Vectorised code for sampling from truncated normal distributions with different intervals
numpy, python, random, sampling, scipy
Solution
One day in the not so distant future, all NumPy/SciPy functions will broadcast all their arguments, and you will be able to do `truncnorm.rvs(a_s, b_s, size=100)`, but since we are not there yet, you could manually generate your random samples from a uniform distribution and the CDF and PPF of a normal distribution:
import numpy as np
from scipy.stats import truncnorm, norm
a_s = np.random.uniform(0, 1, size=100)
b_s = a_s + 0.2
cdf_start = norm.cdf(a_s)
cdf_stop = norm.cdf(b_s)
cdf_samples = np.random.uniform(0, 1, size=(100, 100))
cdf_samples *= (cdf_stop - cdf_start)[:, None]
cdf_samples += cdf_start[:, None]
truncnorm_samples = norm.ppf(cdf_samples)
Problem
The following code generates a sample of size 100 from trunctated normal distributions with different intervals. Is there any effecient(vectorised) way of doing this? ``` from scipy.stats import truncnorm import numpy as np sample=[] a_s=np.random.uniform(0,1,size=100) b_s=a_s+0.2 for i in range(100): sample.append(truncnorm.rvs(a_s[i], b_s[i], size=100)) print sample ```