Understanding Scipy. Stats. Norm. Rvs()?
In Scipy. Stats. Norm. Rvs() the Argument Scale Denotes Standard Deviation but in the Below Piece of Code Sigma_List Refers to an Array. How Does the Code...
In scipy.stats.norm.rvs() the argument scale denotes standard deviation but in the below piece of code sigma_list refers to an array. How does the code actually work?
Where sigma_list is obtained by following code:
sigma=0.06
mask=(x > 0.65) & (x < 0.8)
sigma_list=sigma+mask*0.03
sigma_list
y = sp.stats.norm.rvs(scale=sigma_list, size=200)
Even the standard deviations of both sigma_list and y are also not matching
I want to know the working of the above scipy module
sorry, i didn't mention that x is an array of values between 0 and 1
2 Answers
In your code, the mask will be either True or False here. So if you do some addition or subtraction, it is respectively translated into 1 or 0.
Then the result of sigma_list is not a list nor an array but a floating value. Looking at the documentation, you can see its usage.
rvs(loc=0, scale=1, size=1, random_state=None)
If you look at the code (line 2771) you have:
loc : array_like, optional Location parameter (default=0).
size : int or tuple of ints, optional Defining number of random variates (Default is 1). Note that
sizehas to be given as keyword, not as positional argument.random_state : None or int or
np.random.RandomStateinstance, optional If int or RandomState, use it for drawing the random variates. If None, rely onself.random_state. Default is None.
First you should have created a variable called x. The size of this variable should be 200, since this is the size of the generated random variable y.
import numpy as np
x = np.linspace(0, 1, 200)
Then the mask is selecting every sample of x that is greater than 0.65 and less than 0.8. The variable mask will be a Boolean vector with the same size as x, i.e, 200 samples. This mask will have samples with values True or False. For each sample of the array x that satisfies the condition (0.65 < x < 0.8), the value of the corresponding sample of the mask will be True, otherwise it will be False.
When you multiply a Boolean by a number, the Boolean behaves as an integer with values 0 (False) or 1 (True). So the multiplication mask * 0.03 results in 0.03 where 0.65 < x < 0.8, and 0 otherwise.
So this code does simply that:
- For
0.65 < x < 0.8the standard deviation will be 0.06 + 0.03, i.e., 0.09; - Otherwise the standard deviation will be 0.06
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