weixin_45756392的博客

原创 隐马尔可夫链

import numpy as npimport mathclass Viterbi: def __init__(self, o, A, B, pi, index, N, M): self.N = self.B.shape[1] #shape[1]列数 self.M = self.B.shape[0] #shape[0]行数 self.o = o #可观测状态 self.A = A #状态转移概率矩阵 self.B

原创 随机梯度下降

import numpy as npimport mathimport randomtheta0 = np.random.random()theta1 = np.random.random()theta2 = np.random.random()alpha = 0.01x1 = np.array([2104,1600, 2400,1416,3000])x2 = np.array([3,3,3,2,4])t = np.array([400,330,369,232,540])eps = m

原创 LDA初理解

原创 2021-10-26

原创 演员—评论家算法

import gym, osfrom itertools import countimport paddleimport paddle.nn as nnimport paddle.optimizer as optimimport paddle.nn.functional as Ffrom paddle.distribution import Categoricaldevice = paddle.get_device()env = gym.make("CartPole-v0") sta

原创 手写体识别

#加载飞桨和相关类库import paddlefrom paddle.nn import Linearimport paddle.nn.functional as Fimport osimport numpy as npimport matplotlib.pyplot as plt# 设置数据读取器，API自动读取MNIST数据训练集train_dataset = paddle.vision.datasets.MNIST(mode='train')train_data0 = np.arr

原创 使用卷积神经网络进行图像分类

import paddleimport paddle.nn.functional as Ffrom paddle.vision.transforms import ToTensorimport numpy as npimport matplotlib.pyplot as plttransform = ToTensor()cifar10_train = paddle.vision.datasets.Cifar10(mode='train',

原创 主成分分析

import numpy as npfrom numpy.linalg import eigx = np.array([2.5, 0.5, 2.2, 1.9, 3.1, 2.3, 2, 1, 1.5, 1.1])y = np.array([2.4, 0.7, 2.9, 2.2, 3.0, 2.7, 1.6, 1.1, 1.6, 0.9])x_eve = np.sum(x)/10y_eve = np.sum(y)/10x_norm = np.array(x)-x_evey_norm = np.

原创 改进欧拉算法

import numpy as npimport matplotlib.pyplot as plth = 0.1y = 1x = 0X = np.zeros(100)Y = np.zeros(100)for i in range(1, 100): y = 1.1*y-0.2*(x/y) y1 = 1.1*y-0.2*((x+0.1)/y) y2= 1.05*y-0.1*(x/y)+0.05*y1-0.1*((x+0.1)/y1) x+=h X[i]=

原创 反向传播算法公式推导，

原创 欧拉法解初值

import numpy as npimport mathimport matplotlib.pyplot as plth=0.1y=1x=0X=np.zeros(100)Y=np.zeros(100)for i in range(1,100): y=1.1*y-0.2*(x/y) x+=h X[i]=x Y[i]=y print(x,y) plt.plot(X,Y,color='red')plt.scatter(x,y)plt.sh

原创 感知器算法

import numpy as npimport mathimport randomalpha = 0.01x = np.array([[1,1],[0,0],[0,1],[1,0]])t = np.array([1,0,0,0])w1 = 0w2 = 0eps = 1e-4e1 = 1e2 = 1sigmod = 1#激活函数sigmod()for i in range(0,4): if e1 <=eps or e2 <= eps: w1

原创 numpy作业

1.数组拆分##垂直拆分：numpy.vsplit(数组，份数)->(数组片段)numpy.anange(起点，终点).reshape()arange()用于生成一维数组reshape()将一维数组转换为多维数组import numpy as npc = np.arange(1,13).reshape(6,2)carray([[ 1, 2], [ 3, 4], [ 5, 6], [ 7, 8], [ 9, 10],

原创 梯度下降算法

import numpy as npimport mathimport randomimport matplotlibimport matplotlib.mlab as mlabimport matplotlib.pyplot as pltimport matplotlib.font_manager as fmfrom mpl_toolkits.mplot3d import Axes3D #绘制3D坐标的函数theta0 = np.random.random()theta1 = np.