Revert 566f924..4ba0141

This rolls back to commit 566f9248c05db44133e3cbf145a4cbaf2fed140d.

5 files changed, 6 insertions, 7 deletions

diff --git a/datasets/subset_facebook_SNAPnormalize.txt b/datasets/subset_facebook_SNAPnormalize.txt
index 049260c..30851e3 100644
--- a/datasets/subset_facebook_SNAPnormalize.txt
+++ b/datasets/subset_facebook_SNAPnormalize.txt
@@ -5035,4 +5035,4 @@
 242 107
 317 306
 331 249
-204 255
\ No newline at end of file
+204 255
diff --git a/notes/reportYaron.tex b/notes/reportYaron.tex
index d5822ed..acdfaea 100644
--- a/notes/reportYaron.tex
+++ b/notes/reportYaron.tex
@@ -19,9 +19,7 @@ Given a set of observed cascades, the \textbf{graph reconstruction problem} cons
 
 \section{Related Work}
 
-There have been several works tackling the graph reconstruction problem in variants of the independent cascade. We briefly summarize their results and approaches below.
-
-
+In previous work, this problem has been formulated in different ways, including a convex optimization and a maximum likelihood problem. However, there is no known algorithm for graph reconstruction with theoretical guarantees and with a reasonable required sample size.
 
 \section{The Voter Model}
 
@@ -280,6 +278,7 @@ $\delta_4 = .54$ & $\delta_4 = .37$ & $\delta_4 = .43$ & $\delta_4 = .23$ \\
 The results of our findings on a very small social network (a subset of the famous Karate club), show that as the number of cascades increase the RIP constants decrease and that if $p_\text{init}$ is small then the RIP constant decrease as well. Finally the constants we obtain are either under or close to the $.25$ mark set by the authors of \cite{candes}.
 
 
+
 \subsection{Testing our algorithm}
 
 
diff --git a/src/algorithms.py b/src/algorithms.py
index 39bcbb2..0e240c9 100644
--- a/src/algorithms.py
+++ b/src/algorithms.py
@@ -60,7 +60,7 @@ def correctness_measure(G, G_hat, print_values=False):
     edges_hat = set(G_hat.edges())
     fp = len(edges_hat - edges)
     fn = len(edges - edges_hat)
-    tp = len(edges & edges_hat)
+    tp = len(edges | edges_hat)
     tn = G.number_of_nodes() ** 2 - fp - fn - tp
 
     #Other metrics
diff --git a/src/cascade_creation.py b/src/cascade_creation.py
index 1a71285..9a26c03 100644
--- a/src/cascade_creation.py
+++ b/src/cascade_creation.py
@@ -4,7 +4,7 @@ import collections
 from itertools import izip
 from sklearn.preprocessing import normalize
 
-class InfluenceGraph(nx.DiGraph):
+class InfluenceGraph(nx.Graph):
     """
     networkX graph with mat and logmat attributes
     """
diff --git a/src/make_plots.py b/src/make_plots.py
index 905c731..7c8bebb 100644
--- a/src/make_plots.py
+++ b/src/make_plots.py
@@ -40,7 +40,7 @@ def compare_greedy_and_lagrange_cs284r():
     """
     G = cascade_creation.InfluenceGraph(max_proba = .8)
     G.import_from_file("../datasets/subset_facebook_SNAPnormalize.txt")
-    A = cascade_creation.generate_cascades(G, p_init=.05, n_cascades=100)
+    A = cascade_creation.generate_cascades(G, p_init=.05, n_cascades=50)
 
     #Greedy
     G_hat = algorithms.greedy_prediction(G, A)