vladignatyev

# The MIT License (MIT)
# Copyright (c) 2016 Vladimir Ignatev
#
# Permission is hereby granted, free of charge, to any person obtaining 
# a copy of this software and associated documentation files (the "Software"), 
# to deal in the Software without restriction, including without limitation 
# the rights to use, copy, modify, merge, publish, distribute, sublicense, 
# and/or sell copies of the Software, and to permit persons to whom the Software 
# is furnished to do so, subject to the following conditions:
# 

smithdanielle

# check.packages function: install and load multiple R packages.
# Check to see if packages are installed. Install them if they are not, then load them into the R session.
check.packages <- function(pkg){
    new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
    if (length(new.pkg)) 
        install.packages(new.pkg, dependencies = TRUE)
    sapply(pkg, require, character.only = TRUE)
}

# Usage example

andrewjong

import torch
from torchvision import datasets

class ImageFolderWithPaths(datasets.ImageFolder):
    """Custom dataset that includes image file paths. Extends
    torchvision.datasets.ImageFolder
    """

    # override the __getitem__ method. this is the method that dataloader calls
    def __getitem__(self, index):

omoindrot

"""
Example TensorFlow script for finetuning a VGG model on your own data.
Uses tf.contrib.data module which is in release v1.2
Based on PyTorch example from Justin Johnson
(https://gist.github.com/jcjohnson/6e41e8512c17eae5da50aebef3378a4c)

Required packages: tensorflow (v1.2)
Download the weights trained on ImageNet for VGG:
```
wget http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz

ninjadq

def test_transaction(*args):
    create_sql = "INSERT INTO user (name) VALUES (%s)"
    transaction.set_autocommit(False, using='uic')
    try:
        cursor = connections['uic'].cursor()
        for u in args:
            cursor.execute(create_sql,[u])
    except IntegrityError as e:
        transaction.rollback(using='uic')
    else:

ramhiser

import itertools 

def jaccard(labels1, labels2):
    """
    Computes the Jaccard similarity between two sets of clustering labels.

    The value returned is between 0 and 1, inclusively. A value of 1 indicates
    perfect agreement between two clustering algorithms, whereas a value of 0
    indicates no agreement. For details on the Jaccard index, see:
    http://en.wikipedia.org/wiki/Jaccard_index

juice49

/**
 * Creates a unique filename by appending a number
 *
 * i.e. if image.jpg already exists, returns
 * image2.jpg 
 */
function uniqueFilename($path, $name, $ext) {
	
	$output = $name;
	$basename = basename($name, '.' . $ext);

alceufc

import numpy as np

def softmax(x):
    """
    Compute softmax values for each sets of scores in x.
    
    Rows are scores for each class. 
    Columns are predictions (samples).
    """
    scoreMatExp = np.exp(np.asarray(x))