yym68686

import osimport numpy as npimport matplotlib.pyplot as pltdef Euclidean_distance(point1, point2): #计算两个点之间的欧几里得距离 return np.sqrt(np.sum((point1 - point2) ** 2))def get_Euclidean_distance(point, dataSet): # 得到一个点到数据集里面所有点的距离 distanceSet = []

linearRegCostFunction.mJ = 1 / (2 * m) * sum((X * theta - y) .^ 2) + lambda / (2 * m) * (sum(theta .* theta) - theta(1) * theta(1));gradient = 1 / m * X' * (X * theta - y); %没有sumgrad = gradient + lambda / m * theta;grad(1) = gradient(1); learningCur

nnCostFunction.ma1 = [ones(m, 1) X]; %5000x401z2 = a1 * Theta1'; %5000x25 Theta1 25*401a2 = sigmoid(z2); %5000x25a2 = [ones(m, 1) a2]; %5000x26z3 = a2 * Theta2'; %5000x10 Theta2 10×26a3 = sigmoid(z3); %5000x10h = a3;

lrCostFunction.mfunction [J, grad] = lrCostFunction(theta, X, y, lambda)%LRCOSTFUNCTION Compute cost and gradient for logistic regression with %regularization% J = LRCOSTFUNCTION(theta, X, y, la...

plotData.mfunction plotData(X, y)%PLOTDATA Plots the data points X and y into a new figure % PLOTDATA(x,y) plots the data points with + for the positive examples% and o for the negative exampl...

function [X_norm, mu, sigma] = featureNormalize(X)%FEATURENORMALIZE Normalizes the features in X % FEATURENORMALIZE(X) returns a normalized version of X where% the mean value of each feature is...

基础知识1.数学数学是学不完的，也没有几个人能像博士一样扎实地学好数学基础，入门人工智能领域，其实只需要掌握必要的基础知识就好。AI的数学基础最主要是高等数学、线性代数、概率论与数理统计三门课程，这三门课程是本科必修的。这里整理了一个简易的数学入门文章。数学基础：高等数学https://zhuanlan.zhihu.com/p/36311622数学基础：线性代数https://zhu...