Just out of curiosity, what speed-up factor did you achieve? Regards, David
On 04/02/12 22:20, Naresh wrote: > Warren Weckesser<warren.weckesser<at> enthought.com> writes: > >> >> On Sat, Feb 4, 2012 at 2:35 PM, Benjamin Root<ben.root<at> ou.edu> wrote: >> >> >> On Saturday, February 4, 2012, Naresh Pai<npai<at> uark.edu> wrote:> I am > somewhat new to Python (been coding with Matlab mostly). I am trying to >>> simplify (and expedite) a piece of code that is currently a bottleneck in a > larger >>> code.> I have a large array (7000 rows x 4500 columns) titled say, abc, and > I am trying> to find a fast method to count the number of instances of each > unique value within> it. All unique values are stored in a variable, say, > unique_elem. My current code >> >>> is as follows:> import numpy as np> #allocate space for storing element > count> elem_count = zeros((len(unique_elem),1))> #loop through and count > number > of unique_elem> for i in range(len(unique_elem)): >> >>> elem_count[i]= np.sum(reduce(np.logical_or,(abc== x for x > in [unique_elem[i]])))> This loop is bottleneck because I have about 850 > unique > elements and it takes> about 9-10 minutes. Can you suggest a faster way to do > this? >> >>> Thank you,> Naresh> >> no.unique() can return indices and reverse indices. It would be trivial to > histogram the reverse indices using np.histogram(). >> >> Instead of histogram(), you can use bincount() on the inverse indices:u, inv >> = > np.unique(abc, return_inverse=True)n = np.bincount(inv)u will be an array of > the > unique elements, and n will be an array of the corresponding number of > occurrences.Warren >> >> >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion<at> scipy.org >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> > The histogram() solution works perfect since unique_elem is ordered. I > appreciate everyone's help. > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
