environment test

util/__init__.py

@@ -0,0 +1 @@
+from util.util import *

util/util.py

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+from datetime import datetime
+import numpy as np
+
+def reward(responses):
+    reward = 0.0
+    for response in responses:
+        reward += int(response._recall)
+    return reward
+
+def update_metrics(responses, metrics, info):
+    # print("responses: ", responses)
+    prs = []
+    for response in responses:
+        prs.append(response['pr'])
+    if type(metrics) != list:
+        metrics = [prs]
+    else:
+        metrics.append(prs)
+    # print(metrics)
+    return metrics
+
+def eval_result(train_time, last_review, history, W):
+    with open(f"{datetime.now()}.txt", "w") as f:
+        print(train_time, file=f)
+        print(last_review, file=f)
+        print(history, file=f)
+        print(W, file=f)
+        # np.einsum('ij,ij->i', a, b)
+        last_review = train_time - last_review
+        mem_param = np.exp(np.einsum('ij,ij->i', history, W))
+        pr = np.exp(-last_review / mem_param)
+        print(pr, file=f)
+        print(pr)
+        print("score:", np.sum(pr) / pr.shape[0], file=f)
+        print("score:", np.sum(pr) / pr.shape[0])