liuguaaa的博客

原创 k-means聚类算法

clear all;clc;tic; X=[ 91.8244186,22.3705287; 92.0069949,21.4282895; 90.3917291,23.764319; 90.3858206,22.4929908; 91.8780611,24.9015238; 88.6074625,24.3750433; 89.2166017,23.1468977; ]; k=2; [Idx,Ctrs,SumD,D]=kmeans(X,k); plot(X(Idx=

原创 随机梯度下降法

import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D e0 = 1 e1 = 1 e2 = 1 alpha = 0.01 theta0 = np.random.uniform(0, 1) theta1 = np.random.uniform(0, 1) theta2 = np.random.uniform(0, 1) theta = np.array([theta1, thet

原创 决策树中信息熵和信息增益的推导

原创 Actor Critic Method算法(强化学习)

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 device = paddle.get_device() env = gym.make("CartPole-v0") stat

原创 主成分分析PCA(降维)

import numpy as np #初始化矩阵 data = np.array([ [2.5,2.4], [0.5,0.7], [2.2,2.9], [1.9,2.2], [3.1,3.0], [2.3,2.7], [2, 1.6],

原创 全连接神经网络

import numpy as np import random import sys import math alpha = 0.01 #sigmoid函数 def sigmoid(x): return 1 / (1 + math.exp(-x)) #损失函数 def loss(): sigema1 = (y[0] - t[0]) ** 2 sigema2 = (y[1] - t[1]) ** 2 return sigema1 + sigema2 #t是预测值

原创 改进欧拉算法

import matplotlib.pyplot as plt y = 1 x = 0 xx = [0] * 101 yy = [0] * 101 k1 = y - 0.2 * x / y #k1存储当前点的斜率 k2 = 0.00 #k2存储下一个点的斜率 for i in range (1,101): if(i == 1): #第一次计算的时候只有k1所以需要用朴素欧拉计算第一次的值 y = 1.1 * y - 0.2 *

原创 全连接神经网络公式推导

原创 用segmoid函数拟合阶跃函数

import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D import math import random def sigmoid(x): return 1 / (1 + math.exp(-x)) e0 = 1 e1 = 1 e2 = 1 alpha = 0.001 omega0 = np.random.uniform(0,1) omega1 = np.random.uniform(0,1) omeg

翻译 numpy操纵代码的还原

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], [1...

原创 机器学习：用matplotlib绘制拟合曲线

import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D e0 = 1 e1 = 1 e2 = 1 alpha = 0.01 theta0 = np.random.uniform(0,1) theta1 = np.random.uniform(0,1) theta2 = np.random.uniform(0,1) theta = np.array([theta1, thet.