added alpha model

1 files changed, 0 insertions, 72 deletions

diff --git a/lstm_chem/utils/smiles_tokenizer.py.4faeffb638548d04ca4415dfe32cf8c7.py b/lstm_chem/utils/smiles_tokenizer.py.4faeffb638548d04ca4415dfe32cf8c7.py
deleted file mode 100755
index d15d625..0000000
--- a/lstm_chem/utils/smiles_tokenizer.py.4faeffb638548d04ca4415dfe32cf8c7.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import copy
-import numpy as np
-
-import time
-
-
-class SmilesTokenizer(object):
-    def __init__(self):
-        atoms = [
-            'Li',
-            'Na',
-            'Al',
-            'Si',
-            'Cl',
-            'Sc',
-            'Zn',
-            'As',
-            'Se',
-            'Br',
-            'Sn',
-            'Te',
-            'Cn',
-            'H',
-            'B',
-            'C',
-            'N',
-            'O',
-            'F',
-            'P',
-            'S',
-            'K',
-            'V',
-            'I',
-        ]
-        special = [
-            '(', ')', '[', ']', '=', '#', '%', '0', '1', '2', '3', '4', '5',
-            '6', '7', '8', '9', '+', '-', 'se', 'te', 'c', 'n', 'o', 's'
-        ]
-        padding = ['G', 'A', 'E']
-
-        self.table = sorted(atoms, key=len, reverse=True) + special + padding
-        self.table_len = len(self.table)
-
-        self.one_hot_dict = {}
-        for i, symbol in enumerate(self.table):
-            vec = np.zeros(self.table_len, dtype=np.float32)
-            vec[i] = 1
-            self.one_hot_dict[symbol] = vec
-
-    def tokenize(self, smiles):
-        N = len(smiles)
-        i = 0
-        token = []
-
-        timeout = time.time() + 5   # 5 seconds from now
-        while (i < N):
-            for j in range(self.table_len):
-                symbol = self.table[j]
-                if symbol == smiles[i:i + len(symbol)]:
-                    token.append(symbol)
-                    i += len(symbol)
-                    break
-            if time.time() > timeout:
-                break 
-        return token
-
-    def one_hot_encode(self, tokenized_smiles):
-        result = np.array(
-            [self.one_hot_dict[symbol] for symbol in tokenized_smiles],
-            dtype=np.float32)
-        result = result.reshape(1, result.shape[0], result.shape[1])
-        return result