Line model parameters in the following order :None:None:`origin`, :None:None:`direction`.
In contrast to ordinary least squares line estimation, this estimator minimizes the orthogonal distances of points to the estimated line.
Lines are defined by a point (origin) and a unit vector (direction) according to the following vector equation:
X = origin + lambda * direction
Total least squares estimator for N-dimensional lines.
>>> x = np.linspace(1, 2, 25)This example is valid syntax, but we were not able to check execution
... y = 1.5 * x + 3
... lm = LineModelND()
... lm.estimate(np.array([x, y]).T) True
>>> tuple(np.round(lm.params, 5)) (array([1.5 , 5.25]), array([0.5547 , 0.83205]))This example is valid syntax, but we were not able to check execution
>>> res = lm.residuals(np.array([x, y]).T)This example is valid syntax, but we were not able to check execution
... np.abs(np.round(res, 9)) array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
>>> np.round(lm.predict_y(x[:5]), 3) array([4.5 , 4.562, 4.625, 4.688, 4.75 ])This example is valid syntax, but we were not able to check execution
>>> np.round(lm.predict_x(y[:5]), 3) array([1. , 1.042, 1.083, 1.125, 1.167])See :
The following pages refer to to this document either explicitly or contain code examples using this.
skimage.measure.fit.LineModelND
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