def nearest_neighbour(query_sequence, training_set, params):
distances = [
cdtw_sakoe_chiba(query_sequence, training_sequence, params['r'])
for training_sequence in training_set
]
best_seq_index = argmin(distances)
return best_seq_index, distances
def test_cdtw():
"""Simple test to run cDTW with different values of r"""
print("\n\n== Testing cDTW ==\n")
a = array([1, 2, 2, 2, 3, 4, 5, 6, 7], dtype="float64")
b = array([1, 2, 3, 4, 5, 6, 6, 6, 7], dtype="float64")
results = [(r, cdtw_sakoe_chiba(a, b, r)) for r in [0, 1, 2]]
print("a: {0}".format(a))
print("b: {0}".format(b))
print("cDTW results: (should be 8, 4, and 0)")
for result in results:
print("r={0[0]}: {0[1]}".format(result))
print("\n")
def test_cdtw():
"""Simple test to run cDTW with different values of r"""
print("\n\n== Testing cDTW ==\n")
a = array([1,2,2,2,3,4,5,6,7], dtype="float64")
b = array([1,2,3,4,5,6,6,6,7], dtype="float64")
results = [(r, cdtw_sakoe_chiba(a, b, r)) for r in [0,1,2]]
print("a: {0}".format(a))
print("b: {0}".format(b))
print("cDTW results: (should be 8, 4, and 0)")
for result in results:
print("r={0[0]}: {0[1]}".format(result))
print("\n")
def metric(x, y):
return cdtw_sakoe_chiba(x, y, 2)
def nearest_neighbour(query_sequence, training_set, params):
distances = [cdtw_sakoe_chiba(query_sequence, training_sequence, params['r']) for training_sequence in training_set]
best_seq_index = argmin(distances)
return best_seq_index, distances