The difference in output between numpy linspace and numpy logspace

Question

Numpy Living Space returns evenly distributed numbers over a given interval. Numpy logspace returns numbers evenly distributed across the log scale.

I do not understand why numpy logspace often returns “out of range” values ​​from given boundaries. Take the numbers between 0.02and 2.0:

import numpy as np
print np.linspace(0.02, 2.0, num=20)
print np.logspace(0.02, 2.0, num=20)

Output for the first:

[ 0.02        0.12421053  0.22842105  0.33263158  0.43684211  0.54105263
  0.64526316  0.74947368  0.85368421  0.95789474  1.06210526  1.16631579
  1.27052632  1.37473684  1.47894737  1.58315789  1.68736842  1.79157895
  1.89578947  2.        ]

It looks right. However, the output for is np.logspace()incorrect:

[   1.04712855    1.33109952    1.69208062    2.15095626    2.73427446
    3.47578281    4.41838095    5.61660244    7.13976982    9.07600522
   11.53732863   14.66613875   18.64345144   23.69937223   30.12640904
   38.29639507   48.68200101   61.88408121   78.6664358   100.        ]

Why does he output 1.047to 100.0?

Answer 1

2017: numpy 1.12 , , , .. , .

numpy.geomspace

>>> np.geomspace(0.02, 2.0, 20)
array([ 0.02      ,  0.0254855 ,  0.03247553,  0.04138276,  0.05273302,
        0.06719637,  0.08562665,  0.1091119 ,  0.13903856,  0.17717336,
        0.22576758,  0.28768998,  0.36659614,  0.46714429,  0.59527029,
        0.75853804,  0.96658605,  1.23169642,  1.56951994,  2.        ])

Answer 2

logspace base**start base**stop . base , 10.0.

10**0.02 == 1.047 10**2 == 100.

( np.log10):

>>> np.logspace(np.log10(0.02) , np.log10(2.0) , num=20)
array([ 0.02      ,  0.0254855 ,  0.03247553,  0.04138276,  0.05273302,
        0.06719637,  0.08562665,  0.1091119 ,  0.13903856,  0.17717336,
        0.22576758,  0.28768998,  0.36659614,  0.46714429,  0.59527029,
        0.75853804,  0.96658605,  1.23169642,  1.56951994,  2.        ])

Answer 3

numpy.logspace() -

numpy.logspace(start, stop, num = 50, endpoint = True, base = 10.0, dtype = None)

.

** start ( ) ** (. ).

In your case, the base is 10 by default, so its transition from 10 to 0.02 to 10 is increased to 2 (100).

Answer 4

It is pretty simple.

NumPy gives you numbers evenly distributed in the log space.

i.e. 10 ^ (value). where the value is evenly distributed between the start and stop values.

You will notice that 10 ^ 0.02 is 1.04712 ... and 10 ^ 2 is 100