def mpdist(ts, ts_b, w, threshold=0.05, n_jobs=1):
"""
Computes the MPDist between the two series ts and ts_b. For more details
refer to the paper:
Matrix Profile XII: MPdist: A Novel Time Series Distance Measure to Allow
Data Mining in More Challenging Scenarios. Shaghayegh Gharghabi,
Shima Imani, Anthony Bagnall, Amirali Darvishzadeh, Eamonn Keogh. ICDM 2018
Parameters
----------
ts : array_like
The time series to compute the matrix profile for.
ts_b : array_like
The time series to compare against.
w : int
The window size.
threshold : float, Default 0.05
The percentile in which the distance is taken from. By default it is
set to 0.05 based on empircal research results from the paper.
Generally, you should not change this unless you know what you are
doing! This value must be a float greater than 0 and less than 1.
n_jobs : int, Default = 1
Number of cpu cores to use.
Returns
-------
float : mpdist
The MPDist.
"""
ts = core.to_np_array(ts).astype('d')
ts_b = core.to_np_array(ts_b).astype('d')
n_jobs = core.valid_n_jobs(n_jobs)
if not core.is_one_dimensional(ts):
raise ValueError('ts must be one dimensional!')
if not core.is_one_dimensional(ts_b):
raise ValueError('ts_b must be one dimensional!')
if not isinstance(threshold, float) or threshold <= 0 or threshold >= 1:
raise ValueError('threshold must be a float greater than 0 and less'\
' than 1')
mp, mpi, mpb, mpib = cympx_ab_parallel(ts, ts_b, w, 0, n_jobs)
mp_abba = np.append(mp, mpb)
data_len = len(ts) + len(ts_b)
abba_sorted = np.sort(mp_abba[~core.nan_inf_indices(mp_abba)])
distance = np.inf
if len(abba_sorted) > 0:
upper_idx = int(np.ceil(threshold * data_len)) - 1
idx = np.min([len(abba_sorted) - 1, upper_idx])
distance = abba_sorted[idx]
return distance
def mpdist(ts, ts_b, w, n_jobs=1):
"""
Computes the MPDist between the two series ts and ts_b. For more details
refer to the paper:
Matrix Profile XII: MPdist: A Novel Time Series Distance Measure to Allow
Data Mining in More Challenging Scenarios. Shaghayegh Gharghabi,
Shima Imani, Anthony Bagnall, Amirali Darvishzadeh, Eamonn Keogh. ICDM 2018
Parameters
----------
ts : array_like
The time series to compute the matrix profile for.
ts_b : array_like
The time series to compare against.
w : int
The window size.
n_jobs : int, Default = 1
Number of cpu cores to use.
Returns
-------
float :
The MPDist.
"""
ts = core.to_np_array(ts).astype('d')
ts_b = core.to_np_array(ts_b).astype('d')
n_jobs = core.valid_n_jobs(n_jobs)
if not core.is_one_dimensional(ts):
raise ValueError('ts must be one dimensional!')
if not core.is_one_dimensional(ts_b):
raise ValueError('ts_b must be one dimensional!')
mp, mpi, mpb, mpib = cympx_ab_parallel(ts, ts_b, w, 0, n_jobs)
mp_abba = np.append(mp, mpb)
data_len = len(ts) + len(ts_b)
abba_sorted = np.sort(mp_abba[~core.nan_inf_indices(mp_abba)])
distance = np.inf
if len(abba_sorted) > 0:
idx = np.min([len(abba_sorted) - 1, int(np.ceil(0.05 * data_len)) - 1])
distance = abba_sorted[idx]
return distance
def mpx(ts, w, query=None, cross_correlation=False, n_jobs=1):
"""
The MPX algorithm computes the matrix profile without using the FFT.
Parameters
----------
ts : array_like
The time series to compute the matrix profile for.
w : int
The window size.
query : array_like
Optionally a query series.
cross_correlation : bool, Default=False
Setermine if cross_correlation distance should be returned. It defaults
to Euclidean Distance.
n_jobs : int, Default = 1
Number of cpu cores to use.
Returns
-------
A dict of key data points computed.
{
'mp': The matrix profile,
'pi': The matrix profile 1NN indices,
'rmp': The right matrix profile,
'rpi': The right matrix profile 1NN indices,
'lmp': The left matrix profile,
'lpi': The left matrix profile 1NN indices,
'metric': The distance metric computed for the mp,
'w': The window size used to compute the matrix profile,
'ez': The exclusion zone used,
'join': Flag indicating if a similarity join was computed,
'sample_pct': Percentage of samples used in computing the MP,
'data': {
'ts': Time series data,
'query': Query data if supplied
}
'class': "MatrixProfile"
'algorithm': "mpx"
}
"""
ts = core.to_np_array(ts).astype('d')
n_jobs = core.valid_n_jobs(n_jobs)
is_join = False
if core.is_array_like(query):
query = core.to_np_array(query).astype('d')
is_join = True
mp, mpi, mpb, mpib = cympx_ab_parallel(ts, query, w,
int(cross_correlation), n_jobs)
else:
mp, mpi = cympx_parallel(ts, w, int(cross_correlation), n_jobs)
mp = np.asarray(mp)
mpi = np.asarray(mpi)
distance_metric = 'euclidean'
if cross_correlation:
distance_metric = 'cross_correlation'
return {
'mp': mp,
'pi': mpi,
'rmp': None,
'rpi': None,
'lmp': None,
'lpi': None,
'metric': distance_metric,
'w': w,
'ez': int(np.floor(w / 4)),
'join': is_join,
'sample_pct': 1,
'data': {
'ts': ts,
'query': query
},
'class': 'MatrixProfile',
'algorithm': 'mpx'
}
def mpx(ts, w, query=None, cross_correlation=False, n_jobs=1):
"""
The MPX algorithm computes the matrix profile without using the FFT.
Parameters
----------
ts : array_like
The time series to compute the matrix profile for.
w : int
The window size.
query : array_like
Optionally a query series.
cross_correlation : bool, Default=False
Determine if cross_correlation distance should be returned. It defaults
to Euclidean Distance.
n_jobs : int, Default = 1
Number of cpu cores to use.
Returns
-------
dict : profile
A MatrixProfile data structure.
>>> {
>>> 'mp': The matrix profile,
>>> 'pi': The matrix profile 1NN indices,
>>> 'rmp': The right matrix profile,
>>> 'rpi': The right matrix profile 1NN indices,
>>> 'lmp': The left matrix profile,
>>> 'lpi': The left matrix profile 1NN indices,
>>> 'metric': The distance metric computed for the mp,
>>> 'w': The window size used to compute the matrix profile,
>>> 'ez': The exclusion zone used,
>>> 'join': Flag indicating if a similarity join was computed,
>>> 'sample_pct': Percentage of samples used in computing the MP,
>>> 'data': {
>>> 'ts': Time series data,
>>> 'query': Query data if supplied
>>> }
>>> 'class': "MatrixProfile"
>>> 'algorithm': "mpx"
>>> }
"""
# --- Drew's addition ---
dtype = core.get_dtype(ts)
ts = core.to_np_array(ts).astype(dtype)
#ts = core.to_np_array(ts).astype('d')
n_jobs = core.valid_n_jobs(n_jobs)
is_join = False
if core.is_array_like(query):
query = core.to_np_array(query).astype(dtype)
#query = core.to_np_array(query).astype('d')
is_join = True
mp, mpi, mpb, mpib = cympx_ab_parallel(ts, query, w,
int(cross_correlation), n_jobs)
else:
# --- More changes... ---
if np.issubdtype(dtype, 'U'):
#ts = np.array([ord(x) for x in ts], dtype = 'd')
mp, mpi = mpx_single_char(ts, w)
else:
mp, mpi = cympx_parallel(ts, w, int(cross_correlation), n_jobs)
# --- That's it for now... ---
#mp, mpi = cympx_parallel(ts, w, int(cross_correlation), n_jobs)
mp = np.asarray(mp)
mpi = np.asarray(mpi)
if np.issubdtype(dtype, 'U'):
distance_metric = 'hamming'
else:
distance_metric = 'euclidean'
if cross_correlation:
distance_metric = 'cross_correlation'
return {
'mp': mp,
'pi': mpi,
'rmp': None,
'rpi': None,
'lmp': None,
'lpi': None,
'metric': distance_metric,
'w': w,
'ez': int(np.ceil(w / 4.0)) if is_join else 0,
'join': is_join,
'sample_pct': 1,
'data': {
'ts': ts,
'query': query
},
'class': 'MatrixProfile',
'algorithm': 'mpx'
}