Re: [Numpy-discussion] array vs matrix
On Sun, Jun 8, 2008 at 3:54 AM, Anne Archibald [EMAIL PROTECTED] wrote: 2008/6/7 Robert Kern [EMAIL PROTECTED]: On Sat, Jun 7, 2008 at 14:37, Ondrej Certik [EMAIL PROTECTED] wrote: Hi, what is the current plan with array and matrix with regard of calculating sin(A) ? I.e. elementwise vs sin(A) = Q*sin(D)*Q^T? Is the current approach (elementwise for array and Q*sin(D)*Q^T for matrix) the way to go? I don't believe we intend to make numpy.matrix any more featureful. I don't think it's a good idea for you to base sympy.Matrix's capabilities in lock-step with numpy.matrix, though. There are very different constraints at work. Please, do what you think is best for sympy.Matrix. Let me elaborate somewhat: We recently ran across some painful quirks in numpy's handling of matrices, and they spawned massive discussion. As it stands now, there is significant interest in reimplementing the matrix object from scratch, with different behaviour. So emulating its current behaviour is not a win. For consistency, it makes a certain amount of sense to have sin(A) compute a matrix sine, since A**n computes a matrix power. But looking at the matrix exponential, I see that we have several implementations, none of which is satisfactory for all matrices. I would expect the matrix sine to be similar - particularly when faced with complex matrices - so perhaps needing an explicit matrix sine is a good thing? Also worth noting is that this problem can be evaded with namespaces; matrix sin could be scipy.matrixmath.sin, abbreviated perhaps to mm.sin, as opposed to np.sin. I see. Thanks Robert and Anne for the replies. That's all I needed to know. Ondrej ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] array vs matrix
On Sat, Jun 7, 2008 at 14:37, Ondrej Certik [EMAIL PROTECTED] wrote: Hi, what is the current plan with array and matrix with regard of calculating sin(A) ? I.e. elementwise vs sin(A) = Q*sin(D)*Q^T? Is the current approach (elementwise for array and Q*sin(D)*Q^T for matrix) the way to go? I don't believe we intend to make numpy.matrix any more featureful. I don't think it's a good idea for you to base sympy.Matrix's capabilities in lock-step with numpy.matrix, though. There are very different constraints at work. Please, do what you think is best for sympy.Matrix. -- Robert Kern I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth. -- Umberto Eco ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] array vs matrix
2008/6/7 Robert Kern [EMAIL PROTECTED]: On Sat, Jun 7, 2008 at 14:37, Ondrej Certik [EMAIL PROTECTED] wrote: Hi, what is the current plan with array and matrix with regard of calculating sin(A) ? I.e. elementwise vs sin(A) = Q*sin(D)*Q^T? Is the current approach (elementwise for array and Q*sin(D)*Q^T for matrix) the way to go? I don't believe we intend to make numpy.matrix any more featureful. I don't think it's a good idea for you to base sympy.Matrix's capabilities in lock-step with numpy.matrix, though. There are very different constraints at work. Please, do what you think is best for sympy.Matrix. Let me elaborate somewhat: We recently ran across some painful quirks in numpy's handling of matrices, and they spawned massive discussion. As it stands now, there is significant interest in reimplementing the matrix object from scratch, with different behaviour. So emulating its current behaviour is not a win. For consistency, it makes a certain amount of sense to have sin(A) compute a matrix sine, since A**n computes a matrix power. But looking at the matrix exponential, I see that we have several implementations, none of which is satisfactory for all matrices. I would expect the matrix sine to be similar - particularly when faced with complex matrices - so perhaps needing an explicit matrix sine is a good thing? Also worth noting is that this problem can be evaded with namespaces; matrix sin could be scipy.matrixmath.sin, abbreviated perhaps to mm.sin, as opposed to np.sin. Anne ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] array vs matrix
On Sat, Jun 7, 2008 at 20:54, Anne Archibald [EMAIL PROTECTED] wrote: For consistency, it makes a certain amount of sense to have sin(A) compute a matrix sine, since A**n computes a matrix power. But looking at the matrix exponential, I see that we have several implementations, none of which is satisfactory for all matrices. I would expect the matrix sine to be similar - particularly when faced with complex matrices - so perhaps needing an explicit matrix sine is a good thing? I think so. We already have scipy.linalg.sinm(), cosm(), expm(), expm2(), etc. -- Robert Kern I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth. -- Umberto Eco ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] array vs. matrix performance
Robert Cimrman wrote: I have come to a case where using a matrix would be easier than an array. The code uses lots of dot products, so I tested scipy.dot() performance with the code below and found that the array version is much faster (about 3 times for the given shape). What is the reason for this? Or is something wrong with my measurement? regards, r. --- import timeit setup = import numpy as nm import scipy as sc X = nm.random.rand( 100, 3 ) X = nm.asmatrix( X ) # (un)comment this line. print X.shape tt = timeit.Timer( 'sc.dot( X.T, X )', setup ) print tt.timeit() ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion Confirmed, but for what reason ? 0.5.3.dev3020 1.0.3.dev3792 Array version 6.84843301773 Matrix version 17.1273219585 Nils ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] array vs. matrix performance
On 5/21/07, Charles R Harris [EMAIL PROTECTED] wrote: On 5/21/07, David Cournapeau [EMAIL PROTECTED] wrote: Nils Wagner wrote: Robert Cimrman wrote: I have come to a case where using a matrix would be easier than an array. The code uses lots of dot products, so I tested scipy.dot() performance with the code below and found that the array version is much faster (about 3 times for the given shape). What is the reason for this? Or is something wrong with my measurement? regards, r. --- import timeit setup = import numpy as nm import scipy as sc X = nm.random.rand( 100, 3 ) X = nm.asmatrix( X ) # (un)comment this line. print X.shape tt = timeit.Timer( 'sc.dot( X.T, X )', setup ) print tt.timeit() ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion Confirmed, but for what reason ? 0.5.3.dev3020 1.0.3.dev3792 Array version 6.84843301773 Matrix version 17.1273219585 My guess would be that for such small matrices, the cost of matrix wrapping is not negligeable against the actual computation. This difference disappears for bigger matrices (for example, try 1000 and 5000 instead of 100 for the first dimension). Hmmm, I wonder how Tim's matrix version works. I've attached the code. I am rather fond of the approach and have used it a few times myself. Tim uses the dot product is written so: a(b). That is, the () operator is used instead of the *. Well, it doesn't go. There is a bug in the code and, on a second look, it doesn't use dot. BTW, on my machine numpy dot is faster than the scipy version. Chuck ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
