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#! /usr/bin/env python
from __future__ import print_function
from openturns import *
TESTPREAMBLE()
class FUNC(OpenTURNSPythonFunction):
def __init__(self):
super(FUNC, self).__init__(2, 1)
self.setInputDescription(['R', 'S'])
self.setOutputDescription(['T'])
def _exec(self, X):
Y = [X[0] + X[1]]
return Y
F = FUNC()
print(('in_dim=' + str(F.getInputDimension())
+ ' out_dim=' + str(F.getOutputDimension())))
print((F((10, 5))))
print((F(((10, 5), (6, 7)))))
# Instance creation
myFunc = NumericalMathFunction(F)
# Copy constructor
newFunc = NumericalMathFunction(myFunc)
print(('myFunc input dimension= ' + str(myFunc.getInputDimension())))
print(('myFunc output dimension= ' + str(myFunc.getOutputDimension())))
inPt = NumericalPoint(2, 2.)
print((repr(inPt)))
outPt = myFunc(inPt)
print((repr(outPt)))
outPt = myFunc((10., 11.))
print((repr(outPt)))
inSample = NumericalSample(10, 2)
for i in range(10):
inSample[i] = NumericalPoint((i, i))
print((repr(inSample)))
outSample = myFunc(inSample)
print((repr(outSample)))
outSample = myFunc(((100., 100.), (101., 101.), (102., 102.)))
print((repr(outSample)))
# Test cache behavior
myFunc.enableCache()
print(('calls = ' + str(myFunc.getEvaluationCallsNumber())
+ ' hits = ' + str(myFunc.getCacheHits())))
outPt = myFunc(inPt)
print(('T = ' + repr(outPt)))
print(('calls = ' + str(myFunc.getEvaluationCallsNumber())
+ ' hits = ' + str(myFunc.getCacheHits())))
outPt = myFunc(inPt)
print(('T = ' + repr(outPt)))
print(('calls = ' + str(myFunc.getEvaluationCallsNumber())
+ ' hits = ' + str(myFunc.getCacheHits())))
# duplicate one value
inSample[4] = inSample[5]
outSample = myFunc(inSample)
print(('T = ' + repr(outSample)))
print(('calls = ' + str(myFunc.getEvaluationCallsNumber())
+ ' hits = ' + str(myFunc.getCacheHits())))
outSample = myFunc(inSample)
print(('T = ' + repr(outSample)))
print(('calls = ' + str(myFunc.getEvaluationCallsNumber())
+ ' hits = ' + str(myFunc.getCacheHits())))
myFunc.clearCache()
outSample = myFunc(inSample)
print(('T = ' + repr(outSample)))
print(('calls = ' + str(myFunc.getEvaluationCallsNumber())
+ ' hits = ' + str(myFunc.getCacheHits())))
# test PythonFunction
def a_exec(X):
Y = [0]
Y[0] = X[0] + X[1]
return Y
def a_exec_sample(Xs):
Ys = []
for X in Xs:
Ys.append([X[0] + X[1]])
return Ys
a_sample = ((100., 100.), (101., 101.), (102., 102.))
print('exec')
myFunc = PythonFunction(2, 1, a_exec)
outSample = myFunc(a_sample)
print(outSample)
print('exec + exec_sample')
myFunc = PythonFunction(2, 1, a_exec, a_exec_sample)
outSample = myFunc(a_sample)
print(outSample)
print('exec_sample only on a point')
myFunc = PythonFunction(2, 1, func_sample=a_exec_sample)
outSample = myFunc([100., 100.])
print(outSample)
print('exec_sample only on a sample')
myFunc = PythonFunction(2, 1, func_sample=a_exec_sample)
outSample = myFunc(a_sample)
print(outSample)
def a_grad(X):
# wrong but allows to verify
dY = [[1.], [-1.]]
return dY
print('gradient')
myFunc = PythonFunction(2, 1, a_exec, gradient=a_grad)
grad = myFunc.gradient([100., 100.])
print(grad)
def a_hess(X):
# wrong but allows to verify
d2Y = [[[0.1], [0.3]], [[0.3], [0.1]]]
return d2Y
print('hessian')
myFunc = PythonFunction(2, 1, a_exec, hessian=a_hess)
hess = myFunc.hessian([100., 100.])
print(hess)
print('no func')
try:
myFunc = PythonFunction(2, 1)
outSample = myFunc(a_sample)
except:
# must raise exception
print('no function detected : ok.')
else:
raise Exception('no function not detected!')
def a_exec(X):
Y = [0]
if X[0] == 0.0:
raise RuntimeError('Oups')
elif X[0] == 1.0:
'2' + 2
return Y
for n in range(2):
myFunc = PythonFunction(1, 1, a_exec)
try:
X = NumericalPoint(1, n)
myFunc(X)
except Exception as exc:
# print exc
print('exception handling: ok')
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