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Jörg Thalheim 2016-05-11 11:53:14 +02:00
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requirements.txt Normal file
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pandas
numpy
scipy

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test.py Normal file
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#import pandas as pn
import numpy as np
from numpy.fft import fft, ifft
from scipy.sparse.linalg import eigs
import math
from scipy.stats import zscore
from scipy.ndimage.interpolation import shift
def next_greater_power_of_2(x):
return 2**(x-1).bit_length()
def sbd(x, y):
fft_size = next_greater_power_of_2(len(x))
cc = np.abs(ifft(fft(x, fft_size) * fft(y, fft_size)))
ncc = cc / math.sqrt((sum(x**2) * sum(y**2)))
idx = ncc.argmax()
dist = 1 - ncc[idx]
return dist, shift(y, idx - len(x))
def extract_shape(x, c):
n = len(x)
m = len(x[0])
new_x = np.zeros((n, m))
for i, row in enumerate(x):
_, x_i = sbd(c, row)
new_x[i] = x_i
s = np.dot(new_x.transpose(), new_x)
q = np.identiy(len(s)) - np.ones(len(s)) * 1 / m
M = np.dot(np.dot(q.transpose(), s), q)
_, vec = eigs(M, 1)
return vec[0]
def k_shape(x, k):
iter_ = 0
idx = np.zeros(len(x)) # TODO dimension, random init
old_idx = np.zeros(len(x))
c = np.zeros((k,)) # TODO dimension
while idx != old_idx and iter_ < 100:
old_idx = idx.copy()
for j in range(k):
x_ = []
for i, x_i in enumerate(x):
if idx(i) == j:
x_.append(x_i)
c[j] = extract_shape(x_, c[j])
for i, x_i in enumerate(x):
min_dist = np.inf
for j, c_j in enumerate(c):
dist, _ = sbd(c_j, x_i)
if dist < min_dist:
min_dist = dist
idx[i] = j
def test_extract_shape():
a = zscore(np.ones((3, 10)))
c = np.arange(10)
extract_shape(a, c)
def test_sbd():
a = np.arange(100)
r1 = sbd(zscore(a), zscore(a))
r2 = sbd(zscore(a), zscore(shift(a, 3)))
r3 = sbd(zscore(a), zscore(shift(a, -3)))
r4 = sbd(zscore(a), zscore(shift(a, 30)))
r5 = sbd(zscore(a), zscore(shift(a, -30)))
if __name__ == "__main__":
test_sbd()
test_extract_shape()