bool synaxErrorJudger::isSynaxErrorInCreateTable()
{
if (!createTableRe.match(sqlExp).hasMatch())
return true;
generateTableInfo();
if (!isTableNameValid()) {
throw QString("Synax Error: Table's name is invalid.");
}
if (!isAllAttributeNameValid()) {
throw QString("Synax Error: Attribute's name is invalid.");
}
return false;
}
int main(int argc, const char *argv[])
{
ifstream inputFile; // Input file stream
string singleInstance; // Single line read from the input file
vvs dataTable; // Input data in the form of a vector of vector of strings
inputFile.open(argv[1]);
if (!inputFile) // If input file does not exist, print error and exit
{
cerr << "Error: Training data file not found!" << endl;
exit(-1);
}
/*
* Decision tree training phase
* In this phase, the training data is read
* from the file and stored into a vvs using
* the parse() function. The generateTableInfo()
* function extracts the attribute (column) names
* and also the values that each column can take.
* This information is also stored in a vvs.
* buildDecisionTree() function recursively
* builds trains the decision tree.
*/
while (getline(inputFile, singleInstance)) // Read from file, parse and store data
{
parse(singleInstance, dataTable);
}
inputFile.close(); // Close input file
vvs tableInfo = generateTableInfo(dataTable); // Stores all the attributes and their values in a vector of vector of strings named tableInfo
node* root = new node; // Declare and assign memory for the root node of the Decision Tree
root->isRoot = true;
root = buildDecisionTree(dataTable, root, tableInfo); // Recursively build and train decision tree
string defaultClass = returnMostFrequentClass(dataTable); // Stores the most frequent class in the training data. This is used as the default class label
dataTable.clear(); // clear dataTable of training data to store testing data
/*
* Decision tree testing phase
* In this phase, the testing is read
* from the file, parsed and stored.
* Each row in the table is made to
* traverse down the decision tree
* till a class label is found.
*/
inputFile.clear();
inputFile.open(argv[2]); // Open test file
if (!inputFile) // Exit if test file is not found
{
cerr << "Error: Testing data file not found!" << endl;
exit(-1);
}
while (getline(inputFile, singleInstance)) // Store test data in a table
{
parse(singleInstance, dataTable);
}
vs predictedClassLabels; // Stores the predicted class labels for each row
vs givenClassLabels; // Stores the given class labels in the test data
for (int iii = 1; iii < dataTable.size(); iii++) // Store given class labels in vector of strings named givenClassLabels
{
string data = dataTable[iii][dataTable[0].size()-1];
givenClassLabels.push_back(data);
}
for (int iii = 1; iii < dataTable.size(); iii++) // Predict class labels based on the decision tree
{
string someString = testDataOnDecisionTree(dataTable[iii], root, tableInfo, defaultClass);
predictedClassLabels.push_back(someString);
}
dataTable.clear();
/* Print output */
ofstream outputFile;
outputFile.open("decisionTreeOutput.txt", ios::app);
outputFile << endl << "--------------------------------------------------" << endl;
double accuracy = printPredictionsAndCalculateAccuracy(givenClassLabels, predictedClassLabels); // calculate accuracy of classification
outputFile << "Accuracy of decision tree classifier = " << accuracy << "%"; // Print out accuracy to console
return 0;
}
