1importnumpy as np23a = np.array([[1,2],[3,4]])4a.shape5print(a)6>>>7"""8(2L, 2L)9[[1 2]10[3 4]]11"""12#如果需要在数组上增加维度,输入需要增添维度的轴即可,注意index从零还是13a_add_dimension = np.expand_dims(a,axis=0)14a_add_dimension.shape15>>> (1L, 2L, 2L)1617...
a = np.array([1, 2, 3]) b = np.array([(1, 2, 3), (4, 5, 6)]) print(np.add(a, b)) >>> [[2 4 6] [5 7 9]] #Example of np.roots #Consider a polynomialfunction(x-1)^2 = x^2 - 2*x + 1 #Whose roots are 1,1 >>...
add:数组对应元素相加 subtract:数组对应元素相减 multiply:数组元素相乘 divide:数组元素相除 power:计算A B A^BAB,其中A AA为第一个数组中的元素,B BB为第二个数组中的元素 mod:元素级求模 6 其他方法 (1)转置 进行矩阵运算时,经常需要该操作,如利用np.dot计算矩阵内积X T X X^TXXTX。数组不仅有transpos...
>>> a = array([4.,2.]) >>> b = array([2.,8.]) >>> a[:,newaxis] array([[4.], [2.]]) >>> column_stack((a[:,newaxis],b[:,newaxis])) array([[4., 2.], [2., 8.]]) >>> vstack((a[:,newaxis],b[:,newaxis])) array([[4.], [2.], [2.], [8.]]) r...
>>> a = array([1,2,3,4]) # RIGHT 数组将序列包含序列转化成二维的数组,序列包含序列包含序列转化成三维数组等等。 >>> b = array( [ (1.5,2,3), (4,5,6) ] ) >>> b array([[ 1.5, 2. , 3. ], [ 4. , 5. , 6. ]]) 数组类型可以在创建时显示指定 >>> c = array...
# pytorch中,处理图片需要一个batch一个batch的操作,需要准备的数据格式是[batch_size,n_channels,hight,width]img4=test_transform(image).unsqueeze(0)print('add a dimension: ',img4.shape) 输出如下: ---Start---type(cv2_image):<class'numpy.ndarray'>cv2_image.size:(220,178,3)type(image):<...
加：“+” 或者np.add(a, b)减：“-” 或者np.subtract(a, b)乘：“*” 或者np.multiply(...
dump(file) :将矩阵存储为指定文件,可以通过pickle.loads()或者numpy.loads()如:a.dump(‘d:\\a.txt’) dumps() :将矩阵的数据转存为字符串. fill(value) :将矩阵中的所有元素填充为指定的value flatten([order]) :将矩阵转化为一个一维的形式,但是还是matrix对象 ...
Note that, in linear algebra, the dimension of a vector refers to the number of entries in an array. In NumPy, it instead defines the number of axes. For example, a 1D array is a vector such as[1, 2, 3], a 2D array is a matrix, and so forth. ...
# If a 1d array is added to a 2d array (or the other way), NumPy # chooses the array with smaller dimension and adds it to the one # with bigger dimension a = np.array([1, 2, 3]) b = np.array([(1, 2, 3), (4, 5, 6)]) print(np.add(a, b)) >>> [[2 4 6] ...