AIMContact::AIMContact( Kopete::Account* account, const QString& name, Kopete::MetaContact* parent,
const QString& icon, const Oscar::SSI& ssiItem )
: OscarContact(account, name, parent, icon, ssiItem )
{
mProtocol=static_cast<AIMProtocol *>(protocol());
setOnlineStatus( mProtocol->statusOffline );
m_infoDialog = 0L;
m_warnUserAction = 0L;
mUserProfile="";
m_haveAwayMessage = false;
m_mobile = false;
// Set the last autoresponse time to the current time yesterday
m_lastAutoresponseTime = QDateTime::currentDateTime().addDays(-1);
QObject::connect( mAccount->engine(), SIGNAL( receivedUserInfo( const QString&, const UserDetails& ) ),
this, SLOT( userInfoUpdated( const QString&, const UserDetails& ) ) );
QObject::connect( mAccount->engine(), SIGNAL( userIsOffline( const QString& ) ),
this, SLOT( userOffline( const QString& ) ) );
QObject::connect( mAccount->engine(), SIGNAL( receivedAwayMessage( const QString&, const QString& ) ),
this, SLOT( updateAwayMessage( const QString&, const QString& ) ) );
QObject::connect( mAccount->engine(), SIGNAL( receivedProfile( const QString&, const QString& ) ),
this, SLOT( updateProfile( const QString&, const QString& ) ) );
QObject::connect( mAccount->engine(), SIGNAL( userWarned( const QString&, Q_UINT16, Q_UINT16 ) ),
this, SLOT( gotWarning( const QString&, Q_UINT16, Q_UINT16 ) ) );
QObject::connect( mAccount->engine(), SIGNAL( haveIconForContact( const QString&, QByteArray ) ),
this, SLOT( haveIcon( const QString&, QByteArray ) ) );
QObject::connect( mAccount->engine(), SIGNAL( iconServerConnected() ),
this, SLOT( requestBuddyIcon() ) );
QObject::connect( this, SIGNAL( featuresUpdated() ), this, SLOT( updateFeatures() ) );
}
bool QgsAtlasComposition::beginRender()
{
if ( !mCoverageLayer )
{
return false;
}
bool featuresUpdated = updateFeatures();
if ( !featuresUpdated )
{
//no matching features found
return false;
}
mRestoreLayer = false;
QStringList& layerSet = mComposition->mapRenderer()->layerSet();
if ( mHideCoverage )
{
// look for the layer in the renderer's set
int removeAt = layerSet.indexOf( mCoverageLayer->id() );
if ( removeAt != -1 )
{
mRestoreLayer = true;
layerSet.removeAt( removeAt );
}
}
// special columns for expressions
QgsExpression::setSpecialColumn( "$numpages", QVariant( mComposition->numPages() ) );
QgsExpression::setSpecialColumn( "$numfeatures", QVariant(( int )mFeatureIds.size() ) );
return true;
}
DiscoInfoResponder::DiscoInfoResponder(Swift::IQRouter *router, Config *config) : Swift::GetResponder<DiscoInfo>(router) {
m_config = config;
m_config->onBackendConfigUpdated.connect(boost::bind(&DiscoInfoResponder::updateFeatures, this));
m_buddyInfo = NULL;
m_transportInfo.addIdentity(DiscoInfo::Identity(CONFIG_STRING(m_config, "identity.name"),
CONFIG_STRING(m_config, "identity.category"),
CONFIG_STRING(m_config, "identity.type")));
#if HAVE_SWIFTEN_3
crypto = boost::shared_ptr<CryptoProvider>(PlatformCryptoProvider::create());
#endif
updateFeatures();
}
bool QgsLayoutAtlas::beginRender()
{
if ( !mCoverageLayer )
{
return false;
}
emit renderBegun();
if ( !updateFeatures() )
{
//no matching features found
return false;
}
return true;
}
bool QgsAtlasComposition::beginRender()
{
if ( !mCoverageLayer )
{
return false;
}
emit renderBegun();
bool featuresUpdated = updateFeatures();
if ( !featuresUpdated )
{
//no matching features found
return false;
}
return true;
}
bool QgsAtlasComposition::beginRender()
{
if ( !mCoverageLayer )
{
return false;
}
bool featuresUpdated = updateFeatures();
if ( !featuresUpdated )
{
//no matching features found
return false;
}
// special columns for expressions
QgsExpression::setSpecialColumn( "$numpages", QVariant( mComposition->numPages() ) );
QgsExpression::setSpecialColumn( "$numfeatures", QVariant(( int )mFeatureIds.size() ) );
return true;
}
void AdalineTest::learnTarget(const Vector<double>* targetWeights, Adaline<double>* learner)
{
int nbUpdate = 0;
double threshold = 1e-3;
History<double, 5> history;
history.fill(threshold);
PVector<double> features(targetWeights->dimension());
double target = 0.0f;
while (history.getSum() > threshold)
{
updateFeatures(&features);
target = targetWeights->dot(&features);
double error = learner->predict(&features) - target;
Boundedness::checkValue(error);
history.add(std::fabs(error));
learner->learn(&features, target);
++nbUpdate;
Assert::assertPasses(nbUpdate < 100000);
}
Assert::assertPasses(nbUpdate > 30);
Assert::assertObjectEquals(target, learner->predict(&features), threshold * 10);
}
void DataModel::updateAll() {
updateComponents();
updateFeatures();
updateOrthogonalTable();
}
int main(){
// Load Movie data
loadMovieData();
// printf("test %f %f %f %f\n", userOffset[5], userOffset[num_users - 1], movieOffset[5], movieOffset[num_movies - 1]);
// Set up offsets and implicit data
userOffset = calloc(num_users, sizeof(float));
movieOffset = calloc(num_movies, sizeof(float));
userImplicitData = calloc(num_users*2, sizeof(float));
if (userOffset == NULL || movieOffset == NULL || userImplicitData == NULL) {
printf("Malloc failed\n");
return -1;
}
loadData("../stats/user_offset_reg2.dta", userOffset);
loadData("../stats/movie_offset_reg.dta", movieOffset);
loadUserImplicit("../stats/user_implicit_2.dta", userImplicitData);
// Initialize features
// Initialize random seed
srand (time(NULL));
initializeUserFeatures();
initializeMovieFeatures();
initializeImplicitMovies("../../implicit/user_implicit_movies.dta");
//printf("test %f\n", userImplicitMovies[num_users - 1][2]);
initializeImplicitFeatures();
printf("\n--------------Training --------------\n");
int user, movie, line_number;
float rating, predict, err;
float total_err;
for (int i = 1; i <= epochs; i++) {
total_err = 0;
for (int j = 0; j < num_lines; j++) {
line_number = j * 3;
user = movie_data[line_number];
movie = movie_data[line_number + 1];
rating = (float)movie_data[line_number + 2];
// printf("User %d Movie %d Rating %d Baseling %f\n", user, movie, rating, baseline);
getImplicitC(user);
predict = predictRating(user, movie);
err = rating - predict;
total_err += err * err;
updateFeatures(user, movie, err);
updateBaseline(user, movie, err);
updateImplicitFeatures(user, err);
}
// Update gammas by factor
gamma1 *= gamma_step;
gamma2 *= gamma_step;
printf("Epoch %d RMSE: %f\n", i, sqrt(total_err / num_lines));
}
printf("-----------Saving features-----------\n");
saveOffsets();
saveUserFeatures("f010_e020/user_features.dta");
saveMovieFeatures("f010_e020/movie_features.dta");
saveImplicitFeatures("f010_e020/implicit_features.dta");
free(userOffset);
free(movieOffset);
free(movie_data);
return 0;
}
int main(){
// Load Movie data
loadMovieData();
// printf("test %f %f %f %f\n", userOffset[5], userOffset[num_users - 1], movieOffset[5], movieOffset[num_movies - 1]);
// Set up offsets and implicit data
userOffset = calloc(num_users, sizeof(float));
movieOffset = calloc(num_movies, sizeof(float));
userImplicitData = calloc(num_users*2, sizeof(float));
if (userOffset == NULL || movieOffset == NULL || userImplicitData == NULL) {
printf("Malloc failed\n");
return -1;
}
//loadData("../stats/user_offset_reg2.dta", userOffset);
//loadData("../stats/movie_offset_reg.dta", movieOffset);
loadUserImplicit("../stats/user_implicit_2.dta", userImplicitData);
// Initialize features
// Initialize random seed
srand (time(NULL));
initializeUserFeatures();
initializeMovieFeatures();
initializeImplicitMovies("../../implicit/user_implicit_movies.dta");
//printf("test %f\n", userImplicitMovies[num_users - 1][2]);
initializeImplicitFeatures();
printf("\n--------------Training --------------\n");
unsigned int user, movie, rating, line_number;
unsigned int temp = 0;
float err, feature_c;
float train_err, val_err;
train_errs = calloc(epochs, sizeof(float));
float n = userImplicitData[1*2 + 1]; // get n for first user
float GLOBAL_AVG = 3.609516;
for (unsigned int i = 1; i <= epochs; i++) {
train_err = 0;
val_err = 0;
for (unsigned int j = 0; j < num_lines; j++) {
line_number = j * 3;
user = movie_data[line_number];
movie = movie_data[line_number + 1];
rating = movie_data[line_number + 2];
// Get rating from raw data if we have a new user
if (temp != user) {
// update implict feature for previous user
if (temp > 0) {
updateImplicitFeatures(temp, n);
}
n = userImplicitData[user*2 + 1];
getImplicitC(user, n);
//baseline_c = userOffset[user] + movieOffset[movie];
temp = user;
// if (user == 34821) {
// printf("user %d err %f n %f implicitC %f tempC %f\n", user, total_err, n, implicitC[0], tempImplicitC[0]);
// printf("base %f\n", baseline_c);
// }
}
feature_c = 0;
for (unsigned int i = 0; i < num_features; i++) {
feature_c += (userFeatures[user][i] + tempImplicitC[i])* movieFeatures[movie][i];
}
err = (float) rating - (GLOBAL_AVG + userOffset[user] + movieOffset[movie] + feature_c);
//printf("err %f rating %d predict %f baseline_c %f feature_c %f\n", err, rating, GLOBAL_AVG + baseline_c + feature_c, baseline_c, feature_c);
// if (idx[j] == 4) {
// val_err += err * err;
// }
train_err += err * err;
updateFeatures(user, movie, err, n);
updateBaseline(user, movie, err);
}
// update last user
updateImplicitFeatures(user, n);
// Update gammas by factor
gamma1 *= gamma_step;
gamma2 *= gamma_step;
train_errs[i-1] = sqrt(train_err / num_lines);
printf("Epoch %d Train RMSE: %f\n", i, train_errs[i-1]);
}
printf("-----------Saving features-----------\n");
saveOffsets();
saveUserFeatures("features/f220_e050/user_features.dta");
saveMovieFeatures("features/f220_e050/movie_features.dta");
saveImplicitFeatures("features/f220_e050/implicit_features.dta");
saveErrors("features/f220_e050/error.dta");
free(userOffset);
free(movieOffset);
free(movie_data);
return 0;
}
void OpenNIUser::update()
{
if (trackingEnabled) {
updateFeatures();
}
}
