Faster Alternatives To Using Numpy.random.choice In Python?
My goal is to generate a large 2D array in Python where each number is either a 0 or 1. To do this, I created a nested for-loop as shown below:
for count in range(0,300): block = numpy.zeros((8,300000)) for a in range(0,8): for b in range(0,300000): block[a][b] = numpy.random.choice(2,1, p=[0.9,0.1])
The block has a 90% chance of picking a "0" and a 10% of picking a "1". But it takes over 1 minute for the outer for loop to process once. Is there a more efficient way to pick random numbers for a large number of arrays while stilling being able to use the "P" values? (This is my first post so sorry if the formatting is broken)
The idea behind NumPy is to not loop through 720000000 iterations at Python level. You're supposed to use whole-array operations, like having
numpy.random.choice generate an entire array of choices in one call:
block = numpy.random.choice(2, size=(8, 300000), p=[0.9, 0.1])
This completes almost instantly.
- → What are the pluses/minuses of different ways to configure GPIOs on the Beaglebone Black?
- → Django, code inside <script> tag doesn't work in a template
- → React - Django webpack config with dynamic 'output'
- → GAE Python app - Does URL matter for SEO?
- → Put a Rendered Django Template in Json along with some other items
- → session disappears when request is sent from fetch
- → Python Shopify API output formatted datetime string in django template
- → Shopify app: adding a new shipping address via webhook
- → Shopify + Python library: how to create new shipping address
- → shopify python api: how do add new assets to published theme?
- → Access 'HTTP_X_SHOPIFY_SHOP_API_CALL_LIMIT' with Python Shopify Module