Glad it helped. @larsman's answer on Stackoverflow is more pedagogic
http://stackoverflow.com/questions/26325976/value-error-while-running-svm-in-sklearn
To avoid duplication of effort, if you have posted the question elsewhere,
it is helpful to provide a link when you write to the list (and vice-versa).
Michael
On Sun, Oct 12, 2014 at 5:13 PM, Artur Bercik <vbubbl...@gmail.com> wrote:
> 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
>>>
>>>
>>>
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