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dev/script/fit_radius.py
2018-01-19 10:56:53 +01:00

55 lines
2.0 KiB
Python
Executable File

from mathutils import *
#Fiting center of circle of radius 70 to observerd points
radius = 70.0
x = [30.0, 50.0, 110.0, 35.0, 45.0]
y = [68.0, -6.0, -20.0, 15.0, 97.0]
p = plot(y, xdata=x)[0]
num_samples = len(x)
w = [ 1.0] * num_samples
w = [0.1, 0.1, 1.0, 0.1, 1.0]
#I = MatrixUtils.createRealIdentityMatrix(len(w))
W = MatrixUtils.createRealDiagonalMatrix(w)
# the model function components are the distances to current estimated center,
# they should be as close as possible to the specified radius
class Model(MultivariateJacobianFunction):
def value(self, variables):
(cx,cy) = (variables.getEntry(0), variables.getEntry(1))
value = ArrayRealVector(num_samples)
jacobian = Array2DRowRealMatrix(num_samples, 2)
for i in range(num_samples):
model = math.hypot(cx-x[i], cy-y[i])
value.setEntry(i, model)
# derivative with respect to p0 = x center
jacobian.setEntry(i, 0, (cx - x[i]) / model)
# derivative with respect to p1 = y center
jacobian.setEntry(i, 1, (cy - y[i]) / model)
return Pair(value, jacobian)
model = Model()
# the target is to have all points at the specified radius from the center
target = [radius,] * num_samples
#least squares problem to solve : modeled radius should be close to target radius
initial = [0.0, 0.0 ]
problem = LeastSquaresBuilder().start(initial).model(model).target(target).lazyEvaluation(False).maxEvaluations(1000).maxIterations(1000).weight(W).build()
optimizer = LevenbergMarquardtOptimizer()
optimum = optimizer.optimize(problem)
cx, cy = optimum.getPoint().getEntry(0), optimum.getPoint().getEntry(1)
print "fitted center x: ", cx
print "fitted center y: ", cy
print ""
print "RMS: " , optimum.getRMS()
print "evaluations: " , optimum.getEvaluations()
print "iterations: " , optimum.getIterations()
from plotutils import *
plot_point(p, cx, cy, name="Fit Cente")
plot_circle(p, cx, cy, radius, name="Fit")