complex-numbers

来源： https://stackoverflow.com/questions/61736794/how-does-numpy-multiply-matrices-of-complex-numbers

来源： https://stackoverflow.com/questions/61736794/how-does-numpy-multiply-matrices-of-complex-numbers

问题 The scipy.fftpack.rfft function returns the DFT as a vector of floats, alternating between the real and complex part. This means to multiply to DFTs together (for convolution) I will have to do the complex multiplication "manually" which seems quite tricky. This must be something people do often - I presume/hope there is a simple trick to do this efficiently that I haven't spotted? Basically I want to fix this code so that both methods give the same answer: import numpy as np import scipy

问题 I decided to port some of my Python functions to C, mostly following this simple tutorial. Problem is that my C function returns a complex float, and there's no corresponding type in ctypes's documentation. This is is a problem I couldn't solve myself with my limited knowledge on cross-language coding and C, even extended by Google. My C function works like this: #include <tgmath.h> float _Complex integrand(float _Complex theta1, double r, double z){ //[code] } So, based on the tutorial, the

问题 I am working with complex-valued neural networks. For Complex-valued neural networks Wirtinger calculus is normally used. The definition of the derivate is then (take into acount that functions are non-Holomorphic because of Liouville's theorem): If you take Akira Hirose book "Complex-Valued Neural Networks: Advances and Applications", Chapter 4 equation 4.9 defines: Where the partial derivative is also calculated using Wirtinger calculus of course. Is this the case for tensorflow? or is it

问题 I have run across some confusing behaviour with square roots of complex numbers in python. Running this code: from cmath import sqrt a = 0.2 b = 0.2 + 0j print(sqrt(a / (a - 1))) print(sqrt(b / (b - 1))) gives the output 0.5j -0.5j A similar thing happens with print(sqrt(-1 * b)) print(sqrt(-b)) It appears these pairs of statements should give the same answer? 回答1: Both answers ( +0.5j and -0.5j ) are correct, since they are complex conjugates -- i.e. the real part is identical, and the

问题 I just want to "use" a complex af::array for a Cuda kernel. Unfortunately, the transformation which is described in the af documentation (http://arrayfire.org/docs/interop_cuda.htm) doesn't work here: #include <arrayfire.h> #include <af/cuda.h> #include <thrust/complex.h> #include <cuComplex.h> using namespace af; typedef thrust::complex<double> D2; void test(){ randomEngine en = randomEngine(); dim4 dims(4, 4); array a = randn(dims, c64, en); // array a = randn(dims, f64, en); a.eval(); D2

问题 I have two corresponded (has a relation and has the same dimension) dataset: Time Salinity Some data in salinity dataset is NaN. I can remove the NaN value by: Salinity_new=Salinity(~isnan(Salinity)) But it will not correspond to the Time dataset any more. How can I remove the corresponded time in also? Thanks 回答1: The comments of Diavakar and patrik are correct. To sum it up and get this question answered, some further remarks. mask = isfinite(Salinity) [Time,Salinity] = deal( Time(mask),

问题 I have two corresponded (has a relation and has the same dimension) dataset: Time Salinity Some data in salinity dataset is NaN. I can remove the NaN value by: Salinity_new=Salinity(~isnan(Salinity)) But it will not correspond to the Time dataset any more. How can I remove the corresponded time in also? Thanks 回答1: The comments of Diavakar and patrik are correct. To sum it up and get this question answered, some further remarks. mask = isfinite(Salinity) [Time,Salinity] = deal( Time(mask),

问题 I need to plot a complex function with phase information in MATLAB. To do this I have ploted a surf plot with x,y representing real and imaginary, the height representing the magnitude and colour depending on the phase, as shown below for the example of log(x): xmin=-5; xmax=5; dx=0.1; xReal = xmin:dx:xmax; xImaginary = xmin:dx:xmax; [x,y] = meshgrid(xReal, xImaginary); s = x + 1i*y; z=log(s); magnitude = abs(z1); Phase = angle(z); figure; h(1) = surf(x,y,magnitude,Phase,'EdgeColor','none');