Re: [Numpy-discussion] broadcasting question
On Thursday, August 30, 2012, Neal Becker wrote: I think this should be simple, but I'm drawing a blank I have 2 2d matrixes Matrix A has indexes (i, symbol) Matrix B has indexes (state, symbol) I combined them into a 3d matrix: C = A[:,newaxis,:] + B[newaxis,:,:] where C has indexes (i, state, symbol) That works fine. Now suppose I want to omit B (for debug), like: C = A[:,newaxis,:] In other words, all I want is to add a dimension into A and force it to broadcast along that axis. How do I do that? np.tile would help you there, I think. Ben Root ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] broadcasting question
On Thu, Aug 30, 2012 at 12:49 PM, Neal Becker ndbeck...@gmail.com wrote: I think this should be simple, but I'm drawing a blank I have 2 2d matrixes Matrix A has indexes (i, symbol) Matrix B has indexes (state, symbol) I combined them into a 3d matrix: C = A[:,newaxis,:] + B[newaxis,:,:] where C has indexes (i, state, symbol) That works fine. Now suppose I want to omit B (for debug), like: C = A[:,newaxis,:] In other words, all I want is to add a dimension into A and force it to broadcast along that axis. How do I do that? C, dummy = numpy.broadcast_arrays(A[:,newaxis,:], numpy.empty([1,state,1])) -- Robert Kern ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Broadcasting question
Hi Olivier 2008/12/4 Olivier Grisel [EMAIL PROTECTED]: To avoid the python level loop I then tried to use broadcasting as follows: c = sum((a[:,newaxis,:] - b) ** 2, axis=2) But this build a useless and huge (d1, d2, d3) temporary array that does not fit in memory for large values of d1, d2 and d3... Does numpy.lib.broadcast_arrays do what you need? Regards Stéfan ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Broadcasting question
2008/12/4 Stéfan van der Walt [EMAIL PROTECTED]: Hi Olivier 2008/12/4 Olivier Grisel [EMAIL PROTECTED]: To avoid the python level loop I then tried to use broadcasting as follows: c = sum((a[:,newaxis,:] - b) ** 2, axis=2) But this build a useless and huge (d1, d2, d3) temporary array that does not fit in memory for large values of d1, d2 and d3... Does numpy.lib.broadcast_arrays do what you need? That looks exactly what I am looking for. Apparently this is new in 1.2 since I cannot find it in the 1.1 version of my system. Thanks, -- Olivier ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Broadcasting question
On Thu, Dec 4, 2008 at 8:26 AM, Olivier Grisel [EMAIL PROTECTED]wrote: Hi list, Suppose I have array a with dimensions (d1, d3) and array b with dimensions (d2, d3). I want to compute array c with dimensions (d1, d2) holding the squared euclidian norms of vectors in a and b with size d3. Just to clarify the problem a bit, it looks like you want to compute the squared euclidean distance between every vector in a and every vector in b, i.e., a distance matrix. Is that correct? Also, how big are d1,d2,d3? If you *are* looking to compute the distance matrix I suspect your end goal is something beyond that. Could you describe what you are trying to do? I could be that scipy.spatial or scipy.cluster are what you should look at. snip Chuck ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Broadcasting question
2008/12/4 Charles R Harris [EMAIL PROTECTED]: On Thu, Dec 4, 2008 at 8:26 AM, Olivier Grisel [EMAIL PROTECTED] wrote: Hi list, Suppose I have array a with dimensions (d1, d3) and array b with dimensions (d2, d3). I want to compute array c with dimensions (d1, d2) holding the squared euclidian norms of vectors in a and b with size d3. Just to clarify the problem a bit, it looks like you want to compute the squared euclidean distance between every vector in a and every vector in b, i.e., a distance matrix. Is that correct? Also, how big are d1,d2,d3? I would target d1 d2 ~ d3 with d1 as large as possible to fit in memory and d2 and d3 in the order of a couple hundreds or thousands for a start. If you *are* looking to compute the distance matrix I suspect your end goal is something beyond that. Could you describe what you are trying to do? My end goal it to compute the activation of an array of Radial Basis Function units where the activation of unit with center b_j for data vector a_i is given by: f(a_i, b_j) = exp(-||a_i - bj|| ** 2 / (2 * sigma)) The end goal is to have building blocks of various parameterized array of homogeneous units (linear, sigmoid and RBF) along with their gradient in parameter space so as too build various machine learning algorithms such as multi layer perceptrons with various training strategies such as Stochastic Gradient Descent. That code might be integrated into the Modular Data Processing (MPD toolkit) project [1] at some point. The current stat of the python code is here: http://www.bitbucket.org/ogrisel/oglab/src/186eab341408/simdkernel/src/simdkernel/scalar.py You can find an SSE optimized C implementation wrapped with ctypes here: http://www.bitbucket.org/ogrisel/oglab/src/186eab341408/simdkernel/src/simdkernel/sse.py http://www.bitbucket.org/ogrisel/oglab/src/186eab341408/simdkernel/src/simdkernel/sse.c It could be that scipy.spatial or scipy.cluster are what you should look at. I'll have a look at those, thanks for the pointer. [1] http://mdp-toolkit.sourceforge.net/ -- Olivier ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Broadcasting question
2008/5/2 Chris.Barker [EMAIL PROTECTED]: Hi all, I have a n X m X 3 array, and I a n X M array. I want to assign the values in the n X m to all three of the slices in the bigger array: A1 = np.zeros((5,4,3)) A2 = np.ones((5,4)) A1[:,:,0] = A2 A1[:,:,1] = A2 A1[:,:,2] = A2 However,it seems I should be able to broadcast that, so I don't have to repeat myself, but I can't figure out how. All you need is a newaxis on A2: In [2]: A = np.zeros((3,4)) In [3]: B = np.ones(3) In [4]: A[:,:] = B[:,np.newaxis] In [5]: A Out[5]: array([[ 1., 1., 1., 1.], [ 1., 1., 1., 1.], [ 1., 1., 1., 1.]]) Anne ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Broadcasting question
Charles R Harris wrote: A1 += A2[:,:,newaxis] is one way. Exactly what I was looking for -- thanks Charles (and Ann) -Chris -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/ORR(206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception [EMAIL PROTECTED] ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Broadcasting question
On Thu, May 1, 2008 at 10:58 PM, Chris.Barker [EMAIL PROTECTED] wrote: Hi all, I have a n X m X 3 array, and I a n X M array. I want to assign the values in the n X m to all three of the slices in the bigger array: A1 = np.zeros((5,4,3)) A2 = np.ones((5,4)) A1[:,:,0] = A2 A1[:,:,1] = A2 A1[:,:,2] = A2 A1 += A2[:,:,newaxis] is one way. Or you could just start by stacking copies of A2 and swapping axes around: In [5]: A1 = array([A2.T]*3).T In [6]: A1.shape Out[6]: (4, 5, 3) Chuck ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
