add README and remove intializing

EP/Cora_node_classification/README.md

+# AUTHORS
+Lyuba Dimitrova, Nadia Arslan, Nicolas Weber, Utaemon Toyota
+
+# PROJECT
+Softwareprojekt WS2018/19
+Betreuerin: Prof. Dr. Anette Frank
+Graph Embedding Propagation
+
+# Cora Node Classification
+To evaluate the trained graph and the embeddings the task of node classification will be executed. First, the data of cora will be imported into a networkX graph, which will be saved in a pickle file to use it for the training of the embeddings with our EP-SP algorithm. Afterwards the trained embedding will be evaluated with LibLinear L2-Logistic Regression provided from sklearn.
+
+# Required Data
+- Cora dataset saved in cora_data for building the graph
+- Embeddings for node classification
+
+# Dependencies
+For cora.py
+-networkx	for building the graph
+-numpy		to save one-hot vocabulary vectors
+-pickle		to save data in a pickle file
+
+For node_classification.py
+-cora.py
+-random_nodes_for_node_classification.py	for getting random node sets for test, training and validation sets 
+-pickle
+-numpy
+-sklearn	for evaluation
+-random		for getting random test, trainings and validation sets
+-sys
+-argparse
+-heapq		for getting a heatmap from confusion matrix
+-sklearn	for confusion matrix and f1 score
+
+# Running instructions
+For cora.py
+...
+
+For node_classification.py
+python3 node_classification.py [-g] [-e] [-s] [-i] [-n]
+        -g / --graph        Path to pickled networkX-graph
+        -e / --embeddings   Path to pickled embeddings
+        -s / --seed         Seed for randomization. If this argument is called, only a node classification for this specific seed will be executed
+        -i / --iterations   Number of iterations of node classification. Counter of iteration is equal to random seed
+        -n / --number       Number of instances per class for training
+        -c / --regularization   Inverse of regularization strength

EP/Cora_node_classification/cora.py

 """
 @info
 Getting a networkx graph from Cora. Graph can be saved in txt file. CARE: numpy-arrays are converted to lists due to errors (NumPy array is not JSON serializable).
-Initialize Embeddings for n dimensions with initialize-module.
-Arrays are initialized in normal or uniform random format (default = normal).
 
 @usage
 get_graph(path_nodes="/cora_data/cora.content", path_edges="/cora_data/cora.cites")
 -> return graph with nodes and edges
 To write the graph informations in file:
 def write_graph_to_file(path_nodes="/cora_data/cora.content", path_edges="/cora_data/cora.cites", path_output_graph = "")
-To write the dictionary with initalizing Embeddings in file:
-def write_dict_to_file(rand_type="normal_random", dimension = 128, quantity=1433, path_output_emb = "")
 """
 
 import networkx as nx
 import numpy as np
-import initialize
 import pickle as pkl
 
 def list_of_classes():
@@ -111,9 +106,6 @@ def add_max_values_to_graph(path_nodes, path_edges):                    #update
     Cora_graph.graph["paper_id"]["maxlen_neighbours"] = get_max_neighbours(path_nodes, path_edges)
     return Cora_graph
 
-def get_init_emb(rand_type="normal_random", dimension = 128, quantity=1433):
-    return initialize.get_embeddings(rand_type=rand_type, dimension = dimension, quantity=quantity)
-
 def write_pickle_graph_file(path_nodes="/cora_data/cora.content", path_edges="/cora_data/cora.cites", path_output_graph = ""):
     g = add_max_values_to_graph(path_nodes, path_edges)
     with open(path_output_graph + "graph.pkl", "wb") as output: