Convert sentence to adjancency matrix

src/utils.py

@@ -1,11 +1,51 @@
-def convert_sentence_to_adjancency_matrix(sentence):
+import numpy as np
+
+
+def convert_sentence_to_adjacency_matrix(sentence):
     '''
-    Input: sentence in json?
-    Output: adjancency matrix (gold standard)
+    Input: sentence in json
+    Output: adjacency matrix (gold standard)
     '''
+
+    sentence_len = len(sentence['words'])
+
+    # Initialize a matrix of size N x N
+    adjacency_matrix = np.zeros((sentence_len, sentence_len))
+
     for word in sentence['words']:
-        pass
+        word_id = int(word['id'])
+        head = int(word['head'])
 
+        # Ignore the root(0)-(-1) connection
+        if head == -1:
+            continue
 
-def convert_adjancecy_matrix_to_sentece(matrix):
-    pass
+        adjacency_matrix[head][word_id] = 1
+
+    return adjacency_matrix
+
+def adjacency_matrix_to_tensor(matrix):
+    output = [0] * matrix.shape[0]
+    for i in range(matrix.shape[0]):
+        for j in range(matrix.shape[0]):
+            if matrix[i][j] == 1:
+                output[j] = i
+    output1 = torch.LongTensor(output)
+    return(output1)
+
+test_matrix = np.array(([1, 0, 1, 1, 0],
+                       [0, 0, 0, 0, 0],
+                       [0, 1, 0, 0, 0],
+                       [0, 0, 0, 0, 1],
+                       [0, 0, 0, 0, 0]))
+print(test_matrix)
+print(adjacency_matrix_to_tensor(test_matrix))
+
+#something like this will convert directly
+def convert_sentence_to_tensor(sentence):
+    sentence_len = len(sentence['words'])
+    output = [0] * sentence_len
+    for word in sentence['words']:
+        output[int(word['id'])] = int(word['head'])
+    output1 = torch.LongTensor(output)
+    return output1