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Distributed computing for Big Data
- Why and when does distributed computing matter?
- Ingredients for effiicient distributed computing
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- Review of functional programming
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- Using Hadoop MapReduce
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Hadoop MapReduce on AWS EMR with mrjob
- MapReduce code
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- Now, restart your notebook server
Polyglot programming
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What you should know and learn more about
- Statistical foundations
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- Libraries worth knowing about after numpy, scipy and matplotlib
Wrapping R libraries with Rpy

文江博客开发文档 Computational Statistics in Python 文章详情

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Example: Matrix multiplication

发布于 2025-02-25 23:44:02 字数 43365 浏览 0 评论 0 收藏 0

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@jit
def numba_mult(u, v):
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    n, p = v.shape
    w = np.zeros((m, p))
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%%cython -a
import numpy as np
cimport numpy as np

cimport cython
@cython.boundscheck(False)
@cython.wraparound(False)
def cy_mult(double[:,::1] u, double[:,::1] v):
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    n = u.shape[1]
    p = v.shape[1]

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    for i in range(m):
        for j in range(p):
            for k in range(n):
                w[i, j] += u[i, k] * v[k, j]
    return np.asarray(w)

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m = 50
n = 30
p = 40
u = np.random.random((m, n))
v = np.random.random((n, p))

%timeit -n 10 np.dot(u, v)
%timeit -n 10 py_mult(u, v)
%timeit -n 10 numba_mult(u, v)
%timeit -n 10 cy_mult(u, v)

10 loops, best of 3: 16 µs per loop
10 loops, best of 3: 46 ms per loop
10 loops, best of 3: 74.2 µs per loop
10 loops, best of 3: 205 µs per loop

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