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NumPyãŒç‰©è¶³ã‚Šãªã„人ã¸ã®Cython入門
18 Mar. 2012 第３回Tokyo.Scipyã«ã¦ç™ºè¡¨ã—ãŸã‚¹ãƒ©ã‚¤ãƒ‰ã§ã™ã€‚ Cythonã ã‘ã§ã¯ãªãF2Pyã«ã‚‚触れã¦ã„ã¦ã‹ãªã‚Šã‚«ã‚ªã‚¹ãªå†…容ã§ã™ã€‚Read less
Pythonã§æ•°å€¤è¨ˆç®—ã®ã‚³ãƒ„：for文書ã„ãŸã‚‰è² ã‘ã‹ãªã¨æ€ã£ã¦ã„ã‚‹ – ã¯ã‚€ã‹ãšï¼
転è·ã—ã¦ã‹ã‚‰1å¹´ã¨ã¡ã‚‡ã£ã¨ãŒçµŒã¡ã€Pythonをメイン言語ã¨ã—ã¦ã‹ã‚‰ã‚‚åŒã˜ãらã„ãŒçµŒã£ãŸã€‚最近やã£ã¨numpy/scipyã®ä½¿ã„æ–¹ã®ã‚³ãƒ„ãŒã‚ã‹ã£ã¦ããŸã¨æ€ã†ã®ã§ã€ãƒžã‚µã‚«ãƒªé£›ã‚“ã§ãã‚‹ã®ã‚’覚悟ã§ãªã‚“ã‹æ›¸ã„ã¦ã¿ã‚ˆã†ã¨æ€ã†ã€‚ 転è·ã—ã¦åˆã‚ã¦Pythonを使ã£ãŸã¨ã„ã†ã‚ã‘ã§ã¯ãªã„（実際wafã®wscriptã¨ã‹ã¯æ›¸ã„ãŸã“ã¨ã‚る）ãŒã€ã¾ã‚ã§ã‚‚ã»ã¼åˆå¿ƒè€…åŒç„¶ã ã£ãŸã€‚å¦ç¿’曲線ã§ã„ã†ã¨Pythonã¯ã™ã”ã良ã„言語ã ã¨æ€ã†ã€‚Python本体ã®è¨€èªžä»•æ§˜ã«ã¤ã„ã¦ã¯ã€ã‚ã‚Šã¨ã™ãã«è¦šãˆã‚‹ã“ã¨ãŒã§ããŸã€‚ã ãŒä¸€æ–¹ã€numpy/scipyã«ã¤ã„ã¦ã¯ã€ãã†ç°¡å˜ã§ã¯ãªãç¿’å¾—ã™ã‚‹ã«ã¯ãã‚Œãªã‚Šã«æ™‚é–“ãŒã‹ã‹ã£ãŸã¨æ€ã†ã€‚ ケーススタディ ãŸã¨ãˆã°\(N\times M\)行列\(B\), \( M\times L \)行列\( C \), \( M \)次元ベクトル\(a=(a_k)_{1\leq k \leq M}\)ãŒä¸Žãˆã‚‰ã‚Œã¦
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Tokyo.SciPy #2 ã«ã¦ç™ºè¡¨ã—ãŸã€æ•°å¼(ã‚ã‚‹ã„ã¯æ•°å¼å…¥ã‚Šã®ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ )ã‹ã‚‰å®Ÿè£…ã«è½ã¨ã™å ´åˆã€ä½•ã«æ°—ã‚’ã¤ã‘ã‚‹ã®ã‹ã€ã©ã†è€ƒãˆã‚Œã°ã„ã„ã®ã‹ã€ã¨ã„ã†ãŠè©±ã€‚ 対象ã¯ã€ã©ã†ã‚„ã£ã¦æ•°å¼ã‚’プãƒã‚°ãƒ©ãƒ ã™ã‚Œã°ã„ã„ã‹ã‚ˆãã‚ã‹ã‚‰ãªã„人ã€ã¡ã‚‡ã£ã¨ã‚„ã‚„ã“ã—ã„æ•°å¼ã«ãªã‚‹ã¨å››è‹¦å…«è‹¦ã—ã¦ã—ã¾ã„ã€ã‚³ãƒ¼ãƒ‰ã«è½ã¨ã™ã®ã«ã™ã”ã時間ãŒã‹ã‹ã£ã¦ã—ã¾ã†äººã€ãªã©ã€‚ ã“ã“ã§ã¯å®Ÿè¡Œé€Ÿåº¦ã«ã¤ã„ã¦ã¯ã²ã¨ã¾ãšãŠã„ã¨ã„ã¦ã€ç°¡æ½”ã§é–“é•ã„ã«ãã„ã€ã¡ã‚ƒã‚“ã¨å‹•ãコードを書ãã“ã¨ã‚’目標ã«ã—ã¦ã„ã¾ã™ã€‚ Read less
第2回 Tokyo.SciPy ã§ã€Œæ•°å¼ã‚’ numpy ã«è½ã¨ã—ã“むコツã€ã‚’発表ã—ã¦ãã¾ã—㟠- 木曜ä¸è¶³
10/15 ã« IBM ã•ã‚“ã®æ¸‹è°·ã‚ªãƒ•ã‚£ã‚¹ã«ã¦é–‹å‚¬ã•ã‚ŒãŸ 第2回 Tokyo.SciPy ã«ã®ã“ã®ã“å‚åŠ ã—ã¦ãã¾ã—ãŸã€‚主催㮠@sla ã•ã‚“ã¯ã˜ã‚ã€å‚åŠ è€…ãƒ»ç™ºè¡¨è€…å„ä½ãŠã¤ã‹ã‚Œã•ã¾ã§ã—ãŸï¼†ã‚ã‚ŠãŒã¨ã†ã”ã–ã„ã¾ã—ãŸã€‚ ã›ã£ã‹ãè¡Œããªã‚‰ãªã‚“ã‹ç™ºè¡¨ã—ãŸã„よãã€ã¨ã„ã†ã“ã¨ã§ã€Œæ•°å¼ã‚’ numpy ã«è½ã¨ã—ã“むコツ 〜機械å¦ç¿’ã‚’é¡Œæã«ã€œã€ãªã‚“ã¦ã‚¿ã‚¤ãƒˆãƒ«ã§ã€æ•°å¼(ã‚ã‚‹ã„ã¯æ•°å¼å…¥ã‚Šã®ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ )を実装ã™ã‚‹ã¨ãã«ã€ã©ã†ã„ã†ç‚¹ã«æ³¨ç›®ã™ã‚Œã°æ˜“ã—ãコードを書ã‘ã‚‹ã‹ã€ã«ã¤ã„ã¦ã¡ã‚‰ã¡ã‚‰èªžã£ã¦ã¿ãŸã€‚ ã“ã¡ã‚‰ãŒãã®è³‡æ–™ã€‚ æ•°å¼ã‚’numpyã«è½ã¨ã—ã“むコツ View more presentations from Shuyo Nakatani 例ãˆã°ã€æ©Ÿæ¢°å¦ç¿’ã®(多クラス)ãƒã‚¸ã‚¹ãƒ†ã‚£ãƒƒã‚¯å›žå¸°ã¨ã„ã†æŠ€è¡“ã§ã¯ã€æ¬¡ã®ã‚ˆã†ãªæ•°å¼ãŒç™»å ´ã™ã‚‹ã€‚ (PRML (4.109) å¼) ã“れを一目見ã¦ã™ã‚‰ã™ã‚‰ã¨ã‚³ãƒ¼ãƒ‰ãŒæ›¸ã‘ã‚‹ãªã‚‰è‹¦åŠ´ã¯ãªã„ãŒã€æ…£
ç§è¨³ã€Œæš«å®šçš„ NumPy ãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«ã€ - naoya_t@hatenablog
# 原文：http://www.scipy.org/Tentative_NumPy_Tutorial ã“ã®ãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«ã‚’èªã‚€å‰ã«ã€Pythonã«ã¤ã„ã¦ã¡ã‚‡ã£ã¨ã¯çŸ¥ã£ã¦ã„ã‚‹ã¹ãã 。記憶をリフレッシュã—ãŸã„ã¨æ€ã†ãªã‚‰ã€Pythonãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«ã‚’見ã¦ãã‚‹ãŒã„ã„。 ã“ã®ãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«ã«å‡ºã¦ãる例を試ã—ãŸã„ãªã‚‰ã€ã‚ãªãŸã®PCã«å°‘ãªãã¨ã‚‚ Python NumPy ã¯ã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ«ã•ã‚Œã¦ã„ã‚‹ã¹ãã§ã€ä»–ã«å…¥ã£ã¦ã‚‹ã¨ä¾¿åˆ©ãªã®ã¯ï¼š ipython ã¯æ‹¡å¼µã•ã‚ŒãŸã‚¤ãƒ³ã‚¿ãƒ©ã‚¯ãƒ†ã‚£ãƒ–ãªPythonシェルã§ã€NumPyã®æ©Ÿèƒ½ã‚’探検ã™ã‚‹ã®ã«ã¨ã¦ã‚‚便利 matplotlib ãŒã‚ã‚‹ã¨å›³è¡¨ã®æç”»ãŒå¯èƒ½ã«ãªã‚‹ SciPy ã¯NumPyã®ä¸Šã§å‹•ã科å¦è¨ˆç®—ルーãƒãƒ³ã‚’沢山用æ„ã—ã¦ãれる 基礎 NumPy ã®ä¸»è¦ãªã‚ªãƒ–ジェクトã¯ã€åŒã˜åž‹ï¼ˆæ™®é€šã¯æ•°ï¼‰ã®è¦ç´ ã®ã¿ã‹ã‚‰æˆã‚Šã€æ£ã®æ•´æ•°ã®ã‚¿ãƒ—ルã§æ·»å—付ã‘ã•ã‚ŒãŸã€å‡è³ªãªãƒ†ãƒ¼ãƒ–ル（ã¨ã„ã†ã‹å¤šæ¬¡å…ƒ
Rã¨Python(NumPy)ã®å¯¾å¿œ - Loud Minority
対応表 NumPy for R users: http://mathesaurus.sourceforge.net/r-numpy.html Rã®data.frameã«ç›¸å½“ã™ã‚‹äº‹ã‚’実ç¾ã™ã‚‹ã«ã¯ 作æˆï¼šNumPyã®arrayã§ï¼Œå„列ã«classを指定ã§ãã‚‹ (structured arrays) http://docs.scipy.org/doc/numpy/user/basics.rec.html > import numpy as np > C0=[1,2,3] > C1=['M','F','M'] > C2=[3.3,4.4,5.5] > x=np.array(zip(C0,C1,C2),dtype=[('id', '<i4'), ('gender', '|S1'), ('value', '<f8')]) > x[1] (2, 'F', 4.4) > print(x[x['gender
fonnesbeck/ScipySuperpack @ GitHub
This shell script will build and install the Python scientific stack, including Numpy, Scipy, Matplotlib, IPython, Pandas, Statsmodels, Scikit-Learn, and PyMC for OS X 10.10 (Yosemite) using the Homebrew package manager and pip. The script will use recent development code from each package, which means that though some bugs may be fixed and features added, they also may be more unstable than the o
NumPy for Matlab Users -
Introduction MATLAB® and NumPy/SciPy have a lot in common. But there are many differences. NumPy and SciPy were created to do numerical and scientific computing in the most natural way with Python, not to be MATLAB® clones. This page is intended to be a place to collect wisdom about the differences, mostly for the purpose of helping proficient MATLAB® users become proficient NumPy and SciPy users.
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A simple program to sample functions from a Gaussian process and plot them
Installing lapack for numpy
NumPy for R (and S-Plus) users – Mathesaurus
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