人工智能天气决策树源代码

1、。昆明理工大学信息工程与自动化学院学生实验报告（ 2011 2012学年 第1学期 ）课程名称：人工智能开课实验室： 信自楼计算机机房4442011年 12 月16 日专业班级 0学号200姓名成绩实验名称天气决策树指导教师教该同学是否了解实验原理：A.了解B. 基本了解C. 不了解师该同学的实验能力：A. 强B. 中等C. 差评该同学的实验是否达到要求：A. 达到B. 基本达到C. 未达到实验报告是否规范：A. 规范B. 基本规范C. 不规范语实验过程是否详细记录：A. 详细B. 一般C. 没有教师签名：2011年 12月日一、上机目的及内容1. 上机内容根据下列给定的 14 个数据 , 运

2、用 Information Gain构造一个天气决策树。例子编号属 性分类天况温度湿度风况1晴热大无N2晴热大有N3多云热大无P4雨中大无P5雨冷正常无P6雨冷正常有N7多云冷正常有P8晴中大无N9晴冷正常无P10雨中正常无P11晴中正常有P12多云中大有P。1。13多云热正常无P14雨中大有N2. 上机目的（1）学习用 Information Gain构造决策树的方法；（2）在给定的例子上，构造出正确的决策树；（3）理解并掌握构造决策树的技术要点。二、实验原理及基本技术路线图（方框原理图或程序流程图）（1）设计并实现程序，构造出正确的决策树；（2）对所设计的算法采用大 O符号进行时间复杂性和

3、空间复杂性分析；主函数流程图：Attributevalue.cpp流程图。2。Basefun 流程图：Datapiont.cpp流程图：Dataset主要流程图：。3。三、所用仪器、材料（设备名称、型号、规格等或使用软件）1 台 PC及 VISUAL C+6.0 软件四、实验方法、步骤（或：程序代码或操作过程）工程源代码：Main.cpp:#include #include #include 。4。#include #include #include #include AttributeValue.h#include DataPoint.h#include DataSet.hDataPoint

4、 processLine(std:string const& sLine)std:istringstream isLine(sLine, std:istringstream:in);std:vector attributes;/ TODO: need to handle beginning and ending empty spaces.while( isLine.good() )std:string rawfield;isLine rawfield;attributes.push_back( AttributeValue( rawfield ) );AttributeValue v = at

5、tributes.back();。5。attributes.pop_back();bool type = v.GetType();return DataPoint(attributes, type);void main()std:ifstream ifs(in.txt, std:ifstream:in);DataSet initDataset;while( ifs.good() )/ TODO: need to handle empty lines. std:string sLine;std:getline(ifs, sLine); initDataset.addDataPoint( proc

6、essLine(sLine) );std:list processQ;std:vector finishedDataSet;processQ.push_back(initDataset);。6。while ( processQ.size() 0 )std:vector splittedDataSets;DataSet dataset = processQ.front();dataset.splitDataSet(splittedDataSets);processQ.pop_front();for (int i=0; isplittedDataSets.size(); +i)float prob

7、 = splittedDataSetsi.getPositiveProb();if (prob = 0.0 | prob = 1.0)finishedDataSet.push_back(splittedDataSetsi);elseprocessQ.push_back(splittedDataSetsi);。7。std:cout The dicision tree is: std:endl;for (int i = 0; i finishedDataSet.size(); +i)finishedDataSeti.display();Attributevalue.cpp：#include Att

8、ributeValue.h#include base.hAttributeValue:AttributeValue(std:string const& instring): m_value(instring)bool AttributeValue:GetType()if (m_value = P)return true;else if (m_value = N)return false;elsethrow DataErrException();Basefun.cpp:#include float log2 (float x)。8。return 1.0 / log10(2) * log10(x)

9、;float calEntropy(float prob)float sum=0;if (prob = 0 | prob = 1)return 0;sum -= prob * log2(prob);sum -= (1 - prob) * log2 ( 1 - prob );return sum;Datapiont.cpp:#include #include DataPoint.hDataPoint:DataPoint(std:vector const& attributes, bool type): m_type(type)for (int i=0; iattributes.size(); +

10、i)m_attributes.push_back( attributesi );void DataPoint:display()for (int i=0; im_attributes.size(); +i)std:cout t m_attributesi.getValue();if (true = m_type)std:cout tP;elsestd:cout tN;。9。std:cout std:endl;Dataset.cpp:#include #include #include base.h#include DataSet.hvoid SplitAttributeValue:displa

11、y()std:cout tSplit attribute ID( m_attributeIndex )t;std:cout Split attribute value( m_v.getValue() ) std:endl;void DataSet:addDataPoint(DataPoint const& datapoint)m_data.push_back(datapoint);float DataSet:getPositiveProb()float nPositive = 0;for(int i=0; im_data.size(); +i)if ( m_datai.isPositive()

12、 )nPositive+;return nPositive / m_data.size();struct Statint nPos;int nNeg;int id;。10。void DataSet:splitDataSet(std:vector& splittedSets)/ find all available splitting attributesint nAttributes = m_data0.getNAttributes();int i, j;std:vector splittingAttributeBV;splittingAttributeBV.resize(nAttribute

13、s);for (i=0; inAttributes; +i)splittingAttributeBVi = true;for (i=0; im_splitAttributes.size(); +i)splittingAttributeBV m_splitAttributesi.getAttributeIndex() = false;std:vector splittingAttributeIds;for (i=0; inAttributes; +i)if (true = splittingAttributeBVi)splittingAttributeIds.push_back(i);typed

14、ef std:map AttributeValueStat;typedef std:map:iterator AttributeValueStat_iterator;typedefstd:map:const_iteratorAttributeValueStat_const_iterator;/ go through data once, and collect needed statistics to calculate entropy std:vector splittingStats; splittingStats.resize( splittingAttributeIds.size()

15、);for (i=0; im_data.size(); +i)for (j=0; jsplittingAttributeIds.size(); +j)AttributeValueconst&v=m_datai.getAttribute(splittingAttributeIdsj);AttributeValueStat_iterator it = splittingStatsj.find(v);if ( splittingStatsj.end() = it )Stat stat;。11。if ( m_datai.isPositive() )stat.nPos = 1;stat.nNeg = 0

16、;stat.id= 0;elsestat.nPos = 0;stat.nNeg = 1;stat.id= 0;splittingStatsj.insert(std:pair(v, stat);elseif ( m_datai.isPositive() )it-second.nPos+;elseit-second.nNeg+;/ display collected statisticsfor (j=0; jsplittingAttributeIds.size(); +j)std:cout Attribute( splittingAttributeIdsj ): std:endl; std:cou

17、t tValue t nPos t nNeg std:endl;for (AttributeValueStat_const_iterator it = splittingStatsj.begin(); it != splittingStatsj.end(); +it)std:cout t first.getValue() t second.nPos t second.nNeg std:endl;/ find splitting attribute float minEntropy = 0.0;int splitAttributeId = -1;for (j=0; jsecond.nPos +

18、it-second.nNeg;float p = it-second.nPos;p /= nSamples;entropy += calEntropy(p) * nSamples / n;if (entropy minEntropy | -1 = splitAttributeId)minEntropy = entropy;splitAttributeId = j;std:cout Split at attribute( splittingAttributeIdssplitAttributeId ) std:endl second.id = k+;splittedSets.resize( k);for (i=0; ik; +i)for (j=0; jsecond.id.m_splitAttributes.push_back(SplitAttributeValue(itt -first, attrId);。13。for (i=0; isecond.id.addDataPoint(m_datai);elsethrow Da