def cpd_auto(K, ncp, vmax, desc_rate=1, **kwargs):
"""Main interface
Detect change points automatically selecting their number
K - kernel between each pair of frames in video
ncp - maximum ncp
vmax - special parameter
Optional arguments:
lmin - minimum segment length
lmax - maximum segment length
desc_rate - rate of descriptor sampling (vmax always corresponds to 1x)
Note:
- cps are always calculated in subsampled coordinates irrespective to
desc_rate
- lmin and m should be in agreement
---
Returns: (cps, costs)
cps - best selected change-points
costs - costs for 0,1,2,...,m change-points
Memory requirement: ~ (3*N*N + N*ncp)*4 bytes ~= 16 * N^2 bytes
That is 1,6 Gb for the N=10000.
"""
m = ncp
# print("CP", m)
(_, scores) = cpd_nonlin(K, m, backtrack=False, **kwargs)
N = K.shape[0]
N2 = N * desc_rate # length of the video before subsampling
penalties = np.zeros(m + 1)
# Prevent division by zero (in case of 0 changes)
ncp = np.arange(1, m + 1)
print("calculatedncp: ", ncp)
penalties[1:] = (vmax * ncp / (2.0 * N2)) * (np.log(float(N2) / ncp) + 1)
costs = scores / float(N) + penalties
m_best = np.argmin(costs)
(cps, scores2) = cpd_nonlin(K, m_best, **kwargs)
print("whereitreturns", cps)
return (cps, costs)
return (X, np.array(cps))
if __name__ == "__main__":
from matplotlib import pyplot as plt
plt.ioff()
print "Test 1: 1-dimensional signal"
plt.figure("Test 1: 1-dimensional signal")
n = 1000
m = 10
(X, cps_gt) = gen_data(n, m)
print "Ground truth:", cps_gt
plt.plot(X)
K = np.dot(X, X.T)
cps, scores = cpd_nonlin(K, m, lmin=1, lmax=10000)
print "Estimated:", cps
mi = np.min(X)
ma = np.max(X)
for cp in cps:
plt.plot([cp, cp], [mi, ma], 'r')
plt.show()
print "=" * 79
print "Test 2: multidimensional signal"
plt.figure("Test 2: multidimensional signal")
n = 1000
m = 20
(X, cps_gt) = gen_data(n, m, d=50)
print "Ground truth:", cps_gt
plt.plot(X)