m0_61403154的博客

原创 请对我的服务方法二

原创 而二哥如果仍让人阿

原创 2476218

import gym, os from itertools import count import paddle import paddle.nn as nn import paddle.optimizer as optim import paddle.nn.functional as F from paddle.distribution import Categorical print(paddle.__version__) 2.0.2 device = paddle.get_device() e

原创 2476218

import paddle from paddle.nn import Linear import paddle.nn.functional as F import os import numpy as np import matplotlib.pyplot as plt /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Usi

原创 2476218

import paddle import paddle.nn.functional as F from paddle.vision.transforms import ToTensor import numpy as np import matplotlib.pyplot as plt print(paddle.__version__) transform = ToTensor() cifar10_train = paddle.vision.datasets.Cifar10(mode='train',

原创 Untitled20

import numpy as np import math from numpy import matrix as mat from sympy import * """ 这回是真的一点没看懂，搜了个函数就用了 文件要求的线代水平超过我现有水平了，手算完全不会 """ '\n这回是真的一点没看懂，搜了个函数就用了\n\n文件要求的线代水平超过我现有水平了，手算完全不会\n\n' X0 = (2.5+0.5+2.2+1.9+3.1+2.3+2.0+1.0+1.5+1.1) / 10 Y0 = 1

原创 Viterbi

import numpy as np #条件粘上，根据理论推导的结果,晴雨雨的概率最高 states = ('Rainy', 'Sunny') observations = ('walk', 'shop', 'clean') start_probability = {'Rainy': 0.6, 'Sunny': 0.4} transition_probability = { 'Rainy' : {'Rainy': 0.7, 'Sunny': 0.3}, 'Sunny' : {'Rai

原创 2021-09-29

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原创 Untitled13

''' 改进欧拉算法 ''' import numpy as np import math #定义函数 def f(x,y): return 1.1*y-0.2*x/y def f1(x,y): return y-2*x/y #构造Euler def Euler(x0,y0,t,f,f1): yi=y0 xi=x0 for i in range(1,100): x=xi+t*i yp=yi+t*f(xi,yi) .

原创 Untitled12

''' 尝试下写注释的感觉 ''' import numpy as np import math import random ''' #生成随机函数 def random_number(a,b): return (b-a)*random.random+a ''' #生成矩阵 def makwmatrix(m,n,fill =0.0): a =[] for i in range(20): a.append([fill]*n) return a

原创 2021-09-17

原创 2021-09-15

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原创 Perception

import numpy as np '''''' class per(object): def __init__(self,input_num,step): self.step = step self.weights = [0.0 for _ in range(input_num)] self.bias = 0.0 '''''' def train(self,input_vec

原创 costFunction

import numpy as np import matplotlib import math x1 = np.array([[2104,3],[1600,3],[2400,3],[1416,2]]) x2 = np.array([3,3,3,2]) t = np.array([400,330,369,232]) alpha = 0.01 th0 = np.random.random() th1 = np.random.random() th2 = np.random.random()

原创 Numpy

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