBM by input parameters

	RBM (int,		// N

		int,		// n_visible

		int,		// n_hidden

		double**,	// W

		double*,	// hbias

		double*		// vbias

		);

	// destructor, release all the memory of parameters

	~RBM ();

	// CD-k algorithm to train RBM

	 void contrastive_divergence (int*,	// one input sample

		 double,						// the learning rate

		 int							// the k of CD-k, it is usually 1

		 );

	// these the functions of Gibbs sample 

	// sample the hidden node given the visiable node, 'sample' means calculating

	// 1. the output probability of the hidden node given the input of visiable node

	// and the weight of current RBM; 2. the 0-1 state of hidden node by a binomial

	// distribution given the calculated output probability of this hidden node

	void sample_h_given_v (int*,		// one input sample from visiable nodes -- input

		double*,						// the output probability of hidden nodes -- output

		int*							// the calculated 0-1 state of hidden node -- output

		);

	// sample the visiable node given the hidden node, 'sample' means calculating

	// 1. the output probability of the visiable node given the input of hidden node

	// and the weight of current RBM; 2. the 0-1 state of visiable node by a binomial

	// distribution given the calculated output probability of this visiable node

	void sample_v_given_h (int*,		// one input sample from hidden nodes -- input

		double*,						// the output probability of visiable nodes -- output

		int*							// the calculated 0-1 state of visiable node -- output

		);

	// 'propup' -- probability up. It's called by the 'sample_x_given_x' function and the reconstruct funciton

	//	To calculate the probability in 'upper' node given the input from 'lower' node in RBM

	// note: what is the 'up' and 'down'? the visiable node is below (down) the hidden node.

	// 'probability up' means calculating the probability of hidden node given the visiable node

	// return value: the output probability of the hidden node given the input of visiable node

	// and the weight of current RBM

	// the probability is : p (hi|v) = sigmod ( sum_j(vj * wij) + bi)

	double propup (int*,				// one input sample from visiable node -- input

		double*,						// the weight W connecting one hidden node to all visible node -- input

		double							// the bias for this hidden node -- input

		);

	// 'propdown' -- probability down. It's called by the 'sample_x_given_x' function and the reconstruct funciton

	//	To calculate the probability in 'lower' node given the input from 'upper' node in RBM

	// note: what is the 'up' and 'down'? the visiable node is below (down) the hidden node.

	// 'probability down' means calculating the probability of visiable node given the hidden node

	// return value: the output probability of the visiable node given the input of hidden node

	// and the weight of current RBM

	// the probability is : p (vi|h) = sigmod ( sum_j(hj * wij) + ci)

	double propdown (int*,				// one input sample from hidden node -- input

		int,							// the index of visiable node in the W matrix -- input

		double							// the bias for this visible node -- input

		);

	// 'gibbs_hvh' -- gibbs sample firstly from hidden node to visible node, then sample

	// from visiable node to hidden node. It is called by contrastive_divergence.

	void gibbs_hvh (int*,				// one input sample from hidden node, h0 -- input

		double*,						// the output probability of visiable nodes -- output

		int*,							// the calculated 0-1 state of visiable node -- output

		double*,						// the output probability of reconstructed hidden node  h1 -- output

		int*							// the calculated 0-1 state of reconstructed hidden node h1 -- output

		);

	// reconstruct the input visiable node by the trained RBM (so as to varify the RBM model)

	void reconstruct (int*,				// one input sample from visiable node

		double*							// the reconstructed output by RBM model

		);

};

主要添加了函数说明、参数说明、计算说明、调用关系等。