Data Algorithm learn of Numpy
98285 ワード
Author:チューガー
2018-11-28 19:11:09
IDE: Pycharm2018.03 Anaconda 3.5.1 Python 3.7
KeyWord : NumPy
Explain:更新[Explain:こうしん]
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転載先:https://www.cnblogs.com/caochucheng/p/10034018.html
2018-11-28 19:11:09
IDE: Pycharm2018.03 Anaconda 3.5.1 Python 3.7
KeyWord : NumPy
Explain:更新[Explain:こうしん]
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1 # -*- coding: utf-8 -*-
2 # ---------------------------------
3
4 """
5 # Author : chu ge
6 # Function: Numpy
7 #
8 """
9
10 # ---------------------------------
11 '''
12 # --------------------------------
13 #
14 # 1.
15 # 2.
16 # 3.
17 # --------------------------------
18 '''
19 # 1. 、
20 import sys
21 import os
22
23 # 2.
24 import numpy as np
25 from scipy import linalg
26 import pandas as pd
27 import matplotlib.pyplot as plt
28 import seaborn as sns
29
30 import timeit
31 import numpy.random as np_random
32 from numpy.linalg import inv, qr
33 from random import normalvariate
34
35 import pylab
36 #
37 '''
38 ============================================================================
39 #》》》》》》》》》》》》》》》》》》》》》》》》
40
41 ----------------------------------------------
42 Numpy
43 、 , , Numpy list
44 NumPy Python 。 , 。
45
46 ndarray
47 •
48 •
49 • 、 ,
50 • C、C++、Fortran Python
51 • Python ,NumPy
52 , 。
53
54 Scipy
55 NumPy 、 、 、
56
57 Pandas
58 Numpy , 。
59 n 。
60 n 。
61 n 。
62 n ( ) 。
63 n 。
64 n ( SQL) 。
65 o :Series DataFrame
66
67 matplotlib
68 Python
69
70 nltk
71 (Natural Language Toolkit)
72 n :pip install -U nltk
73 n :import nltk
74 n :nltk.download()
75 n
76 n
77 n
78 n
79 n
80
81 igraph
82
83 o :
84 n pip install -U python-igraph
85 n conda install -c marufr python-igraph=0.7.1.post6
86 Scikit-learn
87 o Scikit-learn Scipy Python 。
88 n :pip install -U scikit-learn / conda install scikit-learn
89 ----------------------------------------------
90
91 Numpy
92 ----------------------------------------------
93
94 array ( 、 、 ) ndarray。
95 dtype, dtype。 。
96 asarray darray, ndarray 。
97 arange range, ndarray 。
98 ones,ones_like dtype 1 。ones_like ,
99 dtype 1 。
100 zeros,zeros_like ones ones_like, 0 。
101 empty, empty_like , 。
102 eye,identity N * N
103
104
105
106 int8, uint8 - i1, u1 / 8
107 int16, uint16 - i2, u2 / 16
108 int32, uint32 - i4, u4 / 32
109 int64, uint64 - i8, u8 / 64
110 float16 - f2
111 float32 - f4 or f , C float 。
112 float64 - f8 or d 。 C double Python float 。
113 float128 - f16 or g
114 complex64/128/256 -c8/16/32 32 ,64 128 。
115 bool - ? True False
116 object - O Python
117 string_ - S 。S10 10 。
118 unicode_ - U unicode
119
120
121 NumPy ndarray
122 •
123 •
124 •
125
126 NumPy ndarray
127 •
128 •
129
130 NumPy ndarray
131 • 。
132 • 、 ( )
133
134 NumPy ndarray
135 • /
136 •
137
138 NumPy ndarray
139 • I
140 abs, fabs 、 。 , fabs。
141 sqrt 。 arr ** 0.5
142 sqare 。 arr ** 2
143 exp e^x
144 log,log10, 、 10 log、 2 log log(1 + x)。
145 log2,log1p
146 sign :1( )、0( )、-1( )。
147 ceil ceiling , 。
148 floor floor , 。
149
150 II
151 rint , dtype。
152 modf 。
153 isnan “ NaN( )”
154 isfinite, “ ( inf, NaN)” “ ”
155 isinf
156 cos,
157 cosh,
158 sin,
159 sinh,
160 tan,
161 tanh
162 arccos,
163 arccosh,
164 arcsin,
165 arcsinh,
166 arctan,
167 arctanh
168 logical_not not x 。 -arr。
169
170 • I
171 add
172 subtract
173 multiply
174 divide,
175 floor_divide
176 power A B, A^B。
177 maximum, fmax 。fmax NaN。
178 minimum, fmin 。fmin NaN。
179 mod
180
181 II
182 copysign
183 greater, , 。
184 greater_equal,
185 less,
186 less_equal,
187 equal,
188 not_equal
189 logical_and, , 。
190 logical_or,
191 logical_xor
192
193
194
195 • NumPy (
196 )。 , 。
197 • Python
198
199
200 •
201 • Python , 。
202 •
203 • where where
204
205
206 •
207 sum 。 sum 0。
208 mean 。 mean NaN。
209 std, var , ( n)。
210 min, max
211 argmin
212 cumsum
213 cumprod
214
215 •
216 • cumsum cumprod
217 • axis
218
219
220
221 • sum True
222 • any all , , 0 True
223
224
225 •
226 •
227 ---- Error
228
229
230 •
231 unique(x) x , 。
232 intersect1d(x, y) x y , 。
233 union1d(x, y) x y , 。
234 in1d(x, y) "x y"
235 setdiff1d(x, y) , x y
236 setxor1d(x, y) , 。
237
238
239 •
240 •
241
242
243 • numpy.linalg I
244 diag ( ), ( 0)。
245 dot
246 trace
247 det
248 eig
249 inv
250 • numpy.linalg II
251 pinv Moore-Penrose
252 qr QR
253 svd
254 solve Ax = b, A
255
256
257
258 seed
259 permutation
260 shuffle
261 rand
262 randint
263 randn ( 0, 1)
264 binomial
265 normal ( )
266 beta Beta
267 chisquare
268 gamma Gamma
269 uniform [0, 1]
270
271
272
273 • reshape
274 • -1
275
276
277
278
279 •
280 concatenate ,
281 vstack, row_stack ( 0)
282 hstack, ( 1)
283 column_stack hstack, 。
284 dstack “ ” ( 2)
285 split
286 hsplit, split , 0、 1 2 。
287 vsplit,
288 dsplit
289
290
291
292
293 • _r
294 • _c
295
296
297 • _tile
298 • _repeat
299
300
301 • take
302 • put
303
304
305
306 m × n X, X = [x1, x2, ... xn], i m 。
307 n × n , Dij = ||xi - xj||2
308 ----------------------------------------------
309
310
311
312 ============================================================================
313 '''
314 #
315
316 #
317 '''
318 # ============================================================================
319 # Function:
320 # Explain :
321 # :
322 # ============================================================================
323 '''
324
325
326 # ============================================================================
327 '''
328 # ============================================================================
329 #
330 # ============================================================================
331 '''
332 if __name__ == "__main__":
333 print("123")
334 # # numpy
335 # print(np.arange(10)**2)
336 # # scipy
337 # var_A = np.array([[1,2],[3,4]])
338 # print(linalg.det(var_A))
339 # # pandas
340 # print(pd.Series([1,3,5,np.nan,6,8]))
341 # print(pd.date_range('20130101',periods=3))
342 # dates = pd.date_range('20130101', periods=6)
343 # df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD'))
344 # print(df)
345 # df1 = df.sort_values(by='B')
346 # print(df1)
347 #
348 # # # mat plot lib
349 # plt.plot([1, 2, 3])
350 # plt.ylabel('some numbers')
351 # plt.show()
352 # # sea born
353 # sns.set(color_codes=True)
354 #
355 # # x = np.random.normal(loc=0.0, scale=1.0,size=100)
356 # # sns.distplot(x)
357
358
359 # numpy
360 #
361 # print(' NumPy ')
362 # data = [6, 7.5, 8, 0, 1]
363 # arr = np.array(data)
364 # print( arr, arr.dtype )
365 # print( ' NumPy ')
366 # data = [[1, 2, 3, 4], [5, 6, 7, 8]]
367 # arr = np.array(data)
368 # print( arr, '
: ',arr.shape) #
369 # print(' zeros/empty')
370 # print('
10 0 :
',np.zeros(10)) # 10 0
371 # print('
3*6 :
', np.zeros((3, 6))) # 3*6
372 # print('
2*3*2 , :
',np.empty((2, 3, 2))) # 2*3*2 ,
373 # print('
:
', np.arange(15))
374 #
375
376
377 # print('
:',)
378 # arr = np.array([1, 2, 3], dtype=np.float64)
379 # arr1 = np.array([1, 2, 3], dtype=np.int32)
380 # print( arr.dtype, arr1.dtype )
381 # print('
astype :', )
382 # int_arr = np.array([1, 2, 3, 4, 5])
383 # float_arr = int_arr.astype(np.float)
384 # print(int_arr.dtype, float_arr.dtype )
385 # print('
astype float int :', )
386 # float_arr = np.array([3.7, -1.2, -2.6, 0.5, 12.9, 10.1])
387 # int_arr = float_arr.astype(dtype=np.int)
388 # print(int_arr)
389 # print('
astype , :')
390 # str_arr = np.array(['1.25', '-9.6', '42'], dtype=np.string_)
391 # float_arr = str_arr.astype(dtype=np.float)
392 # print(float_arr)
393 # print('
astype :')
394 # int_arr = np.arange(10)
395 # float_arr = np.array([.23, 0.270, .357, 0.44, 0.5], dtype=np.float64)
396 # print(int_arr.astype(float_arr.dtype))
397 # print('
astype , :
',int_arr[0], int_arr[1])
398 #
399
400
401 # print(' / , / 。')
402 # arr = np.array([[1.0, 2.0, 3.0], [4., 5., 6.]])
403 # print('>>>1
',arr * arr)
404 # print('>>>2
',arr - arr)
405 # print('
:')
406 # arr = np.array([[1.0, 2.0, 3.0], [4., 5., 6.]])
407 # print('>>>3
', 1 / arr )
408 # print('>>>4
', arr ** 0.5) #
409 #
410
411
412 # print('
')
413 # arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
414 # print(arr[2])
415 # print(arr[1][2])
416 # print(arr[1,2])
417 # print('
')
418 # arr = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])
419 # print('>>>1
', arr[0])
420 # print('>>>2
', arr[1, 0])
421 # old_values = arr[0].copy() # arr[0]
422 # arr[0] = 123456 # arr[0]
423 # print('>>>3
', arr)
424 # arr[0] = old_values #
425 # print('>>>4
', arr)
426 # print('
:')
427 # arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
428 # print('>>>1
',arr[1:6]) # arr[1] arr[5]
429 # arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
430 # print('>>>2
', arr[:2]) # 1、2
431 # print('>>>3
', arr[:2, 1:]) # 1、2 , 2、3
432 # print('>>>4
',arr[:, :1]) #
433 # arr[:2, 1:] = 0 # 1、2 , 2、3 0
434 # print('>>>5
', arr)
435 #
436
437
438 # print(' ')
439 # name_arr = np.array(['Bob', 'Joe', 'Will', 'Bob', 'Will', 'Joe', 'Joe'])
440 # rnd_arr = np_random.randn(7, 4) # 7*4
441 # print('>>>1
', rnd_arr)
442 # print('>>>2
', name_arr == 'Bob') # , 'Bob' True, False。
443 # print('>>>3
', rnd_arr[name_arr == 'Bob']) #
444 # print('>>>4
', rnd_arr[name_arr == 'Bob', :2]) #
445 # print('>>>5
', rnd_arr[~(name_arr == 'Bob')] ) #
446 # mask_arr = (name_arr == 'Bob') | (name_arr == 'Will')
447 # print('>>>6
', rnd_arr[mask_arr]) #
448 # rnd_arr[name_arr != 'Joe'] = 7
449 # print('>>>7
', rnd_arr) # , 7
450 #
451
452
453 # print("
:")
454 # arr = np.empty((8, 4))
455 # for i in range(8):
456 # arr[i] = i
457 # print('>>>1
', arr)
458 # print('>>>2
', arr[[4, 3, 0, 6]]) # arr[4]、arr[3]、arr[0] arr[6]
459 # print('>>>3
', arr[[-3, -5, -7]]) # arr[3]、arr[5] arr[-7]
460 # arr = np.arange(32).reshape((8, 4)) # reshape
461 # print('>>>4
', arr)
462 # print('>>>5
', arr[[1, 5, 7, 2], [0, 3, 1, 2]]) # arr[1, 0]、arr[5, 3],arr[7, 1] arr[2, 2]
463 # print('>>>6
', arr[[1, 5, 7, 2]][:, [0, 3, 1, 2]]) # 1572 0312
464 # print('>>>7
', arr[np.ix_([1, 5, 7, 2], [0, 3, 1, 2])] ) #
465 #
466
467
468 # print("
")
469 # arr = np.arange(15).reshape((3, 5))
470 # print('>>>1
', arr)
471 # print('>>>2
', arr.T)
472 # print("
")
473 # arr = np_random.randn(6, 3)
474 # print('>>>3
', np.dot(arr.T, arr))
475 # print("
")
476 # arr = np.arange(16).reshape((2, 2, 4))
477 # print('>>>4
', arr)
478 # print('>>>5
', arr.transpose((1, 0, 2)))
479 # print('>>>6
', arr.swapaxes(1, 2))
480 # print('>>>7
', arr)
481 # '''
482 # :
483 # arr
484 # - a[0][0] = [0, 1, 2, 3]
485 # - a[0][1] = [4, 5, 6, 7]
486 # - a[1][0] = [8, 9, 10, 11]
487 # - a[1][1] = [12, 13, 14, 15]
488 # transpose , (0, 1, 2, ... , n - 1),
489 # (1, 0, 2) a[x][y][z] = a[y][x][z], 0 1 。
490 # - a'[0][0] = a[0][0] = [0, 1, 2, 3]
491 # - a'[0][1] = a[1][0] = [8, 9, 10, 11]
492 # - a'[1][0] = a[0][1] = [4, 5, 6, 7]
493 # - a'[1][1] = a[1][1] = [12, 13, 14, 15]
494 # '''
495 #
496
497 # print("
")
498 # arr = np.arange(10)
499 # print('>>>1
', arr)
500 # print('>>>2
', np.sqrt(arr))
501 # print("
")
502 # x = np_random.randn(8)
503 # print(x)
504 # y = np_random.randn(8)
505 # print(y)
506 # print('>>>3
', np.maximum(x, y))
507 # print("
modf ")
508 # arr = np_random.randn(7) * 5 # 5
509 # print('>>>4
', np.modf(arr))
510 #
511
512
513 # points = np.arange(-10, 10, 0.005) # 100
514 # xs, ys = np.meshgrid(points, points) # xs, ys
515 # # print('>>>1
', xs, ys)
516 # z = np.sqrt(xs ** 2 + ys ** 2)
517 # # print (z)
518 # print("
")
519 # plt.imshow(z, cmap = plt.cm.gray)#gray
520 # plt.colorbar()
521 # # plt.title("Image plot of $\sqrt{x^2 + y^2}$ for a grid of values")
522 # pylab.show()
523 #
524
525
526 # '''
527 # zip ,zip , 1 tuple 。
528 # zip([1, 2, 3], [4, 5, 6], [7, 8, 9])
529 # :[(1, 4, 7), (2, 5, 8), (3, 6, 9)]
530 # '''
531 # print("
")
532 # x_arr = np.array([1.1, 1.2, 1.3, 1.4, 1.5])
533 # y_arr = np.array([2.1, 2.2, 2.3, 2.4, 2.5])
534 # cond = np.array([True, False, True, True, False])
535 # result = [(x if c else y) for x, y, c in zip(x_arr, y_arr, cond)] #
536 # print('>>>1
', result)
537 # print('>>>2
', np.where(cond, x_arr, y_arr)) # NumPy where
538 # arr = np_random.randn(4, 4)
539 # print('>>>3
', arr)
540 # print('>>>4
', np.where(arr > 0, 2, -2))
541 # print('>>>5
', np.where(arr > 0, 2, arr))
542 # print("
where ")
543 # cond_1 = np.array([True, False, True, True, False])
544 # cond_2 = np.array([False, True, False, True, False])
545 # result = []
546 # for i in range(len(cond)):
547 # if cond_1[i] and cond_2[i]:
548 # result.append(0)
549 # elif cond_1[i]:
550 # result.append(1)
551 # elif cond_2[i]:
552 # result.append(2)
553 # else:
554 # result.append(3)
555 # print('>>>6
', result)
556 # result = np.where(cond_1 & cond_2, 0,np.where(cond_1, 1, np.where(cond_2, 2, 3)))
557 # print('>>>7
', result)
558 #
559
560 # print("
, :")
561 # arr = np.random.randn(5, 5)
562 # print('>>>1
', arr)
563 # print('>>>2
', arr.mean()) #
564 # print('>>>3
', arr.sum()) #
565 # print('>>>4
', arr.mean(axis=1)) #
566 # print('>>>5
', arr.sum(0)) # ,axis
567 # '''
568 # cumsum:
569 # - :a[i][j] += a[i - 1][j]
570 # - :a[i][j] *= a[i][j - 1]
571 # cumprod:
572 # - :a[i][j] += a[i - 1][j]
573 # - :a[i][j] *= a[i][j - 1]
574 # '''
575 # print("
cunsum cumprod ")
576 # arr = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8]])
577 # print('>>>6
', arr.cumsum(1))
578 # print('>>>7
', arr.cumprod(1))
579 # print('>>>8
', arr)
580 # print('>>>9
', arr.cumsum(0))
581 #
582
583
584 # print("
")
585 # arr = np_random.randn(100)
586 # print((arr > 0).sum())
587 # print("
")
588 # bools = np.array([False, False, True, False])
589 # print( bools.any()) # True True
590 # print( bools.all()) # False False
591 #
592
593
594 # print("
")
595 # arr = np_random.randn(20)
596 # arr1 = arr.sort()
597 # print('>>>1
', arr1)
598 # print("
")
599 # arr = np_random.randn(5, 5)
600 # arr2 = arr.sort(1)
601 # print('>>>3
', arr2) #
602 # arr3 = arr.sort(0)
603 # print('>>>4
', arr3)
604 # print("
5% ")
605 # large_arr = np_random.randn(1000)
606 # large_arr.sort()
607 # print('>>>5
', large_arr[int(0.05 * len(large_arr))])
608 #
609
610 # print( '
unique ')
611 # names = np.array(['Bob', 'Joe', 'Will', 'Bob', 'Will', 'Joe', 'Joe'])
612 # print('>>>1
', sorted(set(names))) # Python
613 # print('>>>2
', np.unique(names))
614 # ints = np.array([3, 3, 3, 2, 2, 1, 1, 4, 4])
615 # print('>>>3
', np.unique(ints))
616 # print('
')
617 # values = np.array([6, 0, 0, 3, 2, 5, 6])
618 # print('>>>4
', np.in1d(values, [2, 3, 6]))
619 #
620
621 # test
622 # print( '
: ')
623 # arr = np.arange(10)
624 # np.save('test_some_array', arr)
625 # print(np.load('test_some_array.npy'))
626 # print( '
')
627 # np.savez('test_array_archive.npz', a=arr, b=arr)
628 # arch = np.load('test_array_archive.npz')
629 # print(arch['b'])
630 # #
631 # print('
csv ')
632 # arr = np.loadtxt('array_ex.txt', delimiter=',')
633 # print(arr)
634 #
635
636 # print( '
')
637 # x = np.array([[1., 2., 3.], [4., 5., 6.]])
638 # y = np.array([[6., 23.], [-1, 7], [8, 9]])
639 # print('>>>1
', x.dot(y)) #
640 # print('>>>2
', np.dot(x, np.ones(3)))
641 # x = np_random.randn(5, 5)
642 # print('>>>3
', x)
643 # print(' ')
644 # mat = x.T.dot(x)
645 # print( '>>>4
', inv(mat)) #
646 # print( '>>>5
', mat.dot(inv(mat))) # , 。
647 # print( ' ')
648 # print('>>>6
', mat)
649 # q, r = qr(mat)
650 # print('>>>7
', q)
651 # print('>>>8
', r)
652 #
653
654 # print("
:")
655 # samples = np.random.normal(size=(5, 5))
656 # print('>>>1
', samples)
657 # print("
0 1 ")
658 # N = 10
659 # print([normalvariate(0, 10)for _ in range(N)])
660 # print(np.random.normal(size=N)) #
661 #
662
663
664 # print("
")
665 # arr = np.arange(8)
666 # print('>>>1
', arr.reshape((4, 2)))
667 # print('>>>2
', arr.reshape((4, 2)).reshape((2, 4))) #
668 # print("
")
669 # arr = np.arange(54)
670 # print('>>>3
', arr.reshape((9, -1)))
671 # print("
")
672 # other_arr = np.ones((6, 9))
673 # print('>>>4
', other_arr.shape)
674 # print('>>>5
', arr.reshape(other_arr.shape))
675 # print("
")
676 # arr = np.arange(15).reshape((5, 3))
677 # print('>>>6
', arr.ravel())
678 #
679
680
681 # print("
")
682 # print("
")
683 # arr1 = np.array([[1, 2, 3], [4, 5, 6]])
684 # arr2 = np.array([[7, 8, 9], [10, 11, 12]])
685 # print('>>>1
', np.concatenate([arr1, arr2], axis=0)) #
686 # print('>>>2
', np.concatenate([arr1, arr2], axis=1)) #
687 # print("
stack stack")
688 # print('>>>3
', np.vstack((arr1, arr2))) #
689 # print('>>>4
', np.hstack((arr1, arr2)) ) #
690 # print("
")
691 # arr = np_random.randn(5, 5)
692 # print("
")
693 # first, second, third = np.split(arr, [1, 3], axis=0)
694 # print('>>>5
', first)
695 # print('>>>6
', second)
696 # print('>>>7
', third)
697 # print("
")
698 # first, second, third = np.split(arr, [1, 3], axis=1)
699 # print('>>>8
', first)
700 # print('>>>9
', second)
701 # print('>>>10
', third)
702 # print("
")
703 # arr = np.arange(6)
704 # arr1 = arr.reshape((3, 2))
705 # arr2 = np_random.randn(3, 2)
706 # print("
r_ ")
707 # print('>>>11
', np.r_[arr1, arr2])
708 # print("
c_ ")
709 # print('>>>12
', np.c_[np.r_[arr1, arr2], arr])
710 # print('
')
711 # print('>>>13
', np.c_[1:6, -10:-5])
712 #
713 # try:
714 # print("
")
715 # print("
Repeat: ")
716 # arr = np.arange(4)
717 # print('>>>1
', arr.repeat([1, 2, 3, 4])) # 3 , 2, 3, 4 。
718 # print("
Repeat, ")
719 # arr = np_random.randn(3, 3)
720 # print('>>>2
', arr)
721 # print('>>>3
', arr.repeat(2, axis=0)) # repeat
722 # print('>>>4
', arr.repeat(2, axis=1)) # repeat
723 # print('>>>5
', arr.repeat(2, axis=0)) # repeat
724 # print("
Tile: ")
725 # arr = np_random.randn(2, 2)
726 # print('>>>6
', np.tile(arr, 2))
727 # print('>>>7
', np.tile(arr, (2, 3)) ) # tile
728 # except Exception:
729 # print(" !!!")
730
731 # try:
732 # print("
Fancy Indexing ")
733 # arr = np.arange(10) * 100
734 # print(arr)
735 # inds = [7, 1, 2, 6]
736 # print('>>>1
', arr[inds])
737 # print("
take")
738 # print('>>>2
', arr.take(inds))
739 # print("
put ")
740 # print('>>>3
', arr.put(inds, 50))
741 # print('>>>4
', arr)
742 # print('>>>5
', arr.put(inds, [70, 10, 20, 60]))
743 # print('>>>6
', arr)
744 # print("
take ")
745 # arr = np_random.randn(2, 4)
746 # inds = [2, 0, 2, 1]
747 # print('>>>7
', arr)
748 # print('>>>8
', arr.take(inds, axis=1))
749 # except Exception:
750 # print(" !!!")
751
752
753 try:
754 print("
:")
755 nsteps = 1000
756 draws = np.random.randint(0, 2, size=nsteps)
757 steps = np.where(draws > 0, 1, -1)
758 walk = steps.cumsum()
759 print(" ")
760 plt.title('Random Walk')
761 limit = max(abs(min(walk)), abs(max(walk)))
762 plt.axis([0, nsteps, -limit, limit])
763 x = np.linspace(0, nsteps, nsteps)
764 plt.plot(x, walk, 'g-')
765 plt.show()
766 except Exception:
767 print(" !!!")
768 # print('>>>1
', )
769 # print('>>>2
', )
770 # print('>>>3
', )
771 # print('>>>4
', )
772 # print('>>>5
', )
773 # print('>>>6
', )
774 # print('>>>7
', )
775 # print('>>>8
', )
776 # print('>>>9
', )
--------
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転載先:https://www.cnblogs.com/caochucheng/p/10034018.html