numba

I want to improve the performance of the for loop in this function. import numpy as np import random def play_game(row, n=1000000): """Play the game! This game is a kind of random walk. Arguments: row (int[]): row index to use in the p matrix for each step in the walk. Then length of this array is the same as n. n (int): number of steps in the random walk """ p = np.array([[ 0.499, 0.499, 0.499], [ 0.099, 0.749, 0.749]]) X0 = 100 Y0 = X0 % 3 X = np.zeros(n) tempX = X0 Y = Y0 for j in range(n): tempX = X[j] = tempX + 2 * (random.random() < p.item(row.item(j), Y)) - 1 Y = tempX % 3 return np.r_

I'm using numba to make some functions containing cycles on numpy arrays. Everything is fine and dandy, I can use jit and I learned how to define the signature. Now I tried using jit on a function with optional arguments, e.g.: from numba import jit import numpy as np @jit(['float64(float64, float64)', 'float64(float64, optional(float))']) def fun(a, b=3): return a + b This works, but if instead of optional(float) I use optional(float64) it doesn't (same thing with int or int64 ). I lost 1 hour trying to figure this syntax out (actually, a friend of mine found this solution by chance because

I was trying to parallellize a Monte Carlo simulation that operates on many independent datasets. I found out that numba's parallel guvectorize implementation was barely 30-40% faster than the numba jit implementation. I found these ( 1 , 2 ) comparable topics on Stackoverflow, but they do not really answer my question. In the first case, the implementation is slowed down by a fall back to object mode and in the second case the original poster did not properly use guvectorize - none of these problems apply to my code. To make sure there was no problem with my code, I created this very simple

Compare a pure Python no-op function with a no-op function decorated with @numba.jit , that is: import numba @numba.njit def boring_numba(): pass def call_numba(x): for t in range(x): boring_numba() def boring_normal(): pass def call_normal(x): for t in range(x): boring_normal() If we time this with %timeit , we get the following: %timeit call_numba(int(1e7)) 792 ms ± 5.51 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) %timeit call_normal(int(1e7)) 737 ms ± 2.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) All perfectly reasonable; there's a small overhead for the numba function

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: Found this gist using numba for fast computation of cosine similarity. import numba @numba.jit(target='cpu', nopython=True) def fast_cosine(u, v): m = u.shape[0] udotv = 0 u_norm = 0 v_norm = 0 for i in range(m): if (np.isnan(u[i])) or (np.isnan(v[i])): continue udotv += u[i] * v[i] u_norm += u[i] * u[i] v_norm += v[i] * v[i] u_norm = np.sqrt(u_norm) v_norm = np.sqrt(v_norm) if (u_norm == 0) or (v_norm == 0): ratio = 1.0 else: ratio = udotv / (u_norm * v_norm) return ratio Results look promising (500ns vs. only 200us without jit decorator in

Is there any reason why the following code run in 2s, def euclidean_distance_square(x1, x2): return -2*np.dot(x1, x2.T) + np.expand_dims(np.sum(np.square(x1), axis=1), axis=1) + np.sum(np.square(x2), axis=1) while the following numba code run in 12s? @jit(nopython=True) def euclidean_distance_square(x1, x2): return -2*np.dot(x1, x2.T) + np.expand_dims(np.sum(np.square(x1), axis=1), axis=1) + np.sum(np.square(x2), axis=1) My x1 is a matrix of dimension (1, 512) and x2 is a matrix of dimension (3000000, 512). It is quite weird that numba can be so much slower. Am I using it wrong? I really need

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I'm unable to install Numba (via pip) on my OS X system. I'm using Python: 2.7.11 (Homebrew) pip: 8.1.1 setuptools: 20.6.7 OS X: 10.11.4 (x86_64) Xcode: 7.3 Xcode CLT: 7.3.0.0.1.1457485338 Clang: 7.3 build 703 and have installed the prerequisites (I think) with brew install llvm git clone https://github.com/numba/llvmlite cd llvmlite LLVM_CONFIG=/usr/local/opt/llvm/bin/llvm-config python setup.py install cd .. rm -rf llvmlite and also tried brew install llvm brew link --force llvm # later: brew unlink llvm cd /usr/local/Cellar/llvm/X.X.X