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1 file changed, 5 insertions, 2 deletions
memoization.py
| @@ -1,7 +1,10 @@ | |||
| 1 | - | # example of memoization in pythong for fibonacci | |
| 1 | + | # example of memoization in python for fibonacci | |
| 2 | 2 | ||
| 3 | 3 | # Memoization: Let’s Be Efficient, Shall We? | |
| 4 | - | # Ever ask the same question over and over? Annoying, right? That’s what your computer thinks too. Memoization fixes this by storing results so you don’t have to repeat expensive calculations. | |
| 4 | + | # Ever worry about recursion and the stack and so on... | |
| 5 | + | # Ever ask the same question over and over? | |
| 6 | + | # Annoying, right? That’s what your computer thinks too. | |
| 7 | + | # Memoization fixes this by storing results so you don’t have to repeat expensive calculations. | |
| 5 | 8 | def fib(n, memo={}): | |
| 6 | 9 | if n in memo: | |
| 7 | 10 | return memo[n] | |
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1 file changed, 15 insertions
memoization.py(archivo creado)
| @@ -0,0 +1,15 @@ | |||
| 1 | + | # example of memoization in pythong for fibonacci | |
| 2 | + | ||
| 3 | + | # Memoization: Let’s Be Efficient, Shall We? | |
| 4 | + | # Ever ask the same question over and over? Annoying, right? That’s what your computer thinks too. Memoization fixes this by storing results so you don’t have to repeat expensive calculations. | |
| 5 | + | def fib(n, memo={}): | |
| 6 | + | if n in memo: | |
| 7 | + | return memo[n] | |
| 8 | + | if n < 2: | |
| 9 | + | return n | |
| 10 | + | memo[n] = fib(n - 1, memo) + fib(n - 2, memo) | |
| 11 | + | return memo[n] | |
| 12 | + | ||
| 13 | + | fib(100) | |
| 14 | + | ||
| 15 | + | # 354224848179261915075 | |
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