public static void main(String[] args) throws IOException {
// String fileInputReq = args[0];
// float bid_price = Float.valueOf(args[1]);
// String model_para = args[2];
// String fileOutReq = args[3];
String fileInputReq = "test.txt";
String str = "0.150000000";
float bid_price = Float.parseFloat(str);
System.out.println(bid_price);
String dict = "dict";
String model_para = "train.model";
String resultsOut = "results.txt";
TestDataGen2 saj = new TestDataGen2();
long startTime = System.currentTimeMillis();
String ReqLine = saj.dataMergeReq(fileInputReq, bid_price);
// System.out.println(ReqLine);
String cutdic = saj.MakeDict(ReqLine);
// System.out.println(cutdic);
// String ReqLineFormat = saj.dictGet(cutdic,dict);
String outputs = "result.txt";
saj.dictGet(cutdic, dict, outputs);
// System.out.println(ReqLineFormat);
String[] arg = {"-b", "1", outputs, model_para, resultsOut};
Predict.main(arg);
long endTime = System.currentTimeMillis();
long trainTime = endTime - startTime;
System.out.println(trainTime + "ms\n");
}
public static void RetrieveTrainMatrix() {
try {
BufferedReader flrdr =
new BufferedReader(
new FileReader("D:\\IR\\Assignment7\\Pytest\\part2\\output\\train_matrix.txt"));
String line = "";
int doc_count = 0;
while ((line = flrdr.readLine()) != null) {
SortedSet<Integer> word_ids = new TreeSet<Integer>();
int val_count = 0;
String[] key_value = line.split(" :: ");
key_value[1] = key_value[1].substring(1, key_value[1].length() - 2);
String[] values = key_value[1].split(",");
FeatureNode[] node = new FeatureNode[values.length];
for (String val : values) word_ids.add(Integer.parseInt(val.trim()));
for (int val : word_ids) node[val_count++] = new FeatureNode(val, 1);
if (spam_docs.contains(key_value[0].trim())) ylable[doc_count] = 1;
else ylable[doc_count] = 0;
train_matrix[doc_count++] = node;
}
flrdr.close();
} catch (Exception e) {
e.printStackTrace();
System.exit(0);
}
}
ABSAClassifierHotels(int option, String trainFile, String testFile, String ddgFile)
throws IOException {
rootDirectory = System.getProperty("user.dir");
mainClassifierFunction(option, trainFile, testFile, ddgFile);
// rootDirectory = "D:\\Course\\Semester VII\\Internship\\sentiment\\indian";
// return generateFeature();
}
public static void RetrieveTestMatrix() {
try {
BufferedReader flrdr =
new BufferedReader(
new FileReader("D:\\IR\\Assignment7\\Pytest\\part2\\output\\test_matrix.txt"));
String line = "";
while ((line = flrdr.readLine()) != null) {
SortedSet<Integer> word_ids = new TreeSet<Integer>();
int val_count = 0;
String[] key_value = line.split(" :: ");
if (key_value[1].trim().length() < 1)
System.out.println("Error on Train Doc " + key_value[0]);
key_value[1] = key_value[1].substring(1, key_value[1].length() - 2);
String[] values = key_value[1].split(",");
FeatureNode[] node = new FeatureNode[values.length];
for (String val : values) word_ids.add(Integer.parseInt(val.trim()));
for (int val : word_ids) node[val_count++] = new FeatureNode(val, 1);
double predict = Linear.predict(model, node);
doc_score.put(key_value[0].trim(), predict);
}
flrdr.close();
} catch (Exception e) {
e.printStackTrace();
System.exit(0);
}
}