Chores: small fixes for code in READMEs and versions [skip ci] (#47)

chore: small fixes for code in READMEs and versions [skip ci]

Author: Sebastian Schmidl

grammarviz3/README.md

@@ -49,7 +49,7 @@ def post_grammarviz(algorithm_parameter: AlgorithmParameter, args: dict) -> np.n
     scores = np.zeros_like(sums)
     np.divide(sums, counts, out=scores, where=counts != 0)
     # returns the completely flattened array (from `[[1.2], [2.3]]` to `[1.2, 2.3]`)
-    return scores.A1
+    return scores.A1  # type: ignore
 ```
 <!--END:timeeval-post-->
 

hotsax/README.md

@@ -52,7 +52,7 @@ def post_hotsax(algorithm_parameter: AlgorithmParameter, args: dict) -> np.ndarr
     scores = np.zeros_like(sums)
     np.divide(sums, counts, out=scores, where=counts != 0)
     # returns the completely flattened array (from `[[1.2], [2.3]]` to `[1.2, 2.3]`)
-    return scores.A1
+    return scores.A1  # type: ignore
 ```
 <!--END:timeeval-post-->
 

mscred/README.md

@@ -33,6 +33,6 @@ def post_mscred(scores: np.ndarray, args: dict) -> np.ndarray:
     max_window_size = max(args.get("hyper_params", {}).get("windows", [10, 30, 60]))
     offset = (ds_length - (max_window_size - 1)) % gap_time
     image_scores = ReverseWindowing(window_size=window_size).fit_transform(scores)
-    return np.concatenate([np.repeat(image_scores[:-offset], gap_time), image_scores[-offset:]])
+    return np.concatenate([np.repeat(image_scores[:-offset], gap_time), image_scores[-offset:]])  # type: ignore
 ```
 <!--END:timeeval-post-->

ssa/manifest.json

@@ -3,7 +3,7 @@
     "description": "Segmented Sequence Analysis calculates two piecewise linear models, aligns them and then computes the similarity between them. Finally a treshhold based approach is used to classify data as anomalous.",
     "inputDimensionality": "univariate",
     "learningType": "Unsupervised",
-    "version": "1.0",
+    "version": "1.0.0",
     "authors": "Yuan Yao et al.",
     "language": "Python",
     "type": "Detector",