def getQuadraticModel(self, phi):
# ensure this is an array, I think I was refactoring this to be matrices
phi = asarray(phi).flatten()
c = 0
b = zeros(self.n)
Q = zeros((self.n, self.n))
for j in range(self.powers.shape[1]):
sumpowers = sum(self.powers[:, j])
if sumpowers == 0:
c = phi[j] * self.coeff[j]
elif sumpowers == 1:
b[self.powers[:, j] > 0] = phi[j] * self.coeff[j]
elif sumpowers == 2:
idx1 = -1
idx2 = -1
for i in range(self.powers.shape[0]):
if self.powers[i, j] == 0:
continue
if idx1 < 0:
idx1 = i
else:
idx2 = i
if idx2 < 0:
Q[idx1, idx1] = 2 * phi[j] * self.coeff[j]
else:
Q[idx1, idx2] = phi[j] * self.coeff[j]
Q[idx2, idx1] = phi[j] * self.coeff[j]
else:
pass
return functions.Quadratic(Q, b, c)
def test_simpleValuesMatch(self):
fun1 = functions.Quadratic(arr([[1, 0], [0, -2]]), arr([3, 2]), 4)
fun2 = functions.Quadratic(zeros((2, 2)), arr([3, 2]), 4)
funs = [fun1, fun2]
set = dfo_model.MultiFunctionModel(funs, self.b, self.center,
self.radius)
x = arr([3, 4])
y1 = arr([fun1.evaluate(x), fun2.evaluate(x)])
y2 = set.interpolate(x)
self.assertTrue(norm(y1 - y2) < self.tolerance)
quadmod = set.getQuadraticModels(arr([0, 1], int))
y2 = quadmod.evaluate(x)
self.assertTrue(norm(y1 - y2) < self.tolerance)
newCenter = 10 * (2 * random.random(2) - 1)
for i in range(50):
print("testing " + str(i) + " of " + str(50))
rFactor = self.getRFactor()
newRadius = set.modelRadius * rFactor
newCenter = newCenter + set.modelRadius / newRadius
oldlen = set.history.size()
pva = set.testNewModelCenter(newCenter)
newlen = set.history.size()
self.assertTrue(newlen == oldlen + 1)
deviation = norm(
((pva[:, 0] - pva[:, 1]) / pva[:, 1])) / pva.shape[0]
self.assertTrue(deviation < self.tolerance)
set.setNewModelCenter(newCenter)
set.multiplyRadius(rFactor)
set.improve('images/test_%04d_improve.png' % i)
quadmod = set.getQuadraticModels(arr([0, 1], int))
for j in range(10):
x = 10 * (2 * random.random(2) - 1)
y1 = arr([fun1.evaluate(x), fun2.evaluate(x)])
y2 = quadmod.evaluate(x)
self.assertTrue(norm(y1 - y2) < self.tolerance)
def evaluate(self, x):
qL = full([self.n, self.n], -infinity)
qU = full([self.n, self.n], infinity)
bL = full(self.n, -infinity)
bU = full(self.n, infinity)
cL = -infinity
cU = infinity
for e in self.history:
e.updateQBounds(x, qL, qU, self.maxChangeInParams)
e.updateBBounds(x, bL, bU, self.maxChangeInParams)
cL, cU = e.updateCBounds(x, cL, cU, self.maxChangeInParams)
rand = random([self.n, self.n])
newQ = multiply(rand, qL) + multiply(1 - rand, qU)
rand = random(self.n)
newB = multiply(rand, bL) + multiply(1 - rand, bU)
rand = random()
newC = rand * cL + (1 - rand) * cU
self.history.append(Evaluation(x, newQ, newB, newC))
return functions.Quadratic(2 * newQ, newB, newC)
#log = file if hasattr(file,'write') else sys.stderr
log = sys.stderr
traceback.print_stack(file=log)
log.write(warnings.formatwarning(message, category, filename, lineno, line))
Q = arr([[1, 0],[0, 5]])
b = arr([0, 0])
#b = arr([1, 1])
c=0
# write tests
q = functions.Quadratic(Q, b, c)
c1 = functions.Line(arr([1, 1]), -1)
#c2 = functions.Line(arr([1, -1]), 10)
c2 = functions.Wiggles()
equality = []
equality.append(c1)
inequality = []
inequality.append(c2)
x0 = arr([1, 2])
warnings.showwarning = warn_with_traceback
statement = program.Program(q, equality, inequality, x0)
#statement = program.DfoProgram("wiggles", q, equality, inequality, x0, plotImprovements=True)
from numpy import array
from utilities import functions
from utilities import create_program
import matplotlib.pyplot as plt
quad1 = functions.Quadratic(array([[1, 0], [0, 2]]), array([0, 0]), 5)
quad2 = functions.Quadratic(array([[-1, 0], [0, -2]]), array([10, -1]), -13)
quad3 = functions.Quadratic(array([[-1, 0], [0, -2]]), array([-1, 1]), 50)
func = lambda x: array([quad1.evaluate(x), quad2.evaluate(x), quad3.evaluate(x)])
program, center = create_program.create_program('test', func, 2, 3, num_equality=1)
program.createBasePlotAt(center, 20)
plt.show()
#
#
# from oct2py import octave
# import os
# import numpy as np
# import utilities.cute_dfo_functions as funs
#
# octave.addpath(os.path.dirname(os.path.realpath(__file__)))
#