wav = wav * scalarreturnwav, scalar sr =16000wav = librosa.load("./wavdata/TIMIT.WAV", sr=sr)[0]# (46797,)print(wav.shape) wav, scalar = volumeAument1(wav, dB=15)print(mean_dbfs(wav))# 18.0103004778581wav, scalar = volumeAument2(wav,15)print(mean_dbfs(wav))# 18.010299731550788 ...
print("多分类预测建模的精度acc为: ",accuracy_score(test_labels,y_predict)) print("多分类预测建模的R方为: ",r2_score(test_labels, y_predict)) # print("多分类预测的报告: \n",classification_report(y_predict, test_labels)) 多分类预测建模的精度acc为: 0.780053428317008 多分类预测建模的R方为...
# print(tp+tn,tp+tn+fp+fn) # accuracy = float((tp+tn) / (tp+tn+fp+fn)) # 准确率 # precision = tp / (tp+fp) # 精确率 # recall = tp / (tp+fn) # print(2*precision*recall,precision+recall)# 召回率 # F1 = float((2*precision*recall) / (precision+recall)) # F1 # ret...
import decimal # Set up a context with limited precision c = decimal.getcontext().copy() c.prec = 3 # Create our constant pi = c.create_decimal('3.1415') # The constant value is rounded off print 'PI :', pi # The result of using the constant uses the global context print 'RESULT...
plt.legend(["Without Noise","With Noise","Noise"], loc =2) plt.show()#Extract training from the toy datasetx_train = x[0:80] y_train = y[0:80]print("Shape of x_train:",x_train.shape)print("Shape of y_train:",y_train.shape) ...
withtorch.no_grad(): pred=model(data).max(dim=1)[1] correct=pred.eq(data.y).sum().item() total=len(data.y) precision=correct/total recall=precision print(f"精确度:{precision},召回率:{recall}") 08 Transformer模型 Transformer模型是一类设计用于序列到序列任务(如自然语言处理)的深度学习算法...
And similar, the%operator can be used to format and set the precision of the output: value=34.185609print('The value is:%.3f'%value)# The value is: 34.186 Conclusion¶ In this article, we have discussed the different types of precision handling in Python. We have also discussed the dif...
N = 1000 #number of samples fs = 1000 #sample frequency T = 1/fs #interval time = np.linspace(-2, 2, N) tri = np.where(np.abs(time)<=.5,1,0) tri = np.where(tri==1,.5-np.abs(time),0) print("We consider {} samples".format(N)) scales = np.arange(1,51,1) coef,...
选项precision=2,限定⼩数只有两位。 qcut是⼀个⾮常类似于cut的函数,它可以根据样本分位数对数据进⾏⾯元划分。根据数据的分布情况,cut可能⽆法使各个⾯元中含有相同数量的数据点。 ⽽qcut由于使⽤的是样本分位数,因此可以得到⼤⼩基本相等的⾯元: In [87]: data = np.random.randn(1000...
You can use this method in scientific computations that require high precision. In these situations, you may need to avoid precision loss due to floating-point rounding errors.For example, say that you need to perform computations with the gravitational constant:...