zzl150298的博客

原创 LDA算法

原创 决策树作业

原创 2021-10-21

import gym, osfrom itertools import countimport paddleimport paddle.nn as nnimport paddle.optimizer as optimimport paddle.nn.functional as Ffrom paddle.distribution import Categoricalprint(paddle.version)device = paddle.get_device()env = gym.make(

原创 2021-09-26

import paddlefrom paddle.nn import Linearimport paddle.nn.functional as Fimport osimport numpy as npimport matplotlib.pyplot as plttrain_dataset = paddle.vision.datasets.MNIST(mode=‘train’)train_data0 = np.array(train_dataset[0][0])train_label_0 =

原创 2021-09-17神经网络反向传播

转载 python实现感知器

from __future__ import print_functionfrom functools import reduceclass VectorOp(object): def dot(x, y): """ 计算两个向量x和y的内积 """ # 首先把x[x1,x2,x3...]和y[y1,y2,y3,...]按元素相乘 # 变成[x1*y1, x2*y2, x3*y3] # 然后利用re

原创 监督学习（房产）

import numpy as npimport mathimport randomx1= np.array([2104,1600,2400,1416,3000])x2 = np.array([3,3,3,2,4])t = np.array([400,330,369,232,540])theta0=random.random()theta1=random.random()theta2=random.random()alpla = 0.1e = 0.0000001k0=random.ra

原创 zzl剩余作业

import numpy as npa1=np.random.choice(7,5)a1array([6, 5, 2, 4, 6])a2=np.random.choice([0,1,2,3,4,5,6],5)a2array([0, 1, 6, 3, 5])a3=np.random.choice(np.array([0,1,2,3,4,5,6]),5)a3array([2, 0, 5, 0, 6])a4=np.random.choice([0,1,2,3,4,5,6],

原创 np.prod()计算元素乘积

x=np.array([[1,2,3],[2,3,4]])np.prod(x)144np.prod(x,axis=1)array([ 6, 24])np.prod(x,axis=0)array([ 2, 6, 12])

原创 np.pad()函数进行padding操作

import numpy as npA= np.arange(95,99).reshape(2,2)Aarray([[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,

原创 np.newaxis在特定位置增加一个维度

import numpy as npc=np.array([1,2,5,4])c[:,np.newaxis]array([[1], [2], [5], [4]])c[np.newaxis,:]array([[1, 2, 5, 4]])

原创 np.meshgrid()快速生成网格

import numpy as npx=np.array([1,3,5])y=np.array([4,6])XX,YY=np.meshgrid(x,y)XXarray([[1, 3, 5], [1, 3, 5]])YYarray([[4, 4, 4], [6, 6, 6]])

原创 np.hstack()和np.vstack()用于堆叠矩阵

import numpy as npx=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]])

原创 np.full生成形为shape的元素全为val的矩阵

import numpy as npc=np.array([[1,2],[3,4]])carray([[1, 2], [3, 4]])c.astype(np.float32)array([[1., 2.], [3., 4.]], dtype=float32)

原创 np.argmax

import numpy as npa=np.array([[1,1,1],[2,2,2],[0,3,6]])aarray([[1, 1, 1], [2, 2, 2], [0, 3, 6]])b1=np.argmax(a)b18b2=np.argmax(a,axis=0)b2array([1, 2, 2], dtype=int64)b3=np.argmax(a,axis=1)b3array([0, 0, 2], dtype=int6

原创 insert()函数

import numpy as npmatrix=[ [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) >>>>q1>>>> [[ 1 2 3 4] [ 1 1 1 1] [ 5 6 7 8] [ 9 10

原创 delete()函数

import numpy as npmatrix=[ [1,2,3,4], [5,6,7,8], [9,10,11,12]]p1=np.delete(matrix,1,0)print(' >>>>p1>>>>\n' ,p1) >>>>p1>>>> [[ 1 2 3 4] [ 9 10 11 12]]p2=np.delete(matrix,1,1)prin

原创 append()函数

import numpy as npmatrix=[ [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) >>>>m1>>>> [[ 1 2 3 4] [ 5 6 7 8] [ 9 10 11 12] [

原创 numpy作业

```pythonc = np.arange(1,13).reshape(6,2)``` --------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-1-267063bef37c>...