OpenMP Parallel Optimized Gaussian Naive Bayesian Algorithm - Inferring Gender Through Height, Weight and Lung Capacity (Machine Learning)

Table of contents

OpenMP Parallel Design

Shared Memory Parallel Computing Using OpenMP

Use pragma omp parallel for to improve training speed

code

result

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This project consists of the following four parts, this section is Section 4:

1. Deduce gender by height and weight

2. Estimate gender based on height, weight, and vital capacity

3. MPI optimization

4. OpenMP optimization

OpenMP Parallel Design

Based on the serial code, OpenMP uses the #pragma omp parallel for num_threads(thread_count) instruction to perform multithreading on all for loops involving large arrays, so as to achieve parallel effects.

Shared Memory Parallel Computing Using OpenMP

Use pragma omp parallel for to improve training speed

Since the serialization of this project needs to loop through the entire array for (i=0;i<dataLen;i++) multiple times to evaluate, it is very suitable to use the pragma omp parallel for instruction for shared memory programming. This instruction indicates that all threads equally divide (dataLen / comm_sz) data, and use these data for cyclic calculation, and each thread returns the data to the main thread, which is similar to MPI's MPI_Scatterv() function.

The clauses after the pragma omp parallel for instruction are: num_threads(thread_count), reduction(+:maleNum), shared(dataSet,dataLen), private(i). num_threads(thread_count) indicates the number of threads, reduction(+:A) indicates that maleNum is a reduction variable, and finally A is accumulated by all threads. shared(dataSet,dataLen) means dataSet, dataLen array number can be shared by all threads, private(i) means that i of each thread is private and not affected by other threads.

The reference code is as follows (find sum[2] as an example):

/*初始化，reduction内的变量一定要有初始值*/

double A,B;

A=0;B=0;

double sum[2]={0,0};

# pragma omp parallel for num_threads(thread_count) \

reduction(+:A) reduction(+:B)\

shared(dataSet,dataLen) private(i)

for(i=0;i<dataLen;i++){ ;

A+=dataSet[i*EIGEN_NUM+1];

B+=dataSet[i*EIGEN_NUM+2];}

# pragma omp barrier

sum[0]=A;sum[1]=B;

code

#include <iostream>
#include <vector>
#include <cstdlib>
#include <time.h>
#include <cassert>
#include <cstring>
#include <cmath>
#include<omp.h>


#define PI 3.1415926535898

//单条数据的长度
#define MAX_LINE 20
//数据集的长度(从1开始计算)
#define DATA_LEN 11000000

#define EIGEN_NUM 4

//float dataSet[DATA_LEN * EIGEN_NUM];	//数据集
float (*dataSet)=(float(*))malloc(sizeof(float)*DATA_LEN*EIGEN_NUM);  

int dataLen;//数据集的行数
double maleNum=0;//男性总数
double femaleNum=0;//女性总数

int main(int argc, char **argv) {

	int i=0;
	int j=0;

	double start = omp_get_wtime( );


	/************************读取文件************************/

	char buf[MAX_LINE];		//缓冲区
	FILE *fp;				//文件指针s
	int len;				//行字符个数

		//读取文件
		const char* fileLocation="E:\\test\\addVitalCapacityData.csv";
		fp = fopen(fileLocation,"r");
		if(fp  == NULL)
		{
			perror("fp  == NULL");
			exit (1) ;
		}

		//逐行读取及写入数组
		char *token;
		const char s[2] = ",";
		while(fgets(buf,MAX_LINE,fp) != NULL && i< DATA_LEN)
		{
			len = strlen(buf);
			//删去换行符
			buf[len-1] = '\0';
			//分割字符串
			token = strtok(buf, s);
			//继续分割字符串
			j = 0;
			while( token != NULL ) 
			{
				dataSet[i*EIGEN_NUM + j]=atof(token);
				token = strtok(NULL, s);
				j = j+1;
			 }
			i = i + 1;
		}
		dataLen=i;
		printf("%d行4列的数据读取完毕\n",dataLen);
		fclose(fp);

		

		double readTime = omp_get_wtime( );



	/************************开始OpenMP计算************************/

	int thread_count = strtol(argv[1],NULL,10);

	/***********计算高斯分布***********/
	char *maenInf[6]={"maleLength","maleWeight","maleVC","femaleLength","femaleWeight","femaleVC"};

	double A,B,C,D,E,F,G;
	A=0;B=0;C=0;D=0;E=0;F=0;G=0;

	double sum[6]={0,0,0,0,0,0};
	double mean[6]={0,0,0,0,0,0};


#	pragma omp parallel for num_threads(thread_count) \
	reduction(+:maleNum) reduction(+:femaleNum) \
	reduction(+:A) reduction(+:B) reduction(+:C)\
	reduction(+:D) reduction(+:E) reduction(+:F)\
	shared(dataSet,dataLen) private(i)
	for(i=0;i<dataLen;i++)
	{
		if(dataSet[i*EIGEN_NUM]==1)
		{
			maleNum=maleNum+1;
			A+=dataSet[i*EIGEN_NUM+1];
			B+=dataSet[i*EIGEN_NUM+2];
			C+=dataSet[i*EIGEN_NUM+3];
		}
		else if(dataSet[i*EIGEN_NUM]==2)
		{
			femaleNum=femaleNum+1;
			D+=dataSet[i*EIGEN_NUM+1];
			E+=dataSet[i*EIGEN_NUM+2];
			F+=dataSet[i*EIGEN_NUM+3];
		}
		else
		{
			printf("dataSet[%d]=%f,性别有误\n",i*EIGEN_NUM,dataSet[i*EIGEN_NUM]);
		}
		//printf("sum[0]=%f \n",sum[0]);
		//printf("%d行4列的数据求和完毕\n",i);
	}
#	pragma omp barrier
	sum[0]=A;
	sum[1]=B;
	sum[2]=C;
	sum[3]=D;
	sum[4]=E;
	sum[5]=F;


	//printf("maleNum=%.0f\nfemaleNum=%.0f\n",maleNum,femaleNum);


	/*for(i=0;i<6;i++)
	{
		printf("sum[%d]=%.0f\n",i,sum[i]);
	}*/

	//计算平均值
	for(i=0;i<6;i++)
	{
		if(i<3){mean[i]=sum[i]/maleNum;}
		if(i>2){mean[i]=sum[i]/femaleNum;}
		//printf("mean-%s = %.5f \n",maenInf[i],mean[i]);
	}

	//计算累加
	A=0;B=0;C=0;D=0;E=0;F=0;G=0;
	double Sigma[6]={0,0,0,0,0,0};
#	pragma omp parallel for num_threads(thread_count) default(none) \
	reduction(+:A) reduction(+:B) reduction(+:C)\
	reduction(+:D) reduction(+:E) reduction(+:F)\
	shared(dataSet,dataLen,mean) private(i)
	for(i=0;i<dataLen;i++)
	{
		if(dataSet[i*EIGEN_NUM]==1)
		{
			A+=pow(dataSet[i*EIGEN_NUM+1]-mean[0] , 2 );
			B+=pow(dataSet[i*EIGEN_NUM+2]-mean[1] , 2 );
			C+=pow(dataSet[i*EIGEN_NUM+3]-mean[2] , 2 );
		}
		else if(dataSet[i*EIGEN_NUM]==2)
		{
			D+=pow(dataSet[i*EIGEN_NUM+1]-mean[3] , 2 );
			E+=pow(dataSet[i*EIGEN_NUM+2]-mean[4] , 2 );
			F+=pow(dataSet[i*EIGEN_NUM+3]-mean[5] , 2 );
		}
		else
		{
			printf("dataSet[i*EIGEN_NUM]=%f,性别有误",dataSet[i*EIGEN_NUM]);
		}
	}
#	pragma omp barrier
	Sigma[0]=A;
	Sigma[1]=B;
	Sigma[2]=C;
	Sigma[3]=D;
	Sigma[4]=E;
	Sigma[5]=F;


	//计算标准差
	double standardDeviation[6];	//标准差
	double sexNum;//各性别人数
	for(i=0;i<6;i++){
		if(i<3){sexNum=maleNum;}
		if(i>=3){sexNum=femaleNum;}
		standardDeviation[i]=sqrt(Sigma[i]/sexNum);
		//printf("Sigma[%d]=%f maleNum=%f",i,Sigma[i],sexNum);
		//printf("第%d个标准差=%.5f\n",i,standardDeviation[i]);
		}



	/*********** 朴素贝叶斯 & 准确率测试 ***********/
	//数据集有肺活量(VC),准确度判断
	float preSexID;
	float Right=0;
	float Error=0;
	//声明性别ID判断函数
	int sexIDResult(float height,float weight,float VC,double *mean,double *standardDeviation);

#	pragma omp parallel for num_threads(thread_count)  default(none) \
	reduction(+:Right) reduction(+:Error) \
	shared(dataSet,dataLen,mean,standardDeviation) private(i,preSexID)
	for(i=0;i<dataLen;i++){
		preSexID=sexIDResult(dataSet[i*EIGEN_NUM+1],dataSet[i*EIGEN_NUM+2],dataSet[i*EIGEN_NUM+3],mean,standardDeviation);
		if(dataSet[i*EIGEN_NUM]==preSexID){
			Right=Right+1;
		}
		else{
			Error=Error+1;
			//printf("预测ID:%.0f  实际ID:%.0f \n",preSexID,receiveBuf[i*EIGEN_NUM]);
			//printf("性别:%.0f,身高:%.2f,体重:%.2f,肺活量:%.0f \n",receiveBuf[i*EIGEN_NUM],receiveBuf[i*EIGEN_NUM+1],receiveBuf[i*EIGEN_NUM+2],receiveBuf[i*EIGEN_NUM+3]);
			}
	}

	printf("Right:%.0f\nError:%.0f\n",Right,Error);
	double accuracy  = Right/(Error+Right);
	printf("Accuracy：%f\n",accuracy);

	double end = omp_get_wtime( );

	//printf("start = %.16g\nend = %.16g\ndiff = %.16g\n", start, end, end - start);
	printf("整体耗时 = %.16f\n", end - start);
	printf("读取时长 = %.16f\n", readTime - start);
	printf("计算时长 = %.16f\n", end - readTime);

	return 0;
}






/*****************函数*****************/



/***********高斯分布函数***********/
//求和
double getSum(float *data,int recDatalen,int sex,int column)
{
	double Sum=0;
	for(int i=0;i<(recDatalen/EIGEN_NUM);i++)
	{
		if(data[i*EIGEN_NUM]==sex){
			Sum=Sum+data[i*EIGEN_NUM+column];
		}
	}
	return Sum;
}

//求pow((data[i]-mean),2)的累加
double getSigma(float *data,int recDatalen,double mean,int sex,int column){
	double Sigma=0;
	for(int i=0;i<(recDatalen/EIGEN_NUM);i++){
		if(data[i*EIGEN_NUM]==sex){
			Sigma=Sigma+pow(data[i*EIGEN_NUM+column]-mean , 2 );
			//printf("sex=%d data[i]=%f mean=%f \n",sex,data[i*EIGEN_NUM+column],mean);
		}
	}
	return Sigma;
}



/***********朴素贝叶斯函数***********/

//计算概率p(特征列column = x | 性别)
double getProbability(double x,int column,int sex,double mean,double standardDeviation)
{
	double Probability;	//计算出的概率
	double u = mean;
	double p = standardDeviation;

	//高数分布概率密度函数 x:预测变量 u:样本平均值 p:标准差
	p=pow(p,2);
	Probability = (1 / (2*PI*p)) * exp( -pow((x-u),2) / (2*p) );

	//printf("p(%s=%lf|性别=%s)=%.16lf\n",basicInfo[column],x,gender,Probability);

	return Probability;
}

//返回性别ID结果
int sexIDResult(float height,float weight,float VC,double *mean,double *standardDeviation)
{
	double maleP;//男性概率
	double femaleP;//女性概率
	double a=0.5; //男女比例各50%

	maleP = a * getProbability(height,1,1,mean[0],standardDeviation[0]) * getProbability(weight,2,1,mean[1],standardDeviation[1]) 
		* getProbability(VC,3,1,mean[2],standardDeviation[2]);

	femaleP = a * getProbability(height,1,2,mean[3],standardDeviation[3]) * getProbability(weight,2,2,mean[4],standardDeviation[4]) 
		* getProbability(VC,3,2,mean[5],standardDeviation[5]);

	if(maleP > femaleP){return 1;}
	if(maleP < femaleP){return 2;}
	if(maleP == femaleP){return 0;}
}

result

linux server:

Compile with gcc, code: https://download.csdn.net/download/admiz/16162424