def intersectionRatio(hist1, hist2):
assert(ml.isVector(hist1) and ml.isVector(hist2))
nz_1 = np.nonzero(hist1)[0]
nz_2 = np.nonzero(hist2)[0]
intersecting = len(np.intersect1d(nz_1, nz_2))
dividend = len(nz_1) + len(nz_2) - intersecting
if dividend == 0:
print len(nz_1), len(nz_2), intersecting
print hist1
print hist2
ratio = float(intersecting) /(len(nz_1) + len(nz_2) - intersecting)
return ratio
def truncate(x, d):
assert (ml.isVector(x))
D = x.shape[0]
if d >= D:
return x
ret = np.compress([True for _ in xrange(d)], x, axis=0)
assert (ret.shape == (d, ))
return ret
def imgArrayToBinnedHistogram(imgArray):
assert(ml.isVector(imgArray))
M = len(imgArray)
hist = np.zeros((K * K * K))
i = 0
while i + 2 < M:
r, g, b = imgArray[i], imgArray[i+1], imgArray[i+2]
bucket = RGBToBin(r, g, b)
hist[bucket] += 1
# should use the below instead (sparse matrix)
#return scipy.sparse.csr_matrix(hist.ravel())
return hist
