mrdwab

stratified = function(df, group, size) {
  #  USE: * Specify your data frame and grouping variable (as column 
  #         number) as the first two arguments.
  #       * Decide on your sample size. For a sample proportional to the
  #         population, enter "size" as a decimal. For an equal number 
  #         of samples from each group, enter "size" as a whole number.
  #
  #  Example 1: Sample 10% of each group from a data frame named "z",
  #             where the grouping variable is the fourth variable, use:
  # 

un33k

FILE SPACING:

 # double space a file
 sed G

 # double space a file which already has blank lines in it. Output file
 # should contain no more than one blank line between lines of text.
 sed '/^$/d;G'

fabianp

"""
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.

agramfort

"""
Implementation of pairwise ranking using scikit-learn LinearSVC

Reference: "Large Margin Rank Boundaries for Ordinal Regression", R. Herbrich,
    T. Graepel, K. Obermayer.

Authors: Fabian Pedregosa <[email protected]>
         Alexandre Gramfort <[email protected]>
"""

kevin-smets

Default

Powerlevel10k

dorneanu

#! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8

""" Simple demo of using multiprocessing when parsing files """

import sys
import os
import codecs
from multiprocessing import Pool, Process, Queue, cpu_count

ameasure

from __future__ import absolute_import
from __future__ import print_function
import numpy as np

from keras.datasets import reuters
from keras.models import Sequential
from keras.layers.embeddings import Embedding
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.layers.core import Dense, Dropout, Activation, Flatten, Reshape, Merge

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

Smerity

from __future__ import absolute_import
from __future__ import print_function
from functools import reduce
import re
import tarfile

import numpy as np
np.random.seed(1337)  # for reproducibility

bAs such, I agree strongly with you that this won't make a good test dataset for testing various RNN architectures.from keras.callbacks import EarlyStopping

graphific

#!/usr/bin/env bash
# Installation script for Cuda and drivers on Ubuntu 14.04, by Roelof Pieters (@graphific)
# BSD License
if [ "$(whoami)" == "root" ]; then
  echo "running as root, please run as user you want to have stuff installed as"
  exit 1
fi
###################################
#   Ubuntu 14.04 Install script for:
# - Nvidia graphic drivers for Titan X: 352