Usage | Returns |
---|---|
ConfusionMatrix.order() | List |
Argument | Type | Details |
---|---|---|
this: confusionMatrix | ConfusionMatrix |
Examples
Code Editor (JavaScript)
// Construct an error/confusion matrix from an array (rows are actual values, // columns are predicted values). We construct an error matrix here for brevity // and matrix visualization, in most applications the confusion matrix will be // generated from ee.Classifier.confusionMatrix. 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]]); var cmDefaultOrder = ee.ConfusionMatrix(array); print('Default name and order of the rows and columns', cmDefaultOrder.order()); var order = [11, 22, 42, 52, 71, 81]; var cmSpecifiedOrder = ee.ConfusionMatrix({array: array, order: order}); print('Specified name and order of the rows and columns', cmSpecifiedOrder.order());
import ee import geemap.core as geemap
Colab (Python)
# Construct an error/confusion matrix from an array (rows are actual values, # columns are predicted values). We construct an error matrix here for brevity # and matrix visualization, in most applications the confusion matrix will be # generated from ee.Classifier.confusionMatrix. 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]]) cm_default_order = ee.ConfusionMatrix(array) print('Default name and order of the rows and columns:', cm_default_order.order().getInfo()) order = [11, 22, 42, 52, 71, 81] cm_specified_order = ee.ConfusionMatrix(array, order) print('Specified name and order of the rows and columns:', cm_specified_order.order().getInfo())