sutiedan的博客

原创 随机梯度下降法

import numpy as npfrom mpl_toolkits.mplot3d import Axes3Dimport matplotlib.pyplot as pltalpha = 0.01a0 = np.random.uniform(0, 1)a1 = np.random.uniform(0, 1)a2 = np.random.uniform(0, 1)a = np.array([a1, a2])eps = 1e-4e0 = 1e1 = 1e2 = 1x = np.

原创 2021-11-02

原创 决策树推导

原创 强化学习——Actor Critic Method

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 = gy.

原创 paddlepaddle识别手写体训练

import paddlefrom paddle.nn import Linearimport paddle.nn.functional as Fimport osimport numpy as npimport matplotlib.pyplot as plt# train_dataset = paddle.vision.datasets.MNIST(mode='train')## train_data0 = np.array(train_dataset[0][0])# train_.

原创 主成分分析（PCA)原理

import numpy as npimport mathx = 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])ax = x.mean()ay = y.mean()xx = np.empty(10)yy = np.empty(10)for i in range(0, 10): xx[i] =.

原创 梯度下降法

import numpy as npimport matplotlib.pyplot as pltx=0y=1Y=1xx=np.zeros(100)yy=np.zeros(100)i=0for i in range(100): x=x+0.01 y=y+Y Y=y-(2*x/y) print(x,y) xx[i] = x yy[i] = yplt.plot(xx,yy)结果0.01 20.02 3.990.03 7.969.

原创 神经网络公式推导

原创 欧拉公式推导

import numpy as npimport matplotlib.pyplot as pltx=0y=1Y=1xx=np.zeros(100)yy=np.zeros(100)i=0for i in range(100): x=x+0.01 y=y+Y Y=y-(2*x/y) print(x,y) xx[i] = x yy[i] = yplt.plot(xx,yy)0.01 20.02 3.990.03 7.9699749.

原创 朴素贝叶斯，凉鞋问题

原创 机器学习---感知器

代码import numpy as npimport mathx = np.array([[0,0],[0,1],[1,0],[1,1]])y = np.array([0,1,1,1])alpha = 0.1w0 = np.random.random()w1 = np.random.random()w2 = np.random.random()w = np.array([w1,w2])eps = 1e-4e0 = 2e1 = 2e2 = 2i = 0while e0

原创 监督学习算法基础

监督学习----梯度下降法import numpy as npimport matplotlib.pyplot as pltx = np.array([[2104,3],[1600,3],[2400,3],[1416,2],[3000,4]])t = np.array([400,336,369,232,540])alpha = 0.1a0 = np.random.random()a1 = np.random.random()a2 = np.random.random()a =np.ar

原创 numpy入门-----广播机制

广播机制import numpy as npdata1 = np.array([1,2,3])data2 = np.array([1,2])print(data1,data2)[1 2 3] [1 2]加减乘除 1 1 + 1 2data1:[ 2 ] +(-,*,/) data2:[ 1] = [ 2 + 1 ] = [ 3 ]