The issue is just as the error indicates, time_list is a normal python list, and hence you cannot index it using another list (unless the other list is an array with single element). Example – If you want to do that kind of indexing, you would need to make time_list a numpy.array. Example – Another … Read more
if you give a 2D array to the plot function of matplotlib it will assume the columns to be lines: If x and/or y is 2-dimensional, then the corresponding columns will be plotted. In your case your shape is not accepted (100, 1, 1, 8000). As so you can using numpy squeeze to solve the … Read more
temp[::-1].sort() sorts the array in place, whereas np.sort(temp)[::-1] creates a new array.
You should make x and y numpy arrays, not lists: With this change, it produces the expected plot. If they are lists, m * x will not produce the result you expect, but an empty list. Note that m is anumpy.float64 scalar, not a standard Python float. I actually consider this a bit dubious behavior of Numpy. In normal Python, multiplying a list with an … Read more
You could try to install a specific NumPy version. In your case you could use: In case of conflicts it will show the conflicting packages. For example: In this case blaze and numba prevent NumPy from upgrading.
In newer version of numpy (>1.8.x), numpy.meshgrid() provides a much faster implementation: @pv’s solution numpy.meshgrid() use to be 2D only, now it is capable of ND. In this case, 3D: Note that the order of the final resultant is slightly different.
Is there a numpy method which is equivalent to the builtin pop for python lists? Popping obviously doesn’t work on numpy arrays, and I want to avoid a list conversion
The “official” answer, according to the Scipy documentation, is that there is really no reason to ever since all of the interesting functions in Scipy are actually located in the submodules, which are not automatically imported. Therefore, the recommended method is to use then, functions can be called with Personally, I always use and live with … Read more
Try this testing code and pay attention to np.array([1,2,3], dtype=np.float64). It seems self.svm.decision_function(vectors) returns 1d array instead of 2d. If you replace [1,2,3] to [[1,2,3], [4,5,6]] everything will be ok. Output: So, what your vectors are?
There are the index tricks r_ and c_. If this is performance critical code, you might prefer to use the equivalent np.concatenate rather than the index tricks. There are also np.resize and np.ndarray.resize, but they have some limitations (due to the way numpy lays out data in memory) so read the docstring on those ones. … Read more