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])
这篇关于numpy作业一的文章就介绍到这儿，希望我们推荐的文章对大家有所帮助，也希望大家多多支持为之网！
您可能喜欢
-
Springboot应用的多环境打包入门11-23
-
Springboot应用的生产发布入门教程11-23
-
Python编程入门指南11-23
-
Java创业入门：从零开始的编程之旅11-23
-
Java创业入门：新手必读的Java编程与创业指南11-23
-
Java对接阿里云智能语音服务入门详解11-23
-
Java对接阿里云智能语音服务入门教程11-23
-
JAVA对接阿里云智能语音服务入门教程11-23
-
Java副业入门：初学者的简单教程11-23
-
JAVA副业入门：初学者的实战指南11-23
-
JAVA项目部署入门：新手必读指南11-23
-
Java项目部署入门：新手必看指南11-23
-
Java项目部署入门：新手必读指南11-23
-
Java项目开发入门：新手必读指南11-23
-
JAVA项目开发入门：从零开始的实用教程11-23
栏目导航
