我正在嘗試完成一項(xiàng)奇怪的任務(wù)。我需要在不使用 sklearn 的情況下完成以下操作，最好使用 numpy：給定一個(gè)數(shù)據(jù)集，將數(shù)據(jù)分成 5 個(gè)相等的“折疊”或分區(qū)在每個(gè)分區(qū)內(nèi)，將數(shù)據(jù)拆分為“訓(xùn)練”和“測(cè)試”集，拆分比例為 80/20這里有一個(gè)問(wèn)題：你的數(shù)據(jù)集被標(biāo)記為類(lèi)。以一個(gè)有 100 個(gè)實(shí)例的數(shù)據(jù)集為例，A 類(lèi)有 33 個(gè)樣本，B 類(lèi)有 67 個(gè)樣本。我應(yīng)該創(chuàng)建 5 個(gè) 20 個(gè)數(shù)據(jù)實(shí)例的折疊，其中在每個(gè)折疊中，A 類(lèi)有 6 或 7 (1/3) 個(gè)值，B 類(lèi)有其余的我的問(wèn)題是： 我不知道如何為每個(gè)折疊正確返回測(cè)試和訓(xùn)練集，盡管能夠適當(dāng)?shù)胤指钏?，而且，更重要的是，我不知道如何合并每個(gè)類(lèi)的元素?cái)?shù)量的正確劃分.我當(dāng)前的代碼在這里。有人評(píng)論我被卡住的地方：import numpydef csv_to_array(file): # Open the file, and load it in delimiting on the ',' for a comma separated value file data = open(file, 'r') data = numpy.loadtxt(data, delimiter=',') # Loop through the data in the array for index in range(len(data)): # Utilize a try catch to try and convert to float, if it can't convert to float, converts to 0 try: data[index] = [float(x) for x in data[index]] except Exception: data[index] = 0 except ValueError: data[index] = 0 # Return the now type-formatted data return datadef five_cross_fold_validation(dataset): # print("DATASET", dataset) numpy.random.shuffle(dataset) num_rows = dataset.shape[0] split_mark = int(num_rows / 5) folds = [] temp1 = dataset[:split_mark] # print("TEMP1", temp1) temp2 = dataset[split_mark:split_mark*2] # print("TEMP2", temp2) temp3 = dataset[split_mark*2:split_mark*3] # print("TEMP3", temp3) temp4 = dataset[split_mark*3:split_mark*4] # print("TEMP4", temp4) temp5 = dataset[split_mark*4:] # print("TEMP5", temp5) folds.append(temp1) folds.append(temp2) folds.append(temp3) folds.append(temp4) folds.append(temp5) # folds = numpy.asarray(folds) for fold in folds: # fold = numpy.asarray(fold) num_rows = fold.shape[0] split_mark = int(num_rows * .8) fold_training = fold[split_mark:] fold_testing = fold[:split_mark]
基于內(nèi)部值的 Numpy 數(shù)組操作
胡說(shuō)叔叔
2021-11-30 19:22:43
