/usr/share/pyshared/mlpy/_dwtfs.py is in python-mlpy 2.2.0~dfsg1-2.
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## Discrete Wavelet Transform (DWT).
## This is an implementation of Discrete Wavelet Transform described in:
## Prabakaran Subramani, Rajendra Sahu and Shekhar Verma.
## 'Feature selection using Haar wavelet power spectrum'.
## In BMC Bioinformatics 2006, 7:432.
## This code is written by Giuseppe Jurman, <jurman@fbk.eu> and Davide Albanese, <albanese@fbk.eu>.
## (C) 2008 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__ = ['Dwt', 'haar', 'haar_spectrum']
import math
from numpy import *
SQRT_2 = sqrt(2.0)
LOG_2 = log(2.0)
def haar(d):
"""
Haar wavelet decomposition.
"""
N = log(d.shape[0])
n = int(ceil(N / LOG_2))
two_n = 2**n
dwt = zeros(two_n, dtype = float)
dwt[0: d.shape[0]] = d
for j in range(n, 0, -1):
offset = two_n - 2**j
dproc = dwt[offset::].copy()
for i in range(dproc.shape[0] / 2):
dwt[offset + i] = \
(dproc[2 * i] - dproc[2 * i + 1]) / SQRT_2
dwt[offset + dproc.shape[0] / 2 + i] = \
(dproc[2 * i] + dproc[2 * i + 1]) / SQRT_2
return dwt[::-1]
def haar_spectrum(dwt):
"""
Compute spectrum from wavelet decomposition.
"""
N = log(dwt.shape[0])
n = int(N / LOG_2)
spec = zeros(n + 1, dtype = float)
spec[0] = dwt[0] * dwt[0]
if(dwt[0] < 0.0):
spec[0] = -spec[0]
for j in range(1, n + 1):
spec[j] = sum(dwt[2**(j - 1): 2**j]**2)
return spec
def rpv(s1, s2):
"""
Relative Percentage Variation (RPV).
"""
mean_s1 = mean(s1)
mean_s2 = mean(s2)
return (mean_s1 - mean_s2) / mean_s1 * 100
def arpv(s1, s2):
"""
Absolute Relative Percentage Variation (ARPV).
"""
return sqrt(abs(rpv(s1, s2)) * abs(rpv(s2, s1)))
def crpv(s1, s2, f, y):
"""
Correlation Relative Percentage Variation (CRPV).
"""
return arpv(s1, s2) * abs(correlate(f, y))
def compute_dwt(x, y, specdiff = 'rpv'):
"""
Compute DWT.
"""
pidx = where(y == 1)
nidx = where(y == -1)
w = zeros(x.shape[1], dtype = float)
for f in range(x.shape[1]):
fp = x[pidx, f][0]
fn = x[nidx, f][0]
phaar = haar(fp)
nhaar = haar(fn)
s1 = haar_spectrum(phaar)
s2 = haar_spectrum(nhaar)
if specdiff == 'rpv':
w[f] = rpv(s1, s2)
elif specdiff == 'arpv':
w[f] = arpv(s1, s2)
elif specdiff == 'crpv':
w[f] = crpv(s1, s2, x[:, f], y)
return w
class Dwt:
"""Discrete Wavelet Transform (DWT).
Example:
>>> import numpy as np
>>> import mlpy
>>> xtr = np.array([[1.0, 2.0, 3.1, 1.0], # first sample
... [1.0, 2.0, 3.0, 2.0], # second sample
... [1.0, 2.0, 3.1, 1.0]]) # third sample
>>> ytr = np.array([1, -1, 1]) # classes
>>> mydwt = mlpy.Dwt() # initialize dwt class
>>> mydwt.weights(xtr, ytr) # compute weights on training data
array([ -2.22044605e-14, -2.22044605e-14, 6.34755463e+00, -3.00000000e+02])
"""
SPECDIFFS = ['rpv', 'arpv', 'crpv']
def __init__(self, specdiff = 'rpv'):
"""Initialize the Dwt class.
Input
* *specdiff* - [string] spectral difference method ('rpv', 'arpv', 'crpv')
"""
if not specdiff in self.SPECDIFFS:
raise ValueError("specdiff (spectral difference) must be in %s" % self.SPECDIFFS)
self.__specdiff = specdiff
self.__classes = None
def weights(self, x, y):
"""Return ABSOLUTE feature weights.
:Parameters:
x : 2d ndarray float (samples x feats)
training data
y : 1d ndarray integer (-1 or 1)
classes
:Returns:
fw : 1d ndarray float
feature weights
"""
self.__classes = unique(y)
if self.__classes.shape[0] != 2:
raise ValueError("DTW algorithm works only for two-classes problems")
if self.__classes[0] != -1 or self.__classes[1] != 1:
raise ValueError("DTW algorithm works only for 1 and -1 classes")
w = compute_dwt(x, y, self.__specdiff)
return w
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