A Study on data science project's PERFORMANCE pull request
Use 01_accelerated_crawler to crawl prs, and then use 02_LLM_process to preprocess the crawled prs.
Study on data science project's PERFORMANCE pull request
So I'm just trying to write a simple script to convert RBG to YUV and I have ended up with something like this:
rgb2yuv_matrix = np.array([[0.299, 0.587, 0.114], [-0.1473, -0.28886, 0.436],[0.615, -0.51499, 0.10001]])
for i in range(n_train):
for j in range(32):
for k in range(32):
# Note here that X_train[i, j, k] is a 3 element array
X_train[i,j,k] = np.dot(rgb2yuv_matrix, X_train[i,j,k])
I was wondering if there is a better way to do this since I know a triple loop is very inefficient, but I don't know if there is a better way to do it in numpy.
I have two given arrays: x and y. I want to calculate correlation coefficient between two arrays as follows:
import numpy as np
from scipy.stats import pearsonr
x = np.array([[[1,2,3,4],
[5,6,7,8]],
[[11,22,23,24],
[25,26,27,28]]])
i,j,k = x.shape
y = np.array([[[31,32,33,34],
[35,36,37,38]],
[[41,42,43,44],
[45,46,47,48]]])
xx = np.row_stack(np.dstack(x))
yy = np.row_stack(np.dstack(y))
results = []
for a, b in zip(xx,yy):
r_sq, p_val = pearsonr(a, b)
results.append(r_sq)
results = np.array(results).reshape(j,k)
print results
[[ 1. 1. 1. 1.]
[ 1. 1. 1. 1.]]
The answer is correct. However, would like to know if there are better and faster ways of doing it using numpy and/or scipy.
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