1. Clustering com numpy e Cython
Anderson Berg
XIII Encontro do PUG-PE

2. Agrupamento de dados

3. K-Means

4. Matrizes

7. Exemplo simples
>>> from numpy import *
>>>a = arange(10).reshape(2,5)
>>>a
array([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]])

8. Criação de arrays
>>> a = array( [2,3,4] )
>>> b = array( [ (1.5,2,3), (4,5,6) ] )
>>> b
array([[ 1.5, 2. , 3. ], [ 4. , 5. , 6. ]])
>>> zeros( (3,4) )
array([[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.]])
>>> ones( (2,3,4), dtype=int16 )
array([[[ 1, 1, 1, 1], [ 1, 1, 1, 1], [ 1, 1, 1, 1]], [[ 1, 1,
1, 1], [ 1, 1, 1, 1], [ 1, 1, 1, 1]]], dtype=int16)

9. Outras Operações
>>> b = arange( 4 )
>>> b**2
array([0, 1, 4, 9])
>>> a = random.random((2,3))
>>> a
array([[ 0.6903007 , 0.39168346, 0.16524769],
[ 0.48819875, 0.77188505, 0.94792155]])

10. Ainda outras operações
>>> a.sum()
3.4552372100521485
>>> a.min()
0.16524768654743593
>>> a.max()
0.9479215542670073
>>> a[:,1]
array([0.39168346, 0.77188505])
>>> sum(a[:,1])
1.1635685099999999

12. Primeiros Passos
helloworld.pyx:
print "Hello World"
setup.py:
from distutils.core import setup
from distutils.extension import Extension
from Cython.Distutils import build_ext
setup(
cmdclass = {'build_ext': build_ext},
ext_modules = [Extension("helloworld",
["helloworld.pyx"])] )
$ python setup.py build_ext --inplace
>>> import helloworld
Hello World

13. Advanced Mode
def primes(int kmax):
cdef int n, k, i
cdef int p[1000]
result = []
if kmax > 1000:
kmax = 1000
k = 0
n = 2
while k < kmax:
i = 0
while i < k and n % p[i] != 0:
i = i + 1
if i == k:
p[k] = n
k = k + 1
result.append(n)
n = n + 1
return result

14. Sem Cython Com Cython

15. Clustering com numpy e Cython
Anderson Berg - @berg_pe
XIII Encontro do PUG-PE