Python Implementation of Naive Bayes

Take the test tomorrow. You can bring your computer to your computer and write the program first. Save your effort to use a calculator ...... Directly use the Python source code. [Python] # Naive Bayes # Calculate the Prob. of class: clsdef P (data, cls_val, cls_name = "class"): cnt = 0.0 for e in data: if e [cls_name] = cls_val: cnt + = 1 return cnt/len (data) # Calculate the Prob (attr | cls) def PT (data, cls_val, attr_name, attr_val, cls_name = "class "): cnt1 = 0.0 cnt2 = 0.0 for e in data: if e [cls_name] = cls_val: cnt1 + = 1 if e [attr_name] = attr_val: cnt2 + = 1 return cnt2/cnt1 # Calculate the NBdef NB (data, test, cls_y, cls_n): PY = P (data, cls_y) PN = P (data, cls_n) for key, val in test. items (): print key, val PY * = PT (data, cls_y, key, val) PN * = PT (data, cls_n, key, val) return {cls_y: PY, cls_n: PN} if _ name _ = "_ main _": # data = [{"outlook": "sunny", "temp ": "hot", "humidity": "high", "wind": "weak", "class": "no" },{ "outlook": "sunny ", "temp": "hot", "humidity": "high", "wind": "strong", "class": "no" },{ "outlook ": "overcast", "temp": "hot", "humidity": "high", "wind": "weak", "class": "yes "}, {"outlook": "rain", "temp": "mild", "humidity": "high", "wind": "weak", "class ": "yes"}, {"outlook": "rain", "temp": "cool", "humidity": "normal", "wind": "weak ", "class": "yes" },{ "outlook": "rain", "temp": "cool", "humidity": "normal", "wind ": "strong", "class": "no" },{ "outlook": "overcast", "temp": "cool", "humidity": "normal ", "wind": "strong", "class": "yes" },{ "outlook": "sunny", "temp": "mild", "humidity ": "high", "wind": "weak", "class": "no" },{ "outlook": "sunny", "temp": "cool ", "humidity": "normal", "wind": "weak", "class": "yes" },{ "outlook": "rain", "temp ": "mild", "humidity": "normal", "wind": "weak", "class": "yes" },{ "outlook": "sunny ", "temp": "mild", "humidity": "normal", "wind": "strong", "class": "yes" },{ "outlook ": "overcast", "temp": "mild", "humidity": "high", "wind": "strong", "class": "yes "}, {"outlook": "overcast", "temp": "hot", "humidity": "normal", "wind": "weak", "class ": "yes" },{ "outlook": "rain", "temp": "mild", "humidity": "high", "wind": "strong ", "class": "no"},] # calculate print NB (data, {"outlook": "sunny", "temp": "cool", "humidity ": "high", "wind": "strong"}, "yes", "no") [/python]