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<p>需要一些关于python中naivebayes的代码的建议。我一直在用csv碰到“zero division error:float division by zero”错误(NB.csv公司)但另一个csv(数据.csv)它运行得很好。。。
我运行的是python3.6(也尝试过2.7)。在</p>
<pre><code># Example of Naive Bayes implemented from Scratch in Python
import csv
import random
import math
def loadCsv(filename):
lines = csv.reader(open(filename, "r"))
<a href="https://www.cnpython.com/pypi/dataset" class="inner-link">dataset</a> = 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.<a href="https://www.cnpython.com/list/append" class="inner-link">append</a>(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.items():
summaries[classValue] = summarize(instances)
return summaries
def calculateProbability(x, mean, stdev):
exponent = math.exp(-(math.pow(x - mean, 2) / (2 * math.pow(stdev, 2))))
print (stdev,"||",exponent)
print (2 * math.pow(stdev, 2))
return (1 / (math.sqrt(2 * math.pi) * stdev)) * exponent
def calculateClassProbabilities(summaries, inputVector):
probabilities = {}
for classValue, classSummaries in summaries.items():
probabilities[classValue] = 1
for i in range(len(classSummaries)):
mean, stdev = classSummaries[i]
x = inputVector[i]
print ("x: ",x,"mean: ", mean,"stdev: ", stdev," || ","summaries: " ,summaries,"inputVector: ",inputVector,"i:",[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.items():
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 = 'C:\\Users\\common\\Dropbox\\Project\\NB.csv'
splitRatio = 0.67
dataset = loadCsv(filename)
print ("Load csv")
trainingSet, testSet = splitDataset(dataset, splitRatio)
print('Split ' + str(len(dataset)) + ' rows into train=' + str(len(trainingSet)) + ' and test= '+ str(len(testSet)) +' rows')
# prepare model
summaries = summarizeByClass(trainingSet)
predictions = getPredictions(summaries, testSet)
accuracy = getAccuracy(testSet, predictions)
print('Accuracy: ' + str(accuracy))
main()
</code></pre>
<p>但是代码一直在提示这个错误”</p>
<blockquote>
<p>Traceback (most recent call last): File
"C:/Users/common/PycharmProjects/Lab/NB_raw.py", line 123, in
main() File "C:/Users/common/PycharmProjects/Lab/NB_raw.py", line 117, in main
predictions = getPredictions(summaries, testSet) File "C:/Users/common/PycharmProjects/Lab/NB_raw.py", line 92, in
getPredictions
result = predict(summaries, testSet[i]) File "C:/Users/common/PycharmProjects/Lab/NB_raw.py", line 79, in predict
probabilities = calculateClassProbabilities(summaries, inputVector) File "C:/Users/common/PycharmProjects/Lab/NB_raw.py",
line 74, in calculateClassProbabilities
probabilities[classValue] *= calculateProbability(x, mean, stdev) File "C:/Users/common/PycharmProjects/Lab/NB_raw.py", line 60, in
calculateProbability
exponent = math.exp(-(math.pow(x - mean, 2) / (2 * math.pow(stdev, 2)))) ZeroDivisionError: float division by zero"</p>
</blockquote>