用法 | 返回 |
---|---|
ee.ConfusionMatrix(array, order) | ConfusionMatrix |
参数 | 类型 | 详细信息 |
---|---|---|
array | 对象 | 一个表示混淆矩阵的整数二维方阵。请注意,与 ee.Array 构造函数不同,此实参不能采用列表。 |
order | 列表,默认值:null | 非连续或非零基准矩阵的行和列大小及顺序。 |
示例
代码编辑器 (JavaScript)
// A confusion matrix. Rows correspond to actual values, columns to // predicted values. var array = ee.Array([[32, 0, 0, 0, 1, 0], [ 0, 5, 0, 0, 1, 0], [ 0, 0, 1, 3, 0, 0], [ 0, 1, 4, 26, 8, 0], [ 0, 0, 0, 7, 15, 0], [ 0, 0, 0, 1, 0, 5]]); print('Constructed confusion matrix', ee.ConfusionMatrix(array)); // The "order" parameter refers to row and column class labels. When // unspecified, the class labels are assumed to be a 0-based sequence // incrementing by 1 with a length equal to row/column size. print('Default row/column labels (unspecified "order" parameter)', ee.ConfusionMatrix({array: array, order: null}).order()); // Set the "order" parameter when custom class label integers are required. The // list of integer value labels should correspond to the matrix axes left to // right / top to bottom. var order = [11, 22, 42, 52, 71, 81]; print('Specified row/column labels (specified "order" parameter)', ee.ConfusionMatrix({array: array, order: order}).order());
import ee import geemap.core as geemap
Colab (Python)
from pprint import pprint # A confusion matrix. Rows correspond to actual values, columns to # predicted values. array = ee.Array([[32, 0, 0, 0, 1, 0], [ 0, 5, 0, 0, 1, 0], [ 0, 0, 1, 3, 0, 0], [ 0, 1, 4, 26, 8, 0], [ 0, 0, 0, 7, 15, 0], [ 0, 0, 0, 1, 0, 5]]) print('Constructed confusion matrix:') pprint(ee.ConfusionMatrix(array).getInfo()) # The "order" parameter refers to row and column class labels. When # unspecified, the class labels are assumed to be a 0-based sequence # incrementing by 1 with a length equal to row/column size. print('Default row/column labels (unspecified "order" parameter):', ee.ConfusionMatrix(array, None).order().getInfo()) # Set the "order" parameter when custom class label integers are required. The # list of integer value labels should correspond to the matrix axes left to # right / top to bottom. order = [11, 22, 42, 52, 71, 81] print('Specified row/column labels (specified "order" parameter):', ee.ConfusionMatrix(array, order).order().getInfo())