dound

def upsert(db_cur, table, pk_fields, schema=None, **kwargs):
    """Updates the specified relation with the key-value pairs in kwargs if a
    row matching the primary key value(s) already exists.  Otherwise, a new row
    is inserted.  Returns True if a new row was inserted.

    schema     the schema to use, if any (not sanitized)
    table      the table to use (not sanitized)
    pk_fields  tuple of field names which are part of the primary key
    kwargs     all key-value pairs which should be set in the row
    """

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

jeetsukumaran

#! /usr/bin/env python

import sys
import random
import pymc
import numpy
from dendropy.mathlib import probability as prob
from dendropy.mathlib import statistics as stats

rng = random.Random()

matpalm

#!/usr/bin/env python                                                                                                                                
import json
import urllib

def estimated_count_for(search_term):
  url = 'http://ajax.googleapis.com/ajax/services/search/web?v=1.0&%s' % urllib.urlencode({'q': search_term})
  results = json.loads(urllib.urlopen(url).read())
  try:
      return results['responseData']['cursor']['estimatedResultCount']
  except KeyError:

kevin-smets

Default

Powerlevel10k

sshopov

import functools
import threading
 
# A decorator that will run its wrapped function in a new thread
def run_in_new_thread(function):
    # functool.wraps will copy over the docstring and some other metadata
    # from the original function
    @functools.wraps(function)
    def fn_(*args, **kwargs):
        thread = threading.Thread(target=function, args=args, kwargs=kwargs)

erikerlandson

import java.lang.Math
import org.apache.spark.rdd.RDD
import org.apache.spark.mllib.recommendation.Rating
import org.apache.spark.mllib.recommendation.ALS
import org.apache.spark.mllib.recommendation.MatrixFactorizationModel
import org.apache.spark.mllib.util.MLUtils.kFold

// Preload some Rating data for my own convenience
val txt = sc.textFile("/home/eje/git/ratorade/data/bgr.dat")
val ratings = txt.map(_.split('\t') match { case Array(user, item, rating, _, _) => Rating(user.toInt, item.toInt, rating.toDouble / 100.0)})

cigrainger

from urllib2 import urlopen
from json import load 
import re, nltk
from nltk.stem.wordnet import WordNetLemmatizer
from nltk.corpus import wordnet, stopwords
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', 
    level=logging.INFO)
from gensim import corpora, models, similarities, matutils
import numpy as np

tdhopper

Moved to tdhopper.com.