如何在Python中正确应用赋值运算符?

问题描述

我必须对大型数组进行一些数学运算(例如加法，乘法).为了防止出现任何'MemoryError'，我正在根据此线程的答案进行计算

I have to do some math operations (e.g., add, multiply) on a large array.To prevent any 'MemoryError' , I am doing my computations as suggested on the answer from this thread.

但是，按照线程中的建议应用赋值操作时，我遇到了一些麻烦.我将使用小型3x3阵列演示我的问题.

However, I am running into some trouble while applying the assignment operations as per suggested in the thread. I will demonstrate my problem using a small 3x3 array.

我有以下输入数组K:

array([[ 0.        ,  0.51290339,  0.24675368],
       [ 0.51290339,  0.        ,  0.29440921],
       [ 0.24675368,  0.29440921,  0.        ]])

我想将以下计算应用于输入数组K:

I want to apply the following computation to the input array K:

output = K* (1.5 - 0.5 * K* K)

我使用上面的公式来计算所需的输出，如下所示:

I apply the above equation to compute the desired output as follows in Python:

K*= (1.5+np.dot(np.dot(-0.5,K),K))

但是，输出答案不正确.

However, the output answer is not correct.

我想要的答案应该是:

0.0000000 0.7018904 0.3626184
0.7018904 0.0000000 0.4288546
0.3626184 0.4288546 0.0000000

欢迎任何帮助.

推荐答案

之所以会出现差异，是因为dot计算点积，而*计算逐元素积.尝试使用

The difference arises because dot computes the dot product whereas * computes the element-wise product. Try using

K *= 1.5 - 0.5 * K * K

相反.

添加

不幸的是，这还不能解决内存问题.我建议使用cython来计算所需的函数，而无需分配额外的内存.

Unfortunately, that does not yet solve the memory problems. I would recommend using cython to compute the desired function without allocating extra memory.

# This cython function must be compiled
def evaluate_function_inplace(double[:] values):
    cdef int i
    for i in range(values.shape[0]):
        values[i] *= 1.5 - 0.5 * values[i] * values[i]

随后，您可以像这样使用该功能.

Subsequently, you can use the function like so.

K = ...
evaluate_function_inplace(K.ravel())

K.ravel()调用将使数组变平，但不会分配新的内存.

The K.ravel() call will flatten the array but will not allocate new memory.

当然，您也可以在不依靠cython的情况下使用上述方法，但是在python中对如此大数组的元素进行迭代的性能开销非常大.

Of course, you can also use the above approach without resorting to cython but the performance overhead of iterating over the elements of such a large array in python are very large.

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