def test_hdquantiles(self):
data = self.data
assert_almost_equal(ms.hdquantiles(data, [0., 1.]),
[0.006514031, 0.995309248])
hdq = ms.hdquantiles(data, [0.25, 0.5, 0.75])
assert_almost_equal(hdq, [
0.253210762,
0.512847491,
0.762232442,
])
hdq = ms.hdquantiles_sd(data, [0.25, 0.5, 0.75])
assert_almost_equal(hdq, [
0.03786954,
0.03805389,
0.03800152,
], 4)
data = np.array(data).reshape(10, 10)
hdq = ms.hdquantiles(data, [0.25, 0.5, 0.75], axis=0)
assert_almost_equal(hdq[:, 0],
ms.hdquantiles(data[:, 0], [0.25, 0.5, 0.75]))
assert_almost_equal(hdq[:, -1],
ms.hdquantiles(data[:, -1], [0.25, 0.5, 0.75]))
hdq = ms.hdquantiles(data, [0.25, 0.5, 0.75], axis=0, var=True)
assert_almost_equal(
hdq[..., 0], ms.hdquantiles(data[:, 0], [0.25, 0.5, 0.75],
var=True))
assert_almost_equal(
hdq[..., -1],
ms.hdquantiles(data[:, -1], [0.25, 0.5, 0.75], var=True))
def test_hdquantiles(self):
data = [
0.706560797, 0.727229578, 0.990399276, 0.927065621, 0.158953014,
0.887764025, 0.239407086, 0.349638551, 0.972791145, 0.149789972,
0.936947700, 0.132359948, 0.046041972, 0.641675031, 0.945530547,
0.224218684, 0.771450991, 0.820257774, 0.336458052, 0.589113496,
0.509736129, 0.696838829, 0.491323573, 0.622767425, 0.775189248,
0.641461450, 0.118455200, 0.773029450, 0.319280007, 0.752229111,
0.047841438, 0.466295911, 0.583850781, 0.840581845, 0.550086491,
0.466470062, 0.504765074, 0.226855960, 0.362641207, 0.891620942,
0.127898691, 0.490094097, 0.044882048, 0.041441695, 0.317976349,
0.504135618, 0.567353033, 0.434617473, 0.636243375, 0.231803616,
0.230154113, 0.160011327, 0.819464108, 0.854706985, 0.438809221,
0.487427267, 0.786907310, 0.408367937, 0.405534192, 0.250444460,
0.995309248, 0.144389588, 0.739947527, 0.953543606, 0.680051621,
0.388382017, 0.863530727, 0.006514031, 0.118007779, 0.924024803,
0.384236354, 0.893687694, 0.626534881, 0.473051932, 0.750134705,
0.241843555, 0.432947602, 0.689538104, 0.136934797, 0.150206859,
0.474335206, 0.907775349, 0.525869295, 0.189184225, 0.854284286,
0.831089744, 0.251637345, 0.587038213, 0.254475554, 0.237781276,
0.827928620, 0.480283781, 0.594514455, 0.213641488, 0.024194386,
0.536668589, 0.699497811, 0.892804071, 0.093835427, 0.731107772
]
#
assert_almost_equal(ms.hdquantiles(data, [0., 1.]),
[0.006514031, 0.995309248])
hdq = ms.hdquantiles(data, [0.25, 0.5, 0.75])
assert_almost_equal(hdq, [
0.253210762,
0.512847491,
0.762232442,
])
hdq = ms.hdquantiles_sd(data, [0.25, 0.5, 0.75])
assert_almost_equal(hdq, [
0.03786954,
0.03805389,
0.03800152,
], 4)
#
data = np.array(data).reshape(10, 10)
hdq = ms.hdquantiles(data, [0.25, 0.5, 0.75], axis=0)
assert_almost_equal(hdq[:, 0],
ms.hdquantiles(data[:, 0], [0.25, 0.5, 0.75]))
assert_almost_equal(hdq[:, -1],
ms.hdquantiles(data[:, -1], [0.25, 0.5, 0.75]))
hdq = ms.hdquantiles(data, [0.25, 0.5, 0.75], axis=0, var=True)
assert_almost_equal(
hdq[..., 0], ms.hdquantiles(data[:, 0], [0.25, 0.5, 0.75],
var=True))
assert_almost_equal(
hdq[..., -1],
ms.hdquantiles(data[:, -1], [0.25, 0.5, 0.75], var=True))
def test_hdquantiles_sd(self):
# Standard deviation is a jackknife estimator, so we can check if
# the efficient version (hdquantiles_sd) matches a rudimentary,
# but clear version here.
hd_std_errs = ms.hdquantiles_sd(self.data)
# jacknnife standard error, Introduction to the Bootstrap Eq. 11.5
n = len(self.data)
jdata = np.broadcast_to(self.data, (n, n))
jselector = np.logical_not(np.eye(n)) # leave out one sample each row
jdata = jdata[jselector].reshape(n, n-1)
jdist = ms.hdquantiles(jdata, axis=1)
jdist_mean = np.mean(jdist, axis=0)
jstd = ((n-1)/n * np.sum((jdist - jdist_mean)**2, axis=0))**.5
assert_almost_equal(hd_std_errs, jstd)
# Test actual values for good measure
assert_almost_equal(hd_std_errs, [0.0379258, 0.0380656, 0.0380013])
two_data_points = ms.hdquantiles_sd([1, 2])
assert_almost_equal(two_data_points, [0.5, 0.5, 0.5])
def test_hdquantiles(self):
data = self.data
assert_almost_equal(ms.hdquantiles(data,[0., 1.]),
[0.006514031, 0.995309248])
hdq = ms.hdquantiles(data,[0.25, 0.5, 0.75])
assert_almost_equal(hdq, [0.253210762, 0.512847491, 0.762232442,])
hdq = ms.hdquantiles_sd(data,[0.25, 0.5, 0.75])
assert_almost_equal(hdq, [0.03786954, 0.03805389, 0.03800152,], 4)
data = np.array(data).reshape(10,10)
hdq = ms.hdquantiles(data,[0.25,0.5,0.75],axis=0)
assert_almost_equal(hdq[:,0], ms.hdquantiles(data[:,0],[0.25,0.5,0.75]))
assert_almost_equal(hdq[:,-1], ms.hdquantiles(data[:,-1],[0.25,0.5,0.75]))
hdq = ms.hdquantiles(data,[0.25,0.5,0.75],axis=0,var=True)
assert_almost_equal(hdq[...,0],
ms.hdquantiles(data[:,0],[0.25,0.5,0.75],var=True))
assert_almost_equal(hdq[...,-1],
ms.hdquantiles(data[:,-1],[0.25,0.5,0.75], var=True))
def test_hdquantiles(self):
data = [0.706560797,0.727229578,0.990399276,0.927065621,0.158953014,
0.887764025,0.239407086,0.349638551,0.972791145,0.149789972,
0.936947700,0.132359948,0.046041972,0.641675031,0.945530547,
0.224218684,0.771450991,0.820257774,0.336458052,0.589113496,
0.509736129,0.696838829,0.491323573,0.622767425,0.775189248,
0.641461450,0.118455200,0.773029450,0.319280007,0.752229111,
0.047841438,0.466295911,0.583850781,0.840581845,0.550086491,
0.466470062,0.504765074,0.226855960,0.362641207,0.891620942,
0.127898691,0.490094097,0.044882048,0.041441695,0.317976349,
0.504135618,0.567353033,0.434617473,0.636243375,0.231803616,
0.230154113,0.160011327,0.819464108,0.854706985,0.438809221,
0.487427267,0.786907310,0.408367937,0.405534192,0.250444460,
0.995309248,0.144389588,0.739947527,0.953543606,0.680051621,
0.388382017,0.863530727,0.006514031,0.118007779,0.924024803,
0.384236354,0.893687694,0.626534881,0.473051932,0.750134705,
0.241843555,0.432947602,0.689538104,0.136934797,0.150206859,
0.474335206,0.907775349,0.525869295,0.189184225,0.854284286,
0.831089744,0.251637345,0.587038213,0.254475554,0.237781276,
0.827928620,0.480283781,0.594514455,0.213641488,0.024194386,
0.536668589,0.699497811,0.892804071,0.093835427,0.731107772]
#
assert_almost_equal(ms.hdquantiles(data,[0., 1.]),
[0.006514031, 0.995309248])
hdq = ms.hdquantiles(data,[0.25, 0.5, 0.75])
assert_almost_equal(hdq, [0.253210762, 0.512847491, 0.762232442,])
hdq = ms.hdquantiles_sd(data,[0.25, 0.5, 0.75])
assert_almost_equal(hdq, [0.03786954, 0.03805389, 0.03800152,], 4)
#
data = np.array(data).reshape(10,10)
hdq = ms.hdquantiles(data,[0.25,0.5,0.75],axis=0)
assert_almost_equal(hdq[:,0], ms.hdquantiles(data[:,0],[0.25,0.5,0.75]))
assert_almost_equal(hdq[:,-1], ms.hdquantiles(data[:,-1],[0.25,0.5,0.75]))
hdq = ms.hdquantiles(data,[0.25,0.5,0.75],axis=0,var=True)
assert_almost_equal(hdq[...,0],
ms.hdquantiles(data[:,0],[0.25,0.5,0.75],var=True))
assert_almost_equal(hdq[...,-1],
ms.hdquantiles(data[:,-1],[0.25,0.5,0.75], var=True))
def test_hdquantiles_sd(self):
# Only test that code runs, implementation not checked for correctness
res = ms.hdquantiles_sd(self.data)
assert_(res.size == 3)
def test_hdquantiles_sd(self):
# Only test that code runs, implementation not checked for correctness
res = ms.hdquantiles_sd(self.data)
assert_(res.size == 3)
