{"id":4312,"date":"2017-08-19T01:00:00","date_gmt":"2017-08-18T17:00:00","guid":{"rendered":"http:\/\/swarma.org\/%swarma-paper%\/"},"modified":"2018-12-05T22:01:10","modified_gmt":"2018-12-05T14:01:10","slug":"flownetwork%ef%bc%9a%e6%b5%81%e7%bd%91%e7%bb%9c%e7%9a%84%e5%bc%80%e6%ba%90python%e5%8c%85","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=4312","title":{"rendered":"Flownetwork\uff1a\u6d41\u7f51\u7edc\u7684\u5f00\u6e90Python\u5305"},"content":{"rendered":"
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\"\u96c6\u667a\"<\/span><\/p>\n

\u5f00\u653e\u7684\u6d41\u7f51\u7edc\uff08Open Flow Network\uff09\uff0c\u6216\u6d41\u52a8\u7f51\u7edc\uff0c\u4e5f\u7b80\u79f0\u6d41\u7f51\u7edc(Flow Network)\u662f\u6307\u4e00\u7c7b\u7279\u6b8a\u7684\u52a0\u6743\u6709\u5411\u7684\u590d\u6742\u7f51\u7edc\u3002\u5176\u4e2d\uff0c\u6709\u5411\u8fde\u8fb9\u8868\u793a\u80fd\u91cf\u3001\u7269\u8d28\u3001\u8d27\u5e01\u3001\u4fe1\u606f\u3001\u6ce8\u610f\u529b\u7b49\u6d41\u52a8\u7684\u65b9\u5411\uff0c\u8fde\u8fb9\u7684\u6743\u91cd\u5219\u8868\u793a\u6d41\u91cf\u3002\u5f00\u653e\u6027\u4f53\u73b0\u4e3a\u7f51\u7edc\u4e2d\u5b58\u5728\u7740\u6e90\u548c\u6c47\u4e24\u7c7b\u7279\u6b8a\u7684\u8282\u70b9\u4ee3\u8868\u73af\u5883\u3002\u76ee\u524d\uff0c\u5f00\u653e\u6d41\u7f51\u7edc\u8fd9\u79cd\u6a21\u578b\u5df2\u7ecf\u88ab\u5e94\u7528\u5230\u4e86\u751f\u6001\u80fd\u91cf\u6d41\u5206\u6790\u3001\u56fd\u9645\u8d38\u6613\u6d41\u5206\u6790\u3001\u6295\u5165\u4ea7\u51fa\u5173\u7cfb\u3001\u5927\u89c4\u6a21\u7684\u4eba\u7c7b\u884c\u4e3a\u3001\u96c6\u4f53\u6ce8\u610f\u529b\u6d41\u7b49\u591a\u4e2a\u5b66\u79d1\u80cc\u666f\u4e2d\u53bb\u4e86\u3002
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\u5357\u4eac\u5927\u5b66\u7684\u738b\u6210\u519b\u8001\u5e08\u548c\u829d\u52a0\u54e5\u5927\u5b66\u7684\u5434\u4ee4\u98de\u535a\u58eb\u5f00\u53d1\u4e86\u4e00\u4e2a\u5f00\u6e90\u5de5\u5177\u5305Flownetwork\uff0c\u5c06\u5e38\u7528\u7684\u8ba1\u7b97\u90fd\u96c6\u6210\u5230\u4e86\u4e00\u8d77\u3002\u521d\u5b66\u8005\u53ef\u4ee5\u76f4\u63a5\u8c03\u7528\u8be5\u5305\u5b8c\u6210\u5404\u79cd\u6709\u5173\u5f00\u653e\u6d41\u7f51\u7edc\u7684\u8ba1\u7b97\u3002<\/span><\/inherit><\/p>\n

\"\u96c6\u667a\"<\/div>\n

\u8be5\u5305\u7684\u4e3b\u8981\u529f\u80fd\u5982\u4e0b\uff1ahttps:\/\/github.com\/chengjun\/flownetwork\uff08\u6216\u70b9\u51fb\u9605\u8bfb\u539f\u6587\uff09<\/span><\/p>\n

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\u9996\u5148\u9700\u8981\u5b89\u88c5flownetwork\u7684\u5305\uff0c\u6253\u5f00\u4e00\u4e2a\u7ec8\u7aef\u8f93\u5165\uff1a<\/span><\/p>\n

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pip install flownetwork<\/code><\/pre>\n
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\u7136\u540e\u53ef\u4ee5\u5728jupyter notebook\uff08\u6216\u8005\u5176\u4ed6\u7684\u5730\u65b9\uff09\u4e2d\u5bfc\u5165\u5b89\u88c5\u5305\uff1a<\/span><\/p>\n
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# import packages  from flownetwork import flownetwork as fn import networkx as nx import matplotlib.pyplot as plt  print(fn.__version__)<\/code><\/pre>\n
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\u9996\u5148\u6211\u4eec\u53ef\u4ee5\u7528help\u8bed\u53e5\u6765\u67e5\u770b\u4e00\u4e0b\u8fd9\u4e2a\u6d41\u7f51\u7edc\u7684\u7ed3\u6784\uff1a<\/span><\/p>\n
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help(fn.constructFlowNetwork)<\/code><\/pre>\n
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Help on function constructFlowNetwork in module flownetwork.flownetwork: constructFlowNetwork(C)    C is an array of two dimentions, e.g.,    C = np.array([[user1, item1],       [user1, item2],       [user2, item1],       [user2, item3]])    Return a balanced flow networ<\/code><\/pre>\n
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demo = fn.attention_data gd = fn.constructFlowNetwork(demo)<\/code><\/pre>\n
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# drawing a demo network fig = plt.figure(figsize=(12, 8),facecolor='white') pos={0: np.array([ 0.2 ,  0.8]),  2: np.array([ 0.2,  0.2]),  1: np.array([ 0.4,  0.6]),  6: np.array([ 0.4,  0.4]),  4: np.array([ 0.7,  0.8]),  5: np.array([ 0.7,  0.5]),  3: np.array([ 0.7,  0.2 ]),  'sink': np.array([ 1,  0.5]), 'source': np.array([ 0,  0.5])} width=[float(d['weight']*1.2) for (u,v,d) in gd.edges(data=True)]  edge_labels=dict([((u,v,),d['weight']) for u,v,d in gd.edges(data=True)])nx.draw_networkx_edge_labels(gd,pos,edge_labels=edge_labels, font_size = 15, alpha = .5)  nx.draw(gd, pos, node_size = 3000, node_color = 'orange',  alpha = 0.2, width = width, edge_color='orange',style='solid') nx.draw_networkx_labels(gd,pos,font_size=18)  plt.show()<\/code><\/pre>\n
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nx.info(gd)<\/code><\/pre>\n
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# balancing the network # if it is not balanced  gh = fn.flowBalancing(cd) nx.info(gh)<\/code><\/pre>\n
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# flow matrix
m = fn.getFlowMatrix(gd)  m<\/span><\/code><\/pre>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n
\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u8f93\u51fa\u7684\u6d41\u77e9\u9635\u7684\u4fe1\u606f\u662f\u4e0b\u9762\u8fd9\u6837\u5b50\u7684\uff1a<\/span><\/p>\n
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matrix([[ 0.,  1.,  0.,  0.,  3.,  1.,  0.,  0.,  0.],    [ 0.,  0.,  3.,  0.,  0.,  0.,  0.,  0.,  0.],    [ 0.,  0.,  0.,  2.,  0.,  0.,  0.,  0.,  2.],    [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  2.],    [ 0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  2.],    [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  2.],    [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.],    [ 5.,  2.,  1.,  0.,  0.,  0.,  1.,  0.,  0.],    [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]])<\/code><\/pre>\n
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fn.getMarkovMatrix(m)<\/span><\/code><\/pre>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n
\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u8f93\u51fa\u7684\u9a6c\u5c14\u79d1\u592b\u77e9\u9635\u662f\u4e0b\u5217\u6570\u7ec4\u7684\u5f62\u5f0f\uff1a<\/span><\/p>\n
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array([[ 0.,  0.2   ,  0.,  0.,  0.6   ,0.2   ,  0.,  0.,  0.],   [ 0.,  0.,  1.,  0.,  0.,0.,  0.,  0.,  0.],   [ 0.,  0.,  0.,  0.5   ,  0.,0.,  0.,  0.,  0.5   ],   [ 0.,  0.,  0.,  0.,  0.,0.,  0.,  0.,  1.],   [ 0.,  0.,  0.,  0.,  0.,0.33333333,  0.,  0.,  0.66666667],   [ 0.,  0.,  0.,  0.,  0.,0.,  0.,  0.,  1.],   [ 0.,  0.,  0.,  0.,  0.,0.,  0.,  0.,  1.],   [ 0.55555556,  0.22222222,  0.11111111,  0.,  0.,0.,  0.11111111,  0.,  0.],   [ 0.,  0.,  0.,  0.,  0.,0.,  0.,  0.,  0.]])<\/span><\/code><\/pre>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n
\u63a5\u7740\u6211\u4eec\u8ba1\u7b97\u65e0\u7a77\u6b65\u957f\u7684\u7d2f\u79ef\u9a6c\u5c14\u79d1\u592b\u8f6c\u79fb\u77e9\u9635\uff1a
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fn.getUmatrix(gd)<\/span><\/code><\/pre>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n
\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u8f93\u51fa\u7684\u77e9\u9635\u662f\u4e0b\u9762\u8fd9\u4e2a\u6837\u5b50\u7684\uff1a<\/span><\/p>\n
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matrix([[ 1.,  0.2   ,  0.2   ,  0.1   ,  0.6   ,0.4   ,  0.], [ 0.,  1.,  1.,  0.5   ,  0.,0.,  0.], [ 0.,  0.,  1.,  0.5   ,  0.,0.,  0.], [ 0.,  0.,  0.,  1.,  0.,0.,  0.], [ 0.,  0.,  0.,  0.,  1.,0.33333333,  0.], [ 0.,  0.,  0.,  0.,  0.,1.,  0.], [ 0.,  0.,  0.,  0.,  0.,0.,  1.]])<\/span><\/code><\/pre>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n
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# return dissipationToSink,totalFlow,flowFromSource fn.networkDissipate(gd)
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fn.outflow(gd, 1) fn.inflow(gd, 1)<\/code><\/pre>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n
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fn.averageFlowLength(gd)<\/span><\/code><\/pre>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n
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fn.getAverageTimeMatrix(gd)<\/code><\/pre>\n
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fig=plt.figure(figsize=(9.9)\uff0cfacecolor=\"white\" ax=fig.add_subplot(111) fn.plotTree(gd,ax) plt.show()  from random import random x=np.array(range(1,100)) y=(x+random()*x)**3  plt.plot(x,y) plt.xscale('log') plt.yscale(\"log\") plt.show()<\/code><\/pre>\n
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\u7136\u540e\u6211\u4eec\u5bf9: y = (x+x*[0,1))**3\u7684\u51fd\u6570\u56fe\u50cf\u505a\u6700\u5c0f\u4e8c\u4e58\u6cd5\u7ebf\u6027\u62df\u5408(\u53cc\u5bf9\u6570\u6761\u4ef6\u4e0b,\u8fd1\u4f3c\u76f4\u7ebf) \uff1a<\/span><\/p>\n
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fn.alloRegressPlot(x,y,\"r\",\"s\",\"$x$\",\"$y$\",loglog=True)
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rg = np.array([ 20.7863444 ,   9.40547933,   8.70934714,   8.62690145,  7.16978087,   7.02575052,   6.45280959,   6.44755478,  5.16630287,   5.16092884,   5.15618737,   5.05610068,  4.87023561,   4.66753197,   4.41807645,   4.2635671 ,  3.54454372,   2.7087178 ,   2.39016885,   1.9483156 ,  1.78393238,   1.75432688,   1.12789787,   1.02098332,  0.92653501,   0.32586582,   0.1514813 ,   0.09722761]) fn.powerLawExponentialCutOffPlot(rg, '$x$', '$p(x)$')<\/span><\/code><\/pre>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n
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fn.DGBDPlot(rg)  fn.DGBDPlot(rg)<\/code><\/pre>\n
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from networkx.utils import powerlaw_sequence pl_sequence = powerlaw_sequence(1000,exponent=2.5)  fig = plt.figure(figsize=(4, 4),facecolor='white') ax = fig.add_subplot(111) fn.plotPowerlaw(pl_sequence,ax,'r','$x$')<\/code><\/pre>\n
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fig = plt.figure(figsize=(4, 4),facecolor='white') ax = fig.add_subplot(111) fn.plotCCDF(pl_sequence,ax,'b','$x$')<\/code><\/pre>\n
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bins, result, gini_val = fn.gini_coefficient(np.array(pl_sequence))  plt.plot(bins, bins, '--', label=\"perfect\") plt.plot(bins, result, label=\"observed\") plt.title(\"$GINI: %.4f$\" %(gini_val))  plt.legend(loc = 0, frameon = False) plt.show()<\/code><\/pre>\n
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    http:\/\/wiki.swarma.net\/index.php\/Python \u96c6\u667awiki\u4e0a\u9762\u7f16\u5199\u7684\u5173\u4e8e\u6d41\u7f51\u7edc\u7684\u7b97\u6cd5<\/span><\/p>\n<\/li>\n
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    http:\/\/wiki.swarma.net\/index.php\/%E5%9F%BA%E4%BA%8E%E9%A9%AC%E5%B0%94%E7%A7%91%E5%A4%AB%E9%93%BE%E7%9A%84%E6%B5%81%E7%BD%91%E7%BB%9C%E5%88%86%E6%9E%90 \u96c6\u667awiki\u4e0a\u7f16\u5199\u7684\u5173\u4e8e\u57fa\u4e8e\u9a6c\u5c14\u79d1\u592b\u94fe\u7684\u6d41\u7f51\u7edc\u5206\u6790<\/span><\/p>\n<\/li>\n<\/ol>\n

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    \u96c6\u667aQQ\u7fa4\uff5c292641157
    \u5546\u52a1\u5408\u4f5c\uff5czhangqian@swarma.org
    \u6295\u7a3f\u8f6c\u8f7d\uff5cwangting@swarma.org<\/span><\/strong><\/span><\/p>\n
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    \u8ba9\u82f9\u679c\u7838\u5f97\u66f4\u731b\u70c8\u4e9b\u5427\uff01<\/span><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n
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    \u59cb\u53d1\u4e8e\u5fae\u4fe1\u516c\u4f17\u53f7\uff1a                         \u96c6\u667a\u4ff1\u4e50\u90e8                      <\/p>\n<\/blockquote>\n<\/div>\n","protected":false},"excerpt":{"rendered":"
    \u5f00\u653e\u7684\u6d41\u7f51\u7edc\uff08Open Flow Network\uff09\uff0c\u6216\u6d41\u52a8\u7f51\u7edc\uff0c\u4e5f\u7b80\u79f0\u6d41\u7f51\u7edc(Flow Network)\u662f\u6307\u4e00\u7c7b\u7279\u6b8a\u7684\u52a0\u6743\u6709\u5411\u7684\u590d\u6742\u7f51\u7edc\u3002\u5176\u4e2d\uff0c\u6709\u5411\u8fde\u8fb9\u8868\u793a\u80fd\u91cf\u3001\u7269\u8d28\u3001\u8d27\u5e01\u3001\u4fe1\u606f\u3001\u6ce8\u610f\u529b\u7b49\u6d41\u52a8\u7684\u65b9\u5411\uff0c\u8fde\u8fb9\u7684\u6743\u91cd\u5219\u8868\u793a\u6d41\u91cf\u3002\u5f00\u653e\u6027\u4f53\u73b0\u4e3a\u7f51\u7edc\u4e2d\u5b58\u5728\u7740\u6e90\u548c\u6c47\u4e24\u7c7b\u7279\u6b8a\u7684\u8282\u70b9\u4ee3\u8868\u73af\u5883\u3002\u76ee\u524d\uff0c\u5f00\u653e\u6d41\u7f51\u7edc\u8fd9\u79cd\u6a21\u578b…<\/p>\n","protected":false},"author":1,"featured_media":4313,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[18],"tags":[],"special":[],"_links":{"self":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/4312"}],"collection":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=4312"}],"version-history":[{"count":1,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/4312\/revisions"}],"predecessor-version":[{"id":4331,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/4312\/revisions\/4331"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/media\/4313"}],"wp:attachment":[{"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4312"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4312"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4312"},{"taxonomy":"special","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fspecial&post=4312"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}