On 3/14/2023 3:48 AM, Alexander Nestorov wrote:
I'm working on an NLP and I got bitten by an unreasonably slow behaviour in
Python while operating with small amounts of numbers.
I have the following code:
```python
import random, time
from functools import reduce
def trainPerceptron(perceptron, data):
learningRate = 0.002
weights = perceptron['weights']
error = 0
for chunk in data:
input = chunk['input']
output = chunk['output']
# 12x slower than equivalent JS
sum_ = 0
for key in input:
v = weights[key]
sum_ += v
# 20x slower than equivalent JS
#sum_ = reduce(lambda acc, key: acc + weights[key], input)
actualOutput = sum_ if sum_ > 0 else 0
expectedOutput = 1 if output == perceptron['id'] else 0
currentError = expectedOutput - actualOutput
if currentError:
error += currentError ** 2
change = currentError * learningRate
for key in input:
weights[key] += change
[snip]
Just a speculation, but the difference with the javascript behavior
might be because the JS JIT compiler kicked in for these loops.
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