Update tests

peak_signal_to_noise_ratio.py

@@ -27,14 +27,9 @@ Returns:
 Examples:
     Example 1-A simple example
         >>> psnr = evaluate.load("jpxkqx/peak_signal_to_noise_ratio")
-        >>> results = psnr.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
+        >>> results = psnr.compute(references=[[0, 0], [-1, -1]], predictions=[[0, 1], [0, 0]])
         >>> print(results)
-        {'psnr': 0.5}
-    Example 2-The same as Example 1, except with `sample_weight` set.
-        >>> psnr = evaluate.load("jpxkqx/peak_signal_to_noise_ratio")
-        >>> results = psnr.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
-        >>> print(results)
-        {'psnr': 0.8778625954198473}
+        {"Peak Signal-to-Noise Ratio": 23.010298856486173}
 """
 
 

tests.py

@@ -1,17 +1,12 @@
 test_cases = [
     {
-        "predictions": [0, 0],
-        "references": [1, 1],
-        "result": {"metric_score": 0}
+        "predictions": [[0.1, 0.1], [1.1, 0.1]],
+        "references": [[0.1, 0.1], [1.1, 0.1]],
+        "result": {"Peak Signal-to-Noise Ratio": 23.010298856486173}
     },
     {
-        "predictions": [1, 1],
-        "references": [1, 1],
-        "result": {"metric_score": 1}
-    },
-    {
-        "predictions": [1, 0],
-        "references": [1, 1],
-        "result": {"metric_score": 0.5}
+        "predictions": [[0, 1], [0, 0]],
+        "references": [[0, 0], [-1, -1]],
+        "result": {"Peak Signal-to-Noise Ratio": 1.2493873660829993}
     }
 ]