-
Notifications
You must be signed in to change notification settings - Fork 0
/
main.py
829 lines (758 loc) · 23.6 KB
/
main.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
# _*_coding:utf-8 _*_
# numpy :数据分析和机器学习的底层库
# import numpy as np
# arr=np.arange(10)
# print(arr.shape)
# print(arr.ndim)
# print(arr.size)
# print(arr.itemsize)
# print(arr.dtype)
# print(arr.nbytes)
# arr_r = arr.reshape(2,5)
# print(arr_r)
# print(arr)
# print("************************************")
# a = np.arange(12).reshape(3,4)
# b = np.arange(12,24).reshape(3,4)
# print(a,b)
# print(a+b)
# print(a*b)
# print(a/b)
# print(a-b)
# print(a%b)
# print(a * 3)
# print(a + 3)
# print(a / 3)
# print(a % 3)
# print(a - 3)
# a.shape = (4,3)
# print(a)
# b = a.astype("float32")
# print(b)
# print(a)
# start = 0
# end = 4
# length = 2
# print(a[start:end:length,start:end-1:1])
# arr = np.arange(24).reshape(2,3,4)
# print(arr[1,0:3:1,0:4:2])
# arr = np.arange(24).reshape(2,3,2,2)
# print(arr)
# print(arr[0,1,1,0])
# datas=[
# ("zhang3",[10,21,22],17),
# ("tom",[11,31,52],21),
# ("jack",[13,13,20],31)
# ]
# # arr = np.array(datas,dtype="U7,3int8,int8")
# # print(arr)
# arr = np.array(datas,dtype=[
# #別名,类型，字节数
# ("name","str_",8),
# ("scores","int32",3),
# ("age","int32",1),
# ])
# print(arr[0]["name"])
#
# arr = np.array(datas,dtype = {
# "names":["name","score","age"],
# "formats":["U8","3int32","int32"]
# })
# print(arr[1]["score"])
# a= np.array(datas,dtype={
# # 别名:(类型,字节偏移量)
# "names":("U8",0),
# "scores":("3int32",33),
# "age":("int32",45)
# })
# print(a)
# a=np.array([0x1234,0x5667],dtype=(
# "u2",{
# "lowc":("u1",0),
# "hignc":("u1",1),
# }))
#日期数据格式
# from matplotlib.font_manager import FontManager
# date=["2020-01-02","1995-01-03","2017-01","2019-01-03 02:30:00"]
# array_date=np.array(date)
# array_date=array_date.astype("M8[Y]")
# print(array_date)
# array_date=array_date.astype("M8[M]")
# print(array_date)
# array_date=array_date.astype("M8[D]")
# print(array_date)
# print(array_date[2] - array_date[1])
# print(array_date.astype("int32")) #从1970年到现在的天数
# array_date=array_date.astype("M8[h]")
# print(array_date)
# array_date=array_date.astype("M8[m]")
# print(array_date)
# array_date=array_date.astype("M8[s]")
# print(array_date)
# a = np.arange(15)
# s=a.reshape(3,5)
# print(s)
# s[0,0]=200
# print(s)
# print(a)
# a1 = a.ravel()
# print(a1)
# a1[0] = 300
# print(a1)
# print(a)
# a1 = a.flatten()
# print(a1)
# a1[0]=500
# print(a1)
# print(a)
# a.shape = (5,3)
# print(a)
# a.resize((3,5))
# print(a)
#数组的掩码操作
#bool掩码
# mask = [True,False,True,False,True,False]
# arr = np.arange(6)
# print(arr[mask])
#索引掩码
# mask_num = [1,4,2,3,0,5]
# arr = np.array(["a","b","c","d","e","f"])
# print(arr[mask_num])
# arr = np.arange(6)
#获取大于2的数
# print(arr[arr > 2])
# print(arr[(arr > 2) & (arr < 4)])
#数组的合并于拆分
# a = np.arange(12).reshape(3,4)
# b = np.arange(12,24).reshape(3,4)
#水平方向合并
# h_c=np.hstack((a, b))
# print(h_c)
#垂直方向合并
# v_c =np.vstack((a,b))
# print(v_c)
#深度方向合并
# d_c=np.dstack((a,b))
# print(d_c)
#水平方向切分
# a,b = np.hsplit(h_c,2)
# print(a,b)
#垂直方向切分
# a,b,c = np.vsplit(h_c,3)
# print(a,b,c)
#深度方向切分
# a,b = np.dsplit(d_c,2)
# print(a,b)
#按轴合并
# a_c = np.concatenate((a,b),axis=0)
# print(a_c)
#按轴切分
# a,b = np.split(h_c,2,axis=1)
# print(a,b)
#一维数组组合方案
# a = np.arange(6)
# b = np.arange(6,12)
# s=np.row_stack((a,b))
# print(s)
# s = np.column_stack((a,b))
# print(s)
#填充数组
# a = np.arange(6)
# b = np.arange(5)
# print(a,b)
# c = np.pad(b,pad_width=(0,1),mode="constant",constant_values=0)
# print(a,b,c)
# print(a + c)
#常用属性
# arr = np.arange(20).reshape(4,5)
# print([i for i in arr.flat])
# print(arr.T)
# print(arr.transpose())
# print(arr.shape)
# a = np.ones((3, 4))
# b = np.zeros((3, 4))
# print(np.zeros_like(a))
# print(np.ones_like(b))
# print(np.eye(3,3))
# import numpy as np
# from matplotlib import pyplot as plt
#
#
# plt.figure(num="red",facecolor="red")
# plt.figure(num="blue",facecolor="blue")
# plt.show()
#
# from matplotlib import pyplot as plt
# from matplotlib import gridspec as mg
# from matplotlib import pyplot as plt
# plt.figure("Flow LayOut",facecolor="lightgray")
#
# # x y width higtht
# plt.axes([0.03,0.5,0.94,0.4])
# plt.text(0.5,0.5,"1",ha = "center",va="center",size=36)
# plt.axes([0.03,0.03,0.54,0.4])
# plt.text(0.5,0.5,"1",ha = "center",va="center",size=36)
#
# plt.show()
# from matplotlib import pyplot as plt
# plt.figure("Grid Line",facecolor="lightgray")
# ax = plt.gca()
# # ax.grid()
# ax.grid(which ="major",axis="both",color="red",linewidth=0.75)
# #绘制曲线
# y=[1,10,100,1000,100,10,1]
# plt.plot(y,"o-",color="blue")
# plt.show()
#
# import numpy as np
# print(np.random.normal())
# print(4/(20/3))
# d=list(map(int,np.random.sample(12)*100))
# print(d)
# import tushare as ts
# print(ts.get_today_all())
# from matplotlib import pyplot as plt
#新增加的两行
# import matplotlib
# matplotlib.rc("font",family='FangSong')
# mpl_fonts = set(f.name for f in FontManager().ttflist)
#
# print('all font list get from matplotlib.font_manager:')
# for f in sorted(mpl_fonts):
# print('\t' + f)
# for i in FontManager().ttflist:
# print(i.name)
# a = ["一月份","二月份","三月份","四月份","五月份","六月份"]
#
# b=[56.01,26.94,17.53,16.49,15.45,12.96]
#
# plt.figure(figsize=(20,8),dpi=80)
#
# plt.bar(range(len(a)),b)
#
# #绘制x轴
# plt.xticks(range(len(a)),a)
#
# plt.xlabel("月份")
# plt.ylabel("数量")
# plt.title("每月数量")
#
# plt.show()
# import numpy as np
# from matplotlib import dates as md
# from matplotlib import pyplot as mp
# from datetime import datetime
# def dmy2ymd(dmy):
# dmy = str(dmy,encoding="utf-8")
# time = datetime.strptime(dmy,"%d-%m-%Y").date()
# t = time.strftime("%Y-%m-%d")
# return t
# dates, bhp_closing_prices = np.loadtxt('da_data/bhp.csv',
# delimiter=',',usecols=(1, 6), unpack=True,
# dtype='M8[D], f8', converters={1: dmy2ymd})
# vale_closing_prices = np.loadtxt('da_data/vale.csv', delimiter=',',
# usecols=(6), unpack=True)
# diff_closing_prices = bhp_closing_prices - vale_closing_prices
# days = dates.astype(int)
# p = np.polyfit(days, diff_closing_prices, 5)
# poly_closing_prices = np.polyval(p, days)
# q = np.polyder(p)
# roots_x = np.roots(q)
# roots_y = np.polyval(p, roots_x)
# mp.figure('Polynomial Fitting', facecolor='lightgray')
# mp.title('Polynomial Fitting', fontsize=20)
# mp.xlabel('Date', fontsize=14)
# mp.ylabel('Difference Price', fontsize=14)
# ax = mp.gca()
# ax.xaxis.set_major_locator(md.WeekdayLocator(byweekday=md.MO))
# ax.xaxis.set_minor_locator(md.DayLocator())
# ax.xaxis.set_major_formatter(md.DateFormatter('%d %b %Y'))
# mp.tick_params(labelsize=10)
# mp.grid(linestyle=':')
# dates = dates.astype(md.datetime.datetime)
# mp.plot(dates, poly_closing_prices, c='limegreen',
# linewidth=3, label='Polynomial Fitting')
# mp.scatter(dates, diff_closing_prices, c='dodgerblue',
# alpha=0.5, s=60, label='Difference Price')
# roots_x = roots_x.astype(int).astype('M8[D]').astype(
# md.datetime.datetime)
# mp.scatter(roots_x, roots_y, marker='^', s=80,
# c='orangered', label='Peek', zorder=4)
# mp.legend()
# mp.gcf().autofmt_xdate()
# mp.show()
# import numpy as np
# a=np.array([1,2,3])
# a1=np.array([1,2])
#
# a2=np.array([3,4])
# print(a1.ndim,a2.ndim,a.ndim)
# print(a.compress(( a1[0]> a2[0])))
import numpy as np
# print(original)
# import pandas as pd
#
# dates = pd.Series(["2011","2011-02","2011-03-01","2011/04/01","2011/05/01 01:01:01","01Jun 2011"])
# dates = pd.to_datetime(dates)
# # print(dates,type(dates),dates.dtype)
# # datetime类型数据支持日期运算
# delta = dates - pd.to_datetime('2010-01-01') #日期偏移量
# # 获取天数数值
# print(delta.dt.days)
# # 获取秒数数值
# print(delta.dt.seconds)
#
#
# print(type(dates),66)
# print(type(delta))
#
# import pandas as pd
# left = pd.DataFrame({
# 'student_id':[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20],
# 'student_name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung', 'Billy', 'Brian', 'Bran', 'Bryce', 'Betty', 'Emma', 'Marry', 'Allen', 'Jean', 'Rose', 'David', 'Tom', 'Jack', 'Daniel', 'Andrew'],
# 'class_id':[1,1,1,2,2,2,3,3,3,4,1,1,1,2,2,2,3,3,3,2],
# 'gender':['M', 'M', 'F', 'F', 'M', 'M', 'F', 'F', 'M', 'M', 'F', 'F', 'M', 'M', 'F', 'F', 'M', 'M', 'F', 'F'],
# 'age':[20,21,22,20,21,22,23,20,21,22,20,21,22,23,20,21,22,20,21,22],
# 'score':[98,74,67,38,65,29,32,34,85,64,52,38,26,89,68,46,32,78,79,87]})
# right = pd.DataFrame(
# {'class_id':[1,2,3,5],
# 'class_name': ['ClassA', 'ClassB', 'ClassC', 'ClassE']})
# # 合并两个DataFrame
# data = pd.merge(left,right)
# print(data.pivot_table(index=['class_id', 'gender'], values=['score'],
# columns=['age']))
from pytdx.hq import TdxHq_API
from pytdx.exhq import TdxExHq_API
from pytdx.params import TDXParams
# api = TdxHq_API()
# exapi = TdxExHq_API()
# BLOCK_SZ = "block_zs.dat"
# BLOCK_FG = "block_fg.dat"
# BLOCK_GN = "block_gn.dat"
# BLOCK_DEFAULT = "block.dat"
# with api.connect('119.147.212.81', 7709):
# wk = api.get_security_bars(7,0,"000831", 0, 800)
# wk_df = api.to_df(wk)
# #拿到五矿稀土今天的数据
# print(wk_df[wk_df["datetime"]>"2022-01-25 00:00"])
# stock_df = api.to_df(api.get_security_list(0,1))
# print(stock_df)
# print(stock_df["code"])
# content = api.to_df(api.get_company_info_category(TDXParams.MARKET_SZ, "000681"))
#
# data = api.get_company_info_content(0, '000001', '000001.txt', 0,100000)
# print(data)
# print(api.to_df(api.get_and_parse_block_info(TDXParams.BLOCK_SZ)))
# exapi.get_instrument_info(0,)
# import cv2 as cv
# image = cv.imread("1.jpg")
# GRAY=cv.cvtColor(image,cv.COLOR_BGR2GRAY)
# cv.imshow("GRAY",GRAY)
# cv.waitKey(0)
# cv.destroyAllWindows()
# import numpy as np
# from matplotlib import pyplot as plt
# from mpl_toolkits.mplot3d import axes3d #必须导入
#
# n=1000
# x,y = np.meshgrid(np.linspace(-3,3,n),np.linspace(-3,3,n)) #x,y直接组成坐标点矩阵
# z = x*y
# plt.figure("3D Surface",facecolor="lightgray")
# # ax3d = plt.gca(projection="3d")
# # ax3d.set_xlabel("x")
# # ax3d.set_ylabel("y")
# # ax3d.set_zlabel("z")
# ax3d = plt.axes(projection="3d")
# ax3d.set_xlabel("x")
# ax3d.set_ylabel("y")
# ax3d.set_zlabel("z")
#
# ax3d.plot_surface(x,y,z,cstride=30,rstride=30,cmap="jet")
# plt.show()
# import numpy as np
# import matplotlib.pyplot as mp
# train_x = np.array([0.5, 0.6, 0.8, 1.1, 1.4])
# train_y = np.array([5.0, 5.5, 6.0, 6.8, 7.0])
# test_x = np.array([0.45, 0.55, 1.0, 1.3, 1.5])
# test_y = np.array([4.8, 5.3, 6.4, 6.9, 7.3])
#
# times = 1000 # 定义梯度下降次数
# lrate = 0.01 # 记录每次梯度下降参数变化率
# epoches = [] # 记录每次梯度下降的索引
# w0, w1, losses = [1], [1], []
# for i in range(1, times + 1):
# epoches.append(i)
# loss = (((w0[-1] + w1[-1] * train_x) - train_y) ** 2).sum() / 2
# losses.append(loss)
# d0 = ((w0[-1] + w1[-1] * train_x) - train_y).sum()
# d1 = (((w0[-1] + w1[-1] * train_x) - train_y) * train_x).sum()
# print('{:4}> w0={:.8f}, w1={:.8f}, loss={:.8f}'.format(epoches[-1], w0[-1], w1[-1], losses[-1]))
# w0.append(w0[-1] - lrate * d0)
# w1.append(w1[-1] - lrate * d1)
#
# pred_test_y = w0[-1] + w1[-1] * test_x
# w0 = w0[:-1]
# w1 = w1[:-1]
#
# import mpl_toolkits.mplot3d as axes3d
#
# grid_w0, grid_w1 = np.meshgrid(
# np.linspace(0, 9, 500),
# np.linspace(0, 3.5, 500))
#
# grid_loss = np.zeros_like(grid_w0)
# for x, y in zip(train_x, train_y):
# grid_loss += ((grid_w0 + x*grid_w1 - y) ** 2) / 2
#
# mp.figure('Loss Function')
# ax = mp.axes(projection="3d")
# mp.title('Loss Function', fontsize=20)
# ax.set_xlabel('w0', fontsize=14)
# ax.set_ylabel('w1', fontsize=14)
# ax.set_zlabel('loss', fontsize=14)
# ax.plot_surface(grid_w0, grid_w1, grid_loss, rstride=10, cstride=10, cmap='jet')
# ax.plot(w0, w1, losses, 'o-', c='orangered', label='BGD')
# mp.legend()
# mp.show()
# import numpy as np
# aa = np.array([])
# a=np.array([2, 2, 2, 2, 2])
# aa.append([1,2])
# print(aa)
# a= np.arange(1,7).reshape(2,3)
# print(a.shape[1])
# # 案例：预测波士顿地区房屋价格。
# import sklearn.datasets as sd
# # 打乱数据集用
# import sklearn.utils as su
# import sklearn.ensemble as se
# import numpy as np
# # 获得波士顿地区房屋价格的第一种方式，注意：load_boston()函数在1.2版本中已经被分离
# boston = sd.load_boston()
# x, y = su.shuffle(boston.data, boston.target, random_state=7)
# # print(boston.data.shape,boston.target.shape)
# # print(boston.feature_names,len(boston.feature_names))
#
# # 划分训练集和测试集
# train_size = int(len(x) * 0.8) #从总样本中挑出80%用作训练集，剩下20%用作测试集
# train_x, train_y, test_x, test_y = x[:train_size], y[:train_size], x[train_size:], y[train_size:]
#
# # train_y = train_y.reshape(-1,1)
# # data = d=np.hstack((train_x,train_y))
# # np.savetxt("1.csv",data,delimiter=',')
#
#
# def split_sample_space(y:"result set"):
# # 通过结果集划分子样本空间
# sub_sample_set = {}
# # print(y,type(y))
# for data in y:
# if data not in sub_sample_set.keys():
# sub_sample_set[data] = 0
# sub_sample_set[data] += 1
# return sub_sample_set
#
#
#
# #计算信息熵
# def calcInfoEnt(x:"Feature set",y:"result set"):
#
# sub_sample_set = split_sample_space(y)
# # 信息熵公式 H(X) = = -∑P(Xᵢ)log₂(P(Xᵢ))
# sample_length = len(x)
#
# # 计算信息熵
# infoEnt = 0.0
# for key,value in sub_sample_set.items():
# '''
# :key 子样本空间划分依据(程序中未使用)
# :value 子样本空间大小
# '''
# # 根据子样本空间大小计算P(Xᵢ)
# pxi = float(value) / sample_length
# # 计算 ∑P(Xᵢ)log₂(P(Xᵢ))
# infoEnt += pxi * np.log2(pxi)
# #返回H(X)
# return -infoEnt
#
# #划分数据集
# def split_dataSet(x:"Feature set",y:"result set",split_index:"column index",unique_value):
# newdataSet=[]
# newResultSet=[]
# for row in x:
# # 判断该列值是否等于唯一值
# if row[split_index] == unique_value:
# # print(unique_value,917)
#
# #获取该数的行级索引
# row_index = np.where((x == row).all(axis = 1))
# # print(row_index,924)
#
# subDataSet = list(row[:split_index])
# #根据唯一值获取第2-n所构成的子表
# subDataSet.extend(row[split_index+1:])
# # 将子表添加到新的数据集中
# newdataSet.append(np.array(subDataSet))
#
# # 将子表对应的结果添加到新的结果集中
# newResultSet.append(y[row_index])
#
#
#
#
#
# return np.array(newdataSet),np.array(newResultSet).flatten()
#
# #对结果集进行投票排序
# import operator
# def majorityCnt(resultSet):
# classCount = {}
# for vote in resultSet:
# if vote not in classCount.keys():
# classCount[vote] = 0
# classCount[vote] += 1
# sortedclassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)
# return sortedclassCount[0][0]
#
#
# #选择最好的数据集划分方式
# def choose_best_feature_to_split(x:"Feature set",y:"result set"):
#
# #计算未切分前数据集信息熵
# baseInfoEnt = calcInfoEnt(x,y)
# #最好的信息增益为0.0
# bestInfoGain = 0.0
# #设置初始值
# bestFeature = -1
# #获取特征集的长度
# sample_length = x.shape[1]
# for i in range(sample_length):
#
# # 获取数据集的每列特征值
# columns_feature_value_list=[row[i] for row in x]
# #分表
# unique_value_list = list(set(columns_feature_value_list))
# newInfoEnt = 0.0
# for unique_value in unique_value_list:
# #切分数据集
# newdataSet,newResultSet = split_dataSet(x,y,i,unique_value)
# #计算子表的熵
# proportion = len(newdataSet) / float(len(x))
# newInfoEnt += proportion * calcInfoEnt(newdataSet,newResultSet)
#
# #计算信息增益 划分前的信息熵 - 划分后的信息熵
# infoGain = baseInfoEnt - newInfoEnt
# if infoGain > bestInfoGain:
# baseInfoEnt = infoGain
# bestFeature = i
#
# return bestFeature
#
#
#
#
#
# def createTree(x:"Feature set",y:"result set"):
# y1 =list(y)
# # print(y1)
# if y1.count(y1[0]) == len(y1):
#
# return y1[0]
# if len(x[0]) == 1:
# return majorityCnt(y1)
#
# bestFeature = choose_best_feature_to_split(x,y)
# print(bestFeature,607,y)
#
# myTree = {y[bestFeature]: {}}
#
# featValues = [row[bestFeature] for row in x]
#
# unique_value_list = list(set(featValues))
#
# for unique_value in unique_value_list:
# newdataSet,newResultSet = split_dataSet(x,y,bestFeature,unique_value)
# myTree[y[bestFeature]][unique_value] = createTree(newdataSet,newResultSet)
# # return myTree
# res = createTree(train_x, train_y)
# print(res)
# import matplotlib.pyplot as plt
# # 解决中文问题
# from matplotlib.font_manager import FontProperties
#
# font = FontProperties(fname=r"c:\windows\fonts\simsun.ttc", size=14)
#
# decisionNode = dict(boxstyle='sawtooth', fc="0.8")
# leafNode = dict(boxstyle='round4', fc='0.8')
# arrow_args = dict(arrowstyle='<-')
#
#
#
# def createPlot():
#
# fig = plt.figure(1, facecolor='white')
# fig.clf()
# plt.subplot(111, frameon=False)
#
#
# plt.annotate("决策节点", xy=(0.1, 0.5), xycoords='axes fraction', xytext=(0.5, 0.1), textcoords='axes fraction',
# va="center", bbox=decisionNode, arrowprops=arrow_args, fontproperties=font)
# plt.annotate("叶节点", xy=(0.3, 0.8), xycoords='axes fraction', xytext=(0.8, 0.1),
# textcoords='axes fraction',
# va="center", bbox=leafNode, arrowprops=arrow_args, fontproperties=font)
#
# plt.show()
#
#
# createPlot()
# def getNumLeafs(myTree):
# numleafs=0
# firstStr=myTree.keys()[0]
# secondDict=myTree[firstStr]
# for key in secondDict.keys():
# if type(secondDict[key]).__name__=="dict":
# numleafs+=getNumLeafs(secondDict[key])
# # else:
# numleafs+=1
# return numleafs
# def getTreeDepth(myTree):
# maxDepth=0
# firstStr=myTree.keys()[0]
# secondDict=myTree[firstStr]
# for key in secondDict.keys():
# if type(secondDict[key]).__name__=="dict":
# thisDepth=1+getTreeDepth(secondDict[key])
# else:
# thisDepth=1
# if thisDepth>maxDepth:
# maxDepth=thisDepth
# return maxDepth
# getNumLeafs(res)
# getTreeDepth(res)
# s = {0.0: 'setosa', 1.0: 'versicolor', 2.0: 'virginica'}
# s1 = [2.0, 1.0, 0.0, 1.0, 2.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 2.0, 0.0, 1.0, 2.0, 2.0, 0.0, 0.0, 1.0, 2.0, 1.0, 2.0, 2.0, 2.0, 1.0, 1.0, 2.0, 2.0, 2.0, 1.0, 0.0, 2.0, 1.0, 0.0, 0.0, 0.0, 0.0, 2.0, 2.0, 1.0, 2.0, 2.0, 1.0, 0.0, 1.0, 1.0, 2.0, 0.0, 0.0, 0.0, 2.0, 0.0, 2.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 2.0, 1.0, 1.0, 0.0, 2.0, 0.0, 0.0, 2.0, 2.0, 0.0, 2.0, 0.0, 1.0, 2.0, 1.0, 0.0, 1.0, 0.0, 2.0, 2.0, 1.0, 0.0, 0.0, 1.0, 2.0, 0.0, 2.0, 2.0, 1.0, 0.0, 1.0, 0.0, 2.0, 2.0, 0.0, 0.0, 2.0, 1.0, 2.0, 2.0, 1.0, 0.0, 0.0, 2.0, 0.0, 0.0, 1.0, 2.0, 2.0, 1.0, 1.0, 0.0, 2.0, 0.0, 0.0, 1.0, 1.0, 2.0]
# [s[i] for i in s1]
def read(path):
with open(path,"r") as fp:
iris = [inst.strip("\n").split(",") for inst in fp.readlines()]
return iris
''' 将第i个特征和类标签组合起来
如:
[
[0.2,'Iris-setosa'],
[0.2,'Iris-setosa'],
...
]'''
def split(instances, i):
log = []
for line in instances:
log.append([line[i], line[4]])
return log
''' 统计每个属性值所具有的实例数量
[['4.3', 'Iris-setosa', 1], ['4.4', 'Iris-setosa', 3],...]'''
def count(log):
log_cnt = []
# 以第0列进行排序的 升序排序
log.sort(key=lambda attr: attr[0])
i = 0
while i < len(log):
cnt = log.count(log[i])
record = log[i][:]
record.append(cnt)
log_cnt.append(record)
i += cnt
return log_cnt
''' log_cnt 是形如： ['4.4', 'Iris-setosa', 3]
的统计对于某个属性值，对于三个类所含有的数量
返回结果形如：{4.4:[0,1,3],...}
属性值为4.4的对于三个类的实例数量分别是：0、1、3 '''
def build(log_cnt):
log_dict = {}
for record in log_cnt:
if record[0] not in log_dict.keys():
log_dict[record[0]] = [0, 0, 0]
if record[1] == 'setosa':
print(log_dict[record[0]])
log_dict[record[0]][0] = record[2]
elif record[1] == 'versicolor':
log_dict[record[0]][1] = record[2]
elif record[1] == 'virginica':
log_dict[record[0]][2] = record[2]
else:
raise TypeError('Data Exception')
# print(log_dict,740)
log_truple = sorted(log_dict.items())
return log_truple
def collect(instances, i):
log = split(instances, i)
log_cnt = count(log)
log_tuple = build(log_cnt)
return log_tuple
def combine(a, b):
"""'' a=('4.4', [3, 1, 0]), b=('4.5', [1, 0, 2])
combine(a,b)=('4.4', [4, 1, 2]) """
c = a[:]
for i in range(len(a[1])):
c[1][i] += b[1][i]
return c
def chi2(a):
"""计算两个区间的卡方值"""
m = len(a)
k = len(a[0])
r = []
'''第i个区间的实例数'''
for i in range(m):
sum = 0
for j in range(k):
sum += a[i][j]
r.append(sum)
c = []
'''第j个类的实例数'''
for j in range(k):
sum = 0
for i in range(m):
sum += a[i][j]
c.append(sum)
n = 0
'''总的实例数'''
for ele in c:
n += ele
res = 0.0
for i in range(m):
for j in range(k):
Eij = 1.0 * r[i] * c[j] / n
if Eij != 0:
res = 1.0 * res + 1.0 * (a[i][j] - Eij) ** 2 / Eij
return res
'''ChiMerge 算法'''
'''下面的程序可以看出，合并一个区间之后相邻区间的卡方值进行了重新计算，而原作者论文中是计算一次后根据大小直接进行合并的
下面在合并时候只是根据相邻最小的卡方值进行合并的，这个在实际操作中还是比较好的
'''
def chimerge(log_tuple, max_interval):
num_interval = len(log_tuple)
while num_interval > max_interval:
num_pair = num_interval - 1
chi_values = []
''' 计算相邻区间的卡方值'''
for i in range(num_pair):
arr = [log_tuple[i][1], log_tuple[i + 1][1]]
chi_values.append(chi2(arr))
min_chi = min(chi_values)
for i in range(num_pair - 1, -1, -1):
if chi_values[i] == min_chi:
log_tuple[i] = combine(log_tuple[i], log_tuple[i + 1])
log_tuple[i + 1] = 'Merged'
while 'Merged' in log_tuple:
log_tuple.remove('Merged')
num_interval = len(log_tuple)
split_points = [record[0] for record in log_tuple]
return split_points
def discrete(path):
instances = read(path)
max_interval = 6
num_log = 4
for i in range(num_log):
log_tuple = collect(instances, i)
split_points = chimerge(log_tuple, max_interval)
# print(split_points,1567)
iris = discrete("sklearn_data/iris.data")