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)

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

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

kevin-smets

Default

Powerlevel10k

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 <fabian@fseoane.net>
         Alexandre Gramfort <alexandre.gramfort@inria.fr>
"""

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.

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'

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:
  #