reset the MSM default parameter (cost c) to 1 rather than 0

sktime/contrib/experiments.py

@@ -112,7 +112,11 @@ def set_classifier(cls, resampleId=None):
     elif name == "ps" or name == "proximityStump":
         return ProximityStump(random_state=resampleId)
     elif name == "dtwcv" or name == "kneighborstimeseriesclassifier":
+        return KNeighborsTimeSeriesClassifier(metric="dtwcv")
+    elif name == "dtw" or name == "1nn-dtw":
         return KNeighborsTimeSeriesClassifier(metric="dtw")
+    elif name == "msm" or name == "1nn-msm":
+        return KNeighborsTimeSeriesClassifier(metric="msm")
     elif name == "ee" or name == "elasticensemble":
         return ElasticEnsemble()
     elif name == "shapedtw":
@@ -575,18 +579,13 @@ def test_loading():
             train_file=tf,
         )
     else:  # Local run
-        #        data_dir = "/scratch/univariate_datasets/"
-        #        results_dir = "/scratch/results"
-        #         data_dir = "/bench/datasets/Univariate2018/"
-        #         results_dir = "C:/Users/ajb/Dropbox/Turing Project/Results/"
         print(" Local Run")
-        data_dir = "C:/Code/sktime/sktime/datasets/data/"
+        data_dir = "Z:/ArchiveData/Univariate_ts/"
         results_dir = "C:/Temp/"
-        #        results_dir = "Z:/Results/sktime Bakeoff/"
-        dataset = "UnitTest"
+        dataset = "ArrowHead"
         trainX, trainY = load_ts(data_dir + dataset + "/" + dataset + "_TRAIN.ts")
         testX, testY = load_ts(data_dir + dataset + "/" + dataset + "_TEST.ts")
-        classifier = "KNeighborsTimeSeriesClassifier"
+        classifier = "1NN-MSM"
         resample = 0
         #         for i in range(0, len(univariate_datasets)):
         #             dataset = univariate_datasets[i]

sktime/distances/elastic.py

@@ -234,8 +234,7 @@ def msm_single_channel(first, second, **kwargs) -> float:
         try:
             c = kwargs["c"]
         except KeyError:
-            c = 0.1
-
+            c = 1
         m = len(first)
         n = len(second)
 

sktime/distances/elastic_cython.pyx

@@ -154,12 +154,11 @@ def wddtw_distance(np.ndarray[double, ndim=2] x, np.ndarray[double, ndim=2] y ,
     return wdtw_distance(np.diff(x.T).T,np.diff(y.T).T,g)
 
 
-def msm_distance(np.ndarray[double, ndim=2] x, np.ndarray[double, ndim=2] y, double c = 0, int dim_to_use = 0):
+def msm_distance(np.ndarray[double, ndim=2] x, np.ndarray[double, ndim=2] y, double c = 1, int dim_to_use = 0):
 
     cdef np.ndarray[double, ndim=2] first = x
     cdef np.ndarray[double, ndim=2] second = y
     cdef np.ndarray[double, ndim=2] temp
-
     if len(first) > len(second):
         temp = first
         first = second