Commiting coinToss and updated readme

Author: MichaelSheely

README.md

@@ -21,3 +21,10 @@ connectFour originated as a project in my CS 5 (introduction to computer science
 class in which we designed a basic AI to play against other AIs or against humans
 (or to play two humans against one another).  I made a few improvements to the 
 graphics and user input over the summer, so here it is.
+
+====== Update ======
+
+On November 30, 2014 I added to the file betrayal.py, which is code designed to model
+the tabletop game created by JT Booth on which he, John Phillpot and I have been working.
+This is only one side of the game which we are developing, a tool for the game master to
+use to keep track of his or her players.

coinToss.py

@@ -0,0 +1,63 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Nov 28 20:34:26 2014
+
+@author: Michael
+"""
+
+import math
+from matplotlib import pyplot
+import random
+
+def tossCoin():
+    return random.choice(["H", "T"])
+
+def waitForString(string):
+    """returns the number of flips needed until
+    we get a desired sequence"""
+    n = len(string)
+    lastN = []
+    trials = 0
+    while len(lastN) < len(string):
+        lastN += [tossCoin()]
+        trials += 1
+    while not same(string, lastN):
+        lastN = lastN[1:] + [tossCoin()]
+        trials += 1
+    return trials
+
+def same(string, L):
+    for x in range(len(L)):
+        if string[x] != L[x]:
+            return False
+    return True
+
+def getTstats(string1, string2, trials=1000):
+    """gets a t-score for a pooled test comparing
+    the number of flips needed for two different strings"""
+    r1 = []
+    r2 = []
+    for trial in range(trials):
+        r1.append(waitForString(string1))
+        r2.append(waitForString(string2))
+    avg1 = sum(r1)/float(trials)
+    avg2 = sum(r2)/float(trials)
+    var1 = 1/float(trials-1)*sum([(x - avg1)**2 for x in r1])
+    var2 = 1/float(trials-1)*sum([(x - avg2)**2 for x in r2])
+    S_p  = math.sqrt((trials-1)*(var1 + var2)/float(2*trials - 2))
+    df   = 2*trials - 2
+    print "Difference in variances is: ", (var2 - var1)/((var1 + var2)/2)
+    T = (avg1 - avg2)/(S_p*math.sqrt(2/float(trials)))
+    return T, df
+
+#the main verifies that not all strings
+#of the same length take the same number
+#of flips on average
+trials = 500
+r1 = []
+r2 = []
+for trial in range(trials):
+    r1.append(waitForString(10*"H"))
+    r2.append(waitForString(5*"HT"))
+pyplot.hist(r1)
+pyplot.hist(r2)