mpociot

<?php

namespace App\BotMan\Middleware;

use Mpociot\BotMan\Message;
use Mpociot\BotMan\Http\Curl;
use Mpociot\BotMan\Interfaces\HttpInterface;
use Mpociot\BotMan\Interfaces\DriverInterface;
use Mpociot\BotMan\Interfaces\MiddlewareInterface;

vickyqian

import tweepy
import csv
import pandas as pd
####input your credentials here
consumer_key = ''
consumer_secret = ''
access_token = ''
access_token_secret = ''

auth = tweepy.OAuthHandler(consumer_key, consumer_secret)

jexp

function import_extract_first {
  echo "name:ID(Concept)" > concepts.txt
  cat data-concept-instance-relations.txt | cut -d $'\t' -f 1 | sort | uniq >> concepts.txt
  
  echo "name:ID(Instance)" > instances.txt
  cat data-concept-instance-relations.txt | cut -d $'\t' -f 2 | sort | uniq >> instances.txt

  echo $':END_ID(Concept)\t:START_ID(Instance)	relations:int' > is_a.hdr
  
  $NEO4J_HOME/bin/neo4j-import --into concepts.db --id-type string --delimiter TAB --bad-tolerance 13000000 --skip-duplicate-nodes true --skip-bad-relationships true \

dirko

# Keras==1.0.6
from keras.models import Sequential
import numpy as np
from keras.layers.recurrent import LSTM
from keras.layers.core import TimeDistributedDense, Activation
from keras.preprocessing.sequence import pad_sequences
from keras.layers.embeddings import Embedding
from sklearn.cross_validation import train_test_split
from keras.layers import Merge
from keras.backend import tf

dirko

# Keras==1.0.6
import numpy as np
from keras.models import Sequential
from keras.layers.recurrent import LSTM
from keras.layers.core import TimeDistributedDense, Activation
from keras.preprocessing.sequence import pad_sequences
from keras.layers.embeddings import Embedding
from sklearn.cross_validation import train_test_split
from sklearn.metrics import confusion_matrix, accuracy_score, precision_recall_fscore_support

mcclux

import os
import sys
import dlib
import matplotlib.pyplot as plt
from PIL import Image

# adjust these variables as necessary

# dirname is the directory relative to the script where the files to detect a face and crop live
dirname = "source"

Brainiarc7

Standard FFmpeg How-to

Table of Contents

* Generic Syntax

* Main Options

* Encoding :

protrolium

ffmpeg

Converting Audio into Different Formats / Sample Rates

Minimal example: transcode from MP3 to WMA:
ffmpeg -i input.mp3 output.wma

You can get the list of supported formats with:
ffmpeg -formats

You can get the list of installed codecs with:

syllog1sm

"""A simple implementation of a greedy transition-based parser. Released under BSD license."""
from os import path
import os
import sys
from collections import defaultdict
import random
import time
import pickle

SHIFT = 0; RIGHT = 1; LEFT = 2;

ax3l

In general, check the crt/host_config.h file to find out which versions are supported. Sometimes it is possible to hack the requirements there to get some newer versions working, too :)

Thrust version can be found in $CUDA_ROOT/include/thrust/version.h.

Download Archives: https://developer.nvidia.com/cuda-toolkit-archive

Release notes for CUDA Toolkit (CTK):

• latest: https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html
• archive: https://docs.nvidia.com/cuda/archive/