little bugfixes: id to int, uniform to uniform-random instead of normal

Cora_Preprocessing/cora.py

@@ -8,9 +8,9 @@ Arrays are initialized in normal or uniform random format (default = normal).
 get_graph(path_nodes="/home/utaemon/SP/cora/cora.content", path_edges="/home/utaemon/SP/cora/cora.cites")
 -> return graph with nodes and edges
 To write the graph informations in file:
-def write_graph_to_file(path_nodes="/home/utaemon/SP/cora/cora.content", path_edges="/home/utaemon/SP/cora/cora.cites", path_output_graph = "/home/utaemon/SP/graph.txt")
+def write_graph_to_file(path_nodes="/home/utaemon/SP/cora/cora.content", path_edges="/home/utaemon/SP/cora/cora.cites", path_output_graph = "/home/utaemon/SP/")
 To write the dictionary with initalizing Embeddings in file:
-def write_dict_to_file(quantity=1433, path_output_emb = "/home/utaemon/SP/init.txt")
+def write_dict_to_file(rand_type="normal_random", dimension = 128, quantity=1433, path_output_emb = "/home/utaemon/SP/")
 """
 
 import networkx as nx
@@ -29,9 +29,9 @@ def read_file_and_get_nodes(graph_name, path="/home/utaemon/SP/cora/cora.content
             paper_bow = np.where(all_bow_of_paper == "1")               #get indices which words occur
 
             #add infos to Graph
-            graph_name.add_node(paper_id)
-            graph_name.node[paper_id]["class"] = paper_class
-            graph_name.node[paper_id]["bow"] = paper_bow
+            graph_name.add_node(int(paper_id))
+            graph_name.node[int(paper_id)]["class"] = paper_class
+            graph_name.node[int(paper_id)]["bow"] = paper_bow
 
 def read_file_and_get_edges(graph_name, path="/home/utaemon/SP/cora/cora.cites"):
     with open(path) as file:
@@ -45,8 +45,8 @@ def get_graph(path_nodes="/home/utaemon/SP/cora/cora.content", path_edges="/home
     read_file_and_get_edges(Cora_graph, path_edges)
     return Cora_graph
 
-def get_init_emb(quantity=1433):
-    return initialize.get_embeddings(quantity=quantity)
+def get_init_emb(rand_type="normal_random", dimension = 128, quantity=1433):
+    return initialize.get_embeddings(rand_type=rand_type, dimension = dimension, quantity=quantity)
 
 #This class is from: https://stackoverflow.com/a/47626762
 class NumpyEncoder(json.JSONEncoder):
@@ -55,22 +55,19 @@ class NumpyEncoder(json.JSONEncoder):
             return obj.tolist()
         return json.JSONEncoder.default(self, obj)
 
-def write_graph_to_file(path_nodes="/home/utaemon/SP/cora/cora.content", path_edges="/home/utaemon/SP/cora/cora.cites", path_output_graph = "/home/utaemon/SP/graph.txt"):
+def write_graph_to_file(path_nodes="/home/utaemon/SP/cora/cora.content", path_edges="/home/utaemon/SP/cora/cora.cites", path_output_graph = "/home/utaemon/SP/"):
     """
     JSON kann kein numpy-array verarbeiten. Lösung: zuvor zu einer List umformen und später zurück.
     """
     g = get_graph(path_nodes, path_edges)
-    with open(path_output_graph, "w") as output1:
+    with open(path_output_graph + "graph.txt", "w") as output1:
         output1.write(json.dumps(json_graph.node_link_data(g), cls=NumpyEncoder)) #care, numpy arrays are converted to lists!
 
-def write_dict_to_file(quantity=1433, path_output_emb = "/home/utaemon/SP/init.txt"):
-    with open(path_output_emb, "w") as output2:
-        output2.write(str(get_init_emb(quantity)))
-
-write_dict_to_file()
-#write_graph_to_file()
+def write_dict_to_file(rand_type="normal_random", dimension = 128, quantity=1433, path_output_emb = "/home/utaemon/SP/"):
+    with open(path_output_emb + rand_type +"_" + "init.txt", "w") as output2:
+        output2.write(str(get_init_emb(rand_type=rand_type, dimension=dimension, quantity=quantity)))
 
 if __name__ == "__main__":
     # execute only if run as a script
     get_graph(path_nodes="/home/utaemon/SP/cora/cora.content", path_edges="/home/utaemon/SP/cora/cora.cites")
-    get_init_emb(quantity=1433)
\ No newline at end of file
+    get_init_emb(rand_type="normal_random", dimension = 128, quantity=1433)
\ No newline at end of file

Cora_Preprocessing/initialize.py

@@ -31,7 +31,5 @@ def get_embeddings(rand_type="normal_random", dimension = 128, quantity=1):
             emb_dict[i] = Embeddings.normal_random(dimension = dimension, seed = i)
     elif rand_type == "uniform_random":
         for i in range(0, quantity):
-            emb_dict[i] = Embeddings.normal_random(dimension = dimension, seed = i)
-    return emb_dict
-
-#print (get_embeddings())
\ No newline at end of file
+            emb_dict[i] = Embeddings.uniform_random(dimension = dimension, seed = i)
+    return emb_dict
\ No newline at end of file