Do you understand the concept of cross validation? In k-fold cross validation your data is split in k samples and one sample is used to test the model and k-1 samples are used to train the model. If you set k = 1 (or n = 1 in your example) you won’t be able to … Read more
I can reproduce your error with: In [230]: np.concatenate([],0) ————————————————————————— ValueError Traceback (most recent call last) <ipython-input-230-2a097486ce12> in <module>() —-> 1 np.concatenate([],0) ValueError: need at least one array to concatenate In [231]: np.concatenate([np.atleast_2d(i) for i in ()],0) ————————————————————————— ValueError Traceback (most recent call last) <ipython-input-231-007f2dd2af1b> in <module>() —-> 1 np.concatenate([np.atleast_2d(i) for i in ()],0) ValueError: … Read more
Observe: >>> import numpy as np >>> a, b = np.random.randint(0, 255, (2, 10), dtype=np.uint8) >>> a – b array([ 62, 10, 126, 206, 157, 36, 170, 42, 54, 1], dtype=uint8) >>> b – a array([194, 246, 130, 50, 99, 220, 86, 214, 202, 255], dtype=uint8) >>> a.astype(np.int) – b.astype(np.int) array([ 62, -246, -130, -50, … Read more
you will first have to define how you wanna cluster your data. The scikit-learn’s simple KMeans clustering is designed to work on numbers. However scikit-learn can be also be used to cluster documents by topics using a bag-of-words approach. This is done by extracting the features using scipy.sparse matrix instead of standard numpy arrays One … Read more
It is impossible. The lottery numbers are random – actually to be more specific, the system is chaotic. You would require the initial configuration (positions etc) to insane (possibly infinite) precision to be able to make any predictions. Basically, don’t even try it.