def create_executor(cls, allowCached=True):
if not allowCached:
return pyfora.connect('http://localhost:30000')
if cls.executor is None:
cls.executor = pyfora.connect('http://localhost:30000')
cls.executor.stayOpenOnExit = True
return cls.executor
def create_executor(cls, allowCached=True):
if not allowCached:
return pyfora.connect('http://localhost:30000')
if cls.executor is None:
cls.executor = pyfora.connect('http://localhost:30000')
cls.executor.stayOpenOnExit = True
return cls.executor
def setUpClass(cls):
cls.config = Setup.config()
cls.executor = None
cls.simulation = ClusterSimulation.Simulator.createGlobalSimulator()
cls.simulation.startService()
cls.simulation.getDesirePublisher().desireNumberOfWorkers(1)
cls.ufora = pyfora.connect('http://localhost:30000')
def setUpClass(cls):
cls.config = Setup.config()
cls.executor = None
cls.simulation = ClusterSimulation.Simulator.createGlobalSimulator()
cls.simulation.startService()
cls.simulation.getDesirePublisher().desireNumberOfWorkers(1)
cls.ufora = pyfora.connect('http://localhost:30000')
import pyfora
import pandas as pd
from pyfora.pandas_util import read_csv_from_string
from pyfora.algorithms import linearRegression
print "Connecting..."
executor = pyfora.connect('http://localhost:30000')
print "Importing data..."
#raw_data = executor.importS3Dataset('ufora-test-data',
# 'iid-normal-floats-20GB-20-columns.csv').result()
print "Parsing and regressing..."
#df = pd.read_csv('cara.csv', sep=';')
#print df
#df = pd.read_cdfsv('caracteristicas_images.csv', sep=' ')
#X = df[list(df.columns)[1:]]
#mat = X.as_matrix()
##X.pop(0);
#print mat
#y = df['A58']
data_frame = pd.read_csv('cara.csv',sep=';')#
with executor.remotely:
#data_frame = pd.read_csv('cara.csv',sep=';')#read_csv_from_string(raw_data)
predictors = data_frame.iloc[:, :-1]
responses = data_frame.iloc[:, -1:]
regression_result = linearRegression(predictors, responses)
coefficients = regression_result[:-1]
def impure_method(self, host_and_port):
return pyfora.connect(host_and_port)
def impure_method(self, host_and_port):
return pyfora.connect(host_and_port)
import pyfora
print 'hello'
#import boto.ec2
#conn = boto.ec2.connect_to_region('us-west-1')
ufora = pyfora.connect("http://localhost:30000")
print 'hi'
print 'helloo'
def isPrime(p):
x = 2
while x*x <= p:
if p%x == 0:
return 0
x = x + 1
return 1
print 'hi'
with ufora.remotely.downloadAll():
result = sum(isPrime(x) for x in xrange(10))
print result
import pyfora
ufora = pyfora.connect('http://localhost:30000')
X = [[12,7,3],
[4 ,5,6],
[7 ,8,9]]
Y = [[5,8,1,2],
[6,7,3,0],
[4,5,9,1]]
# result is 3x4
def multi(X,Y):
r2 = []
for i in range(len(X)):
r = []
for j in range(len(Y[0])):
s = 0
for k in range(len(Y)):
s = s + X[i][k] * Y[k][j]
r = r + [s]
r2 = r2 + [r]
#r2[0][0]=999
return r2
def test(A,B):
C=A+B
