Python中的numpy函数的使用ones，zeros，eye

在看别人写的代码时，看到的不知道的函数，就在这里记下来。

原文是这样用的：

weights = ones((numfeatures,1))

在python中help（）：

import numpy as np
help(np.ones)

 Help on function ones in module numpy.core.numeric:

 ones(shape, dtype=None, order='C')
     Return a new array of given shape and type, filled with ones.

     Parameters
     ----------
     shape : int or sequence of ints
         Shape of the new array, e.g., ``(2, 3)`` or ``2``.
     dtype : data-type, optional
         The desired data-type for the array, e.g., `numpy.int8`.  Default is
         `numpy.float64`.
     order : {'C', 'F'}, optional
         Whether to store multidimensional data in C- or Fortran-contiguous
         (row- or column-wise) order in memory.

     Returns
     -------
     out : ndarray
         Array of ones with the given shape, dtype, and order.

     See Also
     --------
     zeros, ones_like

     Examples
     --------
     >>> np.ones(5)
     array([ 1.,  1.,  1.,  1.,  1.])

     >>> np.ones((5,), dtype=np.int)
     array([1, 1, 1, 1, 1])

     >>> np.ones((2, 1))
     array([[ 1.],
            [ 1.]])

     >>> s = (2,2)
     >>> np.ones(s)
     array([[ 1.,  1.],
            [ 1.,  1.]])

zeros：

 Help on built-in function zeros in module numpy.core.multiarray:

 zeros(...)
     zeros(shape, dtype=float, order='C')

     Return a new array of given shape and type, filled with zeros.

     Parameters
     ----------
     shape : int or sequence of ints
         Shape of the new array, e.g., ``(2, 3)`` or ``2``.
     dtype : data-type, optional
         The desired data-type for the array, e.g., `numpy.int8`.  Default is
         `numpy.float64`.
     order : {'C', 'F'}, optional
         Whether to store multidimensional data in C- or Fortran-contiguous
         (row- or column-wise) order in memory.

     Returns
     -------
     out : ndarray
         Array of zeros with the given shape, dtype, and order.

     See Also
     --------
     zeros_like : Return an array of zeros with shape and type of input.
     ones_like : Return an array of ones with shape and type of input.
     empty_like : Return an empty array with shape and type of input.
     ones : Return a new array setting values to one.
     empty : Return a new uninitialized array.

     Examples
     --------
     >>> np.zeros(5)
     array([ 0.,  0.,  0.,  0.,  0.])

     >>> np.zeros((5,), dtype=np.int)
     array([0, 0, 0, 0, 0])

     >>> np.zeros((2, 1))
     array([[ 0.],
            [ 0.]])

     >>> s = (2,2)
     >>> np.zeros(s)
     array([[ 0.,  0.],
            [ 0.,  0.]])

     >>> np.zeros((2,), dtype=[('x', 'i4'), ('y', 'i4')]) # custom dtype
     array([(0, 0), (0, 0)],
           dtype=[('x', '<i4'), ('y', '<i4')])

eye：

 Help on function eye in module numpy.lib.twodim_base:

 eye(N, M=None, k=0, dtype=<class 'float'>)
     Return a 2-D array with ones on the diagonal and zeros elsewhere.

     Parameters
     ----------
     N : int #行数
       Number of rows in the output.
     M : int, optional#列数
       Number of columns in the output. If None, defaults to `N`.
     k : int, optional#对角线的位置，0的时候是正对角线，+1就是对角线向上移，-1就是对角线向下移
       Index of the diagonal: 0 (the default) refers to the main diagonal,
       a positive value refers to an upper diagonal, and a negative value
       to a lower diagonal.
     dtype : data-type, optional
       Data-type of the returned array.

     Returns
     -------
     I : ndarray of shape (N,M)
       An array where all elements are equal to zero, except for the `k`-th
       diagonal, whose values are equal to one.

     See Also
     --------
     identity : (almost) equivalent function
     diag : diagonal 2-D array from a 1-D array specified by the user.

     Examples
     --------
     >>> np.eye(2, dtype=int)
     array([[1, 0],
            [0, 1]])
     >>> np.eye(3, k=1)
     array([[ 0.,  1.,  0.],
            [ 0.,  0.,  1.],
            [ 0.,  0.,  0.]])