There are:
- 100M users/authors
- 20M papers
- 2M topics
The typical user has:
- 1k followers
- 2k followees
- 10 topics that they follow
- 30 papers uploaded
import numpy as np | |
from PIL import Image | |
import cv2 | |
import dlib | |
# Load the image | |
image_path = "/mnt/data/me.jpeg" | |
image = Image.open(image_path) | |
# Convert the image to OpenCV format |
There are:
The typical user has:
Add this into your Vagrantfile:
module OS
def OS.windows?
(/cygwin|mswin|mingw|bccwin|wince|emx/ =~ RUBY_PLATFORM) != nil
end
def OS.mac?
(/darwin/ =~ RUBY_PLATFORM) != nil
import subprocess | |
# Convert GeoJSON to GeoTIFF | |
# ATTRIBUTE_FIELD = ? | |
subprocess.run([ | |
"gdal_rasterize", | |
"-a", "ATTRIBUTE_FIELD", | |
"-ts", "WIDTH", "HEIGHT", | |
"-l", "LAYER_NAME", | |
"i.geojson", |
import subprocess | |
# Step 2: Convert GeoJSON to GeoTIFF | |
# ATTRIBUTE_FIELD = ? | |
subprocess.run([ | |
"gdal_rasterize", | |
"-a", "ATTRIBUTE_FIELD", | |
"-ts", "WIDTH", "HEIGHT", | |
"-l", "LAYER_NAME", | |
"i.geojson", |
Check if a number is a perfect square
import math
number = int(input())
square_number = int(math.sqrt(number))
if (number == square_number * square_number):
print("perfect square: %d * %d = %d"%(square_number,square_number,number))
else:
Stump weights (𝐰̂) and data point weights (𝛼) are two different concepts. Stump weights (𝐰̂) tell you how important each stump is while making predictions with the entire boosted ensemble. Data point weights (𝛼) tell you how important each data point is while training a decision stump.