【Visual Python profiler】http://t
    Sunday, May 22, 2016
    10:21 PM

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    计算机生成了可选文字:
    网路冷眼
    05/20/2016
    【Visual Python profiler】http://t.cn/RGDoAe8 vprof 是一个为不同Python 程序特性提供丰富和交互可视化的包,对程序的运行时和内存使用进行可视化。它支持Python 2.7, Python 3.4, Python 3.5, BSD 授权许可发布。
    计算机生成了可选文字: Flame chart  pezchec .  init_.py:106(<module...  stscn . 226   sc l@ecé!}  w .py:  orthogonal  basic.py:5(<module...  (<module...  miobase.py:7()...  .py:3(  init_.py:93()...  Function name: [module]  Location:  Line number: 168  Time percentage: 14.6 %  Cumulative time: 0.050955 s  Time per call: 0.020272 s  Primitive calls: 1  Ill  ntco..w  setup  init_.py:l _  pylab.py:l()  Object name: test/testscript.py (module)  Total runtime: 0.34965599999999647s  Total calls: 158982  Primitive calls.  • 157123  l*ic..;• 'Gackend_bases.p...  gure....  lot.  1901    in.  ates.p...  finance.py:8()  ylab.py:217()
    计算机生成了可选文字: Memory stats  12.3  12.2  12.1  12.0  11.9  11.8  11.7  11.6  200  Executed line: 1138  Line number: 90  Function name: _bsearchr  Filename: testjtestscript.py  Memory usage: 11.98828125 MB  400  600  800  ,ooo  1 ,200  1 ,400  Object name: test/testscript.py (module)  Total lines executed: 2253  11600
    ![计算机生成了可选文字: Code heatmap Inspected modules test/ testscript. py test/ testscript . py 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 import numpy import pylab import scipy.io dataset = scipy .io.10admat(‘test/ex7data2. mat’ x dataset[ def euclidean distance cxl, xz, yl, y2): um . s rt x EPS = 0.25 centroids = numpy. zerosCCK, 2)) for i in range(K): rand_i = numpy. random. random_integersCx. shape CO centroids[i] di stances = numpy. zerosCCx.shapeCØ], K)) distance_delta = numpy .ones(K) num_iter = history = C) while (distance_delta EPS) .all(): # Calculate distance to centroids. for i in range(x. shape : for j in range(K): distances[i, j) = euclidean_distance( # Pick closest cluster. point_clusters = distances . history. for i in range(K): Execution count: 1 300 prev_cent_x, prev_cent_y = centroidsCi, t], centroidsi, 1] centroidsCi , — numpy. , axis—O) distance_deltaCi] = eucl prev_cent_x, centroidsCi, 0], prev_cent_y, centroidsCi, 1]) num_iter 1 print( ‘ Algorithm converged in %s iterations % num—iter)
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