@@ -15,6 +15,7 @@ using namespace Rcpp;

 #include <boost/algorithm/string.hpp>

 #include <boost/assign.hpp>

 #include <boost/iostreams/filtering_stream.hpp>

+#include <random>

 #include "matrix.h"

 #include "utils.h"

@@ -305,7 +306,7 @@ double objective_em_supervise(Matrix_Double & p, vector<double> & theta)

 //   theta (model parameters), a vector with "T" elements.

 //   q is a matrix of N X T, the tissue-specific posterior probabilty of each read

 //

-void em_supervise(Matrix_Double & p, int max_iter, vector<double> & theta, Matrix_Double& q)

+void em_supervise(Matrix_Double & p, int max_iter, vector<double> & theta, Matrix_Double& q, int random_seed)

 {

 	// cout.precision(15);

 	// cerr.precision(15);

@@ -315,9 +316,24 @@ void em_supervise(Matrix_Double & p, int max_iter, vector<double> & theta, Matri

 	// initialize model parameters as uniform distribution

 	// alternatively, model parameters can be random numbers by satisfying the crition

 	//    (1) \sum_{i=1}^{ncol}{theta_i}=1

-	for (int j=0; j<ncol; j++) {

-		theta[j] = 1/(double)ncol;

+	if (random_seed == 0) {

+	    for (int j=0; j<ncol; j++) {

+	        theta[j] = 1/(double)ncol;

+	    }

+	} else{

+	    double sum = 0.0;

+	    std::default_random_engine generator(random_seed);

+	    std::uniform_real_distribution<double> distribution(0.0, 1.0);

+	    for (int j = 0; j < ncol; j++) {

+	        theta[j] = distribution(generator);

+	        sum += theta[j];

+	    }

+	    // Normalize the values so that they sum up to 1

+	    for (int j = 0; j < ncol; j++) {

+	        theta[j] /= sum;

+	    }

 	}

+

 	// create and initialize q with the same size of p and with all elements initialized as 0

 	//Matrix_Double q(nrow, ncol, 0);

 	//cerr << "iter 0\t" << objective_em_supervise(p, theta) << endl;

@@ -386,7 +402,7 @@ void em_supervise(Matrix_Double & p, int max_iter, vector<double> & theta, Matri

 //

 void em_semisupervise(Matrix_Double & p, vector<int> & Rm, vector<int> & Rl,

 	int max_iter, vector<double> & theta, Matrix_Double& q, vector<double>& q_unknown,

-	vector<double> & m)

+	vector<double> & m, int random_seed)

 {

 	// cout.precision(15);

   // cerr.precision(15);

@@ -408,9 +424,27 @@ void em_semisupervise(Matrix_Double & p, vector<int> & Rm, vector<int> & Rl,

 	// alternatively, theta and m can be random numbers, by satisfying:

 	//    (1) \sum_{i=1}^{ncol+1}{theta_i}=1

 	//    (2) 0 <= m_k <=1 for all k=1,2,...,#marker_covered_by_all_reads

-	for (int j=0; j<ncol+1; j++) {

-		theta[j] = 1/(double)(ncol+1);

+	if (random_seed == 0) {

+	    for (int j=0; j<ncol+1; j++) {

+	        theta[j] = 1/(double)(ncol+1);

+	    }

+	} else{

+	    double sum = 0.0;

+	    std::default_random_engine generator(random_seed);

+	    std::uniform_real_distribution<double> distribution(0.0, 1.0);

+	    for (int j = 0; j < ncol+1; j++) {

+	        theta[j] = distribution(generator);

+	        sum += theta[j];

+	    }

+	    // Normalize the values so that they sum up to 1

+	    for (int j = 0; j < ncol+1; j++) {

+	        theta[j] /= sum;

+	    }

 	}

+	

+	

+	

+	

 	for (int k=0; k<nMarker; k++) {

 		m[k] = 0.5;

 	}

@@ -1,576 +1,576 @@

-// [[Rcpp::depends(BH)]]

-#include <Rcpp.h>

-using namespace Rcpp;

-

-#include <cstdlib>

-#include <cmath>

-#include <sstream>

-#include <iostream>

-#include <fstream>

-#include <map>

-#include <vector>

-#include <string>

-#include <cassert>

-#include <algorithm>

-#include <boost/algorithm/string.hpp>

-#include <boost/assign.hpp>

-#include <boost/iostreams/filtering_stream.hpp>

-#include "matrix.h"

-#include "utils.h"

-

-using namespace std;

-using namespace boost;

-

-#include "template_utils.cpp"

-

-Matrix_Double::Matrix_Double(const unsigned int nrow_, const unsigned int ncol_, const double init_value)

-{

-	nrow = nrow_;

-	ncol = ncol_;

-	empty = false;

-	mat.reserve(nrow_);

-	for (int i=0; i<nrow; i++) {

-		vector<double> temp;

-		temp.reserve(ncol);

-		for (int j=0; j<ncol; j++)

-			temp.push_back(init_value);

-		mat.push_back( temp );

-	}

-	empty = mat.empty();

-}

-

-// a line represents a row of the matrix. Each element in this row is delimited by a TAB.

-void Matrix_Double::process_one_line_of_mat_file(string & line, int num_header_column)

-{

-	vector<string> items;

-	split(items, line, is_any_of("\t"));

-	vector<double> v;

-	for (int i=0; i<items.size(); i++) {

-		if (i==0) row_labels.push_back( atoi(items[0].c_str()) ); // the first column is the label of this row.

-		if (i<num_header_column) continue; // skip the first num_header_column header columns

-		v.push_back( atof(items[i].c_str()) );

-	}

-	mat.push_back( v );

-}

-

-Matrix_Double::Matrix_Double(const string & mat_file,

-	bool header_line=false, int num_header_column=1)

-{

-	unsigned long i_line=0;

-	// input is a plain text file

-	istream * in=&cin; // default is stdin

-	ifstream fin;

-	if ( !(mat_file.compare("stdin")==0)) {

-		fin.open(mat_file.c_str());

-		if (fin.fail()){

-		  Rcpp::Rcerr << "Error: Unable to open " << mat_file << " in Matrix_Double()" << endl;

-			// exit(EXIT_FAILURE);

-		}

-		in = &fin;

-	}

-	string line;

-	while (!(*in).eof()) {

-		getline((*in), line);

-		//cerr << "Line: " << line << endl;

-		if (header_line) {

-			if (i_line==0) {

-				// skip the header line

-				i_line++;

-				continue;

-			}

-		}

-		if (line.empty()) {

-			// this is the last line of the file

-			break;

-		}

-		process_one_line_of_mat_file(line, num_header_column);

-		i_line++;

-	}

-	if ( !(mat_file.compare("stdin")==0)) {

-		fin.close();

-	}

-	empty = mat.empty();

-	nrow = mat.size();

-	if (nrow>=1) ncol = mat[0].size();

-	else ncol = 0;

-}

-

-// return the row index of this vector

-long Matrix_Double::append_row_vector(vector<double>& v_, int row_label_) {

-	if (v_.size()!=ncol) {

-	  Rcpp::Rcerr << "Error of Matrix_Double::append_row_vector: the appended vector size (" << v_.size() << ") does not match the number of column in the matrix (" << ncol << ")!\nExit." << endl;

-		// exit(EXIT_FAILURE);

-	}

-	vector<double> v;

-	for (int i=0; i<v_.size(); i++) {

-		v.push_back( v_[i] );

-	}

-	mat.push_back( v );

-	nrow = mat.size();

-	row_labels.push_back(row_label_);

-	empty=false;

-	return(nrow-1);

-}

-

-//

-// if max(v_)/min(v_) >= min_threshold_maxv_minv, then we append this vector, otherwise ignore it.

-//

-// return row index of this appended vector, if successfully appending; return -1 otherwise.

-//

-long Matrix_Double::append_row_vector_with_filter(vector<double>& v_, int row_label_, double min_threshold_maxv_minv) {

-	if (v_.size()!=ncol) {

-	  Rcpp::Rcerr << "Error of Matrix_Double::append_row_vector_with_filter: the appended vector size (" << v_.size() << ") does not match the number of column in the matrix (" << ncol << ")!\nExit." << endl;

-		// exit(EXIT_FAILURE);

-	}

-	double min_ = *min_element(v_.begin(), v_.end());

-	double max_ = *max_element(v_.begin(), v_.end());

-	//cout << "max: " << max_ << ", min: " << min_ << ", ratio: " << max_/min_ << endl;

-	if (min_==0) {

-		if (max_==0) return(-1);

-	} else {

-		double ratio=max_/min_;

-		if (ratio<min_threshold_maxv_minv) return(-1);

-	}

-	long row_index = append_row_vector(v_, row_label_);

-	return(row_index);

-}

-

-bool Matrix_Double::get_element(const int i, const int j, double & v)

-{

-	if (!empty) {

-		if (i<nrow && j<ncol) {

-			v = mat[i][j];

-			return true;

-		} else {

-		  Rcpp::Rcerr << "Warning: i=" << i << " and j=" << j << " exceed matrix size!" << endl;

-			return false;

-		}

-	} else {

-		v = 0;

-		return false;

-	}

-}

-

-bool Matrix_Double::set_element(const int i, const int j, double v)

-{

-	if (!empty) {

-		if (i<nrow && j<ncol) {

-			mat[i][j] = v;

-			return true;

-		} else {

-		  Rcpp::Rcerr << "Warning: i=" << i << " and j=" << j << " exceed matrix size!" << endl;

-			return false;

-		}

-	} else {

-		return false;

-	}

-}

-

-bool Matrix_Double::get_column_sum(const int j, double & v)

-{

-	if (!empty) {

-		if (j<ncol) {

-			v = 0;

-			for (int k=0; k<nrow; k++) {

-				v += mat[k][j];

-			}

-			return true;

-		} else {

-		  Rcpp::Rcerr << "Warning: j=" << j << " exceeds matrix column size (" << ncol << ")!" << endl;

-			return false;

-		}

-	} else {

-		v = 0;

-		return false;

-	}

-}

-

-bool Matrix_Double::get_row_sum(const int i, double & v)

-{

-	if (!empty) {

-		if (i<nrow) {

-			v = 0;

-			for (int k=0; k<ncol; k++) {

-				v += mat[i][k];

-			}

-			return true;

-		} else {

-		  Rcpp::Rcerr << "Warning: i=" << i << " exceeds matrix row size (" << nrow << ")!" << endl;

-			return false;

-		}

-	} else {

-		v = 0;

-		return false;

-	}

-}

-

-void Matrix_Double::get_unique_row_labels(vector<int> & uniq_labels)

-{

-	map<int, int> counts;

-	for (int i=0; i<row_labels.size(); i++)

-		if (counts.find(row_labels[i]) != counts.end())

-			counts[row_labels[i]] += 1; // found this label, then increment count

-		else {

-			counts.insert(make_pair(row_labels[i],1)); // not found this label, then count it once

-			//counts[row_labels[i]] = 1;

-		}

-	map<int,int>::iterator it;

-	for (it=counts.begin(); it!=counts.end(); ++it)

-		uniq_labels.push_back(it->first);

-}

-

-void Matrix_Double::print_with_additional_column_of_Bins2Value(ostream & os, Bins2Value& additional_column_data)

-{

-	// obtain unique row labels

-	vector<int> unique_row_labels;

-	int row_label_;

-	for (int i=0; i<nrow; i++) {

-		row_label_ = row_labels[i];

-		unique_row_labels.push_back( row_label_ );

-	}

-	Bins2Value::iterator iter;

-	for (iter=additional_column_data.begin(); iter!=additional_column_data.end(); iter++) {

-		row_label_ = iter->first;

-		if (!exist_row_label(row_label_)) {

-			unique_row_labels.push_back( row_label_ );

-		}

-	}

-	sort(unique_row_labels.begin(), unique_row_labels.end());

-	// print both 'mat' and 'additional_column_data' as the last column

-	for (int i=0; i<unique_row_labels.size(); i++) {

-		row_label_ = unique_row_labels[i];

-		os << row_label_;

-		int row_index = get_row_index(row_label_);

-		if (row_index!=-1) {

-			for (int j=0; j<ncol; j++) {

-				os << "\t" << mat[row_index][j];

-			}

-		} else {

-			for (int j=0; j<ncol; j++) {

-				os << "\t0";

-			}

-		}

-		if (additional_column_data.find(row_label_)!=additional_column_data.end()) {

-			os << "\t" << additional_column_data[row_label_];

-		} else {

-			os << "\t0";

-		}

-		os << endl;

-	}

-}

-

-ostream & operator<<(ostream & os, Matrix_Double & mat)

-{

-	double v;

-	int row_label;

-	if (!mat.isempty()) {

-		for (int i=0; i<mat.get_row_num(); i++) {

-			int j;

-			mat.get_row_label(i, row_label);

-			os << row_label << "\t";

-			for (j=0; j<mat.get_column_num()-1; j++) {

-				mat.get_element(i,j,v);

-				os << v << "\t";

-			}

-			mat.get_element(i,j,v);

-			os << v << endl;

-		}

-	}

-	return os;

-}

-

-double objective_em_supervise(Matrix_Double & p, vector<double> & theta)

-{

-	unsigned int ncol = p.get_column_num();

-	unsigned int nrow = p.get_row_num();

-	double v;

-	double obj = 0;

-	for (int i=0; i<nrow; i++) {

-		double sum = 0;

-		for (int j=0; j<ncol; j++) {

-			p.get_element(i,j,v);

-			sum += theta[j]*v;

-		}

-		obj += log(sum);

-	}

-	return obj;

-}

-//

-// There are T known tissues and 1 unknown tissue (described by a double vector "m")

-//

-// input:

-//   p is a matrix of N X T, where N is number of reads and T is number of known tissue

-//

-// output:

-//   theta (model parameters), a vector with "T" elements.

-//   q is a matrix of N X T, the tissue-specific posterior probabilty of each read

-//

-void em_supervise(Matrix_Double & p, int max_iter, vector<double> & theta, Matrix_Double& q)

-{

-	// cout.precision(15);

-	// cerr.precision(15);

-	unsigned int ncol = p.get_column_num();

-	unsigned int nrow = p.get_row_num(); // nrow is number of tissues.

-	theta.resize(ncol, 0); // assign (num_tissues) space and initialize to 0s.

-	// initialize model parameters as uniform distribution

-	// alternatively, model parameters can be random numbers by satisfying the crition

-	//    (1) \sum_{i=1}^{ncol}{theta_i}=1

-	for (int j=0; j<ncol; j++) {

-		theta[j] = 1/(double)ncol;

-	}

-	// create and initialize q with the same size of p and with all elements initialized as 0

-	//Matrix_Double q(nrow, ncol, 0);

-	//cerr << "iter 0\t" << objective_em_supervise(p, theta) << endl;

-	double v1, v2;

-	for (int iter=0; iter<max_iter; iter++) {

-	  Rcpp::Rcerr << iter+1 << "," ;

-		// E-step: estimate q

-		for (int i=0; i<nrow; i++) {

-			double sum = 0;

-			for (int j=0; j<ncol; j++) {

-				p.get_element(i,j,v1);

-				v2 = theta[j]*v1;

-				q.set_element( i, j, v2 );

-				sum += v2;

-			}

-			for (int j=0; j<ncol; j++) {

-				q.get_element(i,j,v2);

-				v2 /= sum;

-				q.set_element( i, j, v2 );

-			}

-		}

-		// M-step: estimate theta

-		for (int j=0; j<ncol; j++) {

-			double sum=0;

-			for (int i=0; i<nrow; i++) {

-				q.get_element(i,j,v2);

-				sum += v2;

-			}

-			theta[j] = sum / nrow;

-		}

-		// for debug

-		//cerr << "iter " << (iter+1) << "\t" << objective_em_supervise(p, theta) << endl;

-	}

-	// last E-step: estimate q

-	for (int i=0; i<nrow; i++) {

-		double sum = 0;

-		for (int j=0; j<ncol; j++) {

-			p.get_element(i,j,v1);

-			v2 = theta[j]*v1;

-			q.set_element( i, j, v2 );

-			sum += v2;

-		}

-		for (int j=0; j<ncol; j++) {

-			q.get_element(i,j,v2);

-			v2 /= sum;

-			q.set_element( i, j, v2 );

-		}

-	}

-	Rcpp::Rcerr << endl;

-}

-

-//

-// There are T known tissues and 1 unknown tissue (described by a double vector "m")

-//

-// input:

-//   p is a matrix of N X T, where N is number of reads and T is number of known tissue

-//     p.row_label is an int vector of N X 1, each element is the marker Id (1-base) of a read.

-//   Rm is a vector of N X 1, each element is number of valid methylated CpG sites in a read

-//   Rl is a vector of N X 1, each element is number of all valid CpG sites in a read

-//

-// output:

-//   theta is a vector of (T+1) X 1, where T is number of known tissue. This vector is already allocated with space of (ncol+1) units.

-//   m is a vector of M X 1, where M is number of markers.

-//   q is a matrix of N X T, the tissue-specific posterior probabilty of each read

-//   q_unknown is a vector of N X 1, the posterior probabilty of each read for the unknown class. It does not need to be allocated space before this function calling.

-//

-void em_semisupervise(Matrix_Double & p, vector<int> & Rm, vector<int> & Rl,

-	int max_iter, vector<double> & theta, Matrix_Double& q, vector<double>& q_unknown,

-	vector<double> & m)

-{

-	// cout.precision(15);

-  // cerr.precision(15);

-	unsigned int ncol = p.get_column_num(); // T, number of known tissues

-	unsigned int nrow = p.get_row_num(); // N, number of reads

-

-	theta.resize(ncol+1, 0); // assign (num_tissues+1) space and initialize to 0s.

-

-	vector<int> uniq_marker_Ids;

-	p.get_unique_row_labels(uniq_marker_Ids);

-	int nMarker = uniq_marker_Ids.size(); // M, number of markers that all N reads cover. Some markers may not be covered by these N reads.

-	m.resize(nMarker, 0); // assign nMarker space and initialize to 0s.

-	map<int,int> markerId2Index; // marker index is 0-based.

-	for (int index=0; index<nMarker; index++) {

-		markerId2Index.insert(make_pair(uniq_marker_Ids[index],index));

-	}

-

-	// initialize model parameters theta as uniform distribution, and m as 0.5

-	// alternatively, theta and m can be random numbers, by satisfying:

-	//    (1) \sum_{i=1}^{ncol+1}{theta_i}=1

-	//    (2) 0 <= m_k <=1 for all k=1,2,...,#marker_covered_by_all_reads

-	for (int j=0; j<ncol+1; j++) {

-		theta[j] = 1/(double)(ncol+1);

-	}

-	for (int k=0; k<nMarker; k++) {

-		m[k] = 0.5;

-	}

-	for (int i=0; i<nrow; i++)

-		q_unknown.push_back(0);

-

-	// EM algorithm

-	for (int iter=0; iter<max_iter; iter++) {

-	  Rcpp::Rcerr << iter+1 << "," ;

-		// E-step: estimate q (for T known tissues) and q_unknown (for one unknown tissue)

-		double v1, v2, likelihood_unknown_class;

-		int markerId, markerIndex;

-		for (int i=0; i<nrow; i++) {

-			// process the first T known tissues

-			double sum = 0;

-			for (int j=0; j<ncol; j++) {

-				p.get_element(i,j,v1);

-				v2 = theta[j]*v1;

-				q.set_element( i, j, v2 );

-				sum += v2;

-			}

-			// process the last unknown tissue

-			p.get_row_label(i, markerId); // get markerId (1-base) of read i

-			markerIndex = markerId2Index[markerId];

-			likelihood_unknown_class = pow(m[markerIndex],Rm[i]) * pow(1-m[markerIndex],Rl[i]-Rm[i]);

-			q_unknown[i] = theta[ncol]*likelihood_unknown_class;

-			sum += q_unknown[i];

-

-			// update q and q_unknown

-			for (int j=0; j<ncol; j++) {

-				q.get_element(i,j,v2);

-				v2 /= sum;

-				q.set_element( i, j, v2 );

-			}

-			q_unknown[i] /= sum;

-		}

-

-		// M-step 1: estimate unknown class's methylation level (m) of each marker

-		double sum1=0, sum2=0;

-		int curr_marker_index=-1, prev_marker_index=-1; // all marker index is 0-base

-		for (int i=0; i<nrow; i++) {

-			// for each row (or read)

-			p.get_row_label(i, markerId); // get the marker_index (0-base) of read i

-			curr_marker_index = markerId2Index[markerId];

-			if (curr_marker_index != prev_marker_index) {

-				// this is the 1st read of a new marker

-				// we need to summarize m value of previous marker

-				if (prev_marker_index!=-1) { // current marker is not the 1st marker

-					m[prev_marker_index] = (sum2!=0 ? sum1/sum2 : 0);

-					sum1 = 0;

-					sum2 = 0;

-				}

-				prev_marker_index = curr_marker_index;

-			}

-			sum1 += q_unknown[i]*Rm[i];

-			sum2 += q_unknown[i]*Rl[i];

-		}

-		m[curr_marker_index] = (sum2!=0 ? sum1/sum2 : 0); // estimate m of the last marker

-

-		//cout << "iter=" << iter << ", " << "m=" << endl; // for debug

-		//print_vec(cout, m, "\n"); // for debug

-		//cout << endl; // for debug

-

-		// M-step 2: estimate theta, which has ncol+1 values

-		double sum;

-		for (int j=0; j<ncol; j++) {

-			sum=0;

-			for (int i=0; i<nrow; i++) {

-				q.get_element(i,j,v2);

-				sum += v2;

-			}

-			theta[j] = sum / nrow;

-		}

-		sum=0;

-		for (int i=0; i<nrow; i++)

-			sum += q_unknown[i];

-		theta[ncol] = sum / nrow;

-

-		//cout << "round " << iter+1 << ", theta="; // for debug

-		//print_vec(cout, theta, ", "); // for debug

-		//cout << endl << endl; // for debug

-	}

-	Rcpp::Rcerr << endl;

-	//cout << "final:" << endl << std::flush;

-	//print_vec(cout, theta, ", "); // for debug

-	//cout << endl << endl << std::flush; // for debug

-}

-

-void readCounts_by_reads_posterior_probability_version_regular(Matrix_Double& q, double unit, Matrix_Double& readCounts)

-{

-	unsigned int ncol = q.get_column_num(); // T, number of known tissues

-	unsigned int nrow = q.get_row_num(); // N, number of reads

-

-	vector<int> uniq_marker_Ids;

-	q.get_unique_row_labels(uniq_marker_Ids);

-	// int nMarker = uniq_marker_Ids.size(); // M, number of markers that all N reads cover. Some markers may not be covered by these N reads.

-

-	double v;

-	int curr_markerId=-1, prev_markerId=-1; // all marker index is 0-base

-	vector<double> readCountsPerMarker(ncol, 0); // a vector of read counts for one marker (T elements) with default values 0

-	for (int i=0; i<nrow; i++) {

-		// for each row (or read)

-		q.get_row_label(i, curr_markerId); // get the marker ID of read i

-		if (curr_markerId != prev_markerId) {

-			// this is the 1st read of a new marker

-			// we need to summarize read counts of each tissue for the previous marker and deposit them to the matrix "readCounts"

-			// then clear readCountsPerMarker for a new read counting of the next new marker

-			if (prev_markerId!=-1) { // current marker is not the 1st marker

-				multi_vec_by_number(readCountsPerMarker, unit); // normalize the raw read counts by unit.

-				readCounts.append_row_vector(readCountsPerMarker, prev_markerId);

-				assign_vector_zeros(readCountsPerMarker);

-			}

-			prev_markerId = curr_markerId;

-		}

-		for (int j=0; j<ncol; j++) {

-			q.get_element(i,j,v);

-			readCountsPerMarker[j] += v;

-		}

-	}

-	multi_vec_by_number(readCountsPerMarker, unit); // normalize the raw read counts by unit.

-	readCounts.append_row_vector(readCountsPerMarker, curr_markerId);

-}

-

-void readCounts_by_reads_posterior_probability_version_unknownclass(Matrix_Double& q, vector<double>& q_unknown, double unit, Matrix_Double& readCounts)

-{

-	unsigned int ncol = q.get_column_num(); // T, number of known tissues

-	unsigned int nrow = q.get_row_num(); // N, number of reads

-	if (nrow!=(unsigned int)q_unknown.size()) {

-	  Rcpp::Rcerr << "Error (readCounts_by_reads_posterior_probability_version_unknownclass): row number of q_unknown does not match with row number of q!" << endl;

-		// exit(EXIT_FAILURE);

-	}

-

-	vector<int> uniq_marker_Ids;

-	q.get_unique_row_labels(uniq_marker_Ids);

-	// int nMarker = uniq_marker_Ids.size(); // M, number of markers that all N reads cover. Some markers may not be covered by these N reads.

-

-	double v;

-	int curr_markerId=-1, prev_markerId=-1; // all marker index is 0-base

-	vector<double> readCountsPerMarker(ncol+1, 0); // a vector of read counts for one marker (T+1 elements) with default values 0. The last element is for the unknown class.

-	for (int i=0; i<nrow; i++) {

-		// for each row (or read)

-		q.get_row_label(i, curr_markerId); // get the marker ID of read i

-		if (curr_markerId != prev_markerId) {

-			// this is the 1st read of a new marker

-			// we need to summarize read counts of each tissue for the previous marker and deposit them to the matrix "readCounts"

-			// then clear readCountsPerMarker for a new read counting of the next new marker

-			if (prev_markerId!=-1) { // current marker is not the 1st marker

-				multi_vec_by_number(readCountsPerMarker, unit); // normalize the raw read counts by unit.

-				readCounts.append_row_vector(readCountsPerMarker, prev_markerId);

-				assign_vector_zeros(readCountsPerMarker);

-			}

-			prev_markerId = curr_markerId;

-		}

-		for (int j=0; j<ncol; j++) {

-			q.get_element(i,j,v);

-			readCountsPerMarker[j] += v;

-		}

-		readCountsPerMarker[ncol] += q_unknown[i];

-	}

-	multi_vec_by_number(readCountsPerMarker, unit); // normalize the raw read counts by unit.

-	readCounts.append_row_vector(readCountsPerMarker, curr_markerId);

-}

-

+// [[Rcpp::depends(BH)]]

+#include <Rcpp.h>

+using namespace Rcpp;

+

+#include <cstdlib>

+#include <cmath>

+#include <sstream>

+#include <iostream>

+#include <fstream>

+#include <map>

+#include <vector>

+#include <string>

+#include <cassert>

+#include <algorithm>

+#include <boost/algorithm/string.hpp>

+#include <boost/assign.hpp>

+#include <boost/iostreams/filtering_stream.hpp>

+#include "matrix.h"

+#include "utils.h"

+

+using namespace std;

+using namespace boost;

+

+#include "template_utils.cpp"

+

+Matrix_Double::Matrix_Double(const unsigned int nrow_, const unsigned int ncol_, const double init_value)

+{

+	nrow = nrow_;

+	ncol = ncol_;

+	empty = false;

+	mat.reserve(nrow_);

+	for (int i=0; i<nrow; i++) {

+		vector<double> temp;

+		temp.reserve(ncol);

+		for (int j=0; j<ncol; j++)

+			temp.push_back(init_value);

+		mat.push_back( temp );

+	}

+	empty = mat.empty();

+}

+

+// a line represents a row of the matrix. Each element in this row is delimited by a TAB.

+void Matrix_Double::process_one_line_of_mat_file(string & line, int num_header_column)

+{

+	vector<string> items;

+	split(items, line, is_any_of("\t"));

+	vector<double> v;

+	for (int i=0; i<items.size(); i++) {

+		if (i==0) row_labels.push_back( atoi(items[0].c_str()) ); // the first column is the label of this row.

+		if (i<num_header_column) continue; // skip the first num_header_column header columns

+		v.push_back( atof(items[i].c_str()) );

+	}

+	mat.push_back( v );

+}

+

+Matrix_Double::Matrix_Double(const string & mat_file,

+	bool header_line=false, int num_header_column=1)

+{

+	unsigned long i_line=0;

+	// input is a plain text file

+	istream * in=&cin; // default is stdin

+	ifstream fin;

+	if ( !(mat_file.compare("stdin")==0)) {

+		fin.open(mat_file.c_str());

+		if (fin.fail()){

+		  Rcpp::Rcerr << "Error: Unable to open " << mat_file << " in Matrix_Double()" << endl;

+			// exit(EXIT_FAILURE);

+		}

+		in = &fin;

+	}

+	string line;

+	while (!(*in).eof()) {

+		getline((*in), line);

+		//cerr << "Line: " << line << endl;

+		if (header_line) {

+			if (i_line==0) {

+				// skip the header line

+				i_line++;

+				continue;

+			}

+		}

+		if (line.empty()) {

+			// this is the last line of the file

+			break;

+		}

+		process_one_line_of_mat_file(line, num_header_column);

+		i_line++;

+	}

+	if ( !(mat_file.compare("stdin")==0)) {

+		fin.close();

+	}

+	empty = mat.empty();

+	nrow = mat.size();

+	if (nrow>=1) ncol = mat[0].size();

+	else ncol = 0;

+}

+

+// return the row index of this vector

+long Matrix_Double::append_row_vector(vector<double>& v_, int row_label_) {

+	if (v_.size()!=ncol) {

+	  Rcpp::Rcerr << "Error of Matrix_Double::append_row_vector: the appended vector size (" << v_.size() << ") does not match the number of column in the matrix (" << ncol << ")!\nExit." << endl;

+		// exit(EXIT_FAILURE);

+	}

+	vector<double> v;

+	for (int i=0; i<v_.size(); i++) {

+		v.push_back( v_[i] );

+	}

+	mat.push_back( v );

+	nrow = mat.size();

+	row_labels.push_back(row_label_);

+	empty=false;

+	return(nrow-1);

+}

+

+//

+// if max(v_)/min(v_) >= min_threshold_maxv_minv, then we append this vector, otherwise ignore it.

+//

+// return row index of this appended vector, if successfully appending; return -1 otherwise.

+//

+long Matrix_Double::append_row_vector_with_filter(vector<double>& v_, int row_label_, double min_threshold_maxv_minv) {

+	if (v_.size()!=ncol) {

+	  Rcpp::Rcerr << "Error of Matrix_Double::append_row_vector_with_filter: the appended vector size (" << v_.size() << ") does not match the number of column in the matrix (" << ncol << ")!\nExit." << endl;

+		// exit(EXIT_FAILURE);

+	}

+	double min_ = *min_element(v_.begin(), v_.end());

+	double max_ = *max_element(v_.begin(), v_.end());

+	//cout << "max: " << max_ << ", min: " << min_ << ", ratio: " << max_/min_ << endl;

+	if (min_==0) {

+		if (max_==0) return(-1);

+	} else {

+		double ratio=max_/min_;

+		if (ratio<min_threshold_maxv_minv) return(-1);

+	}

+	long row_index = append_row_vector(v_, row_label_);

+	return(row_index);

+}

+

+bool Matrix_Double::get_element(const int i, const int j, double & v)

+{

+	if (!empty) {

+		if (i<nrow && j<ncol) {

+			v = mat[i][j];

+			return true;

+		} else {

+		  Rcpp::Rcerr << "Warning: i=" << i << " and j=" << j << " exceed matrix size!" << endl;

+			return false;

+		}

+	} else {

+		v = 0;

+		return false;

+	}

+}

+

+bool Matrix_Double::set_element(const int i, const int j, double v)

+{

+	if (!empty) {

+		if (i<nrow && j<ncol) {

+			mat[i][j] = v;

+			return true;

+		} else {

+		  Rcpp::Rcerr << "Warning: i=" << i << " and j=" << j << " exceed matrix size!" << endl;

+			return false;

+		}

+	} else {

+		return false;

+	}

+}

+

+bool Matrix_Double::get_column_sum(const int j, double & v)

+{

+	if (!empty) {

+		if (j<ncol) {

+			v = 0;

+			for (int k=0; k<nrow; k++) {

+				v += mat[k][j];

+			}

+			return true;

+		} else {

+		  Rcpp::Rcerr << "Warning: j=" << j << " exceeds matrix column size (" << ncol << ")!" << endl;

+			return false;

+		}

+	} else {

+		v = 0;

+		return false;

+	}

+}

+

+bool Matrix_Double::get_row_sum(const int i, double & v)

+{

+	if (!empty) {

+		if (i<nrow) {

+			v = 0;

+			for (int k=0; k<ncol; k++) {

+				v += mat[i][k];

+			}

+			return true;

+		} else {

+		  Rcpp::Rcerr << "Warning: i=" << i << " exceeds matrix row size (" << nrow << ")!" << endl;

+			return false;

+		}

+	} else {

+		v = 0;

+		return false;

+	}

+}

+

+void Matrix_Double::get_unique_row_labels(vector<int> & uniq_labels)

+{

+	map<int, int> counts;

+	for (int i=0; i<row_labels.size(); i++)

+		if (counts.find(row_labels[i]) != counts.end())

+			counts[row_labels[i]] += 1; // found this label, then increment count

+		else {

+			counts.insert(make_pair(row_labels[i],1)); // not found this label, then count it once

+			//counts[row_labels[i]] = 1;

+		}

+	map<int,int>::iterator it;

+	for (it=counts.begin(); it!=counts.end(); ++it)

+		uniq_labels.push_back(it->first);

+}