linemodelnd

LineModelND

class skimage.measure.LineModelND [source]

Bases: skimage.measure.fit.BaseModel

Total least squares estimator for N-dimensional lines.

Lines are defined by a point (origin) and a unit vector (direction) according to the following vector equation:

X = origin + lambda * direction

Attributes

params

(tuple) Line model parameters in the following order origin, direction.

__init__() [source]

estimate(data) [source]

Estimate line model from data.

Parameters:	data : (N, dim) array N points in a space of dimensionality dim >= 2.
Returns:	success : bool True, if model estimation succeeds.

predict(x, axis=0, params=None) [source]

Predict intersection of the estimated line model with a hyperplane orthogonal to a given axis.

Parameters:	x : array coordinates along an axis. axis : int axis orthogonal to the hyperplane intersecting the line. params : (2, ) array, optional Optional custom parameter set in the form (origin, direction).
Returns:	y : array Predicted coordinates. If the line is parallel to the given axis, a ValueError is raised.

predict_x(y, params=None) [source]

Predict x-coordinates for 2D lines using the estimated model.

Alias for:

predict(y, axis=1)[:, 0]

Parameters:	y : array y-coordinates. params : (2, ) array, optional Optional custom parameter set in the form (origin, direction).
Returns:	x : array Predicted x-coordinates.

predict_y(x, params=None) [source]

Predict y-coordinates for 2D lines using the estimated model.

Alias for:

predict(x, axis=0)[:, 1]

Parameters:	x : array x-coordinates. params : (2, ) array, optional Optional custom parameter set in the form (origin, direction).
Returns:	y : array Predicted y-coordinates.

residuals(data) [source]

Determine residuals of data to model.

For each point the shortest distance to the line is returned. It is obtained by projecting the data onto the line.

Parameters:	data : (N, dim) array N points in a space of dimension dim.
Returns:	residuals : (N, ) array Residual for each data point.