Java教程

numpy作业一

本文主要是介绍numpy作业一,对大家解决编程问题具有一定的参考价值,需要的程序猿们随着小编来一起学习吧!
import numpy as np
c= np.arange(1,13).reshape(6,2)
c

array([[ 1,  2],
       [ 3,  4],
       [ 5,  6],
       [ 7,  8],
       [ 9, 10],
       [11, 12]])
np.vsplit(c,3)

[array([[1, 2],
        [3, 4]]),
 array([[5, 6],
        [7, 8]]),
 array([[ 9, 10],
        [11, 12]])]
d =c.T
d
array([[ 1,  3,  5,  7,  9, 11],
       [ 2,  4,  6,  8, 10, 12]])
np.hsplit(d,3)

[array([[1, 3],
        [2, 4]]),
 array([[5, 7],
        [6, 8]]),
 array([[ 9, 11],
        [10, 12]])]
m = np.arange(11,20)
n = np.arange(21,30)
e = np.dstack((m,n))
e
array([[[11, 21],
        [12, 22],
        [13, 23],
        [14, 24],
        [15, 25],
        [16, 26],
        [17, 27],
        [18, 28],
        [19, 29]]])
np.dsplit(e,2)
e
array([[[11, 21],
        [12, 22],
        [13, 23],
        [14, 24],
        [15, 25],
        [16, 26],
        [17, 27],
        [18, 28],
        [19, 29]]])
import numpy as np
import matplotlib.pyplot as plt
x = np.array([[2,3],[4,6],[7,8],[12,15]])
t = np.array([5,8,14,17])
alpha = 0.1
theta0 = np.random.random()
theta1 = np.random.random()
theta2 = np.random.random()
theta = np.array([theta1,theta2])

eps = 1e-4
e0 = 9
e1 = 9
e2 = 9
while e0 >= eps or e1 >=eps or e2>=eps:
    i = 0
    e0 = (np.sum((x[:,i]*theta[i]))+theta0*1 -t[i])
    e1 = e0*x[1,i]
    e2 = e0*x[2,i]
    theta0 = theta0 - alpha*e0
    theta1 = theta1 -alpha*e1
    theta2 = theta2 -alpha*e2
    i +=1import numpy as np
import math
import random


x = np.array([[2104], [1600], [2400], [1416], [3000]])
x1 = np.array([[3],[3],[3],[2],[4]])
t = np.array([[400], [330], [369], [232], [540]])
a = 0.0000100

error = 0.00001

dert = random.random()
dert1 = random.random()
dert2 = random.random()

sit = random.random()
sit2 = random.random()
sit3 = random.random()

for i in range(0, 5):
     while (dert > error or dert1 > error or dert2 > error):
        dert = (sit+sit2*x[i]+sit3*x1[i]-t[i])*1

        dert1 = (sit + sit2 * x[i] + sit3 * x1[i] - t[i]) * x[i]

        dert2 = (sit + sit2 * x[i] + sit3 * x1[i] - t[i]) * x1[i]

        sit = sit - dert * a
        sit2 = sit2 - dert1*a
        sit3 = sit3 - dert2*a

print(dert)
print(dert1)
print(dert2)

print(sit)
print(sit2)
print(sit3)
x3 = 3600
x4 = 5
h = sit + sit2*x3 + sit3*x4
print(h)


  File "<ipython-input-33-e5a5e2cad66a>", line 23
    i +=1import numpy as np
              ^
SyntaxError: invalid syntax
import numpy as np
a = np.array([1,1,1,1])
b = np.array([[1],[1],[1],[1]])
a+b
array([[2, 2, 2, 2],
       [2, 2, 2, 2],
       [2, 2, 2, 2],
       [2, 2, 2, 2]])
c = np.array([[1,1,1,1]])
c+b
array([[2, 2, 2, 2],
       [2, 2, 2, 2],
       [2, 2, 2, 2],
       [2, 2, 2, 2]])
W = np.array([[1,1,1],[2,2,2]])
W[:,1]
array([1, 2])
W[1]
array([2, 2, 2])
W[:,1] = np.array([5,5])
W
array([[1, 5, 1],
       [2, 5, 2]])
import numpy as np
matrix = [
[1,2,3,4],
[5,6,7,8],
[9,10,11,12]
]
p1 = np.delete(matrix, 1, 0)
print('>>>>p1>>>>\n',p1)
p2 = np.delete(matrix, 1, 1)
print('>>>>p2>>>>\n',p2)
p3 = np.delete(matrix, 1)
print('>>>>p3>>>>\n',p3)
p4 = np.delete(matrix, [0,1], 1)
print('>>>>p4>>>>\n',p4)
>>>>p1>>>>
 [[ 1  2  3  4]
 [ 9 10 11 12]]
>>>>p2>>>>
 [[ 1  3  4]
 [ 5  7  8]
 [ 9 11 12]]
>>>>p3>>>>
 [ 1  3  4  5  6  7  8  9 10 11 12]
>>>>p4>>>>
 [[ 3  4]
 [ 7  8]
 [11 12]]
import numpy as np
matrix = [
[1,2,3,4],
[5,6,7,8],
[9,10,11,12]
]
q1 = np.insert(matrix, 1, [1,1,1,1], 0)
print('>>>>q1>>>>\n',q1)
q2 = np.insert(matrix, 0, [1,1,1], 1)
print('>>>>q2>>>>\n',q2)
q3 = np.insert(matrix, 3, [1,1,1,1], 0)
print('>>>>q3>>>>\n',q3)
>>>>q1>>>>
 [[ 1  2  3  4]
 [ 1  1  1  1]
 [ 5  6  7  8]
 [ 9 10 11 12]]
>>>>q2>>>>
 [[ 1  1  2  3  4]
 [ 1  5  6  7  8]
 [ 1  9 10 11 12]]
>>>>q3>>>>
 [[ 1  2  3  4]
 [ 5  6  7  8]
 [ 9 10 11 12]
 [ 1  1  1  1]]
import numpy as np
matrix = [
[1,2,3,4],
[5,6,7,8],
[9,10,11,12]
]
m1 = np.append(matrix,[[1,1,1,1]],axis=0)
print('>>>>m1>>>>\n',m1)
m2 = np.append(matrix,[[1],[1],[1]],axis=1)
print('>>>m2>>>>\n',m2)
m3 = np.append(matrix,[1,1,1,1])
print('>>>>m3>>>>\n',m3)
>>>>m1>>>>
 [[ 1  2  3  4]
 [ 5  6  7  8]
 [ 9 10 11 12]
 [ 1  1  1  1]]
>>>m2>>>>
 [[ 1  2  3  4  1]
 [ 5  6  7  8  1]
 [ 9 10 11 12  1]]
>>>>m3>>>>
 [ 1  2  3  4  5  6  7  8  9 10 11 12  1  1  1  1]
import numpy as np
a1 = np.random.choice(7,5)
a1
array([5, 5, 5, 5, 2])
a2 = np.random.choice([0,1,2,3,4,5,6],5)
a2
array([0, 6, 6, 4, 0])
a3 = np.random.choice(np.array([0,1,2,3,4,5,6]),5)
a3
array([0, 6, 6, 6, 6])
a4 = np.random.choice([0,1,2,3,4,5,6],5,replace=False)
a4
array([4, 6, 3, 1, 2])
a5 = np.random.choice(np.array([0,1,2,3,4,5,6]),5,p=[0.1,0.1,0.1,0.1,0.1,0.1,0.4])
a5
array([3, 1, 6, 4, 2])
import numpy as np
a = np.array([[1,1,1],[2,2,2],[0,3,6]])
a
array([[1, 1, 1],
       [2, 2, 2],
       [0, 3, 6]])
b1 = np.argmax(a) 
b1
8
b2 = np.argmax(a, axis=0) 
b2
array([1, 2, 2], dtype=int32)
b3 = np.argmax(a, axis=1) 
b3
array([0, 0, 2], dtype=int32)
import numpy as np
y1 = np.linspace(-10.0,10.0)
y1
array([-10.        ,  -9.59183673,  -9.18367347,  -8.7755102 ,
        -8.36734694,  -7.95918367,  -7.55102041,  -7.14285714,
        -6.73469388,  -6.32653061,  -5.91836735,  -5.51020408,
        -5.10204082,  -4.69387755,  -4.28571429,  -3.87755102,
        -3.46938776,  -3.06122449,  -2.65306122,  -2.24489796,
        -1.83673469,  -1.42857143,  -1.02040816,  -0.6122449 ,
        -0.20408163,   0.20408163,   0.6122449 ,   1.02040816,
         1.42857143,   1.83673469,   2.24489796,   2.65306122,
         3.06122449,   3.46938776,   3.87755102,   4.28571429,
         4.69387755,   5.10204082,   5.51020408,   5.91836735,
         6.32653061,   6.73469388,   7.14285714,   7.55102041,
         7.95918367,   8.36734694,   8.7755102 ,   9.18367347,
         9.59183673,  10.        ])
y2 = np.linspace(1,10,10)
y2
array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.])
y3 = np.linspace(1,10,10,endpoint=False) 
y3
array([1. , 1.9, 2.8, 3.7, 4.6, 5.5, 6.4, 7.3, 8.2, 9.1])
y4= np.linspace(1, 10, 6, retstep=True) 
y4
(array([ 1. ,  2.8,  4.6,  6.4,  8.2, 10. ]), 1.8)
import numpy as np
x = np.array([[1,2,3],[4,5,6],[1,2,3]])
x.flatten()
array([1, 2, 3, 4, 5, 6, 1, 2, 3])
x.ravel()
array([1, 2, 3, 4, 5, 6, 1, 2, 3])
x.ravel('F')
array([1, 4, 1, 2, 5, 2, 3, 6, 3])
x.flatten('F')
array([1, 4, 1, 2, 5, 2, 3, 6, 3])
x.flatten()[1] = 20
x
array([[1, 2, 3],
       [4, 5, 6],
       [1, 2, 3]])
x.ravel()[1] = 20
x
array([[ 1, 20,  3],
       [ 4,  5,  6],
       [ 1,  2,  3]])
x.reshape(1,-1) 
array([[ 1, 20,  3,  4,  5,  6,  1,  2,  3]])
x = np.array([1,2,3,6,7,8])
x[None,:]
array([[1, 2, 3, 6, 7, 8]])
x[:,None]

array([[1],
       [2],
       [3],
       [6],
       [7],
       [8]])
x[np.newaxis, :]
array([[1, 2, 3, 6, 7, 8]])
x = np.array([[1,2,3],[2,3,4]])
np.prod(x)
144
np.prod(x,axis=1)
array([ 6, 24])
np.prod(x,axis=0)
array([ 2,  6, 12])
import numpy as np
x = np.array([[1,2,3],[-3,2,4],[5,-2,9]])
x
array([[ 1,  2,  3],
       [-3,  2,  4],
       [ 5, -2,  9]])
y1 = np.maximum(0,x)
y1
array([[1, 2, 3],
       [0, 2, 4],
       [5, 0, 9]])
y2 = np.minimum(0,x)
y2
array([[ 0,  0,  0],
       [-3,  0,  0],
       [ 0, -2,  0]])
x1 = x.copy()
x1
array([[ 1,  2,  3],
       [-3,  2,  4],
       [ 5, -2,  9]])
x1[x1 < 0] = 0 
x1
array([[1, 2, 3],
       [0, 2, 4],
       [5, 0, 9]])
x2 = x.copy()
x2[x2 > 0] = 0 
x2
array([[ 0,  0,  0],
       [-3,  0,  0],
       [ 0, -2,  0]])
import numpy as np
x = np.array([[1,2,3],[-3,2,4],[5,-2,9]])
x
array([[ 1,  2,  3],
       [-3,  2,  4],
       [ 5, -2,  9]])
x1 = x.copy() 
x1[x1 > 0] = 0
x1
array([[ 0,  0,  0],
       [-3,  0,  0],
       [ 0, -2,  0]])
x
array([[ 1,  2,  3],
       [-3,  2,  4],
       [ 5, -2,  9]])
x2 = x 
x2
array([[ 1,  2,  3],
       [-3,  2,  4],
       [ 5, -2,  9]])
x2[x2>0] = 0
x2
array([[ 0,  0,  0],
       [-3,  0,  0],
       [ 0, -2,  0]])
x 
array([[ 0,  0,  0],
       [-3,  0,  0],
       [ 0, -2,  0]])
x = np.array([[1,2,3],[-3,2,4],[5,-2,9]])
x3 = x[2] 
x3
array([ 5, -2,  9])
x3[2] = 100 
x 
array([[  1,   2,   3],
       [ -3,   2,   4],
       [  5,  -2, 100]])
import numpy as np
x = np.array([[1,2,3],[4,5,6]])
np.zeros_like(x) 
array([[0, 0, 0],
       [0, 0, 0]])
import numpy as np
n = np.random.rand(3,4)
n
array([[0.13265741, 0.9591874 , 0.86838237, 0.50585336],
       [0.71872155, 0.62327226, 0.87040469, 0.30877465],
       [0.77806554, 0.9002527 , 0.64874138, 0.98064497]])
import numpy as np
x = np.random.randn(2,3)
x
array([[-0.09324644, -1.11793879,  0.66470165],
       [ 2.07949479, -0.65827137,  0.98875917]])
y = np.multiply(0.1,np.random.randn(2,3))+0.5
y
array([[0.42629478, 0.66925894, 0.47400818],
       [0.72116543, 0.49445974, 0.55346324]])
import numpy as np
z = np.random.randint(2,9,(2,3))
z
array([[8, 5, 2],
       [6, 7, 3]])
m = np.random.randint(9,size = (2,3))
m
array([[5, 4, 4],
       [7, 6, 0]])
x = 'You are right'
type(x)
str
b = np.array([[[1,2],[3,4]],[[3,4],[7,8]],[[4,5],[1,2]]])

b
array([[[1, 2],
        [3, 4]],

       [[3, 4],
        [7, 8]],

       [[4, 5],
        [1, 2]]])
A = np.arange(95,99).reshape(2,2)
A
array([[95, 96],
       [97, 98]])
np.pad(A,((3,2),(2,3)),'constant',constant_values = (0,0))
array([[ 0,  0,  0,  0,  0,  0,  0],
       [ 0,  0,  0,  0,  0,  0,  0],
       [ 0,  0,  0,  0,  0,  0,  0],
       [ 0,  0, 95, 96,  0,  0,  0],
       [ 0,  0, 97, 98,  0,  0,  0],
       [ 0,  0,  0,  0,  0,  0,  0],
       [ 0,  0,  0,  0,  0,  0,  0]])
np.pad(b, ((0,0),(1,1),(1,1)), 'constant', constant_values = 0)
array([[[0, 0, 0, 0],
        [0, 1, 2, 0],
        [0, 3, 4, 0],
        [0, 0, 0, 0]],

       [[0, 0, 0, 0],
        [0, 3, 4, 0],
        [0, 7, 8, 0],
        [0, 0, 0, 0]],

       [[0, 0, 0, 0],
        [0, 4, 5, 0],
        [0, 1, 2, 0],
        [0, 0, 0, 0]]])
import numpy as np
x = np.empty([3,2], dtype = int)
print (x)
[[0 0]
 [1 1]
 [1 1]]
import numpy as np
c = np.array([[1,2],[3,4]])
c
array([[1, 2],
       [3, 4]])
c.astype(np.float32)
array([[1., 2.],
       [3., 4.]], dtype=float32)
import numpy as np
x = np.array([1,3,5])
y = np.array([4,6])
XX,YY = np.meshgrid(x,y)
XX
array([[1, 3, 5],
       [1, 3, 5]])
YY
array([[4, 4, 4],
       [6, 6, 6]])
import numpy as np
x = np.array([[3,4,5],[1,3,4]])
y = np.array([[1,1,1],[2,2,2]])
np.hstack((x,y)) 
array([[3, 4, 5, 1, 1, 1],
       [1, 3, 4, 2, 2, 2]])
np.vstack((x,y)) 
array([[3, 4, 5],
       [1, 3, 4],
       [1, 1, 1],
       [2, 2, 2]])
import numpy as np
a = np.array([0.125,0.568,5.688])
np.round(a)
array([0., 1., 6.])
np.round(a,decimals = 2) 
array([0.12, 0.57, 5.69])
np.floor(a) 
array([0., 0., 5.])
np.ceil(a)
array([1., 1., 6.])
import numpy as np
c = np.array([1,2,5,4])
c[:,np.newaxis]
array([[1],
       [2],
       [5],
       [4]])
c[np.newaxis,:]
array([[1, 2, 5, 4]])
import numpy as np
a = np.array([[1,2,3],[4,5,6]])
a = np.array([[1,2,3,6],[4,5,6,6]])
a1 = a.reshape((1,2,4))
a1
array([[[1, 2, 3, 6],
        [4, 5, 6, 6]]])
b = np.array([[3,4,5,6],[1,2,3,4],[4,5,5,5]])
b
array([[3, 4, 5, 6],
       [1, 2, 3, 4],
       [4, 5, 5, 5]])
b1 = b.reshape((1,3,4)).transpose((1,0,2))
b1
array([[[3, 4, 5, 6]],

       [[1, 2, 3, 4]],

       [[4, 5, 5, 5]]])
a1
array([[[1, 2, 3, 6],
        [4, 5, 6, 6]]])
a1+b1
array([[[ 4,  6,  8, 12],
        [ 7,  9, 11, 12]],

       [[ 2,  4,  6, 10],
        [ 5,  7,  9, 10]],

       [[ 5,  7,  8, 11],
        [ 8, 10, 11, 11]]])
c = np.array([[[1,2,5],[3,4,6]],[[4,5,6],[7,8,9]]])
c
array([[[1, 2, 5],
        [3, 4, 6]],

       [[4, 5, 6],
        [7, 8, 9]]])
c.transpose(1,0,2) 
array([[[1, 2, 5],
        [4, 5, 6]],

       [[3, 4, 6],
        [7, 8, 9]]])
c.transpose(1,2,0)
array([[[1, 4],
        [2, 5],
        [5, 6]],

       [[3, 7],
        [4, 8],
        [6, 9]]])
import numpy as np
a = np.array([2,2,3,4,5,5,6,7])
a[0:7:2]
array([2, 3, 5, 6])
import numpy as np
a = np.array([2,2,3,4,5,5,6,7])
a[0::2]
array([2, 3, 5, 6])
a[::-1]
array([7, 6, 5, 5, 4, 3, 2, 2])
import numpy as np
a = np.array([2,2,3,4,5,5,6,7])
s = slice(0,7,2)
a[s]
array([2, 3, 5, 6])

                    
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