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Functions15 in github.com/ZiyaoWei/pyMatrixProfile

⨍ Functions 15◇ Types & classes 3
↓ 3 callersFunction_matrixProfile
(tsA, m, orderClass, distanceProfileFunction, tsB = None)
mp.py:6
↓ 3 callersFunctionzNormalize
Return a z-normalized version of the time series ts. >>> zNormalize(np.array([1.0, 1.0, 1.0])) array([ 0., 0., 0.]) >>> np.round(zNorma
util.py:5
↓ 2 callersMethodnext
(self)
order.py:4
↓ 1 callersFunctionmass
>>> np.round(mass(np.array([0.0, 1.0, -1.0, 0.0]), np.array([-1, 1, 0, 0, -1, 1])), 3) array([ 2. , 2.828, 2. ])
util.py:52
↓ 1 callersFunctionmovstd
>>> np.round(movstd(np.array([1, 2, 3, 10]), 3), 3) array([ 0.816, 3.559])
util.py:37
↓ 1 callersFunctionzNormalizedEuclideanDistance
Return the z-normalized Euclidean Distance between tsA and tsB. >>> zNormalizedEuclideanDistance(np.array([1.0, 1.0, 1.0]), np.array([2.0, 2.0, 2
util.py:21
Method__init__
(self, m)
order.py:9
Method__init__
(self, m)
order.py:22
FunctionnaiveDistanceProfile
Return the distance profile of query against ts. Use the naive all pairs comparison algorithm. >>> np.round(naiveDistanceProfile(np.array([0.0, 1
distanceProfile.py:4
FunctionnaiveMP
>>> np.round(naiveMP(tsA = np.array([0.0, 1.0, -1.0, 0.0, 0.0]), tsB = np.array([-1, 1, 0, 0, -1, 1]), m = 4), 3) array([[ 2., 2., 2.],
mp.py:21
Methodnext
(self)
order.py:13
Methodnext
(self)
order.py:27
Functionstamp
>>> np.round(stmp(tsA = np.array([0.0, 1.0, -1.0, 0.0, 0.0]), tsB = np.array([-1, 1, 0, 0, -1, 1]), m = 4), 3) array([[ 2., 2., 2.],
mp.py:37
FunctionstampDistanceProfile
>>> np.round(stampDistanceProfile(np.array([0.0, 1.0, -1.0, 0.0]), 0, 4, np.array([-1, 1, 0, 0, -1, 1])), 3) array([[ 2. , 2.828, 2. ],
distanceProfile.py:27
Functionstmp
>>> np.round(stmp(tsA = np.array([0.0, 1.0, -1.0, 0.0, 0.0]), tsB = np.array([-1, 1, 0, 0, -1, 1]), m = 4), 3) array([[ 2., 2., 2.],
mp.py:29