clustering_theano.py

__author__ = 'teddy'
# Steven's incremental clustering re-implemented in Theano
from time import time
import math

import theano
import theano.tensor as T

# implementing sample Theano functions

'''
def logFunctionOne(var1, var2):
    x = T.scalar('x')
    y = T.scalar('y')
    z = T.log10(x+y)
    f = theano.function([x,y],z)
    return f(var1, var2)
'''


def logMath(var1, var2):
    return math.log10(var1 + var2)


def logTheano(var1, var2):
    return f(var1, var2)

A = [[10, 10], [100, 100]]
x = T.scalar('x')
y = T.scalar('y')
z = T.log10(x + y)
f = theano.function([x, y], z)
print('Theano')
start = time()
# print start
for I in range(1000):
    ans1 = logTheano(I, 1)
    # print ans

elapsed1 = time() - start
print elapsed1
print('Math Module')
start = time()
for I in range(1000):
    ans2 = logMath(I, 1)
    # print ans

elapsed2 = time() - start
print elapsed2
print('theano w.o function call')
start = time()
for I in range(1000):
    ans1 = f(I, 1)
elapsed3 = time() - start
print elapsed3
# print('Diff %f' % (elapsed1-elapsed2))