implemented monte carlo integration in nim (#356)

Author: c252

contents/monte_carlo_integration/code/nim/monte_carlo.nim

@@ -0,0 +1,27 @@
+import random
+import math
+
+randomize()
+
+proc in_circle(x,y,radius: float): bool =
+    return x * x + y * y < radius * radius
+
+proc monte_carlo(samples: int): float =
+    const radius: float = 1
+    var count: int = 0
+
+    for i in 0 .. < samples:
+        var
+            x: float = random(radius)
+            y: float = random(radius)
+        
+        if in_circle(x,y,radius):
+            count += 1
+    
+    var pi_estimate: float = 4 * count / samples
+    return pi_estimate
+
+let estimate: float = monte_carlo(1000000)
+
+echo "the estimate of pi is ", estimate
+echo "percent error: ", 100 * (abs(estimate - PI)/PI)

contents/monte_carlo_integration/monte_carlo_integration.md

@@ -65,6 +65,8 @@ each point is tested to see whether it's in the circle or not:
 [import:5-7, lang:"python"](code/python/monte_carlo.py)
 {% sample lang="cs" %}
 [import:23-23, lang:"csharp"](code/csharp/Circle.cs)
+{% sample lang="nim" %}
+[import:6-7, lang:"nim"](code/nim/monte_carlo.nim)
 {% endmethod %}
 
 If it's in the circle, we increase an internal count by one, and in the end,
@@ -129,9 +131,12 @@ Feel free to submit your version via pull request, and thanks for reading!
 [import, lang:"csharp"](code/csharp/Circle.cs)
 ##### Program.cs
 [import, lang:"csharp"](code/csharp/Program.cs)
+{% sample lang="nim" %}
+[import, lang:"nim"](code/nim/monte_carlo.nim)
 {% endmethod %}
 
 
+
 <script>
 MathJax.Hub.Queue(["Typeset",MathJax.Hub]);
 </script>