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# Use binary search to find optimal parameters (added simple constraints to speed up)
whileTrue:
state=1
forsinsubset:
...
...
@@ -141,8 +143,6 @@ def preprocess(data, r=None):
elif(i==len(data)):
break
# subset = sample(list(range(len(data))), 200)
dtw_distances=[]
eq_distances=[]
fori,index_1inenumerate(subset):
...
...
@@ -241,7 +241,6 @@ def table_info(data, table):
'max':max_values.tolist(),
'min':min_values.tolist()
})
# distances = [[dtw(np.array(v["average"]), np.array(w["average"]), global_constraint='sakoe_chiba', sakoe_chiba_radius=int(0.05 * 120)) for j, w in enumerate(prototypes)] for i, v in enumerate(prototypes)]