Gqyxzt的博客

原创 梯度下降算法的实现

import numpy as np import os import matplotlib.pyplot as plt w = np.array([5, 7, 4]) sample = np.random.randint(0, 10, (100, 3)) y = np.dot(sample, w) y = y + np.random.randn() * 1 + 8 print(y) w_zz = np.array([1, 1, 1, 1]) learn_rating = 0.0001 p = 0 w =

原创 欧拉算法的实现

代码 from matplotlib import pyplot as plt import numpy as np h = 0.01 yy = [] xx = [] yy.append(1) xx.append(0) def y1(m,n): return m - 2*n/m for i in range(100): k1 = y1(yy[i],xx[i]) y = yy[i] + hy1(yy[i],xx[i]) xx.append(xx[i]+h) k2 = y1(y,xx[i+1]) yy.appe

原创 电磁场天线

原创 2021-11-12

import pandas as pd import matplotlib.pyplot as plt import numpy as np data = pd.read_csv(‘ex1data1.txt’,header = None,names=[‘Population’,‘Profits’]) data.head() data.describe() fig,axes = plt.subplots() data.plot(kind=‘scatter’,x=‘Population’,y=‘Profits’

原创 决策树（西瓜）

原创 2021-10-26

原创 演员–评论家算法

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__) device = paddle.get_device() env = gym

原创 2021-10-19

import paddle from paddle.nn import Linear import paddle.nn.functional as F import os import numpy as np import matplotlib.pyplot as plt train_dataset = paddle.vision.datasets.MNIST(mode=‘train’) train_data0 = np.array(train_dataset[0][0]) train_lable_0 =

原创 2021-09-17

原创 2021-09-11

原创 波士顿房价预测

房价预测 import numpy as np x = np.array([[2104,3], [1600,3], [2400,3], [2416,3], [3000,4]]) t = np.array([400,330,369,232,240 ]) a0 = np.random.random() a1 = np.random.random() a2 = np.random.random() a = np.array([a1,a2]) esp = 1e-4 e0 = 6 e1 = 6 e2 = 6 whil

原创 2021-09-03

numpy中有哪些简单且实用的操作 1、数组拆分 垂直拆分：numpy.vsplit(数组，份数)->(数组片段) 水平拆分：numpy.hsplit(数组，份数)->(数组片段) numpy.dsplit(数组，份数)->(数组片段) 灵活选择引用 numpy基本加减和取行操作 矩阵删除、插入、尾部添加操作(delete,insert,append) numpy矩阵操作主要有delete()、insert()、append()等函数，分别执行删除、插入和添加的操作，注意app