In [40]: a = np.array([[2,2], [2,3]]) In [41]: a.flatten() Out[41]: array([2, 2, 2, 3]) In [43]: a.reshape(-1) Out[43]: array([2, 2, 2, 3]) 但是像这种不规则维度的多维数组就不能转换成功了,还是本身 a = np.array([[[2,3]], [2,3]]) 转换成二维表示的...
in x64 systemsfloat64is equivalent todoublein C. On most systems, this has either an alignment of 4 or 8 bytes (and this can be controlled in GCC by the optionmalign-double). A variable is aligned in memory if its memory offset is a multiple of its alignment....
Equivalent to b[-1, :] array([40, 41, 42, 43]) 当用...时,省略未填写的切片 x[1, 2, ...] is equivalent to x[1, 2, :, :, :], x[..., 3] to x[:, :, :, :, 3] and x[4, ..., 5, :] to x[4, :, :, 5, :] c = np.array([[[ 0, 1, 2], # a ...
inv(C)# equivalent to C.I=> matrix([[0.+2.j ,0.-1.j ], [0.-1.5j,0.+0.5j]]) C.I * C => matrix([[1.00000000e+00+0.j,4.44089210e-16+0.j], [0.00000000e+00+0.j,1.00000000e+00+0.j]]) 行列式 linalg.det(C) => (2.0000000000000004+0j) linalg.det(C.I) => (0.50000...
>>> b[ : ,1] # equivalent to the previous example array([ 1, 11, 21, 31, 41]) >>> b[1:3, : ] # each column in the second and third row of b array([[10, 11, 12, 13], [20, 21, 22, 23]]) 当有些维度没有指定索引时,空缺的维度被默认为取所有元素。
Equivalent to b[-1, :] array([40, 41, 42, 43]) 中括号内的表达式 b[i]被视为 i 其次是尽可能多的实例 :根据需要代表 剩余的轴。 NumPy 还允许您使用点将其编写为 b[i, ...]. _ 点( ...) 代表产生一个所需的冒号 完整的索引元组。 例如,如果 x是一个数组,有 5 轴,然后 ...
Minimizing perplexity is equivalent to maximizing the probability of `words` under the `N`-gram model. It may also be interpreted as the average branching factor when predicting the next word under the language model. Parameters --- N : int The gram-size of the model to calculate perplexity ...
[0:5, 1] # each row in the second column of b array([ 1, 11, 21, 31, 41]) >>> b[ : ,1] # equivalent to the previous example array([ 1, 11, 21, 31, 41]) >>> b[1:3, : ] # each column in the second and third row of b array([[10, 11, 12, 13], [20, ...
byteswap(inplace) Swap the bytes of the array elements choose(choices[, out, mode]) :根据给定的索引得到一个新的数据矩阵(索引从choices给定) clip(a_min, a_max[, out]) :返回新的矩阵,比给定元素大的元素为a_max,小的为a_min compress(condition[, axis, out]) :返回满足条件的矩阵 ...