On Thu, Jan 8, 2009 at 12:34 PM, Eric Firing <[email protected]> wrote: > John Hunter wrote: >> On Wed, Jan 7, 2009 at 5:37 PM, Eric Firing <[email protected]> wrote: >> >>> A couple small changes speed it up quite a bit: >>> >>> efir...@manini:~/temp/nnbf$ python test_nnbf.py >>> loading data... this could take a while >>> testing nnbf... >>> 10 trials: mean=0.0150, min=0.0100 >>> testing numpy... >>> 10 trials: mean=0.0660, min=0.0600 >>> >>> It is all a matter of keeping Python objects and function calls out of inner >>> loops. I suspect there is quite a bit more that could be done in that >>> regard, but I haven't looked. >> >> Much faster, but no longer correct, as you'll see if you uncomment >> out the nose test test_neighbors that compare actual vs desired. >> >> Is the pointer arithmetic correct: >> >> dataptr + i >> >> I would have thought perhaps: >> >> dataptr + i*n >> >> but this is segfaulting. Do we need to use a stride? > > Sorry, I was too hasty. Yes, it seems like i*n should be correct, but > it isn't; we are missing something simple and fundamental here. I don't > see it immediately, and won't be able to look at it for a while.
OK, the code at > svn co https://matplotlib.svn.sourceforge.net/svnroot/matplotlib/trunk/py4science/examples/pyrex/nnbf now passes the correctness tests and is approx ten time faster than the numpy version. I borrowed the "raw_data" idiom from Anne's ckdtree.pyx, though I don't really understand why it is different that what we were doing with the data.data ptr. I am also not sure if I need to be doing any memory management when I resize the buffer and reset raw_data.... """ A brute force nearest neighbor routine with incremental add. The internal array data structure grows as you add points """ import numpy as np cimport numpy as np cdef extern from "math.h": float sqrt(float) cdef inline int is_neighbor(int n, double*row, double*pp, double d2max): """ return 1 if the sum-of-squares of n length array row[j]-pp[j] <= d2max """ cdef int j cdef double d, d2 d2 = 0. for j in range(n): d = row[j] - pp[j] d2 += d*d if d2>d2max: return 0 return 1 cdef class NNBF: cdef readonly object data cdef double* raw_data cdef readonly int n, numrows, numpoints def __init__(self, n): """ create a buffer to hold n dimensional points """ cdef np.ndarray[double, ndim=2] inner_data self.n = n self.numrows = 10000 # XXX how to create empty as contiguous w/o copy? data = np.empty((self.numrows, self.n), dtype=np.float) self.data = np.ascontiguousarray(data, dtype=np.float) inner_data = self.data self.raw_data = <double*>inner_data.data self.numpoints = 0 def add(NNBF self, object point): """ add a point to the buffer, grow if necessary """ cdef np.ndarray[double, ndim=2] inner_data cdef np.ndarray[double, ndim=1] pp pp = np.array(point).astype(np.float) self.data[self.numpoints] = pp self.numpoints += 1 if self.numpoints==self.numrows: ## XXX do I need to do memory management here, eg free ## raw_data if I were using it? self.numrows *= 2 newdata = np.empty((self.numrows, self.n), np.float) newdata[:self.numpoints] = self.data self.data = np.ascontiguousarray(newdata, dtype=np.float) inner_data = self.data self.raw_data = <double*>inner_data.data def get_data(NNBF self): """ return a copy of data added so far as a numpoints x n array """ return self.data[:self.numpoints] def find_neighbors(NNBF self, object point, double radius): """ return a list of indices into data which are within radius from point """ cdef int i, neighbor, n cdef double d2max cdef np.ndarray[double, ndim=1] pp # avoid python array indexing in the inner loop if len(point)!=self.n: raise ValueError('Expected a length %d vector'%self.n) pp = np.asarray(point).astype(np.float) d2max = radius*radius neighbors = [] # don't do a python lookup inside the loop n = self.n for i in range(self.numpoints): neighbor = is_neighbor( n, self.raw_data + i*n, <double*>pp.data, d2max) # if the number of points in the cluster is small, the # python list performance should not kill us if neighbor: neighbors.append(i) return neighbors def find_neighbors_numpy(self, point, radius): """ do a plain ol numpy lookup to compare performance and output *data* is a numpoints x numdims array *point* is a numdims length vector radius is the max distance distance return an array of indices into data which are within radius """ data = self.get_data() distance = data - point r = np.sqrt((distance*distance).sum(axis=1)) return np.nonzero(r<=radius)[0] _______________________________________________ Numpy-discussion mailing list [email protected] http://projects.scipy.org/mailman/listinfo/numpy-discussion
