x= np.arange(3,21,3) width= 0.6axes_width= 3#刻度线宽度axes_length = 6#刻度线长度spines_width = 3#坐标轴的宽度fig, ax = plt.subplots(figsize=(6.4,4.8), dpi=500) labels= ["BerNB","MultiNB","LogReg","SVC","LSVC","NuSVC"] rects1= ax.bar(x - width*1.5, data.bigram_F1Sco...
train_accuracy,test_loss,test_accuracy):_,(loss_plt,accu_plt)=plt.subplots(1,2,figsize=(12,4))x=np.arange(len(train_loss))loss_plt.plot(x,train_loss,'b',label='train')loss_plt.plot(x,test_loss,'g',label='test')loss_plt.set_title('Model Loss')loss_plt.set_xlabel('epoch'...
save_index_file=False): # Convenience function to create HNSWgraph # features : list of lists containingthe embeddings # ef, M: parameters to tune the HNSWalgorithm num_elements = len(features) labels_index =np.arange(num_elements) EMBEDDING_SIZE= len(features[0]) # Declaring index # possi...
读取CSV文件:df.read_csv(filename),结果为DataFrame 数组长度:len(arr) 访问第i个元素:一维数组用a[i],多维数组用a.flat[i] 数组转置:ndarray.T 数组分割:numpy.split(ary, indices_or_sections, axis),第二项的值为整数则表明要创建的等大小的子数组的数量,是一维数组则表明要创建新子数组的点。 追加值...
x = np.arange(0,10,0.1) y_1 = np.sin(x) y_2 = np.sin(x+5) y_3 = np.sin(x+10) y_4 = np.sin(x+15) y_5 = np.sin(x+20) df = pd.DataFrame({'X':x,'y_1':y_1,'y_2':y_2, 'y_3':y_3,'y_4':y_4,'y_5':y_5}) ...
line_idx = torch.arange(len(indicies), device=indicies.device).view(-1, 1) Test code: x = torch.linspace(0, 10*3.14, 100).repeat(3, 1) y = torch.sin(x) y[1] *= 2 y[2] *= 3 sc = torch.linspace(x.min(), x.max(), 10).repeat(3, 1) sc[1] *= 0.8 sc[2] *= ...
- y: Vector of labels, of shape (N,) where y[i] is the label for x[i] and 0 <= y[i] < C Returns a tuple of: - loss: Scalar giving the loss - dx: Gradient of the loss with respect to x"""N=x.shape[0] correct_class_scores=x[np.arange(N), y] ...
Using np.arange()NumPy has its own version of the built-in range(). It’s called np.arange(), and unlike range(), it’s not restricted to just integers. You can use np.arange() in a similar way to range(), using start, stop, and step as the input parameters:Python...
defconvert2DJarrayToNumpy(jArr):arrShape=(len(jArr),len(jArr[0]) )arrType=type(jArr[0][0] )resultArray=empty(arrShape,dtype=arrType)fori,colsinenumerate(jArr[:] ):resultArray[i,:]=cols[:]returnresultArray# 2D case, not so slowsourceDat=arange(1000000,dtype=double).reshape((1000...
x = np.arange(0,10,0.1) y_1 = np.sin(x) y_2 = np.sin(x+5) y_3 = np.sin(x+10) y_4 = np.sin(x+15) y_5 = np.sin(x+20) df = pd.DataFrame({'X':x,'y_1':y_1,'y_2':y_2, 'y_3':y_3,'y_4':y_4,'y_5':y_5}) ...