sirpoot

'''
Copyright (c) <2012> Tarek Galal <[email protected]>

Permission is hereby granted, free of charge, to any person obtaining a copy of this 
software and associated documentation files (the "Software"), to deal in the Software 
without restriction, including without limitation the rights to use, copy, modify, 
merge, publish, distribute, sublicense, and/or sell copies of the Software, and to 
permit persons to whom the Software is furnished to do so, subject to the following 
conditions:

StephenWakely

(ns acme.selecttags
  (:require-macros [cljs.core.async.macros :refer [go alt!]])
  (:require [cljs.core.async :refer [put! <! >! chan timeout]]
            [om.core :as om :include-macros true]
            [om.dom :as dom :include-macros true]
            [acme.shared-controls :as shared]
            [cljs-http.client :as http]
            [acme.server :as server]
            [clojure.string :as str]))

ahoy-jon

;; ; made this macro scraching my head between the simplicity of Haskell for fix
;; ; and the absence of curryfication in Clojure.

;; (P expr-fn) ; makes expr-fn lazy
;; (P P expr-fn) ; curry one time expr-fn
;; (P P P expr-fn) ; curry two time expr-fn
;; (= ((((P P P str) "a") "b") "c") "abc") ;=> true
(defmacro P [& f]
  (let [x (gensym 'x)] ;; cannot be replaced by x# due to nested macro expansion.

tomazzaman

check process hhvm with pidfile /var/run/hhvm/pid
  group hhvm
  start program = "/usr/sbin/service hhvm start" with timeout 60 seconds
  stop program  = "/usr/sbin/service hhvm stop"
  if failed unixsocket /var/run/hhvm/hhvm.sock then restart
  if mem > 400.0 MB for 1 cycles then restart
  if 5 restarts with 5 cycles then timeout

ognis1205

import numpy
import sys
import scipy.stats as stats
import matplotlib.pyplot as plotter
from gensim import corpora, models, similarities, matutils


# Defines dictionary from the specified corpus.
dictionary = corpora.Dictionary(
    line.lower().split() for line in open('corpus_train.txt', 'rb')

hnykda

import pandas as pd  
from random import random

flow = (list(range(1,10,1)) + list(range(10,1,-1)))*100
pdata = pd.DataFrame({"a":flow, "b":flow})  
pdata.b = pdata.b.shift(9)  
data = pdata.iloc[10:] * random()  # some noise  

import numpy as np

viksit

#!/usr/bin/env python
# see http://matpalm.com/blog/2015/03/28/theano_word_embeddings/
import theano
import theano.tensor as T
import numpy as np
import random

E = np.asarray(np.random.randn(6, 2), dtype='float32')
t_E = theano.shared(E)
t_idxs = T.ivector()

DSA101

# Time series forecasting based on multiple time series, including the original one
# This script is based on the following examples and discussions:
# https://gist.github.com/lukovkin/1aefa4509e066690b892
# https://groups.google.com/forum/#!topic/keras-users/9GsDwkSdqBg

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import random
import theano

fchollet

'''Functional Keras is a more functional replacement for the Graph API.
'''

###################
# 2 LSTM branches #
###################
a = Input(input_shape=(10, 32))  # output is a TF/TH placeholder, augmented with Keras attributes
b = Input(input_shape=(10, 32))
encoded_a = LSTM(32)(a)  # output is a TF/TH tensor
encoded_b = LSTM(32)(b)

farizrahman4u

import numpy as np

__author__ = 'Fariz Rahman'


def eq(x, y):
	return x.lower().replace(" ", "") == y.lower().replace(" ", "")

def get_words(x):
	x = x.replace("  ", " ")