Create Exercise 10: Naive Bayes classifier.py

chapter-2/Exercise 10: Naive Bayes classifier.py

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+import numpy as np
+
+p1 = [1/6, 1/6, 1/6, 1/6, 1/6, 1/6]   # normal
+p2 = [0.1, 0.1, 0.1, 0.1, 0.1, 0.5]   # loaded
+
+def roll(loaded):
+    if loaded:
+        print("rolling a loaded die")
+        p = p2
+    else:
+        print("rolling a normal die")
+        p = p1
+
+    # roll the dice 10 times
+    # add 1 to get dice rolls from 1 to 6 instead of 0 to 5
+    sequence = np.random.choice(6, size=10, p=p) + 1 
+    for roll in sequence:
+        print("rolled %d" % roll)
+
+    return sequence
+
+def bayes(sequence):
+    odds = 1.0           # start with odds 1:1
+    for roll in sequence:
+        r = p2[roll-1] / p1[roll-1]               #remember that r = P(loaded ∣ roll)÷P(normal ∣ roll), roll-1 because index starts at 0
+        odds = odds*r                             #update the odds (multiply the old odds with r)
+    if odds > 1:
+        return True
+    else:
+        return False
+
+sequence = roll(True)
+if bayes(sequence):
+    print("I think loaded")
+else:
+    print("I think normal")