--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/PDAUG_Word_Vector_Model/PDAUG_Word_Vector_Model.py	Wed Oct 28 02:12:57 2020 +0000
@@ -0,0 +1,38 @@
+import nltk
+from nltk import trigrams
+import pandas as pd
+from Bio import SeqIO
+import gensim, logging
+import argparse
+
+parser = argparse.ArgumentParser()
+
+parser.add_argument("-I", "--Input", required=True, default=None, help="Path to target fasta file")
+parser.add_argument("-M", "--min_count", required=False, default=0, help="Path to target tsv file")
+parser.add_argument("-W", "--window", required=False, default=5, help="Path to target tsv file")
+parser.add_argument("-O", "--OutFile", required=False, default='model.txt', help="Path to target tsv file")
+
+args = parser.parse_args()
+
+class ProteinSeq(object):
+    def __init__(self):
+        pass
+    def __iter__(self):
+        for index, record in enumerate(SeqIO.parse(args.Input, 'fasta')):
+            for loop_num in range(0, 3):
+                Ngram_list = []
+                tri_tokens = trigrams(record.seq)
+                for index1, item in enumerate(tri_tokens):
+                    if index1 % 3 == loop_num:
+                        tri_pep = item[0] + item[1] + item[2]
+                        Ngram_list.append(tri_pep)
+                yield Ngram_list
+#min_count = 0
+size = 200
+#window = 5
+sg = 1
+
+sentences = ProteinSeq() 
+model = gensim.models.Word2Vec(sentences, min_count=int(args.min_count), size=size, window=int(args.window), sg = sg, workers = 10)
+model.wv.save_word2vec_format(args.OutFile, binary=False)
+