mblondel

"""Kernel K-means"""

# Author: Mathieu Blondel <[email protected]>
# License: BSD 3 clause

import numpy as np

from sklearn.base import BaseEstimator, ClusterMixin
from sklearn.metrics.pairwise import pairwise_kernels
from sklearn.utils import check_random_state

andreif

Performance of Flask, Tornado, GEvent, and their combinations

Wensheng Wang, 10/1/11

Source: http://blog.wensheng.org/2011/10/performance-of-flask-tornado-gevent-and.html

When choosing a web framework, I pretty much have eyes set on Tornado. But I heard good things about Flask and Gevent. So I tested the performance of each and combinations of the three. I chose something just a little more advanced than a "Hello World" program to write - one that use templates. Here are the codes:

1, Pure Flask (pure_flask.py)

frankrowe

 import shapefile
   # read the shapefile
   reader = shapefile.Reader("my.shp")
   fields = reader.fields[1:]
   field_names = [field[0] for field in fields]
   buffer = []
   for sr in reader.shapeRecords():
       atr = dict(zip(field_names, sr.record))
       geom = sr.shape.__geo_interface__
       buffer.append(dict(type="Feature", \

dfm

#!/usr/bin/env python
# -*- coding: utf-8 -*-

from __future__ import division, print_function
import emcee
import numpy as np
import matplotlib.pyplot as pl

np.random.seed(123)

kwmsmith

#cython:boundscheck=False
#cython:wraparound=False

import numpy as np
from cython.parallel cimport prange
from libc.math cimport sqrt

cdef inline double dotp(int i, int j, int N, double[:, ::1] X) nogil:
    cdef:
        int k

glamp

import numpy as np
import pylab as pl

x = np.random.uniform(1, 100, 1000)
y = np.log(x) + np.random.normal(0, .3, 1000)

pl.scatter(x, y, s=1, label="log(x) with noise")
pl.plot(np.arange(1, 100), np.log(np.arange(1, 100)), c="b", label="log(x) true function")
pl.xlabel("x")
pl.ylabel("f(x) = log(x)")

tbarbugli

import collections
import json
import redis
import threading
from tornado import gen
from tornado import ioloop
from tornado import web
from tornado.options import define
from tornado.options import options
import tornadoredis

dwf

"""
A proof-of-concept multi-threaded chunked NPY format reader.
"""
__author__ = "David Warde-Farley"
__credits__ = ["David Warde-Farley"]
__license__ = "3-clause BSD"
__email__ = 'd dot warde dot farley at gmail DOT com"

import threading
from numpy.lib.format import read_magic, read_array_header_1_0

larsmans

cimport numpy as np


def csc_columnwise_max(np.ndarray[np.float64_t, ndim=1] data,
                       np.ndarray[int, ndim=1] indices,
                       np.ndarray[int, ndim=1] indptr,
                       np.ndarray[np.float64_t, ndim=1] out):
    cdef double mx
    cdef int n_features = indptr.shape[0] - 1
    cdef int i, j

craigdub

import tornado.ioloop
import tornado.web
import pika
import logging
from pika.adapters.tornado_connection import TornadoConnection

TORNADO_PORT = 8889

RMQ_USER = 'user'
RMQ_PWD = 'password'