Hi,
I followed documentation for digit recognition, as I was hoping for
something better then OCR with minimal involvement from my side.
Here is example:
http://nbviewer.ipython.org/gist/klonuo/873868aaaa5d0e5a8aa0
So I'm feeding the classifier with my own data compliant to format it
expects and get bogus result. I tried this for many digit samples, and
results are far than I naively expected.
But, this trained dataset consists of 1800 digits, and by giving me results
that none match with digit I feed the predictor, my lucky guess is that I'm
giving wrong parameters or maybe using wrong estimator or else?
Here is the code from link, just in case:
========================================
>>> from pylab import *
>>> from sklearn import svm
>>> from sklearn import datasets
>>> digits = datasets.load_digits()
>>> clf = svm.SVC(gamma=0.001, C=100.)
>>> clf.fit(digits.data[:-1], digits.target[:-1])
>>> imshow(digits.data[-1].reshape(8, 8), interpolation='nearest',
cmap='binary')
>>> clf.predict(digits.data[-1])
array([8])
>>> my_sample = array([0, 0, 1, 15, 15, 15, 15, 0, 0, 15, 15,
13, 0, 12, 15, 15, 12, 15, 10, 0, 0, 0,
15, 15, 15, 15, 0, 0, 0, 0, 15, 15, 15,
15, 0, 0, 0, 0, 15, 15, 15, 15, 0, 0,
0, 10, 15, 0, 15, 15, 0, 0, 4, 15, 8,
0, 0, 7, 15, 15, 15, 1, 0, 0])
>>> imshow(my_sample.reshape(8, 8), interpolation='nearest', cmap='binary')
>>> clf.predict(my_sample) # expecting zero
array([1])
========================================
Thanks,
klo
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