Why would xcorr and xcorr2 be quite different here? M1 and M2 are numpy matrices. M1.shape[0] = M2.shape[0]. xcorr is what I would expect with this operation, but xcorr2 is something totally different and has imaginary numbers. xcorr does not have imaginary numbers. Answer Try giving xcorr and xcorr2 dtype=complex. According to scipy docs, the output from both fft and
Tag: scipy
Subplots won’t display together + griddata() returning “nan” values
I have a 9×9 matrix in which I know 8 values, at the positions contained in array points, with corresponding values in array values. I want to interpolate the unknown values and paste the result on a 200×200 image. I’m a beginner at python and I’m struggling with the following: My subplots won’t display together. Subplot(121) and subplot(122) should create
getting “KeyError” while implementing Z-score on a dataset
I have been trying to implement z-score normalization to all of the numeric values present in combined_data with the following code: Here, combined_data is the combination of training and testing datasets as a dataframe and passed through one-hot encoding. I am seeing the following error: The dataset combined_data contains 257673 rows & 198 columns Here is the sample data of
Which library/function should I use to fit a multivariate polynom to my data?
I have data that depends on 4 independent variables (x1,x2,x3,x4) and I need a model (available in Python) to evaluate f(x1,x2,x3,x4) outside the data points. In principle, if I set 3 of my variables as constant values I can always use a polynomial fit of a reasonable degree (<5) to interpolate the data in the remaining dimension so I would
Linear sum assignment (SciPy) and balancing the costs
I am having difficulty using scipy.optimize.linear_sum_assignment to evenly distribute tasks (costs) to workers, where each worker can be assigned multiple tasks. The cost matrix represents the workload of each task for each worker. We want to minimize the total costs of all workers, while evenly distributing the costs of each worker. In this example, we have 3 workers named a,
Saving Numpy array using scipy.io.savemat MATLAB does not produce the necessary .mat file
I have written a function to convert a NumPy array into a mat file using scipy.io.savemat() but it produces a generic type of file: File with the same name but not of type .mat as expected. The array I want to save is of type <class ‘numpy.ndarray’> as verified by the print statement. I don’t know what may be the
How to run scipy’s BFGS on GPU
I’d like to run scipy implementation of BFGS optimization algorithm on GPU and scipy seems not to support GPUs. The target function which I want to run on GPU is the following one which is part of the implementation of this repository: I know there is Tensorflow Probablity implementation of BFGS, but I couldn’t find out how I can convert
How to generate random values for a predefined function?
I have a predefined function, for example this: How can I generate random values against it so I can plot the results of the function using matplotlib? Answer If you want to plot, don’t use random x values but rather a range. Also you should use numpy.exp that can take a vector as input and your y in the lambda
Fastest way to create a square nxn matrix from 1d array in numpy
Suppose the following numpy array: I want to know the fastest way to generate the following operation: (1) How to efficiently create matrix “result” (because n >> 0 can be very large) ? (2) Does this matrix have a particular name ? Answer This is a bit faster: cursory benchmarks, nothing scientific. (timeit 100 times with arr):
Scipy – probability in binomial distribution
I’m trying to use scipy in order to calculate a probability, given a binomial distribution: The probability: in an exam with 45 questions, each one with 5 items, what is the probability of randomly choose right (instead of wrong) more than half the exam, that is, 22.5? I’ve tried: How do I calculate this with scipy? Answer Assuming there’s exactly