├── .gitignore ├── 1995一种离散化的方法.pdf ├── 2003另一种方法.pdf ├── C++ source Code ├── C4.5.cpp ├── Continuous │ ├── .vscode │ │ ├── c_cpp_properties.json │ │ ├── launch.json │ │ └── settings.json │ ├── CDTree.cpp │ ├── CDTree.hpp │ ├── CMakeCache.txt │ ├── CMakeFiles │ │ ├── 3.5.1 │ │ │ ├── CMakeCXXCompiler.cmake │ │ │ ├── CMakeDetermineCompilerABI_CXX.bin │ │ │ ├── CMakeSystem.cmake │ │ │ └── CompilerIdCXX │ │ │ │ └── CMakeCXXCompilerId.cpp │ │ ├── CMakeDirectoryInformation.cmake │ │ ├── CMakeOutput.log │ │ ├── DtreeMain.dir │ │ │ ├── CXX.includecache │ │ │ ├── DependInfo.cmake │ │ │ ├── build.make │ │ │ ├── cmake_clean.cmake │ │ │ ├── depend.internal │ │ │ ├── depend.make │ │ │ ├── flags.make │ │ │ ├── link.txt │ │ │ └── progress.make │ │ ├── Makefile.cmake │ │ ├── Makefile2 │ │ ├── TargetDirectories.txt │ │ ├── cmake.check_cache │ │ ├── feature_tests.bin │ │ ├── feature_tests.cxx │ │ └── progress.marks │ ├── CMakeLists.txt │ ├── DtreeMain │ ├── Makefile │ ├── RFCSV.hpp │ ├── cmake_install.cmake │ └── main.cpp ├── Discrete │ ├── DTree.cpp │ ├── DTree.hpp │ ├── haha.csv │ ├── haha2.csv │ ├── main.cpp │ ├── out.yml │ ├── pre.csv │ └── tianqi.csv └── data │ ├── ContinuousTest.csv │ ├── ContinuousTrain.csv │ ├── ContinuousValid.csv │ ├── Newslabel.csv │ ├── Newsvector.csv │ ├── 连续样例数据.csv │ └── 连续样例数据.xlsx ├── Coninuous_DecisionTree ├── .~draw.pptx ├── CDTree.png ├── Readme.md ├── cut.png ├── data.png ├── decisiontree.png ├── draw.pptx ├── example.png ├── handsome.png ├── hight.png ├── hight_sort.png ├── interval_tree.png └── result.png ├── Continuous_DecisionTree ├── .~draw.pptx ├── CDTree.png ├── Readme.md ├── cut.png ├── data.png ├── decisiontree.png ├── draw.pptx ├── example.png ├── handsome.png ├── hight.png ├── hight_sort.png ├── interval_tree.png └── result.png ├── Finding Optimal Decision Trees.pdf ├── LICENSE ├── README.md ├── SogouCA.mini.tar.gz ├── SogouCA.mini.txt ├── XGboost.pdf ├── how to deal with continuous attribute.pdf ├── pic ├── data.png ├── predicted.png ├── trainData.png └── 流程.png ├── ppt.pdf ├── 连续数据的决策树.png └── 连续数据的决策树.xmind /.gitignore: -------------------------------------------------------------------------------- 1 | # Prerequisites 2 | *.d 3 | 4 | # Compiled Object files 5 | *.slo 6 | *.lo 7 | *.o 8 | *.obj 9 | 10 | # Precompiled Headers 11 | *.gch 12 | *.pch 13 | 14 | # Compiled Dynamic libraries 15 | *.so 16 | *.dylib 17 | *.dll 18 | 19 | # Fortran module files 20 | *.mod 21 | *.smod 22 | 23 | # Compiled Static libraries 24 | *.lai 25 | *.la 26 | *.a 27 | *.lib 28 | 29 | # Executables 30 | *.exe 31 | *.out 32 | *.app 33 | -------------------------------------------------------------------------------- /1995一种离散化的方法.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PiggyGaGa/MachineLearning-DecisionTree/3c063024405021739509e6cdb3655d5ebf417ebd/1995一种离散化的方法.pdf -------------------------------------------------------------------------------- /2003另一种方法.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PiggyGaGa/MachineLearning-DecisionTree/3c063024405021739509e6cdb3655d5ebf417ebd/2003另一种方法.pdf -------------------------------------------------------------------------------- /C++ source Code/C4.5.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | #include 5 | #include 6 | #include 7 | #include 8 | using namespace std; 9 | 10 | class MatrixCls 11 | { 12 | private: 13 | vector < vector < string > > Matrix; 14 | public: 15 | MatrixCls(string Data_File) 16 | { 17 | Matrix.erase(Matrix.begin(),Matrix.end()); 18 | ifstream Data(Data_File); 19 | string Line; 20 | string Item; 21 | vector < string > Row; 22 | while(!Data.eof()) 23 | { 24 | getline(Data, Line); 25 | istringstream Iss(Line); 26 | while(Iss.good()) 27 | { 28 | getline(Iss, Item, ','); 29 | Item.erase(remove(Item.begin(), Item.begin()+2, ' '), Item.begin()+2); 30 | Item.erase(remove(Item.end()-1, Item.end(), ' '), Item.end()); 31 | Row.push_back(Item); 32 | } 33 | 34 | if(Line.length()) 35 | { 36 | Matrix.push_back(Row); 37 | Row.erase(Row.begin(),Row.end()); 38 | } 39 | } 40 | Data.close(); 41 | } 42 | 43 | MatrixCls(){ }; 44 | ~MatrixCls(){ }; 45 | 46 | string Element(int i,int j) 47 | { 48 | return Matrix[i][j]; 49 | } 50 | 51 | int SizeX() 52 | { 53 | return Matrix[0].size(); 54 | } 55 | 56 | int SizeY() 57 | { 58 | return Matrix.size(); 59 | } 60 | 61 | vector < string > GetVarKinds() 62 | { 63 | vector < string > Kinds; 64 | int j; 65 | for(j = 0; j < SizeX()-1; j++) 66 | { 67 | Kinds.push_back(Matrix[0][j]); 68 | } 69 | return Kinds; 70 | } 71 | 72 | vector < string > GetAttributes() 73 | { 74 | vector < string > Attributes; 75 | int j; 76 | for(j = 0; j < SizeX()-1; j++) 77 | { 78 | Attributes.push_back(Matrix[1][j]); 79 | } 80 | return Attributes; 81 | } 82 | 83 | vector < string > GetScores() 84 | { 85 | vector < string > Scores; 86 | for(int i = 2; i < SizeY(); i++) 87 | { 88 | Scores.push_back(Matrix[i][SizeX()-1]); 89 | } 90 | return Scores; 91 | } 92 | 93 | int GetAttributeIndex(string The_Attribute) 94 | { 95 | { 96 | int Index = 0; 97 | for(int j = 0; j < SizeX(); j++) 98 | { 99 | if(Matrix[1][j].compare(The_Attribute) == 0) 100 | { 101 | Index = j; 102 | break; 103 | } 104 | } 105 | return Index; 106 | } 107 | } 108 | 109 | vector < string > GetAttributeValues(string The_Attribute) 110 | { 111 | vector < string > Values; 112 | int Index = GetAttributeIndex(The_Attribute); 113 | for(int i = 2; i < SizeY(); i++) 114 | { 115 | Values.push_back(Matrix[i][Index]); 116 | } 117 | return Values; 118 | } 119 | 120 | vector < string > GetUniqueAttributeValues(string The_Attribute) 121 | { 122 | vector < string > Values = GetAttributeValues(The_Attribute); 123 | sort(Values.begin(), Values.end()); 124 | Values.erase(unique(Values.begin(), Values.end()), Values.end()); 125 | return Values; 126 | } 127 | 128 | map < string, vector < string > > GetAttributeValuesScores(string The_Attribute) 129 | { 130 | int i,k; 131 | int Index = GetAttributeIndex(The_Attribute); 132 | map < string, vector < string > > Attribute_Values_Scores; 133 | vector < string > Attribute_Values = GetUniqueAttributeValues(The_Attribute); 134 | vector < string > Row; 135 | for(k = 0; k < Attribute_Values.size(); k++) 136 | { 137 | for(i = 2; i < SizeY(); i++) 138 | { 139 | if(Matrix[i][Index].compare(Attribute_Values[k]) == 0) 140 | { 141 | Row.push_back(Matrix[i][SizeX()-1]); 142 | } 143 | } 144 | Attribute_Values_Scores[Attribute_Values[k]] = Row; 145 | Row.erase(Row.begin(),Row.end()); 146 | } 147 | return Attribute_Values_Scores; 148 | } 149 | 150 | vector < string > SortAttributeValues(string The_Attribute) 151 | { 152 | vector < string > Values = GetAttributeValues(The_Attribute); 153 | string Temp; 154 | for(int i = 0; i < Values.size()-1; i++) 155 | { 156 | for(int j = i+1; j < Values.size(); j++) 157 | { 158 | if(stod(Values[i]) - stod(Values[j]) > 1.e-8) 159 | { 160 | Temp = Values[i]; 161 | Values[i] = Values[j]; 162 | Values[j] = Temp; 163 | } 164 | } 165 | } 166 | return Values; 167 | } 168 | 169 | vector < string > SortScoreValues(string The_Attribute) 170 | { 171 | vector < string > Values = GetAttributeValues(The_Attribute); 172 | vector < string > Scores = GetScores(); 173 | string Temp; 174 | 175 | for(int i = 0; i < Values.size()-1; i++) 176 | { 177 | for(int j = i+1; j < Values.size(); j++) 178 | { 179 | if(stod(Values[i]) - stod(Values[j]) > 1.e-8) 180 | { 181 | Temp = Values[i]; 182 | Values[i] = Values[j]; 183 | Values[j] = Temp; 184 | 185 | Temp = Scores[i]; 186 | Scores[i] = Scores[j]; 187 | Scores[j] = Temp; 188 | } 189 | } 190 | } 191 | return Scores; 192 | } 193 | 194 | vector < string > GetBisectNodes(string The_Attribute) 195 | { 196 | vector < string > Bisect_Nodes; 197 | vector < string > SortedValues = SortAttributeValues(The_Attribute); 198 | vector < string > SortedScores = SortScoreValues(The_Attribute); 199 | for(int i = 0; i < SortedValues.size()-1; i++) 200 | { 201 | if(abs(stod(SortedValues[i]) - stod(SortedValues[i+1])) > 1.e-8 & SortedScores[i].compare(SortedScores[i+1]) != 0) 202 | { 203 | Bisect_Nodes.push_back(to_string((stod(SortedValues[i]) + stod(SortedValues[i+1]))/2.)); 204 | } 205 | } 206 | return Bisect_Nodes; 207 | } 208 | 209 | map < string, vector < string > > GetAttributeBisectParts(string The_Attribute, string Bisect_Node) 210 | { 211 | map < string, vector < string > > Bisect_Parts; 212 | vector < string > SortedValues = SortAttributeValues(The_Attribute); 213 | vector < string > SortedScores = SortScoreValues(The_Attribute); 214 | vector < string > Row_1, Row_2, Row_3, Row_4; 215 | for(int i = 0; i < SortedValues.size(); i++) 216 | { 217 | if(stod(SortedValues[i]) - stod(Bisect_Node) < -1.e-8) 218 | { 219 | Row_1.push_back(SortedScores[i]); 220 | Row_3.push_back(SortedValues[i]); 221 | }else{ 222 | Row_2.push_back(SortedScores[i]); 223 | Row_4.push_back(SortedValues[i]); 224 | } 225 | } 226 | Bisect_Parts["Lower_Scores"] = Row_1; 227 | Bisect_Parts["Upper_Scores"] = Row_2; 228 | Bisect_Parts["Lower_Values"] = Row_3; 229 | Bisect_Parts["Upper_Values"] = Row_4; 230 | return Bisect_Parts; 231 | } 232 | 233 | MatrixCls operator()(MatrixCls A_Matrix, string The_Attribute, string The_Value, string Bisect_Node = "") 234 | { 235 | Matrix.erase(Matrix.begin(),Matrix.end()); 236 | int i, j; 237 | int Index = A_Matrix.GetAttributeIndex(The_Attribute); 238 | vector < string > Kinds = A_Matrix.GetVarKinds(); 239 | vector < string > Row; 240 | 241 | if(Kinds[Index].compare("Discrete") == 0) 242 | { 243 | for(i = 0; i < 2; i++) 244 | { 245 | for(j = 0; j < A_Matrix.SizeX(); j++) 246 | { 247 | if(A_Matrix.Element(1,j).compare(The_Attribute) != 0) 248 | { 249 | Row.push_back(A_Matrix.Element(i,j)); 250 | } 251 | } 252 | if(Row.size() != 0) 253 | { 254 | Matrix.push_back(Row); 255 | Row.erase(Row.begin(),Row.end()); 256 | } 257 | } 258 | 259 | for(i = 2; i < A_Matrix.SizeY(); i++) 260 | { 261 | for(j = 0; j < A_Matrix.SizeX(); j++) 262 | { 263 | if(A_Matrix.Element(1,j).compare(The_Attribute) != 0 & A_Matrix.Element(i,Index).compare(The_Value) == 0) 264 | { 265 | Row.push_back(A_Matrix.Element(i,j)); 266 | } 267 | } 268 | 269 | if(Row.size() != 0) 270 | { 271 | Matrix.push_back(Row); 272 | Row.erase(Row.begin(),Row.end()); 273 | } 274 | } 275 | return *this; 276 | } 277 | 278 | else if(Kinds[Index].compare("Continuous") == 0) 279 | { 280 | for(i = 0; i < 2; i++) 281 | { 282 | for(j = 0; j < A_Matrix.SizeX(); j++) 283 | { 284 | Row.push_back(A_Matrix.Element(i,j)); 285 | } 286 | if(Row.size() != 0) 287 | { 288 | Matrix.push_back(Row); 289 | Row.erase(Row.begin(),Row.end()); 290 | } 291 | } 292 | 293 | if(The_Value.compare("Lower_Values") == 0) 294 | { 295 | for(i = 2; i < A_Matrix.SizeY(); i++) 296 | { 297 | for(j = 0; j < A_Matrix.SizeX(); j++) 298 | { 299 | if(stod(A_Matrix.Element(i,Index)) -stod(Bisect_Node) < -1.e-8) 300 | { 301 | Row.push_back(A_Matrix.Element(i,j)); 302 | } 303 | } 304 | 305 | if(Row.size() != 0) 306 | { 307 | Matrix.push_back(Row); 308 | Row.erase(Row.begin(),Row.end()); 309 | } 310 | } 311 | } 312 | else if(The_Value.compare("Upper_Values") == 0) 313 | { 314 | for(i = 2; i < A_Matrix.SizeY(); i++) 315 | { 316 | for(j = 0; j < A_Matrix.SizeX(); j++) 317 | { 318 | if(stod(A_Matrix.Element(i,Index)) - stod(Bisect_Node) > 1.e-8) 319 | { 320 | Row.push_back(A_Matrix.Element(i,j)); 321 | } 322 | } 323 | 324 | if(Row.size() != 0) 325 | { 326 | Matrix.push_back(Row); 327 | Row.erase(Row.begin(),Row.end()); 328 | } 329 | } 330 | } 331 | return *this; 332 | } 333 | } 334 | 335 | void Display() 336 | { 337 | int i, j; 338 | for(i = 0; i < Matrix.size(); i++) 339 | { 340 | for(j = 0; j < Matrix[0].size(); j++) 341 | { 342 | cout << Matrix[i][j] << " \t"; 343 | } 344 | cout << endl; 345 | } 346 | } 347 | }; 348 | 349 | vector < string > UniqueValues(vector < string > A_String) 350 | { 351 | sort(A_String.begin(), A_String.end()); 352 | A_String.erase(unique(A_String.begin(), A_String.end()), A_String.end()); 353 | return A_String; 354 | } 355 | 356 | string FrequentValues(vector < string > A_String) 357 | { 358 | vector < string > Unique_Values = UniqueValues(A_String); 359 | int Count[Unique_Values.size()] = {0}; 360 | for(int i = 0; i < A_String.size(); i++) 361 | { 362 | for(int j = 0; j < Unique_Values.size(); j++) 363 | { 364 | if(A_String[i].compare(Unique_Values[j]) == 0) 365 | { 366 | Count[j] = Count[j] + 1; 367 | } 368 | } 369 | } 370 | 371 | int Max_Count = 0, Max_Index; 372 | for(int i = 0; i < Unique_Values.size(); i++) 373 | { 374 | if(Count[i] > Max_Count) 375 | { 376 | Max_Count = Count[i]; 377 | Max_Index = i; 378 | } 379 | } 380 | return Unique_Values[Max_Index]; 381 | } 382 | 383 | double ComputeScoreEntropy(vector < string > Scores) 384 | { 385 | vector < string > Score_Range = UniqueValues(Scores); 386 | if(Score_Range.size() == 0) 387 | { 388 | return 0.; 389 | } 390 | else 391 | { 392 | double TheEntropy = 0.; 393 | int i, j; 394 | int Count[Score_Range.size()] = {0}; 395 | 396 | for(i = 0; i < Scores.size(); i++) 397 | { 398 | for(j = 0; j < Score_Range.size(); j++) 399 | { 400 | if(Scores[i].compare(Score_Range[j]) == 0) 401 | { 402 | Count[j] = Count[j] + 1; 403 | } 404 | } 405 | } 406 | 407 | double Temp_Entropy; 408 | double Temp_P; 409 | for(j = 0; j < Score_Range.size(); j++) 410 | { 411 | Temp_P = (double)Count[j]/(double)(Scores.size()); 412 | Temp_Entropy = -Temp_P*log(Temp_P)/log(2.); 413 | TheEntropy = TheEntropy + Temp_Entropy; 414 | } 415 | return TheEntropy; 416 | } 417 | } 418 | 419 | double ComputeAttributeEntropy(MatrixCls Remain_Matrix, string The_Attribute) 420 | { 421 | vector < string > Values = Remain_Matrix.GetAttributeValues(The_Attribute); 422 | return ComputeScoreEntropy(Values); 423 | } 424 | 425 | double ComputeAttributeEntropyGain(MatrixCls Remain_Matrix, string The_Attribute, string Bisect_Node = "") 426 | { 427 | int Index = Remain_Matrix.GetAttributeIndex(The_Attribute); 428 | vector < string > Kinds = Remain_Matrix.GetVarKinds(); 429 | double Original_Entropy = 0., Gained_Entropy = 0.; 430 | vector < string > Scores = Remain_Matrix.GetScores(); 431 | Original_Entropy = ComputeScoreEntropy(Scores); 432 | 433 | if(Kinds[Index].compare("Discrete") == 0) 434 | { 435 | map < string, vector < string > > Values_Scores = Remain_Matrix.GetAttributeValuesScores(The_Attribute); 436 | vector < string > Values = Remain_Matrix.GetUniqueAttributeValues(The_Attribute); 437 | 438 | double After_Entropy = 0.; 439 | double Temp_Entropy; 440 | vector < string > Temp_Scores; 441 | int i,j; 442 | for(i = 0; i < Values.size(); i++) 443 | { 444 | Temp_Scores = Values_Scores[Values[i]]; 445 | Temp_Entropy = ComputeScoreEntropy(Temp_Scores)*(double)Temp_Scores.size()/(double)Scores.size(); 446 | After_Entropy = After_Entropy + Temp_Entropy; 447 | } 448 | Gained_Entropy = Original_Entropy - After_Entropy; 449 | return Gained_Entropy; 450 | } 451 | 452 | if(Kinds[Index].compare("Continuous") == 0) 453 | { 454 | map < string, vector < string > > Parts = Remain_Matrix.GetAttributeBisectParts(The_Attribute,Bisect_Node); 455 | double LowerLen = Parts["Lower_Scores"].size(); 456 | double UpperLen = Parts["Upper_Scores"].size(); 457 | double Len = LowerLen + UpperLen; 458 | double After_Entropy, Gained_Entropy; 459 | After_Entropy = LowerLen/Len*ComputeScoreEntropy(Parts["Lower_Scores"]) + UpperLen/Len*ComputeScoreEntropy(Parts["Upper_Scores"]); 460 | Gained_Entropy = Original_Entropy - After_Entropy; 461 | return Gained_Entropy; 462 | } 463 | } 464 | 465 | double GainRatio(MatrixCls Remain_Matrix, string The_Attribute, string Bisect_Node = "") 466 | { 467 | double Attribute_Entropy = ComputeAttributeEntropy(Remain_Matrix, The_Attribute); 468 | double Attribute_Entropy_Gain = ComputeAttributeEntropyGain(Remain_Matrix, The_Attribute, Bisect_Node); 469 | return Attribute_Entropy_Gain/Attribute_Entropy; 470 | } 471 | 472 | class TreeCls 473 | { 474 | public: 475 | string Node; 476 | string Branch; 477 | vector < TreeCls * > Child; 478 | TreeCls(); 479 | TreeCls * BuildTree(TreeCls * Tree, MatrixCls Remain_Matrix); 480 | void Display(int Depth); 481 | string Temp_TestTree(vector < string > Kinds, vector < string > Attributes, vector < string > Value, vector < string > Score_Range); 482 | vector < string > TestTree(MatrixCls Data_Matrix); 483 | }; 484 | 485 | string TreeCls::Temp_TestTree(vector < string > Kinds, vector < string > Attributes, vector < string > Value, vector < string > Score_Range) 486 | { 487 | for(int i = 0; i < Score_Range.size(); i++) 488 | { 489 | if(this->Node.compare(Score_Range[i]) == 0) 490 | { 491 | return this->Node; 492 | } 493 | } 494 | 495 | for(int i = 0; i < Attributes.size(); i++) 496 | { 497 | if(this->Node.compare(Attributes[i]) == 0) 498 | { 499 | if(Kinds[i].compare("Discrete") == 0) 500 | { 501 | for(int j = 0; j < this->Child.size(); j++) 502 | { 503 | if((this->Child[j])->Branch.compare(Value[i]) == 0) 504 | { 505 | for(int k = 0; k < Score_Range.size(); k++) 506 | { 507 | if((this->Child[j])->Node.compare(Score_Range[k]) == 0) 508 | { 509 | return (this->Child[j])->Node; 510 | } 511 | } 512 | 513 | vector < string > New_Kinds; 514 | vector < string > New_Attributes; 515 | vector < string > New_Value; 516 | for(int l = 0; l < Attributes.size(); l++) 517 | { 518 | if( l != i) 519 | { 520 | New_Kinds.push_back(Kinds[l]); 521 | New_Attributes.push_back(Attributes[l]); 522 | New_Value.push_back(Value[l]); 523 | } 524 | } 525 | return (this->Child[j])->Temp_TestTree(New_Kinds, New_Attributes, New_Value, Score_Range); 526 | } 527 | } 528 | } 529 | 530 | else if(Kinds[i].compare("Continuous") == 0) 531 | { 532 | string Threshold = (this->Child[0])->Branch; 533 | Threshold.erase(remove(Threshold.begin(), Threshold.begin()+3, '<'), Threshold.begin()+3); 534 | Threshold.erase(remove(Threshold.begin(), Threshold.begin()+3, ' '), Threshold.begin()+3); 535 | if(stod(Value[i]) - stod(Threshold) < -1.e-8) 536 | { 537 | for(int k = 0; k < Score_Range.size(); k++) 538 | { 539 | if((this->Child[0])->Node.compare(Score_Range[k]) == 0) 540 | { 541 | return (this->Child[0])->Node; 542 | } 543 | } 544 | return (this->Child[0])->Temp_TestTree(Kinds, Attributes, Value, Score_Range); 545 | } 546 | else if(stod(Value[i]) - stod(Threshold) > 1.e-8) 547 | { 548 | for(int k = 0; k < Score_Range.size(); k++) 549 | { 550 | if((this->Child[1])->Node.compare(Score_Range[k]) == 0) 551 | { 552 | return (this->Child[1])->Node; 553 | } 554 | } 555 | return (this->Child[1])->Temp_TestTree(Kinds, Attributes, Value, Score_Range); 556 | } 557 | } 558 | } 559 | } 560 | } 561 | 562 | vector < string > TreeCls::TestTree(MatrixCls Data_Matrix) 563 | { 564 | 565 | int Lines_Number = Data_Matrix.SizeY() - 2; 566 | vector < string > Test_Scores; 567 | int i, j, k, l, m; 568 | vector < string > Kinds = Data_Matrix.GetVarKinds(); 569 | vector < string > Attributes = Data_Matrix.GetAttributes(); 570 | vector < string > Attributes_Value; 571 | vector < string > Score_Range = UniqueValues(Data_Matrix.GetScores()); 572 | 573 | for(i = 0; i < Lines_Number; i++) 574 | { 575 | 576 | for(j = 0; j < Data_Matrix.SizeX() - 1; j++) 577 | { 578 | Attributes_Value.push_back(Data_Matrix.Element((i+2),j)); 579 | } 580 | 581 | string Temp_Score; 582 | Temp_Score = Temp_TestTree(Kinds, Attributes, Attributes_Value, Score_Range); 583 | Test_Scores.push_back(Temp_Score); 584 | Attributes_Value.erase(Attributes_Value.begin(),Attributes_Value.end()); 585 | } 586 | return Test_Scores; 587 | } 588 | 589 | TreeCls::TreeCls() 590 | { 591 | Node = ""; 592 | Branch = ""; 593 | } 594 | 595 | TreeCls * TreeCls::BuildTree(TreeCls * Tree, MatrixCls Remain_Matrix) 596 | { 597 | if(Tree == NULL) 598 | { 599 | Tree = new TreeCls(); 600 | } 601 | 602 | vector < string > Unique_Scores = UniqueValues(Remain_Matrix.GetScores()); 603 | if(Unique_Scores.size() == 1) 604 | { 605 | Tree->Node = Unique_Scores[0]; 606 | return Tree; 607 | } 608 | 609 | //vector < string > Scores = Remain_Matrix.GetScores(); 610 | //if(Scores.size() <= 5) 611 | //{ 612 | // Tree->Node = FrequentValues(Scores); 613 | // return Tree; 614 | //} 615 | 616 | double Gain_Ratio = 0, Entropy_Gain = 0; 617 | double Temp_Gain_Ratio, Temp_Entropy_Gain; 618 | string Max_Attribute; 619 | string Max_Bisect_Node = ""; 620 | int Max_Attribute_Index = 0; 621 | //string Max_Bisect_Node_Index = 0; 622 | vector < string > Attributes = Remain_Matrix.GetAttributes(); 623 | vector < string > Kinds = Remain_Matrix.GetVarKinds(); 624 | int i, j; 625 | for(i = 0; i < Attributes.size(); i++) 626 | { 627 | if(Kinds[i].compare("Discrete") == 0) 628 | { 629 | Temp_Gain_Ratio = GainRatio(Remain_Matrix,Attributes[i]); 630 | } 631 | else if(Kinds[i].compare("Continuous") == 0) 632 | { 633 | vector < string > Bisect_Nodes = Remain_Matrix.GetBisectNodes(Attributes[i]); 634 | for(j = 0; j < Bisect_Nodes.size(); j++) 635 | { 636 | Temp_Entropy_Gain = ComputeAttributeEntropyGain(Remain_Matrix,Attributes[i],Bisect_Nodes[j]); 637 | if(Temp_Entropy_Gain - Entropy_Gain > 1.e-8 ) 638 | { 639 | Entropy_Gain = Temp_Entropy_Gain; 640 | Max_Bisect_Node = Bisect_Nodes[j]; 641 | } 642 | } 643 | Temp_Gain_Ratio = GainRatio(Remain_Matrix,Attributes[i], Max_Bisect_Node); 644 | } 645 | 646 | if(Temp_Gain_Ratio - Gain_Ratio > 1.e-8) 647 | { 648 | Gain_Ratio = Temp_Gain_Ratio; 649 | Max_Attribute = Attributes[i]; 650 | Max_Attribute_Index = i; 651 | } 652 | } 653 | 654 | Tree->Node = Max_Attribute; 655 | vector < string > Values, Branch_Values; 656 | if(Kinds[Max_Attribute_Index].compare("Discrete") == 0) 657 | { 658 | Values = Remain_Matrix.GetUniqueAttributeValues(Max_Attribute); 659 | Branch_Values = Values; 660 | } 661 | else if(Kinds[Max_Attribute_Index].compare("Continuous") == 0) 662 | { 663 | Values = {"Lower_Values", "Upper_Values"}; 664 | string Left_Branch = "< " + Max_Bisect_Node; 665 | string Right_Branch = "> " + Max_Bisect_Node; 666 | Branch_Values = {Left_Branch, Right_Branch}; 667 | } 668 | 669 | int k; 670 | MatrixCls New_Matrix; 671 | for(k = 0; k < Values.size(); k++) 672 | { 673 | New_Matrix = New_Matrix.operator()(Remain_Matrix, Max_Attribute, Values[k], Max_Bisect_Node); 674 | TreeCls * New_Tree = new TreeCls(); 675 | New_Tree->Branch = Branch_Values[k]; 676 | vector < string > New_Unique_Scores = UniqueValues(New_Matrix.GetScores()); 677 | if(New_Unique_Scores.size() == 1) 678 | { 679 | New_Tree->Node = New_Unique_Scores[0]; 680 | } 681 | else 682 | { 683 | BuildTree(New_Tree, New_Matrix); 684 | } 685 | Tree->Child.push_back(New_Tree); 686 | } 687 | return Tree; 688 | } 689 | 690 | void TreeCls::Display(int Depth = 0) 691 | { 692 | for(int i = 0; i < Depth; i++) 693 | { 694 | cout << "\t"; 695 | } 696 | if(this->Branch.compare("") != 0) 697 | { 698 | cout << this->Branch << endl; 699 | for(int i = 0; i < Depth+1; i++) 700 | { 701 | cout << "\t"; 702 | } 703 | } 704 | cout << this->Node << endl; 705 | for(int i = 0; i < this->Child.size(); i++) 706 | { 707 | (this->Child[i])->Display(Depth + 1); 708 | } 709 | } 710 | 711 | void DisplayVector(vector < string > The_Vector) 712 | { 713 | for(int i = 0; i < The_Vector.size(); i++) 714 | { 715 | cout << The_Vector[i] << "\n" ; 716 | } 717 | cout << endl; 718 | } 719 | 720 | int main() 721 | { 722 | MatrixCls Matrix("Golf.dat"); 723 | TreeCls * Tree; 724 | Tree = Tree->BuildTree(Tree, Matrix); 725 | Tree->Display(); 726 | vector < string > Test_Scores = Tree->TestTree(Matrix); 727 | DisplayVector(Test_Scores); 728 | vector < string > Test_String = {"a","b","c","d"}; 729 | DisplayVector(Test_String); 730 | Test_String.erase(Test_String.begin()+0); 731 | //cout << sizeof(Test_String.begin()) << endl; 732 | DisplayVector(Test_String); 733 | } 734 | -------------------------------------------------------------------------------- /C++ source Code/Continuous/.vscode/c_cpp_properties.json: -------------------------------------------------------------------------------- 1 | { 2 | "configurations": [ 3 | { 4 | "name": "Linux", 5 | "includePath": [ 6 | "${workspaceFolder}/**", 7 | "/usr/local/eigen3/" 8 | ], 9 | "defines": [], 10 | "compilerPath": "/usr/bin/clang", 11 | "cStandard": "c11", 12 | "cppStandard": "c++17", 13 | "intelliSenseMode": "clang-x64" 14 | } 15 | ], 16 | "version": 4 17 | } -------------------------------------------------------------------------------- /C++ source Code/Continuous/.vscode/launch.json: -------------------------------------------------------------------------------- 1 | { 2 | // Use IntelliSense to learn about possible attributes. 3 | // Hover to view descriptions of existing attributes. 4 | // For more information, visit: 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-------------------------------------------------------------------------------- 1 | #ifndef CDTree_H 2 | #define CDTree_H 3 | 4 | #include 5 | #include //读取文件内容 6 | #include 7 | #include 8 | #include 9 | #include 10 | #include 11 | 12 | using std::cout; using std::cin; using std::cerr; using std::endl; 13 | using std::string; using std::ifstream; using std::istringstream; 14 | using std::vector; using std::map; using std::ios; 15 | 16 | using Eigen::MatrixXf; //Eigen Matrix float 存储数据 训练数据 17 | using Eigen::MatrixXi; //Eigen Matrix int 存储数据 训练数据和标签 18 | using Eigen::VectorXi; 19 | using Eigen::VectorXf; 20 | 21 | 22 | class CDTree 23 | { 24 | public: 25 | struct TreeNode 26 | { //节点信息 27 | TreeNode* parents; //当前节点的双亲节点 28 | int AttributeIndex; //此节点对应的属性索引 29 | bool LeafNode; //判断是否为叶子节点 30 | vector children; //孩子节点的地址。 31 | int label; //if it is a leaf node then this prameter is useful 32 | 33 | }; 34 | 35 | struct IntervalTree 36 | { 37 | IntervalTree *leftTree; 38 | IntervalTree *rightTree; 39 | bool LeafNode; 40 | float cutValue; 41 | IntervalTree *parients; 42 | }; 43 | 44 | vector ColsIndex; //所有数据的列 45 | TreeNode* root; 46 | int deepestTree; // deepest depth of the tree 47 | int max_bin; // max number of interval on every continous parameter's cut 48 | float thresholdInfoGain; //在计算 49 | int trainX_Dimension; 50 | 51 | 52 | CDTree(int deepestTree, int max_bin, float thresholdInfoGain); 53 | CDTree(); 54 | int buildTree(const MatrixXf &trainX, const MatrixXi &trainY, const MatrixXf &validateX, const MatrixXi &validateY, string Algorithm); 55 | int buildTree(const MatrixXf &trainX, const MatrixXi &trainY, string Algorithm); 56 | MatrixXi predict(const MatrixXf &testX); 57 | ~CDTree(); 58 | 59 | private: 60 | vector>> intervals; // 每个属性的区间分割 61 | vector>> continous2discrete(const MatrixXf &trainX, const MatrixXi &trainY); 62 | IntervalTree* MultiwayPartitionGain(IntervalTree *parientsNode, const vector &continuousVec, const MatrixXf &trainX, const MatrixXi &trainY, int index, float thresholdInfoGain); 63 | int getIntervalsFromTreeStruct(IntervalTree *Intervalroot, vector &intervals); 64 | int sortVectorXf(const VectorXf &vec, VectorXf &sorted_vec, VectorXi &ind); 65 | int cutBranches(const MatrixXf &validateX, const MatrixXi &validateY); 66 | int cutBranches(int deepthTree); 67 | TreeNode* AlgorithmID3(TreeNode *parients, const MatrixXf &trainX, const MatrixXi &trainY, vector attrrbuteIndexOfCurrentData); 68 | TreeNode* AlgorithmC4_5(const MatrixXf &trainX, const MatrixXi &trainY); 69 | TreeNode* AlgorithmCART(const MatrixXf &trainX, const MatrixXi &trainY); 70 | std::pair splitData(const MatrixXf &trainX, const MatrixXi &trainY, std::pair smallInterval, int maxIndex, string flag); 71 | int FindMaxInformationGain(vector s); 72 | vector CalculateInfGain(const MatrixXf &trainX, const MatrixXi &trainY, vector attributeIndexOfCurrentData); 73 | vector getSampleProbability(const MatrixXi &trainY); 74 | vector removeColRepetitionValue(const MatrixXf &Col); 75 | MatrixXi findDvlabel(const MatrixXf &trainX, const MatrixXi &trainY, int index, std::pair smallInterval, string flag); 76 | float calculateEntropy(vector probability); 77 | bool TheSameLabel(const MatrixXi &trainY); 78 | int chooseMostLabel(const MatrixXi &trainY); 79 | int findExistandDealwith(int label, vector> &labelAndNum); 80 | int destroyTree(TreeNode* root); 81 | int destroyIntervalTree(IntervalTree *root); 82 | 83 | template 84 | int KMeans(vector vec, vector &newvec, int index); 85 | int predictTree(TreeNode *node, const MatrixXf testX); 86 | 87 | }; 88 | 89 | #endif -------------------------------------------------------------------------------- /C++ source Code/Continuous/CMakeCache.txt: -------------------------------------------------------------------------------- 1 | # This is the CMakeCache file. 2 | # For build in directory: /home/liana/Desktop/MachineLearning/MachineLearning-DecisionTree/C++ source Code/Continuous 3 | # It was generated by CMake: 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define COMPILER_ID "Borland" 57 | /* __BORLANDC__ = 0xVRR */ 58 | # define COMPILER_VERSION_MAJOR HEX(__BORLANDC__>>8) 59 | # define COMPILER_VERSION_MINOR HEX(__BORLANDC__ & 0xFF) 60 | 61 | #elif defined(__WATCOMC__) && __WATCOMC__ < 1200 62 | # define COMPILER_ID "Watcom" 63 | /* __WATCOMC__ = VVRR */ 64 | # define COMPILER_VERSION_MAJOR DEC(__WATCOMC__ / 100) 65 | # define COMPILER_VERSION_MINOR DEC((__WATCOMC__ / 10) % 10) 66 | # if (__WATCOMC__ % 10) > 0 67 | # define COMPILER_VERSION_PATCH DEC(__WATCOMC__ % 10) 68 | # endif 69 | 70 | #elif defined(__WATCOMC__) 71 | # define COMPILER_ID "OpenWatcom" 72 | /* __WATCOMC__ = VVRP + 1100 */ 73 | # define COMPILER_VERSION_MAJOR DEC((__WATCOMC__ - 1100) / 100) 74 | # define COMPILER_VERSION_MINOR DEC((__WATCOMC__ / 10) % 10) 75 | # if (__WATCOMC__ % 10) > 0 76 | # define COMPILER_VERSION_PATCH DEC(__WATCOMC__ % 10) 77 | # endif 78 | 79 | #elif defined(__SUNPRO_CC) 80 | # define COMPILER_ID "SunPro" 81 | # if __SUNPRO_CC >= 0x5100 82 | /* __SUNPRO_CC = 0xVRRP */ 83 | # define COMPILER_VERSION_MAJOR HEX(__SUNPRO_CC>>12) 84 | # define COMPILER_VERSION_MINOR HEX(__SUNPRO_CC>>4 & 0xFF) 85 | # define COMPILER_VERSION_PATCH HEX(__SUNPRO_CC & 0xF) 86 | # else 87 | /* __SUNPRO_CC = 0xVRP */ 88 | # define COMPILER_VERSION_MAJOR HEX(__SUNPRO_CC>>8) 89 | # define COMPILER_VERSION_MINOR HEX(__SUNPRO_CC>>4 & 0xF) 90 | # define COMPILER_VERSION_PATCH HEX(__SUNPRO_CC & 0xF) 91 | # endif 92 | 93 | #elif defined(__HP_aCC) 94 | # define COMPILER_ID "HP" 95 | /* __HP_aCC = VVRRPP */ 96 | # define COMPILER_VERSION_MAJOR DEC(__HP_aCC/10000) 97 | # define COMPILER_VERSION_MINOR DEC(__HP_aCC/100 % 100) 98 | # define COMPILER_VERSION_PATCH DEC(__HP_aCC % 100) 99 | 100 | #elif defined(__DECCXX) 101 | # define COMPILER_ID "Compaq" 102 | /* __DECCXX_VER = VVRRTPPPP */ 103 | # define COMPILER_VERSION_MAJOR DEC(__DECCXX_VER/10000000) 104 | # define COMPILER_VERSION_MINOR DEC(__DECCXX_VER/100000 % 100) 105 | # define COMPILER_VERSION_PATCH DEC(__DECCXX_VER % 10000) 106 | 107 | #elif defined(__IBMCPP__) && defined(__COMPILER_VER__) 108 | # define COMPILER_ID "zOS" 109 | /* __IBMCPP__ = VRP */ 110 | # define COMPILER_VERSION_MAJOR DEC(__IBMCPP__/100) 111 | # define COMPILER_VERSION_MINOR DEC(__IBMCPP__/10 % 10) 112 | # define COMPILER_VERSION_PATCH DEC(__IBMCPP__ % 10) 113 | 114 | #elif defined(__IBMCPP__) && !defined(__COMPILER_VER__) && __IBMCPP__ >= 800 115 | # define COMPILER_ID "XL" 116 | /* __IBMCPP__ = VRP */ 117 | # define COMPILER_VERSION_MAJOR DEC(__IBMCPP__/100) 118 | # define COMPILER_VERSION_MINOR DEC(__IBMCPP__/10 % 10) 119 | # define COMPILER_VERSION_PATCH DEC(__IBMCPP__ % 10) 120 | 121 | #elif defined(__IBMCPP__) && !defined(__COMPILER_VER__) && __IBMCPP__ < 800 122 | # define COMPILER_ID "VisualAge" 123 | /* __IBMCPP__ = VRP */ 124 | # define COMPILER_VERSION_MAJOR DEC(__IBMCPP__/100) 125 | # define COMPILER_VERSION_MINOR DEC(__IBMCPP__/10 % 10) 126 | # define COMPILER_VERSION_PATCH DEC(__IBMCPP__ % 10) 127 | 128 | #elif defined(__PGI) 129 | # define COMPILER_ID "PGI" 130 | # define COMPILER_VERSION_MAJOR DEC(__PGIC__) 131 | # define COMPILER_VERSION_MINOR DEC(__PGIC_MINOR__) 132 | # if defined(__PGIC_PATCHLEVEL__) 133 | # define COMPILER_VERSION_PATCH DEC(__PGIC_PATCHLEVEL__) 134 | # endif 135 | 136 | #elif defined(_CRAYC) 137 | # define COMPILER_ID "Cray" 138 | # define COMPILER_VERSION_MAJOR DEC(_RELEASE_MAJOR) 139 | # define COMPILER_VERSION_MINOR DEC(_RELEASE_MINOR) 140 | 141 | #elif defined(__TI_COMPILER_VERSION__) 142 | # define COMPILER_ID "TI" 143 | /* __TI_COMPILER_VERSION__ = VVVRRRPPP */ 144 | # define COMPILER_VERSION_MAJOR DEC(__TI_COMPILER_VERSION__/1000000) 145 | # define COMPILER_VERSION_MINOR DEC(__TI_COMPILER_VERSION__/1000 % 1000) 146 | # define COMPILER_VERSION_PATCH DEC(__TI_COMPILER_VERSION__ % 1000) 147 | 148 | #elif defined(__FUJITSU) || defined(__FCC_VERSION) || defined(__fcc_version) 149 | # define COMPILER_ID "Fujitsu" 150 | 151 | #elif defined(__SCO_VERSION__) 152 | # define COMPILER_ID "SCO" 153 | 154 | #elif defined(__clang__) && defined(__apple_build_version__) 155 | # define COMPILER_ID "AppleClang" 156 | # if defined(_MSC_VER) 157 | # define SIMULATE_ID "MSVC" 158 | # endif 159 | # define COMPILER_VERSION_MAJOR DEC(__clang_major__) 160 | # define COMPILER_VERSION_MINOR DEC(__clang_minor__) 161 | # define COMPILER_VERSION_PATCH DEC(__clang_patchlevel__) 162 | # if defined(_MSC_VER) 163 | /* _MSC_VER = VVRR */ 164 | # define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100) 165 | # define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100) 166 | # endif 167 | # define COMPILER_VERSION_TWEAK DEC(__apple_build_version__) 168 | 169 | #elif defined(__clang__) 170 | # define COMPILER_ID "Clang" 171 | # if defined(_MSC_VER) 172 | # define SIMULATE_ID "MSVC" 173 | # endif 174 | # define COMPILER_VERSION_MAJOR DEC(__clang_major__) 175 | # define COMPILER_VERSION_MINOR DEC(__clang_minor__) 176 | # define COMPILER_VERSION_PATCH DEC(__clang_patchlevel__) 177 | # if defined(_MSC_VER) 178 | /* _MSC_VER = VVRR */ 179 | # define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100) 180 | # define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100) 181 | # endif 182 | 183 | #elif defined(__GNUC__) 184 | # define COMPILER_ID "GNU" 185 | # define COMPILER_VERSION_MAJOR DEC(__GNUC__) 186 | # if defined(__GNUC_MINOR__) 187 | # define COMPILER_VERSION_MINOR DEC(__GNUC_MINOR__) 188 | # endif 189 | # if defined(__GNUC_PATCHLEVEL__) 190 | # define COMPILER_VERSION_PATCH DEC(__GNUC_PATCHLEVEL__) 191 | # endif 192 | 193 | #elif defined(_MSC_VER) 194 | # define COMPILER_ID "MSVC" 195 | /* _MSC_VER = VVRR */ 196 | # define COMPILER_VERSION_MAJOR DEC(_MSC_VER / 100) 197 | # define COMPILER_VERSION_MINOR DEC(_MSC_VER % 100) 198 | # if defined(_MSC_FULL_VER) 199 | # if _MSC_VER >= 1400 200 | /* _MSC_FULL_VER = VVRRPPPPP */ 201 | # define COMPILER_VERSION_PATCH DEC(_MSC_FULL_VER % 100000) 202 | # else 203 | /* _MSC_FULL_VER = VVRRPPPP */ 204 | # define COMPILER_VERSION_PATCH DEC(_MSC_FULL_VER % 10000) 205 | # endif 206 | # endif 207 | # if defined(_MSC_BUILD) 208 | # define COMPILER_VERSION_TWEAK DEC(_MSC_BUILD) 209 | # endif 210 | 211 | #elif defined(__VISUALDSPVERSION__) || defined(__ADSPBLACKFIN__) || defined(__ADSPTS__) || defined(__ADSP21000__) 212 | # define COMPILER_ID "ADSP" 213 | #if defined(__VISUALDSPVERSION__) 214 | /* __VISUALDSPVERSION__ = 0xVVRRPP00 */ 215 | # define COMPILER_VERSION_MAJOR HEX(__VISUALDSPVERSION__>>24) 216 | # define COMPILER_VERSION_MINOR HEX(__VISUALDSPVERSION__>>16 & 0xFF) 217 | # define COMPILER_VERSION_PATCH HEX(__VISUALDSPVERSION__>>8 & 0xFF) 218 | #endif 219 | 220 | #elif defined(__IAR_SYSTEMS_ICC__ ) || defined(__IAR_SYSTEMS_ICC) 221 | # define COMPILER_ID "IAR" 222 | 223 | #elif defined(__ARMCC_VERSION) 224 | # define COMPILER_ID "ARMCC" 225 | #if __ARMCC_VERSION >= 1000000 226 | /* __ARMCC_VERSION = VRRPPPP */ 227 | # define COMPILER_VERSION_MAJOR DEC(__ARMCC_VERSION/1000000) 228 | # define COMPILER_VERSION_MINOR DEC(__ARMCC_VERSION/10000 % 100) 229 | # define COMPILER_VERSION_PATCH DEC(__ARMCC_VERSION % 10000) 230 | #else 231 | /* __ARMCC_VERSION = VRPPPP */ 232 | # define COMPILER_VERSION_MAJOR DEC(__ARMCC_VERSION/100000) 233 | # define COMPILER_VERSION_MINOR DEC(__ARMCC_VERSION/10000 % 10) 234 | # define COMPILER_VERSION_PATCH DEC(__ARMCC_VERSION % 10000) 235 | #endif 236 | 237 | 238 | #elif defined(_SGI_COMPILER_VERSION) || defined(_COMPILER_VERSION) 239 | # define COMPILER_ID "MIPSpro" 240 | # if defined(_SGI_COMPILER_VERSION) 241 | /* _SGI_COMPILER_VERSION = VRP */ 242 | # define COMPILER_VERSION_MAJOR DEC(_SGI_COMPILER_VERSION/100) 243 | # define COMPILER_VERSION_MINOR DEC(_SGI_COMPILER_VERSION/10 % 10) 244 | # define COMPILER_VERSION_PATCH DEC(_SGI_COMPILER_VERSION % 10) 245 | # else 246 | /* _COMPILER_VERSION = VRP */ 247 | # define COMPILER_VERSION_MAJOR DEC(_COMPILER_VERSION/100) 248 | # define COMPILER_VERSION_MINOR DEC(_COMPILER_VERSION/10 % 10) 249 | # define COMPILER_VERSION_PATCH DEC(_COMPILER_VERSION % 10) 250 | # endif 251 | 252 | 253 | /* These compilers are either not known or too old to define an 254 | identification macro. Try to identify the platform and guess that 255 | it is the native compiler. */ 256 | #elif defined(__sgi) 257 | # define COMPILER_ID "MIPSpro" 258 | 259 | #elif defined(__hpux) || defined(__hpua) 260 | # define COMPILER_ID "HP" 261 | 262 | #else /* unknown compiler */ 263 | # define COMPILER_ID "" 264 | #endif 265 | 266 | /* Construct the string literal in pieces to prevent the source from 267 | getting matched. Store it in a pointer rather than an array 268 | because some compilers will just produce instructions to fill the 269 | array rather than assigning a pointer to a static array. */ 270 | char const* info_compiler = "INFO" ":" "compiler[" COMPILER_ID "]"; 271 | #ifdef SIMULATE_ID 272 | char const* info_simulate = "INFO" ":" "simulate[" SIMULATE_ID "]"; 273 | #endif 274 | 275 | #ifdef __QNXNTO__ 276 | char const* qnxnto = "INFO" ":" "qnxnto[]"; 277 | #endif 278 | 279 | #if defined(__CRAYXE) || defined(__CRAYXC) 280 | char const *info_cray = "INFO" ":" "compiler_wrapper[CrayPrgEnv]"; 281 | #endif 282 | 283 | #define STRINGIFY_HELPER(X) #X 284 | #define STRINGIFY(X) STRINGIFY_HELPER(X) 285 | 286 | /* Identify known platforms by name. */ 287 | #if defined(__linux) || defined(__linux__) || defined(linux) 288 | # define PLATFORM_ID "Linux" 289 | 290 | #elif defined(__CYGWIN__) 291 | # define PLATFORM_ID "Cygwin" 292 | 293 | #elif defined(__MINGW32__) 294 | # define PLATFORM_ID "MinGW" 295 | 296 | #elif defined(__APPLE__) 297 | # define PLATFORM_ID "Darwin" 298 | 299 | #elif defined(_WIN32) || defined(__WIN32__) || defined(WIN32) 300 | # define PLATFORM_ID "Windows" 301 | 302 | #elif defined(__FreeBSD__) || defined(__FreeBSD) 303 | # define PLATFORM_ID "FreeBSD" 304 | 305 | #elif defined(__NetBSD__) || defined(__NetBSD) 306 | # define PLATFORM_ID "NetBSD" 307 | 308 | #elif defined(__OpenBSD__) || defined(__OPENBSD) 309 | # define PLATFORM_ID "OpenBSD" 310 | 311 | #elif defined(__sun) || defined(sun) 312 | # define PLATFORM_ID "SunOS" 313 | 314 | #elif defined(_AIX) || defined(__AIX) || defined(__AIX__) || defined(__aix) || defined(__aix__) 315 | # define PLATFORM_ID "AIX" 316 | 317 | #elif defined(__sgi) || defined(__sgi__) || defined(_SGI) 318 | # define PLATFORM_ID "IRIX" 319 | 320 | #elif defined(__hpux) || defined(__hpux__) 321 | # define PLATFORM_ID "HP-UX" 322 | 323 | #elif defined(__HAIKU__) 324 | # define PLATFORM_ID "Haiku" 325 | 326 | #elif defined(__BeOS) || defined(__BEOS__) || defined(_BEOS) 327 | # define PLATFORM_ID "BeOS" 328 | 329 | #elif defined(__QNX__) || defined(__QNXNTO__) 330 | # define PLATFORM_ID "QNX" 331 | 332 | #elif defined(__tru64) || defined(_tru64) || defined(__TRU64__) 333 | # define PLATFORM_ID "Tru64" 334 | 335 | #elif defined(__riscos) || defined(__riscos__) 336 | # define PLATFORM_ID "RISCos" 337 | 338 | #elif defined(__sinix) || defined(__sinix__) || defined(__SINIX__) 339 | # define PLATFORM_ID "SINIX" 340 | 341 | #elif defined(__UNIX_SV__) 342 | # define PLATFORM_ID "UNIX_SV" 343 | 344 | #elif defined(__bsdos__) 345 | # define PLATFORM_ID "BSDOS" 346 | 347 | #elif defined(_MPRAS) || defined(MPRAS) 348 | # define PLATFORM_ID "MP-RAS" 349 | 350 | #elif defined(__osf) || defined(__osf__) 351 | # define PLATFORM_ID "OSF1" 352 | 353 | #elif defined(_SCO_SV) || defined(SCO_SV) || defined(sco_sv) 354 | # define PLATFORM_ID "SCO_SV" 355 | 356 | #elif defined(__ultrix) || defined(__ultrix__) || defined(_ULTRIX) 357 | # define PLATFORM_ID "ULTRIX" 358 | 359 | #elif defined(__XENIX__) || defined(_XENIX) || defined(XENIX) 360 | # define PLATFORM_ID "Xenix" 361 | 362 | #elif defined(__WATCOMC__) 363 | # if defined(__LINUX__) 364 | # define PLATFORM_ID "Linux" 365 | 366 | # elif defined(__DOS__) 367 | # define PLATFORM_ID "DOS" 368 | 369 | # elif defined(__OS2__) 370 | # define PLATFORM_ID "OS2" 371 | 372 | # elif defined(__WINDOWS__) 373 | # define PLATFORM_ID "Windows3x" 374 | 375 | # else /* unknown platform */ 376 | # define PLATFORM_ID "" 377 | # endif 378 | 379 | #else /* unknown platform */ 380 | # define PLATFORM_ID "" 381 | 382 | #endif 383 | 384 | /* For windows compilers MSVC and Intel we can determine 385 | the architecture of the compiler being used. This is because 386 | the compilers do not have flags that can change the architecture, 387 | but rather depend on which compiler is being used 388 | */ 389 | #if defined(_WIN32) && defined(_MSC_VER) 390 | # if defined(_M_IA64) 391 | # define ARCHITECTURE_ID "IA64" 392 | 393 | # elif defined(_M_X64) || defined(_M_AMD64) 394 | # define ARCHITECTURE_ID "x64" 395 | 396 | # elif defined(_M_IX86) 397 | # define ARCHITECTURE_ID "X86" 398 | 399 | # elif defined(_M_ARM) 400 | # if _M_ARM == 4 401 | # define ARCHITECTURE_ID "ARMV4I" 402 | # elif _M_ARM == 5 403 | # define ARCHITECTURE_ID "ARMV5I" 404 | # else 405 | # define ARCHITECTURE_ID "ARMV" STRINGIFY(_M_ARM) 406 | # endif 407 | 408 | # elif defined(_M_MIPS) 409 | # define ARCHITECTURE_ID "MIPS" 410 | 411 | # elif defined(_M_SH) 412 | # define ARCHITECTURE_ID "SHx" 413 | 414 | # else /* unknown architecture */ 415 | # define ARCHITECTURE_ID "" 416 | # endif 417 | 418 | #elif defined(__WATCOMC__) 419 | # if defined(_M_I86) 420 | # define ARCHITECTURE_ID "I86" 421 | 422 | # elif defined(_M_IX86) 423 | # define ARCHITECTURE_ID "X86" 424 | 425 | # else /* unknown architecture */ 426 | # define ARCHITECTURE_ID "" 427 | # endif 428 | 429 | #else 430 | # define ARCHITECTURE_ID "" 431 | #endif 432 | 433 | /* Convert integer to decimal digit literals. */ 434 | #define DEC(n) \ 435 | ('0' + (((n) / 10000000)%10)), \ 436 | ('0' + (((n) / 1000000)%10)), \ 437 | ('0' + (((n) / 100000)%10)), \ 438 | ('0' + (((n) / 10000)%10)), \ 439 | ('0' + (((n) / 1000)%10)), \ 440 | ('0' + (((n) / 100)%10)), \ 441 | ('0' + (((n) / 10)%10)), \ 442 | ('0' + ((n) % 10)) 443 | 444 | /* Convert integer to hex digit literals. */ 445 | #define HEX(n) \ 446 | ('0' + ((n)>>28 & 0xF)), \ 447 | ('0' + ((n)>>24 & 0xF)), \ 448 | ('0' + ((n)>>20 & 0xF)), \ 449 | ('0' + ((n)>>16 & 0xF)), \ 450 | ('0' + ((n)>>12 & 0xF)), \ 451 | ('0' + ((n)>>8 & 0xF)), \ 452 | ('0' + ((n)>>4 & 0xF)), \ 453 | ('0' + ((n) & 0xF)) 454 | 455 | /* Construct a string literal encoding the version number components. */ 456 | #ifdef COMPILER_VERSION_MAJOR 457 | char const info_version[] = { 458 | 'I', 'N', 'F', 'O', ':', 459 | 'c','o','m','p','i','l','e','r','_','v','e','r','s','i','o','n','[', 460 | COMPILER_VERSION_MAJOR, 461 | # ifdef COMPILER_VERSION_MINOR 462 | '.', COMPILER_VERSION_MINOR, 463 | # ifdef COMPILER_VERSION_PATCH 464 | '.', COMPILER_VERSION_PATCH, 465 | # ifdef COMPILER_VERSION_TWEAK 466 | '.', COMPILER_VERSION_TWEAK, 467 | # endif 468 | # endif 469 | # endif 470 | ']','\0'}; 471 | #endif 472 | 473 | /* Construct a string literal encoding the version number components. */ 474 | #ifdef SIMULATE_VERSION_MAJOR 475 | char const info_simulate_version[] = { 476 | 'I', 'N', 'F', 'O', ':', 477 | 's','i','m','u','l','a','t','e','_','v','e','r','s','i','o','n','[', 478 | SIMULATE_VERSION_MAJOR, 479 | # ifdef SIMULATE_VERSION_MINOR 480 | '.', SIMULATE_VERSION_MINOR, 481 | # ifdef SIMULATE_VERSION_PATCH 482 | '.', SIMULATE_VERSION_PATCH, 483 | # ifdef SIMULATE_VERSION_TWEAK 484 | '.', SIMULATE_VERSION_TWEAK, 485 | # endif 486 | # endif 487 | # endif 488 | ']','\0'}; 489 | #endif 490 | 491 | /* Construct the string literal in pieces to prevent the source from 492 | getting matched. 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DtreeMain" 165 | @echo "... CDTree.o" 166 | @echo "... CDTree.i" 167 | @echo "... CDTree.s" 168 | @echo "... main.o" 169 | @echo "... main.i" 170 | @echo "... main.s" 171 | .PHONY : help 172 | 173 | 174 | 175 | #============================================================================= 176 | # Special targets to cleanup operation of make. 177 | 178 | # Special rule to run CMake to check the build system integrity. 179 | # No rule that depends on this can have commands that come from listfiles 180 | # because they might be regenerated. 181 | cmake_check_build_system: 182 | $(CMAKE_COMMAND) -H$(CMAKE_SOURCE_DIR) -B$(CMAKE_BINARY_DIR) --check-build-system CMakeFiles/Makefile.cmake 0 183 | .PHONY : cmake_check_build_system 184 | 185 | -------------------------------------------------------------------------------- /C++ source Code/Continuous/RFCSV.hpp: -------------------------------------------------------------------------------- 1 | /* 2 | read data from csv 3 | */ 4 | #ifndef RFCSV_H 5 | #define RFCSV_H 6 | #include 7 | #include 8 | #include 9 | using std::cout; using std::cin; using std::cerr; using std::endl; 10 | using std::string; using std::ifstream; using std::istringstream; 11 | using std::vector; using std::ios; 12 | 13 | template 14 | class RFCSV 15 | { 16 | public: 17 | string csvFileName; 18 | RFCSV() 19 | { 20 | this->csvFileName = ""; 21 | } 22 | RFCSV(string fileName) 23 | { 24 | this->csvFileName = fileName; 25 | } 26 | 27 | std::pair getData(string fileName) 28 | { 29 | 30 | string csvFileName = fileName; 31 | 32 | int cols = 0, rows = 0; //通过文件读取 获取行和列的信息。 33 | vector> strResult; 34 | ifstream read; 35 | read.open(csvFileName, ios::in); 36 | 37 | string headline; 38 | getline(read, headline); 39 | rows++; //每次readline都要加行数一次 40 | if (rows > 0) 41 | { 42 | string attributeName; 43 | char delim = ','; 44 | istringstream stringin(headline); 45 | vector oneLineString; 46 | while (getline(stringin, attributeName, delim)) 47 | { 48 | cols++; 49 | oneLineString.push_back(attributeName); 50 | } 51 | strResult.push_back(oneLineString); 52 | //cout << endl; 53 | } 54 | //获取第一行信息 55 | string line; //从第二行以后开始存储的每一行的数据 56 | //下面获取后面的信息 57 | while (getline(read, line)) 58 | { 59 | rows++; 60 | int realcols = 0; //用来判断此行中数据是否和标题行的数据列数相同，相同才存储，否则不存储 61 | string attributeName; 62 | char delim = ','; 63 | istringstream stringin(line); 64 | vector oneLineString; 65 | while (getline(stringin, attributeName, delim)) 66 | { 67 | realcols++; 68 | //cout << attributeName << " "; 69 | oneLineString.push_back(attributeName); 70 | } 71 | if (realcols == cols) 72 | { //只有和标题行数据列数相同才存储 73 | strResult.push_back(oneLineString); 74 | } 75 | } 76 | dataT Data(rows, cols - 1); 77 | labelT label(rows, 1); 78 | for (int i = 0; i < rows; i++) 79 | { 80 | for (int j = 0; j < cols - 1; j++) 81 | { 82 | Data(i, j) = stringToNum(strResult[i][j]); 83 | } 84 | label(i, 0) = stringToNum(strResult[i][cols - 1]); 85 | } 86 | return std::pair(Data, label); 87 | } 88 | 89 | std::pair getData() 90 | { 91 | std::pair result; 92 | string csvFileName = this->csvFileName; 93 | return result; 94 | } 95 | 96 | template 97 | Type stringToNum(const string& str) 98 | { 99 | istringstream iss(str); 100 | Type num; 101 | iss >> num; 102 | return num; 103 | } 104 | 105 | }; 106 | 107 | #endif -------------------------------------------------------------------------------- /C++ source Code/Continuous/cmake_install.cmake: -------------------------------------------------------------------------------- 1 | # Install script for directory: /home/liana/Desktop/MachineLearning/MachineLearning-DecisionTree/C++ source Code/Continuous 2 | 3 | # Set the install prefix 4 | if(NOT DEFINED CMAKE_INSTALL_PREFIX) 5 | set(CMAKE_INSTALL_PREFIX "/usr/local") 6 | endif() 7 | string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}") 8 | 9 | # Set the install configuration name. 10 | if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME) 11 | if(BUILD_TYPE) 12 | string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" 13 | CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") 14 | else() 15 | set(CMAKE_INSTALL_CONFIG_NAME "debug") 16 | endif() 17 | message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"") 18 | endif() 19 | 20 | # Set the component getting installed. 21 | if(NOT CMAKE_INSTALL_COMPONENT) 22 | if(COMPONENT) 23 | message(STATUS "Install component: \"${COMPONENT}\"") 24 | set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") 25 | else() 26 | set(CMAKE_INSTALL_COMPONENT) 27 | endif() 28 | endif() 29 | 30 | # Install shared libraries without execute permission? 31 | if(NOT DEFINED CMAKE_INSTALL_SO_NO_EXE) 32 | set(CMAKE_INSTALL_SO_NO_EXE "1") 33 | endif() 34 | 35 | if(CMAKE_INSTALL_COMPONENT) 36 | set(CMAKE_INSTALL_MANIFEST "install_manifest_${CMAKE_INSTALL_COMPONENT}.txt") 37 | else() 38 | set(CMAKE_INSTALL_MANIFEST "install_manifest.txt") 39 | endif() 40 | 41 | string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT 42 | "${CMAKE_INSTALL_MANIFEST_FILES}") 43 | file(WRITE "/home/liana/Desktop/MachineLearning/MachineLearning-DecisionTree/C++ source Code/Continuous/${CMAKE_INSTALL_MANIFEST}" 44 | "${CMAKE_INSTALL_MANIFEST_CONTENT}") 45 | -------------------------------------------------------------------------------- /C++ source Code/Continuous/main.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include "CDTree.hpp" 3 | #include "RFCSV.hpp" 4 | #include 5 | using Eigen::MatrixXf; using Eigen::MatrixXi; 6 | int main() 7 | { 8 | 9 | std::pair trainData, testData; 10 | RFCSV readcsv; 11 | trainData = readcsv.getData("../data/ContinuousTrain.csv"); 12 | testData = readcsv.getData("../data/ContinuousTest.csv"); 13 | CDTree myCDTree(4, 3, 0.0001); 14 | myCDTree.buildTree(trainData.first, trainData.second, "ID3"); 15 | MatrixXi predictresult = myCDTree.predict(testData.first); 16 | float accuracy = 0; 17 | for (int i = 0; i < predictresult.size(); i++) 18 | { 19 | if (testData.second(i) == predictresult(i)) 20 | { 21 | accuracy += 1.0 / predictresult.size(); 22 | } 23 | } 24 | cout << "准确率为：" << accuracy * 100 << "\% 你很棒棒哦！" << endl; 25 | } 26 | -------------------------------------------------------------------------------- /C++ source Code/Discrete/DTree.cpp: -------------------------------------------------------------------------------- 1 | #include "DTree.hpp" 2 | 3 | 4 | DTree::DTree() 5 | { 6 | this->root = new TreeNode; 7 | this->trainMatrixInfo.cols = 0; 8 | this->trainMatrixInfo.rows = 0; 9 | 10 | } 11 | 12 | DTree::DTree(string csvfilename) 13 | { 14 | this->root = new TreeNode; 15 | this->trainMatrixInfo.cols = 0; 16 | this->trainMatrixInfo.rows = 0; 17 | this->ReadTrainDataFile(csvfilename); 18 | 19 | } 20 | /* 21 | DTree::TreeNode* DTree::DTree::BuildTree(MatrixXi &data, vector &dataAttr, string Algorithm) 22 | { 23 | if (Algorithm == "ID3") 24 | { 25 | return AlgorithmID3(data, dataAttr); 26 | } 27 | else if (Algorithm == "C4.5") 28 | { 29 | return AlgorithmC4_5(data, dataAttr); 30 | } 31 | else if (Algorithm == "CART") 32 | { 33 | return AlgorithmCART(data, dataAttr); 34 | } 35 | else 36 | { 37 | cerr << "Input error, the program has been stopped!"; 38 | exit(0); 39 | } 40 | return 0; 41 | } 42 | */ 43 | int DTree::DTree::BuildTree(MatrixXi &data, vector &dataAttr, string Algorithm) 44 | { 45 | 46 | if (Algorithm == "ID3") 47 | { 48 | this->root = AlgorithmID3(data, dataAttr); 49 | } 50 | else if (Algorithm == "C4.5") 51 | { 52 | this->root = AlgorithmC4_5(data, dataAttr); 53 | } 54 | else if (Algorithm == "CART") 55 | { 56 | this->root = AlgorithmCART(data, dataAttr); 57 | } 58 | else 59 | { 60 | cerr << "Algorithm name must be ID3,C4.5 or CART, other algorithms are not supported, the program has been stopped!"; 61 | exit(0); 62 | } 63 | return 0; 64 | } 65 | 66 | /* 67 | get data from csv files , the first row of csvfile is the name of attributes, string type 68 | return int type data to this->trainDataMat for later training process 69 | */ 70 | MatrixXi DTree::ReadTrainDataFile(string fileAddress) 71 | { 72 | int cols = 0, rows = 0; //通过文件读取 获取行和列的信息。 73 | ///MatrixXi strResult; 74 | //MatrixXi result; //返回的特征值矩阵。 75 | vector> strResult; 76 | vector headAttr; 77 | ifstream read; 78 | read.open(fileAddress, ios::in); 79 | 80 | string headline; 81 | getline(read, headline); 82 | rows++; //每次readline都要加行数一次 83 | if (rows > 0) 84 | { 85 | string attributeName; 86 | char delim = ','; 87 | istringstream stringin(headline); 88 | vector oneLineString; 89 | while (getline(stringin, attributeName, delim)) 90 | { 91 | cols++; 92 | oneLineString.push_back(attributeName); 93 | } 94 | strResult.push_back(oneLineString); 95 | //cout << endl; 96 | } 97 | //获取第一行信息 98 | string line; //从第二行以后开始存储的每一行的数据 99 | //下面获取后面的信息 100 | while (getline(read, line)) 101 | { 102 | rows++; 103 | int realcols = 0; //用来判断此行中数据是否和标题行的数据列数相同，相同才存储，否则不存储 104 | string attributeName; 105 | char delim = ','; 106 | istringstream stringin(line); 107 | vector oneLineString; 108 | while (getline(stringin, attributeName, delim)) 109 | { 110 | realcols++; 111 | //cout << attributeName << " "; 112 | oneLineString.push_back(attributeName); 113 | } 114 | if (realcols == cols) 115 | { //只有和标题行数据列数相同才存储 116 | strResult.push_back(oneLineString); 117 | } 118 | } 119 | //cout << rows << " " << cols; 120 | //MatrixXi tmpMat(rows - 1, cols, CV_8UC1); 121 | MatrixXi tempMat(rows - 1, cols); 122 | this->trainMatrixInfo.cols = cols; 123 | this->trainMatrixInfo.rows = rows - 1; 124 | stringDataToInt(strResult, tempMat, headAttr); 125 | this->trainDataMat = tempMat; 126 | this->vectorAttr = headAttr; 127 | return tempMat; 128 | } 129 | int DTree::stringDataToInt(const vector> &src, MatrixXi &dataMat, vector &headAttrInfo) 130 | { 131 | int matRows = int(src.size()); 132 | int matCols = int(src[0].size()); 133 | for (int j = 0; j < matCols; j++) 134 | { 135 | vector perColStringVector; //每一列的z字符串种类 136 | map oneColMap; 137 | unsigned char indexNum = 0; //这个数用来产生整型的参数值。 138 | Attr perColAttr; 139 | perColAttr.Attribute = src[0][j]; 140 | perColAttr.colIndex = j; 141 | for (int i = 1; i < matRows; i++) 142 | { 143 | if (StringExistInVector(src[i][j], perColStringVector)) 144 | { 145 | dataMat(i - 1, j) = oneColMap[src[i][j]]; 146 | } 147 | else 148 | { 149 | dataMat(i - 1, j) = indexNum; 150 | perColStringVector.push_back(src[i][j]); 151 | oneColMap.insert(map::value_type(src[i][j], indexNum)); 152 | indexNum++; 153 | } 154 | } 155 | perColAttr.typeMap = oneColMap; 156 | perColAttr.typeNum = indexNum; 157 | perColAttr.AttributeValue = perColStringVector; 158 | headAttrInfo.push_back(perColAttr); 159 | } 160 | return 0; 161 | } 162 | 163 | bool DTree::StringExistInVector(string str, vector strVector) 164 | { 165 | int vectorSize = int(strVector.size()); 166 | for (int i = 0; i < vectorSize; i++) 167 | { 168 | if (strVector[i] == str) 169 | { 170 | return true; 171 | } 172 | } 173 | return false; 174 | } 175 | 176 | DTree::TreeNode* DTree::AlgorithmID3(MatrixXi &data, vector &properties) 177 | { 178 | //对矩阵进行计算，包括entropy,informationGain; 179 | //vector entropy; //信息熵 180 | vector informationGain; //信息增益 181 | int maxIndex; //最大信息增益的属性的索引 182 | //entropy = CalculateEntropy(data); 183 | //cout << "dataSize:" << data.size() << endl; 184 | //cout << data << endl; 185 | informationGain = CalculateInfGain(data); 186 | maxIndex = FindMaxInformationGain(informationGain); 187 | if (data.cols() == 2) 188 | { 189 | TreeNode* leaf = new TreeNode; 190 | string label; 191 | label = MostInMatLabel(data, properties); 192 | leaf->Attribute = properties[0].Attribute; 193 | leaf->LeafNode = true; 194 | return leaf; 195 | } 196 | else if (TheSameLabel(data)) 197 | { 198 | TreeNode* leaf = new TreeNode; 199 | int labelIndex = int(properties.size() - 1); 200 | int labelValue = int(data(0, data.cols() - 1)); 201 | string label = FindAttrString(labelValue, properties[labelIndex]); 202 | leaf->Attribute = label; 203 | leaf->LeafNode = true; 204 | return leaf; 205 | } 206 | else 207 | { 208 | //进行递归 209 | TreeNode* branchNode = new TreeNode; 210 | branchNode->Attribute = properties[maxIndex].Attribute; 211 | branchNode->LeafNode = false; 212 | vector tmpAttr = properties; //构建新的属性向量，用于下次 213 | tmpAttr.erase(tmpAttr.begin() + maxIndex); //删除上一个节点的属性。 214 | vector attributeValue = properties[maxIndex].AttributeValue; 215 | //进行分支 216 | 217 | for (size_t i = 0; i < properties[maxIndex].AttributeValue.size(); i++) 218 | { 219 | TreeNode* childNode = new TreeNode; //声明一个孩子节点 220 | string oneAttributeValue = attributeValue[i]; //每一个属性对应的值，字符串。 221 | if (DataExistAttribute(data, properties, maxIndex, oneAttributeValue)) 222 | { 223 | //每一个属性的其中一个取值生成一个分支节点，所以需要将原来矩阵中的这个数值的这个值的样本去掉 224 | MatrixXi subMat; 225 | subMat = GetNewMat(data, properties, maxIndex, oneAttributeValue); 226 | //这里预留位置，进行预剪枝操作， 227 | //预剪枝操作 228 | 229 | childNode = this->AlgorithmID3(subMat, tmpAttr); 230 | branchNode->children.push_back(childNode); 231 | branchNode->AttributeLinkChildren.insert(map::value_type(oneAttributeValue, childNode)); 232 | } 233 | } 234 | //返回分支节点 235 | return branchNode; 236 | } 237 | } 238 | DTree::TreeNode* DTree::AlgorithmC4_5(MatrixXi &data, vector &a) 239 | { 240 | return nullptr; 241 | } 242 | DTree::TreeNode* DTree::AlgorithmCART(MatrixXi &data, vector &a) 243 | { 244 | return nullptr; 245 | } 246 | vector DTree::CalculateInfGain(MatrixXi &data) //计算InformationGain 247 | { 248 | 249 | vector result; 250 | float dataEntropy; //矩阵的熵 251 | dataEntropy = GetDataEntropy(data); 252 | //vector AttrValueNum; 253 | for (int i = 0; i < data.cols() - 1; i++) //只对属性列进行计算，对标签列不计算，标签列已经计算过了。 254 | { 255 | vector attrValue; //每一列数据公有几种类型存储在这里 256 | map attrLinkToLabel; //每种类型对应的信息熵信息，有可能分类相同，有可能分类不同 257 | 258 | for (int j = 0; j < data.rows(); j++) 259 | { 260 | int value = int(data(j, i)); 261 | if (IntExistInVector(value, attrValue) >= 0) 262 | { 263 | int index = IntExistInVector(int(data(j, data.cols() - 1)), attrLinkToLabel[value].labelValue); 264 | if (index == -1) 265 | { 266 | attrLinkToLabel[value].labelValue.push_back(int(data(j, data.cols() - 1))); 267 | attrLinkToLabel[value].labelValueNum.push_back(1); 268 | } 269 | else 270 | { 271 | attrLinkToLabel[value].labelValueNum[index]++; 272 | } 273 | } 274 | else 275 | { 276 | entropyInfo oneAttrEntropyInfo; 277 | attrValue.push_back(value); 278 | oneAttrEntropyInfo.labelValue.push_back(int(data(j, data.cols() - 1))); 279 | oneAttrEntropyInfo.labelValueNum.push_back(1); 280 | attrLinkToLabel.insert(map::value_type(value, oneAttrEntropyInfo)); 281 | } 282 | } 283 | //计算信息熵 284 | float informationGain; //信息熵 285 | informationGain = InformationGain(attrValue, attrLinkToLabel, dataEntropy, data.rows()); 286 | result.push_back(informationGain); 287 | } 288 | return result; 289 | } 290 | 291 | int DTree::FindMaxInformationGain(vector s) 292 | { 293 | int maxIndex = 0; 294 | float max = 0; 295 | for (size_t i = 0; i < s.size(); i++) 296 | { 297 | if (s[i] > max) 298 | { 299 | max = s[i]; 300 | maxIndex = i; 301 | } 302 | } 303 | return maxIndex; 304 | } 305 | bool DTree::TheSameLabel(MatrixXi &data) 306 | { 307 | unsigned char first = data(0, data.cols() - 1); 308 | for (int i = 1; i < data.rows(); i++) 309 | { 310 | if (data(i, data.cols() - 1) != first) 311 | { 312 | return false; 313 | } 314 | } 315 | return true; 316 | } 317 | /* 318 | 获取新的矩阵，将生成一个属性取值的分支节点的数据去掉 319 | */ 320 | MatrixXi DTree::GetNewMat(MatrixXi &data, vector &properties, int maxIndex, string oneAttributeValue) 321 | { 322 | int matCols = data.cols(), matRows = data.rows(); 323 | int dimensionOFAttr = int(properties.size()); 324 | MatrixXi result; //结果矩阵 325 | unsigned char AttrValue = properties[maxIndex].typeMap[oneAttributeValue]; //字符串属性值在矩阵中对应的数值 326 | if (matCols != dimensionOFAttr || maxIndex >= matCols) 327 | { //做一个数据维数的判断，增加健壮性 328 | cerr << "矩阵维数和属性向量维数不对应"; 329 | getchar(); 330 | exit(0); 331 | } 332 | //下面是生成小矩阵的过程。 333 | 334 | for (int i = 0; i < matRows; i++) 335 | { 336 | MatrixXi newRowValue(1, matCols - 1); 337 | if (data(i, maxIndex) == AttrValue) //如果这一行中的元素是字符属性对应的数值执行下面操作。 338 | { 339 | for (int j = 0; j < matCols; j++) 340 | { 341 | if (j < maxIndex) 342 | { 343 | //newRowValue.push_back(data(i, j)); 344 | newRowValue(0, j) = data(i, j); 345 | } 346 | if (j > maxIndex) 347 | { 348 | newRowValue(0, j - 1) = data(i, j); 349 | } 350 | } 351 | //压入数据 352 | //cout << newRowValue << endl; 353 | if (newRowValue.cols() == matCols - 1) 354 | { //按行压入 355 | //result.push_back(newRowValue); 356 | result.conservativeResize(result.rows() + 1, matCols - 1); 357 | result.row(result.rows() - 1) = newRowValue; 358 | //result << newRowValue; 359 | } 360 | else 361 | { 362 | cerr << "在生成小矩阵时，向量维数和矩阵维数不一致"; 363 | exit(0); 364 | } 365 | } 366 | } 367 | return result; 368 | } 369 | 370 | int DTree::IntExistInVector(int a, vector b) 371 | { //如果在里面则返回对应的索引，否则返回-1 372 | for (size_t i = 0; i < b.size(); i++) 373 | { 374 | if (a == b[i]) 375 | { 376 | return i; 377 | } 378 | } 379 | return -1; 380 | } 381 | 382 | float DTree::GetDataEntropy(MatrixXi &data) 383 | { 384 | vector ratio; 385 | vector label; 386 | map labelNum; 387 | int labelCol = data.cols() - 1; 388 | for (int i = 0; i < data.rows(); i++) 389 | { 390 | int value = int(data(i, labelCol)); 391 | if (IntExistInVector(value, label) >= 0) 392 | { 393 | labelNum[value]++; 394 | } 395 | else 396 | { 397 | label.push_back(value); 398 | labelNum.insert(map::value_type(value, 1)); 399 | } 400 | } 401 | for (size_t i = 0; i < label.size(); i++) 402 | { 403 | ratio.push_back(float(labelNum[label[i]]) / float(data.rows())); 404 | 405 | } 406 | return Entropy(ratio); 407 | } 408 | 409 | float DTree::InformationGain(vector value, map b, float dataEntropy, int matRows) 410 | { 411 | float result = 0; 412 | for (size_t i = 0; i < value.size(); i++) 413 | { 414 | int D_v = 0; 415 | vector ratio; 416 | for (size_t j = 0; j < b[value[i]].labelValueNum.size(); j++) 417 | { 418 | D_v = D_v + b[value[i]].labelValueNum[j]; 419 | } 420 | for (size_t j = 0; j < b[value[i]].labelValueNum.size(); j++) 421 | { 422 | ratio.push_back(float(b[value[i]].labelValueNum[j]) / float(D_v)); 423 | } 424 | result = result + float(float(D_v) / float(matRows)) * Entropy(ratio); 425 | 426 | } 427 | result = dataEntropy - result; 428 | return result; 429 | } 430 | 431 | float DTree::Entropy(vector ratio) 432 | { 433 | float result = 0; 434 | for (size_t i = 0; i < ratio.size(); i++) 435 | { 436 | result = result + ratio[i] * log2(ratio[i]); 437 | } 438 | return -result; 439 | } 440 | bool DTree::DataExistAttribute(MatrixXi &data, vector &properties, int maxIndex, string oneAttributeValue) 441 | { 442 | int stringValue = properties[maxIndex].typeMap[oneAttributeValue]; 443 | for (int i = 0; i < data.rows(); i++) 444 | { 445 | int dataValue = int(data(i, maxIndex)); 446 | if (dataValue == stringValue) 447 | { 448 | return true; 449 | } 450 | } 451 | return false; 452 | } 453 | 454 | vector> DTree::ReadPredictedDataFile(string fileAddress) 455 | { //读取带预测数据 456 | ifstream read; 457 | read.open(fileAddress, ios::in); 458 | vector> predictedStringData; 459 | string rowString; 460 | while (getline(read, rowString)) 461 | { 462 | string oneString; 463 | char dim = ','; 464 | istringstream stringIn(rowString); // 465 | vector oneRowString; 466 | while (getline(stringIn, oneString, dim)) 467 | { 468 | oneRowString.push_back(oneString); 469 | } 470 | if (int(oneRowString.size()) == this->trainMatrixInfo.cols - 1) 471 | { 472 | //只存储和训练数据集匹配的数据 473 | predictedStringData.push_back(oneRowString); 474 | } 475 | else 476 | { 477 | cerr << "待预测数据和训练数据集维数不一致" << endl; 478 | getchar(); 479 | exit(0); 480 | } 481 | 482 | } 483 | this->predictedDataMat = predictedStringData; 484 | return predictedStringData; 485 | } 486 | 487 | vector DTree::Predicted(vector> &predictedData) 488 | { 489 | vector result; 490 | //int matCols = int(predictedData[0].size()); 491 | int matRows = int(predictedData.size()); 492 | for (int i = 0; i < matRows; i++) 493 | { 494 | string answer; 495 | answer = PredictedRecursion(this->root, predictedData[i], this->vectorAttr); 496 | result.push_back(answer); 497 | } 498 | return result; 499 | } 500 | 501 | string DTree::PredictedRecursion(TreeNode* nodeAddress, vector &rowData, vector &vecAttr) 502 | { 503 | //预测的递归函数 504 | if (nodeAddress->LeafNode) 505 | { 506 | return nodeAddress->Attribute; 507 | } 508 | else 509 | { 510 | string nodeString = nodeAddress->Attribute; 511 | int attrIndex = IndexOFAttribute(nodeString, vectorAttr); 512 | if (attrIndex >= 0) 513 | { 514 | string attrValue = rowData[attrIndex]; 515 | TreeNode* newRoot; 516 | newRoot = nodeAddress->AttributeLinkChildren[attrValue]; 517 | return PredictedRecursion(newRoot, rowData, vecAttr); 518 | 519 | } 520 | } 521 | return nullptr; 522 | } 523 | 524 | int DTree::IndexOFAttribute(string nodeString, vector &vectorAttr) 525 | { 526 | for (size_t i = 0; i < vectorAttr.size(); i++) 527 | { 528 | if (nodeString == vectorAttr[i].Attribute) 529 | { 530 | return i; 531 | } 532 | } 533 | return -1; 534 | } 535 | 536 | string DTree::FindAttrString(int a, Attr b) 537 | { 538 | for (size_t i = 0; i < b.AttributeValue.size(); i++) 539 | { 540 | string tmp = b.AttributeValue[i]; 541 | if (b.typeMap[tmp] == a) 542 | { 543 | return tmp; 544 | } 545 | } 546 | cout << "查找出错了" << endl; 547 | return nullptr; 548 | } 549 | 550 | struct MostInMatLabeldata 551 | { 552 | int value; 553 | int num; 554 | }; 555 | static int InStructVector(int v, vector b) 556 | { 557 | for (size_t i = 0; i < b.size(); i++) 558 | { 559 | if (v == b[i].value) 560 | { 561 | return i; 562 | } 563 | } 564 | return -1; 565 | } 566 | string DTree::MostInMatLabel(MatrixXi &data, vector &properties) 567 | { 568 | 569 | 570 | int rows = data.rows(); 571 | vector intVec; 572 | for (int i = 0; i < rows; i++) 573 | { 574 | int labelValue = int(data(i, data.cols() - 1)); 575 | int indexOFVec = InStructVector(labelValue, intVec); 576 | if (indexOFVec >= 0) 577 | { 578 | intVec[indexOFVec].num++; 579 | } 580 | else 581 | { 582 | MostInMatLabeldata tmp; 583 | tmp.value = labelValue; 584 | tmp.num = 1; 585 | intVec.push_back(tmp); 586 | } 587 | } 588 | int maxValue = 0, maxNum = 0; 589 | for (size_t i = 0; i < intVec.size(); i++) 590 | { 591 | if (intVec[i].num >= maxNum) 592 | { 593 | maxValue = intVec[i].value; 594 | } 595 | } 596 | string result = FindAttrString(maxValue, properties[properties.size() - 1]); 597 | return result; 598 | } 599 | 600 | int DTree::destroyTree(TreeNode* root) 601 | { 602 | if (root->LeafNode) 603 | { 604 | delete root; 605 | } 606 | else 607 | { 608 | for (size_t i = 0; i < root->children.size(); i++) 609 | { 610 | destroyTree(root->children[i]); 611 | } 612 | } 613 | return 0; 614 | } 615 | 616 | DTree::~DTree() 617 | { 618 | destroyTree(this->root); 619 | } 620 | -------------------------------------------------------------------------------- /C++ source Code/Discrete/DTree.hpp: -------------------------------------------------------------------------------- 1 | #ifndef DTREE_H 2 | #define DTREE_H 3 | 4 | #include 5 | #include //读取文件内容 6 | #include 7 | #include 8 | #include 9 | #include 10 | #include 11 | 12 | using std::cout; using std::cin; using std::cerr; using std::endl; 13 | using std::string; using std::ifstream; using std::istringstream; 14 | using std::vector; using std::map; using std::ios; 15 | 16 | using Eigen::MatrixXf; //Eigen Matrix float 存储数据 训练数据 17 | using Eigen::MatrixXi; //Eigen Matrix int 存储数据 训练数据和标签 18 | using Eigen::VectorXi; 19 | using Eigen::VectorXf; 20 | 21 | 22 | class DTree 23 | { 24 | public: 25 | struct TreeNode 26 | { //节点信息 27 | string Attribute; //此节点对应的属性 28 | bool LeafNode; //如果是叶子节点，此值反映分类结果。 //其他情况都是0; 29 | vector children; //孩子节点的地址。 30 | map AttributeLinkChildren; //属性指向孩子节点，也就是对应的树形结构中的树枝 31 | 32 | }; 33 | struct Attr //每一列的属性 34 | { 35 | int colIndex; 36 | string Attribute; 37 | int typeNum; //属性取值的个数 38 | vector AttributeValue; 39 | map typeMap; //属性取值对应的整数值; 40 | }; 41 | 42 | private: 43 | struct MatInfo 44 | { 45 | int cols; 46 | int rows; 47 | 48 | }; 49 | struct entropyInfo 50 | { 51 | vector labelValue; 52 | vector labelValueNum; 53 | }; 54 | public: 55 | //MatrixXi trainDataMat; 56 | MatrixXi trainDataMat; 57 | vector> predictedDataMat; 58 | MatInfo trainMatrixInfo; 59 | TreeNode *root; //根节点 60 | vector vectorAttr; //存储所有的矩阵信息，但不存储矩阵。 61 | DTree(); 62 | DTree(string csvfilename); 63 | MatrixXi ReadTrainDataFile(string fileAddress); //数据预处理 64 | //TreeNode* BuildTree(MatrixXi &data, vector &dataAttr, string AlgorithmName); // 指定是哪种算法 65 | int BuildTree(MatrixXi &data, vector &dataAttr, string AlgorithmName); 66 | vector> ReadPredictedDataFile(string fileAddreess); 67 | //vector Predicted(TreeNode* root, vector> &pData); //返回值为int类型表示数据的分类。 68 | vector Predicted(vector> &pData); 69 | ~DTree(); 70 | 71 | private: 72 | int stringDataToInt(const vector> &src, MatrixXi &dataMat, vector &headAttrInfo); 73 | bool StringExistInVector(string aa, vector A); 74 | TreeNode* AlgorithmID3(MatrixXi &data, vector &a); 75 | TreeNode* AlgorithmC4_5(MatrixXi &data, vector &a); 76 | TreeNode* AlgorithmCART(MatrixXi &data, vector &a); 77 | //vector CalculateEntropy(MatrixXi a); 78 | vector CalculateInfGain(MatrixXi &a); 79 | int FindMaxInformationGain(vector s); 80 | bool TheSameLabel(MatrixXi &a); 81 | MatrixXi GetNewMat(MatrixXi &a, vector &properties, int maxIndex, string oneAttributeValue); 82 | int IntExistInVector(int a, vector b); 83 | float GetDataEntropy(MatrixXi &data); 84 | float InformationGain(vector value, map b, float dataEntropy, int rows); 85 | float Entropy(vector ratio); // 计算熵 86 | //判断矩阵中是否有该属性 87 | bool DataExistAttribute(MatrixXi &data, vector &properties, int maxIndex, string oneAttributeValue); 88 | int IndexOFAttribute(string nodeString, vector &vectorAttr); 89 | string PredictedRecursion(TreeNode* nodeAddress, vector &rowData, vector &vecAttr); 90 | string FindAttrString(int a, Attr b); 91 | string MostInMatLabel(MatrixXi &data, vector &properties); 92 | int destroyTree(TreeNode *root); 93 | }; 94 | 95 | #endif -------------------------------------------------------------------------------- /C++ source Code/Discrete/haha2.csv: -------------------------------------------------------------------------------- 1 | type,gender,vage,age,ageg,region,ee,num,cost 2 | B,F,1,18,1,BJ,26.89,20,33973.4 3 | B,F,1,18,1,SH,3.089,2,2167.5 4 | B,F,1,18,1,SZ,3.299,2,2082.5 5 | B,F,1,18,1,CQ,1,0,0 6 | B,F,1,19,1,BJ,31.493,23,36339.2 -------------------------------------------------------------------------------- /C++ source Code/Discrete/main.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include "DTree.hpp" 3 | using std::cout; using std::cin; 4 | int main() 5 | { 6 | 7 | 8 | string fileAddress("./tianqi.csv"); 9 | //cout << "请输入文件的地址："; 10 | //cin >> fileAddress; 11 | //cout << sizeof(unsigned int) << endl; 12 | DTree myDecisionTree; 13 | myDecisionTree.ReadTrainDataFile(fileAddress); 14 | myDecisionTree.BuildTree(myDecisionTree.trainDataMat, myDecisionTree.vectorAttr, "ID3"); 15 | 16 | vector result; 17 | vector> predictedData; 18 | predictedData = myDecisionTree.ReadPredictedDataFile("pre.csv"); 19 | result = myDecisionTree.Predicted(predictedData); 20 | cout << "预测的结果如下：" << endl; 21 | for (size_t i = 0; i < result.size(); i++) 22 | { 23 | cout << result[i] << endl; 24 | } 25 | return 0; 26 | } 27 | -------------------------------------------------------------------------------- /C++ source Code/Discrete/out.yml: -------------------------------------------------------------------------------- 1 | %YAML:1.0 2 | --- 3 | Attribute: Outlook 4 | typeNum: 3 5 | value : sunny 6 | value : overcast 7 | value : rainy 8 | Attribute: Temperature 9 | typeNum: 12 10 | value : "85" 11 | value : "80" 12 | value : "83" 13 | value : "70" 14 | value : "68" 15 | value : "65" 16 | value : "64" 17 | value : "72" 18 | value : "69" 19 | value : "75" 20 | value : "81" 21 | value : "71" 22 | Attribute: Humidity 23 | typeNum: 10 24 | value : "85" 25 | value : "90" 26 | value : "86" 27 | value : "96" 28 | value : "80" 29 | value : "70" 30 | value : "65" 31 | value : "95" 32 | value : "75" 33 | value : "91" 34 | Attribute: Windy 35 | typeNum: 2 36 | value : FALSE 37 | value : TRUE 38 | Attribute: PlayGolf 39 | typeNum: 2 40 | value : no 41 | value : yes 42 | dataMat: !!opencv-matrix 43 | rows: 14 44 | cols: 5 45 | dt: u 46 | data: [ 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 2, 2, 0, 1, 2, 3, 3, 0, 1, 2, 47 | 4, 4, 0, 1, 2, 5, 5, 1, 0, 1, 6, 6, 1, 1, 0, 7, 7, 0, 0, 0, 8, 5, 48 | 0, 1, 2, 9, 4, 0, 1, 0, 9, 5, 1, 1, 1, 7, 1, 1, 1, 1, 10, 8, 0, 1, 49 | 2, 11, 9, 1, 0 ] 50 | -------------------------------------------------------------------------------- /C++ source Code/Discrete/pre.csv: -------------------------------------------------------------------------------- 1 | sunny,85,85,FALSE 2 | sunny,80,90,TRUE 3 | overcast,83,86,FALSE 4 | rainy,70,96,FALSE 5 | rainy,68,80,FALSE 6 | rainy,65,70,TRUE 7 | overcast,64,65,TRUE 8 | sunny,72,95,FALSE 9 | sunny,69,70,FALSE 10 | rainy,75,80,FALSE 11 | sunny,75,70,TRUE 12 | overcast,72,90,TRUE 13 | overcast,81,75,FALSE 14 | rainy,71,91,TRUE -------------------------------------------------------------------------------- /C++ source Code/Discrete/tianqi.csv: -------------------------------------------------------------------------------- 1 | Outlook,Temperature,Humidity,Windy,PlayGolf 2 | sunny,85,85,FALSE,no 3 | sunny,80,90,TRUE,no 4 | overcast,83,86,FALSE,yes 5 | rainy,70,96,FALSE,yes 6 | rainy,68,80,FALSE,yes 7 | rainy,65,70,TRUE,no 8 | overcast,64,65,TRUE,yes 9 | sunny,72,95,FALSE,no 10 | sunny,69,70,FALSE,yes 11 | rainy,75,80,FALSE,yes 12 | sunny,75,70,TRUE,yes 13 | overcast,72,90,TRUE,yes 14 | overcast,81,75,FALSE,yes 15 | rainy,71,91,TRUE,no 16 | -------------------------------------------------------------------------------- /C++ source Code/data/ContinuousTest.csv: -------------------------------------------------------------------------------- 1 | 4.9,3.1,1.5,0.1,1 2 | 5.4,3.7,1.5,0.2,1 3 | 4.8,3.4,1.6,0.2,1 4 | 5.7,2.8,4.5,1.3,2 5 | 5.8,2.7,4.1,1,2 6 | 6.2,2.2,4.5,1.5,2 7 | 5.6,2.5,3.9,1.1,2 8 | 6.4,2.8,5.6,2.2,3 9 | 6.3,2.8,5.1,1.5,3 10 | 6.1,2.6,5.6,1.4,3 11 | 7.7,3,6.1,2.3,3 12 | 6.3,3.4,5.6,2.4,3 13 | 6.4,3.1,5.5,1.8,3 14 | 6,3,4.8,1.8,3 15 | -------------------------------------------------------------------------------- /C++ source Code/data/ContinuousTrain.csv: -------------------------------------------------------------------------------- 1 | 5.1,3.5,1.4,0.2,1 2 | 4.9,3,1.4,0.2,1 3 | 4.7,3.2,1.3,0.2,1 4 | 4.8,3,1.4,0.1,1 5 | 4.3,3,1.1,0.1,1 6 | 5.8,4,1.2,0.2,1 7 | 5.7,4.4,1.5,0.4,1 8 | 5.4,3.9,1.3,0.4,1 9 | 5.1,3.5,1.4,0.3,1 10 | 5.7,3.8,1.7,0.3,1 11 | 5.1,3.8,1.5,0.3,1 12 | 5.4,3.4,1.7,0.2,1 13 | 5.1,3.7,1.5,0.4,1 14 | 4.6,3.6,1,0.2,1 15 | 5.1,3.3,1.7,0.5,1 16 | 4.8,3.4,1.9,0.2,1 17 | 5,3,1.6,0.2,1 18 | 5,3.4,1.6,0.4,1 19 | 5.2,3.5,1.5,0.2,1 20 | 5.2,3.4,1.4,0.2,1 21 | 4.7,3.2,1.6,0.2,1 22 | 4.8,3.1,1.6,0.2,1 23 | 5.4,3.4,1.5,0.4,1 24 | 5.2,4.1,1.5,0.1,1 25 | 5,3.5,1.3,0.3,1 26 | 4.5,2.3,1.3,0.3,1 27 | 4.4,3.2,1.3,0.2,1 28 | 5,3.5,1.6,0.6,1 29 | 5.1,3.8,1.9,0.4,1 30 | 4.8,3,1.4,0.3,1 31 | 5.1,3.8,1.6,0.2,1 32 | 4.6,3.2,1.4,0.2,1 33 | 5.3,3.7,1.5,0.2,1 34 | 5,3.3,1.4,0.2,1 35 | 7,3.2,4.7,1.4,2 36 | 6.4,3.2,4.5,1.5,2 37 | 6.9,3.1,4.9,1.5,2 38 | 5.5,2.3,4,1.3,2 39 | 6.5,2.8,4.6,1.5,2 40 | 5.9,3.2,4.8,1.8,2 41 | 6.1,2.8,4,1.3,2 42 | 6.3,2.5,4.9,1.5,2 43 | 6.1,2.8,4.7,1.2,2 44 | 6.4,2.9,4.3,1.3,2 45 | 6.6,3,4.4,1.4,2 46 | 6.8,2.8,4.8,1.4,2 47 | 6.7,3,5,1.7,2 48 | 6,2.9,4.5,1.5,2 49 | 5.7,2.6,3.5,1,2 50 | 5.5,2.4,3.8,1.1,2 51 | 5.5,2.4,3.7,1,2 52 | 5.8,2.7,3.9,1.2,2 53 | 6,2.7,5.1,1.6,2 54 | 5.4,3,4.5,1.5,2 55 | 6,3.4,4.5,1.6,2 56 | 6.7,3.1,4.7,1.5,2 57 | 6.3,2.3,4.4,1.3,2 58 | 5.6,3,4.1,1.3,2 59 | 5.5,2.5,4,1.3,2 60 | 7.1,3,5.9,2.1,3 61 | 6.3,2.9,5.6,1.8,3 62 | 6.5,3,5.8,2.2,3 63 | 7.6,3,6.6,2.1,3 64 | 4.9,2.5,4.5,1.7,3 65 | 7.3,2.9,6.3,1.8,3 66 | 6.7,2.5,5.8,1.8,3 67 | 7.2,3.6,6.1,2.5,3 68 | 6.5,3.2,5.1,2,3 69 | 6.4,2.7,5.3,1.9,3 70 | 6.8,3,5.5,2.1,3 71 | 5.7,2.5,5,2,3 72 | 5.8,2.8,5.1,2.4,3 73 | 6.4,3.2,5.3,2.3,3 74 | 6.5,3,5.5,1.8,3 75 | 7.7,3.8,6.7,2.2,3 76 | 7.7,2.6,6.9,2.3,3 77 | 6,2.2,5,1.5,3 78 | 6.9,3.2,5.7,2.3,3 79 | 5.6,2.8,4.9,2,3 80 | 7.9,3.8,6.4,2,3 81 | 6.9,3.1,5.4,2.1,3 82 | 6.7,3.1,5.6,2.4,3 83 | 6.9,3.1,5.1,2.3,3 84 | 5.8,2.7,5.1,1.9,3 85 | 6.8,3.2,5.9,2.3,3 86 | 6.7,3.3,5.7,2.5,3 87 | 6.7,3,5.2,2.3,3 88 | 6.3,2.5,5,1.9,3 89 | 6.5,3,5.2,2,3 90 | 6.2,3.4,5.4,2.3,3 91 | 5.9,3,5.1,1.8,3 92 | -------------------------------------------------------------------------------- /C++ source Code/data/ContinuousValid.csv: -------------------------------------------------------------------------------- 1 | 4.6,3.1,1.5,0.2,1 2 | 5,3.6,1.4,0.3,1 3 | 5.4,3.9,1.7,0.4,1 4 | 4.6,3.4,1.4,0.3,1 5 | 5,3.4,1.5,0.2,1 6 | 4.4,2.9,1.4,0.2,1 7 | 5.5,4.2,1.4,0.2,1 8 | 4.9,3.1,1.5,0.2,1 9 | 5,3.2,1.2,0.2,1 10 | 5.5,3.5,1.3,0.2,1 11 | 4.9,3.6,1.4,0.1,1 12 | 4.4,3,1.3,0.2,1 13 | 5.1,3.4,1.5,0.2,1 14 | 6.3,3.3,4.7,1.6,2 15 | 4.9,2.4,3.3,1,2 16 | 6.6,2.9,4.6,1.3,2 17 | 5.2,2.7,3.9,1.4,2 18 | 5,2,3.5,1,2 19 | 5.9,3,4.2,1.5,2 20 | 6,2.2,4,1,2 21 | 6.1,2.9,4.7,1.4,2 22 | 5.6,2.9,3.6,1.3,2 23 | 6.7,3.1,4.4,1.4,2 24 | 5.6,3,4.5,1.5,2 25 | 5.5,2.6,4.4,1.2,2 26 | 6.1,3,4.6,1.4,2 27 | 5.8,2.6,4,1.2,2 28 | 5,2.3,3.3,1,2 29 | 5.6,2.7,4.2,1.3,2 30 | 5.7,3,4.2,1.2,2 31 | 5.7,2.9,4.2,1.3,2 32 | 6.2,2.9,4.3,1.3,2 33 | 5.1,2.5,3,1.1,2 34 | 5.7,2.8,4.1,1.3,2 35 | 6.3,3.3,6,2.5,3 36 | 5.8,2.7,5.1,1.9,3 37 | 7.7,2.8,6.7,2,3 38 | 6.3,2.7,4.9,1.8,3 39 | 6.7,3.3,5.7,2.1,3 40 | 7.2,3.2,6,1.8,3 41 | 6.2,2.8,4.8,1.8,3 42 | 6.1,3,4.9,1.8,3 43 | 6.4,2.8,5.6,2.1,3 44 | 7.2,3,5.8,1.6,3 45 | 7.4,2.8,6.1,1.9,3 46 | -------------------------------------------------------------------------------- /C++ source Code/data/Newslabel.csv: -------------------------------------------------------------------------------- 1 | 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/Coninuous_DecisionTree/.~draw.pptx: -------------------------------------------------------------------------------- 1 | liana liana -------------------------------------------------------------------------------- /Coninuous_DecisionTree/CDTree.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PiggyGaGa/MachineLearning-DecisionTree/3c063024405021739509e6cdb3655d5ebf417ebd/Coninuous_DecisionTree/CDTree.png -------------------------------------------------------------------------------- /Coninuous_DecisionTree/Readme.md: -------------------------------------------------------------------------------- 1 | ##写在前面 2 | 这是好久之前遗留的一篇博客，最近要好好谢谢文字，所以把之前落下的补上，欢迎大家批评指正。关于决策树的理论部分，我之前写过一篇博客，同时也实现了决策树的算法[离散型决策树的C++实现](https://blog.csdn.net/luoluonuoyasuolong/article/details/78696829)，但是当时的那个实现只能针对训练数据是离散型变量的，不能处理连续型数值。这篇文章是继承上面那一篇文章的，所有的理论部分全部都可以在上一篇文章中看到，这篇文章着重处理连续型变量怎样用决策树处理。所以在读这篇文章之前可以看一看前面那篇，兴许会对决策树本身的算法理解上有很大帮助。 3 | 4 | ## 举个简单的例子 5 | 首先我们要弄清楚，什么是离散型随机变量，什么是连续型随机变量。这个问题其实非常的简单，我们可以举个简单的例子，比如女孩子选择男朋友这件事，我们仅仅举一个简单的例子，这里不对这件事情本身做任何讨论，毕竟感情这比较复杂，而分类器比较简单。好，就拿女孩子选择男朋友这件事，假设每个女孩子在自己的心目中都由一棵选择男孩子的决策树，假设女孩子找男朋友只看中四个标准，1. 帅气程度，2.科研能力 3. 身高 4. 体重。在这个四个指标中，我们就可以看出第一项和第二项是明显的离散型变量，因为帅气程度一般我们就分为：帅到爆，一般般，看得过去，ugly。 6 | ![](./handsome.png) 7 | 而科研能力一般就分为：大神(可以科研带飞型)，普通(可以共同学习型)，菜鸟(隐藏的大神)。而第三和第四项是明显的连续型变量，身高大致取值是[150-220cm]这个区间的任意值都有可能，体重同样大概取值在[20-100kg]之间。所以决策树在解决离散型和连续型变量时是有很大差异的。原则上决策树并不能处理连续型数据，只能处理离散型数据，为什么呢？因为在决策树中某个属性有几个值就会有几条分支，而连续型变量有无穷多个取值，但决策树不可能有无穷多个分支，所以要想办法将连续型变量进行离散化，这是问题的关键。 8 | 9 | 我们可以看下面一个既有离散型变量，又有连续型变量的例子 10 | ![](./decisiontree.png) 11 | 上图是女孩A心中的一棵与找男朋友有关的决策树，首先女孩子比较看中帅气程度这个属性，然后比较看中科研能力，再然后是身高、体重。大家可以看到这个女孩子对于帅气程度的要求既不能太高也不能太低，所以我第一个就被pass了，为什么呢，这个我就不多解释了，都是泪，看图，这说明了，我们没有缘分。其次，该女孩可能是一位科研大佬，需要另一半也有很强的科研能力才可以。重点是身高和体重，我们看女孩A心中的决策树把身高划分为>180和<180两个离散变量，这就是连续变量离散化的最简单的一种方式。好了，我们要解决的问题就是上面所述那样，在用决策树处理分类问题时同时解决离散型变量和连续型变量。具体在理论上和实际中是怎么处理这样的问题的呢？下面，我们继续讨论。 12 | 13 | ## 理论部分 14 | 这个问题当时我调研了好久，发现很久很久以前就有人做过相关的工作了，我找到了最早的一篇是 15 | [Compression-Based Discretization of Continuous Attributes](1995)， 16 | 但实际上我编程用的方法是 17 | 18 | [二分法基于C4.5的机制](1993), 19 | 20 | 1996年也有一篇文章： 21 | 22 | [Finding Optimal Multi-Splits for Numerical Attributes in Decision Tree Learning](1996) 23 | 24 | 还有一篇2003年的文章 25 | 26 | [Numerical Attributes in Decision Tress:A Hierarchical Approach](2003) 27 | 28 | 其中各类树形算法比如XGBoost 29 | 30 | [XGBoost: A Scalable Tree Boosting System](xgboost) 31 | 32 | 都使用了类似的连续属性离散化的方法。不同的方法详细内容可以参考论文，但其实背后的原理都比较类似，就是划分区间，只是不同的方法进行划分的方法不同，标准也不同。总之，最终的结果就是将连续的区间划分为多个区间，然后在某个小区间内的所有数值算是一个类别，还是上面的例子，把身高进行划分，按照某个女孩的标准她可能会将男生的身高分为： 33 | ![](./hight.png) 34 | 当然了，不同的人划分方法是不同的，这就是不同离散算法之间的差异啦。 35 | 36 | ### 二分法 37 | 这篇文章只介绍最简单的一种方法：二分法，后面的编程部分也是实现了这种方法，虽然这个方法比较简单，但是比较好理解，最能说明问题，同时该方法也是著名的C4.5决策树采用的机制，这里的介绍同样是借用了周志华老师的那本西瓜书，有书的同学可以直接看书啦。 38 | 39 | 假设给定样本集D，这个样本集就是有很多男孩子的信息啦，和连续属性hight(身高)，假设hight在样本集D中出现了n个不同的取值，将这些值从小到大进行排序，记为$\{h^1,h^2,...h^n\}$。接下来就要在这个数列中找到一个划分t，这个划分将整个数列分为两部分，一部分是t左边的,记为$D^{-}_t$，一部分是t右边的，记为$D^+_t$。关键的问题是怎样取这个划分呢？这个就是二分法的核心：根据信息增益选取划分点。 40 | 41 | 首先将排好序的数列每两个数值算一个平均值，将该平均值作为一个划分 42 | $$ 43 | T = \{t_i=\frac{h^i + h^{i+1}}{2}\mid 1 \leq i \leq n-1\} 44 | $$ 45 | 46 | 然后我们需要找到一个使得信息增益(下面的式子)最大的一个划分$t_j$。 47 | $$ 48 | Gain(D, hight) = \max\limits_{t\in T}Gain(D, hight, t) \\ 49 | =\max\limits_{t\in T} Ent(D) - \sum\limits_{\lambda\in\{-,+\}}\frac{\mid D^{\lambda}_t\mid}{\mid D \mid}Ent(D^{\lambda}_t) 50 | $$ 51 | 这里Ent(D)表示数据集D下的信息熵，这个熵是根据数据集下的标签计算的，具体的计算过程可以参考周志华老师的书，也可以看后面代码部分，这样更容易理解。 52 | 53 | 原理就是这么简单，具体怎么计算呢？我们举个简单的例子，假设有6个男孩子的信息，身高经过排名后分别是 54 | ![](./hight_sort.png) 55 | 生成的5个划分是 56 | ![](./cut.png) 57 | 这里我们省去计算的部分，假设最终计算出来以168.4为切割的信息增益最大，那么我们第一次切割产生的两个区间就是[158.2，168.4]和(168.4, 185]。但是这样也只是产生了两个离散取值，如果连续区间内的取值很多，两个离散取值不能满足我们的要求怎么办呢？不用担心，既然可以解决两个就可以解决多个，古文曾说：一生二，二生三，三生万物，大道至简。 58 | 我们生成了两部分区间后可以分别在某个区间内再进行二分，这样的操作可以一直进行下去，直到你满意为止。 59 | 下图是上面的例子生成的最终三个区间示意图，其实这个过程我们可以理解为二叉树的建立过程，所有的叶子节点都是最终的划分的区间。 60 | ![](./interval_tree.png) 61 | 62 | ## C++代码实现 63 | 开始实践部分，这才是真正遇到的挑战呢，因为在这个过程中我遇到了各种各样的问题，调试了很久的代码才把最终的demo跑通，当然这里面还有很多待完善的内容，后面有时间再慢慢完善吧，但是To be honest,写出来以后就再也没有用心看过它啦，哈哈，懒，太懒。 64 | 65 | 下面的图片是我在写代码过程的总的流程，大致分为3个步骤： 66 | 1. 离散化 67 | 2. 建立树结构 68 | 3. 减枝 69 | 4. 预测 70 | 哦，是4个步骤。 71 | ![](./CDTree.png) 72 | 73 | 值得一提的是，机器学习问题第一个重要的问题是用什么结构存储矩阵，以前我写离散决策树的时候用的是OpenCV的Mat类存储矩阵，确实很方便，但是OpenCV安转起来很麻烦，所以这次我用了另外一个科学计算的C++库:Eign。据说tensorflow的底层计算都是借用这个库，并且这个库只有头文件，不需要安装就可以使用啦，可以说是既方便有高效将，所以这里我用到了Eigen处理矩阵的科学计算部分。 74 | 75 | 怎么跑通我的代码呢？ 76 | 这个很简单啦，我的代码只有下面4个接口 77 | 78 | ``` 79 | 80 | CDTree(int deepestTree, int max_bin, float thresholdInfoGain); 81 | int buildTree(const MatrixXf &trainX, const MatrixXi &trainY, const MatrixXf &validateX, const MatrixXi &validateY, string Algorithm); 82 | int buildTree(const MatrixXf &trainX, const MatrixXi &trainY, string Algorithm); 83 | MatrixXi predict(const MatrixXf &testX); 84 | ``` 85 | `CDTree()`是构造函数，需要输入一些参数 86 | `buildTree()`是构建树的接口，有两个实现，其中一种是有验证集的构建，另一种是没有验证集的构建 87 | `predict`是预测函数 88 | 不出意外的话，这个类的输入输出数据都是通过Eigen结构存储的，那数据是怎样读取到Eigen的结构当中的呢？为了方便，我还写了将csv数据读入到Eigen结构中的类 89 | 90 | ``` 91 | template 92 | class RFCSV 93 | { 94 | public: 95 | string csvFileName; 96 | RFCSV() 97 | { 98 | this->csvFileName = ""; 99 | } 100 | RFCSV(string fileName) 101 | { 102 | this->csvFileName = fileName; 103 | } 104 | 105 | std::pair getData(string fileName) 106 | { 107 | 108 | 109 | } 110 | std::pair getData() 111 | { 112 | 113 | } 114 | 115 | template 116 | Type stringToNum(const string& str) 117 | { 118 | 119 | } 120 | 121 | }; 122 | ``` 123 | 124 | 为了节省空间，类的实现没有写出来，文章最后我将源代码放在git上，感兴趣的同学慢慢看。 125 | 126 | 最后，我们看一个完整的样例： 127 | 128 | 主函数是这样的 129 | ``` 130 | #include 131 | #include "CDTree.hpp" 132 | #include "RFCSV.hpp" 133 | #include 134 | using Eigen::MatrixXf; using Eigen::MatrixXi; 135 | int main() 136 | { 137 | 138 | std::pair trainData, testData; 139 | RFCSV readcsv; 140 | trainData = readcsv.getData("../data/ContinuousTrain.csv"); 141 | testData = readcsv.getData("../data/ContinuousTest.csv"); 142 | CDTree myCDTree(4, 3, 0.0001); 143 | myCDTree.buildTree(trainData.first, trainData.second, "ID3"); 144 | MatrixXi predictresult = myCDTree.predict(testData.first); 145 | float accuracy = 0; 146 | for (int i = 0; i < predictresult.size(); i++) 147 | { 148 | if (testData.second(i) == predictresult(i)) 149 | { 150 | accuracy += 1.0 / predictresult.size(); 151 | } 152 | } 153 | cout << "准确率为：" << accuracy * 100 << "\% 你很棒棒哦！" << endl; 154 | } 155 | ``` 156 | 数据样例是这样的 157 | ![](./data.png) 158 | 前面4列是连续属性，最后一列是标签。 159 | 最终的输出结果是： 160 | ![](./result.png) 161 | 162 | 总结：代码中用到了k-means聚类，合成最小区间数，但是这个操作使得算法稳定性极差，我觉得应该采用更加简单的方法解决这个问题，其实离散属性不宜划分的过多，过多的离散值会造成决策树过拟合，所以什么时候停止划分区间这个问题是亟待解决的。 163 | 164 | 最后，遇到了喜欢的女孩子，希望我能契合她心目中的那棵决策树，从树根到数叶，那不是机器学习的过程，是我们相互学习的过程，不契合也没有关系，能喜欢上你已经是万分荣幸啦！ 165 | 166 | 掌管天堂的空之王者，于地狱唱响容光之歌！ 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-------------------------------------------------------------------------------- /Continuous_DecisionTree/CDTree.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PiggyGaGa/MachineLearning-DecisionTree/3c063024405021739509e6cdb3655d5ebf417ebd/Continuous_DecisionTree/CDTree.png -------------------------------------------------------------------------------- /Continuous_DecisionTree/Readme.md: -------------------------------------------------------------------------------- 1 | ##写在前面 2 | 这是好久之前遗留的一篇博客，最近要好好谢谢文字，所以把之前落下的补上，欢迎大家批评指正。关于决策树的理论部分，我之前写过一篇博客，同时也实现了决策树的算法[离散型决策树的C++实现](https://blog.csdn.net/luoluonuoyasuolong/article/details/78696829)，但是当时的那个实现只能针对训练数据是离散型变量的，不能处理连续型数值。这篇文章是继承上面那一篇文章的，所有的理论部分全部都可以在上一篇文章中看到，这篇文章着重处理连续型变量怎样用决策树处理。所以在读这篇文章之前可以看一看前面那篇，兴许会对决策树本身的算法理解上有很大帮助。 3 | 4 | ## 举个简单的例子 5 | 首先我们要弄清楚，什么是离散型随机变量，什么是连续型随机变量。这个问题其实非常的简单，我们可以举个简单的例子，比如女孩子选择男朋友这件事，我们仅仅举一个简单的例子，这里不对这件事情本身做任何讨论，毕竟感情这比较复杂，而分类器比较简单。好，就拿女孩子选择男朋友这件事，假设每个女孩子在自己的心目中都由一棵选择男孩子的决策树，假设女孩子找男朋友只看中四个标准，1. 帅气程度，2.科研能力 3. 身高 4. 体重。在这个四个指标中，我们就可以看出第一项和第二项是明显的离散型变量，因为帅气程度一般我们就分为：帅到爆，一般般，看得过去，ugly。 6 | ![](./handsome.png) 7 | 而科研能力一般就分为：大神(可以科研带飞型)，普通(可以共同学习型)，菜鸟(隐藏的大神)。而第三和第四项是明显的连续型变量，身高大致取值是[150-220cm]这个区间的任意值都有可能，体重同样大概取值在[20-100kg]之间。所以决策树在解决离散型和连续型变量时是有很大差异的。原则上决策树并不能处理连续型数据，只能处理离散型数据，为什么呢？因为在决策树中某个属性有几个值就会有几条分支，而连续型变量有无穷多个取值，但决策树不可能有无穷多个分支，所以要想办法将连续型变量进行离散化，这是问题的关键。 8 | 9 | 我们可以看下面一个既有离散型变量，又有连续型变量的例子 10 | ![](./decisiontree.png) 11 | 上图是女孩A心中的一棵与找男朋友有关的决策树，首先女孩子比较看中帅气程度这个属性，然后比较看中科研能力，再然后是身高、体重。大家可以看到这个女孩子对于帅气程度的要求既不能太高也不能太低，所以我第一个就被pass了，为什么呢，这个我就不多解释了，都是泪，看图，这说明了，我们没有缘分。其次，该女孩可能是一位科研大佬，需要另一半也有很强的科研能力才可以。重点是身高和体重，我们看女孩A心中的决策树把身高划分为>180和<180两个离散变量，这就是连续变量离散化的最简单的一种方式。好了，我们要解决的问题就是上面所述那样，在用决策树处理分类问题时同时解决离散型变量和连续型变量。具体在理论上和实际中是怎么处理这样的问题的呢？下面，我们继续讨论。 12 | 13 | ## 理论部分 14 | 这个问题当时我调研了好久，发现很久很久以前就有人做过相关的工作了，我找到了最早的一篇是 15 | [Compression-Based Discretization of Continuous Attributes](1995)， 16 | 但实际上我编程用的方法是 17 | 18 | [二分法基于C4.5的机制](1993), 19 | 20 | 1996年也有一篇文章： 21 | 22 | [Finding Optimal Multi-Splits for Numerical Attributes in Decision Tree Learning](1996) 23 | 24 | 还有一篇2003年的文章 25 | 26 | [Numerical Attributes in Decision Tress:A Hierarchical Approach](2003) 27 | 28 | 其中各类树形算法比如XGBoost 29 | 30 | [XGBoost: A Scalable Tree Boosting System](xgboost) 31 | 32 | 都使用了类似的连续属性离散化的方法。不同的方法详细内容可以参考论文，但其实背后的原理都比较类似，就是划分区间，只是不同的方法进行划分的方法不同，标准也不同。总之，最终的结果就是将连续的区间划分为多个区间，然后在某个小区间内的所有数值算是一个类别，还是上面的例子，把身高进行划分，按照某个女孩的标准她可能会将男生的身高分为： 33 | ![](./hight.png) 34 | 当然了，不同的人划分方法是不同的，这就是不同离散算法之间的差异啦。 35 | 36 | ### 二分法 37 | 这篇文章只介绍最简单的一种方法：二分法，后面的编程部分也是实现了这种方法，虽然这个方法比较简单，但是比较好理解，最能说明问题，同时该方法也是著名的C4.5决策树采用的机制，这里的介绍同样是借用了周志华老师的那本西瓜书，有书的同学可以直接看书啦。 38 | 39 | 假设给定样本集D，这个样本集就是有很多男孩子的信息啦，和连续属性hight(身高)，假设hight在样本集D中出现了n个不同的取值，将这些值从小到大进行排序，记为$\{h^1,h^2,...h^n\}$。接下来就要在这个数列中找到一个划分t，这个划分将整个数列分为两部分，一部分是t左边的,记为$D^{-}_t$，一部分是t右边的，记为$D^+_t$。关键的问题是怎样取这个划分呢？这个就是二分法的核心：根据信息增益选取划分点。 40 | 41 | 首先将排好序的数列每两个数值算一个平均值，将该平均值作为一个划分 42 | $$ 43 | T = \{t_i=\frac{h^i + h^{i+1}}{2}\mid 1 \leq i \leq n-1\} 44 | $$ 45 | 46 | 然后我们需要找到一个使得信息增益(下面的式子)最大的一个划分$t_j$。 47 | $$ 48 | Gain(D, hight) = \max\limits_{t\in T}Gain(D, hight, t) \\ 49 | =\max\limits_{t\in T} Ent(D) - \sum\limits_{\lambda\in\{-,+\}}\frac{\mid D^{\lambda}_t\mid}{\mid D \mid}Ent(D^{\lambda}_t) 50 | $$ 51 | 这里Ent(D)表示数据集D下的信息熵，这个熵是根据数据集下的标签计算的，具体的计算过程可以参考周志华老师的书，也可以看后面代码部分，这样更容易理解。 52 | 53 | 原理就是这么简单，具体怎么计算呢？我们举个简单的例子，假设有6个男孩子的信息，身高经过排名后分别是 54 | ![](./hight_sort.png) 55 | 生成的5个划分是 56 | ![](./cut.png) 57 | 这里我们省去计算的部分，假设最终计算出来以168.4为切割的信息增益最大，那么我们第一次切割产生的两个区间就是[158.2，168.4]和(168.4, 185]。但是这样也只是产生了两个离散取值，如果连续区间内的取值很多，两个离散取值不能满足我们的要求怎么办呢？不用担心，既然可以解决两个就可以解决多个，古文曾说：一生二，二生三，三生万物，大道至简。 58 | 我们生成了两部分区间后可以分别在某个区间内再进行二分，这样的操作可以一直进行下去，直到你满意为止。 59 | 下图是上面的例子生成的最终三个区间示意图，其实这个过程我们可以理解为二叉树的建立过程，所有的叶子节点都是最终的划分的区间。 60 | ![](./interval_tree.png) 61 | 62 | ## C++代码实现 63 | 开始实践部分，这才是真正遇到的挑战呢，因为在这个过程中我遇到了各种各样的问题，调试了很久的代码才把最终的demo跑通，当然这里面还有很多待完善的内容，后面有时间再慢慢完善吧，但是To be honest,写出来以后就再也没有用心看过它啦，哈哈，懒，太懒。 64 | 65 | 下面的图片是我在写代码过程的总的流程，大致分为3个步骤： 66 | 1. 离散化 67 | 2. 建立树结构 68 | 3. 减枝 69 | 4. 预测 70 | 哦，是4个步骤。 71 | ![](./CDTree.png) 72 | 73 | 值得一提的是，机器学习问题第一个重要的问题是用什么结构存储矩阵，以前我写离散决策树的时候用的是OpenCV的Mat类存储矩阵，确实很方便，但是OpenCV安转起来很麻烦，所以这次我用了另外一个科学计算的C++库:Eign。据说tensorflow的底层计算都是借用这个库，并且这个库只有头文件，不需要安装就可以使用啦，可以说是既方便有高效将，所以这里我用到了Eigen处理矩阵的科学计算部分。 74 | 75 | 怎么跑通我的代码呢？ 76 | 这个很简单啦，我的代码只有下面4个接口 77 | 78 | ``` 79 | 80 | CDTree(int deepestTree, int max_bin, float thresholdInfoGain); 81 | int buildTree(const MatrixXf &trainX, const MatrixXi &trainY, const MatrixXf &validateX, const MatrixXi &validateY, string Algorithm); 82 | int buildTree(const MatrixXf &trainX, const MatrixXi &trainY, string Algorithm); 83 | MatrixXi predict(const MatrixXf &testX); 84 | ``` 85 | `CDTree()`是构造函数，需要输入一些参数 86 | `buildTree()`是构建树的接口，有两个实现，其中一种是有验证集的构建，另一种是没有验证集的构建 87 | `predict`是预测函数 88 | 不出意外的话，这个类的输入输出数据都是通过Eigen结构存储的，那数据是怎样读取到Eigen的结构当中的呢？为了方便，我还写了将csv数据读入到Eigen结构中的类 89 | 90 | ``` 91 | template 92 | class RFCSV 93 | { 94 | public: 95 | string csvFileName; 96 | RFCSV() 97 | { 98 | this->csvFileName = ""; 99 | } 100 | RFCSV(string fileName) 101 | { 102 | this->csvFileName = fileName; 103 | } 104 | 105 | std::pair getData(string fileName) 106 | { 107 | 108 | 109 | } 110 | std::pair getData() 111 | { 112 | 113 | } 114 | 115 | template 116 | Type stringToNum(const string& str) 117 | { 118 | 119 | } 120 | 121 | }; 122 | ``` 123 | 124 | 为了节省空间，类的实现没有写出来，文章最后我将源代码放在git上，感兴趣的同学慢慢看。 125 | 126 | 最后，我们看一个完整的样例： 127 | 128 | 主函数是这样的 129 | ``` 130 | #include 131 | #include "CDTree.hpp" 132 | #include "RFCSV.hpp" 133 | #include 134 | using Eigen::MatrixXf; using Eigen::MatrixXi; 135 | int main() 136 | { 137 | 138 | std::pair trainData, testData; 139 | RFCSV readcsv; 140 | trainData = readcsv.getData("../data/ContinuousTrain.csv"); 141 | testData = readcsv.getData("../data/ContinuousTest.csv"); 142 | CDTree myCDTree(4, 3, 0.0001); 143 | myCDTree.buildTree(trainData.first, trainData.second, "ID3"); 144 | MatrixXi predictresult = myCDTree.predict(testData.first); 145 | float accuracy = 0; 146 | for (int i = 0; i < predictresult.size(); i++) 147 | { 148 | if (testData.second(i) == predictresult(i)) 149 | { 150 | accuracy += 1.0 / predictresult.size(); 151 | } 152 | } 153 | cout << "准确率为：" << accuracy * 100 << "\% 你很棒棒哦！" << endl; 154 | } 155 | ``` 156 | 数据样例是这样的 157 | ![](./data.png) 158 | 前面4列是连续属性，最后一列是标签。 159 | 最终的输出结果是： 160 | ![](./result.png) 161 | 162 | 总结：代码中用到了k-means聚类，合成最小区间数，但是这个操作使得算法稳定性极差，我觉得应该采用更加简单的方法解决这个问题，其实离散属性不宜划分的过多，过多的离散值会造成决策树过拟合，所以什么时候停止划分区间这个问题是亟待解决的。 163 | 164 | 最后，遇到了喜欢的女孩子，希望我能契合她心目中的那棵决策树，从树根到数叶，那不是机器学习的过程，是我们相互学习的过程，不契合也没有关系，能喜欢上你已经是万分荣幸啦！ 165 | 166 | 掌管天堂的空之王者，于地狱唱响容光之歌！ 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https://raw.githubusercontent.com/PiggyGaGa/MachineLearning-DecisionTree/3c063024405021739509e6cdb3655d5ebf417ebd/Finding Optimal Decision Trees.pdf -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | BSD 2-Clause License 2 | 3 | Copyright (c) 2017, 达达 4 | All rights reserved. 5 | 6 | Redistribution and use in source and binary forms, with or without 7 | modification, are permitted provided that the following conditions are met: 8 | 9 | * Redistributions of source code must retain the above copyright notice, this 10 | list of conditions and the following disclaimer. 11 | 12 | * Redistributions in binary form must reproduce the above copyright notice, 13 | this list of conditions and the following disclaimer in the documentation 14 | and/or other materials provided with the distribution. 15 | 16 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 17 | AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 18 | IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE 19 | DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE 20 | FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL 21 | DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR 22 | SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 23 | CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, 24 | OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 25 | OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 26 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # MachineLearning-DecisionTree 2 | MachineLearning-DecisionTree Algorithm realized by C++ 3 | ## DecisionTree说明文档 4 | ``` 5 | class DedisionTree 6 | { 7 | public: 8 | struct Attr //每一列的属性 9 | { 10 | int colIndex; 11 | string Attribute; 12 | int typeNum; //属性取值的个数 13 | vector AttributeValue; 14 | map typeMap; //属性取值对应的整数值; 15 | }; 16 | struct TreeNode 17 | { //节点信息 18 | string Attribute; //此节点对应的属性 19 | bool LeafNode; //如果是叶子节点，此值反映分类结果。 //其他情况都是0; 20 | vector children; //孩子节点的地址。 21 | map AttributeLinkChildren; 22 | //属性指向孩子节点，也就是对应的树形结构中的树枝 23 | }; 24 | //attributes 25 | cv::Mat trainDataMat; 26 | vector> predictedDataMat; 27 | //MatInfo trainMatrixInfo; 28 | TreeNode *root; //根节点 29 | vector vectorAttr; //存储所有的矩阵信息，但不存储矩阵。 30 | 31 | //functions 32 | DecisionTree(); //默认构造函数 33 | int ReadTrainDataFile(string fileAddress); //数据预处理 34 | TreeNode* BuildTree(cv::Mat &data, vector &dataAttr, string AlgorithmName); 35 | // 指定是哪种算法 36 | vector> ReadPredictedDataFile(string fileAddreess); 37 | vector Predicted(TreeNode* root, vector> &pData); 38 | //返回值为int类型表示数据的分类。 39 | ～DecisionTree(); 40 | } 41 | ``` 42 | **TreeNode** 43 | TreeNode 结构体用来存储树的节点信息， Attribute 是std::string类型的数据用来存储当前节点的属性名称，如果该节点是中间节点(分支节点)，那么该值就是对应的属性名称，如果该节点是叶子节点，那么该值就是对应的分类结果。 44 | ** Attr ** 45 | Attr 结构体用来存储每一种属性的信息，包括这种属性有多少种不同的离散值,如下表所示表示根据不同的信息得出是否去打高尔夫的简单数据，以第一列数据为例，第一列表示天气属性，那么这一列信息对应着一个Attr结构，Attribute在这里的值就是Outlook，LeafNode及是标志，是否为叶子节点，如果是叶子节点，那么Attr存储的是最后一行分类的某个结果，比如说yes，children存储的是孩子节点的地址，AttributeLinkChildren存储的是孩子节点和属性值的映射关系，仍然以第一列数据为例，如果Outlook的结果是sunny那么此时指向sunny对应的叶子节点的地址。 46 | ![](./pic/data.png =100x100) 47 | 48 | **trainDataMat** 49 | cv::Mat trainDataMat 存储训练集的数据，此结构由函数ReadTrainDataFile()生成 50 | 51 | **predictedDataMat** 52 | vector> predictedDataMat 存储的是待预测的数据矩阵，此矩阵由vector> ReadPredictedDataFile()函数生成 53 | 54 | **root** 55 | TreeNode *root 该属性存储决策树的根节点地址 56 | 57 | **vectorAttr** 58 | vector vectorAttr; 存储所有的属性信息 59 | 60 | **DecisionTree()** 默认构造函数 61 | 62 | **ReadTrainDataFile()** 63 | int ReadTrainDataFile(string fileAddress) 64 | 此函数执行读取训练数据的功能，输入值为待训练的数据集的地址，数据集的文件要求是必须为csv文件，关于csv文件参考[百度百科](https://baike.baidu.com/item/CSV/10739?fr=aladdin) 并且第一行的属性名称行是不能省略的，如下图的红色部分。此函数内部运行结束直接生成trainDataMat和vectorAttr个数据，这两个数据在后面的操作中非常重要。 65 | ![](./pic/trainData.png) 66 | 67 | **BuildTree()** 68 | TreeNode* BuildTree(cv::Mat &data, vector &dataAttr, string AlgorithmName) 69 | 此函数用于生成决策树，函数的返回值是决策树根节点的地址，需要将此值返回给root属性，留给后面的预测时使用。 70 | * data 是训练集数据，类型为OpenCV中的Mat类型，对应类中的trainDataMat 71 | * dataAttr 是训练数据的属性信息，对应类中的vectorAttr 72 | * AlgorithmName 是需要指定的决策树划分节点属性类型的方法，这个类中只支持ID3、C4_5、和Gini这三种方法。 73 | 74 | **ReadPredictedDataFile()** 75 | vector> ReadPredictedDataFile(string fileAddreess); 76 | 此函数执行读取待预测数据信息的功能，函数输入为待预测的数据，输出为字符串格式的矩阵。同样该文件是csv文件格式，但和ReadTrainDataFile有所差别就是该数据集不需要第一行的属性信息，如果有第一行的属性信息程序可能会出错，同时也不需要最后一列的标签列，对应的该矩阵的列数一定比训练矩阵的列数少1，行数没有限制。此函数输入的文件格式如下图, 77 | ![](./pic/predicted.png) 78 | 79 | **Predicted()** 80 | vector Predicted(TreeNode* root, vector> &pData) 81 | 此函数执行预测的功能,函数的输入参数包括根节点地址root和预测数据矩阵pData。 82 | 83 | ##Example 84 | 下面是简单的应用举例 85 | ### 输入数据说明 86 | 该类的两次输入分别是训练集和预测的测试集，并且都是csv格式的文件，此外目前算法只支持离散数据的分类，对于连续性数据不能使用，且每一列的属性个数不得多于255种，这是因为在程序设计中，每一列的数据个数的这个属性是采用uchar数据类型，所以不能多于255种，当然我们也可以根据自己的实际情况进行更改。程序的执行流程如下图所示 87 | ![](./pic/流程.png) 88 | 89 | ``` 90 | da::DecisionTree myDecisionTree; 91 | //对训练数据进行操作 92 | myDecisionTree.ReadTrainDataFile("data.csv"); 93 | //构建决策树 94 | myDecisionTree.root = myDecisionTree.BuildTree(myDecisionTree.trainDataMat, myDecisionTree.vectorAttr, "ID3"); 95 | // 96 | vector result; 97 | vector> predictedData; 98 | //对带预测数据进行操作 99 | predictedData = myDecisionTree.ReadPredictedDataFile("pre.csv"); 100 | //预测 101 | result = myDecisionTree.Predicted(myDecisionTree.root, predictedData); 102 | ``` 103 | 在最后一步的预测中也可以不额外开辟 predictedData数据，因为在ReadPredictedDataFile()函数执行过程中已经备份一份数据到对象的predictedDataMat属性中，所以按照下面的方法会节省空间和时间 104 | ``result = myDecisionTree.Predicted(myDecisionTree.root, myDecisionTree.predictedDataMat);``直接调用对象中的数据即可。 105 | 106 | **～DecisionTree()** 107 | ～DecisionTree() 为构析函数 108 | 109 | [连续型的决策树](./Continuous_DecisionTree/Readme.md) 110 | -------------------------------------------------------------------------------- /SogouCA.mini.tar.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PiggyGaGa/MachineLearning-DecisionTree/3c063024405021739509e6cdb3655d5ebf417ebd/SogouCA.mini.tar.gz -------------------------------------------------------------------------------- /SogouCA.mini.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PiggyGaGa/MachineLearning-DecisionTree/3c063024405021739509e6cdb3655d5ebf417ebd/SogouCA.mini.txt -------------------------------------------------------------------------------- /XGboost.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PiggyGaGa/MachineLearning-DecisionTree/3c063024405021739509e6cdb3655d5ebf417ebd/XGboost.pdf -------------------------------------------------------------------------------- /how to deal with continuous attribute.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PiggyGaGa/MachineLearning-DecisionTree/3c063024405021739509e6cdb3655d5ebf417ebd/how to deal with continuous attribute.pdf -------------------------------------------------------------------------------- /pic/data.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PiggyGaGa/MachineLearning-DecisionTree/3c063024405021739509e6cdb3655d5ebf417ebd/pic/data.png -------------------------------------------------------------------------------- /pic/predicted.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PiggyGaGa/MachineLearning-DecisionTree/3c063024405021739509e6cdb3655d5ebf417ebd/pic/predicted.png -------------------------------------------------------------------------------- /pic/trainData.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PiggyGaGa/MachineLearning-DecisionTree/3c063024405021739509e6cdb3655d5ebf417ebd/pic/trainData.png -------------------------------------------------------------------------------- /pic/流程.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PiggyGaGa/MachineLearning-DecisionTree/3c063024405021739509e6cdb3655d5ebf417ebd/pic/流程.png -------------------------------------------------------------------------------- /ppt.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PiggyGaGa/MachineLearning-DecisionTree/3c063024405021739509e6cdb3655d5ebf417ebd/ppt.pdf -------------------------------------------------------------------------------- /连续数据的决策树.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PiggyGaGa/MachineLearning-DecisionTree/3c063024405021739509e6cdb3655d5ebf417ebd/连续数据的决策树.png -------------------------------------------------------------------------------- /连续数据的决策树.xmind: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PiggyGaGa/MachineLearning-DecisionTree/3c063024405021739509e6cdb3655d5ebf417ebd/连续数据的决策树.xmind --------------------------------------------------------------------------------