On 10/9/11 5:39 AM, Luc Maisonobe wrote:
> Hi Phil,
>
> Le 08/10/2011 23:42, Phil Steitz a écrit :
>> On 10/8/11 2:24 PM, Luc Maisonobe wrote:
>>>
>>>
>>> Phil Steitz<phil.ste...@gmail.com> a écrit :
>>>
>>>> I am getting RTE with message above when I try to run the example
>>>> under "updating the base and differentiated objects" in the docs.
>
> Digging into the code, here are the bytecode operations that are
> not supported yet:
>
> DALOAD, DASTORE:
> element access in double arrays
> GETSTATIC, PUTSTATIC, GETFIELD, PUTFIELD:
> field access (instance fields and class fields)
> INVOKEVIRTUAL/INVOKESPECIAL/INVOKESTATIC/INVOKEINTERFACE:
> method calls
> NEWARRAY/ANEWARRAY/MULTIANEWARRAY:
> array creation
>
>>>> Is this example supposed to work with the code in trunk? Also,
>>>> I am
>>> I'll look at this tomorrow, but I think for now you need to have
>>> a standalone function, it cannot be split
>>> as a main function calling subfunctions. The only allowed calls
>>> are the static methods from Math/StrictMath.
>>> I did not add our own FastMath, but it is trivial to do.
>>>
>>> Another limitation is that your function cannot store
>>> intermediate results as clas attributes yet.
>>
>> Thanks, Luc! What I was trying to illustrate was partial
>> derivatives, which IIUC you need something like that example to do.
>> The following almost works:
>>
>> public void testPartialDerivatives() throws Exception {
>> PartialFunction function = new PartialFunction(1);
>> final UnivariateDerivative derivative = new
>> ForwardModeAlgorithmicDifferentiator().differentiate(function);
>> DifferentialPair t = DifferentialPair.newVariable(1);
>> Assert.assertEquals(3,
>> derivative.f(t).getFirstDerivative(), 0);
>> Assert.assertEquals(2, derivative.f(t).getValue(), 0);
>> function.setX(2);
>> Assert.assertEquals(4,
>> derivative.f(t).getFirstDerivative(), 0);
>> Assert.assertEquals(3, derivative.f(t).getValue(), 0);
>> }
>>
>> with
>>
>> public class PartialFunction implements UnivariateDifferentiable {
>> private double x;
>> public PartialFunction(double x) {
>> this.x = x;
>> }
>> public void setX(double x) {
>> this.x = x;
>> }
>> public double getX() {
>> return x;
>> }
>> public double f(double y) {
>> return x * y + y * y;
>> }
>> }
>>
>> But I end up with java.lang.VerifyError: (class:
>> ExampleTest$1PartialFunction$NablaForwardModeUnivariateDerivative,
>> method: f signature:
>> (Lorg/apache/commons/nabla/core/DifferentialPair;)Lorg/apache/commons/nabla/core/DifferentialPair;)
>>
>> Incompatible type for getting or setting field
>> at java.lang.Class.getDeclaredConstructors0(Native Method)
>> at
>> java.lang.Class.privateGetDeclaredConstructors(Class.java:2389)
>> at java.lang.Class.getDeclaredConstructors(Class.java:1836)
>> at
>> org.apache.commons.nabla.algorithmic.forward.ForwardModeAlgorithmicDifferentiator.differentiate(ForwardModeAlgorithmicDifferentiator.java:107)
>>
>> at ExampleTest.testPartialDerivatives(ExampleTest.java:66)
>
> This error seems to be due to the lack of support for the GETFIELD
> instruction. As x is an instance field, the f method reads this
> field before multiplying the result.
>
> I have added a debug display message (to be removed later on) that
> should print the generated bytecode to standard error when a
> VerifyError exception occurs. It' clearly not targeted towards end
> users, but it could help during development.
Thanks, Luc!
Phil
>
> Luc
>
>>>
>>>
>>> You can look at the junit tests for what is supported. Simple
>>> expressions, calls to traditional functions like sin, cos, exp ...,
>>> Simple loops and conditionals, local automatic variables should
>>> all work (I hope ...)
>>
>> Yep, I have gotten all of this to work. Even "knows" the chain
>> rule :)
>>
>>
>> Phil
>>>
>>>> assuming
>>>> s/ForwardAlgorithmicDifferentiator/ForwardModeAlgorithmicDifferentiator
>>>>
>>>> throughout. Correct?
>>> Yes, the name was changed because a distant goal will be to also
>>> support reverse mode, which is especially
>>> useful when computing gradients (i.e. when one scalar function
>>> depends on many inputs and we want all partial
>>> derivatives).
>>>
>>> Luc
>>>
>>>> Phil
>>>>
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>>
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