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Already started the types, but need more functionality (i.e. transposing, reorganizing, sub-scripting, and so forth)
Operations are started, but of course more can always be added. Some difficulties now:
Complex variants of trig functions have a lot of reused code (i.e. sin of complex uses sinh of complex internally), so if we have them all in separate files, there is a lot of duplicated code
Submodules:
nx.rand
Random number generation, the main type is nx.rand.State, which implements methods:
randb(num=1): return 'num' random bytes
randf(shape=none): return random float in [0, 1)
normal(mean=0, stddev=1, shape=none) - Guassian distribution
...
nx.la
Linear algebra library, mainly concerned with solvers, and some BLAS routines. For example:
matmul(X, Y, R=none) - calculate matrix multiply between shapes [..., M, N], [..., N, K] (extra dimensions represent batched operations)
solve(A, b) - solve for 'x' in the equation Ax = b
gmres(A, b, x0, niter=16) - solve for an approximation to the solution Ax = b using the GMres method
nx.fft
FFT (Fast Fourier Transform) library, concerned with applying time and frequency transforms
nx.fft(X, axes=none, R=none) - forward FFT
nx.ifft(X, axes=none, R=none) - inverse FFT
The text was updated successfully, but these errors were encountered:
Already started the types, but need more functionality (i.e. transposing, reorganizing, sub-scripting, and so forth)
Operations are started, but of course more can always be added. Some difficulties now:
Submodules:
nx.rand
Random number generation, the main type is
nx.rand.State
, which implements methods:randb(num=1)
: return 'num' random bytesrandf(shape=none)
: return random float in [0, 1)normal(mean=0, stddev=1, shape=none)
- Guassian distributionnx.la
Linear algebra library, mainly concerned with solvers, and some BLAS routines. For example:
matmul(X, Y, R=none)
- calculate matrix multiply between shapes[..., M, N]
,[..., N, K]
(extra dimensions represent batched operations)solve(A, b)
- solve for 'x' in the equationAx = b
gmres(A, b, x0, niter=16)
- solve for an approximation to the solutionAx = b
using the GMres methodnx.fft
FFT (Fast Fourier Transform) library, concerned with applying time and frequency transforms
nx.fft(X, axes=none, R=none)
- forward FFTnx.ifft(X, axes=none, R=none)
- inverse FFTThe text was updated successfully, but these errors were encountered: