Dear Michael
Thank you very much.
Your solution helped me perfectly.
Artur Bercik
On Sun, Oct 12, 2014 at 11:57 PM, Michael Eickenberg <
michael.eickenb...@gmail.com> wrote:
> Dear Artur,
>
> the shapes of your input arrays got muddled up. As opposed to e.g. matlab,
> in numpy there exist 1D arrays, and these are usually interpreted as line
> vectors. Thus, male_height, male_weight, male_age are all written into the
> same line by hstack. What you are looking to hstack are column vectors,
> which are 2D arrays. You can obtain these in a number of ways. While there
> are more concise ways of doing this, the most useful command (in the sense
> of teaching to fish) I can give you at this stage is reshape (a full numpy
> tutorial can be very useful in general):
>
> male_height = np.array([111,121,137,143,157]).reshape(-1, 1)
> male_weight = np.array([60,70,88,99,75]).reshape(-1, 1)
> male_age = np.array([41,32,73,54,35]).reshape(-1, 1)
>
> The same needs to be done for the females.
>
> Next problem up will be the label vector, which at the moment only has 3
> entries, but should have as many entries as examples. Ie it should be
> labels = np.array([0, 0, 0, 0, 0, 1, 1, 1,1, 1, 2, 2, 2, 2, 2]).
>
> Hope this helps!
> Michael
>
> On Sun, Oct 12, 2014 at 3:57 PM, Artur Bercik <vbubbl...@gmail.com> wrote:
>
>> Dear sklearn users:
>> I am hanging with the following simple problem of doing support vector
>> machine with numpy arrays. I would be grateful if someone answer me.
>>
>> import numpy as np
>> from sklearn import svm
>>
>> ##I have 3 classes/labels ('male', 'female','na') denoted as follows:
>>
>> labels = [0,1,2]
>>
>> ##Each class was defined by 3 variables ('height','weight','age') as the
>> training data:
>>
>> male_height = np.array([111,121,137,143,157])
>> male_weight = np.array([60,70,88,99,75])
>> male_age = np.array([41,32,73,54,35])
>>
>> males = np.hstack([male_height,male_weight,male_age])
>>
>> female_height = np.array([91,121,135,98,90])
>> female_weight = np.array([32,67,98,86,56])
>> female_age = np.array([51,35,33,67,61])
>>
>> females = np.hstack([female_height,female_weight,female_age])
>>
>> na_height = np.array([96,127,145,99,91])
>> na_weight = np.array([42,97,78,76,86])
>> na_age = np.array([56,35,49,64,66])
>>
>> nas = np.hstack([na_height,na_weight,na_age])
>>
>> ##Now I have to fit the support vector machine method for the training
>> data to predict the class given that 3 variable:
>>
>> height_weight_age = [100,100,100]
>>
>> clf = svm.SVC()
>> trainingData = np.vstack([males,females,nas])
>>
>> clf.fit(trainingData, labels)
>>
>> result = clf.predict(height_weight_age)
>>
>> print result
>>
>> #Unfortunately, the following error occurs:
>> # ValueError: X.shape[1] = 3 should be equal to 15, the number of
>> features at #training time
>> #How should I modify the 'trainingData' and 'labels' to get the correct
>> answer?
>>
>>
>> Thanks in the advance.
>> Artur Bercik
>>
>>
>>
>> ------------------------------------------------------------------------------
>> Meet PCI DSS 3.0 Compliance Requirements with EventLog Analyzer
>> Achieve PCI DSS 3.0 Compliant Status with Out-of-the-box PCI DSS Reports
>> Are you Audit-Ready for PCI DSS 3.0 Compliance? Download White paper
>> Comply to PCI DSS 3.0 Requirement 10 and 11.5 with EventLog Analyzer
>> http://p.sf.net/sfu/Zoho
>> _______________________________________________
>> Scikit-learn-general mailing list
>> Scikit-learn-general@lists.sourceforge.net
>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>>
>>
>
>
> ------------------------------------------------------------------------------
> Meet PCI DSS 3.0 Compliance Requirements with EventLog Analyzer
> Achieve PCI DSS 3.0 Compliant Status with Out-of-the-box PCI DSS Reports
> Are you Audit-Ready for PCI DSS 3.0 Compliance? Download White paper
> Comply to PCI DSS 3.0 Requirement 10 and 11.5 with EventLog Analyzer
> http://p.sf.net/sfu/Zoho
> _______________________________________________
> Scikit-learn-general mailing list
> Scikit-learn-general@lists.sourceforge.net
> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>
>
------------------------------------------------------------------------------
Meet PCI DSS 3.0 Compliance Requirements with EventLog Analyzer
Achieve PCI DSS 3.0 Compliant Status with Out-of-the-box PCI DSS Reports
Are you Audit-Ready for PCI DSS 3.0 Compliance? Download White paper
Comply to PCI DSS 3.0 Requirement 10 and 11.5 with EventLog Analyzer
http://p.sf.net/sfu/Zoho
_______________________________________________
Scikit-learn-general mailing list
Scikit-learn-general@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
