static int64_t AddTx(CWallet& wallet, uint32_t lockTime, int64_t mockTime, int64_t blockTime)
{
CMutableTransaction tx;
tx.nLockTime = lockTime;
SetMockTime(mockTime);
CBlockIndex* block = nullptr;
if (blockTime > 0) {
LOCK(cs_main);
auto inserted = mapBlockIndex.emplace(GetRandHash(), new CBlockIndex);
assert(inserted.second);
const uint256& hash = inserted.first->first;
block = inserted.first->second;
block->nTime = blockTime;
block->phashBlock = &hash;
}
CWalletTx wtx(&wallet, MakeTransactionRef(tx));
if (block) {
wtx.SetMerkleBranch(block, 0);
}
{
LOCK(cs_main);
wallet.AddToWallet(wtx);
}
LOCK(wallet.cs_wallet);
return wallet.mapWallet.at(wtx.GetHash()).nTimeSmart;
}
double MlMaximumEntropyModel::calcScoreForClass(const MlSample* sample, int label) const
{
assert( label>=0 && label < static_cast<int>(weights_.size()));
vector<double> wtx(weights_.size(),0.0);
for (size_t c=0; c<weights_.size(); c++)
for (size_t i=0; i<sample->pairs.size(); i++)
wtx[c] += static_cast<double>(weights_[c][sample->pairs[i].index] * sample->pairs[i].value);
double numerator = exp(wtx[static_cast<size_t>(sample->label)]);
double Z=0.0;
for (size_t c=0; c<wtx.size(); c++)
Z+=exp(wtx[c]);
return (numerator/Z);
}
// Check that GetImmatureCredit() returns a newly calculated value instead of
// the cached value after a MarkDirty() call.
//
// This is a regression test written to verify a bugfix for the immature credit
// function. Similar tests probably should be written for the other credit and
// debit functions.
BOOST_FIXTURE_TEST_CASE(coin_mark_dirty_immature_credit, TestChain100Setup)
{
CWallet wallet;
CWalletTx wtx(&wallet, MakeTransactionRef(coinbaseTxns.back()));
LOCK2(cs_main, wallet.cs_wallet);
wtx.hashBlock = chainActive.Tip()->GetBlockHash();
wtx.nIndex = 0;
// Call GetImmatureCredit() once before adding the key to the wallet to
// cache the current immature credit amount, which is 0.
BOOST_CHECK_EQUAL(wtx.GetImmatureCredit(), 0);
// Invalidate the cached value, add the key, and make sure a new immature
// credit amount is calculated.
wtx.MarkDirty();
wallet.AddKeyPubKey(coinbaseKey, coinbaseKey.GetPubKey());
BOOST_CHECK_EQUAL(wtx.GetImmatureCredit(), 50*COIN);
}
void MlMaximumEntropyModel::calcScores(const MlSample* sample, vector<float>& scores) const
{
vector<double> wtx(weights_.size(),0.0);
for (size_t c=0; c<weights_.size(); c++)
for (size_t i=0; i<sample->pairs.size(); i++)
wtx[c] += static_cast<double>(weights_[c][sample->pairs[i].index] * sample->pairs[i].value);
vector<double> e(weights_.size());
double Z=0.0;
for (size_t c=0; c<weights_.size(); c++)
{
e[c]=exp(wtx[c]);
Z+=e[c];
}
scores.resize(weights_.size());
for (size_t c=0; c<weights_.size(); c++)
scores[c]=e[c]/Z;
}
// Check that GetImmatureCredit() returns a newly calculated value instead of
// the cached value after a MarkDirty() call.
//
// This is a regression test written to verify a bugfix for the immature credit
// function. Similar tests probably should be written for the other credit and
// debit functions.
BOOST_FIXTURE_TEST_CASE(coin_mark_dirty_immature_credit, TestChain100Setup)
{
auto chain = interfaces::MakeChain();
CWallet wallet(*chain, WalletLocation(), WalletDatabase::CreateDummy());
CWalletTx wtx(&wallet, m_coinbase_txns.back());
auto locked_chain = chain->lock();
LOCK(wallet.cs_wallet);
wtx.hashBlock = chainActive.Tip()->GetBlockHash();
wtx.nIndex = 0;
// Call GetImmatureCredit() once before adding the key to the wallet to
// cache the current immature credit amount, which is 0.
BOOST_CHECK_EQUAL(wtx.GetImmatureCredit(*locked_chain), 0);
// Invalidate the cached value, add the key, and make sure a new immature
// credit amount is calculated.
wtx.MarkDirty();
wallet.AddKeyPubKey(coinbaseKey, coinbaseKey.GetPubKey());
BOOST_CHECK_EQUAL(wtx.GetImmatureCredit(*locked_chain), 50*COIN);
}
static void add_coin(const CAmount& nValue, int nAge = 6*24, bool fIsFromMe = false, int nInput=0)
{
static int nextLockTime = 0;
CMutableTransaction tx;
tx.nLockTime = nextLockTime++; // so all transactions get different hashes
tx.vout.resize(nInput+1);
tx.vout[nInput].nValue = nValue;
if (fIsFromMe) {
// IsFromMe() returns (GetDebit() > 0), and GetDebit() is 0 if vin.empty(),
// so stop vin being empty, and cache a non-zero Debit to fake out IsFromMe()
tx.vin.resize(1);
}
std::unique_ptr<CWalletTx> wtx(new CWalletTx(&testWallet, MakeTransactionRef(std::move(tx))));
if (fIsFromMe)
{
wtx->fDebitCached = true;
wtx->nDebitCached = 1;
}
COutput output(wtx.get(), nInput, nAge, true /* spendable */, true /* solvable */, true /* safe */);
vCoins.push_back(output);
wtxn.emplace_back(std::move(wtx));
}
void MultisigDialog::commitMultisigTx()
{
CMutableTransaction tx(multisigTx);
try{
#ifdef ENABLE_WALLET
CWalletTx wtx(pwalletMain, tx);
CReserveKey keyChange(pwalletMain);
if (!pwalletMain->CommitTransaction(wtx, keyChange))
throw runtime_error(string("Transaction rejected - Failed to commit"));
#else
uint256 hashTx = tx.GetHash();
CCoinsViewCache& view = *pcoinsTip;
const CCoins* existingCoins = view.AccessCoins(hashTx);
bool fOverrideFees = false;
bool fHaveMempool = mempool.exists(hashTx);
bool fHaveChain = existingCoins && existingCoins->nHeight < 1000000000;
if (!fHaveMempool && !fHaveChain) {
// push to local node and sync with wallets
CValidationState state;
if (!AcceptToMemoryPool(mempool, state, tx, false, NULL, !fOverrideFees)) {
if (state.IsInvalid())
throw runtime_error(strprintf("Transaction rejected - %i: %s", state.GetRejectCode(), state.GetRejectReason()));
else
throw runtime_error(string("Transaction rejected - ") + state.GetRejectReason());
}
} else if (fHaveChain) {
throw runtime_error("transaction already in block chain");
}
RelayTransaction(tx);
#endif
//disable commit if successfully committed
ui->commitButton->setEnabled(false);
ui->signButtonStatus->setText(strprintf("Transaction has been successfully published with transaction ID:\n %s", tx.GetHash().GetHex()).c_str());
}catch(const runtime_error& e){
ui->signButtonStatus->setText(e.what());
}
}
void MlMaximumEntropyModel::learnLMBFGS(MlTrainingContainer* params)
{
params->initialize();
const MlDataSet* trainingDataSet = params->getTrainingDataSet();
const MlDataSet* testDataSet = params->getTestDataSet();
const vector<size_t>& trainingIdxs = params->getTrainingIdxs();
const vector<size_t>& testIdxs = params->getTestIdxs();
const size_t numSamples = trainingIdxs.size();
const bool performTest = (testDataSet && testIdxs.size()>0);
if (trainingDataSet->getNumClasess() < 2)
error("learnLMBFGS accepts only datasets with 2 or more classes, yor data has ",
trainingDataSet->getNumClasess());
const double lambda = params->getLambda();
const size_t memorySize = params->getLmBfgsMemorySize();
const size_t reportFrequency = params->getVerboseLevel();
const double perplexityDelta = params->getPerplexityDelta();
const size_t numClasses = trainingDataSet->getNumClasess();
const size_t numFeatures = trainingDataSet->getNumBasicFeatures(); // F
const size_t numTraining = trainingIdxs.size(); // N
const size_t numRestarts=3;
// data structures used for training
vector< vector<double> >& w = weights_; // class X features
vector< vector<double> > wOld(numClasses, vector<double>(numFeatures,0.0)); // class X features
vector< vector<double> > wtx(numClasses, vector<double>(numTraining, 0.0)); // class X samples
vector< vector<double> > qtx(numClasses, vector<double>(numTraining, 0.0)); // class X samples
vector< vector<double> > q(numClasses, vector<double>(numFeatures,0.0)); // class X features
vector< vector<double> > g(numClasses, vector<double>(numFeatures,0.0)); // class X features
vector< vector<double> > gOld(numClasses, vector<double>(numFeatures,0.0)); // class X features
vector< vector<float> > trainingProbs(numClasses, vector<float>(numTraining));
vector< vector<float> > testProbs(numClasses, vector<float>(numTraining));
vector< vector<double> > bestW(numClasses, vector<double>(numFeatures));
// initialize weights
if (params->getInputPath().length() > 1)
{
const string modelFile = params->getInputPath() + "_scr.txt";
if (readModel(modelFile.c_str()))
params->setIndClearWeights(false);
}
if (params->getIndClearWeights())
weights_.clear();
weights_.resize(numClasses, vector<double>(numFeatures,0.0));
double previousPerplexity = MAX_FLOAT;
float bestTestError=1.0;
size_t bestTestRound=0;
float bestTrainingError=1.0;
size_t bestTrainingRound=0;
bool terminateTraining = false;
size_t totalRounds=0;
size_t megaRound=0;
for ( megaRound=0; megaRound<numRestarts; megaRound++)
{
// first round
computeGradient(trainingDataSet, trainingIdxs, w, wtx, lambda, g);
const double gtg = computeDotProduct(g,g);
const double denominator = 1.0 / sqrt(gtg);
for (size_t c=0; c<numClasses; c++)
for (size_t i=0; i<numFeatures; i++)
q[c][i]=g[c][i]*denominator;
// qtx <- qTx
for (size_t c=0; c<numClasses; c++)
for (size_t i=0; i<numSamples; i++)
{
const MlSample& sample = trainingDataSet->getSample(trainingIdxs[i]);
qtx[c][i]=computeDotProduct(q[c],sample.pairs);
}
// eta <- lineSearch(...)
double eta = lineSearch(trainingDataSet, trainingIdxs, w, wtx, qtx, g, q, lambda);
//cout << "eta = " << eta << endl;
// update wtx <- wtx + eta*qtx
for (size_t c=0; c<numClasses; c++)
for (size_t i=0; i<wtx[c].size(); i++)
wtx[c][i]+=eta*qtx[c][i];
// update wOld<- w ; w <- w + eta *q ; gOld<-g
for (size_t c=0; c<numClasses; c++)
{
memcpy(&wOld[c][0],&w[c][0],sizeof(double)*w[c].size());
memcpy(&gOld[c][0],&g[c][0],sizeof(double)*g[c].size());
for (size_t i=0; i<numFeatures; i++)
w[c][i]+= eta*q[c][i];
}
// initialize memory
vector< vector< vector<double> > > memoryU(memorySize, vector< vector<double> >(numClasses));
vector< vector< vector<double> > > memoryD(memorySize, vector< vector<double> >(numClasses));
vector< double > memoryAlpha(memorySize);
size_t nextMemPosition=0;
size_t numMemPushes=0;
// iterate until convergence
size_t round=1;
while (round<10000)
{
// compute errors and report round results
{
double trainingLogLikelihood=0.0, testLogLikelihood=0.0;
const double trainingError = calcErrorRateWithLogLikelihood(trainingDataSet, trainingIdxs,
false, &trainingLogLikelihood);
double testError=1.0;
if (performTest)
testError = calcErrorRateWithLogLikelihood(testDataSet, testIdxs, false, &testLogLikelihood);
if (reportFrequency>0 && round % reportFrequency == 0)
{
cout << round << "\t" << scientific << setprecision(5) << trainingLogLikelihood << "\t" << fixed << setprecision(5) << trainingError;
if (performTest)
cout <<"\t" << scientific << testLogLikelihood << "\t" << fixed << setprecision(5)<< testError;
cout << endl;
}
if (performTest)
{
if (testError<=bestTestError)
{
bestTestRound=round;
bestTestError=testError;
for (size_t c=0; c<numClasses; c++)
memcpy(&bestW[c][0],&w[c][0],numFeatures*sizeof(double)); // copy weights
}
}
if (trainingError<=bestTrainingError)
{
bestTrainingRound=round;
bestTrainingError=trainingError;
if (! performTest)
{
for (size_t c=0; c<numClasses; c++)
memcpy(&bestW[c][0],&w[c][0],numFeatures*sizeof(double)); // copy weights
}
}
}
// Train new round
computeGradient(trainingDataSet, trainingIdxs, w, wtx, lambda, g);
double alpha=0.0;
double sigma=0.0;
double utu=0.0;
// write u=g'-g and d=w'-w onto memory, use them to compute alpha and sigma
vector< vector<double> >& u = memoryU[nextMemPosition];
vector< vector<double> >& d = memoryD[nextMemPosition];
for (size_t c=0; c<numClasses; c++)
{
const size_t numFeatures = g[c].size();
u[c].resize(numFeatures);
d[c].resize(numFeatures);
for (size_t i=0; i<numFeatures; i++)
{
const double gDiff = g[c][i]-gOld[c][i];
const double wDiff = w[c][i]-wOld[c][i];
u[c][i]=gDiff;
d[c][i]=wDiff;
alpha += gDiff*wDiff;
utu += gDiff*gDiff;
}
}
sigma = alpha / utu;
memoryAlpha[nextMemPosition]=alpha;
// update memory position
nextMemPosition++;
if (nextMemPosition == memorySize)
nextMemPosition = 0;
numMemPushes++;
// q<-g
for (size_t c=0; c<numClasses; c++)
memcpy(&q[c][0],&g[c][0],g[c].size()*sizeof(double));
// determine memory evaluation order 1..M (M is the newest)
vector<size_t> memOrder;
if (numMemPushes<=memorySize)
{
for (size_t i=0; i<numMemPushes; i++)
memOrder.push_back(i);
}
else
{
for (size_t i=0; i<memorySize; i++)
memOrder.push_back((i+nextMemPosition) % memorySize);
}
vector<double> beta(memOrder.size(),0.0);
for (int i=memOrder.size()-1; i>=0; i--)
{
const size_t m = memOrder[static_cast<size_t>(i)];
const double alpha = memoryAlpha[m];
const vector< vector<double> >& dM = memoryD[m];
double& betaM = beta[m];
// compute beta[m] = (memory_d[m] dot g)/alpha[m]
for (size_t c=0; c<dM.size(); c++)
for (size_t i=0; i<dM[c].size(); i++)
betaM += dM[c][i]*g[c][i];
betaM/=alpha;
// q <- q - beta[m]*memory_u[m]
const vector< vector<double> >& uM = memoryU[m];
for (size_t c=0; c<q.size(); c++)
for (size_t i=0; i<q[c].size(); i++)
q[c][i] -= betaM * uM[c][i];
}
// q <- sigma*q
for (size_t c=0; c<q.size(); c++)
for (size_t i=0; i<q[c].size(); i++)
q[c][i]*=sigma;
for (size_t i=0; i<memOrder.size(); i++)
{
const size_t m = memOrder[static_cast<size_t>(i)];
const vector< vector<double> >& uM = memoryU[m];
const vector< vector<double> >& dM = memoryD[m];
const double betaM = beta[m];
const double oneOverAlpha = 1.0 / memoryAlpha[m];
double umq = computeDotProduct(uM,q);
for (size_t c=0; c<numClasses; c++)
for (size_t j=0; j<q[c].size(); j++)
{
const double dq = dM[c][j] * (betaM - umq*oneOverAlpha);
umq += uM[c][j]*dq;
q[c][j] += dq;
}
}
// q<- -q
for (size_t c=0; c<numClasses; c++)
for (size_t i=0; i<q[c].size(); i++)
q[c][i]=-q[c][i];
// qtx = q*X
for (size_t i=0; i<trainingIdxs.size(); i++)
{
const MlSample& sample = trainingDataSet->getSample(trainingIdxs[i]);
for (size_t c=0; c<numClasses; c++)
qtx[c][i]=computeDotProduct(q[c],sample.pairs);
}
bool needToRestart=false;
eta = lineSearch(trainingDataSet, trainingIdxs, w, wtx, qtx, g, q, lambda);
if (eta<= 0.0)
{
// restart ?
needToRestart = true;
}
// update wOld<- w ; w <- w + eta *q ; gOld<- g
for (size_t c=0; c<numClasses; c++)
{
memcpy(&wOld[c][0],&w[c][0],sizeof(double)*w[c].size());
memcpy(&gOld[c][0],&g[c][0],sizeof(double)*g[c].size());
for (size_t i=0; i<numFeatures; i++)
w[c][i]+= eta*q[c][i];
}
for (size_t c=0; c<numClasses; c++)
for (size_t i=0; i<numSamples; i++)
wtx[c][i]+=eta*qtx[c][i];
round++;
totalRounds++;
if (terminateTraining || needToRestart)
break;
}
if (terminateTraining)
break;
}
if (! params->getIndHadInternalError())
{
params->setIndNormalTermination(true);
}
else
cout << "Warning: encountered mathemtical error while training!" << endl;
weights_ = bestW;
cout << "W=" << endl;
printVector(weights_);
cout << endl;
cout << "Terminated after " << totalRounds << " rounds (" << megaRound << " restarts)" << endl;
cout << "Best training error " << fixed << setprecision(8) << bestTrainingError << " (round " << bestTrainingRound << ")" << endl;
if (performTest)
cout << "Best test error " << bestTestError << " (round " << bestTestRound << ")" << endl;
indWasInitialized_ = true;
//this->calcErrorRateWithPerplexity(trainingDataSet, trainingIdxs, true, NULL);
}
