Thank you for the detailed explanation. I think the approach with
the feedback mechanism seems appropriate at this point.
> If you plan to seriously increase the number of documents in your
> corpus you could also try a Rocchio classifier [1] or a k-NN
> classifier. For large text documents colle
Setting a default value for base_estimator solves the problem.
Thank you.
Maheshakya
On Tue, Sep 17, 2013 at 4:59 PM, Lars Buitinck wrote:
> 2013/9/17 Maheshakya Wijewardena :
> > def __init__(self, base_estimator, n_estimators=10, max_samples=1.0,
>
> You need a default value for base_estimat
Thanks guys!
--Issam
On 9/17/2013 12:30 PM, Olivier Grisel wrote:
> 2013/9/17 Vlad Niculae :
>> And under the current implementation, implementing them involves
>> changing only the sampling and energy computation, I think.
>>
>> I discussed this with Gabriel Synnaeve during the sprint and I thin
2013/9/17 Maheshakya Wijewardena :
> def __init__(self, base_estimator, n_estimators=10, max_samples=1.0,
You need a default value for base_estimator.
--
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2013/9/17 Maheshakya Wijewardena :
> this is the __init__ function.
>
>
> def __init__(self, base_estimator, n_estimators=10, max_samples=1.0,
> bootstrap=True, oob_score=False, n_jobs=1,
> random_state=None, verbose=0):
> if base_estimator == None:
> base_estimator = DecisionTr
This is the __init__ function of the parent class.
(Above class is inherited from this and RegressorMixin)
@abstractmethod
def __init__(self, base_estimator=None, n_estimators=10,
max_samples=1.0,
bootstrap=True, oob_score=False, n_jobs=1,
random_st
this is the __init__ function.
def __init__(self, base_estimator, n_estimators=10, max_samples=1.0,
bootstrap=True, oob_score=False, n_jobs=1,
random_state=None, verbose=0):
if base_estimator == None:
base_estimator = DecisionTreeRegressor
super(Baggin
On Tue, Sep 17, 2013 at 03:32:26PM +0530, Maheshakya Wijewardena wrote:
> @Lars: I have followed the coding guidelines.
In particular:
http://scikit-learn.org/dev/developers/index.html#rolling-your-own-estimator
?
> It doesn't seems that this problem is related to that.
The test failure that yo
Email the code of the __init__ function of your classifier.
On 17 September 2013 20:02, Maheshakya Wijewardena
wrote:
> @Lars: I have followed the coding guidelines. It doesn't seems that this
> problem is related to that.
>
> @Robert: I didn't get what do you mean by copy paste. Can you elabora
@Lars: I have followed the coding guidelines. It doesn't seems that this
problem is related to that.
@Robert: I didn't get what do you mean by copy paste. Can you elaborate.
On Tue, Sep 17, 2013 at 2:53 PM, Robert Layton wrote:
> Also, are you able to copy paste your __init__ function?
>
>
>
2013/9/17 Vlad Niculae :
> And under the current implementation, implementing them involves
> changing only the sampling and energy computation, I think.
>
> I discussed this with Gabriel Synnaeve during the sprint and I think
> he was working on the gaussian version, it might be on his repo.
I am
Also, are you able to copy paste your __init__ function?
On 17 September 2013 19:14, Lars Buitinck wrote:
> 2013/9/17 Maheshakya Wijewardena :
> > I have initialized my estimator(those I created) with default classifiers
> > and regressors. But still, I get the same error.
>
> Did you read and
And under the current implementation, implementing them involves
changing only the sampling and energy computation, I think.
I discussed this with Gabriel Synnaeve during the sprint and I think
he was working on the gaussian version, it might be on his repo.
Lars, do you have any practical experi
2013/9/17 Maheshakya Wijewardena :
> I have initialized my estimator(those I created) with default classifiers
> and regressors. But still, I get the same error.
Did you read and follow the coding guidelines?
http://scikit-learn.org/stable/developers/index.html#coding-guidelines
-
2013/9/16 Issam :
> Maybe I could start working on the simple approach - 'Gaussian visible
> nodes'.
There are several variants of the RBM, for various types of input:
* Bernoulli RBM for binary inputs (Bernoulli random variables; this is
what we have)
* Gaussian RBM for real-valued input with Ga
I have initialized my estimator(those I created) with default classifiers
and regressors. But still, I get the same error.
On Tue, Sep 17, 2013 at 10:26 AM, Robert Layton wrote:
> scikit-learn classifiers should have default values for all __init__
> parameters (except, of course `self`). Set a
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