numpy计算两二维数组距离

利用numpy可以很方便的计算两个二维数组之间的距离。二维数组之间的距离定义为：X的维度为(a,c),Y的维度为(b,c),Z为X到Y的距离数组,维度为(a,b)。且Z[0,0]是X[0]到Y[0]的距离。Z(m,n)为X[m]到Y[n]的距离。如下图所示。

代码如下:

#computer the distance between text point x and train point x_trainimport numpy as npX = np.random.random((3,2))X_train = np.random.random((5,2))print('X:')print(X)print('X_train:')print(X_train)dist = np.zeros((X.shape[0],X_train.shape[0]))print('--------------------')#way 1:use two loopsfor i in range(X.shape[0]):    for j in range(X_train.shape[0]):        dist[i,j] = np.sum((X[i,:]-X_train[j,:])**2)print('way 1 result:')print(dist)#way 2:use one loopsfor i in range(X.shape[0]):    dist[i,:] = np.sum((X_train-X[i,:])**2,axis=1)print('--------------------')print('way 2 result:')print(dist)#way 3:use no loopsdist = np.reshape(np.sum(X**2,axis=1),(X.shape[0],1))+ np.sum(X_train**2,axis=1)-2*X.dot(X_train.T)print('--------------------')print('way 3 result:')print(dist)

结果:

X:[[ 0.2892627   0.46569586] [ 0.75739842  0.33398985] [ 0.95221813  0.11192751]]X_train:[[ 0.10710814  0.27200357] [ 0.09082801  0.68378859] [ 0.62707459  0.81271073] [ 0.29597022  0.56699045] [ 0.42406439  0.93302285]]--------------------way 1 result:[[ 0.07069699  0.08694076  0.23453619  0.01030558  0.23656601] [ 0.42671974  0.56667526  0.24615799  0.26720526  0.46995212] [ 0.73983525  1.06901804  0.59681545  0.6377436   0.95314395]]--------------------way 2 result:[[ 0.07069699  0.08694076  0.23453619  0.01030558  0.23656601] [ 0.42671974  0.56667526  0.24615799  0.26720526  0.46995212] [ 0.73983525  1.06901804  0.59681545  0.6377436   0.95314395]]--------------------way 3 result:[[ 0.07069699  0.08694076  0.23453619  0.01030558  0.23656601] [ 0.42671974  0.56667526  0.24615799  0.26720526  0.46995212] [ 0.73983525  1.06901804  0.59681545  0.6377436   0.95314395]]