Changeset 91d4ae in git for machine_learning/model/predictor.py

Timestamp:

Jul 29, 2019, 2:45:44 PM (5 years ago)

Author:

Murray Heymann <heymann.murray@…>

Branches:

(u'fieker-DuVal', '117eb8c30fc9e991c4decca4832b1d19036c4c65')(u'spielwiese', 'c5facdfddea2addfd91babd8b9019161dea4b695')

Children:

eb2904af1f3fb2d653378807ed3b18780cfd4dbc

Parents:

d93ae5668f2da84265ee686cd190828895b49cba

Message:

Optimize methods

File:

• machine_learning/model/predictor.py (modified) (5 diffs)

machine_learning/model/predictor.py

@@ -12 +12 @@
 # Local imports
 from common.keyword_vector import vector_distance, count_occurances, \
-                                    read_dictionary
+        read_dictionary, normalise_vector
 from common.lookuptable import create_table
 from common.constants import KEYWORDS_FILE
@@ -48 +48 @@
         for x in X: # pylint: disable=invalid-name
             # find the closest vector
-
             min_val = float("inf")
-            min_vec = None
+            index = -1
+            i = 0
             for vec in self.vectors:
                 dist = vector_distance(x, vec)
                 if dist < min_val:
                     min_val = dist
-                    min_vec = vec
+                    index = i
+                i = i + 1
 
             # find corresponding filename
-            index = list(self.vectors).index(min_vec)
             file = self.files[index]
             ret_list.append(file)
@@ -70 +70 @@
     print("Running some tests")
     predictor = HelpPagePredictor()
-    vector1 = {"hello":1, "bye":4, "pizza": 10}
-    vector2 = {"hello":2, "bye":3, "pizza": 1}
-    vector3 = {"hello":3, "bye":9, "pizza": 3}
+    vector1 = normalise_vector([1, 4, 10])
+    vector2 = normalise_vector([2, 3, 1])
+    vector3 = normalise_vector([3, 9, 3])
 
     vectors = np.array([vector1, vector2, vector3])
@@ -78 +78 @@
     print(vectors)
     print(files)
+    print()
 
-    testvec = {"hello":1, "bye":1, "pizza": 1}
+    testvec = normalise_vector([1, 1, 1])
+    print("test vector:")
+    print(testvec)
+    print()
 
     print("distance to 1")
@@ -93 +97 @@
     predictor.fit(vectors, files)
     prediction = predictor.predict(np.array([testvec]))
+    print("Prediction:")
     print(prediction)
+    print()
 
     dictionary = read_dictionary(KEYWORDS_FILE)
+
     start = time.time()
     vectors, file_list = create_table(dictionary=dictionary)
     end = time.time()
     print(end - start, "seconds to create_table")
+
     test_vec = count_occurances("extract.lib", dictionary)
     predictor.fit(vectors, file_list)
+
     start = time.time()
     prediction = predictor.predict(np.array([test_vec]))
     end = time.time()
+    print(end - start, "seconds to make prediction")
     print(prediction)
-    print(end - start, "seconds to make prediction")
 
 if __name__ == '__main__':