J_history=np.zeros((num_iters,1))foriterinrange(num_iters):# 对J求导,得到 alpha/m*(WX-Y)*x(i),(3,m)*(m,1)X(m,3)*(3,1)=(m,1)theta=theta-(alpha/m)*(X.T.dot(X.dot(theta)-y))J_history[iter]=computeCost(X,y,theta)returnJ_history,theta iterations=10000#迭代次数 alph...
Code Folders and files Name Last commit message Last commit date Latest commit pedrovma Merge pull request#171from pedrovma/main Feb 11, 2025 d68f3aa·Feb 11, 2025 History 764 Commits .github Fix file name Nov 8, 2024 ci add spsearch to docs, rm sphinx-bibtex pin ...
想了想,一开始那个博客里说,现成的code有两个,一个是R里面写好的SMOTERregression函数,另一个是python里的SMOTE函数,可以先自己给data分群,然后用SMOTE函数给稀疏群手动加点。那我们看看python包里面有没有法子。 看了一下python包,有不少under-sampling的方法,可惜都是对classfication问题用的。R里面为什么没有,我...
If you are one of those who understand code better than formulas, try for yourself. In the following code, I’m using the algebraic solution to OLS to estimate the parameters of the model you just saw (I’m adding the intercept as the final variables, so the first parameter estimate ...
program-synthesissymbolic-regressionscientific-discoveryequation-discoveryai4sciencelarge-language-modelsai4codeai4mathllm-agent UpdatedNov 11, 2024 Python Official repository for the paper "Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery" ...
clc,clear x=load('ex4x.dat') y=load('ex4y.dat') [m, n] = size(x); x = [ones(m, 1), x];%增加一列 % find returns the indices of the % rows meeting the specified condition pos = find(y == 1); neg = find(y == 0); % Assume the features are in the 2nd and 3rd ...
In Machine Learning, predicting the future is very important.How Does it Work?Python has methods for finding a relationship between data-points and to draw a line of linear regression. We will show you how to use these methods instead of going through the mathematic formula....
For more information on the SDK v2, see What is the Azure Machine Learning Python SDK v2 and the SDK v2 reference. Train a linear regression model that predicts car prices using the Azure Machine Learning designer. This tutorial is part one of a two-part series. This tutorial uses ...
Exercise: Using your code from "Python Basics", implement sigmoid(). As you've seen in the figure above, you need to compute $sigmoid( w^T x + b) = \frac{1}{1 + e{-(wT x + b)}}$ to make predictions. Use np.exp(). # GRADED FUNCTION: sigmoid def sigmoid(z): """ Compu...
Logistic regression, despite its name, is a linear model for classification rather than regression. Logistic regression is also known in the literature as logit regression, maximum-entropy classification (MaxEnt) or the log-linear classifier. In this model, the probabilities describing the possible out...