Python可以执行矢量化操作吗？

Question

I want to implement the following Matlab code in Python:

x=1:100;
y=20*log10(x);

I tried using Numpy to do this:

y = numpy.zeros(x.shape)
for i in range(len(x)):
    y[i] = 20*math.log10(x[i])

But this uses a for loop; is there anyway to do a vectorized operation like in Matlab? I know for some simple math such as division and multiplication, it's possible. But what about other more sophisticated operations like logarithm here?

Answer 1

y = numpy.log10(numpy.arange(1, 101)) * 20

---

In [30]: numpy.arange(1, 10)
Out[30]: array([1, 2, 3, 4, 5, 6, 7, 8, 9])

In [31]: numpy.log10(numpy.arange(1, 10))
Out[31]:
array([ 0.        ,  0.30103   ,  0.47712125,  0.60205999,  0.69897   ,
        0.77815125,  0.84509804,  0.90308999,  0.95424251])

In [32]: numpy.log10(numpy.arange(1, 10)) * 20
Out[32]:
array([  0.        ,   6.02059991,   9.54242509,  12.04119983,
        13.97940009,  15.56302501,  16.9019608 ,  18.06179974,  19.08485019])

Answer 2

Yep, there certainly is.

x = numpy.arange(1, 100)
y = 20 * numpy.log10(x)

Answer 3

Numpy has a lot of built-in array operators like log10 . If it's not listed in numpy's documentation and you can't generate it from combining built-in methods, then there's no easy way to do it efficiently. You can implement a C-level function to work on numpy arrays and compile that, but this is a lot more work than one or two lines of Python code.

For your case you almost have the right output already:

y = 20*numpy.log10(x)

Answer 4

You may want to take a look at the Numpy documentation. This is a good place to start:

http://docs.scipy.org/doc/numpy/reference/routines.html

And specifically related to your question:

http://docs.scipy.org/doc/numpy/reference/routines.math.html

Answer 5

If you're not trying to do anything complicated, the original code could be implemented this way as well, without requiring the use of numpy, if I'm not mistaken.

>>> import math
>>> x = range(1, 101)
>>> y = [ 20 * math.log10(z) for z in x ]

Answer 6

Apart from performing vectorized operation using numpy standard vectorized functions, you can also make your custom vectorized function using numpy.vectorize . Here is one example:

    >>> def myfunc(a, b):
    ...     "Return a-b if a>b, otherwise return a+b"
    ...     if a > b:
    ...         return a - b
    ...     else:
    ...         return a + b
    >>>
    >>> vfunc = np.vectorize(myfunc)
    >>> vfunc([1, 2, 3, 4], 2)
    array([3, 4, 1, 2])

As mentioned in documentation, unlike numpy's standard vectorized functions, this won't improve the performance