/usr/share/pyshared/mlpy/_dtw.py is in python-mlpy 2.2.0~dfsg1-2.1.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 | ## This code is written by Davide Albanese, <albanese@fbk.eu>
## (C) 2009 Fondazione Bruno Kessler - Via Santa Croce 77, 38100 Trento, ITALY.
## This program is free software: you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation, either version 3 of the License, or
## (at your option) any later version.
## This program is distributed in the hope that it will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
## GNU General Public License for more details.
## You should have received a copy of the GNU General Public License
## along with this program. If not, see <http://www.gnu.org/licenses/>.
__all__ = ['Dtw']
import numpy as np
import dtwcore
def dtwc(x, y, derivative=False, startbc=True, steppattern='symmetric0', wincond = "nowindow", r=0.0, onlydist=True):
"""Dynamic Time Warping.
Input
* *x* - [1D numpy array float / list] first time series
* *y* - [1D numpy array float / list] second time series
* *derivative* - [bool] Derivative DTW (DDTW).
* *startbc* - [bool] (0, 0) boundary condition
* *steppattern* - [string] step pattern ('symmetric', 'asymmetric', 'quasisymmetric')
* *wincond* - [string] window condition ('nowindow', 'sakoechiba')
* *r* - [float] sakoe-chiba window length
* *onlydist* - [bool] linear space-complexity implementation. Only the current and previous
columns are kept in memory.
Output
* *d* - [float] normalized distance
* *px* - [1D numpy array int] optimal warping path (for x time series) (for onlydist=False)
* *py* - [1D numpy array int] optimal warping path (for y time series) (for onlydist=False)
* *cost* - [2D numpy array float] cost matrix (for onlydist=False)
"""
if steppattern == 'symmetric0':
sp = 0
elif steppattern == 'asymmetric0':
sp = 1
elif steppattern == 'quasisymmetric0':
sp = 2
else:
raise ValueError('step pattern %s is not available' % steppattern)
if wincond == 'nowindow':
wc = 0
elif wincond == 'sakoechiba':
wc = 1
else:
raise ValueError('window condition %s is not available' % wincond)
if derivative:
xi = dtwcore.der(x)
yi = dtwcore.der(y)
else:
xi = x
yi = y
return dtwcore.dtw(xi, yi, startbc=startbc, steppattern=sp, onlydist=onlydist, wincond=wc, r=r)
class Dtw:
"""
Dynamic Time Warping.
Example:
>>> import numpy as np
>>> import mlpy
>>> x = np.array([1,1,2,2,3,3,4,4,4,4,3,3,2,2,1,1])
>>> y = np.array([1,1,1,1,1,1,1,1,1,1,2,2,3,3,4,3,2,2,1,2,3,4])
>>> mydtw = mlpy.Dtw(onlydist=False)
>>> mydtw.compute(x, y)
0.36842105263157893
>>> mydtw.px
array([ 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8,
9, 10, 11, 12, 12, 12, 13, 14, 15], dtype=int32)
>>> mydtw.py
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 14, 14,
14, 15, 15, 16, 17, 18, 19, 20, 21], dtype=int32)
"""
def __init__(self, derivative=False, startbc=True, steppattern='symmetric0', wincond="nowindow", r=0.0, onlydist=True):
"""
:Parameters:
derivative : bool
derivative DTW (DDTW)
startbc : bool
forces x=0 and y=0 boundary condition
steppattern : string ('symmetric', 'asymmetric', 'quasisymmetric')
step pattern
wincond : string ('nowindow', 'sakoechiba')
window condition
r : float
sakoe-chiba window length
onlydist : bool
linear space-complexity implementation. Only the current
and previous columns are kept in memory.
"""
self.derivative = derivative
self.startbc = startbc
self.steppattern = steppattern
self.wincond = wincond
self.r = r
self.onlydist=onlydist
self.px = None
self.py = None
self.cost = None
def compute(self, x, y):
"""
:Parameters:
x : 1d ndarray or list
first time series
y : 1d ndarray or list
second time series
:Returns:
d : float
normalized distance
:Attributes:
Dtw.px : 1d ndarray int32
optimal warping path (for x time series) (if onlydist=False)
Dtw.py : 1d ndarray int32
optimal warping path (for y time series) (if onlydist=False)
Dtw.cost : 2dndarray float
cost matrix (if onlydist=False)
"""
res = dtwc(x=x, y=y, derivative=self.derivative, startbc=self.startbc, steppattern=self.steppattern,
wincond=self.wincond, r=self.r, onlydist=self.onlydist)
if self.onlydist == True:
return res
else:
self.px = res[1]
self.py = res[2]
self.cost = res[3]
return res[0]
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