numpy基础

numpy二维数组切片

row_r1 = a[1, :]    # Rank 1 view of the second row of a    这里是运用行列的方法，输出的某行的数据
row_r2 = a[1:2, :]  # Rank 2 view of the second row of a   这里是运用索引的方法，输出的元素[[]]
print(row_r1, row_r1.shape)  # Prints "[5 6 7 8] (4,)"
print(row_r2, row_r2.shape)  # Prints "[[5 6 7 8]] (1, 4)"

col_r1 = a[:, 1]
col_r2 = a[:, 1:2]
print(col_r1, col_r1.shape)  # Prints "[ 2  6 10] (3,)"
print(col_r2, col_r2.shape)# Prints "[[2],[6],[10]] (3, 1)"  列的话和行也一样

numpy横列索引

import numpy as np
a = np.array([[1,2], [3, 4], [5, 6]])
print(a[[0, 1, 2], [0, 1, 0]])                                    # Prints "[1 4 5]"        相当于输出a[0,0],a[1,1],a[2,0]

print(np.array([a[0, 0], a[1, 1], a[2, 0]]))               # Prints "[1 4 5]"

print(a[[0, 0], [1, 1]])  # Prints "[2 2]"  [0,1],[0,1]

print(np.array([a[0, 1], a[0, 1]]))                           # Prints "[2 2]"

numpy点乘

import numpy as np

x = np.array([[1,2],[3,4]])
y = np.array([[5,6],[7,8]])

v = np.array([9,10])
w = np.array([11, 12])

# Inner product of vectors; both produce 219
print(v.dot(w))
print(np.dot(v, w))

numpy axis

import numpy as np
x = np.array([[1,2],[3,4]])
print(np.sum(x))  # Compute sum of all elements; prints "10"
print(np.sum(x, axis=0))  # Compute sum of each column; prints "[4 6]"
print(np.sum(x, axis=1))  # Compute sum of each row; prints "[3 7]"

numpy转置(.T表示转置)

import numpy as np
x = np.array([[1,2], [3,4]])
print(x)    # Prints "[[1 2],[3 4]]"
print(x.T)  # Prints "[[1 3],[2 4]]"
v = np.array([1,2,3])
print(v)    # Prints "[1 2 3]"
print(v.T)  # Prints "[1 2 3]"

numpy添加向量

import numpy as np

# We will add the vector v to each row of the matrix x,
# storing the result in the matrix y
x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])
v = np.array([1, 0, 1])
y = np.empty_like(x)   # Create an empty matrix with the same shape as x
# Add the vector v to each row of the matrix x with an explicit loop
for i in range(4):
    y[i, :] = x[i, :] + v
# Now y is the following
# [[ 2  2  4]
#  [ 5  5  7]
#  [ 8  8 10]
#  [11 11 13]]
print(y)

numpy叠加

import numpy as np
# We will add the vector v to each row of the matrix x,
# storing the result in the matrix y
x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])
v = np.array([1, 0, 1])
vv = np.tile(v, (4, 1))   # v的四个叠加起来
print(vv)                 # Prints "[[1 0 1]
                          #          [1 0 1]
                          #          [1 0 1]
                          #          [1 0 1]]"
y = x + vv  # Add x and vv elementwise
print(y)  # Prints "[[ 2  2  4
          #          [ 5  5  7]
          #          [ 8  8 10]
          #          [11 11 13]]"

将两个数组加在一起遵循以下规则：              

如果阵列不具有相同的秩，则用1s预先准备低秩阵列的形状，直到两个形状具有相同的长度。              

如果这两个数组在维度上具有相同的大小，或者如果其中一个数组在该维度中具有大小1，则称为两个数组。              

如果它们在所有维度上兼容，则可以将数组加在一起。              

在加之后，每个数组表现得，好像它的尺寸等于两个输入数组的尺寸最大值。              

在任何维度中，一个数组的大小为1，而另一个数组的大小大于1，第一个数组的行为就好像它是沿着该维度复制的。

numpy reshape

import numpy as np
# Compute outer product of vectors
v = np.array([1,2,3])  # v has shape (3,)
w = np.array([4,5])    # w has shape (2,)
# To compute an outer product, we first reshape v to be a column
# vector of shape (3, 1); we can then broadcast it against w to yield
# an output of shape (3, 2), which is the outer product of v and w:
# [[ 4  5]
#  [ 8 10]
#  [12 15]]
print(np.reshape(v, (3, 1)) * w)    #np.reshape(a,(2,2))=a.reshape(2,2)就是将矩阵a变成2*2,但是元素并不变