Hi, I am new to Python language.
I have only 10 days experience on it.
When I start learning there is no difficult, but it make me slow down when
I reach "Object Oriented Concept", especially "Inherited".
Some of my background knowledge about "Inherited is the child class can get
all of characteris
On 1/18/2013 8:03 AM, eryksun wrote:
Yes, it's a mistake in the PCA example from the docs:
http://mlpy.sourceforge.net/docs/3.5/dim_red.html#principal-component-analysis-pca
There seems to be no way to report a bug in that documentation! Or am I
missing something?
--
Bob Gailer
919-636-4239
C
On 18/01/13 11:11, Jose Amoreira wrote:
Suppose I have a list l_obj of similar objects. Is there any way I can
generate a list l_prp of references to a given property of those objects
in such a way that, if change the value of one element in l_prp, the
corresponding object in l_obj gets its prop
Roger Shaw wrote:
>
> Hello,
> I am very new to python.
>
> Wrote a small program to use on my android phone using pickle/shelve to
> access data.
>
> That worked fine but i realised it would be better to use sqlite as a
> database to more easily modify the data.
>
> I havent got a clue about
>> Thanks for your replies. os.putenv() may be easier than os.environ because,
>> hopefully, it takes care of the OS-specific separators of the values
>> (";" for Windows, ":" for Linux, others I don't
> know
>
> I wouldn't count on it. Support for changing environment variables on the
> fl
Hello,
I am very new to python.
Wrote a small program to use on my android phone
using pickle/shelve to access data.
That worked fine but i realised it would be better to use sqlite as a
database to more easily modify the data.
I havent got a clue about sqlite, have a book but cant find
On Fri, Jan 18, 2013 at 3:25 AM, Lie Ryan wrote:
> On 18/01/13 17:11, Gayathri S wrote:
>>
>> >>> import numpy as np
>> >>> import matplotlib.pyplot as plt
>> >>> import mlpy
>> >>> np.random.seed(0)
>> >>> mean,cov,n=[0,0],[[1,1],[1,1.5]],100
>> >>> x=np.random.multivariate_normal(mean,cov,
Thanks, Peter.
I was trying to avoid the regenerate step for updating. But maybe I can
restructure my code and objects to make this simpler. Before that I'll try
this view approach.
Thanks again
Ze
On Fri, Jan 18, 2013 at 11:37 AM, Peter Otten <__pete...@web.de> wrote:
> Jose Amoreira wrote:
>
>
Jose Amoreira wrote:
> Hello
> Suppose I have a list l_obj of similar objects. Is there any way I can
> generate a list l_prp of references to a given property of those objects
> in such a way that, if change the value of one element in l_prp, the
> corresponding object in l_obj gets its property
Hello
Suppose I have a list l_obj of similar objects. Is there any way I can
generate a list l_prp of references to a given property of those objects in
such a way that, if change the value of one element in l_prp, the
corresponding object in l_obj gets its property updated, and vice-versa?
Let giv
On 18/01/13 17:11, Gayathri S wrote:
hi...
I am using principal component analysis for dimensionality
reduction in python. am having this following error...
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> import mlpy
>>> np.random.seed(0)
>>> mean,cov,n=[0,0],[[1,1]
On Thu, Jan 17, 2013 at 10:11 PM, Gayathri S wrote:
> >>> import numpy as np
> >>> import matplotlib.pyplot as plt
> >>> import mlpy
> >>> np.random.seed(0)
> >>> mean,cov,n=[0,0],[[1,1],[1,1.5]],100
> >>> x=np.random.multivariate_normal(mean,cov,n)
> >>> pca.learn(x)
> Traceback (most recent call
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