def test_kurtosis(self):
# Set flags for axis = 0 and fisher=0 (Pearson's definition of kurtosis
# for compatibility with Matlab)
y = mstats.kurtosis(self.testmathworks, 0, fisher=0, bias=1)
assert_almost_equal(y, 2.1658856802973, 10)
# Note that MATLAB has confusing docs for the following case
# kurtosis(x,0) gives an unbiased estimate of Pearson's skewness
# kurtosis(x) gives a biased estimate of Fisher's skewness (Pearson-3)
# The MATLAB docs imply that both should give Fisher's
y = mstats.kurtosis(self.testmathworks, fisher=0, bias=0)
assert_almost_equal(y, 3.663542721189047, 10)
y = mstats.kurtosis(self.testcase, 0, 0)
assert_almost_equal(y, 1.64)
# test that kurtosis works on multidimensional masked arrays
correct_2d = ma.array(
np.array([-1.5, -3.0, -1.47247052385, 0.0, -1.26979517952]),
mask=np.array([False, False, False, True, False], dtype=np.bool),
)
assert_array_almost_equal(mstats.kurtosis(self.testcase_2d, 1), correct_2d)
for i, row in enumerate(self.testcase_2d):
assert_almost_equal(mstats.kurtosis(row), correct_2d[i])
correct_2d_bias_corrected = ma.array(
np.array([-1.5, -3.0, -1.88988209538, 0.0, -0.5234638463918877]),
mask=np.array([False, False, False, True, False], dtype=np.bool),
)
assert_array_almost_equal(mstats.kurtosis(self.testcase_2d, 1, bias=False), correct_2d_bias_corrected)
for i, row in enumerate(self.testcase_2d):
assert_almost_equal(mstats.kurtosis(row, bias=False), correct_2d_bias_corrected[i])
# Check consistency between stats and mstats implementations
assert_array_almost_equal_nulp(mstats.kurtosis(self.testcase_2d[2, :]), stats.kurtosis(self.testcase_2d[2, :]))
def test_kurtosis(self):
# sum((testcase-mean(testcase,axis=0))**4,axis=0)/((sqrt(var(testcase)*3/4))**4)/4
# sum((test2-mean(testmathworks,axis=0))**4,axis=0)/((sqrt(var(testmathworks)*4/5))**4)/5
# Set flags for axis = 0 and
# fisher=0 (Pearson's definition of kurtosis for compatibility with Matlab)
y = mstats.kurtosis(self.testmathworks,0,fisher=0,bias=1)
assert_almost_equal(y, 2.1658856802973,10)
# Note that MATLAB has confusing docs for the following case
# kurtosis(x,0) gives an unbiased estimate of Pearson's skewness
# kurtosis(x) gives a biased estimate of Fisher's skewness (Pearson-3)
# The MATLAB docs imply that both should give Fisher's
y = mstats.kurtosis(self.testmathworks,fisher=0, bias=0)
assert_almost_equal(y, 3.663542721189047,10)
y = mstats.kurtosis(self.testcase,0,0)
assert_almost_equal(y,1.64)
# test that kurtosis works on multidimensional masked arrays
correct_2d = ma.array(np.array([-1.5, -3., -1.47247052385, 0.,
-1.26979517952]),
mask=np.array([False, False, False, True,
False], dtype=np.bool))
assert_array_almost_equal(mstats.kurtosis(self.testcase_2d, 1),
correct_2d)
for i, row in enumerate(self.testcase_2d):
assert_almost_equal(mstats.kurtosis(row), correct_2d[i])
correct_2d_bias_corrected = ma.array(
np.array([-1.5, -3., -1.88988209538, 0., -0.5234638463918877]),
mask=np.array([False, False, False, True, False], dtype=np.bool))
assert_array_almost_equal(mstats.kurtosis(self.testcase_2d, 1,
bias=False),
correct_2d_bias_corrected)
for i, row in enumerate(self.testcase_2d):
assert_almost_equal(mstats.kurtosis(row, bias=False),
correct_2d_bias_corrected[i])
# Check consistency between stats and mstats implementations
assert_array_almost_equal_nulp(mstats.kurtosis(self.testcase_2d[2, :]),
stats.kurtosis(self.testcase_2d[2, :]))