Coursera机器学习课程笔记（九）

Coursera机器学习课程笔记（九）

一、笔记

1.区分一下机器学习中异常检测算法(Anomaly detection)和监督学习(Supervised learning)所适用的不同情况：

• 异常检测算法：极少的正样本（异常），大量的负样本
• 监督学习：正负样本的数量都很多

通常情况下，异常检测算法是通过大量负样本来学习p(x)模型，之所以不用极少的正样本是因为未来的正样本有可能是全新的。

2.在电影推荐系统中，即使不知道该用哪些特征去代表不同的电影，同时也不知道theta值，可以通过协同过滤(collaborative filtering) 算法来同时学习特征与对应的theta参数。

在用该算法学习出合适的特征之后，既可以用来给用户推荐电影(本职工作)，还能用来找出与一本电影相似的其他电影（通过计算不同电影特征向量之间的距离）。

二、课后作业

1.Estimate Gaussian Parameters

estimateGaussian.m文件：

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.
%

for i = 1:n
    mu(i) = mean(X(:,i));
    sigma2(i) = var(X(:,i)) * (m-1) / m;     % 默认的方差函数var是乘1/(m-1)的，而我们需要的是乘1/m的
end

% =============================================================
end

2.Select Threshold

selectThreshold.m文件：

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 predictions

    predictions = (pval < epsilon);           %predicitons矩阵中1为异常(positive)，0为正常(negative)
    tp = sum((predictions == 1) & (yval == 1));       %计算true positive的个数
    fp = sum((predictions == 1) & (yval == 0));       %计算false positive的个数
    fn = sum((predictions == 0) & (yval == 1));       %计算false negative的个数

    P = tp / (tp + fp);     %计算准确度precision
    R = tp / (tp + fn);     %计算recall

    F1 = 2 * P * R / (P + R);
    % =============================================================

    if F1 > bestF1
       bestF1 = F1;
       bestEpsilon = epsilon;
    end
end

end

3.Collaborative Filtering Cost

4.Collaborative Filtering Gradient

5.Regularized Cost

6.Regularized Gradient

3,4,5,6都在一个文件中完成了
cofiCostFunc.m文件：

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
%

sum_Theta = sum(sum(Theta .^ 2));    %正则化的第一项
sum_X = sum(sum(X.^2));                 %正则化的第二项
J = 0.5 * sum(sum((R .* (X * Theta' - Y)) .^ 2)) + lambda / 2 * (sum_Theta + sum_X) ;

for i = 1:num_movies
    X_grad(i,:) = R(i,:) .* (X(i,:) * Theta' - Y(i,:)) * Theta + lambda * X(i,:);
end

for j = 1:num_users
    Theta_grad(j,:) = (R(:,j) .* (X * Theta(j,:)' - Y(:,j)))' * X  + lambda * Theta(j,:);
end

% =============================================================

grad = [X_grad(:); Theta_grad(:)];

end