IanColdwater

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h3

# -*- coding: utf-8 -*-

from __future__ import unicode_literals

import re
import sys


def c(i):
    """

jbochi

dsal1951

def calc_lift(x,y,clf,bins=10):
	"""
	Takes input arrays and trained SkLearn Classifier and returns a Pandas
	DataFrame with the average lift generated by the model in each bin
	
	Parameters
	-------------------
	x:    Numpy array or Pandas Dataframe with shape = [n_samples, n_features]

	y:    A 1-d Numpy array or Pandas Series with shape = [n_samples]

abresler

library(dplyr)
library(tidyr)
library(magrittr)
library(ggplot2)
"http://academic.udayton.edu/kissock/http/Weather/gsod95-current/NYNEWYOR.txt" %>%
	read.table() %>% data.frame %>% tbl_df -> data
names(data) <- c("month", "day", "year", "temp")

data %>%
	group_by(year, month) %>%

axiak

/**
 * Append the form data from a HubSpot form automatically
 * to the redirect URL query parameters. These values can
 * then be used on the form to modify the user experience
 * of the Thank You page
 * 
 * LICENSE
 * Form redirect
 * Written in 2015 by Mike Axiak <maxiak@hubspot.com>
 * To the extent possible under law, the author(s) have dedicated all copyright and related and neighboring rights to this software to the public domain worldwide. This software is distributed without any warranty.

tomhopper

#' @title PRESS
#' @author Thomas Hopper
#' @description Returns the PRESS statistic (predictive residual sum of squares).
#'              Useful for evaluating predictive power of regression models.
#' @param linear.model A linear regression model (class 'lm'). Required.
#' 
PRESS <- function(linear.model) {
  #' calculate the predictive residuals
  pr <- residuals(linear.model)/(1-lm.influence(linear.model)$hat)
  #' calculate the PRESS

baojie

import multiprocessing

# split a list into evenly sized chunks
def chunks(l, n):
    return [l[i:i+n] for i in range(0, len(l), n)]


def do_job(job_id, data_slice):
    for item in data_slice:
        print "job", job_id, item

zacstewart

import os
import numpy
from pandas import DataFrame
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.cross_validation import KFold
from sklearn.metrics import confusion_matrix, f1_score

NEWLINE = '\n'

henrik

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