#include "stdafx.h"
#include "cv.h"
#include "ml.h"
 
// The neural network
CvANN_MLP machineBrain;
 
// Read the training data and train the network.
void trainMachine()
{
	int i;
	//The number of training samples. 
	int train_sample_count;
 
	//The training data matrix. 
	//Note that we are limiting the number of training data samples to 1000 here.
	//The data sample consists of two inputs and an output. That's why 3.
	float td[1000][3];
 
	//Read the training file
	/*
	A sample file contents(say we are training the network for generating 
	the mean given two numbers) would be:
 
	5
	12 16 14
	10 5  7.5
	8  10 9
	5  4  4.5
	12 6  9
 
	*/
	FILE *fin;
	fin = fopen("train.txt", "r");
 
	//Get the number of samples.
	fscanf(fin, "%d", &train_sample_count);
	printf("Found training file with %d samples...\n", train_sample_count);
 
	//Create the matrices
 
	//Input data samples. Matrix of order (train_sample_count x 2)
	CvMat* trainData = cvCreateMat(train_sample_count, 2, CV_32FC1);
 
	//Output data samples. Matrix of order (train_sample_count x 1)
	CvMat* trainClasses = cvCreateMat(train_sample_count, 1, CV_32FC1);
 
	//The weight of each training data sample. We'll later set all to equal weights.
	CvMat* sampleWts = cvCreateMat(train_sample_count, 1, CV_32FC1);
 
	//The matrix representation of our ANN. We'll have four layers.
	CvMat* neuralLayers = cvCreateMat(4, 1, CV_32SC1);
 
	CvMat trainData1, trainClasses1, neuralLayers1, sampleWts1;
 
	cvGetRows(trainData, &trainData1, 0, train_sample_count);
	cvGetRows(trainClasses, &trainClasses1, 0, train_sample_count);
	cvGetRows(trainClasses, &trainClasses1, 0, train_sample_count);
	cvGetRows(sampleWts, &sampleWts1, 0, train_sample_count);
	cvGetRows(neuralLayers, &neuralLayers1, 0, 4);
 
	//Setting the number of neurons on each layer of the ANN
	/* 
	We have in Layer 1: 2 neurons (2 inputs)
	Layer 2: 3 neurons (hidden layer)
	Layer 3: 3 neurons (hidden layer)
	Layer 4: 1 neurons (1 output)
	*/
	cvSet1D(&neuralLayers1, 0, cvScalar(2));
	cvSet1D(&neuralLayers1, 1, cvScalar(3));
	cvSet1D(&neuralLayers1, 2, cvScalar(3));
	cvSet1D(&neuralLayers1, 3, cvScalar(1));
 
	//Read and populate the samples.
	for (i=0;i<train_sample_count;i++)
		fscanf(fin,"%f %f %f",&td[i][0],&td[i][1],&td[i][2]);
 
	fclose(fin);
 
	//Assemble the ML training data.
	for (i=0; i<train_sample_count; i++)
	{
		//Input 1
		cvSetReal2D(&trainData1, i, 0, td[i][0]);
		//Input 2
		cvSetReal2D(&trainData1, i, 1, td[i][1]);
		//Output
		cvSet1D(&trainClasses1, i, cvScalar(td[i][2]));
		//Weight (setting everything to 1)
		cvSet1D(&sampleWts1, i, cvScalar(1));
	}
 
	//Create our ANN.
	machineBrain.create(neuralLayers);//sigmoid 0 0(激活函数的两个参数)
 
	//Train it with our data.	
	machineBrain.train(
		trainData,//输入
		trainClasses,//输出
		sampleWts,//输入项的权值
		0,
		CvANN_MLP_TrainParams(
		cvTermCriteria(
		CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,///类型 CV_TERMCRIT_ITER 和CV_TERMCRIT_EPS二值之一，或者二者的组合
		10000000,//最大迭代次数
		0.00000001//结果的精确性 两次迭代间权值变化量
		),
		CvANN_MLP_TrainParams::BACKPROP,//BP算法
		0.01,//几个可显式调整的参数
		0.05
		)
		);
}
 
// Predict the output with the trained ANN given the two inputs.
void Predict(float data1, float data2)
{
	float _sample[2];
	CvMat sample = cvMat(1, 2, CV_32FC1, _sample);
	float _predout[1];
	CvMat predout = cvMat(1, 1, CV_32FC1, _predout);
	sample.data.fl[0] = data1;
	sample.data.fl[1] = data2;
 
	machineBrain.predict(&sample, &predout);
 
	printf("%f \n",predout.data.fl[0]);
 
}
 
int _tmain(int argc, _TCHAR* argv[])
{
	int wait;
 
	// Train the neural network  with the samples
	trainMachine();
 
	// Now try predicting some values with the trained network
 
 
	Predict(12.0,16.0);// 14
	Predict(10.0,5.0);//  7.5
	Predict(8.0,10.0);// 9
	Predict(5.0,4.0);//  4.5
	Predict(12.0,6.0);//  9
 
	//I'll wait for an integer. :)
	scanf("%d",&wait);
	return 0;
}