def test_linear_regression(self):
"""
Tests :func:`colour.algebra.regression.linear_regression` definition.
"""
np.testing.assert_almost_equal(
linear_regression(Y),
[0.1406015, 1.77368421],
decimal=7)
np.testing.assert_almost_equal(
linear_regression(Y, X1),
[0.12777065, -3.38129694],
decimal=7)
np.testing.assert_almost_equal(
linear_regression(Y, tuple(zip(X1, X2))),
[0.08640901, 0.08760164, -4.10358123],
decimal=7)
def test_linear_regression(self):
"""
Tests :func:`colour.algebra.regression.linear_regression` definition.
"""
np.testing.assert_almost_equal(
linear_regression(Y),
[0.1406015, 1.77368421],
decimal=7)
np.testing.assert_almost_equal(
linear_regression(Y, X1),
[0.12777065, -3.38129694],
decimal=7)
np.testing.assert_almost_equal(
linear_regression(Y, tuple(zip(X1, X2))),
[0.08640901, 0.08760164, -4.10358123],
decimal=7)
def first_order_colour_fit(m1, m2):
"""
Performs a first order colour fit from given :math:`m2` colour matrix to
:math:`m1` colour matrix. The resulting colour matrix is calculated using
multiple linear regression.
The purpose of that object is for example matching of two *ColorChecker*
colour rendition charts together.
Parameters
----------
m1 : array_like, (3, n)
Reference matrix the matrix :math:`m2` will be colour fitted against.
m2 : array_like, (3, n)
Matrix to fit.
Returns
-------
ndarray, (3, 3)
Fitting colour matrix.
Examples
--------
>>> m1 = np.array([
... [0.1722481, 0.0917066, 0.06416938],
... [0.49189645, 0.2780205, 0.21923399],
... [0.10999751, 0.18658946, 0.29938611],
... [0.1166612, 0.14327905, 0.05713804],
... [0.18988879, 0.18227649, 0.36056247],
... [0.12501329, 0.42223442, 0.37027445],
... [0.64785606, 0.22396782, 0.03365194],
... [0.06761093, 0.11076896, 0.39779139],
... [0.49101797, 0.09448929, 0.11623839],
... [0.11622386, 0.04425753, 0.14469986],
... [0.36867946, 0.4454523, 0.06028681],
... [0.61632937, 0.32323906, 0.02437089],
... [0.03016472, 0.06153243, 0.29014596],
... [0.11103655, 0.30553067, 0.08149137],
... [0.4116219, 0.05816656, 0.04845934],
... [0.73339206, 0.53075188, 0.02475212],
... [0.47347718, 0.08834792, 0.30310315],
... [0., 0.25187016, 0.3506245],
... [0.76809639, 0.7848624, 0.77808297],
... [0.53822392, 0.54307997, 0.54710883],
... [0.35458526, 0.35318419, 0.35524431],
... [0.17976704, 0.18000531, 0.17991488],
... [0.09351417, 0.09510603, 0.09675027],
... [0.03405071, 0.03295077, 0.03702047]])
>>> m2 = np.array([
... [0.15579559, 0.09715755, 0.07514556],
... [0.3911314, 0.25943419, 0.21266708],
... [0.12824821, 0.1846357, 0.31508023],
... [0.12028974, 0.13455659, 0.074084],
... [0.19368988, 0.21158946, 0.37955964],
... [0.19957425, 0.36085439, 0.40678123],
... [0.48896605, 0.20691688, 0.05816533],
... [0.09775522, 0.16710693, 0.47147724],
... [0.39358649, 0.122334, 0.10526425],
... [0.10780332, 0.07258529, 0.16151473],
... [0.27502671, 0.34705454, 0.09728099],
... [0.43980441, 0.26880559, 0.05430533],
... [0.05887212, 0.11126272, 0.38552469],
... [0.12705825, 0.2578786, 0.13566464],
... [0.35612929, 0.07933258, 0.05118732],
... [0.48131976, 0.42082843, 0.07120612],
... [0.34665585, 0.15170714, 0.24969804],
... [0.08261116, 0.24588716, 0.48707733],
... [0.66054904, 0.65941137, 0.66376412],
... [0.48051509, 0.47870296, 0.48230082],
... [0.33045354, 0.32904184, 0.33228886],
... [0.18001305, 0.17978567, 0.18004416],
... [0.10283975, 0.1042468, 0.10384975],
... [0.04742204, 0.04772203, 0.04914226]])
>>> first_order_colour_fit(m1, m2) # doctest: +ELLIPSIS
array([[ 1.4043128..., 0.0112806..., -0.2029710...],
[-0.0998911..., 1.5012214..., -0.1856479...],
[ 0.2248369..., -0.0767236..., 1.0496013...]])
"""
x_coefficients = linear_regression(m1[:, 0], m2)
y_coefficients = linear_regression(m1[:, 1], m2)
z_coefficients = linear_regression(m1[:, 2], m2)
return np.array(
[x_coefficients[:3], y_coefficients[:3], z_coefficients[:3]]).reshape(
(3, 3))
def first_order_colour_fit(m1, m2):
"""
Performs a first order colour fit from given :math:`m2` colour matrix to
:math:`m1` colour matrix. The resulting colour matrix is calculated using
multiple linear regression.
The purpose of that object is for example matching of two *ColorChecker*
colour rendition charts together.
Parameters
----------
m1 : array_like, (3, n)
Reference matrix the matrix :math:`m2` will be colour fitted against.
m2 : array_like, (3, n)
Matrix to fit.
Returns
-------
ndarray, (3, 3)
Fitting colour matrix.
Examples
--------
>>> m1 = np.array([
... [0.1722481, 0.0917066, 0.06416938],
... [0.49189645, 0.2780205, 0.21923399],
... [0.10999751, 0.18658946, 0.29938611],
... [0.1166612, 0.14327905, 0.05713804],
... [0.18988879, 0.18227649, 0.36056247],
... [0.12501329, 0.42223442, 0.37027445],
... [0.64785606, 0.22396782, 0.03365194],
... [0.06761093, 0.11076896, 0.39779139],
... [0.49101797, 0.09448929, 0.11623839],
... [0.11622386, 0.04425753, 0.14469986],
... [0.36867946, 0.4454523, 0.06028681],
... [0.61632937, 0.32323906, 0.02437089],
... [0.03016472, 0.06153243, 0.29014596],
... [0.11103655, 0.30553067, 0.08149137],
... [0.4116219, 0.05816656, 0.04845934],
... [0.73339206, 0.53075188, 0.02475212],
... [0.47347718, 0.08834792, 0.30310315],
... [0., 0.25187016, 0.3506245],
... [0.76809639, 0.7848624, 0.77808297],
... [0.53822392, 0.54307997, 0.54710883],
... [0.35458526, 0.35318419, 0.35524431],
... [0.17976704, 0.18000531, 0.17991488],
... [0.09351417, 0.09510603, 0.09675027],
... [0.03405071, 0.03295077, 0.03702047]])
>>> m2 = np.array([
... [0.15579559, 0.09715755, 0.07514556],
... [0.3911314, 0.25943419, 0.21266708],
... [0.12824821, 0.1846357, 0.31508023],
... [0.12028974, 0.13455659, 0.074084],
... [0.19368988, 0.21158946, 0.37955964],
... [0.19957425, 0.36085439, 0.40678123],
... [0.48896605, 0.20691688, 0.05816533],
... [0.09775522, 0.16710693, 0.47147724],
... [0.39358649, 0.122334, 0.10526425],
... [0.10780332, 0.07258529, 0.16151473],
... [0.27502671, 0.34705454, 0.09728099],
... [0.43980441, 0.26880559, 0.05430533],
... [0.05887212, 0.11126272, 0.38552469],
... [0.12705825, 0.2578786, 0.13566464],
... [0.35612929, 0.07933258, 0.05118732],
... [0.48131976, 0.42082843, 0.07120612],
... [0.34665585, 0.15170714, 0.24969804],
... [0.08261116, 0.24588716, 0.48707733],
... [0.66054904, 0.65941137, 0.66376412],
... [0.48051509, 0.47870296, 0.48230082],
... [0.33045354, 0.32904184, 0.33228886],
... [0.18001305, 0.17978567, 0.18004416],
... [0.10283975, 0.1042468, 0.10384975],
... [0.04742204, 0.04772203, 0.04914226]])
>>> first_order_colour_fit(m1, m2) # doctest: +ELLIPSIS
array([[ 1.4043128..., 0.0112806..., -0.2029710...],
[-0.0998911..., 1.5012214..., -0.1856479...],
[ 0.2248369..., -0.0767236..., 1.0496013...]])
"""
x_coefficients = linear_regression(m1[:, 0], m2)
y_coefficients = linear_regression(m1[:, 1], m2)
z_coefficients = linear_regression(m1[:, 2], m2)
return np.array([x_coefficients[:3],
y_coefficients[:3],
z_coefficients[:3]]).reshape((3, 3))
