python遗传算法实现数据拟合
python据说功能强大，触角伸到各个领域，网上搜了一下其科学计算和工程计算能力也相当强，具备各种第三方包，除了性能软肋外，其他无可指摘，甚至可以同matlab等专业工具一较高下。
从网上找了一个使用遗传算法实现数据拟合的例子学习了一下，确实Python相当贴合自然语言，终于编程语言也能说人话了，代码整体简洁、优雅。。
代码功能：给出一个隐藏函数 例如 z=x^2+y^2,生成200个数据，利用这200个数据，使用遗传算法猜测这些数据是什么公式生成的。 （说的太直白，一点都不高大上）
代码如下：
1 # coding=utf-8
2 from random import random, randint, choice,uniform
3 from copy import deepcopy
4 import numpy as np
5 import matplotlib.pyplot as plt
6
7 from random import random, randint, choice
8 from copy import deepcopy
9 import numpy as np
10
11 # 运算类
12 class fwrapper:
13 def __init__(self, function, childcount, name):
14 self.function = function
15 self.childcount = childcount
16 self.name = name
17
18 # 节点类
19 class node:
20 def __init__(self, fw, children):
21 self.function = fw.function
22 self.name = fw.name
23 self.children = children
24 #将inp指定的运算符作用到子节点上
25 def evaluate(self, inp):
26 # 循环调用子节点的子节点的子节点....的evaluate方法
27 results = [n.evaluate(inp) for n in self.children]
28 # 返回运算结果
29 return self.function(results)
30 #打印本节点及所属节点的操作运算符
31 def display(self, indent=0):
32 print(' ' * indent) + self.name
33 for c in self.children:
34 c.display(indent + 1)
35
36 #参数节点类，x+y 其中x，y都是参数节点
37 class paramnode:
38 def __init__(self, idx):
39 self.idx = idx
40 # evaluate方法返回paramnode节点值本身
41 def evaluate(self, inp):
42 return inp[self.idx]
43
44 def display(self, indent=0):
45 print '%sp%d' % (' ' * indent, self.idx)
46
47 # 常数节点
48 class constnode:
49 def __init__(self, v):
50 self.v = v
51
52 def evaluate(self, inp):
53 return self.v
54
55 def display(self, indent=0):
56 print '%s%d' % (' ' * indent, self.v)
57
58 # 操作运算符类
59 class opera:
60 # 使用前面定义的fwrapper类生产常用的加减乘除运算,第一个参数是本运算执行方式，第二个参数是本运算接受的参数个数，第三个参数是本运算名称
61 addw = fwrapper(lambda l: l[0] + l[1], 2, 'add')
62 subw = fwrapper(lambda l: l[0] - l[1], 2, 'subtract')
63 mulw = fwrapper(lambda l: l[0] * l[1], 2, 'multiply')
64
65 def iffunc(l):
66 if l[0] > 0:
67 return l[1]
68 else:
69 return l[2]
70 #定义if运算
71 ifw = fwrapper(iffunc, 3, 'if')
72
73 def isgreater(l):
74 if l[0] > l[1]:
75 return 1
76 else:
77 return 0
78 #定义greater运算
79 gtw = fwrapper(isgreater, 2, 'isgreater')
80 #构建运算符集合
81 flist = [addw, mulw, ifw, gtw, subw]
82
83 #使用node，paramnode，fwrapper构建一个example
84 def exampletree(self):
85 return node(self.ifw, [node(self.gtw, [paramnode(0), constnode(3)]), node(self.addw, [paramnode(1), constnode(5)]),
86 node(self.subw, [paramnode(1), constnode(2)]), ])
87
88
89 # 构建一颗随机运算数，pc为参数（分叉）个数，maxdepth为树的深度，fpr为运算符个数在运算符加节点总数中所占比例，ppr为参数个数在参数加常数个数总数中所占的比例
90 def makerandomtree(self,pc, maxdepth=4, fpr=0.5, ppr=0.6):
91 if random() < fpr and maxdepth > 0:
92 f = choice(self.flist)
93 # 递归调用makerandomtree实现子节点的创建
94 children = [self.makerandomtree(pc, maxdepth - 1, fpr, ppr) for i in range(f.childcount)]
95 return node(f, children)
96 elif random() < ppr:
97 return paramnode(randint(0, pc - 1))
98 else:
99 return constnode(randint(0, 10))
100
101
102 #变异，变异概率probchange=0.1
103 def mutate(self,t, pc, probchange=0.1):
104 # 变异后返回一颗随机树
105 if random() < probchange:
106 return self.makerandomtree(pc)
107 else:
108 result = deepcopy(t)
109 # 递归调用，给其子节点变异的机会
110 if isinstance(t, node):
111 result.children = [self.mutate(c, pc, probchange) for c in t.children]
112 return result
113
114 #交叉
115 def crossover(self,t1, t2, probswap=0.7, top=1):
116 # 如果符合交叉概率，就将t2的值返回，实现交叉；
117 if random() < probswap and not top:
118 return deepcopy(t2)
119 else:
120 #如果本层节点未实现交配，递归询问子节点是否符合交配条件
121 #首先使用deepcopy保存本节点
122 result = deepcopy(t1)
123 if isinstance(t1, node) and isinstance(t2, node):
124 #依次递归询问t1下的各子孙节点交配情况，交配对象为t2的各子孙；t1,t2家族同辈交配
125 result.children = [self.crossover(c, choice(t2.children), probswap, 0) for c in t1.children]
126 return result
127
128 # random2.display()
129 # muttree=mutate(random2,2)
130 # muttree.display()
131 # cross=crossover(random1,random2)
132 # cross.display()
133
134 #设置一个隐藏函数，也就是原始函数
135 def hiddenfunction(self,x, y):
136 return x ** 2+ y**2
137
138 #依照隐藏函数，生成坐标数据
139 def buildhiddenset(self):
140 rows = []
141 for i in range(200):
142 x = randint(0, 10)
143 x=uniform(-1,1)
144 y = randint(0, 10)
145 y=uniform(-1,1)
146 rows.append([x, y, self.hiddenfunction(x, y)])
147 print("rows:",rows)
148 return rows
149
150 #拟合成绩函数，判定拟合函数（实际是一颗图灵树）贴近原始函数的程度
151 def scorefunction(self,tree, s):
152 dif = 0
153 # print("tree:",tree)
154 # print("s:",s)
155 for data in s:
156 # print("data[0]:",data[0])
157 # print("data[1]:",data[1])
158 v = tree.evaluate([data[0],data[1]])
159 #累加每个数据的绝对值偏差
160 dif += abs(v - data[2])
161 return dif
162
163 #返回一个成绩判定函数rankfunction的句柄
164 def getrankfunction(self,dataset):
165 #此函数调用拟合成绩函数，并对成绩排序，返回各个种群的成绩
166 def rankfunction(population):
167 scores = [(self.scorefunction(t, dataset), t) for t in population]
168 scores.sort()
169 return scores
170 return rankfunction
171
172 # hiddenset=buildhiddenset()
173 # scorefunction(random2,hiddenset)
174 # scorefunction(random1,hiddenset)
175
176 def evolve(self,pc, popsize, rankfunction, maxgen=500, mutationrate=0.1, breedingrate=0.4, pexp=0.7, pnew=0.05):
177 #轮盘算法
178 def selectindex():
179 return int(np.log(random()) / np.log(pexp))
180 #使用随机树生成第一代各种群
181 population = [self.makerandomtree(pc) for i in range(popsize)]
182 #计算每一代各种群的成绩，
183 for i in range(maxgen):
184 scores = rankfunction(population)
185 #打印历代最好成绩
186 print('the best score in genneration ',i,':',scores[0][0])
187 #成绩完全吻合原函数的话，退出函数
188 if scores[0][0] == 0:
189 break
190 #创建新一代各种群，成绩前两名的直接进入下一代
191 newpop = [scores[0][1], scores[1][1]]
192 while len(newpop) < popsize:
193 #pnew为引进随机种群概率，未达此概率的，使用原种群的交配、变异生成新种群
194 if random() > pnew:
195 newpop.append(
196 self.mutate(self.crossover(scores[selectindex()][1], scores[selectindex()][1], probswap=breedingrate), pc,
197 probchange=mutationrate))
198 #引入随机种群
199 else:
200 newpop.append(self.makerandomtree(pc))
201 population = newpop
202 #打印历代最好种群
203 # scores[0][1].display()
204 return scores[0][1]
205
206
207
208 def main(argv):
209 e=opera()
210 def exampletree():
211 return node(e.ifw,[node(e.gtw,[paramnode(0),constnode(3)]),node(e.addw,[paramnode(1),constnode(5)]),node(e.subw,[paramnode(1),constnode(2)])])
212
213
214 # random1=e.makerandomtree(2)
215 # random1.evaluate([7,1])
216 # random1.evaluate([2,4])
217 # random2=e.makerandomtree(2)
218 # random2.evaluate([5,3])
219 # random2.evaluate([5,20])
220 # random1.display()
221 # random2.display()
222
223 # exampletree = e.exampletree()
224 # exampletree.display()
225 # print(exampletree.evaluate([6, 1]))
226 # print('after evaluate:')
227 # exampletree.display()
228 # exampletree.evaluate([2, 3])
229 # exampletree.evaluate([5, 3])
230 # exampletree.display()
231
232 a=opera()
233 row2=a.buildhiddenset()
234 # fig=plt.figure()
235 # ax=fig.add_subplot(1,1,1)
236 # plt.plot(np.random.randn(1000).cumsum())
237 # plt.show()
238
239
240
241 from mpl_toolkits.mplot3d import Axes3D
242 fig = plt.figure()
243 ax = fig.add_subplot(111, projection='3d')
244 X = [1, 1, 2, 2]
245 Y = [3, 4, 4, 3]
246 Z = [1, 2, 1, 1]
247 rx=[]
248 ry=[]
249 rz=[]
250 for i in row2:
251 rx.append(i[0])
252 ry.append(i[1])
253 rz.append(i[2])
254
255 ax.plot_trisurf(rx, ry, rz)
256 rz2=[]
257 rf = a.getrankfunction(row2)
258 final = a.evolve(2, 100, rf, mutationrate=0.2, breedingrate=0.1, pexp=0.7, pnew=0.1,maxgen=500)
259 print('__________________is it?_________________________')
260 final.display()
261 for j in range(0,len(rx)):
262 rz2.append(final.evaluate([rx[j],ry[j]]))
263 fig2 = plt.figure()
264 ax2 = fig2.add_subplot(111, projection='3d')
265 ax2.plot_trisurf(rx, ry, rz2)
266
267 plt.show()
268
269
270 # print(rf)
271 # final = a.evolve(2, 500, rf, mutationrate=0.2, breedingrate=0.1, pexp=0.7, pnew=0.1)
272 # print("final:",final)
273 # print(final.evaluate([1,8]))
274 # print(final.evaluate([2,9]))
275
276
277
278
279
280 if __name__=="__main__":
2 from random import random, randint, choice,uniform
3 from copy import deepcopy
4 import numpy as np
5 import matplotlib.pyplot as plt
6
7 from random import random, randint, choice
8 from copy import deepcopy
9 import numpy as np
10
11 # 运算类
12 class fwrapper:
13 def __init__(self, function, childcount, name):
14 self.function = function
15 self.childcount = childcount
16 self.name = name
17
18 # 节点类
19 class node:
20 def __init__(self, fw, children):
21 self.function = fw.function
22 self.name = fw.name
23 self.children = children
24 #将inp指定的运算符作用到子节点上
25 def evaluate(self, inp):
26 # 循环调用子节点的子节点的子节点....的evaluate方法
27 results = [n.evaluate(inp) for n in self.children]
28 # 返回运算结果
29 return self.function(results)
30 #打印本节点及所属节点的操作运算符
31 def display(self, indent=0):
32 print(' ' * indent) + self.name
33 for c in self.children:
34 c.display(indent + 1)
35
36 #参数节点类，x+y 其中x，y都是参数节点
37 class paramnode:
38 def __init__(self, idx):
39 self.idx = idx
40 # evaluate方法返回paramnode节点值本身
41 def evaluate(self, inp):
42 return inp[self.idx]
43
44 def display(self, indent=0):
45 print '%sp%d' % (' ' * indent, self.idx)
46
47 # 常数节点
48 class constnode:
49 def __init__(self, v):
50 self.v = v
51
52 def evaluate(self, inp):
53 return self.v
54
55 def display(self, indent=0):
56 print '%s%d' % (' ' * indent, self.v)
57
58 # 操作运算符类
59 class opera:
60 # 使用前面定义的fwrapper类生产常用的加减乘除运算,第一个参数是本运算执行方式，第二个参数是本运算接受的参数个数，第三个参数是本运算名称
61 addw = fwrapper(lambda l: l[0] + l[1], 2, 'add')
62 subw = fwrapper(lambda l: l[0] - l[1], 2, 'subtract')
63 mulw = fwrapper(lambda l: l[0] * l[1], 2, 'multiply')
64
65 def iffunc(l):
66 if l[0] > 0:
67 return l[1]
68 else:
69 return l[2]
70 #定义if运算
71 ifw = fwrapper(iffunc, 3, 'if')
72
73 def isgreater(l):
74 if l[0] > l[1]:
75 return 1
76 else:
77 return 0
78 #定义greater运算
79 gtw = fwrapper(isgreater, 2, 'isgreater')
80 #构建运算符集合
81 flist = [addw, mulw, ifw, gtw, subw]
82
83 #使用node，paramnode，fwrapper构建一个example
84 def exampletree(self):
85 return node(self.ifw, [node(self.gtw, [paramnode(0), constnode(3)]), node(self.addw, [paramnode(1), constnode(5)]),
86 node(self.subw, [paramnode(1), constnode(2)]), ])
87
88
89 # 构建一颗随机运算数，pc为参数（分叉）个数，maxdepth为树的深度，fpr为运算符个数在运算符加节点总数中所占比例，ppr为参数个数在参数加常数个数总数中所占的比例
90 def makerandomtree(self,pc, maxdepth=4, fpr=0.5, ppr=0.6):
91 if random() < fpr and maxdepth > 0:
92 f = choice(self.flist)
93 # 递归调用makerandomtree实现子节点的创建
94 children = [self.makerandomtree(pc, maxdepth - 1, fpr, ppr) for i in range(f.childcount)]
95 return node(f, children)
96 elif random() < ppr:
97 return paramnode(randint(0, pc - 1))
98 else:
99 return constnode(randint(0, 10))
100
101
102 #变异，变异概率probchange=0.1
103 def mutate(self,t, pc, probchange=0.1):
104 # 变异后返回一颗随机树
105 if random() < probchange:
106 return self.makerandomtree(pc)
107 else:
108 result = deepcopy(t)
109 # 递归调用，给其子节点变异的机会
110 if isinstance(t, node):
111 result.children = [self.mutate(c, pc, probchange) for c in t.children]
112 return result
113
114 #交叉
115 def crossover(self,t1, t2, probswap=0.7, top=1):
116 # 如果符合交叉概率，就将t2的值返回，实现交叉；
117 if random() < probswap and not top:
118 return deepcopy(t2)
119 else:
120 #如果本层节点未实现交配，递归询问子节点是否符合交配条件
121 #首先使用deepcopy保存本节点
122 result = deepcopy(t1)
123 if isinstance(t1, node) and isinstance(t2, node):
124 #依次递归询问t1下的各子孙节点交配情况，交配对象为t2的各子孙；t1,t2家族同辈交配
125 result.children = [self.crossover(c, choice(t2.children), probswap, 0) for c in t1.children]
126 return result
127
128 # random2.display()
129 # muttree=mutate(random2,2)
130 # muttree.display()
131 # cross=crossover(random1,random2)
132 # cross.display()
133
134 #设置一个隐藏函数，也就是原始函数
135 def hiddenfunction(self,x, y):
136 return x ** 2+ y**2
137
138 #依照隐藏函数，生成坐标数据
139 def buildhiddenset(self):
140 rows = []
141 for i in range(200):
142 x = randint(0, 10)
143 x=uniform(-1,1)
144 y = randint(0, 10)
145 y=uniform(-1,1)
146 rows.append([x, y, self.hiddenfunction(x, y)])
147 print("rows:",rows)
148 return rows
149
150 #拟合成绩函数，判定拟合函数（实际是一颗图灵树）贴近原始函数的程度
151 def scorefunction(self,tree, s):
152 dif = 0
153 # print("tree:",tree)
154 # print("s:",s)
155 for data in s:
156 # print("data[0]:",data[0])
157 # print("data[1]:",data[1])
158 v = tree.evaluate([data[0],data[1]])
159 #累加每个数据的绝对值偏差
160 dif += abs(v - data[2])
161 return dif
162
163 #返回一个成绩判定函数rankfunction的句柄
164 def getrankfunction(self,dataset):
165 #此函数调用拟合成绩函数，并对成绩排序，返回各个种群的成绩
166 def rankfunction(population):
167 scores = [(self.scorefunction(t, dataset), t) for t in population]
168 scores.sort()
169 return scores
170 return rankfunction
171
172 # hiddenset=buildhiddenset()
173 # scorefunction(random2,hiddenset)
174 # scorefunction(random1,hiddenset)
175
176 def evolve(self,pc, popsize, rankfunction, maxgen=500, mutationrate=0.1, breedingrate=0.4, pexp=0.7, pnew=0.05):
177 #轮盘算法
178 def selectindex():
179 return int(np.log(random()) / np.log(pexp))
180 #使用随机树生成第一代各种群
181 population = [self.makerandomtree(pc) for i in range(popsize)]
182 #计算每一代各种群的成绩，
183 for i in range(maxgen):
184 scores = rankfunction(population)
185 #打印历代最好成绩
186 print('the best score in genneration ',i,':',scores[0][0])
187 #成绩完全吻合原函数的话，退出函数
188 if scores[0][0] == 0:
189 break
190 #创建新一代各种群，成绩前两名的直接进入下一代
191 newpop = [scores[0][1], scores[1][1]]
192 while len(newpop) < popsize:
193 #pnew为引进随机种群概率，未达此概率的，使用原种群的交配、变异生成新种群
194 if random() > pnew:
195 newpop.append(
196 self.mutate(self.crossover(scores[selectindex()][1], scores[selectindex()][1], probswap=breedingrate), pc,
197 probchange=mutationrate))
198 #引入随机种群
199 else:
200 newpop.append(self.makerandomtree(pc))
201 population = newpop
202 #打印历代最好种群
203 # scores[0][1].display()
204 return scores[0][1]
205
206
207
208 def main(argv):
209 e=opera()
210 def exampletree():
211 return node(e.ifw,[node(e.gtw,[paramnode(0),constnode(3)]),node(e.addw,[paramnode(1),constnode(5)]),node(e.subw,[paramnode(1),constnode(2)])])
212
213
214 # random1=e.makerandomtree(2)
215 # random1.evaluate([7,1])
216 # random1.evaluate([2,4])
217 # random2=e.makerandomtree(2)
218 # random2.evaluate([5,3])
219 # random2.evaluate([5,20])
220 # random1.display()
221 # random2.display()
222
223 # exampletree = e.exampletree()
224 # exampletree.display()
225 # print(exampletree.evaluate([6, 1]))
226 # print('after evaluate:')
227 # exampletree.display()
228 # exampletree.evaluate([2, 3])
229 # exampletree.evaluate([5, 3])
230 # exampletree.display()
231
232 a=opera()
233 row2=a.buildhiddenset()
234 # fig=plt.figure()
235 # ax=fig.add_subplot(1,1,1)
236 # plt.plot(np.random.randn(1000).cumsum())
237 # plt.show()
238
239
240
241 from mpl_toolkits.mplot3d import Axes3D
242 fig = plt.figure()
243 ax = fig.add_subplot(111, projection='3d')
244 X = [1, 1, 2, 2]
245 Y = [3, 4, 4, 3]
246 Z = [1, 2, 1, 1]
247 rx=[]
248 ry=[]
249 rz=[]
250 for i in row2:
251 rx.append(i[0])
252 ry.append(i[1])
253 rz.append(i[2])
254
255 ax.plot_trisurf(rx, ry, rz)
256 rz2=[]
257 rf = a.getrankfunction(row2)
258 final = a.evolve(2, 100, rf, mutationrate=0.2, breedingrate=0.1, pexp=0.7, pnew=0.1,maxgen=500)
259 print('__________________is it?_________________________')
260 final.display()
261 for j in range(0,len(rx)):
262 rz2.append(final.evaluate([rx[j],ry[j]]))
263 fig2 = plt.figure()
264 ax2 = fig2.add_subplot(111, projection='3d')
265 ax2.plot_trisurf(rx, ry, rz2)
266
267 plt.show()
268
269
270 # print(rf)
271 # final = a.evolve(2, 500, rf, mutationrate=0.2, breedingrate=0.1, pexp=0.7, pnew=0.1)
272 # print("final:",final)
273 # print(final.evaluate([1,8]))
274 # print(final.evaluate([2,9]))
275
276
277
278
279
280 if __name__=="__main__":
281 main(0)
看懂不一定写的出来，这是这次写这个程序最大的体会， 得定时拿出来复习复习。
