asmallteapot

# file: /etc/nginx/sites-available/example.com
# nginx configuration for example.com

server {
	listen 80;
	server_name example.com;
	access_log /srv/www/example.com/logs/access.log;
	error_log /srv/www/example.com/logs/error.log;
	
	# pass root to django

dwf

"""
Parallelized k-means module.

By David Warde-Farley, February 2012. Licensed under the 3-clause BSD.
"""
cimport cython
from cython.parallel import prange
import numpy as np
cimport numpy as np
from numpy.random import normal

pprett

"""Parallel grid search for sklearn's GradientBoosting.

This script uses IPython.parallel to run cross-validated
grid search on an IPython cluster. Each cell on the parameter grid
will be evaluated ``K`` times - results are stored in MongoDB.

The procedure tunes the number of trees ``n_estimators`` by averaging
the staged scores of the GBRT model averaged over all K folds.

You need an IPython ipcluster to connect to - for local use simply

peterk87

from scipy.spatial.distance import *
from scipy.cluster.hierarchy import *
import pandas as pd
import numpy
import matplotlib as plt
from matplotlib.pylab import figure
import pylab as pl
import pp

def num_clusters(hc, d):

wting

Choices

1. Install ghc 7.6.3 and Haskell Platform
2. Install ghc 7.8.4 and Cabal

Option GHC 7.6.3 + Haskell Platform

GHC 7.6.3

Install Ubuntu 12.04 depencies:

jsok

import StringIO
from selenium import webdriver
from PIL import Image
 
 
# Install instructions
#
# npm install phantomjs
# sudo apt-get install libjpeg-dev
# pip install selenium pillow

ahwillia

import numpy as np
import tensorflow as tf

# N, size of matrix. R, rank of data
N = 100
R = 5

# generate data
W_true = np.random.randn(N,R)
C_true = np.random.randn(R,N)

romelgomez

import * as server from "./server";
new server.App

rspeare

from sklearn import linear_model
import numpy as np
import scipy.stats as stat

class LogisticReg:
    """
    Wrapper Class for Logistic Regression which has the usual sklearn instance 
    in an attribute self.model, and pvalues, z scores and estimated 
    errors for each coefficient in 
    

rubinrot1

Изучение немецкого языка

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