用Python从零实现贝叶斯分类器的机器学习的教程

# Example of Naive Bayes implemented from Scratch in Python
import csv
import random
import math
 
def loadCsv(filename):
  lines = csv.reader(open(filename, "rb"))
  dataset = list(lines)
  for i in range(len(dataset)):
    dataset[i] = [float(x) for x in dataset[i]]
  return dataset
 
def splitDataset(dataset, splitRatio):
  trainSize = int(len(dataset) * splitRatio)
  trainSet = []
  copy = list(dataset)
  while len(trainSet) < trainSize:
    index = random.randrange(len(copy))
    trainSet.append(copy.pop(index))
  return [trainSet, copy]
 
def separateByClass(dataset):
  separated = {}
  for i in range(len(dataset)):
    vector = dataset[i]
    if (vector[-1] not in separated):
      separated[vector[-1]] = []
    separated[vector[-1]].append(vector)
  return separated
 
def mean(numbers):
  return sum(numbers)/float(len(numbers))
 
def stdev(numbers):
  avg = mean(numbers)
  variance = sum([pow(x-avg,2) for x in numbers])/float(len(numbers)-1)
  return math.sqrt(variance)
 
def summarize(dataset):
  summaries = [(mean(attribute), stdev(attribute)) for attribute in zip(*dataset)]
  del summaries[-1]
  return summaries
 
def summarizeByClass(dataset):
  separated = separateByClass(dataset)
  summaries = {}
  for classValue, instances in separated.iteritems():
    summaries[classValue] = summarize(instances)
  return summaries
 
def calculateProbability(x, mean, stdev):
  exponent = math.exp(-(math.pow(x-mean,2)/(2*math.pow(stdev,2))))
  return (1 / (math.sqrt(2*math.pi) * stdev)) * exponent
 
def calculateClassProbabilities(summaries, inputVector):
  probabilities = {}
  for classValue, classSummaries in summaries.iteritems():
    probabilities[classValue] = 1
    for i in range(len(classSummaries)):
      mean, stdev = classSummaries[i]
      x = inputVector[i]
      probabilities[classValue] *= calculateProbability(x, mean, stdev)
  return probabilities
 
def predict(summaries, inputVector):
  probabilities = calculateClassProbabilities(summaries, inputVector)
  bestLabel, bestProb = None, -1
  for classValue, probability in probabilities.iteritems():
    if bestLabel is None or probability > bestProb:
      bestProb = probability
      bestLabel = classValue
  return bestLabel
 
def getPredictions(summaries, testSet):
  predictions = []
  for i in range(len(testSet)):
    result = predict(summaries, testSet[i])
    predictions.append(result)
  return predictions
 
def getAccuracy(testSet, predictions):
  correct = 0
  for i in range(len(testSet)):
    if testSet[i][-1] == predictions[i]:
      correct += 1
  return (correct/float(len(testSet))) * 100.0
 
def main():
  filename = 'pima-indians-diabetes.data.csv'
  splitRatio = 0.67
  dataset = loadCsv(filename)
  trainingSet, testSet = splitDataset(dataset, splitRatio)
  print('Split {0} rows into train={1} and test={2} rows').format(len(dataset), len(trainingSet), len(testSet))
  # prepare model
  summaries = summarizeByClass(trainingSet)
  # test model
  predictions = getPredictions(summaries, testSet)
  accuracy = getAccuracy(testSet, predictions)
  print('Accuracy: {0}%').format(accuracy)
 
main()