[Numpy-discussion] numpy dot returns [nan nan nan]

[Warren Weckesser]

On 8/24/13, Tom Bennett <tom.bennett at mail.zyzhu.net> wrote:
> Hi Warren,
>
> Yes you are absolutely right. I had some values close to log(x), where x is
> almost 0. That caused the problem.
>
> Thanks,
> Tom


Now the question is: why does `np.dot` mask the overflow warning?

In numpy 1.7.1, the default is that overflow should generate a warning:

In [1]: np.seterr()
Out[1]: {'divide': 'warn', 'invalid': 'warn', 'over': 'warn', 'under': 'ignore'}


But `np.dot` does not generate a warning:

In [2]: x = np.array([1e300])

In [3]: y = np.array([1e10])

In [4]: np.dot(x, y)
Out[4]: inf


Multiplying `x` and `y` generates the warning, as expected:

In [5]: x*y
/home/warren/anaconda/bin/ipython:1: RuntimeWarning: overflow
encountered in multiply
  #!/home/warren/anaconda/bin/python
Out[5]: array([ inf])


Warren


>
>
> On Sat, Aug 24, 2013 at 12:39 PM, Warren Weckesser <
> warren.weckesser at gmail.com> wrote:
>
>> On 8/24/13, Warren Weckesser <warren.weckesser at gmail.com> wrote:
>> > On 8/24/13, Tom Bennett <tom.bennett at mail.zyzhu.net> wrote:
>> >> Hi All,
>> >>
>> >> I have two arrays, A and B.A is 3 x 100,000 and B is 100,000. If I do
>> >> np.dot(A,B), I get [nan, nan, nan].
>> >>
>> >> However, np.any(np.isnan(A))==False and np.any(no.isnan(B))==False.
>> >> And
>> >> also np.seterr(all='print') does not print anything.
>> >>
>> >> I am not wondering what is going on and how to avoid.
>> >>
>> >> In case it is important, A and B are from the normal equation of doing
>> >> regression. I am regressing 100,000 observations on 3 100,000 long
>> >> factors.
>> >>
>> >> Thanks,
>> >> Tom
>> >>
>> >
>> > What are the data types of the arrays, and what are the typical sizes
>> > of the values in these arrays?  I can get all nans from np.dot if the
>> > values are huge floating point values:
>> >
>> > ```
>> > In [79]: x = 1e160*np.random.randn(3, 10)
>> >
>> > In [80]: y = 1e160*np.random.randn(10)
>> >
>> > In [81]: np.dot(x, y)
>> > Out[81]: array([ nan,  nan,  nan])
>> > ```
>>
>> ...and that happens because some intermediate terms overflow to inf or
>> -inf, and adding these gives nan:
>>
>> ```
>> In [89]: x = np.array([1e300])
>>
>> In [90]: y = np.array([1e10])
>>
>> In [91]: np.dot(x,y)
>> Out[91]: inf
>>
>> In [92]: x2 = np.array([1e300, 1e300])
>>
>> In [93]: y2 = np.array([1e10,-1e10])
>>
>> In [94]: np.dot(x2, y2)
>> Out[94]: nan
>> ```
>>
>> Warren
>>
>>
>> >
>> > Warren
>> >
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>