[SciPy-user] Avoiding For Loops Question
Whitcomb, Mr. Tim
tim.whitcomb@nrlmry.navy....
Fri Jun 5 11:37:22 CDT 2009
> Now, is it possible to write get around these types of for
> loops using any tools from scipy?
Numpy, yes.
> for i in xrange(len(x)):
> a[i] = i*(i+1)/2*x[i]
>
The values of i are just indices, and those can be precomputed
beforehand:
i = numpy.arange(len(x))
a[:] = i[:]*(i[:]+1)/2*x[:]
> for i in xrange(y.shape[0]):
> for k in xrange(y.shape[1]):
> a[i] += x[i] + y[i][k]
Break the sum into two pieces - the x component is just repeated
y.shape[1] times, and y is added up along the second axis:
a[:] = x[:]*y.shape[1] + y[:].sum(axis=1)
> for i in xrange(y.shape[0]):
> for k in xrange(y.shape[1]):
> a[i][k] = x[i] + y[i][k]
Here, you are copying y into a, then adding the same value of x across
an entire axis. Use array broadcasting to make x be the same shape as
y, but with each column the same value:
a[:,:] = x[:, numpy.newaxis] + y[:,:]
I don't know what the style standard is regarding using the colons to
indicate entire arrays (i.e.
a = x[:,numpy.newaxis] + y instead), but these should work for you.
Tim
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