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

59 lines
2.0 KiB
Python
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from mathutils import *
from plotutils import *
#Fitting the quadratic function f(x) = a x2 + b x + c
#Data created with [a = 8, b = 10, c = 16] and 0<noise<1
x = [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]
y = [36.0, 66.0, 121.0, 183.0, 263.0, 365.0, 473.0, 603.0, 753.0, 917.0]
num_samples = len(x)
w = [ 1.0] * num_samples
W = MatrixUtils.createRealDiagonalMatrix(w)
p=plot(y, xdata=x)[0]
initial = [1.0, 1.0, 1.0] #a, b, c
class Model(MultivariateJacobianFunction):
def value(self, variables):
value = ArrayRealVector(num_samples)
jacobian = Array2DRowRealMatrix(num_samples, 3)
for i in range(num_samples):
(a,b,c) = (variables.getEntry(0), variables.getEntry(1), variables.getEntry(2))
model = a*x[i]*x[i] + b*x[i] + c
value.setEntry(i, model)
# derivative with respect to a
jacobian.setEntry(i, 0, x[i]*x[i])
# derivative with respect to b
jacobian.setEntry(i, 1, x[i])
# derivative with respect to c
jacobian.setEntry(i, 2, 1.0)
return Pair(value, jacobian)
model = Model()
# the target is to have all points at the specified radius from the center
target = [v for v in y]
problem = LeastSquaresBuilder().start(initial).model(model).target(target).lazyEvaluation(False).maxEvaluations(1000).maxIterations(1000).weight(W).build()
optimizer = LevenbergMarquardtOptimizer()
optimum = optimizer.optimize(problem)
optimalValues = optimum.getPoint()
a,b,c = optimum.getPoint().getEntry(0), optimum.getPoint().getEntry(1), optimum.getPoint().getEntry(2)
print "A: ", a
print "B: ", b
print "C: ", c
print ""
print "RMS: " , optimum.getRMS()
print "evaluations: " , optimum.getEvaluations()
print "iterations: " , optimum.getIterations()
print ""
for i in range (num_samples):
print x[i], y[i], poly(x[i], [c,b,a])
plot_function(p, PolynomialFunction((c,b,a)), "Fit", x)