Funes el memorioso

#!/usr/bin/env python

class Fibo:
	"Class-based Fibonacci recursion"
	def __init__(self, n):
		if n == 1:
			self.value = 1
		elif n == 2:
			self.value = 1
		else:
			A = Fibo(n-1)
			B = Fibo(n-2)
			self.value = A.value + B.value
	def __repr__(self):
		return str(self.value)

if __name__ == "__main__":
	print Fibo(1)
	print Fibo(2)
	print Fibo(3)
	print Fibo(4)
	print Fibo(5)
	print Fibo(6)
	print Fibo(7)
	print Fibo(8)
	print Fibo(9)
	print Fibo(10)

Let’s check CRS (Compressed Row Storage) in Python NumPy.

[yuy121@cyberstar: ~]# module list
Currently Loaded Modulefiles:
  1) realvnc/4.5r21561   2) java/1.6.0_20    3) intel/11.1.073    4) pgi/10.9

Load Python module.

[yuy121@cyberstar: ~]# module load python
[yuy121@cyberstar: ~]# module list
Currently Loaded Modulefiles:
  1) realvnc/4.5r21561   6) gmp/5.0.1          11) gcc/4.5.0
  2) java/1.6.0_20       7) mpfr/2.4.2         12) openmpi/gnu/1.4.2
  3) intel/11.1.073      8) ppl/0.10.2         13) python/2.6.5
  4) pgi/10.9            9) cloog-ppl/0.15.9
  5) mkl/10.2.4.032     10) mpc/0.8.1
[yuy121@cyberstar: ~]# python
Python 2.6.5 (r265:79063, Jul 14 2011, 09:22:34) 
[GCC 4.1.2 20080704 (Red Hat 4.1.2-50)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>>

Check CSR format in NumPy.

Python 2.6.5 (r265:79063, Jul 14 2011, 09:22:34) 
[GCC 4.1.2 20080704 (Red Hat 4.1.2-50)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> from numpy import array, matrix
>>> from scipy.sparse import csr_matrix
>>> A=[[1, 1, 2], [3, 4, 0], [5, 0, 7], [0, 0, 0]]
>>> c = csr_matrix(A)
>>> c
<4x3 sparse matrix of type '<type 'numpy.int64'>'
	with 7 stored elements in Compressed Sparse Row format>
>>> c.__dict__
{'format': 'csr',
 '_shape': (4, 3),
 'indptr': array([0, 3, 5, 7, 7], dtype=int32),
 'indices': array([0, 1, 2, 0, 1, 0, 2], dtype=int32),
 'maxprint': 50,
 'data': array([1, 1, 2, 3, 4, 5, 7])}
>>> c.data
array([1, 1, 2, 3, 4, 5, 7])
>>> c.indices
array([0, 1, 2, 0, 1, 0, 2], dtype=int32)
>>> c.indptr
array([0, 3, 5, 7, 7], dtype=int32)