/*
* Returns a vector of integers containing the counts
* of all the various values of an attribute/column.
*/
vi countDistinct(vvs &table, int column)
{
vs vectorOfStrings;
vi counts;
bool found = false;
int foundIndex;
for (int iii = 1; iii < table.size(); iii++) {
for (int jjj = 0; jjj < vectorOfStrings.size(); jjj++) {
if (vectorOfStrings[jjj] == table[iii][column]) {
found = true;
foundIndex = jjj;
break;
}
else {
found = false;
}
}
if (!found) {
counts.push_back(1);
vectorOfStrings.push_back(table[iii][column]);
}
else {
counts[foundIndex]++;
}
}
int sum = 0;
for (int iii = 0; iii < counts.size(); iii++) {
sum += counts[iii];
}
counts.push_back(sum);
return counts;
}
/*
* Prunes a table based on a column/attribute's name
* and value of that attribute. Removes that column
* and all rows that have that value for that column.
*/
vvs pruneTable(vvs &attributeTable, string &colName, string value)
{
int iii, jjj;
vvs prunedTable;
int column = -1;
vs headerRow;
for (iii = 0; iii < attributeTable[0].size(); iii++) {
if (attributeTable[0][iii] == colName) {
column = iii;
break;
}
}
for (iii = 0; iii < attributeTable[0].size(); iii++) {
if (iii != column) {
headerRow.push_back(attributeTable[0][iii]);
}
}
prunedTable.push_back(headerRow);
for (iii = 0; iii < attributeTable.size(); iii++) {
vs auxRow;
if (attributeTable[iii][column] == value) {
for (jjj = 0; jjj < attributeTable[iii].size(); jjj++) {
if (jjj != column) {
auxRow.push_back(attributeTable[iii][jjj]);
}
}
prunedTable.push_back(auxRow);
}
}
return prunedTable;
}
/*
* Returns a vvs which contains information about
* the data table. The vvs contains the names of
* all the columns and the values that each
* column can take
*/
vvs generateTableInfo(vvs &dataTable)
{
vvs tableInfo;
#pragma omp parallel
{
#pragma omp for schedule(static) ordered
for (int iii = 0; iii < dataTable[0].size(); iii++) {
vs tempInfo;
msi tempMap;
for (int jjj = 0; jjj < dataTable.size(); jjj++) {
if (tempMap.count(dataTable[jjj][iii]) == 0) {
tempMap[dataTable[jjj][iii]] = 1;
tempInfo.push_back(dataTable[jjj][iii]);
}
else {
tempMap[dataTable[jjj][iii]]++;
}
}
#pragma omp ordered
tableInfo.push_back(tempInfo);
}
}
return tableInfo;
}
/*
* Prints a vector of vector of strings
* For debugging purposes only.
*/
void printAttributeTable(vvs &attributeTable)
{
int inner, outer;
for (outer = 0; outer < attributeTable.size(); outer++) {
for (inner = 0; inner < attributeTable[outer].size(); inner++) {
cout << attributeTable[outer][inner] << "\t";
}
cout << endl;
}
}
/*
* Returns an integer which is the
* index of a column passed as a string
*/
int returnColumnIndex(string &columnName, vvs &tableInfo)
{
int iii;
for (iii = 0; iii < tableInfo.size(); iii++) {
if (tableInfo[iii][0] == columnName) {
return iii;
}
}
return -1;
}
/*
* function: naive_bayes::set_data 设置数据集，产生辅助数据
* d: 数据集
* h: 属性, 需保证最后一列为目标属性，且为离散值
* b: 属性是离散值(false), 还是数值型(true)
*
*/
bool naive_bayes::set_data(vvs& d, vs& h, vb b)
{
bool f = clear();
if(!f) return f;
assert(d.size() > 0); // 数据集不能为空
datas = d;
headers = h;
num_attr = (int)headers.size();
num_data = (int)d.size();
is_numeric = b;
is_numeric.resize(num_attr, false);
assert(is_numeric.back() == false); // 目标属性必须为离散值
target_attr = headers.back();
attr_to_int.resize(num_attr);
int_to_attr.resize(num_attr);
attrs_size.resize(num_attr);
for(int i = 0; i < num_data; ++i)
{
auto& e = d[i];
for(int j = 0; j < num_attr; ++j)
{
if(is_numeric[j]) continue; // 数值型数据不需要映射
auto it = attr_to_int[j].find(e[j]);
if(it == attr_to_int[j].end())
{
attr_to_int[j][e[j]] = (int)int_to_attr[j].size();
int_to_attr[j].push_back(e[j]);
}
}
}
for(int i = 0; i < num_attr; ++i)
attrs_size[i] = (int)int_to_attr[i].size();
num_targ = attrs_size.back();
// 目标属性值的下标
for(int i = 0; i < num_data; ++i)
target_to_label.push_back(attr_to_int[num_attr - 1][d[i][num_attr - 1]]);
return true;
}
bool check(vvs graph,
size_t node_number,
size_t color_number) {
size_t num_nodes = graph.size();
vector<size_t> colors(num_nodes, 0);
bool fail = false;
set<size_t> blue;
set<size_t> red;
if (color_number == 1) {
blue.insert(node_number);
colors[node_number] = 1;
}
if (color_number == 2) {
red.insert(node_number);
colors[node_number] = 2;
}
while (!fail && (!blue.empty() || !red.empty())) {
set<size_t>::iterator it;
set<size_t>* target;
set<size_t>* orig;
if (!blue.empty()) {
it = blue.begin();
target = &red;
orig = &blue;
}
else {
it = red.begin();
target = &blue;
orig = &red;
}
size_t node = *it;
orig->erase(it);
size_t target_color;
if (colors[node] == 1) target_color = 2;
if (colors[node] == 2) target_color = 1;
for (size_t n_counter = 0; !fail && n_counter < graph[node].size(); n_counter++) {
size_t n_num = graph[node][n_counter];
if (colors[n_num] && colors[n_num] != target_color)
fail = true;
else
if (!colors[n_num]) {
colors[n_num] = target_color;
target->insert(n_num);
}
}
}
return !fail;
}
/*
* Returns true if all rows in a subtable
* have the same class label.
* This means that that node's class label
* has been decided.
*/
bool isHomogeneous(vvs &table)
{
int iii;
int lastCol = table[0].size() - 1;
string firstValue = table[1][lastCol];
for (iii = 1; iii < table.size(); iii++) {
if (firstValue != table[iii][lastCol]) {
return false;
}
}
return true;
}
void printCase(vs V, vvs A, vd f){
printf("Case Details:\n");
printf("--------------\n");
printf("Number Of Variables n: %d\n\n", V.size());
printf("Rules:\n");
printf("-------\n");
for(int i=0; i<A.size(); i++){
for(int j=0; j<A[i].size(); j++){
printf("%s ", A[i][j].c_str());
}
printf(":: %.3lf\n", f[i]);
}
}
/*
* Decides which column to split on
* based on entropy. Returns the column
* with the least entropy.
*/
string decideSplittingColumn(vvs &table)
{
int column, iii;
double minEntropy = DBL_MAX;
int splittingColumn = 0;
vi entropies;
#pragma omp parallel
{
#pragma omp for
for (column = 0; column < table[0].size() - 1; column++) {
string colName = table[0][column];
msi tempMap;
vi counts = countDistinct(table, column);
vd attributeEntropy;
double columnEntropy = 0.0;
for (iii = 1; iii < table.size() - 1; iii++) {
double entropy = 0.0;
if (tempMap.find(table[iii][column]) != tempMap.end()) { // IF ATTRIBUTE IS ALREADY FOUND IN A COLUMN, UPDATE IT'S FREQUENCY
tempMap[table[iii][column]]++;
}
else { // IF ATTRIBUTE IS FOUND FOR THE FIRST TIME IN A COLUMN, THEN PROCESS IT AND CALCULATE IT'S ENTROPY
tempMap[table[iii][column]] = 1;
vvs tempTable = pruneTable(table, colName, table[iii][column]);
vi classCounts = countDistinct(tempTable, tempTable[0].size() - 1);
int jjj, kkk;
for (jjj = 0; jjj < classCounts.size(); jjj++) {
double temp = (double)classCounts[jjj];
entropy -= (temp / classCounts[classCounts.size() - 1])*(log(temp / classCounts[classCounts.size() - 1]) / log(2));
}
attributeEntropy.push_back(entropy);
entropy = 0.0;
}
}
//for (iii = 0; iii < counts.size() - 1 && ((attributeEntropy.size() - 1) == (counts.size() - 1)); iii++) {
for (iii = 0; iii < counts.size() - 1; iii++) {
columnEntropy += ((double)counts[iii] * (double)attributeEntropy[iii]);
}
columnEntropy = columnEntropy / ((double)counts[counts.size() - 1]);
if (columnEntropy <= minEntropy) {
minEntropy = columnEntropy;
splittingColumn = column;
}
}
}
return table[0][splittingColumn];
}
/*
* Returns the most frequent class from the training data
* This class will be used as the default class label
*/
string returnMostFrequentClass(vvs &dataTable)
{
msi trainingClasses; // Stores the classlabels and their frequency
for (int iii = 1; iii < dataTable.size(); iii++) {
if (trainingClasses.count(dataTable[iii][dataTable[0].size() - 1]) == 0) {
trainingClasses[dataTable[iii][dataTable[0].size() - 1]] = 1;
}
else {
trainingClasses[dataTable[iii][dataTable[0].size() - 1]]++;
}
}
msi::iterator mapIter;
int highestClassCount = 0;
string mostFrequentClass;
for (mapIter = trainingClasses.begin(); mapIter != trainingClasses.end(); mapIter++) {
if (mapIter->second >= highestClassCount) {
highestClassCount = mapIter->second;
mostFrequentClass = mapIter->first;
}
}
return mostFrequentClass;
}
double recur(vs V, vvs A, vd f){
//printCase(V, A, f);
if(V.size()==1) return f[0];
string var = V[0];
int val;
for(int i=0; i<A.size(); i++){
if(A[i].size()>1) continue;
if(var==A[i][0]) {
val = f[i];
break;
}
}
vs Vij = V;
vvs Aij(A.size(), vs());
//step 1
Vij.erase(Vij.begin());
//step 2
for(int i=0; i<A.size(); i++){
for(int j=0; j<A[i].size(); j++){
if(var!=A[i][j]) Aij[i].push_back(A[i][j]);
}
}
// remove empty rules
for(int i=0; i<Aij.size(); i++){
if(Aij[i].size()==0){
Aij.erase(Aij.begin()+i);
i--;
}
}
// remove repeated rules
set<vs> s(Aij.begin(), Aij.end());
Aij.clear();
Aij = vvs(s.begin(), s.end());
// construct DAG
vector<vi> graph(Aij.size(), vi());
vector<vi> graphT(Aij.size(), vi());
for(int i=0; i<Aij.size(); i++){
for(int j=0; j<Aij.size(); j++){
if(i==j||Aij[i].size()>=Aij[j].size()) continue;
bool subset=1;
for(int k=0; k<Aij[i].size()&⊂ k++){
bool found=0;
for(int l=0; l<Aij[j].size(); l++){
if(Aij[i][k]==Aij[j][l]){
found=1;
break;
}
}
subset&=found;
}
if(subset){
graph[i].push_back(j);
graphT[j].push_back(i);
}
}
}
// Try for all values of the number of instances of the cur variable
double maxValue=0;
while(val>0){
vd fij(Aij.size(), INF);
for(int i=0; i<Aij.size(); i++){
vs S = Aij[i];
vs Sv = S;
Sv.push_back(var);
bool foundS=false, foundSv=false;
double foundSval, foundSvVal;
for(int j=0; j<A.size(); j++){
if(S.size()==A[j].size()){
bool sameSet=1;
for(int k=0; k<S.size()&&sameSet; k++){
bool found=0;
for(int l=0; l<S.size(); l++){
if(S[k]==A[j][l]) {
found = 1;
break;
}
}
sameSet&=found;
}
if(sameSet) foundS=true, foundSval=f[j];
}
if(Sv.size()==A[j].size()){
bool sameSet=1;
for(int k=0; k<Sv.size()&&sameSet; k++){
bool found=0;
for(int l=0; l<Sv.size(); l++){
if(Sv[k]==A[j][l]) {
found = 1;
break;
}
}
sameSet&=found;
}
if(sameSet) foundSv=true, foundSvVal=f[j]/val;
}
}
if(!foundSv) fij[i]=foundSval;
else if(!foundS) fij[i]=foundSvVal;
else fij[i]=min(foundSval,foundSvVal);
}
queue<int> q;
for(int i=0; i<Aij.size(); i++){
if(graph[i].size()==0) q.push(i);
}
while(!q.empty()){
int node = q.front(); q.pop();
for(int i=0; i<graph[node].size(); i++){
fij[node]=min(fij[node], fij[graph[node][i]]);
}
for(int i=0; i<graphT[node].size(); i++){
q.push(graphT[node][i]);
}
}
maxValue=max(maxValue, val*recur(Vij, Aij, fij));
val--;
}
return maxValue;
}
/*
* Returns true if the table is empty
* returns false otherwise
*/
bool tableIsEmpty(vvs &table)
{
return (table.size() == 1);
}