zyxar

/* Exercise: Loops and Functions #43 */
package main

import (
	"fmt"
	"math"
)

func Sqrt(x float64) float64 {
	z := float64(2.)

thoemmi

<#
.SYNOPSIS
    Adds an XML fragment to an XmlNode
    
.DESCRIPTION
    Adds an XML fragment to an XmlNode
    
.NOTES 
    Author     : Thomas Freudenberg - info@thomasfreudenberg.com
    

abhinavgupta

import networkx as nx
import numpy as np
 
from nltk.tokenize.punkt import PunktSentenceTokenizer
from sklearn.feature_extraction.text import TfidfTransformer, CountVectorizer
 
def textrank(document):
    sentence_tokenizer = PunktSentenceTokenizer()
    sentences = sentence_tokenizer.tokenize(document)
 

indyfromoz

###################
# compiled source #
###################
*.com
*.class
*.dll
*.exe
*.pdb
*.dll.config
*.cache

adam-p

import sys
import subprocess

def copy(s):
    if sys.platform == 'win32' or sys.platform == 'cygwin':
        subprocess.Popen(['clip'], stdin=subprocess.PIPE).communicate(s)
    else:
        raise Exception('Platform not supported')

'''

damianavila

"""
Usage: python remove_output.py notebook.ipynb [ > without_output.ipynb ]
Modified from remove_output by Minrk

"""
import sys
import io
import os
from IPython.nbformat.current import read, write

berezovskyi

/* Drop all non-system stored procs */
DECLARE @name VARCHAR(128)
DECLARE @SQL VARCHAR(254)

SELECT @name = (SELECT TOP 1 [name] FROM sysobjects WHERE [type] = 'P' AND category = 0 ORDER BY [name])

WHILE @name is not null
BEGIN
    SELECT @SQL = 'DROP PROCEDURE [dbo].[' + RTRIM(@name) +']'
    EXEC (@SQL)

mblondel

# (C) Mathieu Blondel, November 2013
# License: BSD 3 clause

import numpy as np


def ranking_precision_score(y_true, y_score, k=10):
    """Precision at rank k

    Parameters

bsweger

Useful Pandas Snippets

A personal diary of DataFrame munging over the years.

Data Types and Conversion

Convert Series datatype to numeric (will error if column has non-numeric values)
(h/t @makmanalp)

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats