def test_zscore():
x = np.array([[1, 1, 3, 3], [4, 4, 6, 6]])
z = utils.zscore(x)
npt.assert_equal(x.shape, z.shape)
# Default axis is -1
npt.assert_equal(utils.zscore(x), np.array([[-1.0, -1.0, 1.0, 1.0], [-1.0, -1.0, 1.0, 1.0]]))
# Test other axis:
npt.assert_equal(utils.zscore(x, 0), np.array([[-1.0, -1.0, -1.0, -1.0], [1.0, 1.0, 1.0, 1.0]]))
def test_zscore():
x = np.array([[1, 1, 3, 3], [4, 4, 6, 6]])
z = utils.zscore(x)
yield npt.assert_equal, x.shape, z.shape
# Default axis is -1
yield npt.assert_equal, utils.zscore(x), np.array([[-1.0, -1.0, 1.0, 1.0], [-1.0, -1.0, 1.0, 1.0]])
# Test other axis:
yield npt.assert_equal, utils.zscore(x, 0), np.array([[-1.0, -1.0, -1.0, -1.0], [1.0, 1.0, 1.0, 1.0]])
def test_zscore():
x = np.array([[1, 1, 3, 3], [4, 4, 6, 6]])
z = utils.zscore(x)
npt.assert_equal(x.shape, z.shape)
#Default axis is -1
npt.assert_equal(utils.zscore(x),
np.array([[-1., -1., 1., 1.], [-1., -1., 1., 1.]]))
#Test other axis:
npt.assert_equal(utils.zscore(x, 0),
np.array([[-1., -1., -1., -1.], [1., 1., 1., 1.]]))
def z_score(self):
return ts.TimeSeries(tsu.zscore(self.input.data),
sampling_rate=self.input.sampling_rate,
time_unit = self.input.time_unit)
def z_score(self):
return ts.TimeSeries(tsu.zscore(self.input.data),
sampling_rate=self.input.sampling_rate,
time_unit=self.input.time_unit)
def test_zscore():
x = np.array([[1,2,3],[4,5,6]])
z = utils.zscore(x)
npt.assert_equal(x.shape,z.shape)
