jimpick

FROM ubuntu:16.04
RUN apt-get update --yes && apt-get upgrade --yes

RUN apt-get update --yes && apt-get upgrade --yes

RUN apt-get -y install curl

RUN curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | bash

RUN apt-get -y install ffmpeg zlib1g-dev automake autoconf git \

bartolsthoorn

import torch
import torch.nn as nn
import numpy as np
import torch.optim as optim
from torch.autograd import Variable

# (1, 0) => target labels 0+2
# (0, 1) => target labels 1
# (1, 1) => target labels 3
train = []

Arinerron

#!/bin/bash

# Give the usual warning.
clear;
echo "[INFO] Automated Android root script started.\n\n[WARN] Exploit requires sdk module \"NDK\".\nFor more information, visit the installation guide @ https://goo.gl/E2nmLF\n[INFO] Press Ctrl+C to stop the script if you need to install the NDK module. Waiting 10 seconds...";
sleep 10;
clear;

# Download and extract exploit files.
echo "[INFO] Downloading exploit files from GitHub...";

quadrismegistus

def smart_procrustes_align_gensim(base_embed, other_embed, words=None):
	"""Procrustes align two gensim word2vec models (to allow for comparison between same word across models).
	Code ported from HistWords <https://github.com/williamleif/histwords> by William Hamilton <wleif@stanford.edu>.
		(With help from William. Thank you!)

	First, intersect the vocabularies (see `intersection_align_gensim` documentation).
	Then do the alignment on the other_embed model.
	Replace the other_embed model's syn0 and syn0norm numpy matrices with the aligned version.
	Return other_embed.

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

yurivish

// Since `overlapArea` function is monotonic increasing, we can perform a
// simple bisection search to find the distance that leads to an overlap
// area within epsilon of the desired overlap.
function distanceForOverlapArea(r1, r2, desiredOverlap) {
    // Ensure r1 <= r2
    if (r1 > r2) {
        var temp = r2;
        r2 = r1;
        r1 = temp;
    }

joelouismarino

from __future__ import print_function
import imageio
from PIL import Image
import numpy as np
import keras

from keras.layers import Input, Dense, Conv2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Dropout, Flatten, Concatenate, Reshape, Activation
from keras.models import Model
from keras.regularizers import l2
from keras.optimizers import SGD

tom-chaoscube

sudo yum -y install wget
sudo wget -O /etc/yum.repos.d/scylla.repo http://downloads.scylladb.com/rpm/centos/scylla.repo

sudo yum -y install epel-release

sudo yum -y install scylla-server scylla-jmx scylla-tools

# Set the listen_address and seed_provider seeds in scylla.yaml to the internal 10.x.x.x IP address 

sudo /usr/lib/scylla/scylla_setup

jexp

npm install neo4j-driver

node test-neo-driver.js