Question

由于我想要了解的Numpy产品的输出倍增，我遇到了溢出警告。它在我的大项目中的实际使用的简化版本详述如下：

import numpy as np

class MyClass(object):
    def __init__(self,
                 array_1,
                 array_2):

        # Assigning arrays to be used in later methods
        self.array_1 = array_1
        self.array_2 = array_2

        # Assigning some scaling factors to be used in later methods.
        self.value_1 = np.prod(self.array_1.shape)
        self.value_2 = np.prod(self.array_2.shape)
        print("Numpy Product Assignment: {0}, {1}".format(self.value_1, self.value_2))

        # Alternative assignment of scaling factors
        self.alt_value_1 = self.array_1.shape[0] * self.array_1.shape[1]
        self.alt_value_2 = self.array_2.shape[0] * self.array_2.shape[1]
        print("Regular Product Assignment: {0}, {1}".format(self.alt_value_1, self.alt_value_2))

        pass

    def mymethod(self):
        print("Direct Multiplication: {0}".format(80160 * 262144))
        print("Numpy Product Multiplication: {0}".format(self.value_1 * self.value_2))
        print("Regular Product Multiplcation {0}".format(self.alt_value_1 * self.alt_value_2))

if __name__ == '__main__':
    test_array_1 = np.zeros([512, 512], dtype=complex)
    test_array_2 = np.zeros([1002, 80], dtype=complex)

    test_class = MyClass(test_array_1, test_array_2)
    test_class.mymethod()

包括使用类结构的完整性，尽管经过大量编辑，但最低限度。如果我运行此代码（在Python 3.6.0上），我得到以下输出：

C:/somepath/scratch.py:247: RuntimeWarning: overflow encountered in long_scalars
  print("Numpy Product Multiplication: {0}".format(self.value_1 * self.value_2))
Numpy Product Assignment: 262144, 80160
Regular Product Assignment: 262144, 80160
Direct Multiplication: 21013463040
Numpy Product Multiplication: -461373440
Regular Product Multiplcation 21013463040

Process finished with exit code 0

显然，我可以使用常规乘法解决问题，但我想了解为什么存在问题以及是否可以按原样修复。我认为我错过了一些 dtype = X 微妙之处，所以我的问题是导致这些溢出错误的原因是什么？

Answer 1

这看起来像是由32个整数引起的溢出。您可以将值转换为64位，如下所示：

    self.value_1 = np.prod(self.array_1.shape).astype(np.int64)
    self.value_2 = np.prod(self.array_2.shape).astype(np.int64)

如果用于数组构造的值足够小，Numpy会自动选择32位整数类型。在乘法过程中，它们不会自动转换为64位。

为什么我会得到溢出错误乘以Numpy产品输出？

1 个答案: