Network.h

#pragma once
#include "Kcommon.h"
#include <map>
#include <vector>

using namespace std;

class Network {
public:
  Network();
  ~Network();
  int get_no_nodes();

  // for social network
  int get_out_degree(uint n);
  bool read_network_from_file(int no_nodes, string file, bool is_directed);
  void generate_random_network(int no_nodes, double p, bool is_directed);
  uint sample_influence(
      const kseeds &seeds); // estimate influence, input is a list of pairs,
                            // each pair is (node_id, product)
  uint sample_influence_reverse(
      const kseeds &seeds); // estimate influence but use reverse sampling
  uint sample_influence_linear_threshold(const kseeds &seeds);

  // for sensor
  bool read_sensor_data(int no_nodes, string file);
  double get_entropy(const kseeds &seeds);

private:
  void clear();
  void recursive_entropy(int idx, const kseeds &seeds, double &re,
                         double &prob); // used to calculate entropy

  uint number_of_nodes;
  Kcommon *common_instance;

  // for social network
  bool is_directed;
  map<uint, uint> map_node_id; // map from true id -> ordered id (used for read
                               // graph from file)
  vector<vector<uint>> out_neighbors,
      in_neighbors; // map from node_id -> list of out (in) neighbor of the node
  vector<vector<uint>> preferences; // map from node_id -> preferences on
                                    // partition - this impacts the weight of an
                                    // out-edge with adopting different product
  vector<double>
      probabilities; // map from preference -> probability to influence

  // for sensor data
  vector<ksensors> sensor_data; // map from loc id -> kind of sensor (0 temp, 1
                                // humid, 2 light) -> bin
  int max_no_bin;               // no. bins
};