我是Python的新手，我认为它与Java有很大不同。
我查看了其他答案，它们暗示错误的原因是我在期望传递一个值时却传递了一个数组。我不确定这一点。我相当肯定我只是在传递一个值。
第97行的代码是：exponential = math.exp(-(math.pow(feature_value-mean, 2) / (2*math.pow(standard_deviation, 2))))
错误的完整文本是：
Traceback (most recent call last): File "D:/Personal/Python/NB.py", line 153, in <module> main() File "D:/Personal/Python/NB.py", line 148, in main predictions = getPredict(summaries, testing_set) File "D:/Personal/Python/NB.py", line 129, in getPredict classification = predict(results, testData[index]) File "D:/Personal/Python/NB.py", line 117, in predict probabilities = Classify(feature_summaries, classifications) File "D:/Personal/Python/NB.py", line 113, in Classify probabilities[classes] = probabilities[classes] * GaussianProbabilityDensity(feature_value, mean, standard_deviation) File "D:/Personal/Python/NB.py", line 97, in GaussianProbabilityDensity exponential = math.exp(-(math.pow(feature_value-mean, 2) / (2*math.pow(standard_deviation, 2))))TypeError: only size-1 arrays can be converted to Python scalars如果有帮助，以下是csv文件的内容。值得注意的是，我有另外两个算法可以在这个数据集上正常运行。
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math.pow（以及所有math函数）只能处理标量，即单个数字（整数或浮点数）。错误提示说其中一个参数，例如standard_deviation，是一个包含多个元素的numpy数组，因此无法转换为标量并传递给math.pow。
这发生在你自己的代码中，因此追溯这些变量的来源并不困难。
要么是你无意中传递了一个数组给这个函数，要么你需要用np.pow（和np.exp）函数替换math.pow，这些函数可以处理数组。
你从csv文件加载数据时生成了一个numpy数组
data = numpy.loadtxt(data, delimiter=',')# 遍历数组中的数据for index in range(len(data)): # 使用try catch尝试转换为浮点数，如果无法转换为浮点数，则转换为0 try: data[index] = [float(x) for x in data[index]] except ValueError: data[index] = 0loadtxt返回一个数组，默认类型是浮点数（dtype）。它的所有元素都将是浮点数——如果读取到不是有效浮点数的内容，它会引发错误。因此，不需要这个循环。而且这个循环看起来太像是为列表编写的，而不是为数组编写的。
randomize_data不应该返回任何内容。np.random.shuffle会就地操作csv。这不会引起错误。
