def est_plot_last_search( self ):
import solverLib
from solverLib import solve_via_Newtons_method, rand, SearchAnalyticsWrapper
maxStep = [0.5, 0.5]
xRoots = solve_via_Newtons_method( SearchAnalyticsWrapper(self.f1), rand(2)+3, maxStep, x_tol=0, debugPrintLevel=3, f_tol=10**-12)
print( solverLib.analytics['lastSearch'] )
solverLib.analytics['lastSearch'].plot()
def est_plot_last_search( self ):
import solverLib
from solverLib import solve_via_Newtons_method, rand, SearchAnalyticsWrapper
maxStep = [0.5, 0.5]
xRoots = solve_via_Newtons_method( SearchAnalyticsWrapper(self.f1), rand(2)+3, maxStep, x_tol=0, debugPrintLevel=3, f_tol=10**-12)
print( solverLib.analytics['lastSearch'] )
solverLib.analytics['lastSearch'].plot()
def test_solve_via_Newtons_method(self):
from solverLib import solve_via_Newtons_method, rand
maxStep = [0.5, 0.5]
xMin = solve_via_Newtons_method(self.f1,
rand(2) + 3,
maxStep,
x_tol=0,
debugPrintLevel=0)
self.assertAllClose(xMin, [2, -1])
def test_gradient_approx_1( self ):
'test on a function which returns a single value'
from solverLib import GradientApproximatorRandomPoints, GradientApproximatorForwardDifference, GradientApproximatorCentralDifference, rand
grad_f_rp = GradientApproximatorRandomPoints(self.f2)
grad_f_fd = GradientApproximatorForwardDifference(self.f2)
grad_f_cd = GradientApproximatorCentralDifference(self.f2)
for i in range(2):
X = rand(2)*10-5
#print(' X %s' % X)
#print(' grad_f(X) analytical: %s' % grad_f2(X))
#print(' grad_f(X) randomPoints: %s' % grad_f_rp(X))
#print(' grad_f(X) forwardDiff.: %s' % grad_f_fd(X))
#print(' grad_f(X) centralDiff.: %s' % grad_f_cd(X))
#print(' norm(analytical-randomPoints) %e' % norm(grad_f2(X) - grad_f_rp(X)) )
self.assertAllClose( self.grad_f2(X), grad_f_rp(X) )
self.assertAllClose( self.grad_f2(X), grad_f_fd(X) )
self.assertAllClose( self.grad_f2(X), grad_f_cd(X) )
def test_gradient_approx_1(self):
'test on a function which returns a single value'
from solverLib import GradientApproximatorRandomPoints, GradientApproximatorForwardDifference, GradientApproximatorCentralDifference, rand
grad_f_rp = GradientApproximatorRandomPoints(self.f2)
grad_f_fd = GradientApproximatorForwardDifference(self.f2)
grad_f_cd = GradientApproximatorCentralDifference(self.f2)
for i in range(2):
X = rand(2) * 10 - 5
#print(' X %s' % X)
#print(' grad_f(X) analytical: %s' % grad_f2(X))
#print(' grad_f(X) randomPoints: %s' % grad_f_rp(X))
#print(' grad_f(X) forwardDiff.: %s' % grad_f_fd(X))
#print(' grad_f(X) centralDiff.: %s' % grad_f_cd(X))
#print(' norm(analytical-randomPoints) %e' % norm(grad_f2(X) - grad_f_rp(X)) )
self.assertAllClose(self.grad_f2(X), grad_f_rp(X))
self.assertAllClose(self.grad_f2(X), grad_f_fd(X))
self.assertAllClose(self.grad_f2(X), grad_f_cd(X))
def test_gradient_approx_2( self ):
'test on a function which returns multiple values'
from solverLib import GradientApproximatorRandomPoints, GradientApproximatorForwardDifference, GradientApproximatorCentralDifference, rand
grad_f_rp = GradientApproximatorRandomPoints( self.f1 )
grad_f_fd = GradientApproximatorForwardDifference( self.f1 )
grad_f_cd = GradientApproximatorCentralDifference( self.f1 )
for i in range(2):
X = rand(2)*10-5
#print(' X %s' % X)
#print(' grad_f(X) analytical:')
#prettyPrintArray(grad_f1(X), toStdOut, ' ','%1.6e')
#print(' grad_f(X) randomPoints:')
#prettyPrintArray(grad_f_rp(X), toStdOut, ' ','%1.6e')
#print(' grad_f(X) forwardDiff:')
#prettyPrintArray(grad_f_fd(X), toStdOut, ' ','%1.6e')
#print(' grad_f(X) centralDiff:')
#prettyPrintArray(grad_f_cd(X), toStdOut, ' ','%1.6e')
#print(' error rp %e' % norm(grad_f1(X) - grad_f_rp(X)))
self.assertAllClose( self.grad_f1(X), grad_f_rp(X) )
def test_gradient_approx_2(self):
'test on a function which returns multiple values'
from solverLib import GradientApproximatorRandomPoints, GradientApproximatorForwardDifference, GradientApproximatorCentralDifference, rand
grad_f_rp = GradientApproximatorRandomPoints(self.f1)
grad_f_fd = GradientApproximatorForwardDifference(self.f1)
grad_f_cd = GradientApproximatorCentralDifference(self.f1)
for i in range(2):
X = rand(2) * 10 - 5
#print(' X %s' % X)
#print(' grad_f(X) analytical:')
#prettyPrintArray(grad_f1(X), toStdOut, ' ','%1.6e')
#print(' grad_f(X) randomPoints:')
#prettyPrintArray(grad_f_rp(X), toStdOut, ' ','%1.6e')
#print(' grad_f(X) forwardDiff:')
#prettyPrintArray(grad_f_fd(X), toStdOut, ' ','%1.6e')
#print(' grad_f(X) centralDiff:')
#prettyPrintArray(grad_f_cd(X), toStdOut, ' ','%1.6e')
#print(' error rp %e' % norm(grad_f1(X) - grad_f_rp(X)))
self.assertAllClose(self.grad_f1(X), grad_f_rp(X))
def test( self ):
from degreesOfFreedom import PlacementDegreeOfFreedom, LinearMotionDegreeOfFreedom, AxisRotationDegreeOfFreedom, pi, normalize
from numpy.random import rand
from variableManager import VariableManager
#print('creating test FreeCAD document, constraining a single Cube')
import FreeCAD, Part
FreeCAD.newDocument("testDoc")
#FreeCAD.setActiveDocument("box")
#FreeCAD.ActiveDocument = FreeCAD.getDocument("box")
objName = "box"
box = FreeCAD.ActiveDocument.addObject("Part::FeaturePython", objName)
box.Shape = Part.makeBox(2,3,2)
#FreeCAD.ActiveDocument.recompute()
box.Placement.Base.x = rand()
box.Placement.Base.y = rand() + 1
box.Placement.Base.z = rand() + 2
#print(box.Placement)
class FakeSystem:
def __init__(self, variableManager):
self.variableManager = variableManager
vM = VariableManager(FreeCAD.ActiveDocument)
#print(vM.X)
constaintSystem = FakeSystem(vM)
#print('\nTesting PlacementDegreeOfFreedom')
for object_dof in range(6):
d = PlacementDegreeOfFreedom( constaintSystem, objName, object_dof )
#print(d)
for i in range(6):
value = pi*( rand() - 0.5 )
d.setValue(value)
assert d.getValue() == value
#print('\nTesting LinearMotionDegreeOfFreedom')
tol = 10**-14
for i in range(3):
d = LinearMotionDegreeOfFreedom( constaintSystem, objName )
d.setDirection( normalize(rand(3) - 0.5) )
#print(d)
for i in range(12):
value = 12*( rand() - 0.5 )
d.setValue(value)
returnedValue = d.getValue()
if abs(returnedValue - value) > tol :
raise ValueError("d.getValue() - value != %1.0e, [diff %e]" % (tol, returnedValue - value))
#print('\nTesting AxisRotationDegreeOfFreedom')
tol = 10**-12
for i in range(3):
d = AxisRotationDegreeOfFreedom( constaintSystem, objName )
axis_r = normalize(rand(3) - 0.5) #axis in parts co-ordinate system (i.e. relative to part)
axis = normalize(rand(3) - 0.5) # desired axis in global co-ordinate system
d.setAxis( axis, axis_r )
d.setValue(0) #update azi,ela,theta to statify aligment of axis vector
#print(d)
for i in range(6):
value = 2*pi*( rand() - 0.5 )
d.setValue(value)
returnedValue = d.getValue()
#print(' d.getValue() %f value %f, diff %e' % (returnedValue, value, returnedValue - value))
if abs(returnedValue - value) > tol :
raise ValueError("d.getValue() - value != %1.0e, [diff %e]" % (tol, returnedValue - value))
def test_solve_via_Newtons_method( self ):
from solverLib import solve_via_Newtons_method, rand
maxStep = [0.5, 0.5]
xMin = solve_via_Newtons_method( self.f1, rand(2)+3, maxStep, x_tol=0, debugPrintLevel=0 )
self.assertAllClose( xMin, [2, -1 ] )
def test(self):
from degreesOfFreedom import PlacementDegreeOfFreedom, LinearMotionDegreeOfFreedom, AxisRotationDegreeOfFreedom, pi, normalize
from numpy.random import rand
from variableManager import VariableManager
#print('creating test FreeCAD document, constraining a single Cube')
import FreeCAD, Part
FreeCAD.newDocument("testDoc")
#FreeCAD.setActiveDocument("box")
#FreeCAD.ActiveDocument = FreeCAD.getDocument("box")
objName = "box"
box = FreeCAD.ActiveDocument.addObject("Part::FeaturePython", objName)
box.Shape = Part.makeBox(2, 3, 2)
#FreeCAD.ActiveDocument.recompute()
box.Placement.Base.x = rand()
box.Placement.Base.y = rand() + 1
box.Placement.Base.z = rand() + 2
#print(box.Placement)
class FakeSystem:
def __init__(self, variableManager):
self.variableManager = variableManager
vM = VariableManager(FreeCAD.ActiveDocument)
#print(vM.X)
constaintSystem = FakeSystem(vM)
#print('\nTesting PlacementDegreeOfFreedom')
for object_dof in range(6):
d = PlacementDegreeOfFreedom(constaintSystem, objName, object_dof)
#print(d)
for i in range(6):
value = pi * (rand() - 0.5)
d.setValue(value)
assert d.getValue() == value
#print('\nTesting LinearMotionDegreeOfFreedom')
tol = 10**-14
for i in range(3):
d = LinearMotionDegreeOfFreedom(constaintSystem, objName)
d.setDirection(normalize(rand(3) - 0.5))
#print(d)
for i in range(12):
value = 12 * (rand() - 0.5)
d.setValue(value)
returnedValue = d.getValue()
if abs(returnedValue - value) > tol:
raise ValueError(
"d.getValue() - value != %1.0e, [diff %e]" %
(tol, returnedValue - value))
#print('\nTesting AxisRotationDegreeOfFreedom')
tol = 10**-12
for i in range(3):
d = AxisRotationDegreeOfFreedom(constaintSystem, objName)
axis_r = normalize(
rand(3) -
0.5) #axis in parts co-ordinate system (i.e. relative to part)
axis = normalize(rand(3) -
0.5) # desired axis in global co-ordinate system
d.setAxis(axis, axis_r)
d.setValue(
0) #update azi,ela,theta to statify aligment of axis vector
#print(d)
for i in range(6):
value = 2 * pi * (rand() - 0.5)
d.setValue(value)
returnedValue = d.getValue()
#print(' d.getValue() %f value %f, diff %e' % (returnedValue, value, returnedValue - value))
if abs(returnedValue - value) > tol:
raise ValueError(
"d.getValue() - value != %1.0e, [diff %e]" %
(tol, returnedValue - value))