人工智能天氣決策樹源代碼

1、精選優(yōu)質文檔-傾情為你奉上昆明理工大學信息工程與自動化學院學生實驗報告（ 2011 2012 學年 第 1 學期 ）課程名稱：人工智能 開課實驗室：信自樓計算機機房444 2011 年12月 16 日專業(yè)班級0學號200姓名成績實驗名稱天氣決策樹指導教師 教師評語該同學是否了解實驗原理： A.了解B.基本了解 C.不了解該同學的實驗能力：A.強 B.中等 C.差 該同學的實驗是否達到要求：A.達到B.基本達到 C.未達到實驗報告是否規(guī)范：A.規(guī)范B.基本規(guī)范 C.不規(guī)范實驗過程是否詳細記錄：A.詳細B.一般 C.沒有 教師簽名： 2011 年12 月 日一、上機目的及內容1.上機內容根據下列給

2、定的14個數據,運用Information Gain構造一個天氣決策樹。例子編號屬 性分類天況溫度濕度風況1晴熱大無N2晴熱大有N3多云熱大無P4雨中大無P5雨冷正常無P6雨冷正常有N7多云冷正常有P8晴中大無N9晴冷正常無P10雨中正常無P11晴中正常有P12多云中大有P13多云熱正常無P14雨中大有N2.上機目的（1）學習用Information Gain構造決策樹的方法；（2）在給定的例子上，構造出正確的決策樹；（3）理解并掌握構造決策樹的技術要點。二、實驗原理及基本技術路線圖（方框原理圖或程序流程圖）（1）設計并實現程序，構造出正確的決策樹；（2）對所設計的算法采用大O符號進行時間復雜

3、性和空間復雜性分析；主函數流程圖：Attributevalue.cpp流程圖Basefun流程圖：Datapiont.cpp流程圖：Dataset主要流程圖：三、所用儀器、材料（設備名稱、型號、規(guī)格等或使用軟件）1臺PC及VISUAL C+6.0軟件4、 實驗方法、步驟（或：程序代碼或操作過程）工程源代碼：Main.cpp:#include #include #include #include #include #include #include AttributeValue.h#include DataPoint.h#include DataSet.hDataPoint processLin

4、e(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 = attributes.ba

5、ck();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( processLine(sLine) )

6、;std:list processQ;std:vector finishedDataSet;processQ.push_back(initDataset);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 = splittedDataSets

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

8、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)return 1.0 / log10(2) * log10(x);float calEntropy(float pr

9、ob)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(); +i)m_attributes.push_back(

10、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;std:cout std:endl;Dataset.cpp:#include #include #include base.h#include DataSet.hvoid SplitAttributeValue:display()std:cout tSplit attribute

11、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() )nPositive+;return nPositive

12、 / m_data.size();struct Statint nPos;int nNeg;int id;void DataSet:splitDataSet(std:vector& splittedSets)/ find all available splitting attributesint nAttributes = m_data0.getNAttributes();int i, j;std:vector splittingAttributeBV;splittingAttributeBV.resize(nAttributes);for (i=0; inAttributes; +i)spl

13、ittingAttributeBVi = 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);typedef std:map AttributeValueStat;typ

14、edef std:map:iterator AttributeValueStat_iterator;typedef std:map:const_iterator AttributeValueStat_const_iterator;/ go through data once, and collect needed statistics to calculate entropystd:vector splittingStats;splittingStats.resize( splittingAttributeIds.size() );for (i=0; im_data.size(); +i)fo

15、r (j=0; jsplittingAttributeIds.size(); +j)AttributeValue const& v = m_datai.getAttribute(splittingAttributeIdsj);AttributeValueStat_iterator it = splittingStatsj.find(v);if ( splittingStatsj.end() = it )Stat stat;if ( m_datai.isPositive() )stat.nPos = 1;stat.nNeg = 0;stat.id = 0;elsestat.nPos = 0;st

16、at.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:cout tValue t nPos t nNeg std:endl;

17、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 attributefloat minEntropy = 0.0;int splitAttributeId = -1;for (j=0; jsecond.nPos + it-second.nNeg;float p = it-second

18、.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);for (i