稻花香里说丰年
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wJsJwr
parent
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2
.vscode/settings.json
vendored
2
.vscode/settings.json
vendored
@ -1,3 +1,3 @@
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{
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"python.pythonPath": "C:\\Users\\wjs\\Anaconda3\\python.exe"
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"python.pythonPath": "/Users/wjsjwr/.pyenv/versions/anaconda3-5.0.1/bin/python"
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}
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45
dist_of_k.py
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45
dist_of_k.py
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@ -0,0 +1,45 @@
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import json
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from matplotlib import pyplot as plt
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from island.match import Match
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from island.matches import Matches
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from numpy import mean, std
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import numpy as np
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matches = Matches('wos-data-new')
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max_round = 15
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fig = plt.figure(figsize=(6.4, 3.6))
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ax = fig.gca()
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index = np.arange(11)
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ax.set_xlabel('k')
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ax.set_ylabel('p(k)')
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c = ['#00b894','#00cec9','#0984e3','#6c5ce7','#e84393','#d63031','#e17055','#fdcb6e','#2d3436','#6ab04c','#30336b','#ED4C67']
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for i in range(len(matches.data)):
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k = np.zeros(11)
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m = matches.data[i]
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n = {}
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for r in m.query('neighbor', 'create').raw_data:
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if r['a'] in n:
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n[r['a']].append(r['b'])
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else:
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n[r['a']] = [r['b']]
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if r['b'] in n:
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n[r['b']].append(r['a'])
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else:
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n[r['b']] = [r['a']]
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for j in n.keys():
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k[len(n[j])] += 1
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pk = k / np.sum(k)
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print(i)
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ax.scatter(index, pk, color=c[i])
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# ax.set_title('Scores by group and gender')
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fig.tight_layout()
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plt.show()
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# plt.savefig('graph/neigh_per_round.eps')
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@ -95,6 +95,55 @@ class Match:
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return len(self.raw_data)
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def get_tr(self, i, target, nb, sv):
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"""
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# 计算tau_p的方式是统计周围邻居剩余的tr
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# 本函数计算第i轮，所有有消耗tr的player，最后剩余的tr
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# 由于是计算剩余tr，所以不能计算和target之间的交互
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# nb是target的邻居
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"""
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trs = {}
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req = self.query('action', 'request').where(lambda x: x['rno'] == i+1)
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app = self.query('action', 'approve').where(lambda x: x['rno'] == i+1).raw_data
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can = self.query('action', 'cancel').where(lambda x: x['rno'] == i+1).raw_data
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den = self.query('action', 'deny').where(lambda x: x['rno'] == i+1).raw_data
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fr = []
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for r in self.query('action', 'done').where(lambda x: x['rno'] == i and (x['a'] == target or x['b'] == target)).raw_data:
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if r['a'] == target:
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fr.append(r['b'])
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elif r['b'] == target:
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fr.append(r['a'])
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truenb = []
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for r in nb:
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if r not in fr and r in sv:
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truenb.append(r)
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trs[r] = 1440
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for r in req.raw_data:
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if r['from'] in truenb and r['to'] != target :
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if r['from'] not in trs:
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trs[r['from']] = 1440
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trs[r['from']] -= r['tr']
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for r in app:
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if r['from'] in truenb and r['to'] != target:
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if r['from'] not in trs:
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trs[r['from']] = 1440
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trs[r['from']] -= r['tr']
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for r in can:
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if r['from'] in trs and r['to'] != target:
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trs[r['from']] += req.where(lambda x: x['from'] == r['from'] and x['to'] == r['to'] and x['log_id'] < r['log_id']).orderby('log_id').raw_data[-1]['tr']
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for r in den:
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if r['to'] in trs and r['from'] != target:
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trs[r['to']] += req.where(lambda x: x['from'] == r['to'] and x['to'] == r['from'] and x['log_id'] < r['log_id']).orderby('log_id').raw_data[-1]['tr']
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return trs
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@staticmethod
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def read_from_json(json_path):
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"""
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305
tau_p_co.py
305
tau_p_co.py
@ -10,197 +10,150 @@ from matplotlib import markers
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def error(f,x,y):
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return sp.sum((f(x)-y)**2)
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def get_tr(m, i, target, nb, sv):
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"""
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# 计算tau_p的方式是统计周围邻居剩余的tr
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# 本函数计算第i轮，所有有消耗tr的player，最后剩余的tr
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# 由于是计算剩余tr，所以不能计算和target之间的交互
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# nb是target的邻居
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"""
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trs = {}
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req = m.query('action', 'request').where(lambda x: x['rno'] == i+1)
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app = m.query('action', 'approve').where(lambda x: x['rno'] == i+1).raw_data
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can = m.query('action', 'cancel').where(lambda x: x['rno'] == i+1).raw_data
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den = m.query('action', 'deny').where(lambda x: x['rno'] == i+1).raw_data
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if __name__ == '__main__':
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fr = []
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for r in m.query('action', 'done').where(lambda x: x['rno'] == i and (x['a'] == target or x['b'] == target)).raw_data:
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if r['a'] == target:
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fr.append(r['b'])
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elif r['b'] == target:
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fr.append(r['a'])
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matches = Matches('wos-data-new')
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max_round = 15
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truenb = []
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for r in nb:
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if r not in fr and r in sv:
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truenb.append(r)
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trs[r] = 1440
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survivals = {}
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with open('survivals.json', 'r') as f:
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survivals = json.load(f)
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for r in req.raw_data:
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if r['from'] in truenb and r['to'] != target :
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if r['from'] not in trs:
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trs[r['from']] = 1440
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trs[r['from']] -= r['tr']
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neighbors = {}
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coopr = []
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x = np.arange(1, max_round)
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mwCo = {} # Match-wise frequency of cooperation
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mwTau = {} # Match-wise Tau
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bx = []
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tau = []
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for r in app:
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if r['from'] in truenb and r['to'] != target:
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if r['from'] not in trs:
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trs[r['from']] = 1440
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trs[r['from']] -= r['tr']
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for i in range(len(matches.data)):
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m = matches.data[i]
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n = {}
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for r in m.query('neighbor', 'create').raw_data:
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if r['a'] in n:
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n[r['a']].append(r['b'])
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else:
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n[r['a']] = [r['b']]
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for r in can:
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if r['from'] in trs and r['to'] != target:
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trs[r['from']] += req.where(lambda x: x['from'] == r['from'] and x['to'] == r['to'] and x['log_id'] < r['log_id']).orderby('log_id').raw_data[-1]['tr']
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if r['b'] in n:
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n[r['b']].append(r['a'])
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else:
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n[r['b']] = [r['a']]
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neighbors[matches.names[i]] = n
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for r in den:
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if r['to'] in trs and r['from'] != target:
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trs[r['to']] += req.where(lambda x: x['from'] == r['to'] and x['to'] == r['from'] and x['log_id'] < r['log_id']).orderby('log_id').raw_data[-1]['tr']
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for i in range(max_round):
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co = []
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for j in range(len(matches.data)):
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coop = 0
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rows = matches.data[j].query('action', 'done').where(lambda x: x['rno']==i+1).raw_data
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for row in rows:
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if row['act_a'] == 'C':
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coop += 1
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if row['act_b'] == 'C':
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coop += 1
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return trs
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if rows:
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co.append(float(coop) / float(len(rows)*2))
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mwCo["%s-%d"%(j,i)] = co[-1]
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bx.append(co)
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if co:
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coopr.append(np.average(co))
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matches = Matches('wos-data-new')
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max_round = 15
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for i in range(max_round-1):
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tp = []
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for j in range(len(matches.data)):
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if i == 0:
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for r in matches.data[j].query('player', 'join').raw_data:
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t = 0
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k = r['pid']
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if k not in neighbors[matches.names[j]]:
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print("[%s(%d)] alone: %d" % (matches.names[j], i+1, k))
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else:
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t = 1440 * len(neighbors[matches.names[j]][k])
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tp.append(t if t < 1440 else 1440)
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mwTau["%s-%d"%(j,i)] = tp[-1]
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else:
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if str(i) not in survivals[matches.names[j]]:
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continue
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for k in survivals[matches.names[j]][str(i)]:
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t = 0
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if k not in neighbors[matches.names[j]]:
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print("[%s(%d)] alone: %d" % (matches.names[j], i+1, k))
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else:
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trs = matches.data[j].get_tr(i, k, neighbors[matches.names[j]][k], survivals[matches.names[j]][str(i)])
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for l in neighbors[matches.names[j]][k]:
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if l in trs:
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t += trs[l]
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tp.append(t if t < 1440 else 1440)
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mwTau["%s-%d"%(j,i)] = tp[-1]
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survivals = {}
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with open('survivals.json', 'r') as f:
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survivals = json.load(f)
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neighbors = {}
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coopr = []
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x = np.arange(1, max_round)
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mwCo = {} # Match-wise frequency of cooperation
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mwTau = {} # Match-wise Tau
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bx = []
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tau = []
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for i in range(len(matches.data)):
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m = matches.data[i]
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n = {}
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for r in m.query('neighbor', 'create').raw_data:
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if r['a'] in n:
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n[r['a']].append(r['b'])
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if tp:
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tau.append(np.average(tp))
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else:
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n[r['a']] = [r['b']]
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tau.append(0)
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if r['b'] in n:
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n[r['b']].append(r['a'])
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blue = '#0984e3'
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red = '#d63031'
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# p1折线图
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# fig = plt.figure(figsize=(6.4, 3.6))
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# ax = fig.gca()
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# ax.plot(x, coopr, color=blue, linewidth=3)
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# ax.set_ylim(0.5, 1)
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# ax2 = ax.twinx()
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# ax2.plot(x, tau, color=red, linewidth=3)
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# ax2.set_ylim(0,1440)
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# ax.set_xlim(1,14)
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# ax.set_xlabel("Rounds")
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# ax.set_ylabel("Frequency of Cooperation", color=blue)
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# ax.tick_params(axis='y', labelcolor=blue)
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# ax2.set_ylabel("$\\tau_{p}$", family='sans-serif', color=red)
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# ax2.tick_params(axis='y', labelcolor=red)
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# plt.tight_layout()
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# # plt.show()
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# plt.savefig('graph/tau_p_co_plot.eps')
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tau2 = []
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coopr2 = []
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tau_r = []
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coopr_r = []
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for i in range(len(tau)):
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if tau[i] <= 720:
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tau2.append(tau[i])
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coopr2.append(coopr[i])
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else:
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n[r['b']] = [r['a']]
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neighbors[matches.names[i]] = n
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tau_r.append(tau[i])
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coopr_r.append(coopr[i])
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for i in range(max_round):
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co = []
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for j in range(len(matches.data)):
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coop = 0
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rows = matches.data[j].query('action', 'done').where(lambda x: x['rno']==i+1).raw_data
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for row in rows:
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if row['act_a'] == 'C':
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coop += 1
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if row['act_b'] == 'C':
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coop += 1
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# p2散点图
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fig = plt.figure(figsize=(6.4, 3.6))
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ax = fig.gca()
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# ax.set_ylim(0.5, 1)
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fp1,residuals,rank,sv,rcond = sp.polyfit(tau2, coopr2, 1, full=True)
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print("残差：",residuals)
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print('Model parameter:',fp1)
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f1 = sp.poly1d(fp1)
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print("error= %f" % error(f1, tau2, coopr2))
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# fx = sp.linspace(0,max(tau2),1000)
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fx = sp.linspace(0,720,2)
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if rows:
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co.append(float(coop) / float(len(rows)*2))
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mwCo["%s-%d"%(j,i)] = co[-1]
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bx.append(co)
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if co:
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coopr.append(np.average(co))
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for i in range(max_round-1):
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tp = []
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for j in range(len(matches.data)):
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if i == 0:
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for r in matches.data[j].query('player', 'join').raw_data:
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t = 0
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k = r['pid']
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if k not in neighbors[matches.names[j]]:
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print("[%s(%d)] alone: %d" % (matches.names[j], i+1, k))
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else:
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t = 1440 * len(neighbors[matches.names[j]][k])
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tp.append(t if t < 1440 else 1440)
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mwTau["%s-%d"%(j,i)] = tp[-1]
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else:
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if str(i) not in survivals[matches.names[j]]:
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continue
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for k in survivals[matches.names[j]][str(i)]:
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t = 0
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if k not in neighbors[matches.names[j]]:
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print("[%s(%d)] alone: %d" % (matches.names[j], i+1, k))
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else:
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trs = get_tr(matches.data[j], i, k, neighbors[matches.names[j]][k], survivals[matches.names[j]][str(i)])
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for l in neighbors[matches.names[j]][k]:
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if l in trs:
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t += trs[l]
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tp.append(t if t < 1440 else 1440)
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mwTau["%s-%d"%(j,i)] = tp[-1]
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if tp:
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tau.append(np.average(tp))
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else:
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tau.append(0)
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blue = '#0984e3'
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red = '#d63031'
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# p1折线图
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# fig = plt.figure(figsize=(6.4, 3.6))
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# ax = fig.gca()
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# ax.plot(x, coopr, color=blue, linewidth=3)
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# ax.set_ylim(0.5, 1)
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# ax2 = ax.twinx()
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# ax2.plot(x, tau, color=red, linewidth=3)
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# ax2.set_ylim(0,1440)
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# ax.set_xlim(1,14)
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# ax.set_xlabel("Rounds")
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# ax.set_ylabel("Frequency of Cooperation", color=blue)
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# ax.tick_params(axis='y', labelcolor=blue)
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# ax2.set_ylabel("$\\tau_{p}$", family='sans-serif', color=red)
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# ax2.tick_params(axis='y', labelcolor=red)
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# plt.tight_layout()
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# # plt.show()
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# plt.savefig('graph/tau_p_co_plot.eps')
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tau2 = []
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coopr2 = []
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tau_r = []
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coopr_r = []
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for i in range(len(tau)):
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if tau[i] <= 720:
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tau2.append(tau[i])
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coopr2.append(coopr[i])
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else:
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tau_r.append(tau[i])
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coopr_r.append(coopr[i])
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# p2散点图
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fig = plt.figure(figsize=(6.4, 3.6))
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ax = fig.gca()
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# ax.set_ylim(0.5, 1)
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fp1,residuals,rank,sv,rcond = sp.polyfit(tau2, coopr2, 1, full=True)
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print("残差：",residuals)
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print('Model parameter:',fp1)
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f1 = sp.poly1d(fp1)
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print("error= %f" % error(f1, tau2, coopr2))
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# fx = sp.linspace(0,max(tau2),1000)
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fx = sp.linspace(0,720,2)
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plt.plot(fx,f1(fx),linewidth=2,color=red, ls='--', zorder=0)
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plt.scatter(tau2, coopr2, color=blue, linewidths=2, zorder=100)
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plt.scatter(tau_r, coopr_r, color='white', edgecolors=blue, linewidths=2, zorder=101)
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ax.set_xlabel('$\\tau_{p}$', family='sans-serif')
|
||||
ax.set_ylabel('Frequency of Cooperation')
|
||||
ax.set_xlim(0, 1440)
|
||||
ax.set_xticks(sp.linspace(0, 1440, 13))
|
||||
ax.set_ylim(0.5, 1)
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
# plt.savefig('graph/tau_p_co_sca.eps')
|
||||
plt.plot(fx,f1(fx),linewidth=2,color=red, ls='--', zorder=0)
|
||||
plt.scatter(tau2, coopr2, color=blue, linewidths=2, zorder=100)
|
||||
plt.scatter(tau_r, coopr_r, color='white', edgecolors=blue, linewidths=2, zorder=101)
|
||||
ax.set_xlabel('$\\tau_{p}$', family='sans-serif')
|
||||
ax.set_ylabel('Frequency of Cooperation')
|
||||
ax.set_xlim(0, 1440)
|
||||
ax.set_xticks(sp.linspace(0, 1440, 13))
|
||||
ax.set_ylim(0.5, 1)
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
# plt.savefig('graph/tau_p_co_sca.eps')
|
||||
|
||||
|
||||
|
||||
# 皮尔逊相关系数
|
||||
print("pearson: %f, p-value: %f" % pearsonr(tau2, coopr2))
|
||||
# 皮尔逊相关系数
|
||||
print("pearson: %f, p-value: %f" % pearsonr(tau2, coopr2))
|
||||
122
taup_and_new_partner.py
Normal file
122
taup_and_new_partner.py
Normal file
@ -0,0 +1,122 @@
|
||||
import json
|
||||
from matplotlib import pyplot as plt
|
||||
from island.match import Match
|
||||
from island.matches import Matches
|
||||
import numpy as np
|
||||
from scipy.stats import pearsonr
|
||||
|
||||
matches = Matches('wos-data-new')
|
||||
|
||||
k = np.arange(0, 1441, 144)
|
||||
succ = np.zeros(11)
|
||||
total = np.zeros(11)
|
||||
|
||||
survivals = {}
|
||||
with open('survivals.json', 'r') as f:
|
||||
survivals = json.load(f)
|
||||
|
||||
neighbors = {}
|
||||
for i in range(len(matches.data)):
|
||||
m = matches.data[i]
|
||||
n = {}
|
||||
for r in m.query('neighbor', 'create').raw_data:
|
||||
if r['a'] in n:
|
||||
n[r['a']].append(r['b'])
|
||||
else:
|
||||
n[r['a']] = [r['b']]
|
||||
|
||||
if r['b'] in n:
|
||||
n[r['b']].append(r['a'])
|
||||
else:
|
||||
n[r['b']] = [r['a']]
|
||||
neighbors[matches.names[i]] = n
|
||||
|
||||
for m_i in range(len(matches.data)):
|
||||
m = matches.data[m_i]
|
||||
info = m.query('game', 'created').select('info').first()['info']
|
||||
conf = json.loads(info['config'])
|
||||
game_end_at = int(info['game_end_at'])
|
||||
|
||||
for p in m.query('player', 'join').raw_data:
|
||||
pid = p['pid']
|
||||
for i in range(2, game_end_at):
|
||||
neighborhood = []
|
||||
if pid not in neighbors[matches.names[m_i]]:
|
||||
break
|
||||
for j in neighbors[matches.names[m_i]][pid]:
|
||||
if j in survivals[matches.names[m_i]][str(i-1)]:
|
||||
neighborhood.append(j)
|
||||
if len(neighborhood) < 2:
|
||||
break
|
||||
previous_round_partner = []
|
||||
for r in m.query('action', 'done').where(lambda x: x['rno']==i-1 and (x['a']==pid or x['b']==pid)).raw_data:
|
||||
if r['a'] == pid:
|
||||
previous_round_partner.append(r['b'])
|
||||
else:
|
||||
previous_round_partner.append(r['a'])
|
||||
new_partner_request = 0
|
||||
for r in m.query('action', 'request').where(lambda x: x['rno']==i and (x['from']==pid or x['to']==pid)).raw_data:
|
||||
if r['from'] == pid:
|
||||
if r['to'] not in previous_round_partner:
|
||||
new_partner_request += 1
|
||||
else:
|
||||
if r['from'] not in previous_round_partner:
|
||||
new_partner_request += 1
|
||||
if new_partner_request == 0:
|
||||
continue
|
||||
new_partner_succ = 0
|
||||
for r in m.query('action', 'done').where(lambda x: x['rno']==i and (x['a']==pid or x['b']==pid)).raw_data:
|
||||
if r['a'] == pid:
|
||||
if r['b'] not in previous_round_partner:
|
||||
new_partner_succ += 1
|
||||
else:
|
||||
if r['a'] not in previous_round_partner:
|
||||
new_partner_succ += 1
|
||||
t = 0
|
||||
trs = m.get_tr(i, pid, neighbors[matches.names[m_i]][pid], survivals[matches.names[m_i]][str(i-1)])
|
||||
for l in neighborhood:
|
||||
if l in trs:
|
||||
t += trs[l]
|
||||
t = t if t <= 1440 else 1440
|
||||
succ[t//144] += new_partner_succ
|
||||
total[t//144] += new_partner_request
|
||||
|
||||
red = '#d63031'
|
||||
fig = plt.figure(figsize=(6.4, 4))
|
||||
ax = fig.gca()
|
||||
bar_width = 0.35
|
||||
opacity = 1
|
||||
error_config = {'ecolor': '0.3', 'capsize': 4}
|
||||
# rects1 = ax.bar(k, succ, bar_width,
|
||||
# alpha=opacity, color='#00b894',
|
||||
# # yerr=c_req_suc_std, error_kw=error_config,
|
||||
# label='Success')
|
||||
# rects3 = ax.bar(k + bar_width, total, bar_width,
|
||||
# alpha=opacity, color='#fdcb6e',
|
||||
# # yerr=d_req_suc_mean, error_kw=error_config,
|
||||
# label='Requests')
|
||||
|
||||
ax.set_xlabel('k')
|
||||
ax.set_ylabel('Count')
|
||||
# ax.set_title('Scores by group and gender')
|
||||
# ax.set_xticks(k + bar_width / 2)
|
||||
# ax.set_xticklabels(k)
|
||||
ax.legend()
|
||||
ax2 = ax.twinx()
|
||||
ax.plot(k, succ, color='#00b894', lw=2, marker='*', ls='none')
|
||||
ax.plot(k, total, color='#fdcb6e', lw=2, marker='+', ls='none')
|
||||
for i in range(11):
|
||||
if total[i] == 0:
|
||||
total[i] = 1
|
||||
ax2.plot(k, succ/total,linewidth=2,color=red, ls='--')
|
||||
# ax2.set_ylabel("Frequency of new partners", family='sans-serif', color=red)
|
||||
# ax2.tick_params(axis='y', labelcolor=red)
|
||||
ax2.set_ylim(0,1)
|
||||
fig.tight_layout()
|
||||
plt.show()
|
||||
# plt.savefig('graph/k_and_new_partner.eps')
|
||||
|
||||
# print("[succ vs k]pearson: %f, p-value: %f" % pearsonr(succ, k))
|
||||
# print("[total vs k]pearson: %f, p-value: %f" % pearsonr(total, k))
|
||||
# print("[rate vs k]pearson: %f, p-value: %f" % pearsonr(succ/total, k))
|
||||
# print(np.average(succ/total))
|
||||
Loading…
Reference in New Issue
Block a user