numpy.random.standard_normal() function with example in python

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numpy.random.standard_normal(): This function draw samples from a standard Normal distribution (mean=0, stdev=1).

Syntax: numpy.random.standard_normal(size=None)

Parameters:
size : int or tuple of ints, optional
Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned.

Returns:
out : float or ndarray
Drawn samples.

import numpy as np print(np.random.standard_normal(100)) print(np.random.standard_normal(size=(5, 8, 2)))

 

Output:

[-0.5291338   0.25114969  1.5378128  -1.63973282  1.0320552  -0.4202529
  0.82406882 -0.29542388 -1.70828553  0.43826267  0.53104578 -1.13434012
 -1.22876735 -1.08275008  1.40100994  0.41382866  1.55918235 -1.29533476
  1.26030401  0.16943449 -1.58206014  1.79299528 -0.43173701 -0.03169446
 -2.04257878 -0.22568208  2.07711057  0.39271192  0.96842642 -1.29769805
 -0.17483238 -1.86381702 -0.69918152 -0.7101691  -1.98180982 -1.17651837
 -0.81013536 -1.33946398 -1.76591403 -0.383698   -2.01547288 -0.73713832
 -1.48717267 -0.77332307 -0.36504841  0.92279499 -0.70945953 -0.84383588
  0.42332633  0.1986268   0.04934221  2.39169968 -0.05738675  0.24648873
  0.30510696 -0.72184448 -0.32383144  1.40314784 -1.20228738  0.23947403
  0.10805802 -0.35685451  0.37293493  1.01763145  1.69973757 -0.29910403
 -0.38999096 -1.1027519   1.99383797  1.79398941 -1.01370388 -0.46235839
 -0.30242576 -1.68309898 -0.29448376  0.70484318  0.8440772  -0.00269078
  0.30639468  1.33555591  1.09814833  0.73708753  0.16217399  1.33920216
  0.40461777  0.36144542  0.95019119 -1.52830426 -0.06823193 -2.08948367
 -0.74505854  1.33995657  0.57730772 -1.39699287  0.01718719 -0.48423898
 -1.59289307  1.23102743 -0.85701287 -1.14487056]
[[[ 1.12046496  0.23176019]
  [-0.10172677  0.69678938]
  [ 0.66247166  1.4101414 ]
  [-0.44090584  1.01652444]
  [-0.53720987  0.2190571 ]
  [-1.23940663  1.54966558]
  [ 1.1925955   0.96589747]
  [ 0.35671816 -0.61092705]]

 [[ 0.30657018 -0.45851383]
  [ 1.31953118 -1.56726274]
  [-0.66369309 -0.18486426]
  [ 0.43927321  1.9200575 ]
  [ 0.81595253 -3.9343733 ]
  [ 0.47746047 -0.32715014]
  [-1.28496149 -0.566036  ]
  [ 2.09216743 -1.26436457]]

 [[ 0.29849938 -0.24528484]
  [ 0.90502069  1.14294902]
  [ 0.43418159 -0.86029091]
  [ 0.74588543  1.11033204]
  [-0.96354699  0.73395856]
  [-0.11290504 -0.57743276]
  [ 0.33851756  1.38407246]
  [ 0.61166654  0.08970228]]

 [[-1.48647914 -0.11698196]
  [ 1.47465211  0.02251731]
  [ 0.33026543  1.16178706]
  [-0.18313469 -0.04052182]
  [-0.07783125  1.11630188]
  [-0.75639937  1.71109761]
  [ 1.00641598 -0.04895828]
  [ 0.11304988  0.72099454]]

 [[ 0.01828176  1.38728875]
  [ 0.70672059 -0.10517591]
  [ 1.50041882 -0.45176001]
  [ 0.0585002   1.89378955]
  [ 0.61430977 -1.16487575]
  [ 0.33157504 -1.04717619]
  [ 0.87093577 -0.2909944 ]
  [ 1.80564515  0.22990392]]]

 

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