我试过这个
function [Fvalue,precision,recall,accuracy,JaccardIndex,TP,FP,TN,FN,FPrate,TPrate,MCC] = compareBinaryImages( reference, toTest )
%COMPAREBINARYIMAGES Compute various similarity metrics between two binary images
% reference = grouth truth binary image
% toTest = binary image to be compared
我写了下面的代码,但它指定了文件中的行数。我想知道如何更改它,使其读取未知数量的行数?
n = int(input("instance: "))
tp, tn, fp, fn = 0
for i in range(n):
real, predicted = map(int, input().split(' '))
for num in i:
if real == 1 and predicted == 1:
tp += 1
elif real == 0 and predicted == 0:
我试图找到一种有效的方法,根据R中的自定义性能度量为预测模型的概率分数设置分类阈值。值得注意的是,实际数据是不平衡的,在训练集中有35行million+。这就给出了大约3500万的预测分数,可以设定为这两个类别的阈值分割。到目前为止,我已经尝试过两种方法
1. A 'smart', single thread approach trying to do minimal work
2. A brute-force, parallel multi-threaded approach.
方法1的性能要好得多,如下所示,但在实际数据上仍然太慢(在运行了25+小时之后我放弃了)。我
我正在计算一个分割评估工作,其中我有预测和基本事实。预测阵列和地面真实阵列形状(28,1,224,224)。现在计算真阳性(TP)、真阴性(TN)、假阳性(FP)、假阴性(FN)。我曾经使用下面给出的代码来计算这些值,但得到了一些错误。 def perf_measure( y_hat , y_actual):
TP = 0
FP = 0
TN = 0
FN = 0
for i in range(len(y_hat)):
if y_actual[i]==1 and y_hat[i]==1:
TP += 1
if y_hat[i]==1 and y_
有没有一种方法可以在某些条件下沿着一条轴求和?所以我有一个这样的数组,可以沿着0轴求和。
#array creation:
tpfnfpArray = np.zeros((1000,3))
for i in range(1000):
tpfnfpArray[i,:] = (i,i,i)
#first result
tp,fn, fp = np.sum(tpfnfpArray,axis=0)
#preparing second result
tp2,fp2,fn2,tn2 = (0,0,0,0)
到目前一切尚好。现在我想得到另一个结果(tp2,fp2,fn2,tn2),条件如下:
我用sklearn.metrics.confusion_matrix做了一个测试,看看如果预测数组中有一个类不在标签和映射数组中,会发生什么。我的代码是:
from sklearn.metrics import confusion_matrix as cm
a = ["positive\n", "positive\n", "negative\n", "positive\n", "negative\n"]
b = ["negative\n", "negative\n", "d
def answers():
ppv = tp/(tp + fp)
rcl = tp/(tp + fn)
return ppv, rcl
print("enter the value of true positive")
tp = input()
print("enter the value of false negative")
fn = input()
print("enter the value of false positive")
fp = input()
print("enter the value
如何使用python检索混淆矩阵中的假阴性值: Tn:27, FP=0, FN=7, TP=6 如有任何帮助,我们将不胜感激: idx = X[np.where(y_test != y_pred)[0]].sum(axis=1)
np.array(corpus)[idx] # I have tried this I could not able to get those.
idx=X[np.where(y_test != y_pred)[0]].sum(axis=1)
np.array(corpus)[idx]
Tp-6 TN-27 FP=0 FN=7 我想检索错误的负值。我从上面的