def test_maha():
# Want implementation of Mahalanobis distance to match this R session:
# > x1 <- round(rnorm(10,3), 3)
# > x2 <- round(x1 + rnorm(10), 3)
# > x3 <- round(x2 + runif(10), 3)
# > x1
# [1] 3.853 2.401 2.253 3.067 1.887 3.293 3.995 2.559 2.785 2.228
# > x2
# [1] 4.294 1.915 1.315 4.641 1.611 2.838 3.696 1.337 2.853 2.434
# > x3
# [1] 4.785 2.352 2.023 4.978 2.329 3.101 4.494 2.204 3.468 3.075
# > obs <- cbind(x1, x2, x3)
# > S <- var(obs)
# > S
# x1 x2 x3
# x1 0.5020374 0.6667232 0.633355
# x2 0.6667232 1.4434718 1.326026
# x3 0.6333550 1.3260262 1.248315
# > mahalanobis(obs, c(mean(x1), mean(x2), mean(x3)), S)
# [1] 2.1838336 1.9673401 1.3335029 4.9191627 2.1246818 5.3297995 4.9022487
# [8] 2.5335913 0.1952562 1.5105832
from scitbx.array_family import flex
from dials.algorithms.statistics.fast_mcd import cov, maha_dist_sq
# test Mahalanobis distance.
x1 = flex.double(
(3.853, 2.401, 2.253, 3.067, 1.887, 3.293, 3.995, 2.559, 2.785, 2.228))
x2 = flex.double(
(4.294, 1.915, 1.315, 4.641, 1.611, 2.838, 3.696, 1.337, 2.853, 2.434))
x3 = flex.double(
(4.785, 2.352, 2.023, 4.978, 2.329, 3.101, 4.494, 2.204, 3.468, 3.075))
cols = [x1, x2, x3]
center = [flex.mean(e) for e in cols]
covmat = cov(x1, x2, x3)
maha = maha_dist_sq(cols, center, covmat)
from libtbx.test_utils import approx_equal
R_result = [
2.1838336,
1.9673401,
1.3335029,
4.9191627,
2.1246818,
5.3297995,
4.9022487,
2.5335913,
0.1952562,
1.5105832,
]
assert approx_equal(list(maha), R_result)
def test_maha():
# Want implementation of Mahalanobis distance to match this R session:
#> x1 <- round(rnorm(10,3), 3)
#> x2 <- round(x1 + rnorm(10), 3)
#> x3 <- round(x2 + runif(10), 3)
#> x1
# [1] 3.853 2.401 2.253 3.067 1.887 3.293 3.995 2.559 2.785 2.228
#> x2
# [1] 4.294 1.915 1.315 4.641 1.611 2.838 3.696 1.337 2.853 2.434
#> x3
# [1] 4.785 2.352 2.023 4.978 2.329 3.101 4.494 2.204 3.468 3.075
#> obs <- cbind(x1, x2, x3)
#> S <- var(obs)
#> S
# x1 x2 x3
#x1 0.5020374 0.6667232 0.633355
#x2 0.6667232 1.4434718 1.326026
#x3 0.6333550 1.3260262 1.248315
#> mahalanobis(obs, c(mean(x1), mean(x2), mean(x3)), S)
# [1] 2.1838336 1.9673401 1.3335029 4.9191627 2.1246818 5.3297995 4.9022487
# [8] 2.5335913 0.1952562 1.5105832
from scitbx.array_family import flex
from dials.algorithms.statistics.fast_mcd import maha_dist_sq, cov
# test Mahalanobis distance.
x1 = flex.double((3.853, 2.401, 2.253, 3.067, 1.887, 3.293, 3.995, 2.559, 2.785, 2.228))
x2 = flex.double((4.294, 1.915, 1.315, 4.641, 1.611, 2.838, 3.696, 1.337, 2.853, 2.434))
x3 = flex.double((4.785, 2.352, 2.023, 4.978, 2.329, 3.101, 4.494, 2.204, 3.468, 3.075))
cols = [x1, x2, x3]
center = [flex.mean(e) for e in cols]
covmat = cov(x1, x2, x3)
n = len(cols[0])
maha = maha_dist_sq(cols, center, covmat)
from libtbx.test_utils import approx_equal
R_result = [2.1838336, 1.9673401, 1.3335029, 4.9191627, 2.1246818,
5.3297995, 4.9022487, 2.5335913, 0.1952562, 1.5105832]
assert approx_equal(list(maha), R_result)
print "OK"
return