non_zero_indices = np.nonzero(matrix) print(non_zero_indices) 使用索引选出非0元素 non_zero_elements = matrix[non_zero_indices] print(non_zero_elements) 在上述代码中,np.nonzero(matrix)返回一个元组,包含矩阵中非0元素的行索引和列索引。然后,我们使用这些索引对矩阵进行索引,得到非0元素。 3、使...
2. 使用numpy.nonzero numpy.nonzero函数返回非零元素的索引,可以用于查找特定元素。 deffind_element_in_numpy_array_nonzero(arr,target):indices=np.nonzero(arr==target)[0]returnindices# 示例indices=find_element_in_numpy_array_nonzero(my_array,target)print(f"元素{target}在NumPy数组中的索引为:{ind...
A common use fornonzerois to find the indices of an array, where a condition is True. Given an arraya, the conditiona> 3 is a boolean array and since False is interpreted as 0, np.nonzero(a > 3) yields the indices of theawhere the condition is true.这个功能和numpy.where()的一种...
nonzero() Return the indices of the elements that are non-zero. partition(kth[, axis, kind, order]) Rearranges the elements in the array in such a way that value of the element in kth position is in the position it would be in a sorted array. prod([axis, dtype, out, keepdims]) ...
values = array[nonzero_indices] # 定义填补后的栅格范围和分辨率 x_range = np.arange(0, array.shape[1], 1) y_range = np.arange(0, array.shape[0], 1) # 生成填补后栅格网格 grid_x, grid_y = np.meshgrid(x_range, y_range)
fig, axes = pylab.subplots(nrows=2, ncols=1, figsize=(20,15)) pylab.gray() inlier_idxs = np.nonzero(inliers)[0] plot_matches(axes[0], image_original_gray, image_warped_gray, source, destination, np.column_stack((inlier_idxs, inlier_idxs)), matches_color='b') axes[0].axis(...
pd.DataFrame(data) # 指定要查找的值 values_to_find = [2, 40, 300] # 使用isin()方法找出包含这些值的行 rows_with_values = df.isin(values_to_find) # 获取这些行的索引 row_indices = rows_with_values.any(axis=1).to_numpy().nonzero()[0] print("包含指定值的行索引:", row_indices)...
根据所选的方法(1 或 2),该函数调用相应的find_reverse_neighbors或find_reverse_neighbors_v2函数来寻找测试样本x的逆近邻。接着,我们计算这些逆近邻的平均距离(mean_distances)。然后,我们从mean_distances中提取非零元素(non_zero_mean_distances)。如果non_zero_mean_distances非空,我们计算其均值作为score,否则...
- 用户自定义类中,如果定义了__len__()或者__nonzero__(),并且被调用后返回0或者False while循环 while的就是循环和if的综合体,是一种单纯的基于条件的循环,本身没有遍历的意思,这是和for_each的本质差别,这种区别比起C/C++中要明确得多,用法如下: i = 0 while i < 100: # 笑100遍 print("ha")...
fig, axes = pylab.subplots(nrows=2, ncols=1, figsize=(20,15)) pylab.gray() inlier_idxs = np.nonzero(inliers)[0] plot_matches(axes[0], image_original_gray, image_warped_gray, source, destination, np.column_stack((inlier_idxs, inlier_idxs)), matches_color='b') axes[0].axis(...