vault backup: 2023-09-15 16:17:18

.obsidian/workspace.json

@@ -13,7 +13,7 @@
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@@ -97,7 +97,7 @@
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@@ -114,7 +114,7 @@
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@@ -137,7 +137,7 @@
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@@ -170,8 +170,8 @@
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Algo/Séminaire/Chapter 5 - Recursivity.md

@@ -119,6 +119,12 @@ Exemple with egypt (4.10) vs multiply (4.6):
 The best algorithm in term of complexity is the parameter that is constant/linear or logarithmic. If you have an exponential algorithm, you can put it in trash :)
 
 **Exemple with fibonacci algorithm**
+```Ocaml
+# let rec fibo = function 
+	0|1 -> 1
+	| n -> fibo (n-1) + fibo(n-2);;
+```
+
 
 | |res|how (for human) ?|How (for function) ?|
 |:----:|:----:|:----:|:----:|
@@ -133,4 +139,14 @@ The best algorithm in term of complexity is the parameter that is constant/linea
 
 
 <center><img src="https://imgur.com/6OWREOm.png" height=400 width=auto/></center>
-This function is not optimize because the number of calls is growing exponentially
+This function is not optimize because the number of calls is growing exponentially. 
+A good function will be:
+```Ocaml
+# let fibo n = 
+	let rec fib i fi fi_1 = 
+		if n <= i then
+			fi
+		else
+			fib (i+1)(fi+fi_1)fi
+	in fib 1 1 1
+```

Algo/Séminaire/Exercices seminaire.md

@@ -1,5 +1,5 @@
 
-|## Exercise 2.2 (Power)
+## Exercise 2.2 (Power)
 
 ```Ocaml
 (*First version ; 6 multiplications*)
@@ -366,3 +366,11 @@ let rec multiply x y =
 	eg b;;
 	
 ```
+
+## Exercise 4.11
+```Ocaml
+let rec puissance = function
+	|n when n = 0 -> 1
+	|n when n = 1 -> x
+	|n -> 
+```