Re: [Numpy-discussion] ufunc for sum of squared difference

[Matthew Harrigan]

Thanks for pointing me to that.  I think its a great match for a 1D case,
like the inner product of two arrays in terms of signature.  I don't see
how it works with higher dimensional arrays, esp with something like the
axis parameter in ufunc.reduce.  With what I proposed for an array of shape
(M, N) for example, result.shape could be (1,) or (M, 1) or (1, N) for
reducing over the flattened array or either axis.  Can you do something
like that with gufunc or do you need to iterate gufunc calls over
slices/views?  Thanks again.

On Mon, Nov 14, 2016 at 8:49 PM, Stephan Hoyer  wrote:

> On Mon, Nov 14, 2016 at 5:40 PM, Matthew Harrigan <
> harrigan.matt...@gmail.com> wrote:
>
>> Essentially it creates a reduce for a function which isn't binary.  I
>> think this would be generally useful.
>>
>
> NumPy already has a generic enough interface for creating such ufuncs. In
> fact, it's called a "generalized ufunc":
> https://docs.scipy.org/doc/numpy/reference/c-api.generalized-ufuncs.html
>
> I think you could already write "implicit reductions" using gufuncs?
>
>
>
> ___
> NumPy-Discussion mailing list
> NumPy-Discussion@scipy.org
> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>
>
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] ufunc for sum of squared difference

[Jerome Kieffer]

On Mon, 14 Nov 2016 22:38:25 +0200
eat  wrote:

> but it's not so obvious what's happening "under the hoods". Consider this
> (with an old Win7 machine):
> Python 3.5.2 (v3.5.2:4def2a2901a5, Jun 25 2016, 22:18:55) [MSC v.1900 64
> bit (AMD64)]
> np.__version__
> '1.11.1'

What matters is the "blas" library used under the hood, hence the
options passed to numpy at compile time. I notices 20x differences
depending on the blas version.

But more importantly:
* results are the same (at the limit of the numerical precision)
* dot() was always faster than sum(square()), varying from a bit to a lot.

I agree this may change in future version of numpy.

Cheers,
-- 
Jérôme Kieffer
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] ufunc for sum of squared difference

[Stephan Hoyer]

On Mon, Nov 14, 2016 at 5:40 PM, Matthew Harrigan <
harrigan.matt...@gmail.com> wrote:

> Essentially it creates a reduce for a function which isn't binary.  I
> think this would be generally useful.
>

NumPy already has a generic enough interface for creating such ufuncs. In
fact, it's called a "generalized ufunc":
https://docs.scipy.org/doc/numpy/reference/c-api.generalized-ufuncs.html

I think you could already write "implicit reductions" using gufuncs?
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] ufunc for sum of squared difference

[Matthew Harrigan]

Still slower and worse uses 2x the memory for the intermediate temporary
array.

I propose allowing implicit reductions with ufuncs.  Specifically if out is
provided with shape[axis] = 1, then pass it on to the ufunc with a stride
of 0.  That should allow this to work:

x = np.arange(10)
def add_square_diff(x1, x2, x3):
return x1 + (x2-x3)**2
result  =np.zeros(1)
np.frompyfunc(add_square_diff, 3, 1)(result, x, np.mean(x), result)

Essentially it creates a reduce for a function which isn't binary.  I think
this would be generally useful.  For instance, finding the min and max in
one pass would be nice:

def minmax(x1, x2, x3):
return min(x1,x3), max(x2,x3)
minn = np.array([np.inf])
maxx = np.array([-np.inf])
np.frompyfunc(minmax, 3, 2)(minn, maxx, x, minn, maxx)

Note it also allows for arbitrary initial values or identity to be
specified, possibly determined at run time.  I think this would make ufuncs
even more universal.

On Mon, Nov 14, 2016 at 3:38 AM, Jerome Kieffer 
wrote:

> On Fri, 11 Nov 2016 11:25:58 -0500
> Matthew Harrigan  wrote:
>
> > I started a ufunc to compute the sum of square differences here
> > .
> > It is about 4x faster and uses half the memory compared to
> > np.sum(np.square(x-c)).
>
> Hi Matt,
>
> Using *blas* you win already a factor two (maybe more depending on you
> blas implementation):
>
> % python -m timeit -s "import numpy as np;x=np.linspace(0,1,int(1e7))"
> "np.sum(np.square(x-2.))"
> 10 loops, best of 3: 135 msec per loop
>
> % python -m timeit -s "import numpy as np;x=np.linspace(0,1,int(1e7))"
> "y=x-2.;np.dot(y,y)"
> 10 loops, best of 3: 70.2 msec per loop
>
>
> Cheers,
> --
> Jérôme Kieffer
> ___
> NumPy-Discussion mailing list
> NumPy-Discussion@scipy.org
> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] ufunc for sum of squared difference

[eat]

Yeah,

but it's not so obvious what's happening "under the hoods". Consider this
(with an old Win7 machine):
Python 3.5.2 (v3.5.2:4def2a2901a5, Jun 25 2016, 22:18:55) [MSC v.1900 64
bit (AMD64)]
np.__version__
'1.11.1'

On Mon, Nov 14, 2016 at 10:38 AM, Jerome Kieffer 
wrote:

> On Fri, 11 Nov 2016 11:25:58 -0500
> Matthew Harrigan  wrote:
>
> > I started a ufunc to compute the sum of square differences here
> > .
> > It is about 4x faster and uses half the memory compared to
> > np.sum(np.square(x-c)).
>
> Hi Matt,
>
> Using *blas* you win already a factor two (maybe more depending on you
> blas implementation):
>
> % python -m timeit -s "import numpy as np;x=np.linspace(0,1,int(1e7))"
> "np.sum(np.square(x-2.))"
> 10 loops, best of 3: 135 msec per loop
>
> % python -m timeit -s "import numpy as np;x=np.linspace(0,1,int(1e7))"
> "y=x-2.;np.dot(y,y)"
> 10 loops, best of 3: 70.2 msec per loop
>
x= np.linspace(0, 1, int(1e6))

timeit np.sum(np.square(x- 2.))
10 loops, best of 3: 23 ms per loop

y= x- 2.

timeit np.dot(y, y)
The slowest run took 18.60 times longer than the fastest. This could mean
that an intermediate result is being cached.
1000 loops, best of 3: 1.78 ms per loop

timeit np.dot(y, y)
1000 loops, best of 3: 1.73 ms per loop

Best,
eat

>
>
> Cheers,
> --
> Jérôme Kieffer
> ___
> NumPy-Discussion mailing list
> NumPy-Discussion@scipy.org
> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] ufunc for sum of squared difference

[Jerome Kieffer]

On Fri, 11 Nov 2016 11:25:58 -0500
Matthew Harrigan  wrote:

> I started a ufunc to compute the sum of square differences here
> .
> It is about 4x faster and uses half the memory compared to
> np.sum(np.square(x-c)). 

Hi Matt,

Using *blas* you win already a factor two (maybe more depending on you blas 
implementation):

% python -m timeit -s "import numpy as np;x=np.linspace(0,1,int(1e7))" 
"np.sum(np.square(x-2.))"
10 loops, best of 3: 135 msec per loop

% python -m timeit -s "import numpy as np;x=np.linspace(0,1,int(1e7))" 
"y=x-2.;np.dot(y,y)"
10 loops, best of 3: 70.2 msec per loop


Cheers,
-- 
Jérôme Kieffer
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] ufunc for sum of squared difference

[Matthew Harrigan]

My possibly poorly thought out API would be something like
np.add_sq_diff.set_c(42.0).reduce(x), basically add a setter method which
returned self.  Thanks for explaining what data was intended to do.  All of
this is really just a dance around the requirement that reduce only works
on binary functions.  Would there be any interest in extending reduce to
allow for arbitrary numbers of input and output arguments?

On Fri, Nov 11, 2016 at 1:38 PM, Julian Taylor <
jtaylor.deb...@googlemail.com> wrote:

> here is an old unfinished PR adding max_abs which has a similar purpose
> as yours:
> https://github.com/numpy/numpy/pull/5509
>
> So far I know data is set during runtime construction and is part of the
> ufunc object, so it is not really very suitable to pass in constants on
> call.
> Its intended purpose is pass in functions to be called in generic loops
> like PyUFunc_d_d.
>
> Having an option to mark arguments of a ufunc as special in reductions
> could be useful, e.g. it would allow a potential
> (fused-)multiply-and-add ufunc to be used to implement a weighted sum.
>
> On 11.11.2016 17:25, Matthew Harrigan wrote:
> > I started a ufunc to compute the sum of square differences here
> > .
> It
> > is about 4x faster and uses half the memory compared to
> > np.sum(np.square(x-c)).  This could significantly speed up common
> > operations like std and var (where c=np.mean(x).  It faster because its
> > a single pass instead of 3, and also because the inner loop is
> > specialized for the common reduce case, which might be an optimization
> > considering more generally.
> >
> > I think I have answered my question #2&3 above.
> >
> > Can someone please point me to an example where "data" was used in a
> > ufunc inner loop?  How can that value be set at runtime?  Thanks
> >
> > On Fri, Nov 4, 2016 at 5:33 PM, Sebastian Berg
> > > wrote:
> >
> > On Fr, 2016-11-04 at 15:42 -0400, Matthew Harrigan wrote:
> > > I didn't notice identity before.  Seems like frompyfunc always sets
> > > it to None.  If it were zero maybe it would work as desired here.
> > >
> > > In the writing your own ufunc doc, I was wondering if the pointer
> to
> > > data could be used to get a constant at runtime.  If not, what
> could
> > > that be used for?
> > > static void double_logit(char **args, npy_intp *dimensions,
> > > npy_intp* steps, void* data)
> > > Why would the numerical accuracy be any different?  The subtraction
> > > and square operations look identical and I thought np.sum just
> calls
> > > np.add.reduce, so the reduction step uses the same code and would
> > > therefore have the same accuracy.
> > >
> >
> > Sorry, did not read it carefully, I guess `c` is the mean, so you are
> > doing the two pass method.
> >
> > - Sebastian
> >
> >
> > > Thanks
> > >
> > > On Fri, Nov 4, 2016 at 1:56 PM, Sebastian Berg
>  > > s.net > wrote:
> > > > On Fr, 2016-11-04 at 13:11 -0400, Matthew Harrigan wrote:
> > > > > I was reading this and got thinking about if a ufunc could
> > > > compute
> > > > > the sum of squared differences in a single pass without a
> > > > temporary
> > > > > array.  The python code below demonstrates a possible approach.
> > > > >
> > > > > import numpy as np
> > > > > x = np.arange(10)
> > > > > c = 1.0
> > > > > def add_square_diff(x1, x2):
> > > > > return x1 + (x2-c)**2
> > > > > ufunc = np.frompyfunc(add_square_diff, 2, 1)
> > > > > print(ufunc.reduce(x) - x[0] + (x[0]-c)**2)
> > > > > print(np.sum(np.square(x-c)))
> > > > >
> > > > > I have (at least) 4 questions:
> > > > > 1. Is it possible to pass run time constants to a ufunc written
> > > > in C
> > > > > for use in its inner loop, and if so how?
> > > >
> > > > I don't think its anticipated, since a ufunc could in most cases
> > > > use a
> > > > third argument, but a 3 arg ufunc can't be reduced. Not sure if
> > > > there
> > > > might be some trickery possible.
> > > >
> > > > > 2. Is it possible to pass an initial value to reduce to avoid
> the
> > > > > clean up required for the first element?
> > > >
> > > > This is the identity normally. But the identity can only be 0, 1
> or
> > > > -1
> > > > right now I think. The identity is what the output array gets
> > > > initialized with (which effectively makes it the first value
> passed
> > > > into the inner loop).
> > > >
> > > > > 3. Does that ufunc work, or are there special cases which cause
> > > > it to
> > > > > fall apart?
> > > > > 4. Would a very specialized ufunc such as this be considered
> for
> > > > > incorporating in numpy since it would 

Re: [Numpy-discussion] ufunc for sum of squared difference

[Julian Taylor]

here is an old unfinished PR adding max_abs which has a similar purpose
as yours:
https://github.com/numpy/numpy/pull/5509

So far I know data is set during runtime construction and is part of the
ufunc object, so it is not really very suitable to pass in constants on
call.
Its intended purpose is pass in functions to be called in generic loops
like PyUFunc_d_d.

Having an option to mark arguments of a ufunc as special in reductions
could be useful, e.g. it would allow a potential
(fused-)multiply-and-add ufunc to be used to implement a weighted sum.

On 11.11.2016 17:25, Matthew Harrigan wrote:
> I started a ufunc to compute the sum of square differences here
> .  It
> is about 4x faster and uses half the memory compared to
> np.sum(np.square(x-c)).  This could significantly speed up common
> operations like std and var (where c=np.mean(x).  It faster because its
> a single pass instead of 3, and also because the inner loop is
> specialized for the common reduce case, which might be an optimization
> considering more generally.
> 
> I think I have answered my question #2&3 above.
> 
> Can someone please point me to an example where "data" was used in a
> ufunc inner loop?  How can that value be set at runtime?  Thanks
> 
> On Fri, Nov 4, 2016 at 5:33 PM, Sebastian Berg
> > wrote:
> 
> On Fr, 2016-11-04 at 15:42 -0400, Matthew Harrigan wrote:
> > I didn't notice identity before.  Seems like frompyfunc always sets
> > it to None.  If it were zero maybe it would work as desired here.
> >
> > In the writing your own ufunc doc, I was wondering if the pointer to
> > data could be used to get a constant at runtime.  If not, what could
> > that be used for?
> > static void double_logit(char **args, npy_intp *dimensions,
> > npy_intp* steps, void* data)
> > Why would the numerical accuracy be any different?  The subtraction
> > and square operations look identical and I thought np.sum just calls
> > np.add.reduce, so the reduction step uses the same code and would
> > therefore have the same accuracy.
> >
> 
> Sorry, did not read it carefully, I guess `c` is the mean, so you are
> doing the two pass method.
> 
> - Sebastian
> 
> 
> > Thanks
> >
> > On Fri, Nov 4, 2016 at 1:56 PM, Sebastian Berg  > s.net > wrote:
> > > On Fr, 2016-11-04 at 13:11 -0400, Matthew Harrigan wrote:
> > > > I was reading this and got thinking about if a ufunc could
> > > compute
> > > > the sum of squared differences in a single pass without a
> > > temporary
> > > > array.  The python code below demonstrates a possible approach.
> > > >
> > > > import numpy as np
> > > > x = np.arange(10)
> > > > c = 1.0
> > > > def add_square_diff(x1, x2):
> > > > return x1 + (x2-c)**2
> > > > ufunc = np.frompyfunc(add_square_diff, 2, 1)
> > > > print(ufunc.reduce(x) - x[0] + (x[0]-c)**2)
> > > > print(np.sum(np.square(x-c)))
> > > >
> > > > I have (at least) 4 questions:
> > > > 1. Is it possible to pass run time constants to a ufunc written
> > > in C
> > > > for use in its inner loop, and if so how?
> > >
> > > I don't think its anticipated, since a ufunc could in most cases
> > > use a
> > > third argument, but a 3 arg ufunc can't be reduced. Not sure if
> > > there
> > > might be some trickery possible.
> > >
> > > > 2. Is it possible to pass an initial value to reduce to avoid the
> > > > clean up required for the first element?
> > >
> > > This is the identity normally. But the identity can only be 0, 1 or
> > > -1
> > > right now I think. The identity is what the output array gets
> > > initialized with (which effectively makes it the first value passed
> > > into the inner loop).
> > >
> > > > 3. Does that ufunc work, or are there special cases which cause
> > > it to
> > > > fall apart?
> > > > 4. Would a very specialized ufunc such as this be considered for
> > > > incorporating in numpy since it would help reduce time and memory
> > > of
> > > > functions already in numpy?
> > > >
> > >
> > > Might be mixing up things, however, IIRC the single pass approach
> > > has a
> > > bad numerical accuracy, so that I doubt that it is a good default
> > > algorithm.
> > >
> > > - Sebastian
> > >
> > >
> > > > Thank you,
> > > > Matt
> > > > ___
> > > > NumPy-Discussion mailing list
> > > > NumPy-Discussion@scipy.org 
> > > > https://mail.scipy.org/mailman/listinfo/numpy-discussion
> 
> > > 

Re: [Numpy-discussion] ufunc for sum of squared difference

[Matthew Harrigan]

I started a ufunc to compute the sum of square differences here
.
It is about 4x faster and uses half the memory compared to
np.sum(np.square(x-c)).  This could significantly speed up common
operations like std and var (where c=np.mean(x).  It faster because its a
single pass instead of 3, and also because the inner loop is specialized
for the common reduce case, which might be an optimization considering more
generally.

I think I have answered my question #2&3 above.

Can someone please point me to an example where "data" was used in a ufunc
inner loop?  How can that value be set at runtime?  Thanks

On Fri, Nov 4, 2016 at 5:33 PM, Sebastian Berg 
wrote:

> On Fr, 2016-11-04 at 15:42 -0400, Matthew Harrigan wrote:
> > I didn't notice identity before.  Seems like frompyfunc always sets
> > it to None.  If it were zero maybe it would work as desired here.
> >
> > In the writing your own ufunc doc, I was wondering if the pointer to
> > data could be used to get a constant at runtime.  If not, what could
> > that be used for?
> > static void double_logit(char **args, npy_intp *dimensions,
> > npy_intp* steps, void* data)
> > Why would the numerical accuracy be any different?  The subtraction
> > and square operations look identical and I thought np.sum just calls
> > np.add.reduce, so the reduction step uses the same code and would
> > therefore have the same accuracy.
> >
>
> Sorry, did not read it carefully, I guess `c` is the mean, so you are
> doing the two pass method.
>
> - Sebastian
>
>
> > Thanks
> >
> > On Fri, Nov 4, 2016 at 1:56 PM, Sebastian Berg  > s.net> wrote:
> > > On Fr, 2016-11-04 at 13:11 -0400, Matthew Harrigan wrote:
> > > > I was reading this and got thinking about if a ufunc could
> > > compute
> > > > the sum of squared differences in a single pass without a
> > > temporary
> > > > array.  The python code below demonstrates a possible approach.
> > > >
> > > > import numpy as np
> > > > x = np.arange(10)
> > > > c = 1.0
> > > > def add_square_diff(x1, x2):
> > > > return x1 + (x2-c)**2
> > > > ufunc = np.frompyfunc(add_square_diff, 2, 1)
> > > > print(ufunc.reduce(x) - x[0] + (x[0]-c)**2)
> > > > print(np.sum(np.square(x-c)))
> > > >
> > > > I have (at least) 4 questions:
> > > > 1. Is it possible to pass run time constants to a ufunc written
> > > in C
> > > > for use in its inner loop, and if so how?
> > >
> > > I don't think its anticipated, since a ufunc could in most cases
> > > use a
> > > third argument, but a 3 arg ufunc can't be reduced. Not sure if
> > > there
> > > might be some trickery possible.
> > >
> > > > 2. Is it possible to pass an initial value to reduce to avoid the
> > > > clean up required for the first element?
> > >
> > > This is the identity normally. But the identity can only be 0, 1 or
> > > -1
> > > right now I think. The identity is what the output array gets
> > > initialized with (which effectively makes it the first value passed
> > > into the inner loop).
> > >
> > > > 3. Does that ufunc work, or are there special cases which cause
> > > it to
> > > > fall apart?
> > > > 4. Would a very specialized ufunc such as this be considered for
> > > > incorporating in numpy since it would help reduce time and memory
> > > of
> > > > functions already in numpy?
> > > >
> > >
> > > Might be mixing up things, however, IIRC the single pass approach
> > > has a
> > > bad numerical accuracy, so that I doubt that it is a good default
> > > algorithm.
> > >
> > > - Sebastian
> > >
> > >
> > > > Thank you,
> > > > Matt
> > > > ___
> > > > NumPy-Discussion mailing list
> > > > NumPy-Discussion@scipy.org
> > > > https://mail.scipy.org/mailman/listinfo/numpy-discussion
> > > ___
> > > NumPy-Discussion mailing list
> > > NumPy-Discussion@scipy.org
> > > https://mail.scipy.org/mailman/listinfo/numpy-discussion
> > >
> > ___
> > NumPy-Discussion mailing list
> > NumPy-Discussion@scipy.org
> > https://mail.scipy.org/mailman/listinfo/numpy-discussion
>
> ___
> NumPy-Discussion mailing list
> NumPy-Discussion@scipy.org
> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>
>
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] ufunc for sum of squared difference

[Sebastian Berg]

On Fr, 2016-11-04 at 15:42 -0400, Matthew Harrigan wrote:
> I didn't notice identity before.  Seems like frompyfunc always sets
> it to None.  If it were zero maybe it would work as desired here.
> 
> In the writing your own ufunc doc, I was wondering if the pointer to
> data could be used to get a constant at runtime.  If not, what could
> that be used for?
> static void double_logit(char **args, npy_intp *dimensions,
> npy_intp* steps, void* data)
> Why would the numerical accuracy be any different?  The subtraction
> and square operations look identical and I thought np.sum just calls
> np.add.reduce, so the reduction step uses the same code and would
> therefore have the same accuracy.
> 

Sorry, did not read it carefully, I guess `c` is the mean, so you are
doing the two pass method.

- Sebastian


> Thanks
> 
> On Fri, Nov 4, 2016 at 1:56 PM, Sebastian Berg  s.net> wrote:
> > On Fr, 2016-11-04 at 13:11 -0400, Matthew Harrigan wrote:
> > > I was reading this and got thinking about if a ufunc could
> > compute
> > > the sum of squared differences in a single pass without a
> > temporary
> > > array.  The python code below demonstrates a possible approach.
> > >
> > > import numpy as np
> > > x = np.arange(10)
> > > c = 1.0
> > > def add_square_diff(x1, x2):
> > >     return x1 + (x2-c)**2
> > > ufunc = np.frompyfunc(add_square_diff, 2, 1)
> > > print(ufunc.reduce(x) - x[0] + (x[0]-c)**2)
> > > print(np.sum(np.square(x-c)))
> > >
> > > I have (at least) 4 questions:
> > > 1. Is it possible to pass run time constants to a ufunc written
> > in C
> > > for use in its inner loop, and if so how?
> > 
> > I don't think its anticipated, since a ufunc could in most cases
> > use a
> > third argument, but a 3 arg ufunc can't be reduced. Not sure if
> > there
> > might be some trickery possible.
> > 
> > > 2. Is it possible to pass an initial value to reduce to avoid the
> > > clean up required for the first element?
> > 
> > This is the identity normally. But the identity can only be 0, 1 or
> > -1
> > right now I think. The identity is what the output array gets
> > initialized with (which effectively makes it the first value passed
> > into the inner loop).
> > 
> > > 3. Does that ufunc work, or are there special cases which cause
> > it to
> > > fall apart?
> > > 4. Would a very specialized ufunc such as this be considered for
> > > incorporating in numpy since it would help reduce time and memory
> > of
> > > functions already in numpy?
> > >
> > 
> > Might be mixing up things, however, IIRC the single pass approach
> > has a
> > bad numerical accuracy, so that I doubt that it is a good default
> > algorithm.
> > 
> > - Sebastian
> > 
> > 
> > > Thank you,
> > > Matt
> > > ___
> > > NumPy-Discussion mailing list
> > > NumPy-Discussion@scipy.org
> > > https://mail.scipy.org/mailman/listinfo/numpy-discussion
> > ___
> > NumPy-Discussion mailing list
> > NumPy-Discussion@scipy.org
> > https://mail.scipy.org/mailman/listinfo/numpy-discussion
> > 
> ___
> NumPy-Discussion mailing list
> NumPy-Discussion@scipy.org
> https://mail.scipy.org/mailman/listinfo/numpy-discussion

signature.asc
Description: This is a digitally signed message part
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] ufunc for sum of squared difference

[Matthew Harrigan]

I didn't notice identity before.  Seems like frompyfunc always sets it to
None.  If it were zero maybe it would work as desired here.

In the writing your own ufunc doc, I was wondering if the pointer to data
could be used to get a constant at runtime.  If not, what could that be
used for?

static void double_logit(char **args, npy_intp *dimensions,
npy_intp* steps, void* data)

Why would the numerical accuracy be any different?  The subtraction and
square operations look identical and I thought np.sum just calls
np.add.reduce, so the reduction step uses the same code and would therefore
have the same accuracy.

Thanks

On Fri, Nov 4, 2016 at 1:56 PM, Sebastian Berg 
wrote:

> On Fr, 2016-11-04 at 13:11 -0400, Matthew Harrigan wrote:
> > I was reading this and got thinking about if a ufunc could compute
> > the sum of squared differences in a single pass without a temporary
> > array.  The python code below demonstrates a possible approach.
> >
> > import numpy as np
> > x = np.arange(10)
> > c = 1.0
> > def add_square_diff(x1, x2):
> > return x1 + (x2-c)**2
> > ufunc = np.frompyfunc(add_square_diff, 2, 1)
> > print(ufunc.reduce(x) - x[0] + (x[0]-c)**2)
> > print(np.sum(np.square(x-c)))
> >
> > I have (at least) 4 questions:
> > 1. Is it possible to pass run time constants to a ufunc written in C
> > for use in its inner loop, and if so how?
>
> I don't think its anticipated, since a ufunc could in most cases use a
> third argument, but a 3 arg ufunc can't be reduced. Not sure if there
> might be some trickery possible.
>
> > 2. Is it possible to pass an initial value to reduce to avoid the
> > clean up required for the first element?
>
> This is the identity normally. But the identity can only be 0, 1 or -1
> right now I think. The identity is what the output array gets
> initialized with (which effectively makes it the first value passed
> into the inner loop).
>
> > 3. Does that ufunc work, or are there special cases which cause it to
> > fall apart?
> > 4. Would a very specialized ufunc such as this be considered for
> > incorporating in numpy since it would help reduce time and memory of
> > functions already in numpy?
> >
>
> Might be mixing up things, however, IIRC the single pass approach has a
> bad numerical accuracy, so that I doubt that it is a good default
> algorithm.
>
> - Sebastian
>
>
> > Thank you,
> > Matt
> > ___
> > NumPy-Discussion mailing list
> > NumPy-Discussion@scipy.org
> > https://mail.scipy.org/mailman/listinfo/numpy-discussion
> ___
> NumPy-Discussion mailing list
> NumPy-Discussion@scipy.org
> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>
>
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] ufunc for sum of squared difference

[Sebastian Berg]

On Fr, 2016-11-04 at 13:11 -0400, Matthew Harrigan wrote:
> I was reading this and got thinking about if a ufunc could compute
> the sum of squared differences in a single pass without a temporary
> array.  The python code below demonstrates a possible approach.
> 
> import numpy as np
> x = np.arange(10)
> c = 1.0
> def add_square_diff(x1, x2):
>     return x1 + (x2-c)**2
> ufunc = np.frompyfunc(add_square_diff, 2, 1)
> print(ufunc.reduce(x) - x[0] + (x[0]-c)**2)
> print(np.sum(np.square(x-c)))
> 
> I have (at least) 4 questions:
> 1. Is it possible to pass run time constants to a ufunc written in C
> for use in its inner loop, and if so how?

I don't think its anticipated, since a ufunc could in most cases use a
third argument, but a 3 arg ufunc can't be reduced. Not sure if there
might be some trickery possible.

> 2. Is it possible to pass an initial value to reduce to avoid the
> clean up required for the first element?

This is the identity normally. But the identity can only be 0, 1 or -1
right now I think. The identity is what the output array gets
initialized with (which effectively makes it the first value passed
into the inner loop).

> 3. Does that ufunc work, or are there special cases which cause it to
> fall apart?
> 4. Would a very specialized ufunc such as this be considered for
> incorporating in numpy since it would help reduce time and memory of
> functions already in numpy?
> 

Might be mixing up things, however, IIRC the single pass approach has a
bad numerical accuracy, so that I doubt that it is a good default
algorithm.

- Sebastian


> Thank you,
> Matt
> ___
> NumPy-Discussion mailing list
> NumPy-Discussion@scipy.org
> https://mail.scipy.org/mailman/listinfo/numpy-discussion

signature.asc
Description: This is a digitally signed message part
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
https://mail.scipy.org/mailman/listinfo/numpy-discussion