一、ex8.m

%% Machine Learning Online Class
%  Exercise 8 | Anomaly Detection and Collaborative Filtering
%
%  Instructions
%  ------------
%
%  This file contains code that helps you get started on the
%  exercise. You will need to complete the following functions:
%
%     estimateGaussian.m
%     selectThreshold.m
%     cofiCostFunc.m
%
%  For this exercise, you will not need to change any code in this file,
%  or any other files other than those mentioned above.
%%% Initialization
clear ; close all; clc%% ================== Part 1: Load Example Dataset  ===================
%  We start this exercise by using a small dataset that is easy to
%  visualize.
%
%  Our example case consists of 2 network server statistics across
%  several machines: the latency and throughput of each machine.
%  This exercise will help us find possibly faulty (or very fast) machines.
%fprintf('Visualizing example dataset for outlier detection.\n\n');%  The following command loads the dataset. You should now have the
%  variables X, Xval, yval in your environment
load('ex8data1.mat');%  Visualize the example dataset
plot(X(:, 1), X(:, 2), 'bx');
axis([0 30 0 30]);
xlabel('Latency (ms)');
ylabel('Throughput (mb/s)');fprintf('Program paused. Press enter to continue.\n');
pause%% ================== Part 2: Estimate the dataset statistics ===================
%  For this exercise, we assume a Gaussian distribution for the dataset.
%
%  We first estimate the parameters of our assumed Gaussian distribution,
%  then compute the probabilities for each of the points and then visualize
%  both the overall distribution and where each of the points falls in
%  terms of that distribution.
%
fprintf('Visualizing Gaussian fit.\n\n');%  Estimate my and sigma2
[mu sigma2] = estimateGaussian(X);%  Returns the density of the multivariate normal at each data point (row)
%  of X
p = multivariateGaussian(X, mu, sigma2);%  Visualize the fit
visualizeFit(X,  mu, sigma2);
xlabel('Latency (ms)');
ylabel('Throughput (mb/s)');fprintf('Program paused. Press enter to continue.\n');
pause;%% ================== Part 3: Find Outliers ===================
%  Now you will find a good epsilon threshold using a cross-validation set
%  probabilities given the estimated Gaussian distribution
% pval = multivariateGaussian(Xval, mu, sigma2);[epsilon F1] = selectThreshold(yval, pval);
fprintf('Best epsilon found using cross-validation: %e\n', epsilon);
fprintf('Best F1 on Cross Validation Set:  %f\n', F1);
fprintf('   (you should see a value epsilon of about 8.99e-05)\n\n');%  Find the outliers in the training set and plot the
outliers = find(p < epsilon);%  Draw a red circle around those outliers
hold on
plot(X(outliers, 1), X(outliers, 2), 'ro', 'LineWidth', 2, 'MarkerSize', 10);
hold offfprintf('Program paused. Press enter to continue.\n');
pause;%% ================== Part 4: Multidimensional Outliers ===================
%  We will now use the code from the previous part and apply it to a
%  harder problem in which more features describe each datapoint and only
%  some features indicate whether a point is an outlier.
%%  Loads the second dataset. You should now have the
%  variables X, Xval, yval in your environment
load('ex8data2.mat');%  Apply the same steps to the larger dataset
[mu sigma2] = estimateGaussian(X);%  Training set
p = multivariateGaussian(X, mu, sigma2);%  Cross-validation set
pval = multivariateGaussian(Xval, mu, sigma2);%  Find the best threshold
[epsilon F1] = selectThreshold(yval, pval);fprintf('Best epsilon found using cross-validation: %e\n', epsilon);
fprintf('Best F1 on Cross Validation Set:  %f\n', F1);
fprintf('# Outliers found: %d\n', sum(p < epsilon));
fprintf('   (you should see a value epsilon of about 1.38e-18)\n\n');
pause

二、ex8_cofi.m

%% Machine Learning Online Class
%  Exercise 8 | Anomaly Detection and Collaborative Filtering
%
%  Instructions
%  ------------
%
%  This file contains code that helps you get started on the
%  exercise. You will need to complete the following functions:
%
%     estimateGaussian.m
%     selectThreshold.m
%     cofiCostFunc.m
%
%  For this exercise, you will not need to change any code in this file,
%  or any other files other than those mentioned above.
%%% =============== Part 1: Loading movie ratings dataset ================
%  You will start by loading the movie ratings dataset to understand the
%  structure of the data.
%
fprintf('Loading movie ratings dataset.\n\n');%  Load data
load ('ex8_movies.mat');%  Y is a 1682x943 matrix, containing ratings (1-5) of 1682 movies on
%  943 users
%
%  R is a 1682x943 matrix, where R(i,j) = 1 if and only if user j gave a
%  rating to movie i%  From the matrix, we can compute statistics like average rating.
fprintf('Average rating for movie 1 (Toy Story): %f / 5\n\n', ...mean(Y(1, R(1, :))));%  We can "visualize" the ratings matrix by plotting it with imagesc
imagesc(Y);
ylabel('Movies');
xlabel('Users');fprintf('\nProgram paused. Press enter to continue.\n');
pause;%% ============ Part 2: Collaborative Filtering Cost Function ===========
%  You will now implement the cost function for collaborative filtering.
%  To help you debug your cost function, we have included set of weights
%  that we trained on that. Specifically, you should complete the code in
%  cofiCostFunc.m to return J.%  Load pre-trained weights (X, Theta, num_users, num_movies, num_features)
load ('ex8_movieParams.mat');%  Reduce the data set size so that this runs faster
num_users = 4; num_movies = 5; num_features = 3;
X = X(1:num_movies, 1:num_features);
Theta = Theta(1:num_users, 1:num_features);
Y = Y(1:num_movies, 1:num_users);
R = R(1:num_movies, 1:num_users);%  Evaluate cost function
J = cofiCostFunc([X(:) ; Theta(:)], Y, R, num_users, num_movies, ...num_features, 0);fprintf(['Cost at loaded parameters: %f '...'\n(this value should be about 22.22)\n'], J);fprintf('\nProgram paused. Press enter to continue.\n');
pause;%% ============== Part 3: Collaborative Filtering Gradient ==============
%  Once your cost function matches up with ours, you should now implement
%  the collaborative filtering gradient function. Specifically, you should
%  complete the code in cofiCostFunc.m to return the grad argument.
%
fprintf('\nChecking Gradients (without regularization) ... \n');%  Check gradients by running checkNNGradients
checkCostFunction;fprintf('\nProgram paused. Press enter to continue.\n');
pause;%% ========= Part 4: Collaborative Filtering Cost Regularization ========
%  Now, you should implement regularization for the cost function for
%  collaborative filtering. You can implement it by adding the cost of
%  regularization to the original cost computation.
%  %  Evaluate cost function
J = cofiCostFunc([X(:) ; Theta(:)], Y, R, num_users, num_movies, ...num_features, 1.5);fprintf(['Cost at loaded parameters (lambda = 1.5): %f '...'\n(this value should be about 31.34)\n'], J);fprintf('\nProgram paused. Press enter to continue.\n');
pause;%% ======= Part 5: Collaborative Filtering Gradient Regularization ======
%  Once your cost matches up with ours, you should proceed to implement
%  regularization for the gradient.
%%
fprintf('\nChecking Gradients (with regularization) ... \n');%  Check gradients by running checkNNGradients
checkCostFunction(1.5);fprintf('\nProgram paused. Press enter to continue.\n');
pause;%% ============== Part 6: Entering ratings for a new user ===============
%  Before we will train the collaborative filtering model, we will first
%  add ratings that correspond to a new user that we just observed. This
%  part of the code will also allow you to put in your own ratings for the
%  movies in our dataset!
%
movieList = loadMovieList();%  Initialize my ratings
my_ratings = zeros(1682, 1);% Check the file movie_idx.txt for id of each movie in our dataset
% For example, Toy Story (1995) has ID 1, so to rate it "4", you can set
my_ratings(1) = 4;% Or suppose did not enjoy Silence of the Lambs (1991), you can set
my_ratings(98) = 2;% We have selected a few movies we liked / did not like and the ratings we
% gave are as follows:
my_ratings(7) = 3;
my_ratings(12)= 5;
my_ratings(54) = 4;
my_ratings(64)= 5;
my_ratings(66)= 3;
my_ratings(69) = 5;
my_ratings(183) = 4;
my_ratings(226) = 5;
my_ratings(355)= 5;fprintf('\n\nNew user ratings:\n');
for i = 1:length(my_ratings)if my_ratings(i) > 0 fprintf('Rated %d for %s\n', my_ratings(i), ...movieList{i});end
endfprintf('\nProgram paused. Press enter to continue.\n');
pause;%% ================== Part 7: Learning Movie Ratings ====================
%  Now, you will train the collaborative filtering model on a movie rating
%  dataset of 1682 movies and 943 users
%fprintf('\nTraining collaborative filtering...\n');%  Load data
load('ex8_movies.mat');%  Y is a 1682x943 matrix, containing ratings (1-5) of 1682 movies by
%  943 users
%
%  R is a 1682x943 matrix, where R(i,j) = 1 if and only if user j gave a
%  rating to movie i%  Add our own ratings to the data matrix
Y = [my_ratings Y];
R = [(my_ratings ~= 0) R];%  Normalize Ratings
[Ynorm, Ymean] = normalizeRatings(Y, R);%  Useful Values
num_users = size(Y, 2);
num_movies = size(Y, 1);
num_features = 10;% Set Initial Parameters (Theta, X)
X = randn(num_movies, num_features);
Theta = randn(num_users, num_features);initial_parameters = [X(:); Theta(:)];% Set options for fmincg
options = optimset('GradObj', 'on', 'MaxIter', 100);% Set Regularization
lambda = 10;
theta = fmincg (@(t)(cofiCostFunc(t, Y, R, num_users, num_movies, ...num_features, lambda)), ...initial_parameters, options);% Unfold the returned theta back into U and W
X = reshape(theta(1:num_movies*num_features), num_movies, num_features);
Theta = reshape(theta(num_movies*num_features+1:end), ...num_users, num_features);fprintf('Recommender system learning completed.\n');fprintf('\nProgram paused. Press enter to continue.\n');
pause;%% ================== Part 8: Recommendation for you ====================
%  After training the model, you can now make recommendations by computing
%  the predictions matrix.
%p = X * Theta';
my_predictions = p(:,1) + Ymean;movieList = loadMovieList();[r, ix] = sort(my_predictions, 'descend');
fprintf('\nTop recommendations for you:\n');
for i=1:10j = ix(i);fprintf('Predicting rating %.1f for movie %s\n', my_predictions(j), ...movieList{j});
endfprintf('\n\nOriginal ratings provided:\n');
for i = 1:length(my_ratings)if my_ratings(i) > 0 fprintf('Rated %d for %s\n', my_ratings(i), ...movieList{i});end
end

三、estimateGaussian.m

function [mu sigma2] = estimateGaussian(X)
%ESTIMATEGAUSSIAN This function estimates the parameters of a
%Gaussian distribution using the data in X
%   [mu sigma2] = estimateGaussian(X),
%   The input X is the dataset with each n-dimensional data point in one row
%   The output is an n-dimensional vector mu, the mean of the data set
%   and the variances sigma^2, an n x 1 vector
% % Useful variables
[m, n] = size(X);% You should return these values correctly
mu = zeros(n, 1);
sigma2 = zeros(n, 1);% ====================== YOUR CODE HERE ======================
% Instructions: Compute the mean of the data and the variances
%               In particular, mu(i) should contain the mean of
%               the data for the i-th feature and sigma2(i)
%               should contain variance of the i-th feature.
%mu = mean(X);
sigma2 = var(X,opt=1);% =============================================================end

四、selectThreshold.m

function [bestEpsilon bestF1] = selectThreshold(yval, pval)
%SELECTTHRESHOLD Find the best threshold (epsilon) to use for selecting
%outliers
%   [bestEpsilon bestF1] = SELECTTHRESHOLD(yval, pval) finds the best
%   threshold to use for selecting outliers based on the results from a
%   validation set (pval) and the ground truth (yval).
%bestEpsilon = 0;
bestF1 = 0;
F1 = 0;stepsize = (max(pval) - min(pval)) / 1000;
for epsilon = min(pval):stepsize:max(pval)% ====================== YOUR CODE HERE ======================% Instructions: Compute the F1 score of choosing epsilon as the%               threshold and place the value in F1. The code at the%               end of the loop will compare the F1 score for this%               choice of epsilon and set it to be the best epsilon if%               it is better than the current choice of epsilon.%               % Note: You can use predictions = (pval < epsilon) to get a binary vector%       of 0's and 1's of the outlier predictionspredictions = (pval < epsilon);truePositives  = sum((predictions == 1) & (yval == 1));falsePositives = sum((predictions == 1) & (yval == 0));falseNegatives = sum((predictions == 0) & (yval == 1));precision = truePositives / (truePositives + falsePositives);recall = truePositives / (truePositives + falseNegatives);F1 = (2 * precision * recall) / (precision + recall);% =============================================================if F1 > bestF1bestF1 = F1;bestEpsilon = epsilon;end
endend

五、cofiCostFunc.m

function [J, grad] = cofiCostFunc(params, Y, R, num_users, num_movies, ...num_features, lambda)
%COFICOSTFUNC Collaborative filtering cost function
%   [J, grad] = COFICOSTFUNC(params, Y, R, num_users, num_movies, ...
%   num_features, lambda) returns the cost and gradient for the
%   collaborative filtering problem.
%% Unfold the U and W matrices from params
X = reshape(params(1:num_movies*num_features), num_movies, num_features);
Theta = reshape(params(num_movies*num_features+1:end), ...num_users, num_features);% You need to return the following values correctly
J = 0;
X_grad = zeros(size(X));
Theta_grad = zeros(size(Theta));% ====================== YOUR CODE HERE ======================
% Instructions: Compute the cost function and gradient for collaborative
%               filtering. Concretely, you should first implement the cost
%               function (without regularization) and make sure it is
%               matches our costs. After that, you should implement the
%               gradient and use the checkCostFunction routine to check
%               that the gradient is correct. Finally, you should implement
%               regularization.
%
% Notes: X - num_movies  x num_features matrix of movie features
%        Theta - num_users  x num_features matrix of user features
%        Y - num_movies x num_users matrix of user ratings of movies
%        R - num_movies x num_users matrix, where R(i, j) = 1 if the
%            i-th movie was rated by the j-th user
%
% You should set the following variables correctly:
%
%        X_grad - num_movies x num_features matrix, containing the
%                 partial derivatives w.r.t. to each element of X
%        Theta_grad - num_users x num_features matrix, containing the
%                     partial derivatives w.r.t. to each element of Theta
%errors = (X*Theta' - Y) .* R;
regularizationTheta = lambda/2 * sum(sum(Theta.^2));
regularizationX = lambda/2 * sum(sum(X.^2));J = 1/2 * sum(sum(errors .^2)) + regularizationTheta + regularizationX;
X_grad = errors * Theta + lambda * X;
Theta_grad = errors' * X + lambda * Theta;% =============================================================grad = [X_grad(:); Theta_grad(:)];end

Machine Learning week 9 quiz: programming assignment-Anomaly Detection and Recommender Systems相关推荐

Machine Learning week 5 quiz: programming assignment-Multi-Neural Network Learning
一.ex4.m %% Machine Learning Online Class - Exercise 4 Neural Network Learning% Instructions % ------ ...
Machine Learning week 4 quiz: programming assignment-Multi-class Classification and Neural Networks
一.ex3.m %% Machine Learning Online Class - Exercise 3 | Part 1: One-vs-all% Instructions % --------- ...
Machine Learning week 8 quiz: programming assignment-K-Means Clustering and PCA
一.ex7.m %% Machine Learning Online Class % Exercise 7 | Principle Component Analysis and K-Means Clu ...
Machine Learning week 7 quiz: programming assignment-Support Vector Machines
一.ex6.m %% Machine Learning Online Class % Exercise 6 | Support Vector Machines % % Instructions % - ...
Machine Learning week 6 quiz: programming assignment-Regularized Linear Regression and Bias/Variance
一.ex5.m %% Machine Learning Online Class % Exercise 5 | Regularized Linear Regression and Bias-Varia ...
Machine Learning week 3 quiz: programming assignment-Logistic Regression
一.ex2.m: the main .m file to call other function files % matlab%% Machine Learning Online Class - Ex ...
Anomaly Detection and Recommender Systems
异常检测 import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns fr ...
[异常检测]Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos
Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos 单位: Deakin University 会 ...
Machine Learning week 9 quiz: Anomaly Detection
Anomaly Detection 5 试题 1. For which of the following problems would anomaly detection be a suitable ...

Machine Learning week 9 quiz: programming assignment-Anomaly Detection and Recommender Systems

一、ex8.m

二、ex8_cofi.m

三、estimateGaussian.m

四、selectThreshold.m

五、cofiCostFunc.m

Machine Learning week 9 quiz: programming assignment-Anomaly Detection and Recommender Systems相关推荐

最新文章

热门文章