MATLAB一向是理工科学生的必备神器,但随着中美贸易冲突的一再升级,禁售与禁用的阴云也持续笼罩在高等学院的头顶。也许我们都应当考虑更多的途径,来辅助我们的学习和研究工作。 虽然PYTHON和众多模块也属于美国技术的范围,但开源软件的自由度毕竟不是商业软件可比拟的。 本文是一篇入门性文章,以麻省理工学院(MIT) 18.06版
return self.a_ * x_single + self.b_ def __repr__(self): return "SimpleLinearRegression1()" class SimpleLinearRegression2: """初始化Simple Linear Regression 模型""" def __init__(self): self.a_ = None self.b_ = None """根据训练数据集x_train,y_train训练Simple Linear Regression模型...
Fig. 10. An RLP for computing the value function value/1 of a Markov decision process. There is a finite set of states and actions, and the agent receives a reward reward(S,A) for performing and action A in state S, specified in a LogKB. The MDP instance that we used is the well...
Use the objective function -x(1)-x(2)/3. f = [-1 -1/3]; Set options to use the 'dual-simplex' algorithm. options = optimoptions('linprog','Algorithm','dual-simplex'); Solve the linear program and request the function value, exit flag, and output structure. [x,fval,exitflag,...
DE = matlabFunction(ODEF,'Vars',{t,Y,[A,G,k]}); A = 0.3;% Provide Correct Values G = 0.5;% Provide Correct Values k = 0.7;% Provide Correct Values h0 = 0.1;% Provide Correct Values tspan = [0 1];% Provide Correct Values ...
import math; def sum_of_gradient(x, y, thetas): """计算梯度向量,参数分别是x和y轴点坐标数据以及方程参数""" m = len(x); grad0 = 1.0 / m * sum([(thetas[0] + thetas[1] * x[i] - y[i]) for i in range(m)]) grad1 = 1.0 / m * sum([(thetas[0] + thetas[1] *...
Consider the generating function G(x)=F0+F1x+F2x2+…G(x)=F0+F1x+F2x2+… of the sequence. For G(x)G(x) it holds that G(x)=P(x)+∑k=1dakxkG(x),G(x)=P(x)+∑k=1dakxkG(x), where P(x)P(x) is some polynomial of degree less than dd used to calibrate the fact ...
1. logistic function中体现的几率性质 0x3:对数几率回归的优点性质 0x4:求解模型参数(w,b) 4. 广义线性回归 0x1:对数线性回归 0x2:广义线性模型 4. 线性判别分析(Fisher linear discriminant analysis) - 基于线性模型的线性投影判别算法 0x1:LDA的思想 ...
sparse:bool=False,)->Tensor:ifhas_torch_function_variadic(input,weight):returnhandle_torch_function...
defsquared_loss(y_hat, y): return(y_hat-y.view(y_hat.size()))**2/2 # ### 定义优化函数 # 在这里优化函数使用的是小批量随机梯度下降: # $$(\mathbf{w},b) \leftarrow (\mathbf{w},b) - \frac{\eta}{|\mathcal{B}|} \sum_{i \in \mathcal{B}} \partial_{(\mathbf{w},b)...