void getUserStats(std::vector<int>& users, gk_csr_t* mat,
std::unordered_set<int>& filtItems, const char* opFName) {
auto rowColFreq = getRowColFreq(mat);
auto userFreq = rowColFreq.first;
auto itemFreq = rowColFreq.second;
std::vector<std::pair<int, double>> itemFreqP;
for (int i = 0; i < itemFreq.size(); i++) {
itemFreqP.push_back(std::make_pair(i, itemFreq[i]));
}
std::sort(itemFreqP.begin(), itemFreqP.end(), descComp);
std::unordered_set<int> top500Items;
for (int i = 0; i < 500; i++) {
top500Items.insert(itemFreqP[i].first);
}
std::ofstream opFile(opFName);
for (auto&& user: users) {
int nUserItems = 0;
std::unordered_set<int> items;
for (int ii = mat->rowptr[user];
ii < mat->rowptr[user+1]; ii++) {
int item = mat->rowind[ii];
auto search = filtItems.find(item);
if (search != filtItems.end()) {
//found n skip
continue;
}
items.insert(item);
nUserItems += 1;
}
//no. of users that rated the items rated by user
std::unordered_set<int> adjUsers;
//mean freq of items
float meanFreq = 0;
int top500Count = 0;
for (auto&& item: items) {
for (int jj = mat->colptr[item]; jj < mat->colptr[item+1];
jj++) {
int u = mat->colind[jj];
adjUsers.insert(u);
}
meanFreq += itemFreq[item];
auto search = top500Items.find(item);
if (search != top500Items.end()) {
//found in top 500 items
top500Count++;
}
}
meanFreq = meanFreq/items.size();
//analyze adjUsers
double meanAdjUItems = 0;
double meanAdjUItemsFreq = 0;
int nMeanAdjUItems = 0;
int adjTop500Count = 0;
for (auto&& adjUser: adjUsers) {
meanAdjUItems += userFreq[adjUser];
for (int ii = mat->rowptr[adjUser]; ii < mat->rowptr[adjUser+1]; ii++) {
int item = mat->rowind[ii];
auto search = top500Items.find(item);
if (search != top500Items.end()) {
adjTop500Count++;
}
meanAdjUItemsFreq += itemFreq[item];
nMeanAdjUItems++;
}
}
meanAdjUItems = meanAdjUItems/adjUsers.size();
meanAdjUItemsFreq = meanAdjUItemsFreq/nMeanAdjUItems;
opFile << user << " " << nUserItems << " " << adjUsers.size() << " "
<< meanFreq << " " << top500Count << " " << meanAdjUItems << " "
<< meanAdjUItemsFreq << " " << adjTop500Count/adjUsers.size()
<< std::endl;
}
opFile.close();
}
void meanAndVarSameGroundAllUsers(Data& data, Params& params) {
std::cout << "meanAndVarSameGroundAllUsers" << std::endl;
std::vector<ModelMF> mfModels;
for (int i = 1; i <= 20; i++) {
std::string prefix = "mf_1_0_10_" + std::to_string(i);
ModelMF fullModel(params, params.seed);
fullModel.loadFacs(prefix.c_str());
mfModels.push_back(fullModel);
}
std::string uFName = "uFac_" + std::to_string(mfModels[0].nUsers) + "_10_1.txt";
std::string iFName = "iFac_" + std::to_string(mfModels[0].nItems+1) + "_10_1.txt";
ModelMF origModel(params, uFName.c_str(), iFName.c_str(), params.seed);
int nModels = mfModels.size();
int nUsers = data.trainMat->nrows;
int nItems = data.trainMat->ncols;
std::mt19937 mt(params.seed);
std::unordered_set<int> invalidUsers;
std::unordered_set<int> invalidItems;
std::string modelSign = mfModels[0].modelSignature();
std::cout << "\nModel sign: " << modelSign << std::endl;
std::string prefix = std::string(params.prefix) + "_1_" + modelSign + "_invalUsers.txt";
std::vector<int> invalUsersVec = readVector(prefix.c_str());
prefix = std::string(params.prefix) + "_1_" + modelSign + "_invalItems.txt";
std::vector<int> invalItemsVec = readVector(prefix.c_str());
for (auto v: invalUsersVec) {
invalidUsers.insert(v);
}
for (auto v: invalItemsVec) {
invalidItems.insert(v);
}
std::vector<double> rmseModels(nModels, 0);
double avgRMSE = 0;
double nnz = 0;
std::string opFName = std::string(params.prefix) + "_ui_mean_var.txt";
std::ofstream opFile(opFName.c_str());
//#pragma omp parallel for
#pragma omp parallel
{
std::vector<double> loc_rmseModels(nModels, 0);
#pragma omp for reduction(+: avgRMSE, nnz)
for (int item = 0; item < nItems; item++) {
if (invalidItems.count(item) > 0) {
continue;
}
std::unordered_set<int> ratedUsers;
for (int uu = data.trainMat->colptr[item];
uu < data.trainMat->colptr[item+1]; uu++) {
int user = data.trainMat->colind[uu];
ratedUsers.insert(user);
}
std::vector<std::tuple<int, float, float, float>> meanVarErr;
//compute mean, var and abs err for each user
for (int user = 0; user < nUsers; user++) {
if (invalidUsers.count(user) > 0 || ratedUsers.count(user) > 0) {
continue;
}
float uMean = 0, uVar = 0, uErr = 0, diff = 0;
float r_ui = origModel.estRating(user, item);
for (int i = 0; i < nModels; i++) {
float r_ui_est = mfModels[i].estRating(user, item);
diff = r_ui_est - r_ui;
loc_rmseModels[i] += diff*diff;
uMean += r_ui_est;
}
uMean = uMean/nModels;
uErr = fabs(uMean - r_ui);
avgRMSE += uErr*uErr;
diff = 0;
for (int i = 0; i < nModels; i++) {
diff = mfModels[i].estRating(user, item) - uMean;
uVar += diff*diff;
}
uVar = uVar/nModels;
meanVarErr.push_back(std::make_tuple(user, uMean, uVar, uErr));
nnz++;
}
//#pragma omp critical
//{
/*
for (int i = 0; i < meanVarErr.size(); i++) {
int user = std::get<0>(meanVarErr[i]);
float uMean = std::get<1>(meanVarErr[i]);
float uVar = std::get<2>(meanVarErr[i]);
float uErr = std::get<3>(meanVarErr[i]);
opFile << user << " " << item << " ";
opFile << std::fixed << std::setprecision(5) << uMean << " " << uVar << " "
<< uErr << std::endl;
}
*/
//}
}
#pragma omp critical
{
for (int i = 0; i < nModels; i++) {
rmseModels[i] += loc_rmseModels[i];
}
}
}
double minRMSE = 100;
avgRMSE = sqrt(avgRMSE/nnz);
for (int i = 0; i < nModels; i++) {
rmseModels[i] = sqrt(rmseModels[i]/nnz);
if (rmseModels[i] < minRMSE) {
minRMSE = rmseModels[i];
}
std::cout << "Model " << i << " RMSE: " << rmseModels[i] << std::endl;
}
std::cout << "Best model RMSE: " << minRMSE << std::endl;
std::cout << "Average models RMSE: " << avgRMSE << std::endl;
opFile.close();
}
void meanAndVarSameGroundSampUsers(Data& data, Params& params) {
std::cout << "meanAndVarSameGroundSampUsers" << std::endl;
std::vector<ModelMF> mfModels;
for (int i = 1; i <= 20; i++) {
std::string prefix = "mf_0_0_10_" + std::to_string(i);
ModelMF fullModel(params, params.seed);
fullModel.loadFacs(prefix.c_str());
mfModels.push_back(fullModel);
}
std::string uFName = "uFac_" + std::to_string(mfModels[0].nUsers) + "_10_0.txt";
std::string iFName = "iFac_" + std::to_string(mfModels[0].nItems) + "_10_0.txt";
ModelMF origModel(params, uFName.c_str(), iFName.c_str(), params.seed);
int nModels = mfModels.size();
int nUsers = data.trainMat->nrows;
int nItems = data.trainMat->ncols;
std::mt19937 mt(params.seed);
std::unordered_set<int> invalidUsers;
std::unordered_set<int> invalidItems;
std::string modelSign = mfModels[0].modelSignature();
std::cout << "\nModel sign: " << modelSign << std::endl;
std::string prefix = std::string(params.prefix) + "_1_" + modelSign + "_invalUsers.txt";
std::vector<int> invalUsersVec = readVector(prefix.c_str());
prefix = std::string(params.prefix) + "_1_" + modelSign + "_invalItems.txt";
std::vector<int> invalItemsVec = readVector(prefix.c_str());
for (auto v: invalUsersVec) {
invalidUsers.insert(v);
}
for (auto v: invalItemsVec) {
invalidItems.insert(v);
}
std::string opFName = std::string(params.prefix) + "_ui_mean_var.txt";
std::ofstream opFile(opFName.c_str());
std::vector<std::unordered_set<int>> itemUsers;
int sampUserSz = 0.25*nUsers;
for (int item = 0; item < nItems; item++) {
//sample 1% users who have not rated the item
std::unordered_set<int> sampledUsers;
std::unordered_set<int> ratedUsers;
for (int uu = data.trainMat->colptr[item];
uu < data.trainMat->colptr[item+1]; uu++) {
int user = data.trainMat->colind[uu];
ratedUsers.insert(user);
}
for (auto&& user: invalidUsers) {
ratedUsers.insert(user);
}
sampleUsers(nUsers, ratedUsers, sampledUsers, sampUserSz, mt);
itemUsers.push_back(sampledUsers);
}
std::cout << "Sampled " << sampUserSz << " per item." << std::endl;
#pragma omp parallel for
for (int item = 0; item < nItems; item++) {
if (invalidItems.count(item) > 0) {
continue;
}
std::unordered_set<int>& sampledUsers = itemUsers[item];
std::vector<std::tuple<int, float, float, float>> meanVarErr;
//compute mean, var and abs err for each user
for (auto&& user: sampledUsers) {
float uMean = 0, uVar = 0, uErr = 0, diff = 0;
for (int i = 0; i < nModels; i++) {
uMean += mfModels[i].estRating(user, item);
}
uMean = uMean/nModels;
uErr = fabs(uMean - origModel.estRating(user, item));
for (int i = 0; i < nModels; i++) {
diff = mfModels[i].estRating(user, item) - uMean;
uVar += diff*diff;
}
uVar = uVar/nModels;
meanVarErr.push_back(std::make_tuple(user, uMean, uVar, uErr));
}
#pragma omp critical
{
for (int i = 0; i < meanVarErr.size(); i++) {
int user = std::get<0>(meanVarErr[i]);
float uMean = std::get<1>(meanVarErr[i]);
float uVar = std::get<2>(meanVarErr[i]);
float uErr = std::get<3>(meanVarErr[i]);
opFile << user << " " << item << " ";
opFile << std::fixed << std::setprecision(5) << uMean << " " << uVar << " "
<< uErr << std::endl;
}
}
}
opFile.close();
}
void compJaccSimAccu(Data& data, Params& params) {
std::vector<ModelMF> mfModels;
std::vector<ModelMF> origModels;
for (int i = 1; i < 3; i++) {
std::string prefix = "mf_0_0_10_" + std::to_string(i);
ModelMF fullModel(params, params.seed);
fullModel.loadFacs(prefix.c_str());
mfModels.push_back(fullModel);
std::string uFName = "uFac_" + std::to_string(fullModel.nUsers) + "_10_0.txt";
std::string iFName = "iFac_" + std::to_string(fullModel.nItems+1) + "_10_0.txt";
ModelMF origModel(params, uFName.c_str(), iFName.c_str(), params.seed);
origModels.push_back(origModel);
}
/*
for (int i = 0; i < 2; i++) {
std::string prefix = "mfrand_" + std::to_string(i) + "_0_20";
ModelMF fullModel(params, params.seed);
fullModel.loadFacs(prefix.c_str());
mfModels.push_back(fullModel);
std::string uFName = "uFac_" + std::to_string(fullModel.nUsers) + "_20_"
+ std::to_string(i) + ".txt";
std::string iFName = "iFac_" + std::to_string(fullModel.nItems) + "_20_"
+ std::to_string(i) + ".txt";
ModelMF origModel(params, uFName.c_str(), iFName.c_str(), params.seed);
origModels.push_back(origModel);
}
*/
int nModels = mfModels.size();
std::unordered_set<int> invalidUsers;
std::unordered_set<int> invalidItems;
std::string modelSign = mfModels[0].modelSignature();
std::cout << "\nModel sign: " << modelSign << std::endl;
std::string prefix = std::string(params.prefix) + "_" + modelSign + "_invalUsers.txt";
std::vector<int> invalUsersVec = readVector(prefix.c_str());
prefix = std::string(params.prefix) + "_" + modelSign + "_invalItems.txt";
std::vector<int> invalItemsVec = readVector(prefix.c_str());
for (auto v: invalUsersVec) {
invalidUsers.insert(v);
}
for (auto v: invalItemsVec) {
invalidItems.insert(v);
}
for (int i = 0; i < nModels; i++) {
auto& fullModel = mfModels[i];
auto& origModel = origModels[i];
std::cout << "Model: " << i << std::endl;
std::cout << "Train RMSE: " << fullModel.RMSE(data.trainMat, invalidUsers,
invalidItems, origModel) << std::endl;
std::cout << "Test RMSE: " << fullModel.RMSE(data.testMat, invalidUsers,
invalidItems, origModel) << std::endl;
std::cout << "Val RMSE: " << fullModel.RMSE(data.valMat, invalidUsers,
invalidItems, origModel) << std::endl;
//std::cout << "Full RMSE: " << fullModel.fullLowRankErr(data, invalidUsers,
// invalidItems, origModel) << std::endl;
}
std::vector<double> epsilons = {0.025, 0.05, 0.1, 0.25, 0.5, 1.0};
//std::vector<double> epsilons = {0.5};
int nUsers = data.trainMat->nrows;
int nItems = data.trainMat->ncols;
std::cout << "nModels: " << nModels << std::endl;
for (auto&& epsilon: epsilons) {
std::cout << "epsilon: " << epsilon << std::endl;
std::vector<std::vector<float>> itemsJacSims(nItems);
std::vector<std::vector<float>> itemAccuCount(nItems);
std::vector<std::vector<float>> itemPearsonCorr(nItems);
#pragma omp parallel for
for (int item = 0; item < nItems; item++) {
if (invalidItems.count(item) > 0) {
continue;
}
std::vector<std::unordered_set<int>> modelAccuItems(nModels);
std::vector<std::vector<float>> itemErr(nModels);
std::vector<float> itemErrMean(nModels, 0);
std::vector<bool> ratedUsers(nUsers, false);
int count = 0;
for (int uu = data.trainMat->colptr[item];
uu < data.trainMat->colptr[item+1]; uu++) {
ratedUsers[data.trainMat->colind[uu]] = true;
}
for (int user = 0; user < nUsers; user++) {
if (invalidUsers.count(user) > 0) {
continue;
}
if (ratedUsers[user]) {
continue;
}
for (int i = 0; i < nModels; i++) {
auto& mfModel = mfModels[i];
auto& origModel = origModels[i];
float r_ui_est = mfModel.estRating(user, item);
float r_ui = origModel.estRating(user, item);
float diff = fabs(r_ui - r_ui_est);
itemErr[i].push_back(diff);
itemErrMean[i] += diff;
if (diff <= epsilon) {
modelAccuItems[i].insert(user);
}
}
count++;
}
for (int i = 0; i < nModels; i++) {
itemErrMean[i] /= count;
}
for (int i = 0; i < nModels; i++) {
itemAccuCount[item].push_back(modelAccuItems[i].size());
for (int j = i+1; j < nModels; j++) {
//compute overlap between model i and model j
float intersectCount = (float) setIntersect(modelAccuItems[i], modelAccuItems[j]);
float unionCount = (float) setUnion(modelAccuItems[i], modelAccuItems[j]);
float jacSim = 0;
if (unionCount > 0) {
jacSim = intersectCount/unionCount;
}
itemsJacSims[item].push_back(jacSim);
//compute correlation between model i & j for the item
itemPearsonCorr[item].push_back(pearsonCorr(itemErr[i], itemErr[j],
itemErrMean[i], itemErrMean[j]));
}
}
}
std::string opFName = std::string(params.prefix) + "_" +
std::to_string(epsilon) + "_modelsJacSim.txt";
std::string opFName2 = std::string(params.prefix) + "_" +
std::to_string(epsilon) + "_modelsPearson.txt";
std::cout << "Writing... " << opFName << std::endl;
std::ofstream opFile(opFName.c_str());
std::ofstream opFile2(opFName2.c_str());
for (int item = 0; item < nItems; item++) {
opFile << item << " ";
for (auto&& sim: itemsJacSims[item]) {
opFile << sim << " ";
}
for (auto&& accu: itemAccuCount[item]) {
opFile << accu << " ";
}
opFile << std::endl;
opFile2 << item << " ";
for (auto&& corr: itemPearsonCorr[item]) {
opFile2 << corr << " ";
}
opFile2 << std::endl;
}
opFile.close();
opFile2.close();
}
}
void analyzeAccuracy(Data& data, Params& params) {
std::vector<ModelMF> mfModels;
std::vector<ModelMF> origModels;
for (int i = 1; i < 3; i++) {
std::string prefix = "mf_0_0_10_" + std::to_string(i);
ModelMF fullModel(params, params.seed);
fullModel.loadFacs(prefix.c_str());
mfModels.push_back(fullModel);
std::string uFName = "uFac_" + std::to_string(fullModel.nUsers) + "_10_0.txt";
std::string iFName = "iFac_" + std::to_string(fullModel.nItems+1) + "_10_0.txt";
ModelMF origModel(params, uFName.c_str(), iFName.c_str(), params.seed);
origModels.push_back(origModel);
}
/*
for (int i = 0; i < 5; i++) {
std::string prefix = "mf_" + std::to_string(i) + "_0_10";
ModelMF fullModel(params, params.seed);
fullModel.loadFacs(prefix.c_str());
mfModels.push_back(fullModel);
std::string uFName = "uFac_" + std::to_string(fullModel.nUsers) + "_10_"
+ std::to_string(i) + ".txt";
std::string iFName = "iFac_" + std::to_string(fullModel.nItems) + "_10_"
+ std::to_string(i) + ".txt";
ModelMF origModel(params, uFName.c_str(), iFName.c_str(), params.seed);
origModels.push_back(origModel);
}
*/
int nUsers = data.trainMat->nrows;
int nItems = data.trainMat->ncols;
auto rowColFreq = getRowColFreq(data.trainMat);
auto userFreq = rowColFreq.first;
auto itemFreq = rowColFreq.second;
//double epsilon = 0.025;
std::vector<double> epsilons = {0.025, 0.05, 0.1, 0.25, 0.5, 1.0};
std::unordered_set<int> invalidUsers;
std::unordered_set<int> invalidItems;
std::string modelSign = mfModels[0].modelSignature();
std::cout << "\nModel sign: " << modelSign << std::endl;
std::string prefix = std::string(params.prefix) + "_" + modelSign + "_invalUsers.txt";
std::vector<int> invalUsersVec = readVector(prefix.c_str());
prefix = std::string(params.prefix) + "_" + modelSign + "_invalItems.txt";
std::vector<int> invalItemsVec = readVector(prefix.c_str());
for (auto v: invalUsersVec) {
invalidUsers.insert(v);
}
for (auto v: invalItemsVec) {
invalidItems.insert(v);
}
for (int i = 0; i < mfModels.size(); i++) {
for (auto&& epsilon: epsilons) {
auto& fullModel = mfModels[i];
auto& origModel = origModels[i];
modelSign = fullModel.modelSignature();
std::cout << "Model: " << i << " " << modelSign << std::endl;
std::cout << "Train RMSE: " << fullModel.RMSE(data.trainMat, invalidUsers,
invalidItems, origModel) << std::endl;
std::cout << "Test RMSE: " << fullModel.RMSE(data.testMat, invalidUsers,
invalidItems, origModel) << std::endl;
std::cout << "Val RMSE: " << fullModel.RMSE(data.valMat, invalidUsers,
invalidItems, origModel) << std::endl;
//std::cout << "Full RMSE: " << fullModel.fullLowRankErr(data, invalidUsers,
// invalidItems, origModel) << std::endl;
std::vector<std::pair<double, double>> itemsMeanVar = origModel.itemsMeanVar(data.trainMat);
std::vector<std::pair<double, double>> usersMeanVar = origModel.usersMeanVar(data.trainMat);
std::vector<double> itemAccuPreds(nItems, 0);
std::vector<double> userAccuPreds(nUsers, 0);
std::vector<double> itemSecFreq(nItems, 0);
std::vector<double> itemSecVar(nItems, 0);
std::vector<double> itemSecMean(nItems, 0);
std::vector<double> itemSecAccuPreds(nItems, 0);
double avgAccuPreds = 0;
for (int u = 0; u < nUsers; u++) {
if (invalidUsers.find(u) != invalidUsers.end()) {
continue;
}
std::vector<bool> ratedItems(nItems, false);
for (int ii = data.trainMat->rowptr[u]; ii < data.trainMat->rowptr[u+1];
ii++) {
ratedItems[data.trainMat->rowind[ii]] = true;
}
double uAccuPred = 0;
#pragma omp parallel for reduction(+:avgAccuPreds, uAccuPred)
for (int item = 0; item < nItems; item++) {
if (invalidItems.find(item) != invalidItems.end()) {
continue;
}
if (ratedItems[item]) {
continue;
}
double r_ui = origModel.estRating(u, item);
double r_ui_est = fullModel.estRating(u, item);
if (fabs(r_ui - r_ui_est) <= epsilon) {
itemAccuPreds[item] += 1;
uAccuPred += 1;
avgAccuPreds++;
}
}
userAccuPreds[u] += uAccuPred;
}
avgAccuPreds /= nItems;
#pragma omp parallel for
for (int item = 0; item < data.trainMat->ncols; item++) {
int nRatings = data.trainMat->colptr[item+1] - data.trainMat->colptr[item];
for (int uu = data.trainMat->colptr[item];
uu < data.trainMat->colptr[item+1]; uu++) {
int user = data.trainMat->colind[uu];
itemSecFreq[item] += userFreq[user];
itemSecMean[item] += usersMeanVar[user].first;
itemSecVar[item] += usersMeanVar[user].second;
itemSecAccuPreds[item] += userAccuPreds[user];
}
itemSecFreq[item] = itemSecFreq[item]/nRatings;
itemSecMean[item] = itemSecMean[item]/nRatings;
itemSecVar[item] = itemSecVar[item]/nRatings;
itemSecAccuPreds[item] = itemSecAccuPreds[item]/nRatings;
}
std::string opFName = std::string(params.prefix) + "_" + std::to_string(epsilon)
+ "_itemFreqAccu.txt";
std::ofstream opFile(opFName.c_str());
for (int item = 0; item < nItems; item++) {
opFile << item << "\t" << itemFreq[item] << "\t" << itemSecFreq[item]
<< "\t" << itemAccuPreds[item] << "\t" << avgAccuPreds << "\t"
<< itemsMeanVar[item].first << "\t" << itemsMeanVar[item].second
<< "\t" << itemSecMean[item] << "\t" << itemSecVar[item] << std::endl;
}
opFile.close();
}
}
}
void UpdaterDialog::httpDone(bool error)
{
timeOutTimer->stop();
if(error)
{
if(isVisible())
{
if(httpGet->errorString()=="Request aborted")return;
downloadError();
return;
}
if(feedbackMessage)
QMessageBox::information(0,"Qt Bitcoin Trader",julyTr("UPDATE_ERROR","Cannot check for update. Network error: %1").arg(httpGet->errorString()));
exitSlot();
return;
}
if(stateUpdate==0)
{
QByteArray dataReceived(httpGet->readAll().replace("\r",""));
if(dataReceived.size()>10245)exitSlot();
QMap<QString,QString>versionsMap;
QStringList dataList=QString(dataReceived).split("\n");
for(int n=0;n<dataList.count();n++)
{
QString varData=dataList.at(n);
int splitPos=varData.indexOf('=');
if(splitPos>-1)
{
QString varName=varData.left(splitPos);
varData.remove(0,splitPos+1);
versionsMap[varName]=varData;
}
}
QString os="Src";
bool canAutoUpdate=false;
#ifdef Q_OS_MAC
os="Mac";
canAutoUpdate=true;
#endif
#ifdef Q_OS_WIN
os="Win32";
canAutoUpdate=true;
#endif
updateVersion=versionsMap.value(os+"Ver");
updateSignature=versionsMap.value(os+"Signature").toAscii();
if(!updateSignature.isEmpty())updateSignature=QByteArray::fromBase64(updateSignature);
updateChangeLog=versionsMap.value(os+"ChangeLog");
updateLink=versionsMap.value(os+"Bin");
if(updateVersion.toDouble()<=appVerReal)
{
if(feedbackMessage)
QMessageBox::information(0,"Qt Bitcoin Trader",julyTr("UP_TO_DATE","Your version of Qt Bitcoin Trader is up to date."));
exitSlot();
return;
}
stateUpdate=1;
ui.autoUpdateGroupBox->setVisible(canAutoUpdate);
ui.changeLogText->setHtml(updateChangeLog);
ui.versionLabel->setText("v"+updateVersion);
#ifdef Q_OS_WIN
if(QtWin::isCompositionEnabled())
QtWin::extendFrameIntoClientArea(this);
#endif
#ifdef GENERATE_LANGUAGE_FILE
julyTranslator->loadMapFromUi(this);
julyTranslator->saveToFile("LanguageDefault.lng");
#endif
julyTranslator->translateUi(this);
ui.iconLabel->setPixmap(QPixmap(":/Resources/QtBitcoinTrader.png"));
QSize minSizeHint=minimumSizeHint();
if(mainWindow.isValidSize(&minSizeHint))setFixedSize(minimumSizeHint());
show();
}
else
if(stateUpdate==1)
{
QByteArray binData=httpGet->readAll();
QByteArray fileSha1=QCryptographicHash::hash(binData,QCryptographicHash::Sha1);
QFile readPublicKey(":/Resources/Public.key");
if(!readPublicKey.open(QIODevice::ReadOnly)){QMessageBox::critical(this,windowTitle(),"Public.key is missing");return;}
QByteArray publicKey=readPublicKey.readAll();
QByteArray decrypted=JulyRSA::getSignature(updateSignature,publicKey);
if(decrypted==fileSha1)
{
QString curBin=QApplication::applicationFilePath();
QString updBin=curBin+".upd";
QString bkpBin=curBin+".bkp";
if(QFile::exists(updBin))QFile::remove(updBin);
if(QFile::exists(bkpBin))QFile::remove(bkpBin);
if(QFile::exists(updBin)||QFile::exists(bkpBin)){downloadError();return;}
{
QFile wrFile(updBin);
if(wrFile.open(QIODevice::WriteOnly|QIODevice::Truncate))
{
wrFile.write(binData);
wrFile.close();
}else {downloadError();return;}
}
QByteArray fileData;
{
QFile opFile(updBin);
if(opFile.open(QIODevice::ReadOnly))fileData=opFile.readAll();
opFile.close();
}
if(QCryptographicHash::hash(fileData,QCryptographicHash::Sha1)!=fileSha1){downloadError();return;}
QFile::rename(curBin,bkpBin);
if(!QFile::exists(bkpBin)){downloadError();return;}
QFile::rename(updBin,curBin);
if(!QFile::exists(curBin)){QMessageBox::critical(this,windowTitle(),"Critical error. Please reinstall application. Download it from http://sourceforge.net/projects/bitcointrader/<br>File not exists: "+curBin+"\n"+updBin);downloadError();return;}
#ifdef Q_OS_MAC
QFile(curBin).setPermissions(QFile(bkpBin).permissions());
#endif
QMessageBox::information(this,windowTitle(),julyTr("UPDATED_SUCCESSFULLY","Application updated successfully. Please restart application to apply changes."));
exitSlot();
}
}
}
