The numpy docs recommend using array instead of matrix for working with matrices. However, unlike octave (which I was using till recently), * doesn’t perform matrix multiplication, you need to use the function matrixmultipy(). I feel this makes the code very unreadable.
So I am wondering if there’s a more efficient solution in generating a 2-D array using np.random.choice where each row has unique values.
I’m reading a CSV with float numbers like this:
I have to cluster the consecutive elements from a NumPy array. Considering the following example
I have a column in python pandas DataFrame that has boolean True/False values, but for further calculations I need 1/0 representation. Is there a quick pandas/numpy way to do that?
Say I have an array of distances x=[1,2,1,3,3,2,1,5,1,1].
I would like to create a two dimensional numpy array of arrays that has a different number of elements on each row.
I am trying to fit piecewise linear fit as shown in fig.1 for a data set
From the Udacity’s deep learning class, the softmax of y_i is simply the exponential divided by the sum of exponential of the whole Y vector:
I have a bunch of MATLAB code from my MS thesis which I now want to convert to Python (using numpy/scipy and matplotlib) and distribute as open-source. I know the similarity between MATLAB and Python scientific libraries, and converting them manually will be not more than a fortnight (provided that I work towards it every … Read more