Skip to content

Instantly share code, notes, and snippets.

View geekan's full-sized avatar

Alexander Wu geekan

View GitHub Profile
import asyncio
import hiredis
d = {}
def process(req):
cmd = req[0].lower()
if cmd==b'set':
d[req[1]] = req[2]
return b"+OK\r\n"
@geekan
geekan / keras_gensim_embeddings.py
Created March 19, 2017 13:00 — forked from codekansas/keras_gensim_embeddings.py
Using Word2Vec embeddings in Keras models
from __future__ import print_function
import json
import os
import numpy as np
from gensim.models import Word2Vec
from gensim.utils import simple_preprocess
from keras.engine import Input
from keras.layers import Embedding, merge
@geekan
geekan / ranking.py
Created June 29, 2017 14:33 — forked from fabianp/ranking.py
Pairwise ranking using scikit-learn LinearSVC
"""
Implementation of pairwise ranking using scikit-learn LinearSVC
Reference:
"Large Margin Rank Boundaries for Ordinal Regression", R. Herbrich,
T. Graepel, K. Obermayer 1999
"Learning to rank from medical imaging data." Pedregosa, Fabian, et al.,
Machine Learning in Medical Imaging 2012.