rxaviers

People

:bowtie:	😄 :smile:	😆 :laughing:
😊 :blush:	😃 :smiley:	☺️ :relaxed:
😏 :smirk:	😍 :heart_eyes:	😘 :kissing_heart:
😚 :kissing_closed_eyes:	😳 :flushed:	😌 :relieved:
😆 :satisfied:	😁 :grin:	😉 :wink:
😜 :stuck_out_tongue_winking_eye:	😝 :stuck_out_tongue_closed_eyes:	😀 :grinning:
😗 :kissing:	😙 :kissing_smiling_eyes:	😛 :stuck_out_tongue:

satra

from scipy.spatial.distance import pdist, squareform
import numpy as np

from numbapro import jit, float32

def distcorr(X, Y):
    """ Compute the distance correlation function
    
    >>> a = [1,2,3,4,5]
    >>> b = np.array([1,2,9,4,4])

roachhd

EMOJI CHEAT SHEET

Emoji emoticons listed on this page are supported on Campfire, GitHub, Basecamp, Redbooth, Trac, Flowdock, Sprint.ly, Kandan, Textbox.io, Kippt, Redmine, JabbR, Trello, Hall, plug.dj, Qiita, Zendesk, Ruby China, Grove, Idobata, NodeBB Forums, Slack, Streamup, OrganisedMinds, Hackpad, Cryptbin, Kato, Reportedly, Cheerful Ghost, IRCCloud, Dashcube, MyVideoGameList, Subrosa, Sococo, Quip, And Bang, Bonusly, Discourse, Ello, and Twemoji Awesome. However some of the emoji codes are not super easy to remember, so here is a little cheat sheet. ✈ Got flash enabled? Click the emoji code and it will be copied to your clipboard.

People

😄

yrevar

{0: 'tench, Tinca tinca',
 1: 'goldfish, Carassius auratus',
 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',
 3: 'tiger shark, Galeocerdo cuvieri',
 4: 'hammerhead, hammerhead shark',
 5: 'electric ray, crampfish, numbfish, torpedo',
 6: 'stingray',
 7: 'cock',
 8: 'hen',
 9: 'ostrich, Struthio camelus',

albertomontesg

stared

shellqiqi

/*
 * ANSI C code from the article
 * "Contrast Limited Adaptive Histogram Equalization"
 * by Karel Zuiderveld, [email protected]
 * in "Graphics Gems IV", Academic Press, 1994
 *
 *
 *  These functions implement Contrast Limited Adaptive Histogram Equalization.
 *  The main routine (CLAHE) expects an input image that is stored contiguously in
 *  memory;  the CLAHE output image overwrites the original input image and has the

nlsandler

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Building an LSTM from scratch with Keras (if that's not a contradiction)"""

from keras import backend as K
from keras.engine.topology import Layer
import numpy as np
from keras.layers import RNN, Dense, Activation,LSTM
from keras.models import Model
import keras

jayspeidell

# Info on how to get your api key (kaggle.json) here: https://github.com/Kaggle/kaggle-api#api-credentials
!pip install kaggle
api_token = {"username":"USERNAME","key":"API_KEY"}
import json
import zipfile
import os
with open('/content/.kaggle/kaggle.json', 'w') as file:
    json.dump(api_token, file)
!chmod 600 /content/.kaggle/kaggle.json
!kaggle config path -p /content

sadimanna

'''
"Contrast Limited Adaptive Histogram Equalization"
by Karel Zuiderveld, [email protected]
in "Graphics Gems IV", Academic Press, 1994

_Author_ -- Siladittya Manna

The below implementation does not assume that the 
X- and Y image resolutions are an integer multiple
of the X- and Y sizes of the contextual regions.