LineModelND
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class skimage.measure.LineModelND
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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
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__init__()
[source]
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estimate(data)
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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.
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predict(x, axis=0, params=None)
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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.
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predict_x(y, params=None)
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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.
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predict_y(x, params=None)
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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.
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residuals(data)
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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.
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