[FIXED] Given a 2D Numpy array representing a 2D distribution, how to sample data from this distribution with the aid of Numpy or Scipy functions?

Issue

Given a 2D numpy array dist with shape (200,200), where each entry of the array represents the joint probability of (x1, x2) for all x1 , x2 ∈ {0, 1, . . . , 199}. How do I sample bivariate data x = (x1, x2) from this probability distribution with the aid of Numpy or Scipy API?

Solution

This solution works with probability distributions of any number of dimensions, assuming they are a valid probability distribution (its contents must sum to 1, etc.). It flattens the distribution, samples from that, and adjusts the random index to match the original array shape.

# Create a flat copy of the array
flat = array.flatten()

# Then, sample an index from the 1D array with the
# probability distribution from the original array
sample_index = np.random.choice(a=flat.size, p=flat)

# Take this index and adjust it so it matches the original array
adjusted_index = np.unravel_index(sample_index, array.shape)
print(adjusted_index)

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Also, to get multiple samples, add a size keyword argument to the np.random.choice call, and modify adjusted_index before printing it:

adjusted_index = np.array(zip(*adjusted_index))

This is necessary because np.random.choice with a size argument outputs a list of indices for each coordinate dimension, so this zips them into a list of coordinate tuples. This is also much more efficient than simply repeating the first code.

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Relevant documentation:

• np.random.choice
• np.unravel_index

Answered By - applemonkey496