C++ TimeSeries类代码示例

bool FileCSV::read(TimeSeries &ts, int valuesColumn, int timeColumn)
{
    if (!isOpen() ||
            valuesColumn < 0 || valuesColumn >= (int)m_header.size() ||
            timeColumn < 0   || timeColumn >= (int)m_header.size())
        return false;

    seekRow(0);

    Row row;
    ReadResult rr;

    FL::TimeSeries::Data values, time;

    while ( (rr = readRow(row)) == rrOK )
    {
        values.push_back(row[valuesColumn]);
        time.push_back(row[timeColumn]);
    }

    ts.setData(values, time);
    ts.header()[0] = m_header[valuesColumn];
    ts.header()[1] = m_header[timeColumn];

    return rr == rrEmpty;
}

// Iterate from an initial state
void DynSysModel::Iterate( NTuple& initial_state, size_t length, TimeSeries& result )
{
	// Make sure the result is empty
	result.clear();

	// Save the initial state as the first time series entry
	result.push_back( initial_state );

	mState = initial_state;
	NTuple next_state;
	PolyModelIter iter;

	size_t i,k;
	for( i = length-1; i > 0; --i )
	{
		k = 1;
		// For each polynomial;
		next_state.Reset();
		iter = mModel.begin();
		while( iter != mModel.end() )
		{
			// Evaluate the k'th polynomial at the current state
			//   to produce a new value for the k'th variable
			next_state.Assign( k, mModel[k-1].Evaluate( mState ) );

			// next polynomial
			++iter; ++k;
		}
		// Update the current state to be the newly compute state
		mState = next_state;
		result.push_back( next_state );
	}
}

TEST(StatisticsTest, Statistics)
{
  // Create a distribution of 10 values from -5 to 4.
  TimeSeries<double> timeseries;

  Time now = Clock::now();

  for (int i = -5; i <= 5; ++i) {
    now += Seconds(1);
    timeseries.set(i, now);
  }

  Option<Statistics<double>> statistics = Statistics<double>::from(timeseries);

  EXPECT_SOME(statistics);

  EXPECT_EQ(11u, statistics->count);

  EXPECT_DOUBLE_EQ(-5.0, statistics->min);
  EXPECT_DOUBLE_EQ(5.0, statistics->max);

  EXPECT_DOUBLE_EQ(0.0, statistics->p50);
  EXPECT_DOUBLE_EQ(4.0, statistics->p90);
  EXPECT_DOUBLE_EQ(4.5, statistics->p95);
  EXPECT_DOUBLE_EQ(4.9, statistics->p99);
  EXPECT_DOUBLE_EQ(4.99, statistics->p999);
  EXPECT_DOUBLE_EQ(4.999, statistics->p9999);
}

void FloodPlot::timeseriesData(TimeSeries tsData)
{
  if (tsData.values().empty()){
    return;
  }

  m_startDateTime = tsData.firstReportDateTime();
  m_endDateTime = tsData.firstReportDateTime() + Time(tsData.daysFromFirstReport(tsData.daysFromFirstReport().size()-1));
  m_duration = (m_endDateTime-m_startDateTime).totalDays();
  m_xAxisMin = 0.0;
  m_xAxisMax = m_duration;
  if (m_plot2DTimeAxis == NULL) 
  {
    m_plot2DTimeAxis = new Plot2DTimeAxis(m_startDateTime, m_duration);
    m_qwtPlot->setAxisTitle(QwtPlot::xBottom, " Simulation Time");
    m_qwtPlot->setAxisScale(QwtPlot::xBottom, 0, m_duration);
    m_qwtPlot->setAxisScaleDraw(QwtPlot::xBottom, m_plot2DTimeAxis);
    m_qwtPlot->setAxisLabelRotation(QwtPlot::xBottom, -90.0);
    m_qwtPlot->setAxisLabelAlignment(QwtPlot::xBottom, Qt::AlignLeft | Qt::AlignBottom);
  } 
  else 
  {
    m_plot2DTimeAxis->startDateTime(m_startDateTime);
    m_plot2DTimeAxis->duration(m_duration);
  }

  TimeSeriesFloodPlotData::Ptr data = TimeSeriesFloodPlotData::create(tsData);
  floodPlotData(data);
}

void TimeSeriesTest::testConstruction() {

    BOOST_MESSAGE("Testing time series construction...");

    TimeSeries<Real> ts;
    ts[Date(25, March, 2005)] = 1.2;
    ts[Date(29, March, 2005)] = 2.3;
    ts[Date(15, March, 2005)] = 0.3;

    TimeSeries<Real>::const_iterator cur = ts.begin();
    if (cur->first != Date(15, March, 2005)) {
        BOOST_ERROR("date does not match");
    }
    if (cur->second != 0.3) {
        BOOST_ERROR("value does not match");
    }

    ts[Date(15, March, 2005)] = 4.0;
    cur = ts.begin();
    if (cur->second != 4.0) {
        BOOST_ERROR("replaced value does not match" << cur->second << "\n");
    }

    ts[Date(15, March, 2005)] = 3.5;

    if (cur->second != 3.5) {
        BOOST_ERROR("set value operator not match" << cur->second << "\n");
    }
}

FL::ParseResult AB::analyze(
        const TimeSeries &ts, Forest &forest, Patterns::Matcher &matcher, PatternsSet &patterns, MetricsSet &metrics)
{
    ParseResult result;

    try
    {
        if (ts.size() < 2)
            throw EAnalyze(E_INVALID_INPUT);

        Tree *tree = new Tree(ts);
        const int up   = IDGenerator::idOf("a");
        const int down = IDGenerator::idOf("b");

        for (int i = 0; i < ts.size()-1; i += 1)
        {
            const int &id =  (ts.value(i) <= ts.value(i+1))  ?  up  :  down;
            tree->add(new Node(NULL, id, i, i+1, 0));
        }
        forest.push_back(tree);
        result.treesAdded = 1;
        result.nodesAdded = (ts.size() + 1) / 2;
    }
    catch (const EAnalyze &e)
    {
        m_lastError = e;
    }
    return result;
}

Datum blocker(const RealMatrix& Ua, const RealMatrix sa, const vGroup& ensemble, const uint blocksize, uint repeats, ExtractPolicy& policy) {


  
  TimeSeries<double> coverlaps;

  for (uint i=0; i<repeats; ++i) {
    vector<uint> picks = pickFrames(ensemble.size(), blocksize);
    
    if (debug) {
      cerr << "***Block " << blocksize << ", replica " << i << ", picks " << picks.size() << endl;
      dumpPicks(picks);
    }
    
    vGroup subset = subgroup(ensemble, picks);
    boost::tuple<RealMatrix, RealMatrix> pca_result = pca(subset, policy);
    RealMatrix s = boost::get<0>(pca_result);
    RealMatrix U = boost::get<1>(pca_result);

    if (length_normalize)
      for (uint j=0; j<s.rows(); ++j)
        s[j] /= blocksize;

    coverlaps.push_back(covarianceOverlap(sa, Ua, s, U));
  }

  return( Datum(coverlaps.average(), coverlaps.variance(), coverlaps.size()) );

}

 template <typename TimeSeries> typename TimeSeries::value_type empirical_average(TimeSeries const &t) {
   auto si = t.size();
   if (si == 0) return typename TimeSeries::value_type{};
   auto sum = t[0];
   for (int i = 1; i < si; ++i) sum += t[i];
   return sum / t.size();
 }

TimeSeries<double> MLGDao::getSignal( oid_t nodeId, oid_t bottomLayer, oid_t topLayer )
{
#ifdef MLD_SAFE
    if( bottomLayer == Objects::InvalidOID
        || topLayer == Objects::InvalidOID
        || nodeId == Objects::InvalidOID ) {

        LOG(logERROR) << "MLGDao::getSignal: invalid ids";
        return TimeSeries<double>();
    }
#endif

    // Use as less overhead as possible
    TimeSeries<double> res;
    oid_t layer = bottomLayer;
    type_t oType = m_link->olinkType();
    Value v;
    while( layer != topLayer ) {
        oid_t eid = findEdge(oType, layer, nodeId);
        m_g->GetAttribute(eid, m_g->FindAttribute(oType, Attrs::V[OLinkAttr::WEIGHT]), v);
        res.data().push_back(v.GetDouble());
        layer = m_layer->parent(layer);
    }

    // Don't forget last layer, it is inclusive
    oid_t eid = findEdge(oType, layer, nodeId);
    m_g->GetAttribute(eid, m_g->FindAttribute(oType, Attrs::V[OLinkAttr::WEIGHT]), v);
    res.data().push_back(v.GetDouble());
    res.clamp();
    return res;
}

/* ********************************************************** *
* Evaluate the detection score function of a window of frames *
* *********************************************************** */
int evalKer(Eigen::MatrixXd Ds, TimeSeries TS, Eigen::MatrixXd w, double b, int minSegLen, int maxSegLen, int segStride, int d, int sd, int featType, double thresh)
{
	int n = Ds.cols();
	double minth = -125.803;
	double maxth = 130.957;
	
	if ((minSegLen < 1) || (maxSegLen < minSegLen) || (segStride < 1))
		std::cout << "crtFeatWindow: evalKer: invalid option for sOpt";
	if (minSegLen > n)
		std::cout << "crtFeatWindow: evalKer: minimum segment length is greater than the time series length" << std::endl;
	if (maxSegLen > n)
		maxSegLen = n;

	//Time series options
	TS.D = Ds.data();
	TS.n = n;

	if (featType == FEAT_BAG) {
		TS.IntD = new double[d*(n + 1)];
		cmpIntIm(TS.D, d, n, TS.IntD);
	}
	else if (featType == FEAT_ORDER) {
		TS.sd = sd;
	}

	TS.setSegLst(minSegLen, maxSegLen, segStride);
	TS.updateSegLstVals(w.data(), b);

	//Event mxEv;
	double mxVal = -std::numeric_limits<double>::infinity();
	int curIdx = 0, segLstSz = TS.segLst.size();

	for (int t = minSegLen; t<n; t++) {

		if (curIdx < segLstSz) {	// there are more segments to consider
			ExEvent curSeg = TS.segLst[curIdx];
			while (curSeg.e <= t) {
				mxVal = curSeg.val;

				//Detect the Event (if detect score sup to a threshold)
				if (mxVal > thresh) {
					//std::cout<<"sortie "<<mxVal << " > " <<thresh <<std::endl;
					return 0;
				}

				curIdx++;
				if (curIdx >= segLstSz)
					break;

				curSeg = TS.segLst[curIdx];

			}
		}
	}

	//std::cout << "crtFeatWindow: evalKer: No event was found" << std::endl;

	return -1;
}

 binned_series(TimeSeries const& t, int bin_size_)
    : bin_size(bin_size_), binned(t.size() / bin_size_, value_type{}) {
  if (bin_size_ > t.size())
   TRIQS_RUNTIME_ERROR << "bin size (" << bin_size_ << ") cannot be larger than size (" << t.size() << ") of time series";
  for (int i = 0; i < size(); i++) {
   for (int j = 0; j < bin_size; j++) binned[i] += t[i * bin_size + j];
   binned[i] /= bin_size;
  }
 }

    template <typename TimeSeries> typename TimeSeries::value_type empirical_variance(TimeSeries const &t) {
      auto si = t.size();
      if (si == 0) return typename TimeSeries::value_type{};

      auto avg           = empirical_average(t);
      decltype(avg) sum2 = (t[0] - avg) * (t[0] - avg); // also valid if t[0] is an array e.g., i.e. no trivial contructor...
      for (int i = 1; i < si; ++i) sum2 += (t[i] - avg) * (t[i] - avg);
      return sum2 / t.size();
    }

void BFFindMotif::FindMotifSub(std::deque<Point> &window)
{
	size_t i = 0;
	for(i = 0; i < window.size() - m_MotifLength; ++i)
	{
		double distance = 0.0;
		bool newMotif = true;
		TimeSeries ts;
		ts.reserve(m_MotifLength);
		if(m_SlideWindow.size() >= m_MotifLength)		//	Only process slide window larger than motif length
		{
			//	Get time series
			for(size_t j = i; j < i + m_MotifLength; ++j)
			{
				ts.push_back(window[j].second);
			}

			//	Compare with candidate motif
			for(size_t j = 0; j < m_CandidateMotif.size(); ++j)
			{
				distance = EuclideanDistance(m_CandidateMotif[j].second, ts);

				if((2 * m_Radius > distance) && (m_Radius < distance))	//	Neither new motif nor similar motif
				{
					newMotif = false;
				}
				else if(m_Radius > distance)	//	Similar motif
				{
					m_CandidateMotif[j].first++;
					i += m_Step;
					newMotif = false;
					break;		//	Impossible to be similar with other candidates
				}
			}

			if(true == newMotif)	//	New motif
			{
				m_CandidateMotif.push_back(make_pair<long long, TimeSeries>(1, ts));
				i += m_Step;
			}
			
		}
		else
		{
			cerr << "Window size:" << m_SlideWindow.size() << endl;
		}
	
	}
	
	for(size_t k = 0; k < i; ++k)
	{
		window.pop_front();
	}
	
}

 std::vector<Real> IntervalPrice::extractValues(
                                        const TimeSeries<IntervalPrice>& ts,
                                        IntervalPrice::Type t)  {
     std::vector<Real> returnval;
     returnval.reserve(ts.size());
     for (TimeSeries<IntervalPrice>::const_iterator i = ts.begin();
          i != ts.end(); ++i) {
         returnval.push_back(i->second.value(t));
     }
     return returnval;
 }

// Compute HammingDistance between two time series
size_t NTuple::HammingDistance( TimeSeries& t1, TimeSeries& t2 )
{
	size_t h = 0;
	TimeSeriesIter iter1 = t1.begin();
	TimeSeriesIter iter2 = t2.begin();
	while( iter1 != t1.end() && iter2 != t2.end() )
	{
		// Accumulate the Hamming distance for each NTuple
		h += *iter1++ - *iter2++;
	}
	return h;
}