making variational inference work

1 files changed, 0 insertions, 30 deletions

diff --git a/datasets/normalize_dataset.py b/datasets/normalize_dataset.py
deleted file mode 100644
index 25149dd..0000000
--- a/datasets/normalize_dataset.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import numpy as np
-
-def normalize_file(filename):
-    """
-    Normalizes file:
-    If nodes are not numbered 0 to number_of_nodes - 1, then prints
-    normalized version of dataset in the same directory
-    """
-    #Read number of unique node identifications
-    nodes = []
-    with open(filename, "r") as f:
-        for line in f:
-            if "#" not in line:
-                node_1, node_2 = line.split()
-                nodes.append(node_1); nodes.append(node_2)
-        uniq_nodes = np.unique(nodes)
-
-    #Hash nodes to an index between 0 and number_of_nodes - 1
-    hash_nodes = {}
-    for idx, node in enumerate(uniq_nodes):
-        hash_nodes[node] = idx
-
-    #Write to file
-    with open(filename[:-4]+"normalize.txt", "w") as g:
-        with open(filename, "r") as f:
-            for line_f in f:
-                f_node_1, f_node_2 = line_f.split()
-                g_node_1 = hash_nodes[f_node_1]
-                g_node_2 = hash_nodes[f_node_2]
-                g.write(str(g_node_1)+" "+str(g_node_2)+"\n")