Re: [Numpy-discussion] Software Capabilities of NumPy in Our Tensor Survey Paper

2016-01-15 Thread Stephan Hoyer 
Robert beat me to it on einsum, but also check tensordot for general tensor 
contraction.

On Fri, Jan 15, 2016 at 9:30 AM, Nathaniel Smith  wrote:

> On Jan 15, 2016 8:36 AM, "Li Jiajia"  wrote:
>>
>> Hi all,
>> I’m a PhD student in Georgia Tech. Recently, we’re working on a survey
> paper about tensor algorithms: basic tensor operations, tensor
> decomposition and some tensor applications. We are making a table to
> compare the capabilities of different software and planning to include
> NumPy. We’d like to make sure these parameters are correct to make a fair
> compare. Although we have looked into the related documents, please help us
> to confirm these. Besides, if you think there are more features of your
> software and a more preferred citation, please let us know. We’ll consider
> to update them. We want to show NumPy supports tensors, and we also include
> "scikit-tensor” in our survey, which is based on NumPy.
>> Please let me know any confusion or any advice!
>> Thanks a lot! :-)
>>
>> Notice:
>> 1. “YES/NO” to show whether or not the software supports the operation or
> has the feature.
>> 2. “?” means we’re not sure of the feature, and please help us out.
>> 3. “Tensor order” means the maximum number of tensor dimensions that
> users can do with this software.
>> 4. For computational cores,
>> 1) "Element-wise Tensor Operation (A * B)” includes element-wise
> add/minus/multiply/divide, also Kronecker, outer and Katri-Rao products. If
> the software contains one of them, we mark “YES”.
>> 2) “TTM” means tensor-times-matrix multiplication. We distinguish TTM
> from tensor contraction. If the software includes tensor contraction, it
> can also support TTM.
>> 3) For “MTTKRP”, we know most software can realize it through the above
> two operations. We mark it “YES”, only if an specified optimization for the
> whole operation.
> NumPy has support for working with multidimensional tensors, if you like,
> but it doesn't really use the tensor language and notation (preferring
> instead to think in terms of "arrays" as a somewhat more computationally
> focused and less mathematically focused conceptual framework).
> Which is to say that I actually have no idea what all those jargon terms
> you're asking about mean :-) I am suspicious that NumPy supports more of
> those operations than you have marked, just under different names/notation,
> but really can't tell either way for sure without knowing what exactly they
> are.
> (It is definitely correct though that NumPy includes no support for sparse
> tensors, and NumPy itself is not multi-threaded beyond what we get for free
> through the BLAS, though there are external libraries that can perform
> multi-threaded computations on top of data stored in numpy arrays.)
> -n___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] Software Capabilities of NumPy in Our Tensor Survey Paper

2016-01-15 Thread Yuxiang Wang 
I echo with Robert that the contraction can be done with np.einsum().
Also, check out the np.tensordot() as well - it can also be used to
perform contraction.

Shawn

On Fri, Jan 15, 2016 at 12:32 PM, Robert Kern  wrote:
> On Fri, Jan 15, 2016 at 5:30 PM, Nathaniel Smith  wrote:
>>
>> On Jan 15, 2016 8:36 AM, "Li Jiajia"  wrote:
>> >
>> > Hi all,
>> > I’m a PhD student in Georgia Tech. Recently, we’re working on a survey
>> > paper about tensor algorithms: basic tensor operations, tensor 
>> > decomposition
>> > and some tensor applications. We are making a table to compare the
>> > capabilities of different software and planning to include NumPy. We’d like
>> > to make sure these parameters are correct to make a fair compare. Although
>> > we have looked into the related documents, please help us to confirm these.
>> > Besides, if you think there are more features of your software and a more
>> > preferred citation, please let us know. We’ll consider to update them. We
>> > want to show NumPy supports tensors, and we also include "scikit-tensor” in
>> > our survey, which is based on NumPy.
>> > Please let me know any confusion or any advice!
>> > Thanks a lot! :-)
>> >
>> > Notice:
>> > 1. “YES/NO” to show whether or not the software supports the operation
>> > or has the feature.
>> > 2. “?” means we’re not sure of the feature, and please help us out.
>> > 3. “Tensor order” means the maximum number of tensor dimensions that
>> > users can do with this software.
>> > 4. For computational cores,
>> > 1) "Element-wise Tensor Operation (A * B)” includes element-wise
>> > add/minus/multiply/divide, also Kronecker, outer and Katri-Rao products. If
>> > the software contains one of them, we mark “YES”.
>> > 2) “TTM” means tensor-times-matrix multiplication. We distinguish TTM
>> > from tensor contraction. If the software includes tensor contraction, it 
>> > can
>> > also support TTM.
>> > 3) For “MTTKRP”, we know most software can realize it through the above
>> > two operations. We mark it “YES”, only if an specified optimization for the
>> > whole operation.
>>
>> NumPy has support for working with multidimensional tensors, if you like,
>> but it doesn't really use the tensor language and notation (preferring
>> instead to think in terms of "arrays" as a somewhat more computationally
>> focused and less mathematically focused conceptual framework).
>>
>> Which is to say that I actually have no idea what all those jargon terms
>> you're asking about mean :-) I am suspicious that NumPy supports more of
>> those operations than you have marked, just under different names/notation,
>> but really can't tell either way for sure without knowing what exactly they
>> are.
>
> In particular check if your operations can be expressed with einsum()
>
> http://docs.scipy.org/doc/numpy-1.10.1/reference/generated/numpy.einsum.html
>
> --
> Robert Kern
>
> ___
> NumPy-Discussion mailing list
> NumPy-Discussion@scipy.org
> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>



-- 
Yuxiang "Shawn" Wang
Gerling Haptics Lab
University of Virginia
yw...@virginia.edu
+1 (434) 284-0836
https://sites.google.com/a/virginia.edu/yw5aj/
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] Software Capabilities of NumPy in Our Tensor Survey Paper

2016-01-15 Thread Robert Kern 
On Fri, Jan 15, 2016 at 5:30 PM, Nathaniel Smith  wrote:
>
> On Jan 15, 2016 8:36 AM, "Li Jiajia"  wrote:
> >
> > Hi all,
> > I’m a PhD student in Georgia Tech. Recently, we’re working on a survey
paper about tensor algorithms: basic tensor operations, tensor
decomposition and some tensor applications. We are making a table to
compare the capabilities of different software and planning to include
NumPy. We’d like to make sure these parameters are correct to make a fair
compare. Although we have looked into the related documents, please help us
to confirm these. Besides, if you think there are more features of your
software and a more preferred citation, please let us know. We’ll consider
to update them. We want to show NumPy supports tensors, and we also include
"scikit-tensor” in our survey, which is based on NumPy.
> > Please let me know any confusion or any advice!
> > Thanks a lot! :-)
> >
> > Notice:
> > 1. “YES/NO” to show whether or not the software supports the operation
or has the feature.
> > 2. “?” means we’re not sure of the feature, and please help us out.
> > 3. “Tensor order” means the maximum number of tensor dimensions that
users can do with this software.
> > 4. For computational cores,
> > 1) "Element-wise Tensor Operation (A * B)” includes element-wise
add/minus/multiply/divide, also Kronecker, outer and Katri-Rao products. If
the software contains one of them, we mark “YES”.
> > 2) “TTM” means tensor-times-matrix multiplication. We distinguish TTM
from tensor contraction. If the software includes tensor contraction, it
can also support TTM.
> > 3) For “MTTKRP”, we know most software can realize it through the above
two operations. We mark it “YES”, only if an specified optimization for the
whole operation.
>
> NumPy has support for working with multidimensional tensors, if you like,
but it doesn't really use the tensor language and notation (preferring
instead to think in terms of "arrays" as a somewhat more computationally
focused and less mathematically focused conceptual framework).
>
> Which is to say that I actually have no idea what all those jargon terms
you're asking about mean :-) I am suspicious that NumPy supports more of
those operations than you have marked, just under different names/notation,
but really can't tell either way for sure without knowing what exactly they
are.

In particular check if your operations can be expressed with einsum()

http://docs.scipy.org/doc/numpy-1.10.1/reference/generated/numpy.einsum.html

--
Robert Kern
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] Software Capabilities of NumPy in Our Tensor Survey Paper

2016-01-15 Thread Nathaniel Smith 
On Jan 15, 2016 8:36 AM, "Li Jiajia"  wrote:
>
> Hi all,
> I’m a PhD student in Georgia Tech. Recently, we’re working on a survey
paper about tensor algorithms: basic tensor operations, tensor
decomposition and some tensor applications. We are making a table to
compare the capabilities of different software and planning to include
NumPy. We’d like to make sure these parameters are correct to make a fair
compare. Although we have looked into the related documents, please help us
to confirm these. Besides, if you think there are more features of your
software and a more preferred citation, please let us know. We’ll consider
to update them. We want to show NumPy supports tensors, and we also include
"scikit-tensor” in our survey, which is based on NumPy.
> Please let me know any confusion or any advice!
> Thanks a lot! :-)
>
> Notice:
> 1. “YES/NO” to show whether or not the software supports the operation or
has the feature.
> 2. “?” means we’re not sure of the feature, and please help us out.
> 3. “Tensor order” means the maximum number of tensor dimensions that
users can do with this software.
> 4. For computational cores,
> 1) "Element-wise Tensor Operation (A * B)” includes element-wise
add/minus/multiply/divide, also Kronecker, outer and Katri-Rao products. If
the software contains one of them, we mark “YES”.
> 2) “TTM” means tensor-times-matrix multiplication. We distinguish TTM
from tensor contraction. If the software includes tensor contraction, it
can also support TTM.
> 3) For “MTTKRP”, we know most software can realize it through the above
two operations. We mark it “YES”, only if an specified optimization for the
whole operation.

NumPy has support for working with multidimensional tensors, if you like,
but it doesn't really use the tensor language and notation (preferring
instead to think in terms of "arrays" as a somewhat more computationally
focused and less mathematically focused conceptual framework).

Which is to say that I actually have no idea what all those jargon terms
you're asking about mean :-) I am suspicious that NumPy supports more of
those operations than you have marked, just under different names/notation,
but really can't tell either way for sure without knowing what exactly they
are.

(It is definitely correct though that NumPy includes no support for sparse
tensors, and NumPy itself is not multi-threaded beyond what we get for free
through the BLAS, though there are external libraries that can perform
multi-threaded computations on top of data stored in numpy arrays.)

-n
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] Software Capabilities of NumPy in Our Tensor Survey Paper

2016-01-15 Thread Bryan Van de Ven 
Your first citation is incorrect. It is "VAN DER WALT" (missing V in yours)

Bryan 

> On Jan 15, 2016, at 10:36 AM, Li Jiajia  wrote:
> 
> Hi all,
>   I’m a PhD student in Georgia Tech. Recently, we’re working on a survey 
> paper about tensor algorithms: basic tensor operations, tensor decomposition 
> and some tensor applications. We are making a table to compare the 
> capabilities of different software and planning to include NumPy. We’d like 
> to make sure these parameters are correct to make a fair compare. Although we 
> have looked into the related documents, please help us to confirm these. 
> Besides, if you think there are more features of your software and a more 
> preferred citation, please let us know. We’ll consider to update them. We 
> want to show NumPy supports tensors, and we also include "scikit-tensor” in 
> our survey, which is based on NumPy.
>   Please let me know any confusion or any advice! 
>   Thanks a lot! :-)
> 
> Notice: 
> 1. “YES/NO” to show whether or not the software supports the operation or has 
> the feature.
> 2. “?” means we’re not sure of the feature, and please help us out. 
> 3. “Tensor order” means the maximum number of tensor dimensions that users 
> can do with this software. 
> 4. For computational cores, 
>   1) "Element-wise Tensor Operation (A * B)” includes element-wise 
> add/minus/multiply/divide, also Kronecker, outer and Katri-Rao products. If 
> the software contains one of them, we mark “YES”.
>   2) “TTM” means tensor-times-matrix multiplication. We distinguish TTM 
> from tensor contraction. If the software includes tensor contraction, it can 
> also support TTM.
>   3) For “MTTKRP”, we know most software can realize it through the above 
> two operations. We mark it “YES”, only if an specified optimization for the 
> whole operation.
> 
> Software Name
> 
> NumPy
> 
> Computational Cores
> 
> Element-wise Tensor Operation (A * B)
> 
> YES
> 
> Tensor Contraction (A Xmn B)
> 
> NO
> 
> TTM ( A Xn B)
> 
> NO
> 
> Matriced Tensor Times Khatri-Rao Product (MTTKRP)
> 
> NO
> 
> Tensor Decomposition
> 
> CP
> 
> NO
> 
> Tucker
> 
> NO
> 
> Hierarchical Tucker (HT)
> 
> NO
> 
> Tensor Train (TT)
> 
> NO
> 
> Tensor Features
> 
> Tensor Order
> 
> Arbitrary
> 
> Dense Tensors
> 
> YES
> 
> Sparse Tensors
> 
> NO ?
> 
> Parallelized
> 
> NO ?
> 
> Software Information
> 
> Application Domain
> 
> General 
> 
> Programming Environment
> 
> Python
> 
> Latest Version
> 
> 1.10.4
> Release Date
> 
> 2016
> 
> Citation: 
> 1. AN DER WALT, S., COLBERT, S., AND VAROQUAUX, G. The NumPy array: A 
> structure for efficient numerical computation. Computing in Science 
> Engineering 13, 2 (March 2011), 22–30.
> 2. OLIPHANT, T. E. Python for scientific computing. Computing in Science 
> Engineering 9, 3 (May 2007), 10–20.
> 3. NumPy (Version1.10.4).Available from http://www.numpy.org, Jan 2016.
> 
> Best regards!
> Jiajia Li
> 
> --
> E-mail: jiaji...@gatech.edu
> Tel: +1 (404)9404603
> Computational Science & Engineering
> Georgia Institute of Technology
> 
> ___
> 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] Software Capabilities of NumPy in Our Tensor Survey Paper

2016-01-15 Thread Li Jiajia 
Hi all,
I’m a PhD student in Georgia Tech. Recently, we’re working on a survey 
paper about tensor algorithms: basic tensor operations, tensor decomposition 
and some tensor applications. We are making a table to compare the capabilities 
of different software and planning to include NumPy. We’d like to make sure 
these parameters are correct to make a fair compare. Although we have looked 
into the related documents, please help us to confirm these. Besides, if you 
think there are more features of your software and a more preferred citation, 
please let us know. We’ll consider to update them. We want to show NumPy 
supports tensors, and we also include "scikit-tensor” in our survey, which is 
based on NumPy.
Please let me know any confusion or any advice! 
Thanks a lot! :-)

Notice: 
1. “YES/NO” to show whether or not the software supports the operation or has 
the feature.
2. “?” means we’re not sure of the feature, and please help us out. 
3. “Tensor order” means the maximum number of tensor dimensions that users can 
do with this software. 
4. For computational cores, 
1) "Element-wise Tensor Operation (A * B)” includes element-wise 
add/minus/multiply/divide, also Kronecker, outer and Katri-Rao products. If the 
software contains one of them, we mark “YES”.
2) “TTM” means tensor-times-matrix multiplication. We distinguish TTM 
from tensor contraction. If the software includes tensor contraction, it can 
also support TTM.
3) For “MTTKRP”, we know most software can realize it through the above 
two operations. We mark it “YES”, only if an specified optimization for the 
whole operation.

 <> <>Software Name <>  
NumPy

Computational Cores

Element-wise Tensor Operation (A * B)

YES

Tensor Contraction (A Xmn B)

NO

TTM ( A Xn B)

NO

Matriced Tensor Times Khatri-Rao Product (MTTKRP)

NO

Tensor Decomposition

CP

NO

Tucker

NO

Hierarchical Tucker (HT)

NO

Tensor Train (TT)

NO

Tensor Features

Tensor Order

Arbitrary

Dense Tensors

YES

Sparse Tensors

NO ?

Parallelized

NO ?

Software Information

Application Domain

General 

Programming Environment

Python

Latest Version

1.10.4
Release Date

2016

Citation: 
1. AN DER WALT, S., COLBERT, S., AND VAROQUAUX, G. The NumPy array: A structure 
for efficient numerical computation. Computing in Science Engineering 13, 2 
(March 2011), 22–30.
2. OLIPHANT, T. E. Python for scientific computing. Computing in Science 
Engineering 9, 3 (May 2007), 10–20.
3. NumPy (Version1.10.4).Available from http://www.numpy.org, Jan 
 2016.

Best regards!
Jiajia Li

--
E-mail: jiaji...@gatech.edu
Tel: +1 (404)9404603
Computational Science & Engineering
Georgia Institute of Technology

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