random

3 files changed, 15 insertions, 7 deletions

diff --git a/paper/paper.tex b/paper/paper.tex
index a3ec412..f922b3b 100644
--- a/paper/paper.tex
+++ b/paper/paper.tex
@@ -117,6 +117,7 @@
 \input{sections/linear_threshold.tex}
 
 \section{Experiments}
+\input{sections/experiments.tex}
 
 \section{Discussion}
 
diff --git a/src/algorithms.py b/src/algorithms.py
index 0afee24..9f6d3ea 100644
--- a/src/algorithms.py
+++ b/src/algorithms.py
@@ -61,9 +61,6 @@ def correctness_measure(G, G_hat, print_values=False):
     edges = set(G.edges())
     edges_hat = set(G_hat.edges())
 
-    print(sorted(edges))
-    print(sorted(edges_hat))
-
     fp = len(edges_hat - edges)
     fn = len(edges - edges_hat)
     tp = len(edges & edges_hat)
diff --git a/src/cascade_creation.py b/src/cascade_creation.py
index db0ea7a..c647b9a 100644
--- a/src/cascade_creation.py
+++ b/src/cascade_creation.py
@@ -18,6 +18,17 @@ class InfluenceGraph(nx.DiGraph):
         G = nx.erdos_renyi_graph(n, p, directed=True)
         self.add_edges_from(G.edges())
 
+    def connected_watts_strogatz(self, n, k, p):
+        """
+        n : number of nodes
+        k : number of nearest neighbor
+        p : proba of rewiring edge
+        """
+        G = nx.connected_watts_strogatz_graph(n, k, p)
+        self.add_edges_from(G.edges())
+        #TODO: Make the graph directed!
+        self.add_edges_from(G.edges())
+
     def import_from_file(self, file_name):
         """
         Takes a file from the Stanford collection of networks
@@ -31,8 +42,8 @@ class InfluenceGraph(nx.DiGraph):
     @property
     def mat(self):
         if not hasattr(self, '_mat'):
-            self._mat = (self.max_proba * np.random.rand(len(self), len(self))
-                         * np.asarray(nx.adjacency_matrix(self).todense()))
+            self._mat = self.max_proba * np.random.rand(len(self), len(self)
+                        ) * np.asarray(nx.adjacency_matrix(self).todense().T)
         return self._mat
 
     @property
@@ -145,8 +156,7 @@ def add_edges_from_proba_vector(G, p_node, node, floor_cstt):
     floor_parent = np.nonzero(p_node*(p_node > floor_cstt))
     print(floor_parent)
     for parent in floor_parent[0]:
-        #SOMEHOW THERE WAS A BUG HERE!
-        G.add_edge(node, parent)
+        G.add_edge(parent, node)
     #TODO: update G's mat matrix
     return G